diff options
42 files changed, 1405 insertions, 2956 deletions
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index c78f6d85..2342796a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -11,7 +11,7 @@ on: jobs: unittest: runs-on: ubuntu-latest - container: bonfacekilz/python3-genenetwork2:0bf4ee6 + container: bonfacekilz/python3-genenetwork2:ad741c1 steps: # First start with mariadb set then checkout. The checkout gives diff --git a/wqflask/base/trait.py b/wqflask/base/trait.py index e0afa915..ec8c40a0 100644 --- a/wqflask/base/trait.py +++ b/wqflask/base/trait.py @@ -265,14 +265,17 @@ def get_sample_data(): trait_dict['species'] = trait_ob.dataset.group.species trait_dict['url'] = url_for( 'show_trait_page', trait_id=trait, dataset=dataset) - trait_dict['description'] = trait_ob.description_display if trait_ob.dataset.type == "ProbeSet": trait_dict['symbol'] = trait_ob.symbol trait_dict['location'] = trait_ob.location_repr + trait_dict['description'] = trait_ob.description_display elif trait_ob.dataset.type == "Publish": + trait_dict['description'] = trait_ob.description_display if trait_ob.pubmed_id: trait_dict['pubmed_link'] = trait_ob.pubmed_link trait_dict['pubmed_text'] = trait_ob.pubmed_text + else: + trait_dict['location'] = trait_ob.location_repr return json.dumps([trait_dict, {key: value.value for key, value in list( diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py new file mode 100644 index 00000000..5b621264 --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_gemma_mapping.py @@ -0,0 +1,205 @@ +# test for wqflask/marker_regression/gemma_mapping.py +import unittest +import random +from unittest import mock +from wqflask.marker_regression.gemma_mapping import run_gemma +from wqflask.marker_regression.gemma_mapping import gen_pheno_txt_file +from wqflask.marker_regression.gemma_mapping import gen_covariates_file +from wqflask.marker_regression.gemma_mapping import parse_gemma_output +from wqflask.marker_regression.gemma_mapping import parse_loco_output + + +class AttributeSetter: + def __init__(self, obj): + for key, val in obj.items(): + setattr(self, key, val) + + +class MockGroup(AttributeSetter): + def get_samplelist(self): + return None + + +class TestGemmaMapping(unittest.TestCase): + + @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + def test_run_gemma_firstrun_set_false(self, mock_parse_loco): + """add tests for gemma function where first run is set to false""" + dataset = AttributeSetter( + {"group": AttributeSetter({"genofile": "genofile.geno"})}) + + output_file = "file1" + mock_parse_loco.return_value = [] + this_trait = AttributeSetter({"name": "t1"}) + + result = run_gemma(this_trait=this_trait, this_dataset=dataset, samples=[], vals=[ + ], covariates="", use_loco=True, first_run=False, output_files=output_file) + + expected_results = ([], "file1") + self.assertEqual(expected_results, result) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") + @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMAOPTS", "-debug") + @mock.patch("wqflask.marker_regression.gemma_mapping.GEMMA_WRAPPER_COMMAND", "ghc") + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.gemma_mapping.parse_loco_output") + @mock.patch("wqflask.marker_regression.gemma_mapping.logger") + @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") + @mock.patch("wqflask.marker_regression.gemma_mapping.gen_covariates_file") + @mock.patch("wqflask.marker_regression.run_mapping.random.choice") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + @mock.patch("wqflask.marker_regression.gemma_mapping.gen_pheno_txt_file") + def test_run_gemma_firstrun_set_true(self, mock_gen_pheno_txt, mock_os, mock_choice, mock_gen_covar, mock_flat_files, mock_logger, mock_parse_loco): + """add tests for run_gemma where first run is set to true""" + chromosomes = [] + for i in range(1, 5): + chromosomes.append(AttributeSetter({"name": f"CH{i}"})) + chromo = AttributeSetter({"chromosomes": chromosomes}) + dataset_group = MockGroup( + {"name": "GP1", "genofile": "file_geno"}) + dataset = AttributeSetter({"group": dataset_group, "name": "dataset1_name", + "species": AttributeSetter({"chromosomes": chromo})}) + trait = AttributeSetter({"name": "trait1"}) + samples = [] + mock_gen_pheno_txt.return_value = None + mock_os.path.isfile.return_value = True + mock_gen_covar.return_value = None + mock_choice.return_value = "R" + mock_flat_files.return_value = "/home/genotype/bimbam" + mock_parse_loco.return_value = [] + results = run_gemma(this_trait=trait, this_dataset=dataset, samples=[ + ], vals=[], covariates="", use_loco=True) + system_calls = [mock.call('ghc --json -- -debug -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt -a /home/genotype/bimbam/file_snps.txt -gk > /home/user/data//gn2/GP1_K_RRRRRR.json'), + mock.call('ghc --json --input /home/user/data//gn2/GP1_K_RRRRRR.json -- -debug -a /home/genotype/bimbam/file_snps.txt -lmm 2 -g /home/genotype/bimbam/file_geno.txt -p /home/user/data//gn2/trait1_dataset1_name_pheno.txt > /home/user/data//gn2/GP1_GWA_RRRRRR.json')] + mock_os.system.assert_has_calls(system_calls) + mock_gen_pheno_txt.assert_called_once() + mock_parse_loco.assert_called_once_with(dataset, "GP1_GWA_RRRRRR") + mock_os.path.isfile.assert_called_once_with( + ('/home/user/imgfile_output.assoc.txt')) + self.assertEqual(mock_logger.debug.call_count, 2) + self.assertEqual(mock_flat_files.call_count, 4) + self.assertEqual(results, ([], "GP1_GWA_RRRRRR")) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/user/data") + def test_gen_pheno_txt_file(self): + """add tests for generating pheno txt file""" + with mock.patch("builtins.open", mock.mock_open())as mock_open: + gen_pheno_txt_file(this_dataset={}, genofile_name="", vals=[ + "x", "w", "q", "we", "R"], trait_filename="fitr.re") + mock_open.assert_called_once_with( + '/home/user/data/gn2/fitr.re.txt', 'w') + filehandler = mock_open() + values = ["x", "w", "q", "we", "R"] + write_calls = [mock.call('NA\n'), mock.call('w\n'), mock.call( + 'q\n'), mock.call('we\n'), mock.call('R\n')] + + filehandler.write.assert_has_calls(write_calls) + + @mock.patch("wqflask.marker_regression.gemma_mapping.flat_files") + @mock.patch("wqflask.marker_regression.gemma_mapping.create_trait") + @mock.patch("wqflask.marker_regression.gemma_mapping.create_dataset") + def test_gen_covariates_file(self, create_dataset, create_trait, flat_files): + """add tests for generating covariates files""" + covariates = "X1:X2,Y1:Y2,M1:M3,V1:V2" + samplelist = ["X1", "X2", "X3", "X4"] + create_dataset_side_effect = [] + create_trait_side_effect = [] + + for i in range(4): + create_dataset_side_effect.append(AttributeSetter({"name": f'name_{i}'})) + create_trait_side_effect.append( + AttributeSetter({"data": [f'data_{i}']})) + + create_dataset.side_effect = create_trait_side_effect + create_trait.side_effect = create_trait_side_effect + + group = MockGroup({"name": "group_X", "samplelist": samplelist}) + this_dataset = AttributeSetter({"group": group}) + flat_files.return_value = "Home/Genenetwork" + + with mock.patch("builtins.open", mock.mock_open())as mock_open: + gen_covariates_file(this_dataset=this_dataset, covariates=covariates, + samples=["x1", "x2", "X3"]) + + create_dataset.assert_has_calls( + [mock.call('X2'), mock.call('Y2'), mock.call('M3'), mock.call('V2')]) + mock_calls = [] + trait_names = ["X1", "Y1", "M1", "V1"] + + for i, trait in enumerate(create_trait_side_effect): + mock_calls.append( + mock.call(dataset=trait, name=trait_names[i], cellid=None)) + + create_trait.assert_has_calls(mock_calls) + + flat_files.assert_called_once_with('mapping') + mock_open.assert_called_once_with( + 'Home/Genenetwork/group_X_covariates.txt', 'w') + filehandler = mock_open() + filehandler.write.assert_has_calls([mock.call( + '-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('-9\t'), mock.call('\n')]) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img/") + def test_parse_gemma_output(self): + """add test for generating gemma output with obj returned""" + file = """X/Y\t gn2\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +X/Y\tgn2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +chr\tgn1\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7 +X\tgn7\t2324424\tQ\tE\tA\tP\tMMB\tCDE\t0.4 +125\tgn9\t433575\tQ\tE\tA\tP\tMMB\tCDE\t0.67 +""" + with mock.patch("builtins.open", mock.mock_open(read_data=file)) as mock_open: + results = parse_gemma_output(genofile_name="gema_file") + expected = [{'name': ' gn2', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, {'name': 'gn2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, + {'name': 'gn7', 'chr': 'X', 'Mb': 2.324424, 'p_value': 0.4, 'lod_score': 0.3979400086720376}, {'name': 'gn9', 'chr': 125, 'Mb': 0.433575, 'p_value': 0.67, 'lod_score': 0.17392519729917352}] + mock_open.assert_called_once_with( + "/home/user/img/gema_file_output.assoc.txt") + self.assertEqual(results, expected) + + @mock.patch("wqflask.marker_regression.gemma_mapping.webqtlConfig.GENERATED_IMAGE_DIR", "/home/user/img") + def test_parse_gemma_output_with_empty_return(self): + """add tests for parse gemma output where nothing returned""" + output_file_results = """chr\t today""" + with mock.patch("builtins.open", mock.mock_open(read_data=output_file_results)) as mock_open: + results = parse_gemma_output(genofile_name="gema_file") + self.assertEqual(results, []) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + @mock.patch("wqflask.marker_regression.gemma_mapping.json") + def test_parse_loco_outputfile_found(self, mock_json, mock_os): + """add tests for parse loco output file found""" + mock_json.load.return_value = { + "files": [["file_name", "user", "~/file1"], + ["file_name", "user", "~/file2"]] + } + return_file_1 = """X/Y\t L1\t21\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +X/Y\tL2\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5 +chr\tL3\t12312\tQ\tE\tA\tP\tMMB\tCDE\t0.7""" + return_file_2 = """chr\tother\t21322\tQ\tE\tA\tP\tMMB\tCDE\t0.5""" + mock_os.path.isfile.return_value = True + file_to_write = """{"files":["file_1","file_2"]}""" + with mock.patch("builtins.open") as mock_open: + + handles = (mock.mock_open(read_data="gwas").return_value, mock.mock_open( + read_data=return_file_1).return_value, mock.mock_open(read_data=return_file_2).return_value) + mock_open.side_effect = handles + results = parse_loco_output( + this_dataset={}, gwa_output_filename=".xw/") + expected_results = [{'name': ' L1', 'chr': 'X/Y', 'Mb': 2.1e-05, 'p_value': 0.5, 'lod_score': 0.3010299956639812}, { + 'name': 'L2', 'chr': 'X/Y', 'Mb': 0.021322, 'p_value': 0.5, 'lod_score': 0.3010299956639812}] + + self.assertEqual(expected_results, results) + + @mock.patch("wqflask.marker_regression.gemma_mapping.TEMPDIR", "/home/tmp") + @mock.patch("wqflask.marker_regression.gemma_mapping.os") + def test_parse_loco_outputfile_not_found(self, mock_os): + """add tests for parse loco output where output file not found""" + + mock_os.path.isfile.return_value = False + file_to_write = """{"files":["file_1","file_2"]}""" + + with mock.patch("builtins.open", mock.mock_open(read_data=file_to_write)) as mock_open: + results = parse_loco_output( + this_dataset={}, gwa_output_filename=".xw/") + self.assertEqual(results, []) diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py new file mode 100644 index 00000000..5eec93f1 --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_plink_mapping.py @@ -0,0 +1,85 @@ +# test for wqflask/marker_regression/plink_mapping.py +import unittest +from unittest import mock +from wqflask.marker_regression.plink_mapping import build_line_list +from wqflask.marker_regression.plink_mapping import get_samples_from_ped_file +from wqflask.marker_regression.plink_mapping import flat_files +from wqflask.marker_regression.plink_mapping import gen_pheno_txt_file_plink +from wqflask.marker_regression.plink_mapping import parse_plink_output + + +class AttributeSetter: + def __init__(self, obj): + for key, val in obj.items(): + setattr(self, key, val) +class TestPlinkMapping(unittest.TestCase): + + + def test_build_line_list(self): + """test for building line list""" + line_1 = "this is line one test" + irregular_line = " this is an, irregular line " + exp_line1 = ["this", "is", "line", "one", "test"] + + results = build_line_list(irregular_line) + self.assertEqual(exp_line1, build_line_list(line_1)) + self.assertEqual([], build_line_list()) + self.assertEqual(["this", "is", "an,", "irregular", "line"], results) + + @mock.patch("wqflask.marker_regression.plink_mapping.flat_files") + def test_get_samples_from_ped_file(self, mock_flat_files): + """test for getting samples from ped file""" + dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})}) + file_sample = """Expected_1\tline test +Expected_2\there + Expected_3\tthree""" + mock_flat_files.return_value = "/home/user/" + with mock.patch("builtins.open", mock.mock_open(read_data=file_sample)) as mock_open: + results = get_samples_from_ped_file(dataset) + mock_flat_files.assert_called_once_with("mapping") + mock_open.assert_called_once_with("/home/user/n_1.ped", "r") + self.assertEqual( + ["Expected_1", "Expected_2", "Expected_3"], results) + + @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.plink_mapping.get_samples_from_ped_file") + def test_gen_pheno_txt_file_plink(self, mock_samples): + """test for getting gen_pheno txt file""" + mock_samples.return_value = ["Expected_1", "Expected_2", "Expected_3"] + + trait = AttributeSetter({"name": "TX"}) + dataset = AttributeSetter({"group": AttributeSetter({"name": "n_1"})}) + vals = ["value=K1", "value=K2", "value=K3"] + with mock.patch("builtins.open", mock.mock_open()) as mock_open: + results = gen_pheno_txt_file_plink(this_trait=trait, dataset=dataset, + vals=vals, pheno_filename="ph_file") + mock_open.assert_called_once_with( + "/home/user/data/ph_file.txt", "wb") + filehandler = mock_open() + calls_expected = [mock.call('FID\tIID\tTX\n'), + mock.call('Expected_1\tExpected_1\tK1\nExpected_2\tExpected_2\tK2\nExpected_3\tExpected_3\tK3\n')] + + filehandler.write.assert_has_calls(calls_expected) + + filehandler.close.assert_called_once() + + @mock.patch("wqflask.marker_regression.plink_mapping.TMPDIR", "/home/user/data/") + @mock.patch("wqflask.marker_regression.plink_mapping.build_line_list") + def test_parse_plink_output(self, mock_line_list): + """test for parsing plink output""" + chromosomes = [0, 34, 110, 89, 123, 23, 2] + species = AttributeSetter( + {"name": "S1", "chromosomes": AttributeSetter({"chromosomes": chromosomes})}) + + fake_file = """0 AACCAT T98.6 0.89\n2 AATA B45 0.3\n121 ACG B56.4 NA""" + + mock_line_list.side_effect = [["0", "AACCAT", "T98.6", "0.89"], [ + "2", "AATA", "B45", "0.3"], ["121", "ACG", "B56.4", "NA"]] + with mock.patch("builtins.open", mock.mock_open(read_data=fake_file)) as mock_open: + parse_results = parse_plink_output( + output_filename="P1_file", species=species) + mock_open.assert_called_once_with( + "/home/user/data/P1_file.qassoc", "rb") + expected = (2, {'AACCAT': 0.89, 'AATA': 0.3}) + + self.assertEqual(parse_results, expected) diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py new file mode 100644 index 00000000..b47f877a --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_qtlreaper_mapping.py @@ -0,0 +1,21 @@ +import unittest +from unittest import mock +from wqflask.marker_regression.qtlreaper_mapping import gen_pheno_txt_file + +#issues some methods in genofile object are not defined +#modify samples should equal to vals +class TestQtlReaperMapping(unittest.TestCase): + @mock.patch("wqflask.marker_regression.qtlreaper_mapping.TEMPDIR", "/home/user/data") + def test_gen_pheno_txt_file(self): + vals=["V1","x","V4","V3","x"] + samples=["S1","S2","S3","S4","S5"] + trait_filename="trait_file" + with mock.patch("builtins.open", mock.mock_open())as mock_open: + gen_pheno_txt_file(samples=samples,vals=vals,trait_filename=trait_filename) + mock_open.assert_called_once_with("/home/user/data/gn2/trait_file.txt","w") + filehandler=mock_open() + write_calls= [mock.call('Trait\t'),mock.call('S1\tS3\tS4\n'),mock.call('T1\t'),mock.call('V1\tV4\tV3')] + + filehandler.write.assert_has_calls(write_calls) + + diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py new file mode 100644 index 00000000..c585f1df --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_rqtl_mapping.py @@ -0,0 +1,48 @@ +import unittest +from unittest import mock +from wqflask import app +from wqflask.marker_regression.rqtl_mapping import get_trait_data_type +from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_phenotype +from wqflask.marker_regression.rqtl_mapping import sanitize_rqtl_names + +class TestRqtlMapping(unittest.TestCase): + + def setUp(self): + self.app_context=app.app_context() + self.app_context.push() + + def tearDown(self): + self.app_context.pop() + + + @mock.patch("wqflask.marker_regression.rqtl_mapping.g") + @mock.patch("wqflask.marker_regression.rqtl_mapping.logger") + def test_get_trait_data(self,mock_logger,mock_db): + """test for getting trait data_type return True""" + query_value="""SELECT value FROM TraitMetadata WHERE type='trait_data_type'""" + mock_db.db.execute.return_value.fetchone.return_value=["""{"type":"trait_data_type","name":"T1","traid_id":"fer434f"}"""] + results=get_trait_data_type("traid_id") + mock_db.db.execute.assert_called_with(query_value) + self.assertEqual(results,"fer434f") + + def test_sanitize_rqtl_phenotype(self): + """test for sanitizing rqtl phenotype""" + vals=['f',"x","r","x","x"] + results=sanitize_rqtl_phenotype(vals) + expected_phenotype_string='c(f,NA,r,NA,NA)' + + self.assertEqual(results,expected_phenotype_string) + + def test_sanitize_rqtl_names(self): + """test for sanitzing rqtl names""" + vals=['f',"x","r","x","x"] + expected_sanitized_name="c('f',NA,'r',NA,NA)" + results=sanitize_rqtl_names(vals) + self.assertEqual(expected_sanitized_name,results) + + + + + + + diff --git a/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py b/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py new file mode 100644 index 00000000..4129cc0c --- /dev/null +++ b/wqflask/tests/unit/wqflask/marker_regression/test_run_mapping.py @@ -0,0 +1,284 @@ +import unittest +import datetime +from unittest import mock + +from wqflask.marker_regression.run_mapping import get_genofile_samplelist +from wqflask.marker_regression.run_mapping import geno_db_exists +from wqflask.marker_regression.run_mapping import write_input_for_browser +from wqflask.marker_regression.run_mapping import export_mapping_results +from wqflask.marker_regression.run_mapping import trim_markers_for_figure +from wqflask.marker_regression.run_mapping import get_perm_strata +from wqflask.marker_regression.run_mapping import get_chr_lengths + + +class AttributeSetter: + def __init__(self, obj): + for k, v in obj.items(): + setattr(self, k, v) + + +class MockGroup(AttributeSetter): + + def get_genofiles(self): + return [{"location": "~/genofiles/g1_file", "sample_list": ["S1", "S2", "S3", "S4"]}] + + +class TestRunMapping(unittest.TestCase): + def setUp(self): + + self.group = MockGroup( + {"genofile": "~/genofiles/g1_file", "name": "GP1_", "species": "Human"}) + chromosomes = { + "3": AttributeSetter({ + "name": "C1", + "length": "0.04" + }), + "4": AttributeSetter({ + "name": "C2", + "length": "0.03" + }), + "5": AttributeSetter({ + "name": "C4", + "length": "0.01" + }) + } + self.dataset = AttributeSetter( + {"fullname": "dataser_1", "group": self.group, "type": "ProbeSet"}) + + self.chromosomes = AttributeSetter({"chromosomes": chromosomes}) + self.trait = AttributeSetter( + {"symbol": "IGFI", "chr": "X1", "mb": 123313}) + + def tearDown(self): + self.dataset = AttributeSetter( + {"group": {"location": "~/genofiles/g1_file"}}) + + def test_get_genofile_samplelist(self): + + results_1 = get_genofile_samplelist(self.dataset) + self.assertEqual(results_1, ["S1", "S2", "S3", "S4"]) + self.group.genofile = "~/genofiles/g2_file" + result_2 = get_genofile_samplelist(self.dataset) + self.assertEqual(result_2, []) + + @mock.patch("wqflask.marker_regression.run_mapping.data_set") + def test_if_geno_db_exists(self, mock_data_set): + mock_data_set.create_dataset.side_effect = [ + AttributeSetter({}), Exception()] + results_no_error = geno_db_exists(self.dataset) + results_with_error = geno_db_exists(self.dataset) + + self.assertEqual(mock_data_set.create_dataset.call_count, 2) + self.assertEqual(results_with_error, "False") + self.assertEqual(results_no_error, "True") + + def test_trim_markers_for_figure(self): + + markers = [{ + "name": "MK1", + "chr": "C1", + "cM": "1", + "Mb": "12000", + "genotypes": [], + "dominance":"TT", + "additive":"VA", + "lod_score":0.5 + }, + { + "name": "MK2", + "chr": "C2", + "cM": "15", + "Mb": "10000", + "genotypes": [], + "lod_score":0.7 + }, + { + "name": "MK1", + "chr": "C3", + "cM": "45", + "Mb": "1", + "genotypes": [], + "dominance":"Tt", + "additive":"VE", + "lod_score":1 + }] + + marker_2 = [{ + "name": "MK1", + "chr": "C1", + "cM": "1", + "Mb": "12000", + "genotypes": [], + "dominance":"TT", + "additive":"VA", + "p_wald":4.6 + }] + results = trim_markers_for_figure(markers) + result_2 = trim_markers_for_figure(marker_2) + expected = [ + { + "name": "MK1", + "chr": "C1", + "cM": "1", + "Mb": "12000", + "genotypes": [], + "dominance":"TT", + "additive":"VA", + "lod_score":0.5 + }, + { + "name": "MK1", + "chr": "C3", + "cM": "45", + "Mb": "1", + "genotypes": [], + "dominance":"Tt", + "additive":"VE", + "lod_score":1 + } + + ] + self.assertEqual(results, expected) + self.assertEqual(result_2, marker_2) + + def test_export_mapping_results(self): + """test for exporting mapping results""" + datetime_mock = mock.Mock(wraps=datetime.datetime) + datetime_mock.now.return_value = datetime.datetime( + 2019, 9, 1, 10, 12, 12) + + markers = [{ + "name": "MK1", + "chr": "C1", + "cM": "1", + "Mb": "12000", + "genotypes": [], + "dominance":"TT", + "additive":"VA", + "lod_score":3 + }, + { + "name": "MK2", + "chr": "C2", + "cM": "15", + "Mb": "10000", + "genotypes": [], + "lod_score":7 + }, + { + "name": "MK1", + "chr": "C3", + "cM": "45", + "Mb": "1", + "genotypes": [], + "dominance":"Tt", + "additive":"VE", + "lod_score":7 + }] + + with mock.patch("builtins.open", mock.mock_open()) as mock_open: + + with mock.patch("wqflask.marker_regression.run_mapping.datetime.datetime", new=datetime_mock): + export_mapping_results(dataset=self.dataset, trait=self.trait, markers=markers, + results_path="~/results", mapping_scale="physic", score_type="-log(p)") + + write_calls = [ + mock.call('Time/Date: 09/01/19 / 10:12:12\n'), + mock.call('Population: Human GP1_\n'), mock.call( + 'Data Set: dataser_1\n'), + mock.call('Gene Symbol: IGFI\n'), mock.call( + 'Location: X1 @ 123313 Mb\n'), + mock.call('\n'), mock.call('Name,Chr,'), + mock.call('Mb,-log(p)'), mock.call('Cm,-log(p)'), + mock.call(',Additive'), mock.call(',Dominance'), + mock.call('\n'), mock.call('MK1,C1,'), + mock.call('12000,'), mock.call('1,'), + mock.call('3'), mock.call(',VA'), + mock.call(',TT'), mock.call('\n'), + mock.call('MK2,C2,'), mock.call('10000,'), + mock.call('15,'), mock.call('7'), + mock.call('\n'), mock.call('MK1,C3,'), + mock.call('1,'), mock.call('45,'), + mock.call('7'), mock.call(',VE'), + mock.call(',Tt') + + ] + mock_open.assert_called_once_with("~/results", "w+") + filehandler = mock_open() + filehandler.write.assert_has_calls(write_calls) + + @mock.patch("wqflask.marker_regression.run_mapping.random.choice") + def test_write_input_for_browser(self, mock_choice): + """test for writing input for browser""" + mock_choice.side_effect = ["F", "i", "l", "e", "s", "x"] + with mock.patch("builtins.open", mock.mock_open()) as mock_open: + expected = ['GP1__Filesx_GWAS', 'GP1__Filesx_ANNOT'] + + results = write_input_for_browser( + this_dataset=self.dataset, gwas_results={}, annotations={}) + self.assertEqual(results, expected) + + def test_get_perm_strata(self): + categorical_vars = ["C1", "C2", "W1"] + used_samples = ["S1", "S2"] + sample_list = AttributeSetter({"sample_attribute_values": { + "S1": { + "C1": "c1_value", + "C2": "c2_value", + "W1": "w1_value" + + }, + "S2": { + "W1": "w2_value", + "W2": "w2_value" + + }, + "S3": { + + "C1": "c1_value", + "C2": "c2_value" + + }, + + }}) + + results = get_perm_strata(this_trait={}, sample_list=sample_list, + categorical_vars=categorical_vars, used_samples=used_samples) + self.assertEqual(results, [2, 1]) + + def test_get_chr_length(self): + """test for getting chromosome length""" + chromosomes = AttributeSetter({"chromosomes": self.chromosomes}) + dataset = AttributeSetter({"species": chromosomes}) + results = get_chr_lengths( + mapping_scale="physic", mapping_method="reaper", dataset=dataset, qtl_results=[]) + chr_lengths = [] + for key, chromo in self.chromosomes.chromosomes.items(): + chr_lengths.append({"chr": chromo.name, "size": chromo.length}) + + self.assertEqual(chr_lengths, results) + + qtl_results = [{ + "chr": "16", + "cM": "0.2" + }, + { + "chr": "12", + "cM": "0.5" + }, + { + "chr": "18", + "cM": "0.1" + }, + { + "chr": "22", + "cM": "0.4" + }, + ] + + result_with_other_mapping_scale = get_chr_lengths( + mapping_scale="other", mapping_method="reaper", dataset=dataset, qtl_results=qtl_results) + expected_value = [{'chr': '1', 'size': '0'}, { + 'chr': '16', 'size': '500000.0'}, {'chr': '18', 'size': '400000.0'}] + + self.assertEqual(result_with_other_mapping_scale, expected_value) diff --git a/wqflask/tests/unit/wqflask/test_markdown_routes.py b/wqflask/tests/unit/wqflask/test_markdown_routes.py index 3adf63e5..90e0f17c 100644 --- a/wqflask/tests/unit/wqflask/test_markdown_routes.py +++ b/wqflask/tests/unit/wqflask/test_markdown_routes.py @@ -3,28 +3,25 @@ import unittest from unittest import mock +from dataclasses import dataclass from wqflask.markdown_routes import render_markdown +@dataclass class MockRequests404: - @property - def status_code(self): - return 404 + status_code: int = 404 -class MockRequests200: - @property - def status_code(self): - return 200 - @property - def content(self): - return b""" +@dataclass +class MockRequests200: + status_code: int = 200 + content: str = b""" # Glossary This is some content ## Sub-heading -This is another sub-heading - """ +This is another sub-heading""" + class TestMarkdownRoutesFunctions(unittest.TestCase): """Test cases for functions in markdown_routes""" @@ -32,27 +29,26 @@ class TestMarkdownRoutesFunctions(unittest.TestCase): @mock.patch('wqflask.markdown_routes.requests.get') def test_render_markdown_when_fetching_locally(self, requests_mock): requests_mock.return_value = MockRequests404() - markdown_content = render_markdown("glossary.md") + markdown_content = render_markdown("general/glossary/glossary.md") requests_mock.assert_called_with( "https://raw.githubusercontent.com" - "/genenetwork/genenetwork2/" - "wqflask/wqflask/static/" - "glossary.md") + "/genenetwork/gn-docs/" + "master/general/" + "glossary/glossary.md") self.assertRegexpMatches(markdown_content, - "Glossary of Terms and Features") + "Content for general/glossary/glossary.md not available.") @mock.patch('wqflask.markdown_routes.requests.get') def test_render_markdown_when_fetching_remotely(self, requests_mock): requests_mock.return_value = MockRequests200() - markdown_content = render_markdown("glossary.md") + markdown_content = render_markdown("general/glossary/glossary.md") requests_mock.assert_called_with( "https://raw.githubusercontent.com" - "/genenetwork/genenetwork2/" - "wqflask/wqflask/static/" - "glossary.md") + "/genenetwork/gn-docs/" + "master/general/" + "glossary/glossary.md") self.assertEqual("""<h1>Glossary</h1> <p>This is some content</p> <h2>Sub-heading</h2> -<p>This is another sub-heading</p> -""", +<p>This is another sub-heading</p>""", markdown_content) diff --git a/wqflask/tests/wqflask/show_trait/testSampleList.py b/wqflask/tests/wqflask/show_trait/testSampleList.py new file mode 100644 index 00000000..34c51e3e --- /dev/null +++ b/wqflask/tests/wqflask/show_trait/testSampleList.py @@ -0,0 +1,16 @@ +import unittest +import re +from unittest import mock +from wqflask.show_trait.SampleList import natural_sort + + +class TestSampleList(unittest.TestCase): + def test_natural_sort(self): + "Sort the list into natural alphanumeric order." + + characters_list = ["z", "f", "q", "s", "t", "a", "g"] + names_list = ["temp1", "publish", "Sample", "Dataset"] + sorted_list_a=natural_sort(characters_list) + sorted_list_b=natural_sort(names_list) + self.assertEqual(sorted_list_a, ["a", "f", "g", "q", "s", "t", "z"]) + self.assertEqual(sorted_list_b,["Dataset", "Sample", "publish", "temp1"]) diff --git a/wqflask/tests/wqflask/show_trait/test_show_trait.py b/wqflask/tests/wqflask/show_trait/test_show_trait.py index 24150738..8c866874 100644 --- a/wqflask/tests/wqflask/show_trait/test_show_trait.py +++ b/wqflask/tests/wqflask/show_trait/test_show_trait.py @@ -11,6 +11,7 @@ from wqflask.show_trait.show_trait import get_trait_units from wqflask.show_trait.show_trait import get_nearest_marker from wqflask.show_trait.show_trait import get_genotype_scales from wqflask.show_trait.show_trait import requests +from wqflask.show_trait.show_trait import get_scales_from_genofile class TraitObject: @@ -240,3 +241,38 @@ class TestTraits(unittest.TestCase): expected_results = {f"{file_location}": [["physic", "Mb"]]} self.assertEqual(get_genotype_scales(file_location), expected_results) mock_get_scales.assert_called_once_with(file_location) + + + @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") + def test_get_scales_from_genofile_found(self, mock_ignore_location): + """"add test for get scales from genofile where file is found""" + mock_ignore_location.return_value = True + geno_file = """ + #sample line with no @scales:other\n + #sample line @scales and :separated by semicolon\n + This attempts to check whether\n + """ + + geno_file_string = "@line start with @ and has @scale:morgan" + + file_location = "~/data/file.geno" + + mock_open_geno_file = mock.mock_open(read_data=geno_file) + with mock.patch("builtins.open", mock_open_geno_file): + results = get_scales_from_genofile(file_location) + self.assertEqual(results, [["morgan", "cM"]]) + + mock_open_string = mock.mock_open(read_data=geno_file_string) + + with mock.patch("builtins.open", mock_open_string): + result2 = get_scales_from_genofile(file_location) + self.assertEqual([['morgan', 'cM']], result2) + + @mock.patch("wqflask.show_trait.show_trait.locate_ignore_error") + def test_get_scales_from_genofile_not_found(self, mock_location_ignore): + mock_location_ignore.return_value = False + + expected_results = [["physic", "Mb"]] + results = get_scales_from_genofile("~/data/file") + mock_location_ignore.assert_called_once_with("~/data/file", "genotype") + self.assertEqual(results, expected_results) diff --git a/wqflask/wqflask/__init__.py b/wqflask/wqflask/__init__.py index 874cde17..0564cfa7 100644 --- a/wqflask/wqflask/__init__.py +++ b/wqflask/wqflask/__init__.py @@ -7,6 +7,11 @@ from flask import g from flask import Flask from utility import formatting from wqflask.markdown_routes import glossary_blueprint +from wqflask.markdown_routes import references_blueprint +from wqflask.markdown_routes import links_blueprint +from wqflask.markdown_routes import policies_blueprint +from wqflask.markdown_routes import environments_blueprint +from wqflask.markdown_routes import facilities_blueprint app = Flask(__name__) @@ -19,6 +24,11 @@ app.jinja_env.globals.update( # Registering blueprints app.register_blueprint(glossary_blueprint, url_prefix="/glossary") +app.register_blueprint(references_blueprint, url_prefix="/references") +app.register_blueprint(links_blueprint, url_prefix="/links") +app.register_blueprint(policies_blueprint, url_prefix="/policies") +app.register_blueprint(environments_blueprint, url_prefix="/environments") +app.register_blueprint(facilities_blueprint, url_prefix="/facilities") @app.before_request def before_request(): @@ -39,4 +49,4 @@ from wqflask import db_info from wqflask import user_login from wqflask import user_session -import wqflask.views +import wqflask.views diff --git a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py index a394f548..0269ce68 100644 --- a/wqflask/wqflask/correlation_matrix/show_corr_matrix.py +++ b/wqflask/wqflask/correlation_matrix/show_corr_matrix.py @@ -55,11 +55,7 @@ class CorrelationMatrix(object): self.do_PCA = True this_group = self.trait_list[0][1].group.name #ZS: Getting initial group name before verifying all traits are in the same group in the following loop for trait_db in self.trait_list: - if trait_db[1].group.name != this_group: - self.insufficient_shared_samples = True - break - else: - this_group = trait_db[1].group.name + this_group = trait_db[1].group.name this_trait = trait_db[0] self.traits.append(this_trait) this_sample_data = this_trait.data @@ -68,119 +64,115 @@ class CorrelationMatrix(object): if sample not in self.all_sample_list: self.all_sample_list.append(sample) - if self.insufficient_shared_samples: - pass - else: - self.sample_data = [] - for trait_db in self.trait_list: - this_trait = trait_db[0] - this_sample_data = this_trait.data + self.sample_data = [] + for trait_db in self.trait_list: + this_trait = trait_db[0] + this_sample_data = this_trait.data - this_trait_vals = [] - for sample in self.all_sample_list: - if sample in this_sample_data: - this_trait_vals.append(this_sample_data[sample].value) - else: - this_trait_vals.append('') - self.sample_data.append(this_trait_vals) - - if len(this_trait_vals) < len(self.trait_list): #Shouldn't do PCA if there are more traits than observations/samples - self.do_PCA = False - - self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning) - - self.corr_results = [] - self.pca_corr_results = [] - self.shared_samples_list = self.all_sample_list - for trait_db in self.trait_list: - this_trait = trait_db[0] - this_db = trait_db[1] - - this_db_samples = this_db.group.all_samples_ordered() - this_sample_data = this_trait.data - - corr_result_row = [] - pca_corr_result_row = [] - is_spearman = False #ZS: To determine if it's above or below the diagonal - for target in self.trait_list: - target_trait = target[0] - target_db = target[1] - target_samples = target_db.group.all_samples_ordered() - target_sample_data = target_trait.data - - this_trait_vals = [] - target_vals = [] - for index, sample in enumerate(target_samples): - if (sample in this_sample_data) and (sample in target_sample_data): - sample_value = this_sample_data[sample].value - target_sample_value = target_sample_data[sample].value - this_trait_vals.append(sample_value) - target_vals.append(target_sample_value) - else: - if sample in self.shared_samples_list: - self.shared_samples_list.remove(sample) - - this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(this_trait_vals, target_vals) - - if num_overlap < self.lowest_overlap: - self.lowest_overlap = num_overlap - if num_overlap < 2: - corr_result_row.append([target_trait, 0, num_overlap]) - pca_corr_result_row.append(0) - else: - pearson_r, pearson_p = scipy.stats.pearsonr(this_trait_vals, target_vals) - if is_spearman == False: - sample_r, sample_p = pearson_r, pearson_p - if sample_r == 1: - is_spearman = True - else: - sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) - - corr_result_row.append([target_trait, sample_r, num_overlap]) - pca_corr_result_row.append(pearson_r) - - self.corr_results.append(corr_result_row) - self.pca_corr_results.append(pca_corr_result_row) - - self.trait_data_array = [] - for trait_db in self.trait_list: - this_trait = trait_db[0] - this_db = trait_db[1] - this_db_samples = this_db.group.all_samples_ordered() - this_sample_data = this_trait.data + this_trait_vals = [] + for sample in self.all_sample_list: + if sample in this_sample_data: + this_trait_vals.append(this_sample_data[sample].value) + else: + this_trait_vals.append('') + self.sample_data.append(this_trait_vals) + + if len(this_trait_vals) < len(self.trait_list): #Shouldn't do PCA if there are more traits than observations/samples + self.do_PCA = False + + self.lowest_overlap = 8 #ZS: Variable set to the lowest overlapping samples in order to notify user, or 8, whichever is lower (since 8 is when we want to display warning) + + self.corr_results = [] + self.pca_corr_results = [] + self.shared_samples_list = self.all_sample_list + for trait_db in self.trait_list: + this_trait = trait_db[0] + this_db = trait_db[1] + + this_db_samples = this_db.group.all_samples_ordered() + this_sample_data = this_trait.data + + corr_result_row = [] + pca_corr_result_row = [] + is_spearman = False #ZS: To determine if it's above or below the diagonal + for target in self.trait_list: + target_trait = target[0] + target_db = target[1] + target_samples = target_db.group.all_samples_ordered() + target_sample_data = target_trait.data this_trait_vals = [] - for index, sample in enumerate(this_db_samples): - if (sample in this_sample_data) and (sample in self.shared_samples_list): + target_vals = [] + for index, sample in enumerate(target_samples): + if (sample in this_sample_data) and (sample in target_sample_data): sample_value = this_sample_data[sample].value + target_sample_value = target_sample_data[sample].value this_trait_vals.append(sample_value) - self.trait_data_array.append(this_trait_vals) + target_vals.append(target_sample_value) + else: + if sample in self.shared_samples_list: + self.shared_samples_list.remove(sample) - corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results)) - corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen) + this_trait_vals, target_vals, num_overlap = corr_result_helpers.normalize_values(this_trait_vals, target_vals) - groups = [] - for sample in self.all_sample_list: - groups.append(1) - - try: - if self.do_PCA == True: - self.pca_works = "True" - self.pca_trait_ids = [] - pca = self.calculate_pca(list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors) - self.loadings_array = self.process_loadings() + if num_overlap < self.lowest_overlap: + self.lowest_overlap = num_overlap + if num_overlap < 2: + corr_result_row.append([target_trait, 0, num_overlap]) + pca_corr_result_row.append(0) else: - self.pca_works = "False" - except: - self.pca_works = "False" + pearson_r, pearson_p = scipy.stats.pearsonr(this_trait_vals, target_vals) + if is_spearman == False: + sample_r, sample_p = pearson_r, pearson_p + if sample_r == 1: + is_spearman = True + else: + sample_r, sample_p = scipy.stats.spearmanr(this_trait_vals, target_vals) + + corr_result_row.append([target_trait, sample_r, num_overlap]) + pca_corr_result_row.append(pearson_r) - self.js_data = dict(traits = [trait.name for trait in self.traits], - groups = groups, - cols = list(range(len(self.traits))), - rows = list(range(len(self.traits))), - samples = self.all_sample_list, - sample_data = self.sample_data,) - # corr_results = [result[1] for result in result_row for result_row in self.corr_results]) + self.corr_results.append(corr_result_row) + self.pca_corr_results.append(pca_corr_result_row) + + self.trait_data_array = [] + for trait_db in self.trait_list: + this_trait = trait_db[0] + this_db = trait_db[1] + this_db_samples = this_db.group.all_samples_ordered() + this_sample_data = this_trait.data + + this_trait_vals = [] + for index, sample in enumerate(this_db_samples): + if (sample in this_sample_data) and (sample in self.shared_samples_list): + sample_value = this_sample_data[sample].value + this_trait_vals.append(sample_value) + self.trait_data_array.append(this_trait_vals) + + corr_result_eigen = np.linalg.eig(np.array(self.pca_corr_results)) + corr_eigen_value, corr_eigen_vectors = sortEigenVectors(corr_result_eigen) + + groups = [] + for sample in self.all_sample_list: + groups.append(1) + + try: + if self.do_PCA == True: + self.pca_works = "True" + self.pca_trait_ids = [] + pca = self.calculate_pca(list(range(len(self.traits))), corr_eigen_value, corr_eigen_vectors) + self.loadings_array = self.process_loadings() + else: + self.pca_works = "False" + except: + self.pca_works = "False" + + self.js_data = dict(traits = [trait.name for trait in self.traits], + groups = groups, + cols = list(range(len(self.traits))), + rows = list(range(len(self.traits))), + samples = self.all_sample_list, + sample_data = self.sample_data,) def calculate_pca(self, cols, corr_eigen_value, corr_eigen_vectors): base = importr('base') diff --git a/wqflask/wqflask/docs.py b/wqflask/wqflask/docs.py index d653c269..23fc3cad 100644 --- a/wqflask/wqflask/docs.py +++ b/wqflask/wqflask/docs.py @@ -19,8 +19,10 @@ class Docs(object): self.title = self.entry.capitalize() self.content = "" else: + self.title = result[0] - self.content = result[1] + self.content = result[1].decode("utf-8") + self.editable = "false" # ZS: Removing option to edit to see if text still gets vandalized diff --git a/wqflask/wqflask/markdown_routes.py b/wqflask/wqflask/markdown_routes.py index 33092947..601845d7 100644 --- a/wqflask/wqflask/markdown_routes.py +++ b/wqflask/wqflask/markdown_routes.py @@ -2,37 +2,72 @@ Render pages from github, or if they are unavailable, look for it else where """ -import os import requests -import mistune +import markdown from flask import Blueprint from flask import render_template glossary_blueprint = Blueprint('glossary_blueprint', __name__) +references_blueprint = Blueprint("references_blueprint", __name__) +environments_blueprint = Blueprint("environments_blueprint", __name__) +links_blueprint = Blueprint("links_blueprint", __name__) +policies_blueprint = Blueprint("policies_blueprint", __name__) +facilities_blueprint = Blueprint("facilities_blueprint", __name__) def render_markdown(file_name): """Try to fetch the file name from Github and if that fails, try to -look for it inside the file system - - """ - markdown_url = (f"https://raw.githubusercontent.com" - f"/genenetwork/genenetwork2/" - f"wqflask/wqflask/static/" - f"{file_name}") - md_content = requests.get(markdown_url) +look for it inside the file system """ + github_url = ("https://raw.githubusercontent.com/" + "genenetwork/gn-docs/master/") + + md_content = requests.get(f"{github_url}{file_name}") if md_content.status_code == 200: - return mistune.html(md_content.content.decode("utf-8")) - with open(os.path.join(os.path.abspath(os.path.dirname(__file__)), - f"static/markdown/{file_name}")) as md_file: - markdown = md_file.read() - return mistune.html(markdown) + return markdown.markdown(md_content.content.decode("utf-8"), extensions=['tables']) + # TODO: Add fallback on our git server by checking the mirror. + # Content not available + return (f"\nContent for {file_name} not available. " + "Please check " + "(here to see where content exists)" + "[https://github.com/genenetwork/gn-docs]. " + "Please reach out to the gn2 team to have a look at this") @glossary_blueprint.route('/') def glossary(): return render_template( "glossary.html", - rendered_markdown=render_markdown("glossary.md")), 200 + rendered_markdown=render_markdown("general/glossary/glossary.md")), 200 + + +@references_blueprint.route('/') +def references(): + return render_template( + "references.html", + rendered_markdown=render_markdown("general/references/references.md")), 200 + + +@environments_blueprint.route("/") +def environments(): + return render_template("environment.html", rendered_markdown=render_markdown("general/environments/environments.md")), 200 + + +@links_blueprint.route("/") +def links(): + return render_template( + "links.html", + rendered_markdown=render_markdown("general/links/links.md")), 200 + + +@policies_blueprint.route("/") +def policies(): + return render_template( + "links.html", + rendered_markdown=render_markdown("general/policies/policies.md")), 200 + + +@facilities_blueprint.route("/") +def facilities(): + return render_template("facilities.html", rendered_markdown=render_markdown("general/help/facilities.md")), 200 diff --git a/wqflask/wqflask/marker_regression/display_mapping_results.py b/wqflask/wqflask/marker_regression/display_mapping_results.py index 6d6572ff..08c2d750 100644 --- a/wqflask/wqflask/marker_regression/display_mapping_results.py +++ b/wqflask/wqflask/marker_regression/display_mapping_results.py @@ -74,6 +74,14 @@ DARKVIOLET = ImageColor.getrgb("darkviolet") MEDIUMPURPLE = ImageColor.getrgb("mediumpurple") # ---- END: Define common colours ---- # +# ZS: List of distinct colors for manhattan plot if user selects "varied" +COLOR_CODES = ["#FF0000", "#00FF00", "#0000FF", "#FFFF00", "#FF00FF", "#00FFFF", + "#000000", "#800000", "#008000", "#000080", "#808000", "#800080", + "#008080", "#808080", "#C00000", "#00C000", "#0000C0", "#C0C000", + "#C000C0", "#00C0C0", "#C0C0C0", "#400000", "#004000", "#000040"] + +DISTINCT_COLOR_LIST = [ImageColor.getrgb(color) for color in COLOR_CODES] + # ---- FONT FILES ---- # VERDANA_FILE = "./wqflask/static/fonts/verdana.ttf" VERDANA_BOLD_FILE = "./wqflask/static/fonts/verdanab.ttf" @@ -293,6 +301,12 @@ class DisplayMappingResults(object): self.plotScale = "physic" self.manhattan_plot = start_vars['manhattan_plot'] + if self.manhattan_plot: + self.color_scheme = "alternating" + if 'color_scheme' in start_vars: + self.color_scheme = start_vars['color_scheme'] + if self.color_scheme == "single": + self.manhattan_single_color = ImageColor.getrgb("#" + start_vars['manhattan_single_color']) if 'permCheck' in list(start_vars.keys()): self.permChecked = start_vars['permCheck'] @@ -2076,7 +2090,7 @@ class DisplayMappingResults(object): if self.lrsMax <= 0: #sliding scale if "lrs_value" in self.qtlresults[0]: LRS_LOD_Max = max([result['lrs_value'] for result in self.qtlresults]) - if self.LRS_LOD == "LOD" or self.LRS_LOD == "-log(p)": + if self.LRS_LOD == "LOD" or self.LRS_LOD == "-logP": LRS_LOD_Max = LRS_LOD_Max / self.LODFACTOR if self.permChecked and self.nperm > 0 and not self.multipleInterval: self.significant = min(self.significant / self.LODFACTOR, webqtlConfig.MAXLRS) @@ -2172,7 +2186,7 @@ class DisplayMappingResults(object): TEXT_X_DISPLACEMENT = -12 else: TEXT_X_DISPLACEMENT = -30 - if self.LRS_LOD == "-log(p)": + if self.LRS_LOD == "-logP": TEXT_Y_DISPLACEMENT = -242 else: TEXT_Y_DISPLACEMENT = -210 @@ -2397,7 +2411,7 @@ class DisplayMappingResults(object): if 'lrs_value' in qtlresult: - if self.LRS_LOD == "LOD" or self.LRS_LOD == "-log(p)": + if self.LRS_LOD == "LOD" or self.LRS_LOD == "-logP": if qtlresult['lrs_value'] > 460 or qtlresult['lrs_value']=='inf': #Yc = yZero - webqtlConfig.MAXLRS*LRSHeightThresh/(LRSAxisList[-1]*self.LODFACTOR) Yc = yZero - webqtlConfig.MAXLRS*LRSHeightThresh/(LRS_LOD_Max*self.LODFACTOR) @@ -2424,10 +2438,16 @@ class DisplayMappingResults(object): Yc = yZero - qtlresult['lod_score']*LRSHeightThresh/LRS_LOD_Max if self.manhattan_plot == True: - if self.selectedChr == -1 and (previous_chr_as_int % 2 == 1): - point_color = RED + if self.color_scheme == "single": + point_color = self.manhattan_single_color + elif self.color_scheme == "varied": + point_color = DISTINCT_COLOR_LIST[previous_chr_as_int] else: - point_color = BLUE + if self.selectedChr == -1 and (previous_chr_as_int % 2 == 1): + point_color = RED + else: + point_color = BLUE + im_drawer.text( text="5", xy=( diff --git a/wqflask/wqflask/marker_regression/gemma_mapping.py b/wqflask/wqflask/marker_regression/gemma_mapping.py index 68a8d5ba..02f91a32 100644 --- a/wqflask/wqflask/marker_regression/gemma_mapping.py +++ b/wqflask/wqflask/marker_regression/gemma_mapping.py @@ -31,16 +31,11 @@ def run_gemma(this_trait, this_dataset, samples, vals, covariates, use_loco, maf gwa_output_filename = this_dataset.group.name + "_GWA_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) this_chromosomes = this_dataset.species.chromosomes.chromosomes - chr_list_string = "" - for i in range(len(this_chromosomes)): - if i < (len(this_chromosomes) - 1): - chr_list_string += this_chromosomes[i+1].name + "," - else: - chr_list_string += this_chromosomes[i+1].name + this_chromosomes_name=[chromosome.name for chromosome in this_chromosomes] + chr_list_string=",".join(this_chromosomes_name) if covariates != "": gen_covariates_file(this_dataset, covariates, samples) - if use_loco == "True": generate_k_command = GEMMA_WRAPPER_COMMAND + ' --json --loco ' + chr_list_string + ' -- ' + GEMMAOPTS + ' -g %s/%s_geno.txt -p %s/gn2/%s.txt -a %s/%s_snps.txt -gk > %s/gn2/%s.json' % (flat_files('genotype/bimbam'), genofile_name, diff --git a/wqflask/wqflask/marker_regression/plink_mapping.py b/wqflask/wqflask/marker_regression/plink_mapping.py index fd91b6ca..5d675c38 100644 --- a/wqflask/wqflask/marker_regression/plink_mapping.py +++ b/wqflask/wqflask/marker_regression/plink_mapping.py @@ -9,11 +9,11 @@ import utility.logger logger = utility.logger.getLogger(__name__ ) def run_plink(this_trait, dataset, species, vals, maf): - plink_output_filename = webqtlUtil.genRandStr("%s_%s_"%(dataset.group.name, this_trait.name)) + plink_output_filename = webqtlUtil.genRandStr(f"{dataset.group.name}_{this_trait.name}_") gen_pheno_txt_file(dataset, vals) - plink_command = PLINK_COMMAND + ' --noweb --bfile %s/%s --no-pheno --no-fid --no-parents --no-sex --maf %s --out %s%s --assoc ' % ( - flat_files('mapping'), dataset.group.name, maf, TMPDIR, plink_output_filename) + + plink_command = f"{PLINK_COMMAND} --noweb --bfile {flat_files('mapping')}/{dataset.group.name} --no-pheno --no-fid --no-parents --no-sex --maf {maf} --out { TMPDIR}{plink_output_filename} --assoc " logger.debug("plink_command:", plink_command) os.system(plink_command) @@ -29,12 +29,12 @@ def gen_pheno_txt_file(this_dataset, vals): """Generates phenotype file for GEMMA/PLINK""" current_file_data = [] - with open("{}/{}.fam".format(flat_files('mapping'), this_dataset.group.name), "r") as outfile: + with open(f"{flat_files('mapping')}/{this_dataset.group.name}.fam", "r") as outfile: for i, line in enumerate(outfile): split_line = line.split() current_file_data.append(split_line) - with open("{}/{}.fam".format(flat_files('mapping'), this_dataset.group.name), "w") as outfile: + with open(f"{flat_files('mapping')}/{this_dataset.group.name}.fam","w") as outfile: for i, line in enumerate(current_file_data): if vals[i] == "x": this_val = -9 @@ -44,8 +44,8 @@ def gen_pheno_txt_file(this_dataset, vals): def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''): ped_sample_list = get_samples_from_ped_file(dataset) - output_file = open("%s%s.txt" % (TMPDIR, pheno_filename), "wb") - header = 'FID\tIID\t%s\n' % this_trait.name + output_file = open(f"{TMPDIR}{pheno_filename}.txt", "wb") + header = f"FID\tIID\t{this_trait.name}\n" output_file.write(header) new_value_list = [] @@ -65,7 +65,7 @@ def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''): for i, sample in enumerate(ped_sample_list): j = i+1 value = new_value_list[i] - new_line += '%s\t%s\t%s\n'%(sample, sample, value) + new_line += f"{sample}\t{sample}\t{value}\n" if j%1000 == 0: output_file.write(newLine) @@ -78,7 +78,7 @@ def gen_pheno_txt_file_plink(this_trait, dataset, vals, pheno_filename = ''): # get strain name from ped file in order def get_samples_from_ped_file(dataset): - ped_file= open("{}{}.ped".format(flat_files('mapping'), dataset.group.name), "r") + ped_file= open(f"{flat_files('mapping')}{dataset.group.name}.ped","r") line = ped_file.readline() sample_list=[] @@ -98,7 +98,7 @@ def parse_plink_output(output_filename, species): threshold_p_value = 1 - result_fp = open("%s%s.qassoc"% (TMPDIR, output_filename), "rb") + result_fp = open(f"{TMPDIR}{output_filename}.qassoc","rb") line = result_fp.readline() @@ -154,7 +154,7 @@ def parse_plink_output(output_filename, species): # function: convert line from str to list; # output: lineList list ####################################################### -def build_line_list(line=None): +def build_line_list(line=""): line_list = line.strip().split(' ')# irregular number of whitespaces between columns line_list = [item for item in line_list if item !=''] line_list = [item.strip() for item in line_list] diff --git a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py index 78b1f7b0..505ae295 100644 --- a/wqflask/wqflask/marker_regression/qtlreaper_mapping.py +++ b/wqflask/wqflask/marker_regression/qtlreaper_mapping.py @@ -17,22 +17,29 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo else: genofile_name = this_dataset.group.name - trait_filename = str(this_trait.name) + "_" + str(this_dataset.name) + "_pheno" + trait_filename =f"{str(this_trait.name)}_{str(this_dataset.name)}_pheno" gen_pheno_txt_file(samples, vals, trait_filename) - output_filename = this_dataset.group.name + "_GWA_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) + output_filename = (f"{this_dataset.group.name}_GWA_"+ + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) + ) bootstrap_filename = None permu_filename = None opt_list = [] if boot_check and num_bootstrap > 0: - bootstrap_filename = this_dataset.group.name + "_BOOTSTRAP_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) + bootstrap_filename = (f"{this_dataset.group.name}_BOOTSTRAP_" + + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) + ) opt_list.append("-b") - opt_list.append("--n_bootstrap " + str(num_bootstrap)) - opt_list.append("--bootstrap_output " + webqtlConfig.GENERATED_IMAGE_DIR + bootstrap_filename + ".txt") + opt_list.append(f"--n_bootstrap{str(num_bootstrap)}") + opt_list.append(f"--bootstrap_output{webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt") if num_perm > 0: - permu_filename = this_dataset.group.name + "_PERM_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) + permu_filename =("{this_dataset.group.name}_PERM_" + + ''.join(random.choice(string.ascii_uppercase + + string.digits) for _ in range(6)) + ) opt_list.append("-n " + str(num_perm)) opt_list.append("--permu_output " + webqtlConfig.GENERATED_IMAGE_DIR + permu_filename + ".txt") if control_marker != "" and do_control == "true": @@ -40,13 +47,15 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo if manhattan_plot != True: opt_list.append("--interval 1") - reaper_command = REAPER_COMMAND + ' --geno {0}/{1}.geno --traits {2}/gn2/{3}.txt {4} -o {5}{6}.txt'.format(flat_files('genotype'), - genofile_name, - TEMPDIR, - trait_filename, - " ".join(opt_list), - webqtlConfig.GENERATED_IMAGE_DIR, - output_filename) + reaper_command = (REAPER_COMMAND + + ' --geno {0}/{1}.geno --traits {2}/gn2/{3}.txt {4} -o {5}{6}.txt'.format(flat_files('genotype'), + + genofile_name, + TEMPDIR, + trait_filename, + " ".join(opt_list), + webqtlConfig.GENERATED_IMAGE_DIR, + output_filename)) logger.debug("reaper_command:" + reaper_command) os.system(reaper_command) @@ -61,12 +70,13 @@ def run_reaper(this_trait, this_dataset, samples, vals, json_data, num_perm, boo suggestive = permu_vals[int(num_perm*0.37-1)] significant = permu_vals[int(num_perm*0.95-1)] - return marker_obs, permu_vals, suggestive, significant, bootstrap_vals, [output_filename, permu_filename, bootstrap_filename] + return (marker_obs, permu_vals, suggestive, significant, bootstrap_vals, + [output_filename, permu_filename, bootstrap_filename]) def gen_pheno_txt_file(samples, vals, trait_filename): """Generates phenotype file for GEMMA""" - with open("{}/gn2/{}.txt".format(TEMPDIR, trait_filename), "w") as outfile: + with open(f"{TEMPDIR}/gn2/{trait_filename}.txt","w") as outfile: outfile.write("Trait\t") filtered_sample_list = [] @@ -90,7 +100,7 @@ def parse_reaper_output(gwa_filename, permu_filename, bootstrap_filename): only_cm = False only_mb = False - with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, gwa_filename)) as output_file: + with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{gwa_filename}.txt") as output_file: for line in output_file: if line.startswith("ID\t"): if len(line.split("\t")) < 8: @@ -137,13 +147,13 @@ def parse_reaper_output(gwa_filename, permu_filename, bootstrap_filename): permu_vals = [] if permu_filename: - with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, permu_filename)) as permu_file: + with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{permu_filename}.txt") as permu_file: for line in permu_file: permu_vals.append(float(line)) bootstrap_vals = [] if bootstrap_filename: - with open("{}{}.txt".format(webqtlConfig.GENERATED_IMAGE_DIR, bootstrap_filename)) as bootstrap_file: + with open(f"{webqtlConfig.GENERATED_IMAGE_DIR}{bootstrap_filename}.txt") as bootstrap_file: for line in bootstrap_file: bootstrap_vals.append(int(line)) diff --git a/wqflask/wqflask/marker_regression/run_mapping.py b/wqflask/wqflask/marker_regression/run_mapping.py index fa61272f..c474e0e0 100644 --- a/wqflask/wqflask/marker_regression/run_mapping.py +++ b/wqflask/wqflask/marker_regression/run_mapping.py @@ -138,7 +138,12 @@ class RunMapping(object): mapping_results_filename = self.dataset.group.name + "_" + ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(6)) self.mapping_results_path = "{}{}.csv".format(webqtlConfig.GENERATED_IMAGE_DIR, mapping_results_filename) - if start_vars['manhattan_plot'] == "true": + if start_vars['manhattan_plot']: + self.color_scheme = "alternating" + if "color_scheme" in start_vars: + self.color_scheme = start_vars['color_scheme'] + if self.color_scheme == "single": + self.manhattan_single_color = start_vars['manhattan_single_color'] self.manhattan_plot = True else: self.manhattan_plot = False @@ -228,7 +233,7 @@ class RunMapping(object): self.output_files = start_vars['output_files'] if 'first_run' in start_vars: #ZS: check if first run so existing result files can be used if it isn't (for example zooming on a chromosome, etc) self.first_run = False - self.score_type = "-log(p)" + self.score_type = "-logP" self.manhattan_plot = True with Bench("Running GEMMA"): if self.use_loco == "True": @@ -327,7 +332,7 @@ class RunMapping(object): self.control_marker, self.manhattan_plot) elif self.mapping_method == "plink": - self.score_type = "-log(p)" + self.score_type = "-logP" self.manhattan_plot = True results = plink_mapping.run_plink(self.this_trait, self.dataset, self.species, self.vals, self.maf) #results = self.run_plink() @@ -414,7 +419,7 @@ class RunMapping(object): highest_chr = marker['chr'] if ('lod_score' in marker.keys()) or ('lrs_value' in marker.keys()): if 'Mb' in marker.keys(): - marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.3f}".format(marker['Mb']) + marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.6f}".format(marker['Mb']) elif 'cM' in marker.keys(): marker['display_pos'] = "Chr" + str(marker['chr']) + ": " + "{:.3f}".format(marker['cM']) else: @@ -539,8 +544,8 @@ def export_mapping_results(dataset, trait, markers, results_path, mapping_scale, output_file.write("Location: " + str(trait.chr) + " @ " + str(trait.mb) + " Mb\n") output_file.write("\n") output_file.write("Name,Chr,") - if score_type.lower() == "-log(p)": - score_type = "-log(p)" + if score_type.lower() == "-logP": + score_type = "-logP" if 'Mb' in markers[0]: output_file.write("Mb," + score_type) if 'cM' in markers[0]: diff --git a/wqflask/wqflask/show_trait/SampleList.py b/wqflask/wqflask/show_trait/SampleList.py index a535c493..00495377 100644 --- a/wqflask/wqflask/show_trait/SampleList.py +++ b/wqflask/wqflask/show_trait/SampleList.py @@ -8,6 +8,7 @@ from pprint import pformat as pf from utility import Plot from utility import Bunch + class SampleList(object): def __init__(self, dataset, @@ -72,7 +73,8 @@ class SampleList(object): self.sample_list.append(sample) self.se_exists = any(sample.variance for sample in self.sample_list) - self.num_cases_exists = any(sample.num_cases for sample in self.sample_list) + self.num_cases_exists = any( + sample.num_cases for sample in self.sample_list) first_attr_col = self.get_first_attr_col() for sample in self.sample_list: @@ -115,7 +117,7 @@ class SampleList(object): self.attributes[key].name = name self.attributes[key].distinct_values = [ item.Value for item in values] - natural_sort(self.attributes[key].distinct_values) + self.attributes[key].distinct_values=natural_sort(self.attributes[key].distinct_values) all_numbers = True for value in self.attributes[key].distinct_values: try: @@ -167,7 +169,8 @@ class SampleList(object): return first_attr_col -def natural_sort(list, key=lambda s: s): + +def natural_sort(a_list, key=lambda s: s): """ Sort the list into natural alphanumeric order. """ @@ -175,4 +178,5 @@ def natural_sort(list, key=lambda s: s): def convert(text): return int(text) if text.isdigit() else text return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))] sort_key = get_alphanum_key_func(key) - list.sort(key=sort_key)
\ No newline at end of file + sorted_list = sorted(a_list, key=sort_key) + return sorted_list diff --git a/wqflask/wqflask/static/images/edit.png b/wqflask/wqflask/static/images/edit.png Binary files differnew file mode 100644 index 00000000..571b08cd --- /dev/null +++ b/wqflask/wqflask/static/images/edit.png diff --git a/wqflask/wqflask/static/markdown/glossary.md b/wqflask/wqflask/static/markdown/glossary.md deleted file mode 100644 index db94ae18..00000000 --- a/wqflask/wqflask/static/markdown/glossary.md +++ /dev/null @@ -1,618 +0,0 @@ -# Glossary of Terms and Features - -<div id="index"></div> - -[A](#a) | [B](#b) | [C](#c)| [D](#d) | [E](#e) | [F](#f) | [G](#g) | [H](#h) | [I](#i) | [J](#j) | [K](#k) | [L](#l) | [M](#m) | [N](#n) | [O](#o) | [P](#p) | [Q](#q) | [R](#r) | [S](#s) | [T](#t) | [U](#u) | [V](#v) | [W](#w) | [X](#x) | [Y](#y) | [Z](#z) - -<small>You are welcome to cite or reproduce these glossary -definitions. Please cite or link: Author AA. "Insert Glossary Term -Here." From The WebQTL Glossary--A GeneNetwork -Resource. gn1.genenetwork.org/glossary.html</small> - -<div id="a"></div> - -## A - -<div id="additive"></div> - -#### Additive Allele Effect: - -The additive allele effect is an estimate of the change in the average phenotype that would be produced by substituting a single allele of one type with that of another type (e.g., a replaced by A) in a population. In a standard F2 intercross between two inbred parental lines there are two alleles at every polymorphic locus that are often referred to as the little "a" allele and big "A" allele. F2 progeny inherit the a/a, a/A, or A/A genotypes at every genetic locus in a ratio close to 1:2:1. The additive effect is half of the difference between the mean of all cases that are homozygous for one parental allele (aa) compared to the mean of all cases that are homozygous for the other parental allele (AA): - -<math>[(mean of AA cases)-(mean of aa cases)]/2</math> - -GeneNetwork displays the additive values on the far right of many trait/QTL maps, usually as red or green lines along the maps. The units of measurement of additive effects (and dominance effects) are defined by the trait itself and are shown in **Trait Data and Analysis** windows. For mRNA estimates these units are usually normalized log2 expression values. For this reason an additive effect of 0.5 units indicates that the A/A and a/a genotypes at a particular locus or marker differ by 1 unit (twice the effect of swapping a single A allele for an a allele). On this log2 scale this is equivalent to a 2-fold difference (2 raised to the power of 1). - -On the QTL map plots the polarity of allele effects is represented by the color of the line. For example, in mouse BXD family maps, if the DBA/2J allele produces higher values than the C57BL/6J allele then the additive effect line is colored in green. In contrast, if the C57BL/6J allele produces higher values then the line is colored in red. For computational purposes, C57BL/6J red values are considered negative. - -The dominance effects of alleles are also computed on maps for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. [Please also see entry on **Dominance Effects**: Williams RW, Oct 15, 2004; Sept 3, 2005; Dec 4, 2005; Oct 25, 2011] - -[Go back to index](#index) - -<div id="b"></div> - -<div id="bootstrap"></div> - -#### Bootstrap: - -A [bootstrap sample](http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29) is a randomly drawn sample (or resample) that is taken from the original data set and that has the same number of samples as the original data set. In a single bootstrap sample, some cases will by chance be represented one or more times; other cases may not be represented at all (in other words, the sampling is done "with replacement" after each selection). To get a better intuitive feel for the method, imagine a bag of 26 Scrabble pieces that contain each letter of the English alphabet. In a bootstrap sample of these 26 pieces, you would shake the bag, insert your hand, and draw out one piece. You would then write down that letter on a piece of paper, and the place that Scrabble piece back in the bag in preparation for the next random selection. You would repeat this process (shake, draw, replace) 25 more times to generate a single bootstrap resample of the alphabet. Some letters will be represented several time in each sample and others will not be represented at al. If you repeat this procedure 1000 times you would have a set of bootstrap resamples of the type that GN uses to remap data sets. - -Bootstrap resampling is a method that can be used to estimate statistical parameters and error terms. GeneNetwork uses a bootstrap procedure to evaluate approximate confidence limits of QTL peaks using a method proposed by Peter Visscher and colleagues ([1996](http://www.genetics.org/content/143/2/1013.full.pdf)). We generate 2000 bootstraps, remap each, and keep track of the location of the single locus with the highest LRS score locations (equivalent to a "letter" in the Scrabble example). The 2000 "best" locations are used to produce the yellow histograms plotted on some of the QTL maps. If the position of a QTL is firm, then the particular composition of the sample, will not shift the position of the QTL peak by very much. In such a case, the histogram of "best QTLs" (yellow bars in the maps) that is displayed in WebQTL maps will tend to have a sharp peak (the scale is the percentage of bootstrap resamples that fall into each bar of the bootstrap histogram). In contrast, if the the yellow bootstrap histograms are spread out along a chromosome, then the precise location of a QTL may not be accurate, even in the original correct data set. Bootstrap results naturally vary between runs due to the random generation of the samples. See the related entry "Frequency of Peak LRS." - -KNOWN PROBLEMS and INTERPRETATION of BOOTSTRAP RESULTS: The reliability of bootstrap analysis of QTL confidence intervals has been criticized by Manichaikul and colleagues ([2006](http://www.genetics.org/cgi/content/full/174/1/481)). Their work applies in particular to standard intercrosses and backcrosses in which markers are spaced every 2 cM. They recommend that confidence intervals be estimated either by a conventional 1.5 to 2.0 LOD drop-off interval or by a Bayes credible Interval method. - -There is a known flaw in the way in which GeneNetwork displays bootstrap results (Sept 2011). If a map has two or more adjacent markers with identical LOD score and identical strain distribution patterns, all of the bootstrap results are assigned incorrectly to just one of the "twin" markers. This results in a false perception of precision. - -QTL mapping methods can be highly sensitive to cases with very high or very low phenotype values (outliers). The bootstrap method does not provide protection against the effects of outliers and their effects on QTL maps. Make sure you review your data for outliers before mapping. Options include (1) Do nothing, (2) Delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the mapping results to be quite volatile. In general, if the results (QTL position or value) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. [Williams RW, Oct 15, 2004, Mar 15, 2008, Mar 26, 2008; Sept 2011] - -[Go back to index](#index) - -<div id="c"></div> - -#### CEL and DAT Files (Affymetrix): - -Array data begin as raw image files that are generated using a confocal microscope and video system. Affymetrix refers to these image data files as DAT files. The DAT image needs to be registered to a template that assigns pixel values to expected array coordinates (cells). The result is an assignment of a set of image intensity values (pixel intensities) to each probe. For example, each cell/probe value generated using Affymetrix arrays is associated with approximately 36 pixels (a 6x6 set of pixels, usually with an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is has rank 24 from low to high. The range of variation in intensity amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] - -#### Cluster Map or QTL Cluster Map: - -Cluster maps are sets of QTL maps for a group of traits. The QTL maps for the individual traits (up to 100) are run side by side to enable easy detection of common and unique QTLs. Traits are clustered along one axis of the map by phenotypic similarity (hierarchical clustering) using the Pearson product-moment correlation r as a measurement of similarity (we plot 1-r as the distance). Traits that are positively correlated will be located near to each other. The genome location is shown along the other, long axis of the cluster map, marker by marker, from Chromosome 1 to Chromosome X. Colors are used to encode the probability of linkage, as well as the additive effect polarity of alleles at each marker. These QTL maps are computed using the fast Marker Regression algorithm. P values for each trait are computed by permuting each trait 1000 times. Cluster maps could be considered trait gels because each lane is loaded with a trait that is run out along the genome. Cluster maps are a unique feature of the GeneNetwork developed by Elissa Chesler and implemented in WebQTL by J Wang and RW Williams, April 2004. [Williams RW, Dec 23, 2004, rev June 15, 2006 RWW]. - -#### Collections and Trait Collections: - -One of the most powerful features of GeneNetwork (GN) is the ability to study large sets of traits that have been measured using a common genetic reference population or panel (GRP). This is one of the key requirements of systems genetics--many traits studied in common. Under the main GN menu **Search** heading you will see a link to **Trait Collections**. You can assemble you own collection for any GRP by simply adding items using the Add to Collection button that you will find in many windows. Once you have a collection you will have access to a new set of tools for analysis of your collection, including **QTL Cluster Map, Network Graph, Correlation Matrix**, and **Compare Correlates**. [Williams RW, April 7, 2006] - -#### Complex Trait Analysis: - -Complex trait analysis is the study of multiple causes of variation of phenotypes within species. Essentially all traits that vary within a population are modulated by a set of genetic and environmental factors. Finding and characterizing the multiple genetic sources of variation is referred to as "genetic dissection" or "QTL mapping." In comparison, complex trait analysis has a slightly broader focus and includes the analysis of the effects of environmental perturbation, and gene-by-environment interactions on phenotypes; the "norm of reaction." Please also see the glossary term "Systems Genetics." [Williams RW, April 12, 2005] - -#### Composite Interval Mapping: - -Composite interval mapping is a method of mapping chromosomal regions that controls for some fraction of the genetic variability in a quantitative trait. Unlike simple interval mapping, composite interval mapping usually controls for variation produced at one or more background marker loci. These background markers are generally chosen because they are already known to be close to the location of a significant QTL. By factoring out a portion of the genetic variance produced by a major QTL, one can occasionally detect secondary QTLs. WebQTL allows users to control for a single background marker. To select this marker, first run the **Marker Regression** analysis (and if necessary, check the box labeled display all LRS, select the appropriate locus, and the click on either **Composite Interval Mapping** or **Composite Regression**. A more powerful and effective alternative to composite interval mapping is pair-scan analysis. This latter method takes into accounts (models) both the independent effects of two loci and possible two-locus epistatic interactions. [Williams RW, Dec 20, 2004] - -<div id="Correlations"></div> - -#### Correlations: Pearson and Spearman: - -GeneNetwork provides tools to compute both Pearson product-moment correlations (the standard type of correlation), Spearman rank order correlations. [Wikipedia](http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient) and introductory statistics text will have a discussion of these major types of correlation. The quick advice is to use the more robust Spearman rank order correlation if the number of pairs of observations in a data set is less than about 30 and to use the more powerful but much more sensitive Pearson product-moment correlation when the number of observations is greater than 30 AND after you have dealt with any outliers. GeneNetwork automatically flags outliers for you in the Trait Data and Analysis form. GeneNetwork also allows you to modify values by either deleting or winsorising them. That means that you can use Pearson correlations even with smaller sample sizes after making sure that data are well distributed. Be sure to view the scatterplots associated with correlation values (just click on the value to generate a plot). Look for bivariate outliers. - -#### Cross: - -The term Cross refers to a group of offspring made by mating (crossing) one strain with another strain. There are several types of crosses including intercrosses, backcrosses, advanced intercrosses, and recombinant inbred intercrosses. Genetic crosses are almost always started by mating two different but fully inbred strains to each other. For example, a B6D2F2 cross is made by breeding C57BL/6J females (B6 or B for short) with DBA/2J males (D2 or D) and then intercrossing their F1 progeny to make the second filial generation (F2). By convention the female is always listed first in cross nomenclature; B6D2F2 and D2B6F2 are therefore so-called reciprocal F2 intercrosses (B6D2F1 females to B6D2F1 males or D2B6F1 females to D2B6F1 males). A cross may also consist of a set of recombinant inbred (RI) strains such as the BXD strains, that are actually inbred progeny of a set of B6D2F2s. Crosses can be thought of as a method to randomize the assignment of blocks of chromosomes and genetic variants to different individuals or strains. This random assignment is a key feature in testing for causal relations. The strength with which one can assert that a causal relation exists between a chromosomal location and a phenotypic variant is measured by the LOD score or the LRS score (they are directly convertable, where LOD = LRS/4.61) [Williams RW, Dec 26, 2004; Dec 4, 2005]. - -[Go back to index](#index) - -<div id="d"></div> - -#### Dominance Effects: - -The term dominance indicates that the phenotype of intercross progeny closely resemble one of the two parental lines, rather than having an intermediate phenotype. Geneticists commonly refer to an allele as having a dominance effect or dominance deviation on a phenotype. Dominance deviation at a particular marker are calculated as the difference between the average phenotype of all cases that have the Aa genotype at that marker and the expected value half way between the all casese that have the aa genotype and the AA genotype. For example, if the average phenotype value of 50 individuals with the aa genotype is 10 units whereas that of 50 individuals with the AA genotype is 20 units, then we would expect the average of 100 cases with the Aa genotype to be 15 units. We are assuming a linear and perfectly additive model of how the a and A alleles interact. If these 100 Aa cases actually have a mean of 11 units, then this additive model would be inadequate. A non-linear dominance terms is now needed. In this case the low a alleles is almost perfectly dominant (or semi-dominant) and the dominance deviation is -4 units. - -The dominance effects are computed at each location on the maps generated by the WebQTL module for F2 populations (e.g., B6D2F2 and B6BTBRF2). Orange and purple line colors are used to distinguish the polarity of the dominance effects. Purple is the positive dominance effect that matches the polarity of the green additive effect, whereas orange is the negative dominance effect that matches the polarity of the red additive effect. - -Note that dominance deviations cannot be computed from a set of recombinant inbred strains because there are only two classes of genotypes at any marker (aa and AA, more usuually written AA and BB). However, when data for F1 hybrids are available one can estimate the dominance of the trait. This global phenotypic dominance has almost nothing to do with the dominance deviation at a single marker in the genome. In other words, the dominance deviation detected at a single marker may be reversed or neutralized by the action of many other polymorphic genes. [Williams RW, Dec 21, 2004; Sept 3, 2005] - -[Go back to index](#index) - -<div id="e"></div> - -#### Epistasis: - -Epistasis means that combined effects of two or more different loci or polymorphic genes are not what one would expect given the addition of their individual effects. There is, in other words, evidence for non-linear interactions among two or more loci. This is similar to the dominance effects mentioned above, but now generalized to two or more distinct loci, rather than to two or more alleles at a single locus. For example, if QTL 1 has an A allele that has an additive effects of +5 and QTL 2 has an A alleles that has an additive effect of +2, then the two locus genotype combination A/A would be expected to boost the mean by +7 units. But if the value of these A/A individuals was actually -7 we would be quite surprised and would refer to this as an epistatic interaction between QTL 1 and QTL 2. WebQTL will search for all possible epistatic interactions between pairs of loci in the genome. This function is called a **Pair Scan** becasue the software analyzes the LRS score for all possible pairs of loci. Instead of viewing an LRS plot along a single dimension, we now view a two-dimensional plot that shows a field of LRS scores computed for pairs of loci. Pair scan plots are extremely sensitive to outlier data. Be sure to review the primary data carefully using **Basic Statistics**. Also note that this more sophisiticated method also demands a significantly larger sample size. While 25 to 50 cases may be adequate for a conventional LRS plot (sometimes called a "main scan"), a **Pair-Scan** is hard to apply safely with fewer than 60 cases. [Williams RW, Dec 21, 2004; Dec 5, 2005] - -#### Effect Size of a QTL: - -QTLs can be ranked by the amount of variance that they explain--their so-called "effect size"--when they are included in a statistical model. The concept of a genetic model may seem odd to some users of GeneNetwork. A model is just an explicit hypothesis of how QTLs and other factors cause variation in a trait. QTL mapping involves comparisons of the explanatory power of different models. Effect sizes can be measured in different units including (1) the percentage of total or genetic variance that is explained by adding the QTL into the model, (2) the mean shift in Z score, or (3) the additive effect size expressed in the original measurement scale. Effects of single QTLs are often dependent on genetic background (i.e., other QTLs and their interactions) and on the numbers and types of cases used in a study. For example, the variance explained is influenced strongly by whether the sample are from a family cohort, a case-control cohort, a group of fully inbred strains such as recombinant inbred lines, an outcross or backcross population. - -Please note that the functional importance of a locus, QTL, or GWAS hit can not be predicted by the size of its effect on the trait in one environment, at one stage of development, and in one population. Estimates of the effect size of QTLs are usually both noisy and upwardly biased (overestimated), and both of these problems are particularly acute when sample sizes are small. - -Estimates of effect size for families of inbred lines, such as the BXD, HXB, CC, and hybrid diversity panels (e.g. the hybrid mouse diversity panel and the hybrid rat diversity panel) are typically (and correctly) much higher than those measured in otherwise similar analysis of intercrosses, heterogeneous stock (HS), or diversity outbred stock. Two factors contribute to the much higher level of explained variance of QTLs when using inbred strain panels. - - -1. **Replication Rate:** The variance that can be explained by a locus is increased by sampling multiple cases that have identical genomes and by using the strain mean for genetic analysis. Increasing replication rates from 1 to 6 can easily double the apparent heritability of a trait and therefore the effect size of a locus. The reason is simple—resampling decrease the standard error of mean, boosting the effective heritability (see Glossary entry on *Heritability* and focus on figure 1 from the Belknap [1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf) paper reproduced below).<br/>Compare the genetically explained variance (labeled h2RI in this figure) of a single case (no replication) on the x-axis with the function at a replication rate of 4 on the y-axis. If the explained variance is 0.1 (10% of all variance explained) then the value is boosted to 0.3 (30% of strain mean variance explained) with n = 4. - -2. **Homozygosity:** The second factor has to do with the inherent genetic variance of populations. Recombinant inbred lines are homozygous at nearly all loci. This doubles the genetic variance in a family of recombinant inbred lines compared to a matched number of F2s. This also quadruples the variance compared to a matched number of backcross cases. As a result 40 BXDs sampled just one per genometype will average 2X the genetic variance and 2X the heritability of 40 BDF2 cases. Note that panels made up of isogenic F1 hybrids (so-called diallel crosses, DX) made by crossing recombinant inbred strains (BXD, CC, or HXB) are no longer homozygous at all loci, and while they do expose important new sources of variance associated with dominance, they do not benefit from the 2X gain in genetic variance relative to an F2 intercross. - -<img width="600px" src="/static/images/Belknap_Fig1_1998.png" alt="Homozygosity" /> - -For the reasons listed above a QTL effect size of 0.4 detected a panel of BXD lines replicated four times each (160 cases total), corresponds approximately to an effect size of 0.18 in BXDs without replication (40 cases total), and to an effect size of 0.09 in an F2 of 40 cases total. [Williams RW, Dec 23, 2004; updated by RWW July 13, 2019] - -#### eQTL, cis eQTL, trans eQTL - -An expression QTL or eQTL. Differences in the expression of mRNA or proteins are often treated as standard phenotypes, much like body height or lung capacity. The variation in these microscopic traits (microtraits) can be mapped using conventional QTL methods. [Damerval](http://www.genetics.org/cgi/reprint/137/1/289) and colleagues were the first authors to use this kind of nomenclature and in their classic study of 1994 introduced the term PQLs for protein quantitative trait loci. Schadt and colleagues added the acronym eQTL in their early mRNA study of corn, mouse, and humans. We now are "blessed" with all kinds of prefixes to QTLs that highlight the type of trait that has been measured (m for metabolic, b for behavioral, p for physiological or protein). - -eQTLs of mRNAs and proteins have the unique property of (usually) having a single parent gene and genetic location. An eQTL that maps to the location of the parent gene that produces the mRNA or protein is referred to as a **cis eQTL** or local eQTL. In contrast, an eQTL that maps far away from its parent gene is referred to as a **trans eQTL**. You can use special search commands in GeneNetwork to find cis and trans eQTLs. [Williams RW, Nov 23, 2009, Dec 2009] - -[Go back to index](#index) - -<div id="f"></div> - -## F - -#### Frequency of Peak LRS: - -The height of the yellow bars in some of the Map View windows provides a measure of the confidence with which a trait maps to a particular chromosomal region. WebQTL runs 2000 bootstrap samples of the original data. (A bootstrap sample is a "sample with replacement" of the same size as the original data set in which some samples will by chance be represented one of more times and others will not be represented at all.) For each of these 2000 bootstraps, WebQTL remaps each and keeps track of the location of the single locus with the highest LRS score. These accumulated locations are used to produce the yellow histogram of "best locations." A frequency of 10% means that 200 of 2000 bootstraps had a peak score at this location. It the mapping data are robust (for example, insensitive to the exclusion of an particular case), then the bootstrap bars should be confined to a short chromosomal interval. Bootstrap results will vary slightly between runs due to the random generation of the bootstrap samples. [Williams RW, Oct 15, 2004] - -#### False Discovery Rate (FDR): - -A [false discovery](http://en.wikipedia.org/wiki/False_discovery_rate) is an apparently significant finding--usually determined using a particular P value alpha criterion--that given is known to be insignificant or false given other information. When performing a single statistical test we often accept a false discovery rate of 1 in 20 (p = .05). False discovery rates can climb to high levels in large genomic and genetic studies in which hundreds to millions of tests are run and summarized using standard "single test" p values. There are various statistical methods to estimate and control false discovery rate and to compute genome-wide p values that correct for large numbers of implicit or explicit statistical test. The Permutation test in GeneNetwork is one method that is used to prevent and excessive number of false QTL discoveries. Methods used to correct the FDR are approximations and may depend on a set of assumptions about data and sample structure. [Williams RW, April 5, 2008] - -[Go back to index](#index) - -<div id="g"></div> - -## G - -#### Genes, GenBankID, UniGeneID, GeneID, LocusID: - -GeneNetwork provides summary information on most of the genes and their transcripts. Genes and their alternative splice variants are often are poorly annotated and may not have proper names or symbols. However, almost all entries have a valid GenBank accession identifier. This is a unique code associated with a single sequence deposited in GenBank (Entrez Nucleotide). A single gene may have hundreds of GenBank entries. GenBank entries that share a genomic location and possibly a single gene are generally combined into a single UniGene entry. For mouse, these always begin with "Mm" (Mus musculus) and are followed by a period and then a number. More than half of all mouse UniGene identifiers are associated with a reputable gene, and these genes will have gene identifiers (GeneID). GeneIDs are identical to LocusLink identifiers (LocusID). Even a 10 megabase locus such as human Myopia 4 (MYP4) that is not yet associated with a specific gene is assigned a GeneID--a minor misnomer and one reason to prefer the term LocusID. - -See the related [FAQ](http://gn1.genenetwork.org/faq.html#Q-6) on "How many genes and transcripts are in your databases and what fraction of the genome is being surveyed?" [Williams RW, Dec 23, 2004, updated Jan 2, 2005] - -#### Genetic Reference Population (GRP): - -A genetic reference population consists of a set of genetically well characterized lines that are often used over a long period of time to study a multitude of different phenotypes. Once a GRP has been genotyped, subsequent studies can focus on the analysis of interesting and important phenotypes and their joint and independent relations. Most of the mouse GRPs, such as the BXDs used in the GeneNetwork, have been typed using a common set of over 14,000 makers (SNPs and microsatellites). Many of these same GRPs have been phenotyped extensively for more than 25 years, resulting in rich sets of phenotypes. A GRP is an ideal long-term resource for systems genetics because of the relative ease with which vast amounts of diverse data can be accumulated, analyzed, and combined. - -The power of GRPs and their compelling scientific advantages derive from the ability to study multiple phenotypes and substantial numbers of genetically defined individuals under one or more environmental conditions. When accurate phenotypes from 20 or more lines in a GRP have been acquired it becomes practical to explore and test the genetic correlations between that trait and any previously measured trait in the same GRP. This fact underlies the use of the term **reference** in GRP. Since each genetic individual is represented by an entire isogenic line--usually an inbred strain or an isogenic F1 hybrid--it is possible to obtain accurate mean phenotypes associated with each line simply by typing several individuals. GRPs are also ideal for developmental and aging studies because the same genetic individual can be phenotyped at multiple stages. - -A GRP can also be used a conventional mapping panel. But unlike most other mapping panel, a GRP can be easily adapted to jointly map sets of functionally related traits (multitrait mapping); a more powerful method to extract causal relations from networks of genetic correlations. - -The largest GRPs now consist of more than 400 recombinant inbred lines of *Arabidopsis* and [maize](http://www.maizegdb.org/cgi-bin/stockadvquery.cgi?check=true&name=&typebox=true&type=701&linkage_group=0&genvar1=&genvar2=&genvar3=&karyovar=0&phenotype=0&attribution=&avail_from=0&parent=0). The BayxSha Arabidopsis set in the GeneNetwork consists of 420 lines. Pioneer Hi-Bred International is rumored to have as many as 4000 maize RI lines. The largest mammalian GRPs are the LXS and BXD RI sets in the GeneNetwork. The Collaborative Cross is the largest mammalian GRP, and over 600 of these strains are now being bred by members of the Complex Trait Consortium. - -There are several subtypes of GRPs. In addition to recombinant inbred strains there are - - -- Recombinant congenic ([RCC](http://research.jax.org/grs/type/recombcong.htmll)) strains such as the [AcB](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=11374899&query_hl=4) set Consomic or chromosome substitution strains ([CSS](http://research.jax.org/grs/type/consomic.html)) of mice (Matin et al., [1999](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=10508525&query_hl=11)) and rats (Roman et al., [2002](http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12858554&query_hl=7)) - -- Recombinant intercross ([RIX](http://www.ncbi.nlm.nih.gov/pubmed/?term=15879512)) F1 sets made by mating different RI strains to each other to generate large set of R! first generation (F1) progeny (RIX). This is a standard ([diallel cross](http://en.wikipedia.org/wiki/Diallel_cross)) of RI inbred strains. Genetic analysis of a set of RIX progeny has some advantages over a corresponding analysis of RI strains. The first of these is that while each set of F1 progeny is fully isogenic (AXB1 x AXB2 gives a set of isogenic F1s), these F1s are not inbred but are heterozygous at many loci across the genome. RIX therefore retain the advance of being genetically defined and replicable, but without the disadvantage of being fully inbred. RIX have a genetic architecture more like natural populations. The second correlated advantage is that it is possible to study patterns of dominance of allelic variants using an RIX cross. Almost all loci or genes that differs between the original stock strains (A and B) will be heterozygous among a sufficiently larges set of RIX. A set of RIX progeny can therefore be mapped using the same methods used to map an F2 intercross. Mapping of QTLs may have somewhat more power and precision than when RI strains are used alone. A third advantage is that RIX sets make it possible to expand often limited RI resources to very large sizes to confirm and extend models of genetic or GXE effects. For example a set of 30 AXB strains can be used to generate a full matrix of 30 x 29 unique RIX progeny. The main current disadvantage of RIX panels is the comparative lack of extant phenotype data. - -- Recombinant F1 line sets can also be made by backcrossing an entire RI sets to a single inbred line that carries an interesting mutation or transgene (RI backcross or RIB). GeneNetwork includes one RI backcross sets generated by Kent Hunter. In this RIB each of 18 AKXD RI strains were crossed to an FVB/N line that carries a tumor susceptibility allele (polyoma middle T). - -All of these sets of lines are GRPs since each line is genetically defined and because the set as a whole can in principle be easily regenerated and phenotyped. Finally, each of these resources can be used to track down genetic loci that are causes of variation in phenotype using variants of standard linkage analysis. - -A Diversity Panel such as that used by the Mouse Phenome Project is not a standard GRPs, although its also shares the ability to accumulate and study networks of phenotypes. The main difference is that a Diversity Panel cannot be used for conventional linkage analysis. A sufficiently large Diversity Panel could in principle be used for the equivalent of an assocation study. However, these are definitely NOT in silico studies, because hundreds of individuals need to be phenotyped for every trait. Surveys of many diverse isogenic lines (inbred or F1 hybrids) is statistically the equivalent of a human association study (the main difference is the ability to replicate measurements and study sets of traits) and therefore, like human association studies, does require very high sample size to map polygenic traits. Like human association studies there is also a high risk of false positive results due to population stratification and non-syntenic marker association. - -A good use of a Diversity Panel is as a fine-mapping resource with which to dissect chromosomal intervals already mapped using a conventional cross or GRP. GeneNetwork now includes Mouse Diversity Panel (MDP) data for several data sets. We now typically include all 16 sequenced strains of mice, and add PWK/PhJ, NZO/HiLtJ (two of the eight members of the Collaborative Cross), and several F1 hybrids. The MDP data is often appended at the bottom of the GRP data set with which is was acquired (e.g., BXD hippocampal and BXD eye data sets). [Williams RW, June 19, 2005; Dec 4, 2005] - -#### Genotype - -The state of a gene or DNA sequence, usually used to describe a contrast between two or more states, such as that between the normal state (wildtype) and a mutant state (mutation) or between the alleles inherited from two parents. All species that are included in GeneNetwork are diploid (derived from two parents) and have two copies of most genes (genes located on the X and Y chromosomes are exceptions). As a result the genotype of a particular diploid individual is actually a pair of genotypes, one from each parents. For example, the offspring of a mating between strain A and strain B will have one copy of the A genotype and one copy of the B genotype and therefore have an A/B genotype. In contrast, offspring of a mating between a female strain A and a male strain A will inherit only A genotypes and have an A/A genotype. - -Genotypes can be measured or inferred in many different ways, even by visual inspection of animals (e.g. as Gregor Mendel did long before DNA was discovered). But now the typical method is to directly test DNA that has a well define chromosomal location that has been obtained from one or usually many cases using molecular tests that often rely on polymerase chain reaction steps and sequence analysis. Each case is genotyped at many chromosomal locations (loci, markers, or genes). The entire collection of genotypes (as many a 1 million for a single case) is also sometimes referred to as the cases genotype, but the word "genometype" might be more appropriate to highlight the fact that we are now dealing with a set of genotypes spanning the entire genome (all chromosomes) of the case. - -For gene mapping purposes, genotypes are often translated from letter codes (A/A, A/B, and B/B) to simple numerical codes that are more suitable for computation. A/A might be represented by the value -1, A/B by the value 0, and B/B by the value +1. This recoding makes it easy to determine if there is a statistically significant correlation between genotypes across of a set of cases (for example, an F2 population or a Genetic Reference Panel) and a variable phenotype measured in the same population. A sufficiently high correlation between genotypes and phenotypes is referred to as a quantitative trait locus (QTL). If the correlation is almost perfect (r > 0.9) then correlation is usually referred to as a Mendelian locus. Despite the fact that we use the term "correlation" in the preceding sentences, the genotype is actually the cause of the phenotype. More precisely, variation in the genotypes of individuals in the sample population cause the variation in the phenotype. The statistical confidence of this assertion of causality is often estimated using LOD and LRS scores and permutation methods. If the LOD score is above 10, then we can be extremely confident that we have located a genetic cause of variation in the phenotype. While the location is defined usually with a precision ranging from 10 million to 100 thousand basepairs (the locus), the individual sequence variant that is responsible may be quite difficult to extract. Think of this in terms of police work: we may know the neighborhood where the suspect lives, we may have clues as to identity and habits, but we still may have a large list of suspects. - -Text here [Williams RW, July 15, 2010] - -[Go back to index](#index) - -<div id="h"></div> - -## H - -#### Heritability, h<sup>2</sup>: - -Heritability is a rough measure of the ability to use genetic information to predict the level of variation in phenotypes among progeny. Values range from 0 to 1 (or 0 to 100%). A value of 1 or 100% means that a trait is entirely predictable based on paternal/materinal and genetic data (in other words, a Mendelian trait), whereas a value of 0 means that a trait is not at all predictable from information on gene variants. Estimates of heritability are highly dependent on the environment, stage, and age. - -Important traits that affect fitness often have low heritabilities because stabilizing selection reduces the frequency of DNA variants that produce suboptimal phenotypes. Conversely, less critical traits for which substantial phenotypic variation is well tolerated, may have high heritability. The environment of laboratory rodents is unnatural, and this allows the accumulation of somewhat deleterious mutations (for example, mutations that lead to albinism). This leads to an upward trend in heritability of unselected traits in laboratory populations--a desirable feature from the point of view of the biomedical analysis of the genetic basis of trait variance. Heritability is a useful parameter to measure at an early stage of a genetic analysis, because it provides a rough gauge of the likelihood of successfully understanding the allelic sources of variation. Highly heritable traits are more amenable to mapping studies. There are numerous ways to estimate heritability, a few of which are described below. [Williams RW, Dec 23, 2004] - -#### h<sup>2</sup> Estimated by Intraclass Correlation: - -Heritability can be estimated using the intraclass correlation coefficient. This is essentially a one-way repeated measures analysis of variance (ANOVA) of the reliability of trait data. Difference among strains are considered due to a random effect, whereas variation among samples within a single strain are considered due to measurement error. One can use the method implemented by SAS (PROC VARCOMP) that exploits a restricted maximum likelihood (REML) approach to estimate the intraclass correlation coefficient instead of an ordinary least squares method. The general equation for the intraclass correlation is: - -<math>r = (Between-strain MS - Within-strain MS)/(Between-strain MS + (n-1)x Within-strain MS)</math> - -where n is the average number of cases per strain. The intraclass correlation approaches 1 when there is minimal variation within strains, and strain means differ greatly. In contrast, if difference between strains are less than what would be predicted from the differences within strain, then the intraclass correlation will produce negative estimates of heritability. Negative heritability is usually a clue that the design of the experiment has injected excessive within-strain variance. It is easy for this to happen inadvertently by failing to correct for a batch effect. For example, if one collects the first batch of data for strains 1 through 20 during a full moon, and a second batch of data for these same strains during a rare blue moon, then the apparent variation within strain may greatly exceed the among strain variance. A technical batch effect has been confounded with the within-strain variation and has swamped any among-strain variance. What to do? Fix the batch effect, sex effect, age effect, etc., first! [Williams RW, Chesler EJ, Dec 23, 2004] - -#### h<sup>2</sup> Estimated using Hegmann and Possidente's Method (Adjusted Heritability in the Basic Statisics): - -A simple estimate of heritability for inbred lines involves comparing the variance between strain means (Va) to the total variance (Vt) of the phenotype, where Va is the a rough estimate of the additive genetic variance and Vt is the equal to Va and the average environmental variance, Ve. For example, if we study 10 cases of each of 20 strains, we have a total variance of the phenotype across 200 samples, and a strain mean variance across 20 strain averages. We can use this simple equation to estimate the heritability: - -<math>h<sup>2</sup> = Va / Vt</math> - -This estimate of heritability will be an **overestimate**, and the severity of this bias will be a function of the within-strain standard error of the mean. Even a random data set of 10 each of 20 strains that should have an h2 of 0, will often give h2 values of 0.10 to 0.20. (Try this in a spreadsheet program using random numbers.) - -However, this estimate of h2 cannot be compared directly to those calculated using standard intercrosses and backcrosses. The reason is that all cases above are fully inbred and no genotypes are heterozygous. As a result the estimate of Va will be inflated two-fold. Hegmann and Possidente (1981 suggested a simple solution; adjust the equation as follows: - -<math>h<sup>2</sup> = 0.5Va / (0.5Va+Ve)</math> - -The factor 0.5 is applied to Va to adjust for the overestimation of additive genetic variance among inbred strains. This estimate of heritability also does not make allowances for the within-strain error term. The 0.5 adjustment factor is not recommended any more because h2 is severely **underestimated**. This adjustment is really only needed if the goal is to compare h2 between intercrosses and those generated using panels of inbred strains. - -#### h<sup>2</sup>RIx̅ - -Finally, heritability calculations using strain means, such as those listed above, do not provide estimates of the effective heritability achieved by resampling a given line, strain, or genometype many times. Belknap ([1998](http://gn1.genenetwork.org/images/upload/Belknap_Heritability_1998.pdf)) provides corrected estimates of the effective heritability. Figure 1 from his paper (reproduced below) illustrates how resampling helps a great deal. Simply resampling each strain 8 times can boost the effective heritability from 0.2 to 0.8. The graph also illustrates why it often does not make sense to resample much beyond 4 to 8, depending on heritability. Belknap used the term h2RIx̅ in this figure and paper, since he was focused on data generated using recombinant inbred (RI) strains, but the logic applies equally well to any panel of genomes for which replication of individual genometypes is practical. This h2RIx̅ can be calculated simply by: -<math>h<sup>2</sup><sub>RIx̅</sub> = V<sub>a</sub> / (V<sub>a</sub>+(V<sub>e</sub>/n))</math> where V<sub>a</sub> is the genetic variability (variability between strains), V<sub>e</sub> is the environmental variability (variability within strains), and n is the number of within strain replicates. Of course, with many studies the number of within strain replicates will vary between strains, and this needs to be dealt with. A reasonable approach is to use the harmonic mean of n across all strains. - -<img width="600px" src="/static/images/Belknap_Fig1_1998.png" alt="Homozygosity" /> - -An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different heritability values QTL effect sizes, cohort sizes, and replication rates: - -**[Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/)** - -We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Feb 1, 2019] [Chesler EJ, Dec 20, 2004; RWW updated March 7, 2018; Ashbrook DG, updated Feb 1, 2019] - -#### Hitchhiking Effect: - -Conventional knockout lines (KOs) of mice are often mixtures of the genomes of two strains of mice. One important consequence of this fact is that a conventional comparison of wildtype and KO litter mates does not only test of the effects of the KO gene itself but also tests the effects of thousands of "hitchhiking" sequence polymorphisms in genes that flank the KO gene. This experimental confound can be difficult to resolve (but see below). This problem was first highlighted by Robert Gerlai ([1996](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)). - -**Genetics of KO Lines**. The embryonic stem cells used to make KOs are usually derived from a 129 strain of mouse (e.g., 129/OlaHsd). Mutated stem cells are then added to a C57BL/6J blastocyst to generate B6x129 chimeric mice. Germline transmission of the KO allele is tested and carriers are then used to establish heterozygous +/- B6.129 KO stock. This stock is often crossed back to wildtype C57BL/6J strains for several generations. At each generation the transmission of the KO is checked by genotyping the gene or closely flanking markers in each litter of mice. Carriers are again selected for breeding. The end result of this process is a KO congenic line in which the genetic background is primarily C57BL/6J except for the region around the KO gene. - -It is often thought that 10 generations of backcrossing will result in a pure genetic background (99.8% C57BL/6J). Unfortunately, this is not true for the region around the KO, and even after many generations of backcrossing of KO stock to C57BL/6J, a large region around the KO is still derived from the 129 substrain (see the residual white "line" at N10 in the figure below. - -<img width="300px" src="/static/images/Congenic.png" alt="Congenic" /> - -After 20 generations of backcrossing nearly +/-5 cM on either side of the KO will still usually be derived from 129 (see [Figure 3.6](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)) This amounts to an average of +/- 10 megabases of DNA around the KO. The wildtype littermates do NOT have this flanking DNA from 129 and they will be like a true C57BL/6J. The +/- 10 megabases to either side of the KO is known as the "hitchhiking" chromosomal interval. Any polymorphism between 129 and B6 in this interval has the potential to have significant downstream effects on gene expression, protein expression, and higher order traits such as anxiety, activity, and maternal behavior. Much of the conventional KO literature is highly suspect due to this hitchhiker effect (see Gerlai R, [Trends in Neurosci 1996 19:177](http://gn1.genenetwork.org/images/upload/Gerlai_TINS_1996.pdf)). - -As one example, consider the thyroid alpha receptor hormone gene Thra and its KO. Thra maps to Chr 11 at about 99 Mb. A conventional KO made as described above will have a hitchhiking 129 chromosomal interval extending from about 89 Mb to 109 Mb even after 20 generations of backcrossing to B6. Since the mouse genome is about 2.6 billion base pairs and contains about 26,000 genes, this 20 Mb region will typically contain about 200 genes. The particular region of Chr 11 around Thra has an unusually high density of genes (2-3X) and includes many highly expressed and polymorphic genes, including *Nog*, *Car10*, *Cdc34*, *Col1a1*, *Dlx4*, *Myst2*, *Ngfr*, *Igf2bp1*, *Gip*, the entire *Hoxb* complex, *Sp6*, *Socs7*, *Lasp1*, *Cacnb1*, *Pparbp*, *Pnmt*, *Erbb2*, *Grb7*, *Nr1d1*, *Casc3*, *Igfbp4*, and the entire *Krt1* complex. Of these gene roughly half will be polymorphic between B6 and 129. It is like having a busload of noisy and possibly dangerous hitchhikers. Putative KO effects may be generated by a complex subset of these 100 polymorphic genes. - -What is the solution? - -1. Do not use litter mates as controls without great care. They are not really the correct genetic control. The correct genetic control is a congenic strain of the same general type without the KO or with a different KO in a nearby gene. These are often available as KOs in neighboring genes that are not of interest. For example, the gene *Casc3* is located next to Thra. If a KO in Casc3 is available, then compare the two KOs and see if phenotypes of the two KOs differ ways predicted given the known molecular functions of the gene. - -2. Use a KO in which the KO has been backcrossed to a 129 strain--ideally the same strain from which ES cells were obtained. This eliminates the hitchhiker effect entirely and the KO, HET, and WT littermates really can be compared. - -3. Use a conditional KO. - -4. Compare the phenotype of the two parental strains--129 and C57BL/6J and see if they differ in ways that might be confounded with the effects of the KO. - -<img width="600px" src="/static/images/SilverFig3_6.png" alt="Homozygosity" /> - -Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silver/index.shtml) - -[Go back to index](#index) - -<div id="i"></div> - -## I - -#### Interquartile Range: - -The interquartile range is the difference between the 75% and 25% percentiles of the distribution. We divide the sample into a high and low half and then compute the median for each of these halves. In other words we effectively split our sample into four ordered sets of values known as quartiles. The absolute value of the difference between the median of the lower half and the median of the upper half is also called the interquartile range. This estimate of range is insenstive to outliers. If you are curious you might double the IQR to get an interquartile-range-based estimate of the full range. Of course, keep in mind that range is dependent on the sample size. For theis reason the coeffficient of variation (the standard deviation divided by the mean) is a better overall indicator of dispersion of values around the mean that is less sensitive to sample size. [Williams RW, Oct 20, 2004; Jan 23, 2005] - -#### Interval Mapping: - -Interval mapping is a process in which the statistical significance of a hypothetical QTL is evaluated at regular points across a chromosome, even in the absence of explicit genotype data at those points. In the case of WebQTL, significance is calculated using an efficient and very rapid regression method, the Haley-Knott regression equations ([Haley CS, Knott SA. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers; Heredity 69:315–324](http://www.ncbi.nlm.nih.gov/pubmed/16718932)), in which trait values are compared to the known genotype at a marker or to the probability of a specific genotype at a test location between two flanking markers. (The three genotypes are coded as -1, 0, and +1 at known markers, but often have fractional values in the intervals between markers.) The inferred probability of the genotypes in regions that have not been genotyped can be estimated from genotypes of the closest flanking markers. GeneNetwork/WebQTL compute linkage at intervals of 1 cM or less. As a consequence of this approach to computing linkage statistics, interval maps often have a characteristic shape in which the markers appear as sharply defined inflection points, and the intervals between nodes are smooth curves. [Chesler EJ, Dec 20, 2004; RWW April 2005; RWW Man 2014] - -#### Interval Mapping Options: - -- _Permutation Test_: Select this option to determine the approximate LRS value that matches a genome-wide p-value of .05. - -- _Bootstrap Test_: Select this option to evaluate the consistency with which peak LRS scores cluster around a putative QTL. Deselect this option if it obscures the SNP track or the additive effect track. - -- _Additive Effect_: The additive effect (shown by the red lines in these plots) provide an estimate of the change in the average phenotype that is brought about by substituting a single allele of one type with that of another type. - -- _SNP Track_: The SNP Seismograph Track provides information on the regional density of segregating variants in the cross that may generate trait variants. It is plotted along the X axis. If a locus spans a region with both high and low SNP density, then the causal variant has a higher prior probability to be located in the region with high density than in the region with low density. - -- _Gene Track_: This track overlays the positions of known genes on the physical Interval Map Viewer. If you hover the cursor over genes on this track, minimal information (symbol, position, and exon number) will appear. - -- _Display from X Mb to Y Mb_: Enter values in megabases to regenerate a smaller or large map view. - -- _Graph width (in pixels)_: Adjust this value to obtain larger or smaller map views (x axis only). - -[Go back to index](#index) - -<div id="j"></div> - -## J - -[Go back to index](#index) - -<div id="k"></div> - -## K - -[Go back to index](#index) - -<div id="l"></div> - -## L - -<div id="Literature"></div> - -#### Literature Correlation: - -The literature correlation is a unique feature in GeneNetwork that quantifies the similarity of words used to describe genes and their functions. Sets of words associated with genes were extracted from MEDLINE/PubMed abstracts (Jan 2017 by Ramin Homayouni, Diem-Trang Pham, and Sujoy Roy). For example, about 2500 PubMed abstracts contain reference to the gene "Sonic hedgehog" (Shh) in mouse, human, or rat. The words in all of these abstracts were extracted and categorize by their information content. A word such as "the" is not interesting, but words such as "dopamine" or "development" are useful in quantifying similarity. Sets of informative words are then compared—one gene's word set is compared the word set for all other genes. Similarity values are computed for a matrix of about 20,000 genes using latent semantic indexing [(see Xu et al., 2011)](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018851). Similarity values are also known as literature correlations. These values are always positive and range from 0 to 1. Values between 0.5 and 1.0 indicate moderate-to-high levels of overlap of vocabularies. - -The literature correlation can be used to compare the "semantic" signal-to-noise of different measurements of gene, mRNA, and protein expression. Consider this common situation:There are three probe sets that measure Kit gene expression (1459588\_at, 1415900\_a\_at, and 1452514\_a\_at) in the Mouse BXD Lung mRNA data set (HZI Lung M430v2 (Apr08) RMA). Which one of these three gives the best measurement of Kit expression? It is impractical to perform quantitative rtPCR studies to answer this question, but there is a solid statistical answer that relies on **Literature Correlation**. Do the following: For each of the three probe sets, generate the top 1000 literature correlates. This will generate three apparently identical lists of genes that are known from the PubMed literature to be associated with the Kit oncogene. But the three lists are NOT actually identical when we look at the **Sample Correlation** column. To answer the question "which of the three probe sets is best", review the actual performance of the probe sets against this set of 1000 "friends of Kit". Do this by sorting all three lists by their Sample Correlation column (high to low). The clear winner is probe set 1415900_a_at. The 100th row in this probe set's list has a Sample Correlation of 0.620 (absolute value). In comparison, the 100th row for probe set 1452514_a_at has a Sample Correlation of 0.289. The probe set that targets the intron comes in last at 0.275. In conclusion, the probe set that targets the proximal half of the 3' UTR (1415900_a_at) has the highest "agreement" between Literature Correlation and Sample Correlation, and is our preferred measurement of Kit expression in the lung in this data set. (Updated by RWW and Ramin Homayouni, April 2017.) - -<div id="LOD"></div> - -#### LOD: - -The logarithm of the odds (LOD) provides a measure of the association between variation in a phenotype and genetic differences (alleles) at a particular chromosomal locus (see Nyholt [2000](http://www.sciencedirect.com/science/article/pii/S0002929707626391) for a lovely review of LOD scores). - -A LOD score is defined as the logarithm of the ratio of two likelihoods: (1) in the numerator the likelihood for the alternative hypothesis, namely that there is linkage at the chromosomal marker, and (2) the likelihood of the null hypothesis that there is no linkage. Likelihoods are probabilities, but they are not Pr(hypothesis | data) but rather Pr(data | two alternative hypotheses). That's why they are called likelihoods rather than probabilities. (The "|" symbol above translates to "given the"). Since LOD and LRS scores are associated with two particular hypotheses or models, they are also associated with the degrees of freedom of those two alternative models. When the model only has one degree of freedom this conversion between LOD to p value will work: -<pre> - lodToPval <- - function(x) - { - pchisq(x*(2*log(10)),df=1,lower.tail=FALSE)/2 - } - # (from https://www.biostars.org/p/88495/ ) -</pre> - -In the two likelihoods, one has maximized over the various nuisance parameters (the mean phenotypes for each genotype group, or overall for the null hypothesis, and the residual variance). Or one can say, one has plugged in the maximum likelihood estimates for these nuisance parameters. - -With complete data at a marker, the log likelihood for the normal model reduces to the (-n/2) times the log of the residual sum of squares. - -LOD values can be converted to LRS scores (likelihood ratio statistics) by multiplying by 4.61. The LOD is also roughly equivalent to the -log(P), where P is the probability of linkage (P = 0.001 => 3). The LOD itself is not a precise measurement of the probability of linkage, but in general for F2 crosses and RI strains, values above 3.3 will usually be worth attention for simple interval maps. [Williams RW, June 15, 2005, updated with text from Karl Broman, Oct 28, 2010, updated Apr 21, 2020 with Nyholt reference]. - -<div id="LRS"></div> - -#### LRS: - -In the setting of mapping traits, the likelihood ratio statistic is used as a measurement of the association or linkage between differences in traits and differences in particular genotype markers. LRS or LOD values are usually plotted on the y-axis, whereas chromosomal location of the marker are usually plotted on the x-axis. In the case of a whole genome scan--a sequential analysis of many markers and locations across the entire genome--LRS values above 10 to 15 will usually be worth attention for when mapping with standard experimental crosses (e.g., F2 intercrosses or recombinant inbred strains). The term "likelihood ratio" is used to describe the relative probability (likelihood) of two different explanations of the variation in a trait. The first explanation (or model or hypothesis H1) is that the differences in the trait ARE associated with that particular DNA sequence difference or marker. Very small probability values indicate that H1 is probably true. The second "null" hypothesis (Hnull or H0) is that differences in the trait are NOT associated with that particular DNA sequence. We can use the ratio of these two probabilities and models (H1 divided by H0) as our score. The math is a little bit more complicated and the LRS score is actually equal to -2 times the ratio of the natural logarithms of the two probabilities. For example, if the probability of H0 is 0.05 (only a one-in-twenty probability that the marker is associated with the trait by chance), whereas and the probability of H1 is 1 (the marker is certainly not linked to the trait), then the LRS value is 5.991. In Excel the equation giving the LRS result of 5.991 would look like this "=-2*(LN(0.05)-LN(1)). [Williams RW, Dec 13, 2004, updated Nov 18, 2009, updated Dec 19, 2012] - -[Go back to index](#index) - -<div id="m"></div> - -## M - -Marker Regression: - -The relationship between differences in a trait and differences in alleles at a marker (or gene variants) can be computed using a regression analysis (genotype vs phenotype) or as a simple Pearson product moment correlation. Here is a simple example that you can try in Excel to understand marker-phenotype regression or marker-phenotype correlation: enter a row of phenotype and genotype data for 20 strains in an Excel spreadsheet labeled "Brain weight." The strains are C57BL/6J, DBA/2J, and 20 BXD strains of mice (1, 2, 5, 6, 8, 9, 12, 13, 14, 15, 16, 18, 21, 22, 23, 24, 25, 27, 28, and 29. The brains of these strains weigh an average (in milligrams) of 465, 339, 450, 390, 477, 361, 421, 419, 412, 403, 429, 429, 436, 427, 409, 431, 432, 380, 394, 381, 389, and 375. (These values are taken from BXD Trait 10032; data by John Belknap and colleagues, 1992. Notice that data are missing for several strains including the extinct lines BXD3, 4, and 7. Data for BXD11 and BXD19 (not extinct) are also missing. In the second row enter the genotypes at a single SNP marker on Chr 4 called "rs13478021" for the subset of strains for which we have phenotype data. The genotypes at rs1347801 are as follows for 20 BXDs listed above: D B D B D B D D D D D B D B D B D B D B. This string of alleles in the parents and 20 BXDs is called a strains distribution pattern (SDP). Let's convert these SDP letters into more useful numbers, so that we can "compute" with genotypes. Each B allele gets converted into a -1 and each D allele gets converted into a +1. In the spreadsheet, the data set of phenotypes and genotypes should look like this. - -<pre> - Strain BXD1 BXD2 BXD5 6 8 9 12 13 14 15 16 18 21 22 23 24 25 27 28 29 - Brain_weight 450 390 477 361 421 419 412 403 429 429 436 427 409 431 432 380 394 381 389 375 - Marker_rs1347801 D B D B D B D D D D D B D B D B D B D B - Marker_code 1 -1 1 -1 1 -1 1 1 1 1 1 -1 1 -1 1 -1 1 -1 1 -1 -</pre> - -To compute the marker regression (or correlation) we just compare values in Rows 2 and 4. A Pearson product moment correlation gives a value of r = 0.494. A regression analysis indicates that on average those strains with a D allele have a heavier brain with roughly a 14 mg increase for each 1 unit change in genotype; that is a total of about 28 mg if all B-type strains are compared to all D-type strains at this particular marker. This difference is associated with a p value of 0.0268 (two-tailed test) and an LRS of about 9.8 (LOD = 9.8/4.6 or about 2.1). Note that the number of strains is modest and the results are therefore not robust. If you were to add the two parent strains (C57BL/6J and DBA/2J) back into this analysis, which is perfectly fair, then the significance of this maker is lost (r = 0.206 and p = 0.3569). Bootstrap and permutation analyses can help you decide whether results are robust or not and whether a nominally significant p value for a single marker is actually significant when you test many hundreds of markers across the whole genome (a so-called genome-wide test with a genome-wide p value that is estimated by permutation testing). [RWW, Feb 20, 2007, Dec 14, 2012] - -[Go back to index](#index) - -<div id="n"></div> - -## N - -#### Normal Probability Plot: - -A [normal probability plot](http://en.wikipedia.org/wiki/Normal_probability_plot) is a powerful tool to evaluate the extent to which a distribution of values conforms to (or deviates from) a normal Gaussian distribution. The Basic Statistics tools in GeneNetwork provides these plots for any trait. If a distribution of numbers is normal then the actual values and the predicted values based on a z score (units of deviation from the mean measured in standard deviation units) will form a nearly straight line. These plots can also be used to efficiently flag outlier samples in either tail of the distribution. - -In genetic studies, the probability plot can be used to detect the effects of major effect loci. A classical Mendelian locus will typically be associated with either a bimodal or trimodal distribution. In the plot below based on 99 samples, the points definitely do not fall on a single line. Three samples (green squares) have unusually high values; the majority of samples fall on a straight line between z = -0.8 to z = 2; and 16 values have much lower trait values than would be predicted based on a single normal distribution (a low mode group). The abrupt discontinuity in the distribution at -0.8 z is due to the effect of a single major Mendelian effect. - -Deviations from normality of the sort in the figure below should be considered good news from the point of view of likely success of tracking down the locations of QTLs. However, small numbers of outliers may require special statistical handling, such as their exclusion or [winsorising](http://en.wikipedia.org/wiki/Winsorising) (see more below on "Winsorizing"). [RWW June 2011] - -<img width="600px" src="/static/images/Normal_Plot.gif" alt="Homozygosity" /> - -[Go back to index](#index) - -<div id="o"></div> - -## O - -#### Outliers: (also see [Wikipedia](http://en.wikipedia.org/wiki/Outlier)) - -Statistical methods often assume that the distribution of trait values is close to a Gaussian normal bell-shaped curve and that there are no outlier values that are extremely high or low compared to the average. Some traits can be clearly split into two or more groups (affected cases and unaffected cases) and this is not a problem as long as the number of cases in each group is close to the number that you expected by chance and that your sample size is reasonable high (40 or more for recombinant inbred strains). Mapping functions and most statistical procedure in GeneNetwork should work reasonable well (the pair scan function for epistatic interactions is one possible exception). - -However, correlations and QTL mapping methods can be highly sensitive to outlier values. Make sure you review your data for outliers before mapping. GeneNetwork flags all outliers for you in the Trait Data and Analysis window and gives you the option of zapping these extreme values. Options include (1) do nothing, (2) delete the outliers and see what happens to your maps, (3) [Winsorize](http://en.wikipedia.org/wiki/Winsorising) the values of the outliers. You might try all three options and determine if your main results are stable or not. With small samples or extreme outliers, you may find the correlation and mapping results to be volatile. In general, if results (correlations, QTL positions or QTL LRS score) depend highly on one or two outliers (5-10% of the samples) then you should probably delete or winsorize the outliers. - -In order to calculate outliers, we first determine the Q1(25%) and Q3(75%) values and then multiply by a constant (in our case 1.5; a higher constant is less sensitive to outliers). This value is then subtracted from the Q1 value and added to the Q3 value in order to determine the lower and upper bounds. Values that fall above the upper bound or below the lower bound are considered outliers. - -<small>The method is summarized [here](http://www.wikihow.com/Calculate-Outliers). [Sloan ZA, Oct 2013] </small> - -[Go back to index](#index) - -<div id="p"></div> - -## P - -#### Pair-Scan, 2D Genome Scan, or Two-QTL Model: - -The pair scan function evaluates pairs of intervals (loci) across the genome to determine how much of the variability in the trait can be explained jointly by two putative QTLs. The pair scan function in GeneNetwork is used to detect effects of pairs of QTLs that have epistatic interactions, although this function also evaluates summed additive effects of two loci. Trait variance is evaluated using a general linear model that has this structure (called a "model"): - -<math>Variance V(trait) = QTL1 + QTL2 + QTL1xQTL2 + error</math> (where the = sign should be read "a function of" - -This model is also known as the Full Model (LRS Full in the output table), where QTL1 and QTL2 are the independent additive effects associated with two unlinked loci (the so-called main effects) and QTL1xQTL2 is the interaction term (LRS Interact in the output table). An LRS score is computed for this full model. This is computation identical to computing an ANOVA that allows for an interaction term between two predictors. The additive model that neglects the QTL1XQTL2 term is also computed. - -The output table in GeneNetwork list the the two intervals at the top of the table (Interval 1 to the left and Interval 2 to the far right). The LRS values for different components of the model are shown in the middle of the table (LRS Full, LRS Additive, LRS Interact, LRS 1, and LRS 2). Note that LRS 1 and LRS 2 will usually NOT sum to LRS Additive. - -CAUTIONS and LIMITATIONS: Pair-scan is only implemented for recombinant inbred strains. We do not recommend the use of this function with sample sizes of less than 60 recombinant inbred strains. Pair-scan procedures need careful diagnostics and an be very sensitive to outliers and to the balance among the four possible two-locus genotype classes among a set of RI strains. Pair-scan is not yet implemented for F2 progeny. - -GeneNetwork implements a rapid but non-exhaustive DIRECT algorithm (Lundberg et al., [2004](http://bioinformatics.oxfordjournals.org/content/20/12/1887.full.pdf)) that efficiently searches for epistatic interactions. This method is so fast that it is possible to compute 500 permutations to evaluate non-parametric significance of the joint LRS value within a minute. This makes DIRECT ideal for an interactive web service. Karl Broman's [R/qtl](http://www.rqtl.org/tutorials/rqtltour.pdf) implements an exhaustive search using the "scantwo" function. [RWW, May 2011] - -#### Partial Correlation: - -Partial correlation is the correlation between two variables that remains after controlling for one or more other variables. Idea and techniques used to compute partial correlations are important in testing causal models ([Cause and Correlation in Biology](http://www.amazon.com/Cause-Correlation-Biology-Structural-Equations/dp/0521529212), Bill Shipley, 2000). For instance, r1,2||3,4 is the partial correlation between variables 1 and 2, while controlling for variables 3 and 4 (the || symbol is equivalent to "while controlling for"). We can compare partial correlations (e.g., r1,2||3,4) with original correlations (e.g., r1,2). If there is an insignificant difference, we infer that the controlled variables have minimal effect and may not influence the variables or even be part of the model. In contrast, if the partial correlations change significantly, the inference is that the causal link between the two variables is dependent to some degree on the controlled variables. These control variables are either anteceding causes or intervening variables. (text adapted from D Garson's original by RWW). - -For more on [partial correlation](http://faculty.chass.ncsu.edu/garson/PA765/partialr.htm) please link to this great site by David Garson at NC State. - -For more on dependence separation ([d-separation](http://www.andrew.cmu.edu/user/scheines/tutor/d-sep.html)) and constructing causal models see Richard Scheines' site. - -Why would you use of need partial correlations in GeneNetwork? It is often useful to compute correlations among traits while controlling for additional variables. Partial correlations may reveal more about the causality of relations. In a genetic context, partial correlations can be used to remove much of the variance associated with linkage and linkage disequilibrium. You can also control for age, age, and other common cofactors. - -Please see the related Glossary terms "Tissue Correlation". [RWW, Aug 21, 2009; Jan 30, 2010] - -#### PCA Trait or Eigentrait: - -If you place a number of traits in a Trait Collection you can carry out some of the key steps of a principal component analysis, including defining the variance directed along specific principal component eigenvectors. You can also plot the positions of cases against the first two eigenvectors; in essence a type of scatterplot. Finally, GeneNetwork allows you to exploit PCA methods to make new "synthetic" eigentraits from collections of correlated traits. These synthetic traits are the values of cases along specific eigenvectors and they may be less noisy than single traits. If this seems puzzling, then have a look at these useful PCA explanation by [G. Dallas](http://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/) and by [Powell and Lehe](http://setosa.io/ev/principal-component-analysis/). **How to do it:** You can select and assemble many different traits into a single **Trait Collection** window using the check boxes and **Add To Collection** buttons. One of the most important function buttons in the **Collection** window is labeled **Correlation Matrix**. This function computes Pearson product moment correlations and Spearman rank order correlations for all possible pairs of traits in the Collection window. It also perfoms a principal component or factor analysis. For example, if you have 20 traits in the Collection window, the correlation matrix will consist of 20*19 or 190 correlations and the identity diagonal. Principal components analysis is a linear algebraic procedure that finds a small number of independent factors or principal components that efficiently explain variation in the original 20 traits. It is a effective method to reduce the dimensionality of a group of traits. If the 20 traits share a great deal of variation, then only two or three factors may explain variation among the traits. Instead of analyzing 20 traits as if they were independent, we can now analyze the main principal components labeled PC01, PC02, etc. PC01 and PC02 can be treated as new synthetic traits that represent the main sources of variation among original traits. You can treat a PC trait like any other trait except that it is not stored permanently in a database table. You can put a PC trait in your Collection window and see how well correlated each of the 20 original traits is with this new synthetic trait. You can also map a PC trait. [RWW, Aug 23, 2005] - -<div id="Permutation"></div> - -#### Permutation Test: - -A permutation test is a computationally intensive but conceptually simple method used to evaluate the statisical significance of findings. Permutation tests are often used to evaluate QTL significance. _Some background_: In order to detect parts of chromosomes that apparently harbor genes that contribute to differences in a trait's value, it is common to search for associations (linkage) across the entire genome. This is referred to as a "whole genome" scan, and it usually involves testing hundreds of independently segregating regions of the genome using hundreds, or even thousands of genetic markers (SNPs and microsatellites). A parametric test such as a conventional t test of F test can be used to estimate the probability of the null hypothesis at any single location in the genome (the null hypothesis is that there is no QTL at this particular location). But a parametric test of this type makes assumptions about the distribution of the trait (its normality), and also does not provide a way to correct for the large number of independent tests that are performed while scanning the whole genome. We need protection against many false discoveries as well as some assurance that we are not neglecting truly interesting locations. A permutation test is an elegant solution to both problems. The procedure involves randomly reassigning (permuting) traits values and genotypes of all cases used in the analysis. The permuted data sets have the same set of phenotypes and genotypes (in other words, distributions are the same), but obviously the permutation procedure almost invariably obliterates genuine gene-to-phenotype relation in large data sets. We typically generate several thousand permutations of the data. Each of these is analyzed using precisely the same method that was used to analyze the correctly ordered data set. We then compare statistical results of the original data set with the collection of values generated by the many permuted data sets. The hope is that the correctly ordered data are associated with larger LRS and LOD values than more than 95% of the permuted data sets. This is how we define the p = .05 whole genome significance threshold for a QTL. Please see the related Glossary terms "Significant threshold" and "Suggestive threshold". [RWW, July 15, 2005] - -#### Power to detect QTLs: - -An analysis of statistical power is useful to estimate numbers of replicates and strains needed to detect and resolve major sources of trait variance and covariance. A versatile method has been developed by Sen and colleagues (Sen et al., 2007) and implemented in the R program. qtlDesign. David Ashbrook implemented a version of this within Shiny that can help you estimate power for different QTL effect sizes, cohort sizes, and replication rates: - -#### [Power Calculator (D. Ashbrook)](https://dashbrook1.shinyapps.io/bxd_power_calculator_app/) - -We can see that in all situations power is increased more by increasing the number of lines than by increasing the number of biological replicates. Dependent upon the heritability of the trait, there is little gain in power when going above 4-6 biological replicates. [DGA, Mar 3, 2018] - -#### Probes and Probe Sets: - -In microarray experiments the probe is the immobilized sequence on the array that is complementary to the target message washed over the array surface. Affymetrix probes are 25-mer DNA sequences synthesized on a quartz substrate. There are a few million of these 25-mers in each 120-square micron cell of the array. The abundance of a single transcript is usualy estimated by as many as 16 perfect match probes and 16 mismatch probes. The collection of probes that targets a particular message is called a probe set. [RWW, Dec 21, 2004] - - -[Go back to index](#index) - -<div id="q"></div> - -## Q - -#### QTL: - -A quantitative trait locus is a chromosome region that contains one or more sequence variants that modulates the distribution of a variable trait measured in a sample of genetically diverse individuals from an interbreeding population. Variation in a quantitative trait may be generated by a single QTL with the addition of some environmental noise. Variation may be oligogenic and be modulated by a few independently segregating QTLs. In many cases however, variation in a trait will be polygenic and influenced by large number of QTLs distributed on many chromosomes. Environment, technique, experimental design and a host of other factors also affect the apparent distribution of a trait. Most quantitative traits are therefore the product of complex interactions of genetic factors, developmental and epigenetics factors, environmental variables, and measurement error. [Williams RW, Dec 21, 2004] - -[Go back to index](#index) - -<div id="r"></div> - -## R - -#### Recombinant Inbred Strain (RI or RIS) or Recombinant Inbred Line (RIL): - -An inbred strain whose chromosomes incorporate a fixed and permanent set of recombinations of chromosomes originally descended from two or more parental strains. Sets of RI strains (from 10 to 5000) are often used to map the chromosomal positions of polymorphic loci that control variance in phenotypes. - -For a terrific short summary of the uses of RI strains see [2007](http://www.informatics.jax.org/silverbook/chapters/9-2.shtml)). - -Chromosomes of RI strains typically consist of alternating haplotypes of highly variable length that are inherited intact from the parental strains. In the case of a typical rodent RI strain made by crossing maternal strain C with paternal strain B (called a CXB RI strain), a chromosome will typically incorporate 3 to 5 alternating haplotype blocks with a structure such as BBBBBCCCCBBBCCCCCCCC, where each letter represents a genotype, series of similar genotype represent haplotypes, and where a transition between haplotypes represents a recombination. Both pairs of each chromosome will have the same alternating pattern, and all markers will be homozygous. Each of the different chromosomes (Chr 1, Chr 2, etc.) will have a different pattern of haplotypes and recombinations. The only exception is that the Y chromosome and the mitochondial genome, both of which are inherited intact from the paternal and maternal strain, respectively. For an RI strain to be useful for mapping purposes, the approximate position of recombinations along each chromsome need to be well defined either in terms of centimorgan or DNA basepair position. The precision with which these recombinations are mapped is a function of the number and position of the genotypes used to type the chromosomes--20 in the example above. Because markers and genotypes are often space quite far apart, often more than 500 Kb, the actual data entered into GeneNetwork will have some ambiguity at each recombination locus. The haplotype block BBBBBCCCCBBBCCCCCCCC will be entered as BBBBB?CCCC?BBB?CCCCCCCC where the ? mark indicates incomplete information over some (we hope) short interval. - -RI strains are almost always studied in sets or panels. All else being equal, the larger the set of RI strains, the greater the power and precision with which phenotypes can be mapped to chromosomal locations. The first set of eight RIs, the CXB RIs, were generated by Donald Bailey (By) from an intercross between a female BALB/cBy mouse (abbreviated C) and a male C57BL/6By mouse in the 1960s. The small panel of 8 CXB strains was originally used to determine if the major histocompatibility (MHC) locus on proximal Chr 17 was a key factor accounting for different immune responses such as tissue rejection. The methods used to determine the locations of recombinations relied on visible markers (coat color phenotypes such as the C and B loci) and the electrophoretic mobility of proteins. Somewhat larger RI sets were generated by Benjamin Taylor to map Mendelian and other major effect loci. In the 1990s the utility of RI sets for mapping was significantly improved thanks to higher density genotypes made possible by the use of microsatellite markers. Between 2005 and 2017, virtually all extant mouse and rat RI strains were regenotyped at many thousands of SNP markers, providing highly accurate maps of recombinations. - -While the potential utility of RI strains in mapping complex polygenic traits was obvious from the outset, the small number of strains only made it feasible to map quantitative traits with large effects. The first large RI sets were generated by plant geneticists (Burr et al. [2000](http://demeter.bio.bnl.gov/RIchap_rev.pdf)) and this the plant genetics community holds a strong lead in the production of very large RI sets to study multigenic and polygenic traits and trait covariance and pleiotropy. - -By 2010 the number of mouse RI strains had increased to the point where defining causal gene and sequence variant was more practical. As of 2018 there are about 150 BXD strains (152 have been fully sequenced), ~100 Collaborative Cross strains (also all fully sequenced), and at least another 100 RI strains belonging to smaller sets that have been extremely well genotyped. - -**Making RI strains**: The usual procedure typically involves sib mating of the progeny of an F1 intercross for more than 20 generations. Even by the 5th filial (F) generation of successive matings, the RI lines are homozygous at 50% of loci and by F13, the value is above 90%. At F20 the lines are nearly fully inbred (~98%) and by convention are now referred to as inbred strains rather than inbred lines. - -<img width="600px" src="/static/images/SilverFig3_2.png" alt="" /> -[Go back to index](#index) - -<small>Legend:from [Silver, L. (1995) Oxford University Press](http://www.informatics.jax.org/silverbook/frames/frame3-3.shtml)</small> - -[Williams RW, June 20, 2005; significant extension, Sept 21, 2007, added Crow ref, Oct 2009] - -<div id="s"></div> - -## S - -#### Scree Plots: - -GeneNetwork will often automatically generate a [Scree Plot](http://www.improvedoutcomes.com/docs/WebSiteDocs/PCA/Creating_a_Scree_Plot.htm) and the associated principal components (PCs) when you compute a Correlation Matrix for a group of traits that you have placed in your Trait Collection (a set of phenotypes and/or expression data for a specific population). Here is a nice definition of what a Scree plot is trying to tell you adopted and adapted from IOS (www.improvedoutcomes.com). - -A Scree Plot is a simple line segment plot that shows the fraction of total variance in the data as explained or represented by each PC. The PCs are ordered, and by definition are therefore assigned a number label, by decreasing order of contribution to total variance. The PC with the largest fraction contribution is labeled PC01. Such a plot when read left-to-right across the abscissa can often show a clear separation in fraction of total variance where the 'most important' components cease and the 'least important' components begin. The point of separation is often called the 'elbow'. (In the PCA literature, the plot is called a 'Scree' Plot because it often looks like a 'scree' slope, where rocks have fallen down and accumulated on the side of a mountain.) [Williams RW, Dec 20, 2008] - -#### Significant threshold: - -The significant threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.05, or a 5% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This threshold is computed by evaluating the distribution of highest LRS scores generated by a set of 2000 random permutations of strain means. For example, a random permutation of the correctly ordered data may give a peak LRS score of 10 somewhere across the genome. The set of 1000 or more of these highest LRS scores is then compared to the actual LRS obtained for the correctly ordered (real) data at any location in the genome. If fewer than 50 (5%) of the 1000 permutations have peak LRS scores anywhere in the genome that exceed that obtained at a particular locus using the correctly ordered data, then one can usually claim that a QTL has been defined at a genome-wide p-value of .05. The threshold will vary slightly each time it is recomputed due to the random generation of the permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. WebQTL does make it possible to search through hundreds of traits for those that may have significant linkage somewhere in the genome. Keep in mind that this introduces a second tier of multiple testing problems for which the permutation test will not usually provide adequate protection. If you anticipate mapping many independent traits, then you will need to correct for the number of traits you have tested. [Williams RW, Nov 14, 2004] - -<div id="snpSeismograph"></div> - -#### SNP Seismograph Track: - -SNP is an acronym for single nucleotide polymorphisms (SNPs). SNPs are simple one base pair variants that distinguish individuals and strains. The SNP Seismograph track is a unique feature of physical maps in the GeneNetwork. Each track is customized for a particular cross and shows only those SNPs that differ between the two parental strains. For example, on mouse BXD maps, only the SNPs that differ between C57BL/6J and DBA/2J will be displayed. Regions with high numbers of SNPs are characterised by wider excursions of the yellow traces that extends along the x axis. Since these regions have many SNPs they have a higher prior probability of containing functional sequence differences that might have downstream effects on phenotypes. Large genes with many SNPs close to the peak LRS and that also have a biological connection with the trait ypu are studying are high priority candidate genes. - -The SNP track in WebQTL exploits the complete Celera Discovery System SNP set but adds an additional 500,000 inferred SNPs in both BXD and AXB/BXA crosses. These SNPs were inferred based on common haplotype structure using an Monte Carlo Markov chain algorithm developed by Gary Churchill and Natalie Blades and implemented by Robert Crowell, and RWW in July 2004. Raw data used to generate the SNP seismograph track were generated by Alex Williams and Chris Vincent, July 2003. The BXD track exploits a database of 1.75 million B vs D SNPs, whereas the AXB/BXA track exploits a database of 1.80 million A vs B SNPs. The names, sequences, and precise locations of most of these SNPs are the property of Celera Discovery Systems, whom we thank for allowing us to provide this level of display in WebQTL. - -Approximately 2.8 million additional SNPs generated by Perlegen for the NIEHS have been added to the SNP track by Robert Crowell (July-Aug 2005). We have also added all Wellcome-CTC SNPs and all relevant mouse SNPs from dbSNP. [Williams RW, Dec 25, 2004; Sept 3, 2005] - -#### Standard Error of the Mean (SE or SEM): - -In most GeneNetwork data sets, the SEM is computed as: -Standard Deviation (SD) divided by the square root of n - 1 -where n is the number of independent biological samples used to estimate the population mean. What this means in practice is that when n = 2 (as in many microarray data sets), the SEM and the SD are identical. This method of computing the SEM is conservative, but corrects to some extent for well known bias of the SEM discussed by Gurland and Tripathi (1971, A simple approximation for unbiased estimation of the standard deviation. Amer Stat 25:30-32). [Williams RW, Dec 17, 2008] - -#### Strain Distribution Pattern: - -A marker such as a SNP or microsatellite is genotyped using DNA obtained from each member of the mapping population. In the case of a genetic reference population, such as the BXD strains or the BayXSha Arabadopsis lines, this results in a text string of genotypes (e.g., BDDDBDBBBBDDBDDDBBBB... for BXD1 through BXD100). Each marker is associated with its own particular text string of genotypes that is often called the **strain distribution pattern** of the marker. (A more appropriate term would be the **marker genotype string**.) This string is converted to a numerical version, a genotype vector: -1111-11-1-1-1-111-1111-1-1-1-1..., where D=1, B=-1, H=0. Mapping a trait boils down to performing correlations between each trait and all of the genotype vectors. The genotype vector with the highest correlation (absolute value) is a good candidate for a QTL. [Williams RW, June 18, 2005] - -#### Suggestive Threshold: - -The suggestive threshold represents the approximate LRS value that corresponds to a genome-wide p-value of 0.63, or a 63% probability of falsely rejecting the null hypothesis that there is no linkage anywhere in the genome. This is not a typographical error. The Suggestive LRS threshold is defined as that which yields, on average, one false positive per genome scan. That is, roughly one-third of scans at this threshold will yield no false positive, one-third will yield one false positive, and one-third will yield two or more false positives. This is a very permissive threshold, but it is useful because it calls attention to loci that may be worth follow-up. Regions of the genome in which the LRS exceeds the suggestive threshold are often worth tracking and screening. They are particularly useful in combined multicross metaanalysis of traits. If two crosses pick up the same suggestive locus, then that locus may be significant when the joint probability is computed. The suggestive threshold may vary slightly each time it is recomputed due to the random generation of permutations. You can view the actual histogram of the permutation results by selecting the "Marker Regression" function in the **Analysis Tools** area of the **Trait Data and Editing Form**. [Williams RW and Manly KF, Nov 15, 2004] - -#### Systems Genetics: - -Systems genetics or "network genetics" is an emerging new branch of genetics that aims to understand complex causal networks of interactions at multiple levels of biological organization. To put this in a simple context: Mendelian genetics can be defined as the search for linkage between a single trait and a single gene variant (1 to 1); complex trait analysis can be defined as the search for linkage between a single trait and a set of gene variants (QTLs, QTGs, and QTNs) and environmental cofactors (1 to many); and systems genetics can be defined as the search for linkages among networks of traits and networks of gene and environmental variants (many to many). - -A hallmark of systems genetics is the simultaneous consideration of groups (systems) of phenotypes from the primary level of molecular and cellular interactions that ultimately modulate global phenotypes such as blood pressure, behavior, or disease resistance. Changes in environment are also often important determinants of multiscalar phenotypes; reversing the standard notion of causality as flowing inexorably upward from the genome. Scientists who use a systems genetics approach often have a broad interest in modules of linked phenotypes. Causality in these complex dynamic systems is often contingent on environmental or temporal context, and often will involve feedback modulation. A systems genetics approach can be unusually powerful, but does require the use of large numbers of observations (large sample size), and more advanced statistical and computational models. - -Systems genetics is not really a new field and traces back to [Sewall Wright's](http://www.amphilsoc.org/library/mole/w/wrights.htm) classical paper (Wright, 1921, "Correlation and Causation") that introduced path analysis to study systems of related phenotypes. Two factors have invigorated this field. The first factor is the advent of more sophisticated statistical methods including Structural [Equation Modeling](http://userwww.sfsu.edu/~efc/classes/biol710/path/SEMwebpage.htm) (SEM), [System Dynamics Modeling](http://www.public.asu.edu/~kirkwood/sysdyn/SDIntro/SDIntro.htm), and [Bayesian Network Modeling](http://bnj.sourceforge.net/) combined with powerful computer systems and efficient algorithms. The second factor is the relative ease with which it is now possible to acquire extensive and diverse phenotype data sets across genetic reference populations such as the BXD set of mice, the HXB set of rats, and the BayXSha lines of Arabidopsis (data are incorporated in the GeneNetwork). In the case of the BXD strains, a large research community has collectively generated hundreds of thousands of transcript phenotypes in different tissues and cells (level of expression), as well as hundreds of protein, cellular, pharmacological, and behavioral data types across a single genetic reference panel. Evaluating and modeling the associative and causal relations among these phenotypes is a major, and still relatively new area of research. Complex trait analysis and QTL mapping are both part of systems genetics in which causality is inferred using conventional genetic linkage (Li et al., [2005](http://hmg.oupjournals.org/cgi/content/abstract/ddi124v1)). One can often assert with confidence that a particular module of phenotypes (component of the variance and covariance) is modulated by sequence variants at a common locus. This provides a causal constraint that can be extremely helpful in more accurately modeling network architecture. Most models are currently static, but as the field matures, more sophisticated dynamic models will supplant steady-state models. - -The term "systems genetics" was coined by Grant Morahan, October 2004, during a visit to Memphis, as a more general and appropriate term to use instead of "genetical genomics." [Williams RW, April 11, 2005, revised Oct 22, 2005, April, 2008] - -[Go back to index](#index) - -<div id="t"></div> - -## T - -<div id="tissueCorr"></div> - -#### Tissue Correlation: - -The tissue correlation is an estimate of the similarity of expression of two genes across different cells, tissues, or organs. In order to compute this type of correlation we first generate expression data for multiple different cell types, tissues, or whole organs from a single individual. There will be significant differences in gene expression across this sample and this variation can then be used to compute either Pearson product-moment correlations (r) or Spearman rank order correlations (rho) between any pair of genes, transcripts, or even exons. Since the samples are ideally all from one individual there should not be any genetic or environmental differences among samples. The difficulty in computing tissue correlations is that samples are not independent. For example, three samples of the small intestine (jejunum, ilieum, and duodenum) will have expression patterns that are quite similar to each other in comparison to three other samples, such as heart, brain, and bone. For this reason the nature of the sampling and how those samples are combined will greatly affect the correlation values. The tissue correlations in GeneNetwork were computed in a way that attempts to reduce the impact of this fact by combining closely related sample types. For example multiple data sets for different brain region were combined to generate a single average CNS tissue sample (generating a whole brain sample would have been an alternative method). - -However, there is really not optimal way to minimize the effects of this type of non-independence of samples. Some genes will have high expression in only a few tissues, for example the cholinergic receptor, nicotinic, alpha polypeptide 1 gene Chrna1 has high expression in muscle tissues (skeletal muscle = Mus, tongue = Ton, and esophagus = Eso) but lower expression in most other tissues. The very high correlation between Chrna1 and other genes with high expression only in muscle reflects their joint bimodality of expression. It does not mean that these genes or their proteins necessarily cooperate directly in molecular processes. [Williams RW, Dec 26, 2008] - -<img width="600px" src="/static/images/Chrna1vsMyf6.gif" alt="" /> - -#### Transcript Location: - -The small orange triangle on the x-axis indicates the approximate position of the gene that corresponds to the transcript. These values were taken from the latest assembly of genome of the particular species. - -#### Transform: - -Most of the data sets in the GeneNetwork are ultimately derived from high resolution images of the surfaces of microarrays. Estimates the gene expression therefore involves extensive low-level image analysis. These processesing steps attempt to compensate for low spatial frequency "background" variation in image intensity that cannot be related to the actual hybridization signal, for example, a gradation of intensity across the whole array surface due to illumination differences, uneven hybridization, optical performance, scanning characteristics, etc. High spatial frequeny artifacts are also removed if they are likely to be artifacts: dust, scrathes on the array surface, and other "sharp" blemishes. The raw image data (for example, the Affymetrix DAT file) also needs to be registered to a template that assigns pixel values to expected array spots (cells). This image registration is an important process that users can usually take for granted. The end result is the reliable assignment of a set of image intensity values (pixels) to each probe. Each cell value generated using the Affymetrix U74Av2 array is associated with approximately 36 pixel intensity values (a 6x6 set of pixels, usually an effective 11 or 12-bit range of intensity). Affymetrix uses a method that simply ranks the values of these pixels and picks as the "representative value" the pixel that is closest to a particular rank order value, for example, the 24th highest of 36 pixels. The range of variation in intensity values amoung these ranked pixels provides a way to estimate the error of the estimate. The Affymetrix CEL files therefore consist of XY coordinates, the consensus value, and an error term. [Williams RW, April 30, 2005] - -#### Transgression: - -Most of us are familiar with the phrase "regression toward the mean." This refers to the tendency of progeny of a cross to have phenotype that are intermediate to those of the parents. Transgression refers to the converse: progeny that have more phenotypes that are higher and lower than those of either parent. Transgression is common, and provided that a trait is influenced by many independent sequence variants (a polygenic trait), transgression is the expectation. This is particularly true if the parents are different genetically, but by chance have similar phenotypes. Consider a trait that is controlled by six independent genes, A through F. The "0" allele at these size genes lowers body weight whereas the "1" allele increases body weight. If one parent has a 000111 6-locus genotype and the other parent has 111000 genotype, then they will have closely matched weight. But their progeny may inherit combinations as extreme as 000000 and 111111. - -Transgression means that you can rarely predict the distribution of phenotypes among a set of progeny unless you already have a significant amount of information about the genetic architecture of a trait (numbers of segregating variants that affect the trait, either interactions, and GXE effects). In practical terms this means that if the parents of a cross do NOT differ and you have good reasons to believe that the trait you are interested in is genetically complex, then you can be fairly confident that the progeny will display a much wider range of variation that the parents. [May 2011 by RWW]. - -[Go back to index](#index) - -<div id="u"></div> - -## U - -[Go back to index](#index) - -<div id="v"></div> - -## V - -[Go back to index](#index) - -<div id="w"></div> - -## W - -#### Winsorize, Winsorise: - -QTL mapping results can be greatly affected by inclusion of outlier data. GeneNetwork will do its best to flag outliers for you in the **Trait Data and Analysis** pages (yellow highlighting). Before mapping, review the data, and if necessary, change values. Options for handling outliers include: (1) do nothing, (2) delete the outliers (trimming), (3) transform the data (e.g., logarithmic, arcsine, or logistic regression transforms), or (4) [winsorize](http://en.wikipedia.org/wiki/Winsorising) the distribution of values. Winsorizing is usually the easiest method to implement directly in GeneNetwork. - -**How to winsorize**: First review the distribution of values and define outliers. You should only do this one time, so think before you leap. Look at the **Probability Plot** of the trait by going to **Trait Data and Analysis** page and selecting **Basic Statistics**). For example, the figure below from GeneNetwork shows that at many as seven cases have relatively high values and as many as three have relatively low values (this trait is taken from Species = Mouse, Group = LXS, Type = Phenotype, Trait 10182). GeneNetwork code only declares the highest two values to be outliers, but you can use a more liberal definition and give all seven high values a haircut. It is advisable to winsorizes equal numbers of cases on each side of the distribution (high and low cases). In this case, the seven highest values were changed to match that of the 8th highest value (0.860). To retain the original rank order I added an incremental value of 0.01 to each (0.861, 0.862, etc). I did the same thing to the lowest seven values. Adding this increment is not necessary. - -The result in this case: a suggestive QTL on Chr 16 now reaches the significance threshold. - -The **danger of winsorizing** is doing it multiple times in different ways. You should transform or winsorize the data before mapping. And you should ideally only do any transformation/correction one time. If you fool around with different methods of transforming your data then you are asking for trouble by adding yet another level of multiple testing. If you feel compelled to experiment with different transforms, then you should/must report this in publications and explain why you did so. Demonstrating that mapping results are robust even using multiple transforms is one good excuse. [Williams RW, Jan 2, 2014] - -<img width="600px" src="/static/images/Winsorize1.png" alt="" /> -<img width="600px" src="/static/images/Winsorize3.png" alt="" /> - - -[Go back to index](#index) - -<div id="x"></div> - -## X - -[Go back to index](#index) - -<div id="y"></div> - -## Y -[Go back to index](#index) - - -<div id="z"></div> - -## Z - -[Go back to index](#index) diff --git a/wqflask/wqflask/static/new/css/markdown.css b/wqflask/wqflask/static/new/css/markdown.css index 91167908..dca3e31d 100644 --- a/wqflask/wqflask/static/new/css/markdown.css +++ b/wqflask/wqflask/static/new/css/markdown.css @@ -2,7 +2,10 @@ padding: 20px; } -#markdown h2, #markdown h3, #markdown h4, #markdown h5 { +#markdown h2, +#markdown h3, +#markdown h4, +#markdown h5 { font-weight: bold; } @@ -11,3 +14,51 @@ margin-right: auto; margin-left: auto; } + +.github-btn-container { + margin: 20px 10px; + display: flex; + width: 90vw; + justify-content: flex-end; +} + +.github-btn { + display: flex; + justify-content: center; + border: 3px solid #357ebd; + background: lightgrey; + padding: 3px 4px; + width: 200px; + border-radius: 16px; +} + +.github-btn a { + align-self: center; + font-weight: bold; + color: #357ebd; +} + +.github-btn a img { + height: 40px; + width: 40px; + padding-left:5px; +} + + +.container { + width: 80vw; + margin: auto; + max-width: 80vw; + +} + +.container p { + font-size: 17px; + word-spacing: 0.2em; +} + +@media(max-width:650px) { + .container { + width: 100vw; + } +}
\ No newline at end of file diff --git a/wqflask/wqflask/static/new/css/show_trait.css b/wqflask/wqflask/static/new/css/show_trait.css index 5e1a279b..39c6ba53 100644 --- a/wqflask/wqflask/static/new/css/show_trait.css +++ b/wqflask/wqflask/static/new/css/show_trait.css @@ -145,11 +145,11 @@ input.corr-location { display: inline; } -div.block-by-index-div { +div.block-div { margin-bottom: 10px; } -div.block-by-attribute-div { +div.block-div-2 { margin-top:10px; margin-bottom:10px; } diff --git a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js index 4ec62157..626357d4 100644 --- a/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js +++ b/wqflask/wqflask/static/new/javascript/get_traits_from_collection.js @@ -2,10 +2,6 @@ var add_trait_data, assemble_into_json, back_to_collections, collection_click, collection_list, color_by_trait, create_trait_data_csv, get_this_trait_vals, get_trait_data, process_traits, selected_traits, submit_click, this_trait_data, trait_click, __indexOf = [].indexOf || function(item) { for (var i = 0, l = this.length; i < l; i++) { if (i in this && this[i] === item) return i; } return -1; }; -console.log("before get_traits_from_collection"); - -//collection_list = null; - this_trait_data = null; selected_traits = {}; @@ -69,7 +65,7 @@ if ( ! $.fn.DataTable.isDataTable( '#collection_table' ) ) { collection_click = function() { var this_collection_url; - //console.log("Clicking on:", $(this)); + this_collection_url = $(this).find('.collection_name').prop("href"); this_collection_url += "&json"; collection_list = $("#collections_holder").html(); @@ -87,9 +83,7 @@ submit_click = function() { $('#collections_holder').find('input[type=checkbox]:checked').each(function() { var this_dataset, this_trait, this_trait_url; this_trait = $(this).parents('tr').find('.trait').text(); - console.log("this_trait is:", this_trait); this_dataset = $(this).parents('tr').find('.dataset').text(); - console.log("this_dataset is:", this_dataset); this_trait_url = "/trait/get_sample_data?trait=" + this_trait + "&dataset=" + this_dataset; return $.ajax({ dataType: "json", @@ -147,7 +141,7 @@ create_trait_data_csv = function(selected_traits) { } all_vals.push(this_trait_vals); } - console.log("all_vals:", all_vals); + trait_vals_csv = trait_names.join(","); trait_vals_csv += "\n"; for (index = _k = 0, _len2 = samples.length; _k < _len2; index = ++_k) { @@ -168,7 +162,7 @@ create_trait_data_csv = function(selected_traits) { trait_click = function() { var dataset, this_trait_url, trait; - console.log("Clicking on:", $(this)); + trait = $(this).parent().find('.trait').text(); dataset = $(this).parent().find('.dataset').text(); this_trait_url = "/trait/get_sample_data?trait=" + trait + "&dataset=" + dataset; @@ -182,7 +176,6 @@ trait_click = function() { trait_row_click = function() { var dataset, this_trait_url, trait; - console.log("Clicking on:", $(this)); trait = $(this).find('.trait').text(); dataset = $(this).find('.dataset').data("dataset"); this_trait_url = "/trait/get_sample_data?trait=" + trait + "&dataset=" + dataset; @@ -208,13 +201,17 @@ populate_cofactor_info = function(trait_info) { if (trait_info['type'] == "ProbeSet"){ $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name']) $('#cofactor1_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) - } else { + } else if (trait_info['type'] == "Publish") { $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) if ('pubmed_link' in trait_info) { $('#cofactor1_description').html('<a href=\"' + trait_info['pubmed_link'] + '\">PubMed: ' + trait_info['pubmed_text'] + '</a><br>' + trait_info['description']) } else { - $('#cofactor1_description').html('PubMed: ' + trait_info['pubmed_text'] + '<br>' + trait_info['description']) + $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor1_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) } + } else { + $('#cofactor1_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor1_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n") } $('#select_cofactor1').text("Change Cofactor 1"); $('#cofactor1_info_container').css("display", "inline"); @@ -224,13 +221,17 @@ populate_cofactor_info = function(trait_info) { if (trait_info['type'] == "ProbeSet"){ $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name']) $('#cofactor2_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) - } else { + } else if (trait_info['type'] == "Publish") { $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) if ('pubmed_link' in trait_info) { $('#cofactor2_description').html('<a href=\"' + trait_info['pubmed_link'] + '\">PubMed: ' + trait_info['pubmed_text'] + '</a><br>' + trait_info['description']) } else { - $('#cofactor2_description').html('PubMed: ' + trait_info['pubmed_text'] + '<br>' + trait_info['description']) + $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor2_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) } + } else { + $('#cofactor2_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor2_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n") } $('#select_cofactor2').text("Change Cofactor 2"); $('#cofactor2_info_container').css("display", "inline"); @@ -240,13 +241,17 @@ populate_cofactor_info = function(trait_info) { if (trait_info['type'] == "ProbeSet"){ $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['tissue'] + " " + trait_info['db'] + ": " + trait_info['name']) $('#cofactor3_description').text("[" + trait_info['symbol'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) - } else { + } else if (trait_info['type'] == "Publish") { $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) if ('pubmed_link' in trait_info) { $('#cofactor3_description').html('<a href=\"' + trait_info['pubmed_link'] + '\">PubMed: ' + trait_info['pubmed_text'] + '</a><br>' + trait_info['description']) } else { - $('#cofactor3_description').html('PubMed: ' + trait_info['pubmed_text'] + '<br>' + trait_info['description']) + $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor3_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n" + trait_info['description']) } + } else { + $('#cofactor3_trait_link').text(trait_info['species'] + " " + trait_info['group'] + " " + trait_info['db'] + ": " + trait_info['name']) + $('#cofactor3_description').text("[" + trait_info['name'] + " on " + trait_info['location'] + " Mb]\n") } $('#select_cofactor3').text("Change Cofactor 3"); $('#cofactor3_info_container').css("display", "inline"); @@ -256,7 +261,6 @@ populate_cofactor_info = function(trait_info) { get_trait_data = function(trait_data, textStatus, jqXHR) { var sample, samples, this_trait_vals, trait_sample_data, vals, _i, _len; trait_sample_data = trait_data[1]; - console.log("IN GET TRAIT DATA") if ( $('input[name=allsamples]').length ) { samples = $('input[name=allsamples]').val().split(" "); } else { @@ -362,13 +366,11 @@ assemble_into_json = function(this_trait_vals) { }; color_by_trait = function(trait_sample_data, textStatus, jqXHR) { - console.log('in color_by_trait:', trait_sample_data); return root.bar_chart.color_by_trait(trait_sample_data); }; process_traits = function(trait_data, textStatus, jqXHR) { var the_html, trait, _i, _len; - console.log('in process_traits with trait_data:', trait_data); the_html = "<button id='back_to_collections' class='btn btn-inverse btn-small'>"; the_html += "<i class='icon-white icon-arrow-left'></i> Back </button>"; the_html += " <button id='submit' class='btn btn-primary btn-small'> Submit </button>"; @@ -397,13 +399,11 @@ process_traits = function(trait_data, textStatus, jqXHR) { }; back_to_collections = function() { - collection_list_html = $('#collection_list_html').html() - $("#collections_holder").html(collection_list_html); + $("#collections_holder").html(collection_list); $(document).on("click", ".collection_line", collection_click); return $('#collections_holder').colorbox.resize(); }; -console.log("inside get_traits_from_collection"); $(".collection_line").on("click", collection_click); $("#submit").on("click", submit_click); if ($('#scatterplot2').length){ diff --git a/wqflask/wqflask/static/new/javascript/show_trait.js b/wqflask/wqflask/static/new/javascript/show_trait.js index a34811f8..87c35984 100644 --- a/wqflask/wqflask/static/new/javascript/show_trait.js +++ b/wqflask/wqflask/static/new/javascript/show_trait.js @@ -568,14 +568,29 @@ create_value_dropdown = function(value) { populate_sample_attributes_values_dropdown = function() { var attribute_info, key, sample_attributes, selected_attribute, value, _i, _len, _ref, _ref1, _results; $('#attribute_values').empty(); - sample_attributes = {}; - attr_keys = Object.keys(js_data.attributes).sort(); - for (i=0; i < attr_keys.length; i++) { - attribute_info = js_data.attributes[attr_keys[i]]; - sample_attributes[attribute_info.name] = attribute_info.distinct_values; - } - selected_attribute = $('#exclude_menu').val().replace("_", " "); - _ref1 = sample_attributes[selected_attribute]; + sample_attributes = []; + + var attributes_as_list = Object.keys(js_data.attributes).map(function(key) { + return [key, js_data.attributes[key].name]; + }); + + attributes_as_list.sort(function(first, second) { + if (second[1] > first[1]){ + return -1 + } + if (first[1] > second[1]){ + return 1 + } + return 0 + }); + + for (i=0; i < attributes_as_list.length; i++) { + attribute_info = js_data.attributes[attributes_as_list[i][0]] + sample_attributes.push(attribute_info.distinct_values); + } + + selected_attribute = $('#exclude_column').val() + _ref1 = sample_attributes[selected_attribute - 1]; _results = []; for (_i = 0, _len = _ref1.length; _i < _len; _i++) { value = _ref1[_i]; @@ -590,25 +605,37 @@ if (Object.keys(js_data.attributes).length){ populate_sample_attributes_values_dropdown(); } -$('#exclude_menu').change(populate_sample_attributes_values_dropdown); +$('#exclude_column').change(populate_sample_attributes_values_dropdown); block_by_attribute_value = function() { var attribute_name, cell_class, exclude_by_value; - attribute_name = $('#exclude_menu').val(); + + let exclude_group = $('#exclude_by_attr_group').val(); + let exclude_column = $('#exclude_column').val(); + + if (exclude_group === "other") { + var table_api = $('#samples_other').DataTable(); + } else { + var table_api = $('#samples_primary').DataTable(); + } + exclude_by_value = $('#attribute_values').val(); - cell_class = ".column_name-" + attribute_name; - return $(cell_class).each((function(_this) { - return function(index, element) { - var row; - if ($.trim($(element).text()) === exclude_by_value) { - row = $(element).parent('tr'); - return $(row).find(".trait-value-input").val("x"); - } - }; - })(this)); + + let val_nodes = table_api.column(3).nodes().to$(); + let exclude_val_nodes = table_api.column(attribute_start_pos + parseInt(exclude_column)).nodes().to$(); + + for (i = 0; i < exclude_val_nodes.length; i++) { + let this_col_value = exclude_val_nodes[i].childNodes[0].data; + let this_val_node = val_nodes[i].childNodes[0]; + + if (this_col_value == exclude_by_value){ + this_val_node.value = "x"; + } + } }; -$('#exclude_group').click(block_by_attribute_value); +$('#exclude_by_attr').click(block_by_attribute_value); + block_by_index = function() { - var end_index, error, index, index_list, index_set, index_string, start_index, _i, _j, _k, _len, _len1, _ref, _results; + var end_index, error, index, index_list, index_set, index_string, start_index, _i, _j, _k, _len, _len1, _ref; index_string = $('#remove_samples_field').val(); index_list = []; _ref = index_string.split(","); @@ -630,7 +657,7 @@ block_by_index = function() { index_list.push(index); } } - _results = []; + let block_group = $('#block_group').val(); if (block_group === "other") { table_api = $('#samples_other').DataTable(); @@ -645,6 +672,65 @@ block_by_index = function() { } }; +filter_by_value = function() { + let filter_logic = $('#filter_logic').val(); + let filter_column = $('#filter_column').val(); + let filter_value = $('#filter_value').val(); + let block_group = $('#filter_group').val(); + + if (block_group === "other") { + var table_api = $('#samples_other').DataTable(); + } else { + var table_api = $('#samples_primary').DataTable(); + } + + let val_nodes = table_api.column(3).nodes().to$(); + if (filter_column == "value"){ + var filter_val_nodes = table_api.column(3).nodes().to$(); + } + else if (filter_column == "stderr"){ + var filter_val_nodes = table_api.column(5).nodes().to$(); + } + else if (!isNaN(filter_column)){ + var filter_val_nodes = table_api.column(attribute_start_pos + parseInt(filter_column)).nodes().to$(); + } + else { + return false + } + + for (i = 0; i < filter_val_nodes.length; i++) { + if (filter_column == "value" || filter_column == "stderr"){ + var this_col_value = filter_val_nodes[i].childNodes[0].value; + } else { + var this_col_value = filter_val_nodes[i].childNodes[0].data; + } + let this_val_node = val_nodes[i].childNodes[0]; + + if(!isNaN(this_col_value) && !isNaN(filter_value)) { + if (filter_logic == "greater_than"){ + if (parseFloat(this_col_value) <= parseFloat(filter_value)){ + this_val_node.value = "x"; + } + } + else if (filter_logic == "less_than"){ + if (parseFloat(this_col_value) >= parseFloat(filter_value)){ + this_val_node.value = "x"; + } + } + else if (filter_logic == "greater_or_equal"){ + if (parseFloat(this_col_value) < parseFloat(filter_value)){ + this_val_node.value = "x"; + } + } + else if (filter_logic == "less_or_equal"){ + if (parseFloat(this_col_value) > parseFloat(filter_value)){ + this_val_node.value = "x"; + } + } + } + } +}; + hide_no_value = function() { return $('.value_se').each((function(_this) { return function(_index, element) { @@ -1528,6 +1614,12 @@ $('#block_by_index').click(function(){ block_by_index(); edit_data_change(); }); + +$('#filter_by_value').click(function(){ + filter_by_value(); + edit_data_change(); +}) + $('#exclude_group').click(edit_data_change); $('#block_outliers').click(edit_data_change); $('#reset').click(edit_data_change); diff --git a/wqflask/wqflask/templates/base.html b/wqflask/wqflask/templates/base.html index 0e572fcf..faa59deb 100644 --- a/wqflask/wqflask/templates/base.html +++ b/wqflask/wqflask/templates/base.html @@ -64,13 +64,14 @@ <li class=""> <a href="/help" class="dropdow-toggle" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">Help <span class="caret"></a> <ul class="dropdown-menu"> - <li><a href="/references">References</a></li> + <li><a href="{{ url_for('references_blueprint.references') }}">References</a></li> <li><a href="/tutorials">Tutorials/Primers</a></li> <li><a href="{{ url_for('glossary_blueprint.glossary') }}">Glossary of Term</a></li> <li><a href="http://gn1.genenetwork.org/faq.html">FAQ</a></li> - <li><a href="/policies">Policies</a></li> - <li><a href="/links">Links</a></li> - <li><a href="/environments">Environments</a></li> + <li><a href="{{ url_for('policies_blueprint.policies') }}">Policies</a></li> + <li><a href="{{ url_for('links_blueprint.links') }}">Links</a></li> + <li><a href="{{ url_for('facilities_blueprint.facilities') }}">Facilities</a></li> + <li><a href="{{ url_for('environments_blueprint.environments') }}">Environments</a></li> <li><a href="/news">GN1 News</a></li> </ul> </li> diff --git a/wqflask/wqflask/templates/collections/add.html b/wqflask/wqflask/templates/collections/add.html index 62b6abb5..b4e5385b 100644 --- a/wqflask/wqflask/templates/collections/add.html +++ b/wqflask/wqflask/templates/collections/add.html @@ -50,4 +50,7 @@ <script> $('#add_form').parsley(); + $('#add_form').on('submit', function(){ + parent.jQuery.colorbox.close(); + }); </script> diff --git a/wqflask/wqflask/templates/corr_scatterplot.html b/wqflask/wqflask/templates/corr_scatterplot.html index 1fd5cd15..1133fcd2 100644 --- a/wqflask/wqflask/templates/corr_scatterplot.html +++ b/wqflask/wqflask/templates/corr_scatterplot.html @@ -81,9 +81,9 @@ <div id="cofactor_color_selector" style="margin-bottom: 10px; display:none;"> <hr> <b>Cofactor Color Range:</b> - <input class="chartupdatedata" name="color2" type="hidden" id="cocolorfrom" value="#D9D9D9"> + <input class="chartupdatedata" name="color2" type="hidden" id="cocolorfrom" value="#0000FF"> <button class="chartupdatedata jscolor {valueElement: 'cocolorfrom'}">Low</button> - <input class="chartupdatedata" name="color1" type="hidden" id="cocolorto" value="#000000"> + <input class="chartupdatedata" name="color1" type="hidden" id="cocolorto" value="#FF0000"> <button class="chartupdatedata jscolor {valueElement: 'cocolorto'}">High</button> <hr> </div> diff --git a/wqflask/wqflask/templates/correlation_matrix.html b/wqflask/wqflask/templates/correlation_matrix.html index d556f31a..4e150618 100644 --- a/wqflask/wqflask/templates/correlation_matrix.html +++ b/wqflask/wqflask/templates/correlation_matrix.html @@ -51,8 +51,12 @@ {% if result[0].name == trait.name and result[0].dataset == trait.dataset %} <td nowrap="ON" align="center" bgcolor="#cccccc" style="padding: 3px; line-height: 1.1;"><a href="/show_trait?trait_id={{ trait.name }}&dataset={{ trait.dataset.name }}"><font style="font-size: 12px; color: #3071a9; font-weight: bold;" ><em>n</em><br>{{ result[2] }}</font></a></td> {% else %} + {% if result[1] == 0 %} + <td nowrap="ON" align="middle" bgcolor="#eeeeee" style="padding: 3px; line-height: 1.1;">N/A</td> + {% else %} <td nowrap="ON" align="middle" class="corr_cell" style="padding: 3px; line-height: 1.1;"><a href="/corr_scatter_plot?dataset_1={% if trait.dataset.name == 'Temp' %}Temp_{{ trait.dataset.group.name }}{% else %}{{ trait.dataset.name }}{% endif %}&dataset_2={% if result[0].dataset.name == 'Temp' %}Temp_{{ result[0].dataset.group.name }}{% else %}{{ result[0].dataset.name }}{% endif %}&trait_1={{ trait.name }}&trait_2={{ result[0].name }}"><font style="font-size: 12px; color: #3071a9; font-weight: bold;" ><span class="corr_value">{{ '%0.2f' % result[1] }}</span><br>{{ result[2] }}</font></a></td> {% endif %} + {% endif %} {% endfor %} </tr> {% endfor %} diff --git a/wqflask/wqflask/templates/environment.html b/wqflask/wqflask/templates/environment.html new file mode 100644 index 00000000..94b31464 --- /dev/null +++ b/wqflask/wqflask/templates/environment.html @@ -0,0 +1,40 @@ +{% extends "base.html" %} + +{% block title %}Glossary{% endblock %} + +{% block css %} +<link rel="stylesheet" type="text/css" href="/static/new/css/markdown.css" /> +{% endblock %} + +{% block content %} + + <div class="github-btn-container"> + <div class="github-btn "> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> +<div id="markdown" class="container"> + + <div class="cls-table-style">{{ rendered_markdown|safe }} </div> +</div> + +<style type="text/css"> +table { + width: 100%; +} +table, th, td { + border: 1px solid black; + +} + +td,th{ + padding-top:8px; + padding-bottom: 8px; + text-align: center; +} + +</style> +{% endblock %} diff --git a/wqflask/wqflask/templates/environments.html b/wqflask/wqflask/templates/environments.html deleted file mode 100644 index 5ef91a95..00000000 --- a/wqflask/wqflask/templates/environments.html +++ /dev/null @@ -1,83 +0,0 @@ -{% extends "base.html" %} - -{% block title %}Environments{% endblock %} - -{% block content %} -<div class="container"> - <h3>System Properties</h3> - <table class="table table-bordered table-striped table-hover" style="width: 800px;"> - <tbody> - <tr><td colspan="4">Operating System</td></tr> - <tr> - <td style="text-align: right;">Ubuntu</td> - <td>12.04.5 LTS 64-bit</td> - <td><a target="_blank" href="http://www.ubuntu.com/">http://www.ubuntu.com/</a></td> - <td>Free software licenses (mainly GPL)</td> - </tr> - <tr><td colspan="4">Database</td></tr> - <tr> - <td style="text-align: right;">MySQL</td> - <td>5.5.40</td> - <td><a target="_blank" href="http://www.mysql.com/">http://www.mysql.com/</a></td> - <td>GPL (version 2) or proprietary</td> - </tr> - <tr><td colspan="4">Python</td></tr> - <tr> - <td style="text-align: right;">Python</td> - <td>2.7.3</td> - <td><a target="_blank" href="http://www.python.org/">http://www.python.org/</a></td> - <td>Python Software Foundation License</td> - </tr> - <tr> - <td style="text-align: right;">Flask</td> - <td>0.9</td> - <td><a target="_blank" href="http://flask.pocoo.org/">http://flask.pocoo.org/</a></td> - <td>BSD</td> - </tr> - <tr> - <td style="text-align: right;">Jinja2</td> - <td>2.6</td> - <td><a target="_blank" href="http://jinja.pocoo.org/">http://jinja.pocoo.org/</a></td> - <td>BSD</td> - </tr> - <tr> - <td style="text-align: right;">NumPy</td> - <td>1.7.0</td> - <td><a target="_blank" href="http://www.numpy.org/">http://www.numpy.org/</a></td> - <td>BSD-new</td> - </tr> - <tr> - <td style="text-align: right;">SciPy</td> - <td>0.11.0</td> - <td><a target="_blank" href="http://www.scipy.org/">http://www.scipy.org/</a></td> - <td>BSD-new</td> - </tr> - <tr><td colspan="4">JavaScript / CSS</td></tr> - <tr> - <td style="text-align: right;">jQuery</td> - <td>1.10.2</td> - <td><a target="_blank" href="http://jquery.com/">http://jquery.com/</a></td> - <td>MIT</td> - </tr> - <tr> - <td style="text-align: right;">Bootstrap</td> - <td>2.3.1</td> - <td><a target="_blank" href="http://getbootstrap.com/">http://getbootstrap.com/</a></td> - <td>MIT License (Apache License 2.0 prior to 3.1.0)</td> - </tr> - <tr> - <td style="text-align: right;">DataTables</td> - <td>1.9.4</td> - <td><a target="_blank" href="http://www.datatables.net/">http://www.datatables.net/</a></td> - <td>MIT</td> - </tr> - <tr> - <td style="text-align: right;">CKEditor</td> - <td>4.4.5</td> - <td><a target="_blank" href="http://ckeditor.com/">http://ckeditor.com/</a></td> - <td>GPL, LGPL and MPL</td> - </tr> - </tbody> - </table> -</div> -{% endblock %} diff --git a/wqflask/wqflask/templates/facilities.html b/wqflask/wqflask/templates/facilities.html new file mode 100644 index 00000000..a022b657 --- /dev/null +++ b/wqflask/wqflask/templates/facilities.html @@ -0,0 +1,24 @@ +{% extends "base.html" %} + +{% block title %}Facilities{% endblock %} + +{% block css %} +<link rel="stylesheet" type="text/css" href="/static/new/css/markdown.css" /> +{% endblock %} + +{% block content %} + + <div class="github-btn-container"> + <div class="github-btn"> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> +<div id="markdown" class="container"> + {{ rendered_markdown|safe }} + +</div> + +{% endblock %}
\ No newline at end of file diff --git a/wqflask/wqflask/templates/glossary.html b/wqflask/wqflask/templates/glossary.html index 146c7e86..752c4b12 100644 --- a/wqflask/wqflask/templates/glossary.html +++ b/wqflask/wqflask/templates/glossary.html @@ -8,9 +8,16 @@ {% block content %} +<div class="github-btn-container"> + <div class="github-btn"> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> <div id="markdown" class="container"> - <small> - <a href="https://raw.githubusercontent.com/genenetwork/genenetwork2/wqflask/wqflask/static/glossary.md" target="_">[Edit on Github]</a></small> + {{ rendered_markdown|safe }} </div> {% endblock %} diff --git a/wqflask/wqflask/templates/links.html b/wqflask/wqflask/templates/links.html index 6ff7fc6c..072e8429 100644 --- a/wqflask/wqflask/templates/links.html +++ b/wqflask/wqflask/templates/links.html @@ -1,451 +1,24 @@ {% extends "base.html" %} -{% block title %}Links{% endblock %} -{% block content %} - -<Table width= "100%" cellSpacing=0 cellPadding=5><TR> - <!-- Body Start from Here --> - <TD vAlign=top width="100%" height=200> - - -<P class="title">Links for Exploring Networks of Genes and Phenotypes </P> - - -<Blockquote class="subtitle">GeneNetwork and WebQTL have integrated links to the following resources</Blockquote> -<Blockquote> -<Blockquote>GN PARTNERS, DEVELOPMENT SITES, AND MIRRORS</Blockquote> -<UL> -<LI><a href="http://genenetwork.helmholtz-hzi.de">German Helmholtz Foundation GeneNetwork</a> at the <A HREF="http://www.helmholtz-hzi.de/en/" target="_empty">Helmholtz Centre for Infection Research (HZI)</A>, Braunschweig Germany, in the <a href="hzi.de/en/research/research_groups/experimental_mouse_genetics/experimental_mouse_genetics/team/" target="_empty">research group</A> of Dr. Klaus Schughart. - -<LI><a href="http://genenetwork.epfl.ch" target="_empty">Swiss GeneNetwork</A> in the Laboratory of Integrative Systems Physiology lead by Dr. Johan Auwerx at the EPFL in Lausanne (initiated Nov 9, 2009) - -<LI><a href="http://www.genenetwork.waimr.uwa.edu.au">Australian GeneNetwork</a> mirror and development sites, Centre for Diabetes Research, University of Western Australia, in the research group of Dr. Grant Morahan <!--was http://130.95.9.19/--> (initiated May 2007) - -<LI><a href="http://webqtl.bic.nus.edu.sg/">Singapore GeneNetwork</a> mirror site at the National University of Singapore, Bioinformatics Center, Dr. Mark De Silva (initiated Oct 2009) - -<LI><a href="http://gn.genetics.ucla.edu" target="_empty">California GeneNetwork</A> mirror site in the Department of Genetics, University of California at Los Angeles, in the research group of Dr. A. Jake Lusis (initiated Aug 2008) - -<LI><a href="http://gnat.versailles.inra.fr/" target="_empty">INRA IJPB, Versailles</A> mirror site in the VAST lab of Dr. Olivier Loudet, Plant Breeding and Genetics Unit (initiated Dec 2008) - - -<LI><a href="http://xzhou3.memphis.edu/" target="_empty">University of Memphis</A> mirror sites in the Bioinformatics Program and research group of Drs. Ramin Homayouni and Mohammed Yazin (initiated June 2009) - -<LI><a href="http://www.computablegenomix.com/">Computable Genomix </a> and the <A HREF="https://grits.eecs.utk.edu/sgo/sgo.html" target="_empty">Semantic Gene Organizer</A> systems are the source of GeneNetwork literature correlation data sets. - -<BR><BR><BR> -<LI><a href="http://genenetwork.org/share/annotations/">Custom Microarray Annotation Files</a> used by GeneNetwork -</UL> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">KEY RESOURCES CONNECTED WITH GENENETWORK</Blockquote> -<Blockquote> -<UL> -<LI><a href="http://mus.well.ox.ac.uk/mouse/HS/" target="_empty">Wellcome Trust Oxford Mouse Resource Portal</a> -<LI><a href="http://www.sanger.ac.uk/mouseportal/" target="_empty">WTSI Mouse Resource Portal</a> - -<LI><a href="http://www.sanger.ac.uk/cgi-bin/modelorgs/mousegenomes/lookseq/index.pl?show=1:10000000-10000000,paired_pileup&win=100&lane=129S1.bam&width=700" target="_empty">Sanger Mouse Sequencing Portal</a> (David Adams and colleagues) - -<LI><a href="http://www.grissom.gr/stranger/StRAnGER.php" target="_empty">StRAnGER</a> Gene Ontology and KEGG pathway analyzer - -<LI><a href="http://phenogen.ucdenver.edu/PhenoGen/index.jsp" target="_empty">PhenoGen Informatics</a> -<LI><a href="http://www.brain-map.org/welcome.do" target="_empty">The Allen Mouse Brain Atlas</a> -<LI><a href="http://amigo.geneontology.org/cgi-bin/amigo/go.cgi" target="_empty">AmiGO</a> -<LI><a href="http://syndb.cbi.pku.edu.cn/" target="_empty">Synapse DB</a> -<LI><a href="https://www.pantherdb.org" target="_empty">ABI Panther</a> -<LI><a href="http://biogps.gnf.org" target="_empty">BioGPS</A> <font>and its predecessor </font> <a href="http://symatlas.gnf.org/SymAtlas/" target="_empty">SymAtlas</A> -<LI><a href="http://mouse.perlegen.com/mouse/download.html -" target="_empty">Perlegen/NIEHS Mouse Sequence Data</A> -<LI><a href="http://www.ontologicaldiscovery.org/" target="_empty">Ontological Discovery Environment</a> -<LI><a href="https://shad.eecs.utk.edu/sgo/sgo.html" target="_empty">Semantic Gene Organizer</a> -<LI><a href="http://genome.ucsc.edu/" target="_empty">UCSC Genome Browser</A> and <a href="http://genome.brc.mcw.edu/" target="_empty">mirror</A> -<LI><a href="http://www.ensembl.org/Mus_musculus/index.html" target="_empty">Ensembl</A> -<LI><a href="http://www.ncbi.nlm.nih.gov/gquery/gquery.fcgi?itool=toolbar" target="_empty">NCBI</A> -<LI><a href="http://phenome.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home" target="_empty">Mouse Phenome Database</A> and the <a href="http://phenome.jax.org/pub-cgi/phenome/mpdcgi?rtn=snps/list_pre" target="_empty">MPP SNP Browser</A> -<LI><a href="http://bioinfo.vanderbilt.edu/webgestalt/" target="_empty">WebGestalt</A> -<LI><a href="http://grappa.eecs.utk.edu" target="_empty">GrAPPA</A> Clique-based clustering tool -</UL> -</Blockquote> - -<BR> -<Blockquote class="subtitle">Resources for Analysis of Single Genes, SNPs, mRNAs, and Proteins</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://harvester.fzk.de/harvester/" target="_empty">Harvester</a> retrieves summary data on any one of 57,000 proteins from several bioinformatic resources. -<SMALL>[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> - -<Blockquote> -<a href="http://mus.well.ox.ac.uk/cgi/gscan/wwwqtl.cgi" target="_empty">GSCAN</a>: the Oxford Wellcome Trust Genome Viewer retrieves mapping data for many mouse experimental mapping populations including the Heterogenous Stock, the Pre-Collaborative Cross mice, and Mouse Diversity Panel. -<SMALL>[Added Oct 13, 2010; last site review Oct 13, 2010 by RWW.]</SMALL> -</Blockquote> - - - - -<Blockquote> -<a href="http://nif-apps-stage.neuinfo.org/nif/nifgwt.html" target="_empty">NIF</a>: The Neuroscience Information Framework retrieves summary from a wide variety of neuroscience and bioinformatic resources. -<SMALL>[Added Dec 3, 2009; last site review Dec 3, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://genomics.senescence.info/" target="_empty">Genomics of aging resources</a>. A collection of databases and tools designed to help researchers understand the genetics of human ageing through a combination of functional genomics and evolutionary biology. -<SMALL>[Added Feb 28, 2010; last site review Feb 28, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.hgmd.cf.ac.uk/ac/index.php" target="_empty">Human Disease Gene Database</a> from Cardiff (requires log in) -<SMALL>[Added Nov 5, 2009; last site review Nov 5, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.snp-nexus.org/" target="_empty">SNPnexus</a> database of human SNPs is designed to simplify and assist in the selection of functionally relevant SNPs for large-scale genotyping studies of multifactorial disorders.<SMALL>[Added Jan 15, 2010; last site review Jan 15, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.snpedia.com/index.php/SNPedia" target="_empty">SNPedia</a> is a wiki investigating human genetics. We share information about the effects of variations in DNA, citing peer-reviewed scientific publications. It is used by Promethease to analyze and help explain your DNA.<SMALL>[Added Jan 15, 2010; last site review Jan 15, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="https://www.genoglyphix.com/ggx_browser/search/ -" target="_empty">Genoglyphix Browser</a> includes extensive data on human copy number variants as well as maps of low copy number repeat regions. -<SMALL>[Added Nov 5, 2009; last site review Nov 5, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.hprd.org/query" target="_empty">Human Protein Reference DB</a>: a manually curated resource with data on 20,000 proteins. Very effective interface and rich data. -<SMALL>[Added Oct 30, 2005; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://xmap.picr.man.ac.uk" target="_empty">x:map</a> is a terrific visual display tool for exploring Affymetrix Exon array data sets for mouse and human transcriptomes. -<SMALL>[Added April 18, 2008; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.pdg.cnb.uam.es/UniPub/iHOP/" target="_empty">iHOP</a> retrieves PubMed sentences that report interactions between a reference gene and associate genes and proteins. It allows the assembly of complex graphs that plot the literature interactions of genes. Effective interface for humans and machines. -<SMALL>[Added Dec 25, 2004; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.proteinatlas.org/" target="_empty">Human Protein Atlas</a> displays expression and localization of proteins in a large variety of normal human tissues and cancer cells as high resolution images of immunohistochemically stained tissues and cell lines. -<SMALL>[Added Sept 22, 2007; last site review Sept 22, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.gopubmed.com/" target="_empty">GoPubMed</a> is a simple tool that searches PubMed and sorts the results by GO and MeSH terms. -<SMALL>[Added July 5, 2007 by RWW; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.ncbi.nih.gov/IEB/Research/Acembly/index.html?human" target="_empty">AceView</a> and the <a href="http://www.ebi.ac.uk/astd/main.html" target="_empty">Alternative Splicing and Transcript Diversity database</A> provide excellent resources for systematic information about the many alternative transcripts produced from single genes. -<SMALL>[Added Jan 1, 2005; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> - -<Blockquote> -<a href="http://www.informatics.jax.org/menus/allsearch_menu.shtml" target="_empty">MGI</a> and <a href="http://rgd.mcw.edu/tool-entry.shtml" target="_empty">RGD</a> are reference sites for mouse and rat genetics, respectively. -<SMALL>[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> - - -<Blockquote> -<a href="http://www.dsi.univ-paris5.fr/genatlas/" target="_empty">GenAtlas</a> provides summary data for approximately 19300 human genes and has a useful link that will fetch 10 Kb of upstream sequence for promoter analysis. -[Added Jan 9, 2005; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://polly.wustl.edu/promolign/main.html" target="_empty">PromoLign</a> aligns homologous regions of mouse and human promoters and highlights SNPs and transcription factor binding sites. Check the quick tutorial to see how to extract key data. This site requires an SVG plugin that may not be supported by some browsers and operating systems. -[Added May 10, 2005; FAILED: last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://gai.nci.nih.gov/cgi-bin/GeneViewer.cgi?" target="_empty">CGAP SNP Viewer</a> allows users to view SNPs in the context of transcripts, ORFs and protein motifs for either human or mouse genes. Try the <I>Ahr</I> gene in mouse as an example. -<SMALL>[Added April 10, 2006; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://syndb.cbi.pku.edu.cn/" target="_empty">Synapse Database</a> (SynDB) is a comprehensive database of genes and proteins associated with the neuronal or neuromuscular synapse. Many <B>Trait Data and Analysis</B> pages provide links to SynDB. -<SMALL>[Added May 29, 2005; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://crb118.med.upenn.edu/syn/dev/syndb/main.php" target="_empty">Synapse Database</a> at University of Pennsylvania is a comprehensive database of roughly 200 genes and proteins associated with the synapse. -<SMALL>[Added Nov 26, 2006; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://biosingapore.org/index.php/Databases_and_tools" target="_empty">Singapore</a> Bio Databases and Tool. -<SMALL>[Added Dec 22, 2004; Dragon Genome Explorer site FAILED last site review, changed link; Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> - <a href="http://mutdb.org/AnnoSNP/" target="_empty" >MutDB</a> and <a href="http://www.snps3d.org/modules.php?name=Search&op=advanced%20search" target="_empty">SNPs3D</a> provide great data on functional SNPs in human genes. To analyze the functional impact of non-synonymous SNPs you will also find <a href="http://snpanalyzer.uthsc.edu/" target="_empty" >SNP Analyzer</a> useful because it evaluates SNP impact in terms of the whole protein structural context. -<SMALL>[Added Dec 22, 2004; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://moult.umbi.umd.edu/mouse2004/modules.php?name=Targets" target="_empty" >Alternative Splicing</a> Project provides great summaries and output graphs on splice variants in human, mouse, and Drosophila. -<SMALL>[Added Nov 8, 2005; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">Resources on Imprinting and Parental Origin Effects</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://www.geneimprint.org/site/genes-by-species" target="_empty">Geneimprint</a> is a portal into the burgeoning field of genomic imprinting, collecting relevant articles and reviews, press reports, video and audio lectures, and genetic information -<SMALL>[Added June 23, 2010 by RWW; last site review June 23, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://igc.otago.ac.nz/Search.html" target="_empty">Catalogue of Parent of Origin Effects</a> provides a list of imprinted and putatively imprinted genes with commentary by Ian Morison (University of Otago, New Zealand). Database was last updated in 2008. -<SMALL>[Added June 23, 2010 by RWW; last site review June 23, 2010 by RWW.]</SMALL> -</Blockquote> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">Resources for the Spatial Analysis of Gene and Protein Expression</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://bioinformatics.ubc.ca/resources/links_directory/?subcategory_id=101 -" target="_empty">UBC Bioinformatic and Gene Expression Links</a> is a very extensive and well curated collection of on-line resources for the analysis of biological data sets. -<SMALL>[Added May 24, 2007 by RWW; last site review Sept 19, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -The <a href="http://mamep.molgen.mpg.de/mamep/search.php?searchtype=simple" target="_empty">mamep GeneExpression Links</a> image database of whole-mounted in situ hybridization of mid-gestation mouse embryos. Try entering the symbol <I>Ptch1</I>. -<SMALL>[Added May 28, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -The <a href="http://www.genes2cognition.org/genetics.html" target="_empty">Genes to Cognition</a> databases have a focus on proteins expressed in several key cellular compartments related to synpase function. -<SMALL>[Added Aug 9, 2007 by RWW.]</SMALL> -</Blockquote> - -<Blockquote> -<a href="http://www.cdtdb.brain.riken.jp/CDT/CDTSearch.jsp" target="_empty">Cerebellar Development Transcriptome Database</a>. Expression data for the mouse cerebellum, both microarray and in situ. -<SMALL>[Added Sept 1, 2010; last site review Sept 1, 2010 by RWW.]</SMALL> -</Blockquote> - - -<Blockquote> -Several excellent resources can be used to explore patterns of gene expression primarily in C57BL/6J mice. This strain is one of the parents of the BXD, AXB/BXA, BXH, and CXB genetic reference populations that are key resources in the Gene Network and its companion site, the <a href="http://www.mbl.org" target="_empty" >Mouse Brain Library</A>. -<UL> -<LI><a href="http://www.stjudebgem.org/web/search/keyword/searchByKeyWordForm.php" target="_empty">BGEM</a> and <a href="http://www.ncbi.nlm.nih.gov/projects/gensat/" target="_empty">GENSAT</a> provide images of gene expression in brains of embryos, neonates, and adult mice (roughly 2008 genes as of July 2005). -<BR> -<BR> -<LI><a href="http://www.genepaint.org/Frameset.html" target="_empty">GenePaint</a> and <a href="http://www.geneatlas.org/gene/search/searchgene.jsp" target="_empty">GeneAtlas</a> are companion sites that also provide expression data in embryos, neonates, and adults at high spatial resolution. GeneAtlas has excellent but slow image searching and matching capabilities. -<BR> -<BR> -<LI><a href="http://www.brain-map.org/index.jsp" target="_empty">Allen Brain Atlas</a> has expression data for ~12000 transcripts (adult males in the sagittal plane). -<BR> -<BR> -<LI><a href="http://genex.hgu.mrc.ac.uk/Emage/database/emageIntro.html" target="_empty">EMAP</a> (Edinburgh Mouse Atlas Project) provides data on expression of ~800 genes during development (in situ, immunohistochemistry, and reporter knock-in expression patterns). Most data are from wholemounts between Theiler stages 11 and 20 (embryonic days E7 to E13). EMAP can be used as a Java WebStart application. -<BR> -<BR> -<LI><a href="http://www.mouseatlas.org/data/mouse/project_tissue_view" target="_empty">Mouse Atlas of Gene Expression</a> is a massive SAGE library. The Atlas has quantified the normal state for many tissues by determining the number and identity of genes expressed throughout development. The scope of the project encompasses multiple stages of development of C57BL/6J mouse, from the single cell zygote to the adult, and includes an extensive initial collection of 200 tissues. DiscoverySpace is a WebStart application for use with The Mouse Atlas of Gene Expression. -<BR> -<BR> -<LI><a href="http://mahoney.chip.org/mahoney/database.html" target="_empty">Mahoney</a> Center maintains a rich image collection for ~1000 transcription factors expressed in brain (developmental stages, coronal plane). -<SMALL>[Added Dec 22, 2004; sites reviewed last on Sept 26, 2005 by RWW.]</SMALL> -<BR> -<BR> -</UL> -</Blockquote> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">Resources for the Analysis of Sets and Networks of Transcripts, Genes, Proteins, and SNPs</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://www.genemania.org/full.jsf" target="_empty">GeneMANIA</a> helps you predict the function of your favourite genes and gene sets. Powerful and fast computational methods and a great use of Cytoscape Web. (<A HREF="http://nar.oxfordjournals.org/cgi/content/full/38/suppl_2/W214" target="_empty">2010 PDF</A>). -<SMALL>[Added July 1, 2010; last site review Aug 8, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://toppgene.cchmc.org/" target="_empty">ToppGene Suite</a> A one-stop portal for gene list enrichment analysis and candidate gene prioritization based on functional annotations and protein interactions network -<SMALL>[Added Jan 16, 2010; last site review Jan 16, 2010 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.wikipathways.org/index.php/WikiPathways" target="_empty">WikiPathways</a> (WGCNA) is an open, public platform dedicated to the curation of biological pathways by and for the scientific community. -<SMALL>[Added Nov 12, 2009; last site review Nov 12, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.pathwaycommons.org/pc/" target="_empty">Pathway Commons</a> (WGCNA) is a search tool to find and visualize public biological pathway information. This site collates from several major sites. -<SMALL>[Added Nov 12, 2009; last site review Nov 12, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.sanger.ac.uk/cgi-bin/modelorgs/mousegenomes/snps.pl" target="_empty">Sanger Mouse Genome Project SNP Finder</a> provides access to SNP and indels generated by sequencing 17 strains of mice (plus C57BL/6J). Marvelous. -<SMALL>[Added Nov 18, 2009; last site review Nov 18, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/" target="_empty">Weighted Gene Coexpression Network Analysis</a> (WGCNA) is a collection of R functions to perform weighted correlation network analysis that includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. See the recent paper on <A HREF="http://www.biomedcentral.com/1471-2105/9/559">WGCNA</A>. -<SMALL>[Added Aug 21, 2009; last site review Aug 21, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.genome.jp/" target="_empty">GenomeNet</a> is a terrific site for the analysis of molecular networks. Download the very effective <a href="http://www.genome.jp/download/" target="_empty">KegArray</A> 0.2.6beta package (May 2005) for exploratory data analysis of microarray data set. This package is as good as most commerical software and includes with built-in linkage to the KEGG databases. Versions are available for Mac OS X and Windows. -<SMALL>[Added Jan 3, 2005; last site review Aug 5, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.bioinf.ebc.ee/EP/EP/" target="_empty">Expression Profiler at http://ep.ebi.ac.uk/</a> is a set of tools for clustering, analysis and visualization of gene expression and other genomic data. Tools in the Expression Profiler allow you to perform cluster analysis, pattern discovery, pattern visualization, study and search Gene Ontology categories, generate sequence logos, extract regulatory sequences, study protein interactions, as well as to link analysis results to external tools and databases. -<SMALL>[Added May 20, 2008; last site review May 20, 2008 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.thebiogrid.org/" target="_empty">BioGRID</a>: the Biological General Repository for Interaction Datasets is a freely accessible database of protein and genetic interactions from Mt. Sinai, Toronto. -<SMALL>[Added July 28, 2007; last site review July 28, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://cismols.cchmc.org/" target="_empty">CisMols Analyzer</a> at Cincinnati Children's Hospital (Aronow and colleagues) is a server and database for the analysis of cis element co-occurences in the promoters of a list of genes. The <a href="http://polydoms.cchmc.org/polydoms/" target="_empty">PolyDoms Analyzer</a> is a tool for scanning through gene lists for those members of a pathway, ontolog, or disease that contain potentially harmful protein-coding SNPs. <a href="http://genometrafac.cchmc.org/" target="_empty">GenomeTraFaC</a> is a comparative genomics-based resource for initial characterization of gene models and the identification of putative cis-regulatory regions of RefSeq Gene Orthologs. -<SMALL>[Added Sept 23, 2006; last site review Sept 23, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://compbio.uthsc.edu/miRSNP/" target="_empty">PolymiRTS</a> database that searches for microRNA (miRNA) targets in transcripts that overlap SNPs. This database will also search for genes with associated phenotype variants that may have variants in miRNA target sequence (Yan Cui, Lei Bao and colleagues). - <SMALL>[Added Sept 23, 2006; last site review Sept 23, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.broad.mit.edu/gsea/msigdb/msigdb_index.html" target="_empty">MSigDB</a>: The Molecular Signature Database is part of the Broad Institute Gene Set Enrichment Analysis suite. MSigDB contains large numbers of static and partly annotated sets of genes/transcripts. Registration is not actually required to download data sets. - <SMALL>[Added Jan 18, 2007; last site review Jan 18, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://inia.uchsc.edu/INIA/index.jsp" target="_empty">C-INIA</a> MAGIC-B microarray knowledgebase from the Department of Pharmacology, University of Colorado, Denver, part of the NIAAA INIA project. Extensive public and privated brain array data sets in a powerful analytic web environment. - <SMALL>[Added May 31, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.genmapp.org/introduction.asp" target="_empty">GenMAPP 2.0</a> (2004), the Gene Map Annotation and Pathway Prolifer, is a free Windows application (simple registration required) with which you can visualize expression and other genomic data sets on maps of biological pathways. Very flexible suite of programs that you can also use to make custom gene annotation maps (and more). -<SMALL>[Added Aug 5, 2005; last site review Aug 5, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://bind.ca/" target="_empty">BIND</a> and <a href="http://string.embl.de/" target="_empty">STRING</a> and <a href="http://www.ebi.ac.uk/intact/index.jsp" target="_empty">IntAct</a> are great sites that provide access to well curated data on protein-protein interactions. BIND and IntAct focus on experimentally verified interactions whereas STRING and preBIND incorporate inferred interaction based on other data types, including gene expression. Links to BIND and STRING have been added to the Trait Data and Analysis forms on the GeneNetwork BETA site. -<SMALL>[Added Aug 21, 2005; last site reviews Aug 27, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://ai.stanford.edu/~erans/cancer/index.html" target="_empty">Microarray Module Maps</a> is a great site that databases a large number of coexpression modules defined using many cancer array studies. -<SMALL>[Added Aug 26, 2005; last site review Aug 26, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.geneontology.org/GO.tools.microarray.shtml" target="_empty">The Gene Ontology Consortium</a> maintains a well annotated list of open resources for the analysis of large expression data sets and gene ontologies. Note that there are several different lists, each with valuable links. -<SMALL>[Added July 15, 2005; last site review July 15, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.prioritizer.nl/" target="_empty">Prioritizer</a>: Prioritizer is a stand-alone Java program that uses a functional human gene network, available at <a href="http://www.genenetwork.nl" target="_empty">www.genenetwork.nl</a>, to prioritize positional candidate genes that reside within susceptibility loci, by assuming that real disease genes, residing within different loci are functionally closely related within the gene network.</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.bioinformatics.ic.ac.uk/eqtl/" target="_empty">eQTL Explorer</a> is a Java WebStart application that has also been designed for the calculation and display of QTL maps for large rat data sets, particuarly those generated using the HXB strains. Locations of QTLs for both mRNA traits and conventional physiological traits are displayed on chromosome ideograms. High precision QTL maps can also be generated. A password is required to gain access to the primary data files. -<SMALL>[Added January 7, 2006; last site review Jan 7, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://cliquego.uthsc.edu/" target="_empty">Clique-GO Analysis</a> is a novel tool for extracting cliques of coregulated transcripts. The current data requires Affymetrix U74Av2 probe set IDs as input. Try "103370_at" (the gene <I>Lin7c</I>) as an example. -<SMALL>[Added Jan 4, 2005; last site review Jan 4, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.bioinformatics.ubc.ca/pavlidis/lab/software.html" target="_empty">Gemma and ErmineJ</a> are powerful resources for analysis and metaanalysis of gene expression data sets at UBC. Pavlidis and colleagues also provide updated <A HREF="http://bioinformatics.ubc.ca/microannots/ -">GO data</A> for common microarray platforms. -<SMALL>[Added Jan 4, 2005; last site review June 7, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.cytoscape.org/index.php" target="_empty">Cytoscape</a> is one of several <a href="http://sbml.org/index.psp" target="_empty">SBML</a>-compatible open source programs for visualizing molecular interaction networks and overlaying these networks with gene expression profiles and other data sets to generate and test specific hypotheses. -<SMALL>[Added Jan 5, 2005; last site review Jan 5, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://cgap.nci.nih.gov/Pathways/Pathway_Searcher" target="_empty">Pathway Searcher</a> provides fast access to gene/protein interaction pathways. An intuitive interface. -<SMALL>[Added Dec 30, 2004; last site review Dec 30, 2004 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://genome.ucsc.edu/cgi-bin/hgNear" target="_empty">Gene Sorter</a> is a tool for generating and sorting sets of genes using a wide variety of information integrated into UCSC's Genome Brower. -<SMALL>[Added Dec 31, 2004; last site review Dec 31, 2004 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.cisreg.ca/cgi-bin/oPOSSUM/opossum" target="_empty">oPPOSUM</a> is a tool for finding over-represented transcription factor binding sites in lists of mouse and human genes. It handles about 100 out of greater than 600 TFBSs. -<SMALL>[Added Jan 27, 2005; last site review Nov 21, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://motif.genome.jp/" target="_empty">MOTIF</a> and <a href="http://www.dbi.tju.edu/dbi/tools/paint/index.php?op=FnetBuilder" target="_empty">PAINT</a> search for motifs in submitted sequences or lists of genes. Paint makes use of the TRANSFAC Pro database. -<SMALL>[Added Dec 22, 2004; last site review Dec 25, 2004 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://arrowsmith.psych.uic.edu/arrowsmith_uic/index.html" target="_empty">Arrowsmith</a> provides a fast way to evaluate known interactions or common mechanisms between two genes or proteins. It carries out a sophisticated comparison of the current PubMed database. -<SMALL>[Added Dec 22, 2004; last site review June 7, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.chilibot.net/" target="_empty">Chilibot</a> applies natural-language processing to the PubMed database to hunt for directed relationships among pairs or sets of genes, proteins, and keywords. <SMALL>[Added Dec 30, 2004; last site review Aug 13, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -Mouse Imprinting Resources: -<a href="http://www.mgu.har.mrc.ac.uk/research/imprinting" target="_empty">The Harwell Mouse Imprinting Resource</a>, -<a href="http://www.geneimprint.com/" target="_empty">Duke University - Jirtle's Laboratory</a>, -<a href="http://fantom2.gsc.riken.go.jp/imprinting/" target="_empty">RIKEN Candidate Imprinted Transcript Maps</a>, and -<a href="http://igc.otago.ac.nz/home.html">Imprinted Gene Catalogue - University of Otago</a>. -<SMALL>[Added Oct 20, 2006; last site review Oct 20, 2006 by RWW.]</SMALL> -</Blockquote> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">Resources for the Analysis of Phenotypes in Genetic Reference Populations</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://www.mbl.org/NewMBL_MySQL/tmbl.php" target="_empty">MBL</a> is a extensive image database of brain sections from genetic reference populations of mice, including the BXD, AXB, CXB, BXH strains included in WebQTL. The MBL is a companion database of WebQTL. -<SMALL>[Added Dec 22, 2004; last site review Aug 6, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://snp.ucsd.edu/mouse/" target="_empty">MPAD</a> Mouse Phenome Association Database v 1.0, by Eleazar Eskin and Hyun Min Kang. This resource performs genome-wide association mapping. Phenotype data sets are derived from the Mouse Phenome Project set of standard mouse strains. The permutation procedures account for the genetic relations among these strains and provide much more appropriate genome-wide significance thresholds than previous mouse association mapping methods. -<SMALL>[Added Nov 19, 2006; last site review Nov 19, 2006 by RWW. Link broken June 2007 probably due to move from UCSD to UCLA; check with EE.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://gscan.well.ox.ac.uk/gs/wwwqtl.cgi#" target="_empty">GScan</a> at the Wellcome Trust, Oxford, is a sophisticated viewer and analysis tool with which to explore the genetic control of diverse phenotypes (including array data) generated using heterogeneous stock mice (Flint, Mott, and colleagues). -<SMALL>[Added May 28, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://phenome.jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home" target="_empty">Phenome Project</a> provides access to a wide variety of phenotype data many common and wild inbred strains of mice. -<SMALL>[Added Dec 22, 2004; last site review Dec 25, 2004 by RWW.]</SMALL> -</Blockquote> -</Blockquote> - -<BR> - -<Blockquote class="subtitle">QTL Mapping Resources</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://compbio.uthsc.edu/QSB/" target="_empty">QSB</a>: QSB is a stand-alone JAVA program with a sophisticated GUI developed for genetical genomics or systems genetics, an emerging field that combines quantitative genetics and genomics. QSB stands for QTL mapping, Sequence polymorphism analysis (or SNP analysis) and Bayesian network analysis. QSB takes marker and array data from a segregating population as input and identifies significant QTLs and then evaluated networks of candidate genes associated with these QTLs. -<SMALL>[Added July 29, 2005; last site review Jan 7, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://omicspace.riken.jp/PosMed" target="_empty">Positional Medline (PosMed)</A> is a knowledge-based ranking system of candidate genes within QTL intervals for human, mouse, rat, Arabidopsis, and rice. -<SMALL>[Added Nov 4, 2009; last site review Nov 4, 2009 by RWW.]</SMALL> -</Blockquote> -<Blockquote><a href="http://omicspace.riken.jp" target="_empty">Genome - Phenome Superbrain Project</a> integrates various databases to build a comprehensive computerized encyclopedia of omic sciences in several species, including mouse, rat, human, and arabidopsis, etc. The goal is to evolve this intelligent system into a form of artificial intelligence that can solve a researcher's problems by exploiting a vast amount of information accumulated in documents and published data ranging from genomes to phenomes. <SMALL>[Added Sept 13, 2007; last site review Sept12, 2007 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://qtlreaper.sourceforge.net/" target="_empty">QTL Reaper</a> is software, written in C and compiled as a Python module, for rapidly scanning microarray expression data for QTLs. It is essentially the batch-oriented version of WebQTL. It requires, as input, expression data from members of a set of recombinant inbred lines and genotype information for the same lines. It searches for an association between each expression trait and all genotypes and evaluates that association by a permutation test. For the permutation test, it performs only as many permutations as are necessary to define the empirical P-value to a reasonable precision. It also performs bootstrap resampling to estimate the confidence region for the location of a putative QTL. -<SMALL>[Added Jan 27, 2005; last site review Jan 27, 2005 by KFM.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://ibi.zju.edu.cn/software/qtlnetwork/" target="_empty">QTLNetwork 2.0</a> is a software package ofr mapping QTLs with epistatic and GXE interaction effects in experimental populations including double-haploid, recombinant inbred, backcross, F2, IF2 and BxFy populations. The program provides graphical presentations of QTL mapping results. The software is programmed by C++ programming language under Microsoft Visual C++ 6.0 environment. It works with Microsoft Windows operating systems, including Windows 95/98, NT, 2000, XP, 2003server. A new version of QTLNetwork is under developing, and its functions will be extended to include linkage group construction and marker-assisted virtual breeding.<SMALL>[Added June 21, 2007.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.broad.mit.edu/personal/claire/MouseHapMap/Inbred.htm" target="_empty">MouseHapMap</a> project genotypes from Mark Daly and colleagues. Approximately 140,000 SNPs across 49 strains. Updated Feb 2006.used to explore the Oxford Wellcome Heterogeneous stock QTL mapping project population. It currently includes mapping data for 100+ phenotypes typed across 2000 animals and 13,000 SNPs. -<SMALL>[Added May 10, 2006; last site review May 10, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -A valuable list of <a href="http://www.stat.wisc.edu/%7Eyandell/qtl/software/" target="_empty">Software for QTL Data Analysis</a> and <B>Gene Expression Microarray Software</B> is managed by Brian Yandell at University of Wisconsin. -<SMALL>[Added May 16, 2006; last site review May 16, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://gscan.well.ox.ac.uk/gs/wwwqtl.cgi" target="_empty">GSCAN DB</a> is a browser used to explore the Oxford Wellcome Heterogeneous stock QTL mapping project population. It currently includes mapping data for 100+ phenotypes typed across 2000 animals and 13,000 SNPs. -<SMALL>[Added May 10, 2006; last site review May 10, 2006 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://www.well.ox.ac.uk/mouse/INBREDS/" target="_empty">The Wellcome Trust-CTC SNP Data Set</a> consists of high density SNP data for approximately 490 strains of mice at 13,377 SNPs. These data fiels were processed slightly to generate many of the mouse mapping files used in WebQTL. -<SMALL>[Added Sept 27, 2005; last site review Sept 27, 2005 by RWW.]</SMALL> -</Blockquote> -<Blockquote> -<a href="http://mouse.perlegen.com/mouse/summary_reports.html" target="_empty">The NIEHS-Perlegen Mouse Strain Resequencing Project</a> provides links to SNP data for up to 15 strains of mice. Very high density data for many chromosomes. These data are integrated to some extent in the GeneNetwork. -<SMALL>[Added Sept 25, 2005; last site review Sept 25, 2005 by RWW.]</SMALL> -</Blockquote> -</Blockquote> +{% block title %}Links{% endblock %} -<BR> +{% block css %} +<link rel="stylesheet" type="text/css" href="/static/new/css/markdown.css" /> +{% endblock %} -<Blockquote class="subtitle">Affymetrix Array Annotation Resources</Blockquote> -<Blockquote> -<Blockquote> -<a href="http://research.stowers-institute.org/efg/ScientificSoftware/Applications/Affy/Annotations/" target="_empty">Affy MOE430A and MOE430B Annotation</a> files are explained more clearly that Affymetrix has ever done by Earl F Glynn at the Stowers Institute. (efg@stowers-insitute.org). -<SMALL>[Added July 17, 2006; last site review June 7, 2007 by RWW. This Oct 7, 2005 file caused Grace Wheeler's Mac internet connection to break.]</SMALL> -</Blockquote> -</Blockquote> +{% block content %} -<BR> +<div class="github-btn-container"> + <div class="github-btn "> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> -<Blockquote class="subtitle">Information about this HTML page: </Blockquote> -<Blockquote> -<Blockquote><P><P>This text originally generated by RWW, Dec 21, 2004. Updated by EJC, Feb 27, 2005; by RWW, July 15, Sept 25. -<P>Management of GeneNetwork access and trait <A HREF="http://www.genenetwork.org/manager.html">pages</A>. -</Blockquote> -</Blockquote> - - </TD> - </TR></TABLE> +<div id="markdown" class="container"> + {{ rendered_markdown|safe }} -{% endblock %} +</div> +{% endblock %}
\ No newline at end of file diff --git a/wqflask/wqflask/templates/mapping_results.html b/wqflask/wqflask/templates/mapping_results.html index e68a792a..9542c29d 100644 --- a/wqflask/wqflask/templates/mapping_results.html +++ b/wqflask/wqflask/templates/mapping_results.html @@ -40,6 +40,9 @@ <input type="hidden" name="use_loco" value="{{ use_loco }}"> <input type="hidden" name="selected_chr" value="{{ selectedChr }}"> <input type="hidden" name="manhattan_plot" value="{{ manhattan_plot }}"> + {% if manhattan_plot == True %} + <input type="hidden" name="color_scheme" value="alternating"> + {% endif %} <input type="hidden" name="num_perm" value="{{ nperm }}"> <input type="hidden" name="perm_info" value=""> <input type="hidden" name="perm_strata" value="{{ perm_strata }}"> @@ -55,7 +58,7 @@ <input type="hidden" name="wanted_inputs" value=""> <input type="hidden" name="form_url" value="/run_mapping"> - <div class="container"> + <div class="container" style="min-width: 1400px;"> <div class="col-xs-5"> <h2>Map Viewer: Whole Genome</h2><br> <b>Population:</b> {{ dataset.group.species|capitalize }} {{ dataset.group.name }}<br> @@ -77,7 +80,7 @@ <table> <tr> <td><b>Chr: </b></td> - <td> + <td style="padding: 5px;"> <select name="chromosomes" size="1"> {% for chr in ChrList %} <option value="{{ chr[1] }}" {% if (chr[1] + 1) == selectedChr %}selected{% endif %}>{{ chr[0] }}</option> @@ -87,7 +90,7 @@ </td> </tr> <tr> - <td><b>View: </b></td> + <td ><b>View: </b></td> <td style="padding: 5px;"> <input type="text" name="startMb" size="7" value="{% if startMb != -1 %}{{ startMb }}{% endif %}"> to <input type="text" name="endMb" size="7" value="{% if endMb != -1 %}{{ endMb }}{% endif %}"> </td> @@ -99,7 +102,7 @@ <input type="radio" name="LRSCheck" value="LRS" {% if LRS_LOD == "LRS" %}checked{% endif %}>LRS </label> <label class="radio-inline"> - <input type="radio" name="LRSCheck" value="{% if LRS_LOD == "-log(p)" %}-log(p){% else %}LOD{% endif %}" {% if LRS_LOD == "LOD" or LRS_LOD == "-log(p)" %}checked{% endif %}>{% if LRS_LOD == "-log(p)" %}-log(p){% else %}LOD{% endif %} + <input type="radio" name="LRSCheck" value="{% if LRS_LOD == "-logP" %}-logP{% else %}LOD{% endif %}" {% if LRS_LOD == "LOD" or LRS_LOD == "-logP" %}checked{% endif %}>{% if LRS_LOD == "-logP" %}-logP{% else %}LOD{% endif %} </label> <a href="http://genenetwork.org/glossary.html#LOD" target="_blank"> <sup style="color:#f00"> ?</sup> @@ -114,11 +117,31 @@ </tr> <tr> <td><b>Width: </b></td> - <td> + <td style="padding: 5px;"> <input type="text" name="graphWidth" value="{% if graphWidth is defined %}{{ graphWidth }}{% else %}1600{% endif %}" size="5"><span style="font-size: 12px;"> pixels (minimum=900)</span> </td> </tr> </table> + {% if manhattan_plot == True and selectedChr == -1 %} + <table style="margin-top: 10px;"> + <tr> + <td> + <b>Manhattan Plot Color Scheme: </b> + </td> + <td> + <select id="color_scheme"> + <option value="alternating" {% if color_scheme == "alternating" %}selected{% endif %}>Alternating</option> + <option value="varied" {% if color_scheme == "varied" %}selected{% endif %}>Varied by Chr</option> + <option value="single" {% if color_scheme == "single" %}selected{% endif %}>Single Color</option> + </select> + </td> + <td> + <input name="manhattan_single_color" type="hidden" id="point_color" value={% if manhattan_single_color %}{{ manhattan_single_color }}{% else %}"#D9D9D9"{% endif %}> + <button style="display: none; margin-left: 5px;" id="point_color_picker" class="jscolor {valueElement: 'point_color'}">Choose Color</button> + </td> + </tr> + </table> + {% endif %} </div> <div class="col-xs-4" style="padding: 0px;"> {% if (mapping_method == "reaper" or mapping_method == "rqtl_geno") and nperm > 0 %} @@ -235,8 +258,8 @@ <th></th> <th>Row</th> <th>Marker</th> - {% if LRS_LOD == "-log(p)" %} - <th><div style="text-align: right;">–log(p)</div></th> + {% if LRS_LOD == "-logP" %} + <th><div style="text-align: right;">–logP</div></th> {% else %} <th><div style="text-align: right;">{{ LRS_LOD }}</div></th> {% endif %} @@ -259,7 +282,7 @@ </td> <td align="right">{{ loop.index }}</td> <td>{% if geno_db_exists == "True" %}<a href="/show_trait?trait_id={{ marker.name }}&dataset={{ dataset.group.name }}Geno">{{ marker.name }}</a>{% else %}{{ marker.name }}{% endif %}</td> - {% if LRS_LOD == "LOD" or LRS_LOD == "-log(p)" %} + {% if LRS_LOD == "LOD" or LRS_LOD == "-logP" %} {% if 'lod_score' in marker %} <td align="right">{{ '%0.2f' | format(marker.lod_score|float) }}</td> {% else %} @@ -328,6 +351,9 @@ <script type="text/javascript" src="{{ url_for('js', filename='underscore-string/underscore.string.min.js') }}"></script> <script type="text/javascript" src="{{ url_for('js', filename='d3-tip/d3-tip.js') }}"></script> <script type="text/javascript" src="/static/new/js_external/plotly-latest.min.js"></script> + {% if manhattan_plot == True and selectedChr == -1 %} + <script type="text/javascript" src="/static/new/js_external/jscolor.js"></script> + {% endif %} <script language="javascript" type="text/javascript" src="{{ url_for('js', filename='DataTables/js/jquery.dataTables.min.js') }}"></script> <script language="javascript" type="text/javascript" src="https://cdn.datatables.net/buttons/1.0.0/js/dataTables.buttons.min.js"></script> @@ -423,7 +449,7 @@ var mapping_input_list = ['temp_uuid', 'trait_id', 'dataset', 'tool_used', 'form_url', 'method', 'transform', 'trimmed_markers', 'selected_chr', 'chromosomes', 'mapping_scale', 'score_type', 'suggestive', 'significant', 'num_perm', 'permCheck', 'perm_output', 'perm_strata', 'categorical_vars', 'num_bootstrap', 'bootCheck', 'bootstrap_results', - 'LRSCheck', 'covariates', 'maf', 'use_loco', 'manhattan_plot', 'control_marker', 'control_marker_db', 'do_control', 'genofile', + 'LRSCheck', 'covariates', 'maf', 'use_loco', 'manhattan_plot', 'color_scheme', 'manhattan_single_color', 'control_marker', 'control_marker_db', 'do_control', 'genofile', 'pair_scan', 'startMb', 'endMb', 'graphWidth', 'lrsMax', 'additiveCheck', 'showSNP', 'showGenes', 'viewLegend', 'haplotypeAnalystCheck', 'mapmethod_rqtl_geno', 'mapmodel_rqtl_geno', 'temp_trait', 'group', 'species', 'reaper_version', 'primary_samples', 'n_samples'] @@ -449,10 +475,21 @@ remap = function() { $('input[name=selected_chr]').val($('select[name=chromosomes]').val()); + $('input[name=color_scheme]').val($('select#color_scheme').val()); $('#marker_regression_form').attr('action', '/loading'); return $('#marker_regression_form').submit(); }; + {% if manhattan_plot == True and selectedChr == -1 %} + $('#color_scheme').change(function(){ + if ($(this).val() == "single"){ + $('#point_color_picker').show(); + } else { + $('#point_color_picker').hide(); + } + }); + {% endif %} + {% if mapping_method != "gemma" and mapping_method != "plink" %} $('#download_perm').click(function(){ perm_info_dict = { diff --git a/wqflask/wqflask/templates/policies.html b/wqflask/wqflask/templates/policies.html index 83b6b3c0..4e0985d3 100644 --- a/wqflask/wqflask/templates/policies.html +++ b/wqflask/wqflask/templates/policies.html @@ -1,18 +1,23 @@ {% extends "base.html" %} -{% block title %}Policies{% endblock %} + +{% block title %}Links{% endblock %} + +{% block css %} +<link rel="stylesheet" type="text/css" href="/static/new/css/markdown.css" /> +{% endblock %} + {% block content %} -<Table width= "100%" cellSpacing=0 cellPadding=5><TR> -<!-- Body Start from Here --> -<TD valign="top" height="200" width="100%"> - <P class="title">WebQTL Conditions of Use</P> - <UL> - <LI><A HREF="conditionsofUse.html">WebQTL Usage Conditions and Limitations</A><P></P> - <LI><A HREF="dataSharing.html">WebQTL Data Sharing Policy</A><P></P> - <LI><A HREF="statusandContact.html">Status and investigators to contact on data use and publication</A><P></P> - </UL> - <P></P> -</TD> -</TR></TABLE> + <div class="github-btn-container"> + <div class="github-btn "> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> +<div id="markdown" class="container"> + {{ rendered_markdown|safe }} +</div> {% endblock %}
\ No newline at end of file diff --git a/wqflask/wqflask/templates/reference.html b/wqflask/wqflask/templates/reference.html deleted file mode 100644 index d95d22e3..00000000 --- a/wqflask/wqflask/templates/reference.html +++ /dev/null @@ -1,1497 +0,0 @@ -{% extends "base.html" %} -{% block title %}References{% endblock %} -{% block content %} - -<h2> Papers and References to GeneNetwork</A></h2> - -<BLOCKQUOTE> - -<left> -<a href="#New">Highlighted References</a> | -<a href="#Key">Key References</a> | -<a href="#Background">Background References</a> | -</left> - -<BR> <BR> -<left> -<a href="#2020">2020</a> | -<a href="#2019">2019</a> | -<a href="#2018">2018</a> | -<a href="#2017">2017</a> | -<a href="#2016">2016</a> | -<a href="#2015">2015</a> | -<a href="#2014">2014</a> | -<a href="#2013">2013</a> | -<a href="#2012">2012</a> | -<a href="#2011">2011</a> | -<a href="#2010">2010</a> | -<a href="#2009">2009</a> | -<a href="#2008">2008</a> | -<a href="#2007">2007</a> | -<a href="#2006">2006</a> | -<a href="#2005">2005</a> | -<a href="#2004">2004</a> | -<a href="#2003">2003</a> | -</left> -</BLOCKQUOTE> - -<DIR> - <A HREF="https://scholar.google.com/scholar?as_q=&as_epq=&as_oq=webqtl+%22genenetwork%22&as_eq=&as_occt=any&as_sauthors=&as_publication=&as_ylo=&as_yhi=&btnG=&hl=en&as_sdt=0%2C5"target="_blank" class="fwn">Google Scholar</A> search for <I> "genenetwork" OR "webqtl" </I> generated: -<OL> -<LI>1430 hits on 2016/09/08 -<LI>1730 hits on 2017/10/17 -<LI>2020 hits on 2019/05/13) -</OL> - -<A HREF="https://scholar.google.com/scholar?as_q=&as_epq=&as_oq=genenetwork.org&as_eq=&as_occt=any&as_sauthors=&as_publication=&as_ylo=&as_yhi=&btnG=&hl=en&as_sdt=0%2C5"target="_blank" class="fwn">Google Scholar</A> search for <I> "genenetwork.org" </I> generated: - -<OL> -<LI>1030 hits on 2019/05/13 -<LI> 105 hits from 2018/01/01 to 2019/05/13 -</OL> - -</DIR> - - -<BLOCKQUOTE> - <A NAME="New" class="subtitle">Highlighted References</A> -<BLOCKQUOTE style="font-size: 14px;"> - -<B><I>Please send us citations to articles that we may have missed.</I></B> - -<OL> - -<LI> Mulligan MK, Mozhui K, Prins P, Williams RW (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27933521" target="_blank" class="fwn">2017</A>) GeneNetwork: A Toolbox for Systems Genetics. In <I>Systems Genetics</I>, Methods in Molecular Biology 1488:75-120 -[An updated primer in using GeneNetwork for molecular analysis of mouse and human cohorts.] -<SMALL><A HREF="/images/upload/Mulligan_How_To_Use_GeneNetwork_2017_v1.pdf" target="_blank" class="fwn">PDF version</A> -</SMALL> -<BR> -<BR> - -<LI> Williams RW, Williams EG (<A HREF="/images/upload/Williams_ResourcesSystemsGenetics_MethodsMolBio_2016.pdf" target="_blank" class="fwn"" target="_blank" class="fwn">2017</A>) Resources for Systems Genetics. In <I>Systems Genetics</I>, Methods in Molecular Biology 1488:3-29. -[This review is intended to help you when making the hard choices about types of resources to use in system genetic studies. The short answer: embrace diversity in your resources. The computational barriers to joint analysis are now minimal.] -<SMALL><A HREF="/images/upload/Williams_ResourcesSystemsGenetics_MethodsMolBio_2016.pdf" target="_blank" class="fwn">PDF version</A> -</SMALL> -<BR> -<BR> - -<LI> Williams RW (<A HREF="http://journal.frontiersin.org/article/10.3389/neuro.01.016.2009/full" target="_blank" class="fwn">2009</A>) Herding cats: the sociology of data integration. Frontier in Neuroscience 3(2):154-6. -[An short but amusing commentary on predictive (someday perhaps even precision) medicine and how to get there using genetic reference populations such as the BXD and Collaborative Cross families.] -<SMALL><A HREF="http://journal.frontiersin.org/article/10.3389/neuro.01.016.2009/full" target="_blank" class="fwn">PDF version</A> -</SMALL> -<BR> -<BR> - - -<LI> -Pinelli M, Carissimo A, Cutillo L, Lai CH, Mutarelli M, Moretti MN, Singh MV, Karali M, Carrella D, Pizzo M, Russo F, Ferrari S, Ponzin D, Angelini C, Banfi S, di Bernardo D (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27235414" target="_blank" class="fwn">2016</A>) An atlas of gene expression and gene co-regulation in the human retina.Nucleic Acids Res. 2016 Jul 8;44(12):5773-84 -<SMALL><A HREF="http://retina.tigem.it" target="_blank" class="fwn">RETINA database</A>, and see the <A HREF="/images/upload/Pinelli_diBerardo_2016_AtlasHumanRetina.pdf" target="_blank" class="fwn">PDF</A>. These data are now in GeneNetwork (Species = Human, Group = Retina). -</SMALL> - -<BR> -<BR> - - -<LI> -Williams EG, Wu Y, Pooja J, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N, Aebersold R, Auwerx J (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27284200" target="_blank" class="fwn">2016</A>) Systems proteomics and trans-omic integration illuminate new mechanisms in mitochondrial function. Science 352(6291):aad0189 -<SMALL><A HREF="http://science.sciencemag.org/content/352/6291/aad0189.full" target="_blank" class="fwn">HTML version</A>, <A HREF="/images/upload/WilliamsEG_Auwerx_Systems_Science-2016.pdf " target="_blank" class="fwn">PDF</A> -</SMALL> -<BR> -<BR> - - - -<LI> -Sloan Z, Arends D, Broman KW, Centeno A, Furlotte N, Nijveen H, Yan L, Zhou X, Williams RW, Prins P (2016) GenetNetwork: framework for web-based genetics. The Journal of Open Source Software http://joss.theoj.org/papers/10.21105/joss.00025 (http://dx.doi.org/10.21105/joss.00025) -<SMALL><A HREF="http://joss.theoj.org/papers/10.21105/joss.00025" target="_blank" class="fwn">HTML version</A> -</SMALL> -<BR> -<BR> - -<LI>Alam G, Miller DB, O'Callaghan JP, Lu L, Williams RW, Jones BC (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27182044" target="_blank" class="fwn">2016</A>) MPTP neurotoxicity is highly concordant between the sexes among BXD recombinant inbred mouse strains. Neurotoxicology in press -<SMALL><A HREF="/images/upload/Alam_Jones_MPTP_Neurotox_2016.pdf" target="_blank" class="fwn">PDF version</A> -</SMALL> -<BR> -<BR> - - -<LI>Merkwirth C, Jovaisaite V, Durieux J, Matilainen O, Jordan SD, Quiros PM, Steffen KK, Williams EG, Mouchiroud L, Tronnes SU, Murillo V, Wolff SC, Shaw RJ, Auwerx J, Dillin A (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27133168" target="_blank" class="fwn">2016</A>) Two conserved histone demethylases regulate mitochondrial stress-induced longevity. Cell 165:1209-1223 -<BR> -<BR> - - -<LI> Wang X, Pandey AK, Mulligan MK, Williams EG, Mozhui K, Li Z, Jovaisaite V, Quarles LD, Xiao Z, Huang J, Capra JA, Chen Z, Taylor WL, Bastarache L, Niu X, Pollard KS, Ciobanu DC, Reznik AO, Tishkov AV, Zhulin IB, Peng J, Nelson SF, Denny JC, Auwerx J, Lu L, Williams RW (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26833085" target="_blank" class="fwn">2016</A>). Joint mouse-human phenome-wide association to test gene function and disease risk. Nature Communications 7:10464 -[Best reference on the BXD family of strains and the wide range of phenotypes that have been generated over the past 40 years.] - -<SMALL><A HREF="/images/upload/Wang_PheWas_NatComm_2016.pdf" target="_blank" class="fwn">PDF version</A> and the - -<A HREF="/images/upload/Wang_SupplementalTables_NatComm_2016.xlsx" target="_blank" class="fwn">Supplementary Tables in one Excel file</A> -</SMALL> - -<BR> -<BR> - - -<LI> Xue Y, Li J, Yan L, Lu L, Liao FF (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26092713" target="_blank" class="fwn">2015</A>) Genetic variability to diet-induced hippocampal dysfunction in BXD recombinant inbred (RI) mouse strains. Behav Brain Res 292:83-94 -<BR> -<BR> - -<LI>Chaum E, Winborn CS, Bhattacharya S (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25963977" target="_blank" class="fwn">2015</A>) Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Mamm Genome 26:210-221 -<BR> -<BR> - -<LI>Cheng Q, Seltzer Z, Sima C, Lakschevitz FS, Glogauer M. (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25942439" target="_blank" class="fwn">2015</A>) Quantitative Trait Loci and Candidate Genes for Neutrophil Recruitment in Sterile Inflammation Mapped in AXB-BXA Recombinant Inbred Mice. PLoS One 10:e0124117 -<BR> -<BR> - -<LI>Scott RE, Ghule PN, Stein JL, Stein GS (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25215496" target="_blank" class="fwn">2015</A>) Cell cycle gene expression networks discovered using systems biology: Significance in carcinogenesis. J Cell Physiol 230:2533-42 -<BR> -<BR> - -<LI> Williams EG, Auwerx J (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26140590" target="_blank" class="fwn">2015</A>) The convergence of systems and reductionist approaches in complex trait analysis. Cell 162:23-32. -<BR> -<BR> - -<LI>Wu Y, Williams EG, Dubuis S, Mottis A, Jovaisaite V, Houten SM, Argmann CA, Faridi P, Wolski W, Kutalik Z, Zamboni N, Auwerx J, Aebersold R (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25215496" target="_blank" class="fwn">2014</A>) Multilayered genetic and omics dissection of mitochondrial activity in a mouse reference population. Cell 158:1415-1430 -<BR> -<BR> - -<LI> -Hayes SK, Hager R, Grencis RK (2014) Sex-dependent genetics effects on immune responses to parasitic nematodes. BMC Genomics 15:193 -<BR> -<BR> - -<LI>Harenza JL, Muldoon PP, De Biasi M, Damaj MI, Miles MF (2014) Genetic variation within the Chrna7 gene modulates nicotine reward-like phenotypes in mice. Genes Brain Behav 13:213-225 -<BR> -<BR> - - -<LI> -Ye R, Carneiro AM, Airey D, Sanders-Bush E, Williams RW, Lu L, Wang J, Zhang B, Blakely RD (<A HREF="https://ncbi.nlm.nih.gov/pubmed/24102824" target="_blank" class="fwn">2014</A>) Evaluation of heritable determinants of blood and brain serotonin homeostasis using recombinant inbred mice. Genes Brain Behav. 13:247-260 - -<BR> -<BR> -<LI> -Houtkooper RH, Mouchiroud L, Ryu D, Moullan N, Katsyuba E, Knott G, Williams RW, Auwerx J (<A HREF="https://ncbi.nlm.nih.gov/pubmed/23698443" target="_blank" class="fwn">2013</A>) Mitonuclear protein imbalance as a conserved longevity mechanism. Nature 497:451-457 <A HREF="/images/upload/Houtkooper_Williams_Auwerx_Nature_2013.pdf" target="_blank" class="fwn">PDF version</A> - -<BR> -<BR> - -</OL> - -</Blockquote> - - - - - - - - - - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="Key" class="subtitle">Key References</A> -<BLOCKQUOTE > - -<P>The first section lists key technical papers that are appropriate references when citing GeneNetwork and WebQTL. The second section lists publications and other resources that have made use of GeneNetwork.<P> - -</Blockquote> -<OL> - - -<LI> Mulligan MK, Mozhui K, Prins P, Williams RW (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26092713" target="_blank" class="fwn">2016</A>) GeneNetwork – A toolbox for systems genetics. In <I>Systems Genetics</I>, Methods in Molecular Biology; Schughart K, Williams RW eds.; Humana Press, in press -[This is a currently the most comprehensive protocol and guide (20 Mb) for GeneNetwork.] -<SMALL><A HREF="/images/upload/Mulligan_How_To_Use_GeneNetwork_2016_v1.pdf" target="_blank" class="fwn">PDF version</A></SMALL> - - -<LI> -Williams RW, Mulligan MK (<A HREF="https://ncbi.nlm.nih.gov/pubmed/23195314" target="_blank" class="fwn">2012</A>) Genetic and molecular network analysis of behavior. Int Rev Neurobiol. 104:135-57. [Explains the use of GeneNetwork in behavioral neurogenetics] -<SMALL><A HREF="/images/upload/Williams_Mulligan_Bioinformatics of Brain Short 2012.pdf" target="_blank" class="fwn">PDF version</A> -</SMALL> - -<LI> Williams EG, Auwerx J (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26140590" target="_blank" class="fwn">2015</A>) The convergence of systems and reductionist approaches in complex trait analysis. Cell 162:23-32. [Research into the genetic and environmental factors behind complex trait variation has traditionally been segregated into distinct scientific camps. The reductionist approach aims to decrypt phenotypic variability bit by bit, founded on the underlying hypothesis that genome-to-phenome relations are largely constructed from the additive effects of their molecular players. In contrast, the systems approach aims to examine large-scale interactions of many components simultaneously, on the premise that interactions in gene networks can be both linear and non-linear. Both approaches are complementary, and they are becoming increasingly intertwined due to developments in gene editing tools, omics technologies, and population resources. Together, these strategies are beginning to drive the next era in complex trait research, paving the way to improve agriculture and toward more personalized medicine.] <SMALL><A HREF="/images/upload/WilliamsEG_Auwerx_Cell2015.pdf" target="_blank" class="fwn">PDF version</A> -</SMALL> - -<LI> -Wang J, Williams RW, Manly KF (<a href="http://journals.humanapress.com/ArticleDetail.pasp?issn=1539-2791&acode=NI:1:4:299" target="_blank" class="fwn">2003</a>) WebQTL: Web-based complex trait analysis. Neuroinformatics 1: 299-308 <A href="http://www.genenetwork.org/pdf/webqtl.pdf" class="fwn"><I>Full Text PDF Version</I></A>. [A good technical reference to WebQTL and GeneNetwork] -<LI> -Williams RW, Gu J, Qi S, Lu L (<a href="http://journals.humanapress.com/ArticleDetail.pasp?issn=1539-2791&acode=NI:1:4:299" target="_blank" class="fwn">2003</a>) The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis. Genome Biology 2(11)<A href="http://genomebiology.com/content/2/11/RESEARCH0046" class="fwn"><I>Full Text Version</I></A>. [A detailed analysis of the genetics of recombinant inbred strains] -<LI> -Chesler EJ, Wang J, Lu L, Qu Y, Manly KF, Williams RW (<a href="http://journals.humanapress.com/ArticleDetail.pasp?issn=1539-2791&acode=NI:1:4:343" target="_blank" class="fwn">2003</a>) Genetic correlates of gene expression in recombinant inbred strains: a relational model to explore for neurobehavioral phenotypes. Neuroinformatics 1: 343-357. <A href="http://www.nervenet.org/pdf/Genetic_Correlation_webQTL.pdf" class="fwn"><I>Full Text PDF Version</I></A>. [Best reference regarding interpretation of genetic correlations.] -<LI> -Chesler EJ, Lu L, Wang J, Williams RW, Manly KF (<a href="http://www.nature.com/cgi-taf/DynaPage.taf?file=/neuro/journal/v7/n5/full/nn0504-485.html" target="_blank" class="fwn">2004</a>) WebQTL: rapid exploratory analysis of gene expression and genetic networks for brain and behavior. Nature Neuroscience 7: 485-486. <A href="http://www.genenetwork.org/pdf/nn0504-485.pdf" class="fwn"><I>Full Text PDF Version</I></A> [A short review] -<LI> -Chesler EJ, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15474587" target="_blank" class="fwn">2004</a>) Brain gene expression: genomics and genetics. International Review of Neurobiology 60:59-95. (<I>DNA Arrays in Neurobiology</I>, edited by MF Miles, Elsevier, Amsterdam). [A longer discussion, both statistical and conceptual, on the genetic analysis of gene expression and relations to higher order behavioral phenotypes]</A> -<LI> -Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI, Vellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC, Williams RW, Cooke M, de Haan G (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15711547" target="_blank" class="fwn">2005</A>) Uncovering regulatory pathways affecting hematopoietic stem cell function using "genetical genomics." Nature Genetics 37:225-232. [Please cite this article if you have used the GNF-Groningen hematopoietic stem cell data set.] -<LI> -Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J, Hsu HC, Mountz JD, Baldwin N, Langston MA, Threadgill DW, Manly KF, Williams RW (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15711545" target="_blank" class="fwn">2005</A>) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nature Genetics 37: 233-242. [Please cite this article if you have used one of the UTHSC brain data sets.] -<LI> -Damerval C, Maurice A, Josse JM, de Vienne D (<A HREF="http://www.genetics.org/cgi/content/abstract/137/1/289" class="fwn" target="_blank">1994</A>) Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics 137: 289-301 <A href="http://www.genetics.org/cgi/reprint/137/1/289" class="fwn" target="_blank"><I>Full Text PDF Version</I></A> [The first published paper on system genetics. Impressive; before its time.] - -<Blockquote><SMALL>[Damerval et al., 1994 abstract] A methodology to dissect the genetic architecture of quantitative variation of numerous gene products simultaneously is proposed. For each individual of a segregating progeny, proteins extracted from a given organ are separated using two-dimensional electrophoresis, and their amounts are estimated with a computer-assisted system for spot quantification. Provided a complete genetic map is available, statistical procedures allow determination of the number, effects and chromosomal locations of factors controlling the amounts of individual proteins. This approach was applied to anonymous proteins of etiolated coleoptiles of maize, in an F(2) progeny between two distant lines. The genetic map included both restriction fragment length polymorphism and protein markers. Minimum estimates of one to five unlinked regulatory factors were found for 42 of the 72 proteins analyzed, with a large diversity of effects. Dominance and epistasis interactions were involved in the control of 38% and 14% of the 72 proteins, respectively. Such a methodology might help understanding the architecture of regulatory networks and the possible adaptive or phenotypic significance of the polymorphism of the genes involved. -</SMALL></Blockquote> - -<LI> -Grisham W, Schottler NA, Valli-Marill J, Beck L, Beatty J (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20516355" target="_blank" class="fwn" >2010</a>) Teaching bioinformatics and neuroinformatics by using free web-based tools. CBE--Life Sciences Education 9: 98-107 <A href="http://www.lifescied.org/content/9/2/98.full.pdf+html" target="_blank" class="fwn" ><I>Full Text PDF Version</I></A> -<BR><SMALL> A terrific introduction to a wide range of bioinformatic resources, including GeneNetwork, that have been assembled as a coherent teaching module. A detailed student/instructor’s manual, PDFs of handouts, PowerPoint slides, and sample exams are available for free at <A HREF="http://mdcune.psych.ucla.edu/modules/bioinformatics" target="_blank" class="fwn" >http://mdcune.psych.ucla.edu/modules/bioinformatics</A>. - -<BR><B>Youtube videos of Dr. William Grisham teaching neurogenetics at UCLA</B>: -<BR><A HREF="http://www.youtube.com/watch?v=5UniEc_pzs0" target="_blank" class="fwn" >Part 1</A> -<BR><A HREF="http://www.youtube.com/watch?v=zjdOWC7zxt0" target="_blank" class="fwn" >Part 2</A> -<BR><A HREF="http://www.youtube.com/watch?v=caC0YGhDoo8" target="_blank" class="fwn" >Part 3</A> -<BR><A HREF="http://www.youtube.com/watch?v=eTzIcM3aspM" target="_blank" class="fwn">Part 4</A> -<BR><A HREF="http://www.youtube.com/watch?v=Dnq7w4RIAXI" target="_blank" class="fwn">Part 5</A> -</SMALL> -<BR> -<BR> -<LI> -Grisham W (<a href="http://www.funjournal.org/downloads/200981/grisham81.pdf" target="_blank" class="fwn">2009</a>) Modular digitial course in undergraduate neuroscience education (MDCUNE): A website offering free digital tools for neuroscience educators. Journal of Undergraduate Neuroscience Education 8:A26-A31 <A href="http://www.funjournal.org/downloads/200981/grisham81.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<BR><SMALL> -An excellent example of how resources such as GeneNetwork and the Mouse Brain Library can be used in class room labs. -</SMALL> -<LI> -Hübner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F, Mueller M, Hummel O, Monti J, Zidek V, Musilova A, Kren V, Causton H, Game L, Born G, Schmidt S, Muller A, Cook SA, Kurtz TW, Whittaker J, Pravenec M, Aitman TJ (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15711544" target="_blank" class="fwn">2005</A>) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nature Genetics 37: 243-253. [Please cite this article if you have used one of the rat HXB kidney or peritoneal fat data sets.] -<LI> -Kang HM, Ye C, Eskin E (2008) Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots. Genetics 180:1909-1925 -<A href="http://www.genetics.org/cgi/content/abstract/genetics.108.094201v1" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>. [An important method that can greatly improve the ability to resolve true genetic interactions in expression genetic studies.] -<LI> -Ljungberg K, Holmgren S, Carlborg O (<A HREF="http://bioinformatics.oupjournals.org/cgi/content/abstract/bth175?ijkey=21Pp0pgOuBL6Q&keytype=ref" class="fwn" target="_blank">2004</A>) Simultaneous search for multiple QTL using the global optimization algorithm DIRECT. Bioinformatics 20:1887-1895. [Please review and cite this article if you have exploited the DIRECT pair-scan tool and output graphs in WebQTL.] -<LI> -Manly KF, Wang J, Williams RW (<A HREF="http://genomebiology.com/2005/6/3/R27" class="fwn" target="_blank">2005</A>) Weighting by heritability for detection of quantitative trait loci with microarray estimates of gene expression. Genome Biology 6: R27 <A href="http://genomebiology.com/2005/6/3/R27" class="fwn" target="_blank"><I>Full Text HTML and PDF Version</I></A>. [Please cite this article if you have used one of the HWT (Heritability Weighted Transform) data sets.] -<LI> -Manly K, Williams RW (2001) WEBQTL—WWW service for mapping quantitative trait loci. International Mouse Genome Conference 15: 74. [First published abstract on WebQTL, presented Oct 2001, Edinburgh; also see <A HREF="http://www.complextrait.org/archive/2002/HTML/manly.html" class="fwn" target="_blank">2002</A> CTC abstract] -<LI> -Taylor BA, Heiniger HJ, Meier H (<A HREf="http://www.ncbi.nlm.nih.gov/pubmed/4719448">1973</A>) Genetic analysis of resistance to cadmium-induced teticular damage in mice. Proc Soc Exp Biol Med 143:629-33 [This is one of the first full paper on the use of recombinant inbred strains in biomedical research and the first paper to use BXD lines of mice. The <I>cdm</I> locus that they mapped to distal Chr 3 was subsequently defined as the <I>Slc38a8</I> metal testicular metal ion transporter. <A HREF="/images/upload/Taylor_1973.pdf"> Full text</A> -<LI> -Webster JA, Gibbs JR, Clarke J, Ray M, Zhang W, Holmans P, Rohrer K, Zhao A, Marlowe L, Kaleem M, McCorquodale DS 3rd, Cuello C, Leung D, Bryden L, Nath P, Zismann VL, Joshipura K, Huentelman MJ, Hu-Lince D, Coon KD, Craig DW, Pearson JV; NACC-Neuropathology Group, Heward CB, Reiman EM, Stephan D, Hardy J, Myers AJ (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/19361613" class="fwn" target="_blank">2009</A>) Genetic control of human brain transcript expression in Alzheimer disease. Am J Hum Genet 84:445-58. [Please review and cite this article if you have used the HUMAN data set by Myers and colleagues in GeneNetwork.] -<LI> -Williams RW, Shou S, Lu L, Wang J, Manly KF, Hsu HC, Mountz J, Threadgill DW (<A HREF="http://www.complextrait.org/ctc2002/williams.html" class="fwn" target="_blank">2002</A>) Genomic analysis of transcriptional networks: combining microarrays with complex trait analysis. Complex Trait Consortium 1 [One of the first published abstracts on system genetic analysis of microarray data sets, presented May 2002.] -<LI> -Zhang B, Schmoyer D, Kirov S, Snoddy J (<a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=14975175" target="_blank" class="fwn">2004</a>) GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics 5:16. [This reference describes GOTM--the predecessor of WebGestalt that is now used to analyze sets of covarying transcripts.] - -</OL> -</Blockquote> - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2019" class="subtitle"">GeneNetwork (2019) </A> (not complete) -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI> -Théberge ET, Baker JA, Dubose C, Boyle JK, Balce K, Goldowitz D, Hamre KM (<A HREF="https://ncbi.nlm.nih.gov/pubmed/30589433" target="_blank" class="fwn">2019</A>) Genetic influences on the amount of cell death in the neural tube of BXD mice exposed to acute ethanol at midgestation. Alcohol Clin Exp Res 43:439-452 -<LI> -Roy S, Zaman KI, Williams RW, Homayouni R (<A HREF="https://ncbi.nlm.nih.gov/pubmed/30871457" target="_blank" class="fwn">2019</A>) Evaluation of Sirtuin-3 probe quality and co-expressed genes using literature cohesion. BMC Bioinformatics 20(Suppl 2):104 - - -</OL> -</Blockquote> - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2018" class="subtitle"">GeneNetwork (2018) </A> (not complete) -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI>Ashbrook DG, Mulligan MK, Williams RW Post-genomic behavioral genetics: From revolution to routine (<A HREF="https://ncbi.nlm.nih.gov/pubmed/29193773" target="_blank" class="fwn">2018</A>) Genes Brain Behav 17:e12441 -<LI> -de Vries M, Faiz A, Woldhuis RR, Postma DS, de Jong TV, Sin DD, Bossé Y, Nickle DC, Guryev V, Timens W, van den Berge M, Brandsma CA (<A HREF="https://ncbi.nlm.nih.gov/pubmed/29212667" target="_blank" class="fwn">2018</A>) Lung tissue gene-expression signature for the ageing lung in COPD. Thorax 73:609-617 -<LI> -Diessler S, Jan M, Emmenegger Y, Guex N, Middleton B, Skene DJ, Ibberson M, Burdet F, Götz L, Pagni M, Sankar M, Liechti R, Hor CN, Xenarios I, Franken P (<A HREF="https://ncbi.nlm.nih.gov/pubmed/30091978" target="_blank" class="fwn">2018</A>) A systems genetics resource and analysis of sleep regulation in the mouse. PLoS Biology 16(8):e2005750 -<LI> -King R, Struebing FL, Li Y, Wang J, Koch AA, Cooke Bailey JN, Gharahkhani P; International Glaucoma Genetics Consortium; NEIGHBORHOOD Consortium, MacGregor S, Allingham RR, Hauser MA, Wiggs JL, Geisert EE (<A HREF="https://ncbi.nlm.nih.gov/pubmed/29370175" target="_blank" class="fwn">2018</A>) Genomic locus modulating corneal thickness in the mouse identifies POU6F2 as a potential risk of developing glaucoma. PLoS Genet 14:e1007145 -<LI> -Lu Y, Zhou D, King R, Zhu S, Simpson CL, Jones BC, Zhang W, Geisert EE, Lu L (<A HREF="https://ncbi.nlm.nih.gov/pubmed/30619460" target="_blank" class="fwn">2018</A>) The genetic dissection of Myo7a gene expression in the retinas of BXD mice. Mol Vis 24:115-126 -<LI> -Struebing FL, King R, Li Y, Cooke Bailey JN; NEIGHBORHOOD consortium, Wiggs JL, Geisert EE (<A HREF="https://ncbi.nlm.nih.gov/pubmed/29421330" target="_blank" class="fwn">2018</A>) Genomic loci modulating retinal ganglion cell death following elevated IOP in the mouse. Exp Eye Res 169:61-67 - - -</OL> -</Blockquote> - - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2017" class="subtitle"">GeneNetwork (2017) </A> (not complete) -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI> -Baker JA, Li J, Zhou D, Yang M, Cook MN, Jones BC, Mulligan MK, Hamre KM, Lu L (<A HREF="https://ncbi.nlm.nih.gov/pubmed/28027852" target="_blank" class="fwn">2017</A>) Analyses of differentially expressed genes after exposure to acute stress, acute ethanol, or a combination of both in mice. Alcohol 58:139-151 -<LI> -Baud A, Mulligan MK, Casale FP, Ingels JF, Bohl CJ, Callebert J, Launay JM, Krohn J, Legarra A, Williams RW, Stegle O (<A HREF="https://ncbi.nlm.nih.gov/pubmed/28121987" target="_blank" class="fwn">2017</A>) Genetic variation in the social environment contributes to health and disease. PLoS Genet 13(1):e1006498 -<LI>Brockmann GA, Arends D, Heise S, Dogan A (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27933540" target="_blank" class="fwn">2017</A>) Systems genetics of obesity. Methods Mol Biol. 2017;1488:481-497 -<LI> -Grisham W, Brumberg JC, Gilbert T, Lanyon L, Williams RW, Olivo R (<A HREF="https://ncbi.nlm.nih.gov/pubmed/29371844" target="_blank" class="fwn">2017</A>) Teaching with Big Data: Report from the 2016 Society for Neuroscience Teaching Workshop. J Undergrad Neurosci Educ 16:A68-A76 -<LI> - -Jones BC, Jellen LC (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27933539" target="_blank" class="fwn">2017</A>) Systems genetics analysis of iron and its regulation in brain and periphery. Methods Mol Biol 1488:467-480 -<LI> -Lopez MF, Miles MF, Williams RW, Becker HC (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27793543" target="_blank" class="fwn">2017</A>) Variable effects of chronic intermittent ethanol exposure on ethanol drinking in a genetically diverse mouse cohort. Alcohol 58:73-82 -<LI> -Parker CC, Dickson PE, Philip VM, Thomas M, Chesler EJ (<A HREF="https://ncbi.nlm.nih.gov/pubmed/28398643" target="_blank" class="fwn">2017</A>) Curr Protoc Neurosci 79:8.39.1-8.39.20 -<LI> -Porcu P, O'Buckley TK, Lopez MF, Becker HC, Miles MF, Williams RW, Morrow AL (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27884493" target="_blank" class="fwn">2017</A>) Initial genetic dissection of serum neuroactive steroids following chronic intermittent ethanol across BXD mouse strains. Alcohol 58:107-125 -<LI> -Rinker JA, Fulmer DB, Trantham-Davidson H, Smith ML, Williams RW, Lopez MF, Randall PK, Chandler LJ, Miles MF, Becker HC, Mulholland PJ (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27884493" target="_blank" class="fwn">2017</A>) Differential potassium channel gene regulation in BXD mice reveals novel targets for pharmacogenetic therapies to reduce heavy alcohol drinking. Alcohol 58:33-45 -<LI> -van der Vaart AD, Wolstenholme JT, Smith ML, Harris GM, Lopez MF, Wolen AR, Becker HC, Williams RW, Miles MF (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27838001" target="_blank" class="fwn">2017</A>) The allostatic impact of chronic ethanol on gene expression: A genetic analysis of chronic intermittent ethanol treatment in the BXD cohort. Alcohol 58:93-106 - - -</OL> -</Blockquote> - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2016" class="subtitle"">GeneNetwork (2016) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI> -Alam G, Miller DB, O'Callaghan JP, Lu L, Williams RW, Jones BC (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27182044" target="_blank" class="fwn">2016</A>) MPTP neurotoxicity is highly concordant between the sexes among BXD recombinant inbred mouse strains. Neurotoxicology 55:40-7 -<LI> -Chintalapudi SR, Wang X, Li H, Lau YH, Williams RW, Jablonski MM (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27881906" target="_blank" class="fwn">2016</A>) Genetic and immunohistochemical analysis of HSPA5 in mouse and human retinas. Molecular Vision 22:1318-1331 -<LI> -Lu H, Lu L, Williams RW, Jablonski MM (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27011731" target="_blank" class="fwn">2016</A>) Iris transillumination defect and its gene modulators do not correlate with intraocular pressure in the BXD family of mice. Molecular Vision 22:224-233 -<LI> -Merkwirth C, Jovaisaite V, Durieux J, Matilainen O, Jordan SD, Quiros PM, Steffen KK, Williams EG, Mouchiroud L, Tronnes SU, Murillo V, Wolff SC, Shaw RJ, Auwerx J, Dillin A (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27133168" target="_blank" class="fwn">2016</A>) Two conserved histone demethylases regulate mitochondrial stress-induced longevity. Cell 165:1209-1223 -<LI> -Neuner SM, Garfinkel BP, Wilmott LA, Ignatowska-Jankowska BM, Citri A, Orly J, Lu L, Overall RW, Mulligan MK, Kempermann G, Williams RW, O'Connell KM, Kaczorowski CC (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27460150" target="_blank" class="fwn">2016</A>) Systems genetics identifies Hp1bp3 as a novel modulator of cognitive aging. Neurobiol Aging 46:58-67 -<LI> -Shi X, Walter NA, Harkness JH, Neve KA, Williams RW, Lu L, Belknap JK, Eshleman AJ, Phillips TJ, Janowsky A (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27031617" target="_blank" class="fwn">2016</A>) Genetic polymorphisms affect mouse and human Trace Amine-Associated Receptor 1 function. PLoS One 11(3):e0152581 -<LI> -Schultz NG, Ingels J, Hillhouse A, Wardwell K, Chang PL, Cheverud JM, Lutz C, Lu L, Williams RW, Dean MD (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26935419" target="_blank" class="fwn">2016</A>) The genetic basis of baculum size and shape variation in mice. G3 (Bethesda) 6(5):1141-1151 -<LI> -Sloan Z, Arends D, Broman KW, Centeno A, Furlotte N, Nijveen H, Yan L, Zhou X, Williams RW, Prins P (2016) GenetNetwork: framework for web-based genetics. The Journal of Open Source Software http://joss.theoj.org/papers/10.21105/joss.00025 (http://dx.doi.org/10.21105/joss.00025) -<LI> -Wang X, Pandey AK, Mulligan MK, Williams EG, Mozhui K, Li Z, Jovaisaite V, Quarles LD, Xiao Z, Huang J, Capra JA, Chen Z, Taylor WL, Bastarache L, Niu X, Pollard KS, Ciobanu DC, Reznik AO, Tishkov AV, Zhulin IB, Peng J, Nelson SF, Denny JC, Auwerx J, Lu L, Williams RW (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26833085" target="_blank" class="fwn">2016</A>) Joint mouse-human phenome-wide association to test gene function and disease risk. Nat Commun 7:10464 - <LI> -Williams EG, Wu Y, Jha P, Dubuis S, Blattmann P, Argmann CA, Houten SM, Amariuta T, Wolski W, Zamboni N, Aebersold R, Auwerx J (<A HREF="https://ncbi.nlm.nih.gov/pubmed/27284200" target="_blank" class="fwn">2016</A>) Systems proteomics of liver mitochondria function. Science 352(6291):aad0189 - - - -</OL> -</Blockquote> - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2015" class="subtitle"">GeneNetwork (2015) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI> -Ashbrook DG, Gini B, Hager R (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26701914" target="_blank" class="fwn">2015</A>) Genetic variation in offspring indirectly influences the quality of maternal behaviour in mice. eLIFE 4:e11814 -<LI> -Ashbrook DG, Williams RW, Lu L, Hager R (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26190982" target="_blank" class="fwn">2015</A>) A cross-species genetic analysis identifies candidate genes for mouse anxiety and human bipolar disorder. Frontiers in Behavioral Neuroscience 9:171 -<LI> -Chaum E, Winborn CS, Bhattacharya S (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25963977" target="_blank" class="fwn">2015</A>) Genomic regulation of senescence and innate immunity signaling in the retinal pigment epithelium. Mammalian Genome 26:210-221 <A HREF="/images/upload/ChaumMammGenome2015.pdf"> Full text</A> -<LI> -Cheng Q, Seltzer Z, Sima C, Lakschevitz FS, Glogauer M (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25608512" target="_blank" class="fwn">2015</A>) Quantitative trait loci and candidate genes for neutrophil recruitment in sterile inflammation mapped in AXB-BXA recombinant inbred mice. PLoS One 10:e0124117 -<LI> -Cook MN, Baker JA, Heldt SA, Williams RW, Hamre KM, Lu L (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25991709" target="_blank" class="fwn">2015</A>) Identification of candidate genes that underlie the QTL on chromosome 1 that mediates genetic differences in stress-ethanol interactions. Physiol Genomics 47:308-317 -<LI> -Delprato A, Bonheur B, Algéo MP, Rosay P, Lu L, Williams RW, Crusio WE (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26449520" target="_blank" class="fwn">2015</A>) Systems genetic analysis of hippocampal neuroanatomy and spatial learning in mice. Genes Brain Behav 14(8):591-606 -<LI> -Ferguson B, Ram R, Handoko HY, Mukhopadhyay P, Muller HK, Soyer HP, Morahan G, Walker GJ (<A HREF="https://ncbi.nlm.nih.gov/pubmed/25088201" target="_blank" class="fwn">2015</A>) Melanoma susceptibility as a complex trait: genetic variation controls all stages of tumor progression. Oncogene 34:2879-86 -<LI> -King R, Lu L, Williams RW, Geisert EE. (<A HREF="https://ncbi.nlm.nih.gov/pubmed/26604663" target="_blank" class="fwn">2015</A>) Transcriptome networks in the mouse retina: An exon level BXD RI database. 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Journal of Heredity 2010; doi: 10.1093/jhered/esq023. <A href="http://http://jhered.oxfordjournals.org/cgi/reprint/esq023?ijkey=BbuUGMafKrjFO5f&keytype=ref" class="fwn" target="_blank"><I>Full Text PDF Version</I></A> -<LI> -Loguercio S, Overall RW, Michaelson JJ, Wiltshire T, Pletcher MT, Miller BH, Walker JR, Kempermann G, Su AI, Beyer A <a href="http://www.ncbi.nlm.nih.gov/pubmed/21085707" target="_blank" class="fwn" >2010</a> Integrative analysis of low- and high-resolution eQTL. PLoS One 5(11):e13920 <A href="http://www.plosone.org/article/fetchObjectAttachment.action;jsessionid=C93DEB44991B6401001E7585AC4C4393.ambra02?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0013920&representation=PDF" class="fwn" target="_blank"><I>Full Text PDF Version</I></A> -<LI> -Lynch RM, Naswa S, Rogers Jr GL, Kanla SA, Das S, Chesler EJ, Saxton AM, Langston MA, Voy, BH (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20179155" target="_blank" class="fwn" >2010</a>) Identifying genetic loci and spleen gene coexpression networks underlying immunophenotypes in the BXD recombinant inbred mice. Physiological Genomics 41:244-253 <A href="/images/upload/BXD_immunophenotypes_2010.pdf" target="_blank" class="fwn" ><I>Full Text PDF version</I> </A> -<LI>Lynch RM (<a href="http://trace.tennessee.edu/utk_graddiss/727" target="_blank" class="fwn" >2010</a>) A systems genetics approach to the characterization of differential low dose radiation responses in the BXD recombinant inbred mice. PhD diss., University of Tennessee <a href="http://trace.tennessee.edu/utk_graddiss/727" target="_blank" class="fwn" >http://trace.tennessee.edu/utk_graddiss/727</a> -<LI> -Malkki HA, Donga LA, de Groot SE, Battaglia FP; NeuroBSIK Mouse Phenomics Consortium, Pennartz CM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/21119771" target="_blank" class="fwn" >2010</a>) Appetitive operant conditioning in mice: heritability and dissociability of training stages. Front Behav Neuroscience 4:171 <A href="/images/upload/BXD_immunophenotypes_2010.pdf" target="_blank" class="fwn" ><I>Full Text PDF version</I> </A> -<LI> -Mulligan MK, Lu L, Overall RW, Kempermann G, Rogers GL, Langston MA, Williams RW (<a href="http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5510847" target="_blank" class="fwn" >2010</a>) Genetic analysis of BDNF expression cliques and adult neurogenesis in the hippocampus. Biomedical Sciences and Engineering Conference (BSEC) DOI: 10.1109/BSEC.2010.5510848 <A href="/images/upload/BDNF_Clique_Apr13.pdf" target="_blank" class="fwn" ><I>Full Text PDF version</I> </A> -<LI> -Peidis P, Giannakouros T, Burow ME, Williams RW, Scott RE (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20184719" target="_blank" class="fwn" >2010</a>) Systems genetics analyses predict a transcription role for P2P-R: molecular confirmation that P2P-R is a transcriptional co-repressor. BMC Systems Biology 4:14 <A href="http://www.biomedcentral.com/content/pdf/1752-0509-4-14.pdf" target="_blank" class="fwn" ><I>Full Text PDF version</I> and </A> -<LI> -Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Laviviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19958391" target="_blank" class="fwn">2010</a>) High-throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains. Genes, Brain and Behavior 8:129-159 <A href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2855868/" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Reinius B, Shi C, Hengshuo L, Sandhu KS, Radomska KJ, Rosen GD, Lu L, Kullander K, Williams RW, Jazin E. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/21047393" target="_blank" class="fwn">2010</a>) Female-biased expression of long non-coding RNAs in domains that escape X-inactivation in mouse. BMC Genomics. 2010 Nov 3;11:614 <A href="http://www.biomedcentral.com/1471-2164/11/614" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Rulten SL, Ripley TL, Hunt CL, Stephens DN, Mayne LV (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16594979" target="_blank" class="fwn">2010</a>) Sp1 and NFkappaB pathways are regulated in brain in response to acute and chronic ethanol. Genes, Brain and Behavior 5:257-73. <A href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2855868/" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Suwanwela J, Farber CR, Haung BL, Song B, Pan C, Lyons KM, Lusis AJ (<a href=""/images/upload/Suwanwela-1.JBMR.10.pdf" target="_blank" class="fwn" >2010</a>) Systems genetics analysis of mouse chondrocyte differentiation. JBMR, in press <A href="/images/upload/Suwanwela-1.JBMR.10.pdf" target="_blank" class="fwn" ><I>Full Text PDF version</I> </A> -<LI> -Wang X, Chen Y, Wang X, Lu L. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20054728" target="_blank" class="fwn" >2010</a>) Genetic regulatory network analysis for <I>App</I> based on genetical genomics approach. Exp Aging Res 36:79-93 - - - - - - - - - - - - - - - - - -</OL> -</Blockquote> - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2009" class="subtitle"">GeneNetwork (2009) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> - -<LI> -Badea A, Johnson GA, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19349225" target="_blank" class="fwn">2009</a>) Genetic dissection of the mouse brain using high-field magnetic resonance microscopy. Neuroimaging 45:1067-79 -<LI> -Boon AC, deBeauchamp J, Hollmann A, Luke J, Kotb M, Rowe S, Finkelstein D, Neale G, Lu L, Williams RW, Webby RJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19706712" target="_blank" class="fwn">2009</a>) Host genetic variation affects resistance to infection with a highly pathogenic H5N1 influenza A virus in mice. J Virol 83:10417-26 PMID: 19706712 <A HREF="http://www.genenetwork.org/images/upload/Boon_H5N1_2009.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Brigman JL, Mathur P, Lu L, Williams RW, Holmes A (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18830130" target="_blank" class="fwn">2009</a>) Genetic relationship between anxiety- and fear-related behaviors in BXD recombinant inbred mice. Behavioral Pharmacology 20:204-209 -<A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=18830130" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<LI> -Carneiro AM, Airey DC, Thompson B, Zhu CB, Lu L, Chesler EJ, Erikson KM, Blakely RD (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19179283" target="_blank" class="fwn">2009</a>) Functional coding variation in recombinant inbred mouse lines reveals multiple serotonin transporter-associated phenotypes. Proc Natl Acad Sci USA 106:2047-2052 -<A href="http://www.pnas.org/content/early/2009/01/28/0809449106" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A href="http://www.pnas.org/content/early/2009/01/28/0809449106.full.pdf+html" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Cowley MJ, Cotsapas CJ, Williams RB, Chan EK, Pulvers JN, Liu MY, Luo OJ, Nott DJ, Little PF (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19424753" target="_blank" class="fwn">2009</a>) Intra- and inter-individual genetic differences in gene expression. Mamm Genome 20:281-295 -<A href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690833/?tool=pubmed" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Davies MN, Lawn S, Whatley S, Fernandes C, Williams RW, Schalkwyk LC (<a href="http://frontiersin.org/neurogenomics/paper/10.3389/neuro.15/002.2009/" target="_blank" class="fwn">2009</a>) To what extent is blood a reasonable surrogate for brain gene expression studies: estimation from mouse hippocampus and spleen. Front. Neurogen. 1:2. doi:10.3389/neuro.15.002.2009 -<LI> -Foreman JE, Lionikas A, Lang DH, Gyekis JP, Krishnan M, Sharkey NA, Gerhard GS, Grant MD, Vogler GP, Mack HA, Stout JT, Griffith JW, Lakoski JM, Hofer SM, McClearn GE, Vandenbergh DJ, Blizard DA (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19671078" target="_blank" class="fwn">2009</a>) Genetic architecture for hole-board behaviors across substantial time intervals in young, middle-aged and old mice. Genes Brain Behav 8:714-27 -<LI> -Gaglani SM, Lu L, Williams RW, Rosen GD (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19426526" target="_blank" class="fwn">2009</a>) The genetic control of neocortex volume and covariation with patterns of gene expression in mice. BMC Neuroscience 10:44 -<A href="http://www.biomedcentral.com/1471-2202/10/44" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A href="http://www.biomedcentral.com/content/pdf/1471-2202-10-44.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Gatti DM, Harrill AH, Wright FA, Threadgill DW, Rusyn I. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19609828" target="_blank" class="fwn">2009</a>) Replication and narrowing of gene expression quantitative trait loci using inbred mice. Mamm Genome 20:437-46. -<LI> -Geisert EE, Lu L, Freeman-Anderson ME, Wang X, Gu W, Jiao Y, Williams RW (<A HREF="http://www.molvis.org/molvis/v15/a185" target="_blank" class="fwn">2009</A>) Gene expression landscape of the mammalian eye: A global survey and database of mRNAs of 103 strains of mice. <I>Molecular Vision</I> 15:1730-1763 PMID:19727342 <A href="http://www.molvis.org/molvis/v15/a185" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A HREF="http://www.molvis.org/molvis/v15/a185/mv-v15-a185-geisert.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Grisham W (<a href="http://www.funjournal.org/downloads/200981/grisham81.pdf" target="_blank" class="fwn">2009</a>) Modular digitial course in undergraduate neuroscience education (MDCUNE): A website offering free digital tools for neuroscience educators. Journal of Undergraduate Neuroscience Education 8:A26-A31 <A href="http://www.funjournal.org/downloads/200981/grisham81.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<BR><SMALL> -An excellent example of how resources such as GeneNetwork and the Mouse Brain Library can be used in class room labs. -</SMALL> -<LI> -Jellen LC, Beard JL, Jones BC (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19393285" target="_blank" class="fwn">2009</a>) Systems genetics analysis of iron regulation. Biochemie in press. -19393285 -<LI> -Koutnikova H, Markku L, Lu L, Combe R, Paananen J, Kuulasmaa T, Kuusisto J, Häring H, Hansen T, Pedersen,O, Smith U, Hanefel M, Williams RW, Auwerx J (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19662162" target="_blank" class="fwn">2009</a>) Identification of UBP1 as a critical blood pressure determinant. PLoS Genetics 5:e1000591 -<A href="http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1000591" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A HREF="http://www.plosgenetics.org/article/fetchObjectAttachment.action?uri=info%3Adoi%2F10.1371%2Fjournal.pgen.1000591&representation=PDF" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Michaelson JJ, Loguercio S, Beyer A (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19303049" target="_blank" class="fwn">2009</a>) Detection and interpretation of expression quantitative trait loci (eQTL). Methods 48:265-276 -<A href="http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WN5-4VVXSVN-2&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1065487428&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=df0a2763fb94c714825793af492de045" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Overall RW, Kempermann G, Peirce J, Lu L, Goldowitz D, Gage FH, Goodwin S, Smit AB, Airey DC, Rosen GD, Schalkwyk LC, Sutter TR, Nowakowski RS, Whatley S, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/20582282" target="_blank" class="fwn">2009</a>) Genetics of the hippocampal transcriptome in mice: a systematic survey and online neurogenomic resource. Frontiers in Neuroscience 3:55 -<A href="http://www.frontiersin.org/neurogenomics/10.3389/neuro.15.003.2009/full" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A HREF="javascript:__doPostBack('ctl00$ContentAreaRightMenu$ArchiveRightMenu$lnkPDF','')" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Overton JD, Adams GS, McCall RD, Kinsey ST (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19100334" target="_blank" class="fwn">2009</a>)High energy phosphate concentrations and AMPK phosphorylation in skeletal muscle from mice with inherited differences in hypoxic exercise tolerance. Comp Biochem Physiol A Mol Integr Physiol 152:478-85 -<LI> -Philip VM, Duvvuru S, Gomero B, Ansah TA, Blaha CD, Cook MN, Hamre KM, Laviviere WR, Matthews DB, Mittleman G, Goldowitz D, Chesler EJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19958391" target="_blank" class="fwn">2009</a>) High-throughput behavioral phenotyping in the expanded panel of BXD recombinant inbred strains. Genes, Brain and Behavior 8:in press PMID: 19958391 <A href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2855868/" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Ruden DM, Chen L, Possidente D, Possidente B, Rasouli P, Wang L, Lu X, Garfinkel MD, Hirsch HV, Page GP (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19737576" target="_blank" class="fwn">2009</a>) Genetical toxicogenomics in Drosophila identifies master-modulatory loci that are regulated by developmental exposure to lead. Neurotoxicology 30:898-914 -<LI> -Rosen GL, Pung C, Owens C, Caplow J, Kim H, Lu L, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19191878" target="_blank" class="fwn">2009</a>) Genetic modulation of striatal volume in BXD recombinant inbred mice. Genes, Brain & Behavior 8:296-308 -<LI> -Saccone SF, Bierut LJ, Chesler EJ, Kalivas PW, Lerman C, Saccone NL, Uhl GR, Li CY, Philip VM, Edenberg HJ, Sherry ST, Feolo M, Moyzis RK, Rutter JL (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19381300" target="_blank" class="fwn">2009</a>) Supplementing high-density SNP microarrays for additional coverage of disease-related genes: addiction as a paradigm. PLoS ONE 4:e5225. <A href="http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0005225" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, -<A HREF=http://www.plosone.org/article/fetchObjectAttachment.action?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0005225&representation=PDF" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>. [Supplementary table 1 provides links to many transcripts and genes, some of which were mined from an analysis of GeneNetwork data sets.] -<LI> -Silva GL, Junta CM, Sakamoto-Hojo ET, Donadi EA, Louzada-Junior P, Passos GA (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19758195" target="_blank" class="fwn">2009</a>) Genetic susceptibility loci in rheumatoid arthritis establish transcriptional regulatory networks with other genes. Ann N Y Acad Sci 1173:521-37 -<LI> -Tapocik JD, Letwin N, Mayo CL, Frank B, Luu T, Achinike O, House C, Williams R, Elmer GI, Lee NH (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19386926" target="_blank" class="fwn">2009</a>) Identification of candidate genes and gene networks specifically associated with analgesic tolerance to morphine. J Neurosci 2:5295-307 <A href="http://www.jneurosci.org/cgi/content/full/29/16/5295" target="_blank" class="fwn"><I>Full Text HTML</I></A> -<LI> -Thomas C, Gioiello A, Noriega L, Strehle A, Oury J, Rizzo G, Macchiarulo A, Yamamoto H, Mataki C, Pruzanski M, Pellicciari R, Auwerx J, Schoonjans K (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19723493" target="_blank" class="fwn">2009</a>) TGR5-mediated bile acid sensing controls glucose homeostasis. Cell Metab 10:167-77 -<A href="http://www.genenetwork.org/images/upload/Thomas_Auwerx_2009.pdf" target="_blank" class="fwn"><I>Full Text PDF</I></A> -<LI> -Webb BT, McClay JL, Vargas-Irwin C, York TP, van den Oord EJCG (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19723493" target="_blank" class="fwn">2009</a>) In silico whole genome association scan for murine prepulse inhibition. PLoS ONE 4: e5246. doi:10.1371/journal.pone.0005246 -<A href="http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0005246" target="_blank" class="fwn"><I>Full Text HTML</I></A>, -<A href="http://www.plosone.org/article/fetchObjectAttachment.action;jsessionid=18CBF1DF825C97C45A98D0256635C650?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0005246&representation=PDF" target="_blank" class="fwn"><I>Full Text HTML</I></A> -<LI> -Weng J, Symons MN, Singh SM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19107586" target="_blank" class="fwn">2009</a>) Studies on syntaxin 12 and alcohol preference involving C57BL/6J and DBA/2J strains of mice. Behav Genet. 39:183-191 -<A href="http://www.iovs.org/cgi/content/abstract/50/5/1996" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Whitney IE, Raven MA, Ciobanu DC, Williams RW, Reese BE (<a href="http://www.iovs.org/cgi/content/abstract/50/5/1996" target="_blank" class="fwn">2009</a>) Multiple genes on chromosome 7 regulate dopaminergic amacrine cell number in the mouse. Investigative Ophthalmology & Visual Science 50:1996-2003. -<A href="http://www.iovs.org/cgi/content/abstract/50/5/1996" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. -<LI> -Wu S, Lusis AJ, Drake TA (<a href="http://www.jlr.org/cgi/reprint/R800067-JLR200v1.pdf" target="_blank" class="fwn">2009</a>) A systems-based framework for understanding complex metabolic and cardiovascular disorders. Journal of Lipid Research, in press <A href="http://www.jlr.org/cgi/reprint/R800067-JLR200v1.pdf" target="_blank" class="fwn"><I>Full Text PDF</I></A> -<LI> -Zheng QY, Ding D, Yu H, Salvi RJ, Johnson KR (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18280008" target="_blank" class="fwn">2009</a>) A locus on distal chromosome 10 (<I>ahl4</I>) affecting age-related hearing loss in A/J mice. Neurobiol Aging. 2009 Oct;30(10):1693-705. - -</OL> -</Blockquote> - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2008" class="subtitle"">GeneNetwork (2008) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> -<LI> -Abdeltawab NF, Aziz RK, Kansal R, Rowe SL, Su Y, Gardner L, Brannen C, Nooh MM, Attia RR, Abdelsamed HA, Taylor WL, Williams RW, Kotb M (2008) An unbiased systems genetics approach to mapping genetic loci modulating susceptibility to severe streptococcal sepsis. PLoS Pathogens 4:e1000042 -<A href="http://www.plospathogens.org/article/info%3Adoi%2F10.1371%2Fjournal.ppat.1000042" target="_blank" class="fwn"><I>Full Text HTML Version</I>, -<A href="http://www.plospathogens.org/article/fetchObjectAttachment.action?uri=info%3Adoi%2F10.1371%2Fjournal.ppat.1000042&representation=PDF" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> - -<LI> -Bertrand L, Fan Y, Nissanov J, Rioux L (2008) Genetic regulation of the anatomy of the olfactory bulb. Society for Neuroscience <A HREF="http://www.sfn.org/skins/main/pdf/abstracts/am2008/poster_presentations/tuesday_pm.pdf" target="_blank" class="fwn">2008</A> - -<!-- -Abstract: Development of the nervous system results from an interaction between environmental and genetic factors. Neurodevelopmental diseases with a strong genetic etiology like schizophrenia and autism are associated with functional and structural abnormalities in various areas of the brain, including the olfactory system. Because these diseases are characterized by numerous subjective symptoms, research has begun to identify objectively measurable symptomatology; these quantitative traits are called endophenotypes. One such trait associated with schizophrenia is abnormality of the olfactory bulb (OB). We sought to model the structural aspect of OB abnormalities by examining strain variability of OB volumes in mice. Through the analysis of parental strains, C57BL/6J (B6) and DBA/2J (D2), as well as 35 BXD recombinant inbred strains, mapping of a quantitative trait locus (QTL) associated with OB variability could be the first step in the identification of potential genes correlated with this -olfactory endophenotype. Our study was conducted entirely in silico through the use of the Mouse Brain Library (www.mbl.org), imaging analysis softwares (NIH Object Image, Skill Image and Matlab), and a web-based QTL analysis tool (www.webqtl.org). 4 um/pixel images of sectioned mouse brains (300 um apart) were downloaded from the MBL website. The sample included 11 B6, 8 D2, and 157 BXDs mice. The OB consists of concentric layers: olfactory nerve, glomerular, plexiform, mitral cell and granule cell layers. The main OB and its layers were delineated and their volume was estimated using Cavalieri formula. Results show that B6 main OB volume is larger than that of D2. While the olfactory neuron and glomerular layers are larger in B6, the external plexiform and granular layers are larger in D2. This suggests that the development of the various OB layers is modulated by different genes. Residual volumes were calculated by removal of variance attributed to age and sex. Using the web-based QTL analysis tool, main OB volume was mapped to a significant linkage peak on chromosome 16. This QTL contains genes controlling the development of the main OB and potential candidate genes for schizophrenia. Determination of QTLs for individual layers is in progress. ---> - - -<LI> -Bhoumik A, Singha N, O'Connell MJ, Ronai ZA (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18397884" target="_blank" class="fwn">2008</a>) Regulation of TIP60 by ATF2 modulates ATM activation. J Biol Chem 283:17605-14 -<A href="http://www.jbc.org/content/283/25/17605.long" target="_blank" class="fwn"><I>Full Text HTML Version</I>, <http://www.jbc.org/content/early/2008/04/08/jbc.M802030200.full.pdf -" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> - - - -<LI> -Bjork K, Rimondini R, Hansson AC, Terasmaa A, Hyytiä P, Heilig M, Sommer WH (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18367649" target="_blank" class="fwn">2008</a>) Modulation of voluntary ethanol consumption by beta-arrestin 2. FASEB J 22:2552-60 -<A href="http://www.fasebj.org/cgi/content/full/22/7/2552" target="_blank" class="fwn"><I>Full Text HTML Version</I>, -<A href="http://www.fasebj.org/cgi/reprint/22/7/2552.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Boone EM, Hawks BW, Li W, Garlow SJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18199428" target="_blank" class="fwn">2008</a>) Genetic regulation of hypothalamic cocaine and amphetamine-regulated transcript (CART) in BxD inbred mice. Brain Res 1194:1-7 -<LI> -Crawford NP, Alsarraj J, Lukes L, Walker RC, Officewala JS, Yang HH, Lee MP, Ozato K, Hunter KW (2008) Bromodomain 4 activation predicts breast cancer survival. Proc Natl Acad Sci USA 105:6380-6385 -<A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=18427120" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Crawford NP, Walker RC, Lukes L, Officewala JS, Williams RW, Hunter KW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18301994 -" target="_blank" class="fwn">2008</a>) The Diasporin Pathway: a tumor progression-related transcriptional network that predicts breast cancer survival. Clin Exp Metastasis 25:357-69 -<A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=18301994" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Danciger M, Ogando D, Yang H, Matthes MT, Yu N, Ahern K, Yasumura D, Williams RW, Lavail MM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18344445 -" target="_blank" class="fwn">2008</a>) Genetic modifiers of retinal degeneration in the <I>rd3</I> mouse. Invest Ophthalmol Vis Sci 49:2863-2869 -<A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=18344445" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Druka A, Druka I, Centeno AG, Li H, Sun Z, Thomas WT, Bonar N, Steffenson BJ, Ullrich SE, Kleinhofs A, Wise RP, Close TJ, Potokina E, Luo Z, Wagner C, Schweizer GF, Marshall DF, Kearsey MJ, Williams RW, Waugh R (<a href="http://www.ncbi.nlm.nih.gov/pubmed/19017390 -" target="_blank" class="fwn">2008</a>) Towards systems genetic analyses in barley: Integration of phenotypic, expression and genotype data into GeneNetwork. BMC Genet 9:73 -<A href="http://www.biomedcentral.com/1471-2156/9/73" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Dykstra B, de Haan G (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18008087 -" target="_blank" class="fwn">2008</a>) Hematopoietic stem cell aging and self-renewal. 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[Phenotype data described in this paper are accessible in GeneNetwork in the LXS Phenotype database; ID numbers 10126, 10127, 10132, 10134, 10138, and 10139 (select Species=MOUSE, Group=LXS, Type=PHENOTYPES and search the ALL field with the term "functional" or just enter the ID numbers above.] -<LI> -Cervino ACL, Darvasi A, Fallah Mi, Mader CC, Tsinoremas NF (<a href="http://www.genetics.org/cgi/content/full/175/1/321" target="_blank" class="fwn">2007</a>) An integrated in silico gene mapping strategy in inbred mice. Genetics 175:321-333 -<A href="http://www.genetics.org/cgi/reprint/175/1/321" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Chesler EJ (<a href="http://www3.interscience.wiley.com/cgi-bin/summary/114171173/SUMMARY" target="_blank" class="fwn">2007</a>) Bioinformatics of behavior. 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Genome Biology 8:R221 -<A href="http://www.plosgenetics.org/article/fetchObjectAttachment.action?uri=info%3Adoi%2F10.1371%2Fjournal.pgen.0030214&representation=PDF" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://genomebiology.com/2007/8/10/R221" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Crawford NP, Qian X, Ziogas A, Papageorge AG, Boersma BJ, Walker RC, Lukes L, Rowe WL, Zhang J, Ambs S, Lowy DR, Anton-Culver H, Hunter KW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/18081427" target="_blank" class="fwn">2007</a>) Rrp1b, a new candidate susceptibility gene for breast cancer progression and metastasis. 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Biological Aging: Methods and Protocols, ed Tollefsbol TO, Humana Press, Inc., Netlibrary, Springer -<A href="http://books.google.com/books?id=fbTsCdQv7cEC&pg=RA4-PA332&lpg=RA4-PA332&dq=genenetwork+webqtl&source=web&ots=PawLDYAUam&sig=hOHsKDfumr3U-ZEUtT7Ag6gJAYg&hl=en&sa=X&oi=book_result&resnum=9&ct=result#PRA4-PA321,M1" target="_blank" class="fwn"><I>Google Book Search</I></A> -<LI> -Jawad M, Giotopoulos G, Fitch S, Cole C, Plumb M, Talbot CJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17185011" target="_blank" class="fwn">2007</a>) Mouse bone marrow and peripheral blood erythroid cell counts are regulated by different autosomal genetic loci. Blood Cells Mol Disease 38:69-77 -<LI> -Jessberger S, Gage FH (<a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1800354" target="_blank" class="fwn">2007</a>) ZOOMING IN: a new high-resolution gene expression atlas of the brain. Mol Syst Biol. 3:75 -<LI> -Jones BC, Beard JL, Gibson JN, Unger EL, Allen RP, McCarthy KA, Earley CJ (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17475678" target="_blank" class="fwn">2007</a>) Systems genetic analysis of peripheral iron parameters in the mouse. American Journal of Physiology Regul Integr Comp Physiol 293: R116-124 -<A href="http://ajpregu.physiology.org/cgi/content/full/293/1/R116" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>, and see <A href="http://www.genenetwork.org/images/upload/Jones et al 1999 Pharmacogenetics3-0.pdf" target="_blank" class="fwn"><I>full text of Jones et al., 1999.</I></A> -<LI> -Jones BC and Mormede JP (2007) Neurobehavioral genetics: methods and applications. (<a href="http://books.google.com/books?id=P85FPhRKFVMC&pg=PA102&lpg=PA102&dq=genenetwork+webqtl&source=web&ots=-qg3S-C0xQ&sig=HoOCQpZrV3i45I0vPfDb3PfDMvY" target="_blank" class="fwn">2007</a>)" CRC Press, Taylor & Francis Group, Boca Raton, Florida, p. 102, ISBN 084931903X -<LI> -Kadarmideen HN, Reverter A (2007) Combined genetic, genomic and transcriptomic methods in the analysis of animal traits. CABI Review: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 2: No. 042 (16 pp) -<A href="/images/upload/Kadarmideen_Reverter_2007.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Korostynski M, Piechota M, Kaminska D, Solecki W. Przewlocki R (<a href="http://genomebiology.com/2007/8/6/R128" target="_blank" class="fwn">2007</a>) Morphine effects on striatal transcriptome in mice. Genome Biology 8:R128 -<LI> -Lad HV, Liu L, Payá-Cano JL, Fernandes C, Schalkwyk LC (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1998875/?tool=pubmed" target="_blank" class="fwn">2007</a>) Quantitative traits for the tail suspension test: automation, optimization, and BXD RI mapping. Mamm Genome. 2007 Jul;18(6-7):482-91 -<A href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1998875/?tool=pubmed" target="_blank" class="fwn"><I>Full Text PubMed Central version</I></A> -<LI> -Liang Y, Jansen M, Aronow B, Geiger H, Van Zant G (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17220891" target="_blank" class="fwn">2007</a>) The quantitative trait gene latexin influences the size of the hematopoietic stem cell population in mice. Nature Genetics 39:141-142 (doi: 10.1038/ng1938). [Using the BXD mouse strains, Liang, Van Zant and colleagues, demonstrate that sequence variants in <I>Lxn</I> modulate cell cycling kinetics of bone marrow stem cells. High expression of <I>Lxn</I> mRNA and protein is associated with the C57BL/6J allele and with lower proliferative activity. The authors made good use of the <A HREF="http://www.genenetwork.org/dbdoc/HC_U_0304_R.html" class="fwn" target="_blank" >BXD Hematopoietic Stem Cell</A> data set and <I>Lxn</I> Affymetrix probe set 96065_at. Figures 1 and 5 were generated using GeneNetwork.] -<LI> -Llamas B, Bélanger S, Picard S, Deschepper CF (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17566079" target="_blank" class="fwn">2007</a>) Cardiac mass and cardiomyocyte size are governed by different genetic loci on either autosomes or chromosome Y in recombinant inbred mice. Physiol Genomics 31:176-82 -<LI> -Meng PH, Macquet A, Loudet O, Marion-Poll A, North HM (<a href="http://mplant.oxfordjournals.org/cgi/content/full/ssm014v1" target="_blank" class="fwn">2007</a>) Analysis of natural allelic variation controlling Arabidopsis thaliana seed germinability in response to cold and dark: identification of three major quantitative trait loci. Molecular Plant, doi:10.1093/mp/ssm014 -<LI> -Mouse Phenotype Database Integration Consortium (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17436037" target="_blank" class="fwn">2007</a>) Integration of mouse phenome data resources. Mammalian Genome, 18:157-163. -<A href="http://www.har.mrc.ac.uk/research/bioinformatics/publications/Preprint1.pdf" target="_blank" class="fwn"><I>PDF Preprint Version</I></A> -<LI> -Miyairi I, Tatireddigari VR, Mahdi OS, Rose LA, Belland RJ, Lu L, Williams RW, Byrne GI (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17641048" target="_blank" class="fwn">2007</a>) The p47 GTPases Iigp2 and Irgb10 regulate innate immunity and inflammation to murine Chlamydia psittaci. J Immunol 179:1814-1824 -<LI> -Mozhui K, Hamre KM, Holmes A, Lu L, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17131200" target="_blank" class="fwn">2007</a>) Genetic and structural analysis of the basolateral amygdala complex in BXD recombinant inbred mice. Behav Genet. 37:223-243 -<A href="http://www.genenetwork.org/images/upload/Mozhui_et_al_2007.doc" target="_blank" class="fwn"><I>Full Text MS Word Version (authors' copy)</I></A> -<LI> -Peirce JL, Broman KW, Lu L, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17400728" target="_blank" class="fwn">2007</a>) A simple method for combining genetic mapping data from multiple crosses and experimental designs. PLoS ONE 2:e1036 -<A href="http://www.plosone.org/article/fetchObjectAttachment.action;jsessionid=009B2741B48AC1CB94E68107E714A1B0?uri=info%3Adoi%2F10.1371%2Fjournal.pone.0001036&representation=PDF" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://www.plosone.org/article/info:doi%2F10.1371%2Fjournal.pone.0001036" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Pérez-Enciso M, Quevedo JR, Bahamonde A (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17352813" target="_blank" class="fwn">2007</a>) Genetical genomics: use all data. BMC Genomics 8:69. -<A href="http://www.biomedcentral.com/content/pdf/1471-2164-8-69.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://www.biomedcentral.com/1471-2164/8/69" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Rosen GD, Bai J, Wang Y, Fiondella CG, Threlkeld SW, LoTurco JJ, Galaburda AM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17218481" target="_blank" class="fwn">2007</a>) Disruption of neuronal migration by RNAi of Dyx1c1 results in neocortical and hippocampal malformations. Cereb Cortex 17:2562-72. -<A href="http://cercor.oxfordjournals.org/cgi/reprint/bhl162v1.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<LI> -Rosen GD, Chesler EJ, Manly KF, Williams RW (2007) An informatics approach to systems neurogenetics. Methods Mol Biol 401:287-303 -<LI> -Sunkin SM, Hohmann JG (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17911164" target="_blank" class="fwn">2007</a>) Insights from spatially mapped gene expression in the mouse brain. Human Molecular Genetics 16: R209-219 -<A href="http://hmg.oxfordjournals.org/cgi/reprint/16/R2/R209.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://hmg.oxfordjournals.org/cgi/content/full/16/R2/R209" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Swertz MA, Jansen RC (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17297480" target="_blank" class="fwn">2007</a>) Beyond standardization: dynamic software infrastructure for systems biology. Nature Reviews Genetics:235-243 -<LI> -Taylor M, Valdar W, Kumar A, Flint J, Mott R (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17400728" target="_blank" class="fwn">2007</a>) Management, presentation and interpretation of genome scans using GSCANDB. Bioinformatics 23:1545-1549 -<A href="http://bioinformatics.oxfordjournals.org/cgi/reprint/23/12/1545" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://bioinformatics.oxfordjournals.org/cgi/content/full/23/12/1545" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Vazquez-Chona F, Lu L, Williams RW, Geisert EE (2007) Genetic influences on retinal gene expression and wound healing. Gene Regulation and Systems Biology 1:327-348 -<A href="/images/upload/Vazquez2007.pdf" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -von Rohr P, Friberg M, Kadarmideen HN (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17688316" target="_blank" class="fwn">2007</a>) Prediction of transcription factor binding sites using results from genetical genomics investigations. Journal of Bioinformatics and Computational Biology 5:773-793 -<A href="/images/upload/vonRohr_etal_2007.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Wan X, Pavlidis P (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17917127" target="_blank" class="fwn">2007</a>) Sharing and reusing gene expression profiling data in neuroscience. Neuroinformatics 5:161-75. -<A href="http://www.bioinformatics.ubc.ca/pavlidis/lab/reuse/walkthrough.pdf" target="_blank" class="fwn"><I>HTML example of using GEMMA and GeneNetwork</I></A>. -<LI> -Zou W, Aylor DL, Zeng ZB (<a href="http://www.biomedcentral.com/1471-2105/8/7" target="_blank" class="fwn">2007</a>) eQTL Viewer: visualizing how sequence variation affects genome-wide transcription. BMC Bioinformatics 8:7. -<A href="http://www.biomedcentral.com/content/pdf/1471-2105-8-7.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://www.biomedcentral.com/1471-2105/8/7" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. -</OL> - -</Blockquote> - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2006" class="subtitle"">GeneNetwork (2006) </A> -<BLOCKQUOTE> - -</Blockquote> - -<OL> -<LI> -Aliesky HA, Pichurin PN, Chen CR, Williams RW, Rapoport B, McLachlan SM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16543368" class="fwn">2006</a>) Probing the genetic basis for thyrotropin receptor antibodies and hyperthyroidism in immunized CXB recombinant inbred mice. Endocrinology 147:2789-800. -<A href="http://endo.endojournals.org/cgi/reprint/147/6/2789.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://endo.endojournals.org/cgi/content/full/147/6/2789" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. -<LI> -Bao L, Wei L, Peirce JL, Homayouni R, Li H, Zhou M, Chen H, Lu L, Williams RW, Pfeffer LM, Goldowitz D, Cui Y (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16783639" class="fwn">2006</a>) Combining gene expression QTL mapping and phenotypic spectrum analysis to uncover gene regulatory relationships. Mammalian Genome 17:575-583 -<A href="http://www.springerlink.com/content/v32720xj70343812/fulltext.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://www.springerlink.com/content/v32720xj70343812/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. -<LI> -Beatty J, Laughlin RE (<a href="http://www.biomedcentral.com/1471-2202/7/16" class="fwn">2006</a>) Genomic regulation of natural variation in cortical and noncortical brain volume. BMC Neuroscience 7:16 [This mapping study made use of both the <A HREF="www.mbl.org" target="_blank" class="fwn">Mouse Brain Libray</A> online collection of sections of BXD brains and GeneNetwork/WebQTL.] -<LI> -Bennett B, Carosone-Link P, Zahniser NR, Johnson TE (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16803863&query_hl=2&itool=pubmed_docsum" target="_blank" class="fwn">2006</a>) Confirmation and fine mapping of ethanol sensitivity quantitative trait loci, and candidate gene testing in the LXS recombinant inbred mice. J Pharmacol Exp Ther 319:299-307 (doi: 10.1038/ng1938) -<A href="http://jpet.aspetjournals.org/cgi/reprint/319/1/299.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Bennett B, Downing C, Parker C, Johnson TE (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16730093" target="_blank" class="fwn">2006</a>) Mouse genetic models in alcohol research. Trends Genet 22:367-74 -<LI> -Cervino ACL, Darvasi A, Fallahi M, Mader CC, Tsinoremas NF (2006) An integrated in silico gene mapping strategy in inbred mice (<a href="http://www.genetics.org/cgi/content/full/175/1/321" target="_blank" class="fwn">2006</a>) Genetics 175:321-333 -<LI> -Chesler EJ, Langston MA (<a href="http://www.springerlink.com/content/ut638ph1845x1768/" target="_blank" class="fwn">2006</a>) Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data. In: Lecture Notes in Computer Science. Springer, Heidelberg, Vol. 4023: 150-165. -<LI> -De Haro L, Panda S(<a href="http://www.ncbi.nlm.nih.gov/pubmed/17107940" target="_blank" class="fwn">2006</a>) Systems biology of circadian rhythms: an outlook. Journal of Biological Rhythms 21:507 -<A href="http://jbr.sagepub.com/cgi/reprint/21/6/507.pdf " target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Jones LC, McCarthy KA, Beard JL, Keen CL, Jones BC (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16910173" target="_blank" class="fwn">2006</a>) Quantitative genetic analysis of brain copper and zinc in BXD recombinant inbred mice. Nutritional Neuroscience 9:81-92 [This paper includes a large data set consisting of 16 traits related to copper and zinc metabolism in the CNS that is part of the Mouse BXD Phenotype database in GeneNetwork. The entire data set can be found by entering the search term "Jones LC" in the ALL search field.] -<LI> -Kadarmideen HN, Janss LLG (<a href="http://www.ncbi.nlm.nih.gov//pubmed/17132818" class="fwn" target="_blank" >2006</a>) Population and systems genetics analyses of cortisol in pigs divergently selected for stress. Physiological Genomics 29:57-65 <A href="http://physiolgenomics.physiology.org/cgi/reprint/29/1/57?ck=nck" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://physiolgenomics.physiology.org/cgi/content/full/29/1/57?ck=nck" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. [One of several great papers in a freely available (open) Special Issue of Mammalian Genome devoted to QTLs, Expression and Complex Trait Analysis. This study makes great use of the BXD INIA Brain mRNA M430 PDNN data set and GeneNetwork/WebQTL.] -<LI> -Kadarmideen HN, von Rohr, P, Janss LLG (<a href="http://www.ncbi.nlm.nih.gov//pubmed/16783637" class="fwn" target="_blank" >2006</a>) From genetical genomics to systems genetics: potential applications in quantitative genomics and animal breeding. Mammalian Genome 17:548-564 <A href="http://www.springerlink.com/media/788y661n1l5tvhac6eet/contributions/4/4/2/5/4425640024004428.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://www.springerlink.com/media/12d237nvvh7krwan8q9x/contributions/4/4/2/5/4425640024004428_html/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. [One of several great papers in a freely available (open) Special Issue of Mammalian Genome devoted to QTLs, Expression and Complex Trait Analysis. This study makes great use of the BXD INIA Brain mRNA M430 PDNN data set and GeneNetwork/WebQTL.] -<LI> -Kamminga LM, Bystrykh LV, de Boer A, Houwer S, Douma J, Weersing E, Dontje B, de Haan G (<A HREF="http://www.ncbi.nlm.nih.gov//pubmed/16293602" target="_blank" class="fwn">2006</A>) The Polycomb group gene Ezh2 prevents hematopoietic stem cell exhaustion. Blood 107:2170-2179 -<A href="http://bloodjournal.hematologylibrary.org/cgi/reprint/2005-09-3585v1.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Keeley MB, Wood MA, Isiegas C, Stein J, Hellman K, Hannenhalli S, Abel T (<a href="http://www.learnmem.org/cgi/reprint/13/2/135.pdf?ck=nck" class="fwn" target="_blank>2006</A>) Differential transcriptional response to nonassociative and associative components of classical fear conditioning in the amygdala and hippocampus. Learning and Memory 13:135-142103:780-785. -<LI> -Kempermann G, Chesler EJ, Lu L, Williams RW, Gage FH (<a href="http://www.pnas.org/cgi/content/full/103/3/780" class="fwn" target="_blank>2006</A>) Natural variation and genetic covariance in adult hippocampal neurogenesis. Proceedings of the National Academy of Science 103:780-785. -<LI> -Korostynski M, Kaminska-Chowaniec D, Piechota M, Przewlocki R (<a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1553451" target="_blank" class="fwn">2006</a>) Gene expression profiling in the striatum of inbred mouse strains with distinct opioid-related phenotypes. BMC Genomics 7: 146 (doi: 10.1186/1471-2164-7-146). <A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1553451" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. [A combination of data sources including the Rosen HBP Striatum data set used to study opioid-related traits. Their <a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1553451&rendertype=figure&id=F3" target="_blank" class="fwn">Fig 3C</a> and <a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1553451&rendertype=figure&id=F5" target="_blank" class="fwn">Fig 5</a> take good advantage of GeneNetwork and WebQTL output graphs.] -<LI> -Lan H, Chen M, Flowers JB, Yandell BS, Stapleton DS, Mata CM, Mui ET, Flowers MT, Schueler KL, Manly KF, Williams RW, Kendziorski C, Attie A (<a href="http://genetics.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pgen.0020006" target="_blank" class="fwn">2006</a>) Combined expression trait correlations and expression quantitative trait locus mapping. PLoS Genetics 2:e6 [Please cite this work if you make use of the B6BTBRF2 liver transcriptome data.] -<LI> -Liu Y, Li J, Sam L, Goh CS, Gerstein M, Lussier YA (<a href="http://compbiol.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pcbi.0020159" target="_blank" class="fwn">2006</a>) An integrative genomic approach to uncover molecular mechanisms of prokaryotic traits. PLoS Computional Biology 2:e159 -<LI> -Loney KD, Uddin RK, Singh SM (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16433728" target="_blank" class="fwn">2006</a>) Analysis of metallothionein brain gene expression in relation to ethanol preference in mice using cosegregation and gene knockouts. Alcohol Clin Exp Res 30:15-25 -<LI> -Martin MV, Dong H, Vallera D, Lee D, Lu L, Williams RW, Rosen GD, Cheverud JM, Csernansky JG (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=17081266&query_hl=1&itool=pubmed_docsum" target="_blank" class="fwn">2006</a>) Independent quantitative trait loci influence ventral and dorsal hippocampal volume in recombinant inbred strains of mice. Gene, Brain and Behavior 5:614-623. -<LI> -van Os R, Ausema A, Noach EJ, van Pelt K, Dontje BJ, Vellenga E, de Haan G (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16371023" target="_blank" class="fwn">2006</a>) Identification of quantitative trait loci regulating haematopoietic parameters in B6AKRF2 mice. British Journal of Haematolology 132:80-90. -<LI> -Peirce JL, Li H, Wang J, Manly KF, Hitzemann RJ, Belknap JK, Rosen GD, Goodwin S, Sutter TR, Williams RW, Lu L (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstract&list_uids=16783644&query_hl=1&itool=pubmed_docsum" class="fwn">2006</a>) How replicable are mRNA expression QTL? Mammalian Genome 17:643-656. <A href="http://www.springerlink.com/content/a23g75163p624324/fulltext.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://www.springerlink.com/content/a23g75163p624324/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. [An important paper in which four matched expression data sets (whole brain and striatum) generated using both recombinant inbred and F2 intercrosses were directly compared.] -<LI> -Ponomarev I, Maiya R, Harnett MT, Schafer GL, Ryabinin AE, Blednov YA, Morikawa H, Boehm SL 2nd, Homanics GE, Berman AE, Lodowski KH, Bergeson SE, Harris RA (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16783644" class="fwn">2006</a>) Transcriptional signatures of cellular plasticity in mice lacking the alpha1 subunit of GABAA receptors. J Neurosci 26:5673-83 -<A href="http://www.jneurosci.org/cgi/reprint/26/21/5673.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://www.jneurosci.org/cgi/content/full/26/21/5673" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Radcliffe RA, Lee MJ, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstract&list_uids=16783639&query_hl=1&itool=pubmed_docsum" class="fwn">2006</a>) Prediction of cis-QTLs in a pair of inbred mouse strains with the use of expression and haplotype data from public databases. Mammalian Genome 17:629-642 <A href="http://www.springerlink.com/content/3u641hgk642221r6/fulltext.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://www.springerlink.com/content/3u641hgk642221r6/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Rulten SL, Ripley TL, Hunt CL, Stephens DN, Mayne LV (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16594979&query_hl=2&itool=pubmed_docsum" target="_blank" class="fwn">2006</a>) Sp1 and NFkappaB pathways are regulated in brain in response to acute and chronic ethanol. Genes Brain Behav 5:257-273 [This study exploited the original GeneNetwork Affymetrix U74Av2 brain data set to validate a set of transcripts modulated by ethanol treatment.] -<LI> -Saba L, Bhave SV, Grahame N, Bice P, Lapadat R, Belknap J, Hoffman PL, Tabakoff B (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16783646&query_hl=8&itool=pubmed_docsum" class="fwn">2006</a>) Candidate genes and their regulatory elements: alcohol preference and tolerance. Mammalian Genome 17:669-688 <A href="http://www.springerlink.com/content/224237686jx80v61/fulltext.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://www.springerlink.com/content/224237686jx80v61/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> [A lovely paper describing results of a whole brain BXD study involving males from 30 strains (four individual arrays (not pooled) per strain). The data described in this study have been made accessible from GeneNetwork.] -<LI> -Shifman S, Bell JT, Copley BR, Taylor M, Williams RW, Mott R, Flint J (<A HREF="http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040395" target="_blank" class="fwn">2006</A>) A high resolution single nucleotide polymorphism genetic map of the mouse genome. PLoS Biology 4:2227-2237 <A HREF="http://biology.plosjournals.org/archive/1545-7885/4/12/pdf/10.1371_journal.pbio.0040395-L.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> [A comprehensive analysis of recombination rates using several populations of mice, including most major genetic reference populations.] -<LI> -van Os R, Ausema A, Noach EJ, van Pelt K, Dontje BJ, Vellenga E, de Haan G (<a href="http://www.blackwell-synergy.com/doi/abs/10.1111/j.1365-2141.2005.05835.x" class="fwn">2006</a>) Identification of quantitative trait loci regulating haematopoietic parameters in B6AKRF2 mice. British Journal of Haematology 132:80-90 -<LI> -Veenstra-VanderWeele J, Qaadir A, Palmer AA, Cook EH Jr, de Wit H. (<a href="http://www.ncbi.nlm.nih.gov/pubmed/16237383" class="fwn">2006</a>) Association between the casein kinase 1 epsilon gene region and subjective response to D-amphetamine. Neuropsychopharmacology 31:1056-1063 <A href="http://www.nature.com/npp/journal/v31/n5/full/1300936a.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> [This mapping study made use of GeneNetwork/WebQTL to show that "the region that contains Csnk1e was also found to contain a QTL for MA sensitivity, and complementary data from WebQTL (Chesler et al, 2005) showed that this same region of mouse chromosome 15 influenced Csnk1e transcript abundance, indicating the presence of a cis-acting eQTL."] -<LI> -Voy BH, Scharff JA, Perkins AD, Saxton AM, Borate B, Chesler EJ, Branstetter LK, Langston MA (<a href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=16854212" target="_blank" class="fwn">2006</a>) Extracting gene networks for low-dose radiation using graph theoretical algorithms. PLoS Computational Biology 2:e89. [A paper that introduces the use of cliques in analyzing microarray data sets. One of the GeneNetwork databases (BXD Hematopoietic Stem Cells) was used to follow up on a strong candidate gene, Tubby-like protein 4 <I>Tulp4</I>) that may have a role in immune function. -<LI> -Williams RH, Cotsapas CJ, Cowley MJ, Chan E, Nott DJ, Little PF (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstract&list_uids=16874319&query_hl=3&itool=pubmed_docsum" target="_blank" class="fwn">2006</a>) Normalization procedures and detection of linkage signal in genetical-genomics experiments. Nature Genetics 38:855-856. [A set of letters to the editor by Rohan Williams and colleagues, Chesler and colleagues, and Petretto and colleagues. All three letters highlight some of the technical challenges of analyzing expression data in a genetic context and point to the need for great care and attention to normalization methods. Well worth reviewing. Normalization is still very much an art, and it is quite likely that no one normalization procedure will be optimal for all different research questions. The old adage: "different horses for different courses" is likley to apply.] -<LI> -Williams RW (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=Abstract&list_uids=16783631&query_hl=1&itool=pubmed_docsum" class="fwn">2006</a>) Expression genetics and the phenotype revolution. Mammalian Genome 17:496-502 <A href="http://www.springerlink.com/content/921734n56j08400n/fulltext.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, <A href="http://www.springerlink.com/content/921734n56j08400n/fulltext.html" target="_blank" class="fwn"><I>Full Text HTML Version</I></A>. [My take on where systems genetics may take us in the next few years.] - -</OL> - - - - - - - - - - -</Blockquote> - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2005" class="subtitle"">GeneNetwork (2005) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> -<LI>Alberts R, Terpstra P, Bystrykh LV, de Haan G, Jansen RC (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed&dopt=Abstract&list_uids=15711547&query_hl=10" target="_blank" class="fwn">2005</A>) A statistical multiprobe model for analyzing cis and trans genes in genetical genomics experiments with short-oligonucleotide arrays Genetics 171:1437-1439 -<LI>Alberts R, Fu J, Swertz MA, Lubbers LA, Albers CJ, Jansen RC (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/15975223" target="_blank" class="fwn">2005</A>) Combining microarrays and genetic analysis. Brief Bioinform. 6:135-145 -<A href="http://bib.oxfordjournals.org/cgi/reprint/6/2/135.pdf" target="_blank" class="fwn"><I> Full text PDF version</I></A> -<LI> -Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T, Su AI, Vellenga E, Wang J, Manly KF, Lu L, Chesler EJ, Alberts R, Jansen RC, Williams RW, Cooke M, de Haan G (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/15711547" target="_blank" class="fwn">2005</A>) Uncovering regulatory pathways affecting hematopoietic stem cell function using “genetical genomics." Nature Genetics, 37:225-232 -<LI> -Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J, Hsu HC, Mountz JD, Baldwin N, Langston MA, Threadgill DW, Manly KF, Williams RW (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15711545&query_hl=10" target="_blank" class="fwn">2005</A>) Genetic dissection of gene expression reveals polygenic and pleiotropic networks modulating brain structure and function. Nature Genetics 37: 233-242 -<LI> -Flaherty L, Herron B, Symula D (<A HREF="http://www.genome.org/cgi/content/abstract/15/12/1741" target="_blank" class="fwn">2005</A>) Genome Research 15:1741-1745 <A href="http://www.genome.org/cgi/reprint/15/12/1741.pdf" target="_blank" class="fwn"><I> Full text PDF version</I></A> -<LI> -Garlow SJ, Boone E, Li W, Owens MJ, Nemeroff CB (<A HREF="http://endo.endojournals.org/cgi/content/abstract/146/5/2362" target="_blank" class="fwn">2005</A>) Genetic analysis of hypothalamic corticotropin-releasing factor system. Endocrinology 146: 2362-2368 -<LI> -Gammie SC, Hasen NS, Awad TA, Auger AP, Jessen HM, Panksepp JB, Bronikowski AM (<A HREF="http://www.eeob.iastate.edu/faculty/BronikoA/annespdfs/MBres.pdf" target="_blank" class="fwn">2005</A>) Gene array profiling of large hypothalamic CNS regions in lactating and randomly cycling virgin mice. Molecular Brain Research 139: 201-211 -<LI> -Kerns RT, Ravindranathan A, Hassan S, Cage MP, York T, Williams RW, Miles MF (<A HREF="http://www.jneurosci.org/cgi/content/full/25/9/2255" target="_blank" class="fwn">2005</A>) Ethanol-responsive brain region expression networks: implications for behavioral responses to acute ethanol in DBA/2J versus C57BL/6J mice. Journal of Neuroscience 25: 2255-2266 [<A HREF="http://www.brainchip.vcu.edu/kerns_apptable1.pdf" target="_blank" class="fwn">www.brainchip.vcu.edu/kerns_apptable1.pdf</A> for a complete table of modulated transcripts.] -<LI> -Li HQ, Lu L, Manly KF, Wang J, Zhou M, Williams RW, Cui Y (<A HREF="http://hmg.oupjournals.org/cgi/content/abstract/ddi124v1" target="_blank" class="fwn">2005</A>) Inferring gene transcriptional modulatory relations: a genetical genomics approach. Human Molecular Genetics 14: 1119-1125 -<LI> -Li J, Burmeister M (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16244315&query_hl=5&itool=pubmed_docsum" target="_blank" class="fwn">2005</A>) Genetical genomics: combining genetics with gene expression analysis. Human Molecular Genetics 14:163-169. - [<A href="/images/upload/Li_Burmeister_2005.pdf" target="_blank" class="fwn"><I> Full text PDF version</I></A>] - -<LI> -Lozier JN, Tayebi N, Zhang P (<A HREF="http://www.bloodjournal.org/cgi/content/abstract/" target="_blank" class="fwn">2005</A>) Mapping of genes that control the antibody response to human factor IX in mice. Blood 105: 1029-1035 -<LI> -MacLaren EJ, Sikela JM (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16205357&query_hl=3&itool=pubmed_docsum" target="_blank" class="fwn">2005</A>) Cerebellar gene expression profiling and eQTL analysis in inbred mouse strains selected for ethanol sensitivity. Alcoholism: Clinical and Experimental Research 29: 1568-1579. -<A HREF="http://www.alcoholism-cer.com/pt/re/alcoholism/abstract.00000374-200509000-00002.htm;jsessionid=HBGNJpQldfCKvpYr6XLCzW3fF4YT1mzCVTXGcCMNYCL6N4WlDLlK!901085598!181195628!8091!-1" target="_blank" class="fwn"><I>HTML</I></A> and -<A HREF="http://www.alcoholism-cer.com/pt/re/alcoholism/pdfhandler.00000374-200509000-00002.pdf;jsessionid=HBGNJpQldfCKvpYr6XLCzW3fF4YT1mzCVTXGcCMNYCL6N4WlDLlK!901085598!181195628!8091!-1" target="_blank" class="fwn"><I>PDF</I> reprints.</A> -<LI> -Matthews B, Bhave SV, Belknap JK, Brittingham C, Chesler EJ, Hitzemann RJ, Hoffman PL, Lu L, McWeeney S, Miles MR, Tabakoff B, Williams RW (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&dopt=AbstractPlus&list_uids=16205371&query_hl=1&itool=pubmed_docsum" target="_blank" class="fwn">2005</A>) Complex genetics of interactions of alcohol and CNS function and behavior. Alcoholism: Clinical and Experimental Research 29:1706-1719 -<LI> -Mountz JD, Yang P, Wu Q, Zhou J, Tousson A, Fitzgerald A, Allen J, Wang X, Cartner S, Grizzle WE, Yi N, Lu L, Williams RW, Hsu HC (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15683449&query_hl=4" target="_blank" class="fwn">2005</A>) Genetic segregation of spontaneous erosive arthritis and generalized autoimmune disease in BXD2 recombinant inbred strain of mice. Scadinavian Journal of Immunology 61:1-11 -<A href="/images/upload/Mountz2005.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> -<LI> -Li CX, Wei X, Lu L, Peirce JL, Wiliams RW, Waters RS (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=16338823&query_hl=12&itool=pubmed_docsum" target="_blank" class="fwn">2005</A>) Genetic analysis of barrel field size in the first somatosensory area (S1) in inbred and recombinant inbred strains of mice. Somatosensory and Motor Research 22:141-150 -<LI> -Palmer AA, Verbitsky M, Suresh R, Kamen HM, Reed CI, Li N, Burkhart-Kasch S, McKinnon CS, Belknap JK, Gilliam TC, Phillips TJ (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/16104378" target="_blank" class="fwn">2005</A>) Gene expression differences in mice divergently selected for methamphetamine sensitivity. Mammalian Genome 16:291-305 -<LI> -Pravenec M, Kren V (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/15728137" target="_blank" class="fwn">2005</A>) Genetic analysis of complex cardiovascular traits in the spontaneously hypertensive rat. Exp Physiol 90:273-276 -<A href="http://ep.physoc.org/cgi/reprint/90/3/273.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>, -<A href="http://ep.physoc.org/cgi/content/full/90/3/273" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Scott RE, White-Grindley E, Ruley HE, Chesler EJ, Williams RW (<A HREF="http://www3.interscience.wiley.com/cgi-bin/fulltext/109860478/HTMLSTART" target="_blank" class="fwn">2005</A>) P2P-R expression is genetically coregulated with components of the translation machinery and with PUM2, a translational repressor that associates with the P2P-R mRNA. Journal of Cellular Physiology 204:99-105 <A href="http://www3.interscience.wiley.com/cgi-bin/fulltext/109860478/HTMLSTART" target="_blank" class="fwn"><I> Full text HTML version</I></A> -<LI> -Vazquez-Chona FR, Khan AN, Chan CK, Moore AN, Dash PK, Rosario Hernandez R, Lu L, Chesler EJ, Manly KF, Williams RW, Geisert Jr EE (<A HREF="http://www.molvis.org/molvis/v11/a115/" target="_blank" class="fwn">2005</A>) Genetic networks controlling retinal injury. Molecular Vision 11:958-970 -<LI> -Yang H, Crawford N, Lukes L, Finney R, Lancaster M, Hunter KW (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/15728137" target="_blank" class="fwn">2005</A>) Metastasis predictive signature profiles pre-exist in normal tissues. Clin Exp Metastasis 22:593-603 -<A href="http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pubmed&pubmedid=16475030" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> - - - -</OL> -</Blockquote> - - - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2004" class="subtitle"">GeneNetwork (2004) </A> -<BLOCKQUOTE> - -</Blockquote> -<OL> -<LI>Baldwin NE, Chesler EJ, Kirov S, Langston MA, Snoddy JR, Williams RW, Zhang B (2004) Computational, integrative and comparative methods for the elucidation of genetic co-expression networks. Journal of Biomedicine and Biotechnology 2:172-180 -<A HREF="http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=16046823" target="_blank" class="fwn"><I>HTML</I></A> and -<A HREF="http://www.pubmedcentral.nih.gov/picrender.fcgi?artid=1184052&blobtype=pdf" target="_blank" class="fwn"><I>PDF</I> reprints.</A> -<LI> -Carlborg O, De Koning DJ, Chesler EJ, Manly KM, Williams RW, Haley CS (<A HREF="http://bioinformatics.oupjournals.org/cgi/content/abstract/bti241v1" target="_blank" class="fwn">2004</A>) Methological aspects of the genetic dissection of gene expression. Bioinformatics 10:1093 -<LI> -Chesler EJ, Williams RW (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15474587&query_hl=10" target="_blank" class="fwn">2004</A>) Brain gene expression: genomics and genetics. International Review of Neurobiology 60:59-95 -<LI> -Fernandes K, Paya-Cano JL, Sluyter F, D'Souza U, Plomin R, Schalkwyk LC (<A HREF="http://www.blackwell-synergy.com/links/doi" target="_blank" class="fwn">2004</A>) Hippocampal gene expression profiling across eight mouse inbred strains: towards understanding the molecular basis for behaviour. European Journal of Neuroscience 19:2576-2582 -<A href="http://www.bloodjournal.org/cgi/content/full/104/2/374" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Henckaerts E, Langer JC, Hans-Willem Snoeck HW (2004) Quantitative genetic variation in the hematopoietic stem cell and progenitor cell compartment and in lifespan are closely linked at multiple loci in BXD recombinant inbred mice. Blood 104:374-379 <A href="http://www.bloodjournal.org/cgi/content/full/104/2/374" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Hitzemann R, Reed C, Malmanger B, Lawler M, Hitzemann B, Cunningham B, McWeeney S, Belknap J, Harrington C, Buck K, Phillips T, Crabbe J (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15597075" target="_blank" class="fwn">2004</a>) On the integration of alcohol-related quantitative trait loci and gene expression analyses. Alcoholism: Clinical and Experimental Research 28:1437-1448 [Please cite this paper is you plan to use the B6D2F2 OHSU data set.] -<LI> -Maiya RP (<a href="http://64.233.179.104/search?q=cache:3ajOyi3RpywJ:www.lib.utexas.edu/etd/d/2004/maiyar34683/maiyar34683.pdf+webqtl&hl=en" target="_blank" class="fwn">2004</a>) Regulation of dopamine transporter: a role for ethanol and protein interactions. Dissertation, University of Texas, Austin -<LI> -Orth AP, Batalow S, Perrone M, Chanda SK (<a href="http://www.ashley-pub.com/doi/abs/10.1517/14728222.8.6.587" target="_blank" class="fwn">2004</a>) The promise of genomics to identify novel therapeutic targets. Expert Opion of Therapeutic Targets 8:587-596 -<LI> -Pomp D, Allan MF, Wesolowski SR (<a href="http://www.animal-science.org/cgi/content/full/82/13_suppl/E300" target="_blank" class="fwn">2004</a>) Quantitative genomics: Exploring the genetic architecture of complex trait predisposition. J Anim Sci 82:E300-E312 -<A href="http://jas.fass.org/cgi/reprint/82/13_suppl/E300.pdf" target="_blank" class="fwn"><I>Full Text HTML Version</I></A> -<LI> -Ponomarev I, Schafer GL, Blednov YA, Williams RW, Iver VR, Harris A (<A HREF="http://www.ncbi.nlm.nih.gov/pubmed/15544578" target="_blank" class="fwn">2004</A>) Convergent analysis of cDNA and short oligomer microarrays, mouse null mutant, and bioinformatics resources to study complex traits. Genes, Brain and Behavior 3:360-368 -<LI> -Simon P, Schott K, Williams RW, Schaeffel F (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15610169" target="_blank" class="fwn">2004</A>) Post-translational regulation of the immediate early gene EGR1 by light in the mouse retina. European Journal of Neuroscience 20:3371-3377 -<LI> -Zareparsi S, Hero A, Zack DJ, Williams RW, Swaroop A (<A HREF="http://www.iovs.org/cgi/content/full/45/8/2457" target="_blank" class="fwn">2004</A>) Seeing the unseen: microarray-based gene expression profiling in vision. Investigative Ophthalmology and Visual Science 45:2457-2462 <A href="http://www.iovs.org/cgi/content/full/45/8/2457" target="_blank" class="fwn"><I> Full text HTML version</I></A> -</OL - - - - - - - - - - - - - - -</Blockquote> - - - - - -<BLOCKQUOTE style="font-size: 14px;"> - <A NAME="2003" class="subtitle"">GeneNetwork (2003) </A> -<BLOCKQUOTE> - -</Blockquote> - -<OL> -<LI> -Bolivar V, Flaherty L (2003) A region on chromosome 15 controls intersession habituation in mice. Journal of Neuroscience 23: 9435-9438 <A href="http://www.jneurosci.org/cgi/content/full/23/28/9435" target="_blank" class="fwn"><I> Full Text HTML and PDF Versions</I></A> -<LI> -Jones BC, Reed CL, Hitzemann R, Wiesinger JA, McCarthy KA, Buwen JP, Beard JL <A HREF="http://www.ncbi.nlm.nih.gov/pubmed/14744041" target="_blank" class="fwn">2003</A>) Quantitative genetic analysis of ventral midbrain and liver iron in BXD recombinant inbred mice. Nutr Neuroscience 6:369-77 -<A href="images/upload/Jones_2003_NN1203.pdf" target="_blank" class="fwn"><I> Full Text PDF Version</I></A> - -<LI> -Hitzemann R, Hitzemann B, Rivera S, Gatley J, Thanos P, Shou S, Lu L, Williams RW (<a href="http://www.ncbi.nlm.nih.gov/pubmed/12543998" target="_blank" class="fwn">2003</a>) Dopamine D2 receptor binding, Drd2 expression and the number of dopamine neurons in the BXD recombinant inbred series: genetic relationships to alcohol and other drug associated phenotypes. Alcoholism: Clinical and Experimental Research 27:1-11 -<!-- <A href="http://www.alcoholism-cer.com/pt/re/alcoholism/abstract.00000374-200301000-00002.htm;jsessionid=Bd5vCAYkgweCpQQfVUDL32NyFP8vi8xbCG1JwnOLMdUZFaIgvgU5!1564676086!-949856032!9001!-1 -" target="_blank" class="fwn"><I> Full Text HTML and PDF Versions</I></A> ---> -<LI> -Hitzemann R, Malmanger B, Reed C, Lawler M, Hitzemann B, Coulombe S, Buck K, Rademacher B, Walter N, Polyakov Y, Sikela J, Williams RW, Flint J, Talbot C (<a href="http://www.ncbi.nlm.nih.gov/pubmed/14722723" target="_blank" class="fwn">2003</a>) A strategy for integration of QTL, gene expression, and sequence analyses. Mammalian Genome 14:733-747 -<LI> -Lionikas A, Blizard DA, Vandenbergh DJ, Glover MG, Stout JT, Vogler GP, McClearn GE, Larsson L (<a href="http://physiolgenomics.physiology.org/cgi/content/full/16/1/141" target="_blank" class="fwn">2003</a>) Genetic architecture of fast- and slow-twitch skeletal muscle weight in 200-day-old mice of the C57BL/6J and DBA/2J lineage. Physiological Genomics 16:141-152 -<LI> -Peirce J, Chesler EJ, Williams RW, Lu L (2003) Genetic architecture of the mouse hippocampus: identification of gene loci with regional effects. Genes, Brain and Behavior 2:238–252 -<A href="images/upload/Peirce_Lu_2003.pdf" target="_blank" class="fwn"><I> Full Text PDF Version</I></A> -<LI> -Rosen GD, La Porte NT, Diechtiareff B, Pung, CJ, Nissanov J, Gustafson C, Bertrand L, Gefen S, Fan Y, Tretiak OJ, Manly KF, Parks MR, Williams AG, Connolly MT, Capra JA, Williams RW (2003) Informatics center for mouse genomics: the dissection of complex traits of the nervous system. Neuroinformatics 1:327–342 -<A href="images/upload/Rosen_2003.pdf" target="_blank" class="fwn"><I> Full Text PDF Version</I></A> - -</OL> - -</Blockquote> - -<Blockquote class="fwn"><A NAME="Background" class="subtitle">Background references on inbred strains and other key resources</A> - -<Blockquote style="font-size: 14px;"> -Williams RW, Gu J, Qi S, Lu L (<a href="https://ncbi.nlm.nih.gov/pubmed/11737945" class="fwn" target="_blank">2001</a>) The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis. Genome Biology 2:46.1–46.18 <A href="http://genomebiology.com/content/2/11/RESEARCH0046" target="_blank" class="fwn"><I> Full Text HTML and PDF Version</I></A>. [General background on recombinant inbred strains.] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Peirce JL, Lu L, Gu J, Silver LM, Williams RW (<a href="http://www.biomedcentral.com/1471-2156/5/7" target="_blank" class="fwn">2004</a>) A new set of BXD recombinant inbred lines from advanced intercross populations in mice. BMC Genetics 5:7 <A href="http://www.genenetwork.org/pdf/Peirce_and_Lu_2004.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>. [Background on the expanded set of BXD strains.] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Williams RW, Bennett B, Lu L, Gu J, DeFries JC, Carosone-Link PJ, Rikke BA, Belknap JK, Johnson TE (<a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=15457343" target="_blank" class="fwn">2004</a>) Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis. Mammalian Genome 15:637-647 [Background on origins and the genetic structure large panel of LXS strains. Please cite this paper is you have used LXS data sets.] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Grubb SC, Churchill GA, Bogue MA (<a href="http://www.ncbi.nlm.nih.gov/pubmed/15130929" target="_blank" class="fwn">2004</a>) A collaborative database of inbred mouse strain characteristics. Bioinformatics 20:2857-2859 [One of two key papers on the Mouse Phenome Project and database at The Jackson Laboratory. The mouse diversity panel (MDP) is a superset of strains that are part of the Phenome Project. <a href="http://bioinformatics.oxfordjournals.org/cgi/reprint/20/16/2857" target="_blank" class="fwn"><I>Full Text PDF Version</I></A>.] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Bogue MA, Grubb SC (<a href="http://www.ncbi.nlm.nih.gov/pubmed/15619963" target="_blank" class="fwn">2004</a>) The mouse phenome project. Genetica 122:71-74 [One of two key papers on the Mouse Phenome Project and database at The Jackson Laboratory. The mouse diversity panel (MDP) is a superset of strains that are part of the Phenome Project. Please contact Dr. Molly Bogue for information on the Phenome Project: mollyb@jax.org] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Frazer KA, Eskin E, Kang HM, Bogue MA, Hinds DA, Beilharz EJ, Gupta RV, Montgomery J, Morenzoni MM, Nilsen GB, Pethiyagoda CL, Stuve LL, Johnson FM, Daly MJ, Wade CM, Cox DR (<a href="http://www.ncbi.nlm.nih.gov/pubmed/17660834" target="_blank" class="fwn">2007</a>) A sequence-based variation map of 8.27 million SNPs in inbred mouse strains. Nature 448, 1050-3 -</Blockquote> - - -<Blockquote style="font-size: 14px;"> -Loudet O, Chaillou S, Camilleri C, Bouchez D, Daniel-Vedele F (<A HREF="http://www.inra.fr/qtlat/BayxSha/Loudet2002.pdf" target="_blank" class="fwn">2002</A>) Bay-0 x Shahdara recombinant inbred line population: a powerful tool for the genetic dissection of complex traits in Arabidopsis. Theoretical and Applied Genetics 104:1173-1184 <A HREF="http://www.inra.fr/qtlat/BayxSha/Loudet2002.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> [Please cite this paper is you have used the BXS Arabidopsis data sets.] -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Jirout M, Krenova D, Kren V, Breen L, Pravenec M, Schork NJ, Printz MP (<A HREF="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12925886&query_hl=3" target="_blank" class="fwn">2003</A>) A new framework marker-based linkage map and SDPs for the rat HXB/BXH strain set. expression differences in mice diverently selected for methamphetamine sensitivity. Mammalian Genome 14: 537-546 -</Blockquote> - -<Blockquote style="font-size: 14px;"> -Shifman S, Bell JT, Copley BR, Taylor M, Williams RW, Mott R, Flint J (<A HREF="http://biology.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pbio.0040395" target="_blank" class="fwn">2006</A>) A high resolution single nucleotide polymorphism genetic map of the mouse genome. PLoS Biology 4:2227-2237 <A HREF="http://biology.plosjournals.org/archive/1545-7885/4/12/pdf/10.1371_journal.pbio.0040395-L.pdf" target="_blank" class="fwn"><I>Full Text PDF Version</I></A> [A comprehensive analysis of recombination rates using several populations of mice, including most major GRPs.] -</Blockquote> - - -</Blockquote> -<Blockquote class="subtitle">Information about this HTML page:</P> </Blockquote> -<Blockquote style="font-size: 14px;"> -<Blockquote ><P><P>This text originally generated by RWW, April 2003. Updated by RWW, Oct 15, Dec 17, 2004; RWW Jan 19, 2005; EJC Mar 9; RWW Mar 29, Apr 15; RWW Jan 7, 2006, Jan 21, July 26, Aug 26, 2006s; May 2007; Feb 2008; March 19 2008 -</P></Blockquote> - - </Blockquote> - <P></P> - - - -{% endblock %} diff --git a/wqflask/wqflask/templates/references.html b/wqflask/wqflask/templates/references.html new file mode 100644 index 00000000..f723a1e8 --- /dev/null +++ b/wqflask/wqflask/templates/references.html @@ -0,0 +1,19 @@ +{% extends "base.html" %} +{% block title %}Reference{% endblock %} +{% block css %} +<link rel="stylesheet" type="text/css" href="/static/new/css/markdown.css" /> +{% endblock %} +{% block content %} +<div class="github-btn-container"> + <div class="github-btn"> + <a href="https://github.com/genenetwork/gn-docs"> + Edit Text + <img src="/static/images/edit.png"> + </a> + </div> +</div> +<div id="markdown" class="container"> + {{ rendered_markdown|safe }} +</div> + +{% endblock %}
\ No newline at end of file diff --git a/wqflask/wqflask/templates/search_result_page.html b/wqflask/wqflask/templates/search_result_page.html index c163a99a..2a8d6931 100644 --- a/wqflask/wqflask/templates/search_result_page.html +++ b/wqflask/wqflask/templates/search_result_page.html @@ -2,6 +2,7 @@ {% block title %}Search Results{% endblock %} {% block css %} <link rel="stylesheet" type="text/css" href="{{ url_for('css', filename='DataTables/css/jquery.dataTables.css') }}" /> + <link rel="stylesheet" type="text/css" href="{{ url_for('css', filename='fontawesome/css/font-awesome.min.css') }}" /> <link rel="stylesheet" type="text/css" href="{{ url_for('js', filename='DataTablesExtensions/scroller/css/scroller.dataTables.min.css') }}"> <link rel="stylesheet" type="text/css" href="{{ url_for('js', filename='DataTablesExtensions/buttonStyles/css/buttons.dataTables.min.css') }}"> <link rel="stylesheet" type="text/css" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.15.1/css/all.min.css"> @@ -440,8 +441,6 @@ 'processing': 'Loading...' } } ); - - console.timeEnd("Creating table"); $('.toggle-vis').on( 'click', function (e) { e.preventDefault(); @@ -472,5 +471,4 @@ }); </script> -{% endblock %} - +{% endblock %}
\ No newline at end of file diff --git a/wqflask/wqflask/templates/show_trait_transform_and_filter.html b/wqflask/wqflask/templates/show_trait_transform_and_filter.html index 0418d972..d7eac378 100644 --- a/wqflask/wqflask/templates/show_trait_transform_and_filter.html +++ b/wqflask/wqflask/templates/show_trait_transform_and_filter.html @@ -4,7 +4,7 @@ <strong>Reset</strong> option as needed. </p> - <div id="blockMenuSpan" class="input-append block-by-index-div"> + <div id="blockMenuSpan" class="input-append block-div"> <label for="remove_samples_field">Block samples by index:</label> <input type="text" id="remove_samples_field" placeholder="Example: 3, 5-10, 12"> <select id="block_group" size="1"> @@ -21,19 +21,64 @@ Please check that your input is formatted correctly, e.g. <strong>3, 5-10, 12</strong> </div> {% if sample_groups[0].attributes %} - <div class="input-append block-by-attribute-div"> - <label for="exclude_menu">Block samples by group:</label> - <select id="exclude_menu" size=1> + <div class="input-append block-div-2"> + <label for="exclude_column">Block samples by group:</label> + <select id="exclude_column" size=1> {% for attribute in sample_groups[0].attributes %} - <option value="{{ sample_groups[0].attributes[attribute].name.replace(' ', '_') }}"> - {{ sample_groups[0].attributes[attribute].name }}</option> + {% if sample_groups[0].attributes[attribute].distinct_values|length <= 10 %} + <option value="{{ loop.index }}"> + {{ sample_groups[0].attributes[attribute].name }} + </option> + {% endif %} {% endfor %} </select> <select id="attribute_values" size=1> </select> - <input type="button" id="exclude_group" class="btn" value="Block"> + <select id="exclude_by_attr_group" size="1"> + <option value="primary"> + {{ sample_group_types['samples_primary'] }} + </option> + <option value="other"> + {{ sample_group_types['samples_other'] }} + </option> + </select> + <input type="button" id="exclude_by_attr" class="btn btn-danger" value="Block"> </div> {% endif %} + <div id="filterMenuSpan" class="input-append block-div-2"> + <label for="filter_samples_field">Filter samples by {% if not sample_groups[0].attributes %}value{% endif %} </label> + {% if sample_groups[0].attributes %} + <select id="filter_column"> + <option value="value">Value</option> + {% if js_data.se_exists %} + <option value="stderr">SE</option> + {% endif %} + {% for attribute in sample_groups[0].attributes %} + + <option value="{{ loop.index }}"> + {{ sample_groups[0].attributes[attribute].name }} + </option> + + {% endfor %} + </select> + {% endif %} + <select id="filter_logic" size="1"> + <option value="greater_than">></option> + <option value="less_than"><</option> + <option value="greater_or_equal">≥</option> + <option value="less_or_equal">≤</option> + </select> + <input type="text" id="filter_value" placeholder="Example: 3, 10, 15"> + <select id="filter_group" size="1"> + <option value="primary"> + {{ sample_group_types['samples_primary'] }} + </option> + <option value="other"> + {{ sample_group_types['samples_other'] }} + </option> + </select> + <input type="button" id="filter_by_value" class="btn btn-danger" value="Filter"> + </div> <div> <input type="button" id="hide_no_value" class="btn btn-default" value="Hide No Value"> <input type="button" id="block_outliers" class="btn btn-default" value="Block Outliers"> diff --git a/wqflask/wqflask/views.py b/wqflask/wqflask/views.py index b7c4d142..3557a62a 100644 --- a/wqflask/wqflask/views.py +++ b/wqflask/wqflask/views.py @@ -301,28 +301,13 @@ def news(): doc = Docs("news", request.args) return render_template("docs.html", **doc.__dict__) -@app.route("/references") -def references(): - doc = Docs("references", request.args) - return render_template("docs.html", **doc.__dict__) - #return render_template("reference.html") @app.route("/intro") def intro(): doc = Docs("intro", request.args) return render_template("docs.html", **doc.__dict__) -@app.route("/policies") -def policies(): - doc = Docs("policies", request.args) - #return render_template("policies.html") - return render_template("docs.html", **doc.__dict__) -@app.route("/links") -def links(): - #doc = Docs("links", request.args) - #return render_template("docs.html", **doc.__dict__) - return render_template("links.html") @app.route("/tutorials") def tutorials(): @@ -336,12 +321,6 @@ def credits(): #return render_template("docs.html", **doc.__dict__) return render_template("credits.html") -@app.route("/environments") -def environments(): - doc = Docs("environments", request.args) - return render_template("docs.html", **doc.__dict__) - #return render_template("environments.html", **doc.__dict__) - @app.route("/update_text", methods=('POST',)) def update_page(): update_text(request.form) @@ -718,6 +697,8 @@ def mapping_results_page(): 'maf', 'use_loco', 'manhattan_plot', + 'color_scheme', + 'manhattan_single_color', 'control_marker', 'control_marker_db', 'do_control', |
