merge main

10 files changed, 490 insertions, 13 deletions

diff --git a/gn3/api/rqtl.py b/gn3/api/rqtl.py
new file mode 100644
index 0000000..ebb746c
--- /dev/null
+++ b/gn3/api/rqtl.py
@@ -0,0 +1,58 @@
+"""Endpoints for running the rqtl cmd"""
+import os
+
+from flask import Blueprint
+from flask import current_app
+from flask import jsonify
+from flask import request
+
+from gn3.computations.rqtl import generate_rqtl_cmd, process_rqtl_output, process_perm_output
+from gn3.computations.gemma import do_paths_exist
+
+rqtl = Blueprint("rqtl", __name__)
+
+@rqtl.route("/compute", methods=["POST"])
+def compute():
+    """Given at least a geno_file and pheno_file, generate and
+run the rqtl_wrapper script and return the results as JSON
+
+    """
+    genofile = request.form['geno_file']
+    phenofile = request.form['pheno_file']
+
+    if not do_paths_exist([genofile, phenofile]):
+        raise FileNotFoundError
+
+    # Split kwargs by those with values and boolean ones that just convert to True/False
+    kwargs = ["model", "method", "nperm", "scale", "control_marker"]
+    boolean_kwargs = ["addcovar", "interval", "pstrata"]
+    all_kwargs = kwargs + boolean_kwargs
+
+    rqtl_kwargs = {"geno": genofile, "pheno": phenofile}
+    rqtl_bool_kwargs = []
+    for kwarg in all_kwargs:
+        if kwarg in request.form:
+            if kwarg in kwargs:
+                rqtl_kwargs[kwarg] = request.form[kwarg]
+            if kwarg in boolean_kwargs:
+                rqtl_bool_kwargs.append(kwarg)
+
+    rqtl_cmd = generate_rqtl_cmd(
+        rqtl_wrapper_cmd=current_app.config.get("RQTL_WRAPPER_CMD"),
+        rqtl_wrapper_kwargs=rqtl_kwargs,
+        rqtl_wrapper_bool_kwargs=rqtl_bool_kwargs
+    )
+
+    rqtl_output = {}
+    if not os.path.isfile(os.path.join(current_app.config.get("TMPDIR"),
+                                       "output", rqtl_cmd.get('output_file'))):
+        os.system(rqtl_cmd.get('rqtl_cmd'))
+
+    rqtl_output['results'] = process_rqtl_output(rqtl_cmd.get('output_file'))
+
+    rqtl_output['results'] = process_rqtl_output(rqtl_cmd.get('output_file'))
+    if int(rqtl_kwargs['nperm']) > 0:
+        rqtl_output['perm_results'], rqtl_output['suggestive'], rqtl_output['significant'] = \
+        process_perm_output(rqtl_cmd.get('output_file'))
+
+    return jsonify(rqtl_output)
diff --git a/gn3/app.py b/gn3/app.py
index dc89f55..046b5de 100644
--- a/gn3/app.py
+++ b/gn3/app.py
@@ -5,6 +5,7 @@ from typing import Dict
 from typing import Union
 from flask import Flask
 from gn3.api.gemma import gemma
+from gn3.api.rqtl import rqtl
 from gn3.api.general import general
 from gn3.api.correlation import correlation
 from gn3.api.data_entry import data_entry
@@ -28,6 +29,7 @@ def create_app(config: Union[Dict, str, None] = None) -> Flask:
             app.config.from_pyfile(config)
     app.register_blueprint(general, url_prefix="/api/")
     app.register_blueprint(gemma, url_prefix="/api/gemma")
+    app.register_blueprint(rqtl, url_prefix="/api/rqtl")
     app.register_blueprint(correlation, url_prefix="/api/correlation")
     app.register_blueprint(data_entry, url_prefix="/api/dataentry")
     return app
diff --git a/gn3/commands.py b/gn3/commands.py
index 4b0d62d..14bd295 100644
--- a/gn3/commands.py
+++ b/gn3/commands.py
@@ -30,6 +30,20 @@ def compose_gemma_cmd(gemma_wrapper_cmd: str = "gemma-wrapper",
         cmd += " ".join([f"{arg}" for arg in gemma_args])
     return cmd
 
+def compose_rqtl_cmd(rqtl_wrapper_cmd: str,
+                     rqtl_wrapper_kwargs: Dict,
+                     rqtl_wrapper_bool_kwargs: list) -> str:
+    """Compose a valid R/qtl command given the correct input"""
+    # Add kwargs with values
+    cmd = f"Rscript { rqtl_wrapper_cmd } " + " ".join(
+        [f"--{key} {val}" for key, val in rqtl_wrapper_kwargs.items()])
+
+    # Add boolean kwargs (kwargs that are either on or off, like --interval)
+    if rqtl_wrapper_bool_kwargs:
+        cmd += " "
+        cmd += " ".join([f"--{val}" for val in rqtl_wrapper_bool_kwargs])
+
+    return cmd
 
 def queue_cmd(conn: Redis,
               job_queue: str,
diff --git a/gn3/computations/correlations.py b/gn3/computations/correlations.py
index eae7ae4..bc738a7 100644
--- a/gn3/computations/correlations.py
+++ b/gn3/computations/correlations.py
@@ -69,8 +69,8 @@ pearson,spearman and biweight mid correlation return value is rho and p_value
         "spearman": scipy.stats.spearmanr
     }
     use_corr_method = corr_mapping.get(corr_method, "spearman")
-    corr_coeffient, p_val = use_corr_method(primary_values, target_values)
-    return (corr_coeffient, p_val)
+    corr_coefficient, p_val = use_corr_method(primary_values, target_values)
+    return (corr_coefficient, p_val)
 
 
 def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
@@ -85,13 +85,13 @@ def compute_sample_r_correlation(trait_name, corr_method, trait_vals,
 
     if num_overlap > 5:
 
-        (corr_coeffient, p_value) =\
+        (corr_coefficient, p_value) =\
             compute_corr_coeff_p_value(primary_values=sanitized_traits_vals,
                                        target_values=sanitized_target_vals,
                                        corr_method=corr_method)
 
-        if corr_coeffient is not None:
-            return (trait_name, corr_coeffient, p_value, num_overlap)
+        if corr_coefficient is not None:
+            return (trait_name, corr_coefficient, p_value, num_overlap)
     return None
 
 
@@ -145,10 +145,10 @@ def compute_all_sample_correlation(this_trait,
 
         for sample_correlation in results:
             if sample_correlation is not None:
-                (trait_name, corr_coeffient, p_value,
+                (trait_name, corr_coefficient, p_value,
                  num_overlap) = sample_correlation
                 corr_result = {
-                    "corr_coeffient": corr_coeffient,
+                    "corr_coefficient": corr_coefficient,
                     "p_value": p_value,
                     "num_overlap": num_overlap
                 }
@@ -156,7 +156,7 @@ def compute_all_sample_correlation(this_trait,
                 corr_results.append({trait_name: corr_result})
     return sorted(
         corr_results,
-        key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coeffient"]))
+        key=lambda trait_name: -abs(list(trait_name.values())[0]["corr_coefficient"]))
 
 
 def benchmark_compute_all_sample(this_trait,
@@ -179,12 +179,12 @@ def benchmark_compute_all_sample(this_trait,
             trait_vals=this_vals,
             target_samples_vals=target_vals)
         if sample_correlation is not None:
-            (trait_name, corr_coeffient,
+            (trait_name, corr_coefficient,
              p_value, num_overlap) = sample_correlation
         else:
             continue
         corr_result = {
-            "corr_coeffient": corr_coeffient,
+            "corr_coefficient": corr_coefficient,
             "p_value": p_value,
             "num_overlap": num_overlap
         }
@@ -200,20 +200,20 @@ def tissue_correlation_for_trait(
         compute_corr_p_value: Callable = compute_corr_coeff_p_value) -> dict:
     """Given a primary tissue values for a trait and the target tissues values
     compute the correlation_cooeff and p value the input required are arrays
-    output -> List containing Dicts with corr_coefficient value,P_value and
+    output -> List containing Dicts with corr_coefficient value, P_value and
     also the tissue numbers is len(primary) == len(target)
 
     """
 
     # ax :todo assertion that length one one target tissue ==primary_tissue
 
-    (tissue_corr_coeffient,
+    (tissue_corr_coefficient,
      p_value) = compute_corr_p_value(primary_values=primary_tissue_vals,
                                      target_values=target_tissues_values,
                                      corr_method=corr_method)
 
     tiss_corr_result = {trait_id: {
-        "tissue_corr": tissue_corr_coeffient,
+        "tissue_corr": tissue_corr_coefficient,
         "tissue_number": len(primary_tissue_vals),
         "tissue_p_val": p_value}}
 
diff --git a/gn3/computations/rqtl.py b/gn3/computations/rqtl.py
new file mode 100644
index 0000000..0433b3f
--- /dev/null
+++ b/gn3/computations/rqtl.py
@@ -0,0 +1,103 @@
+"""Procedures related rqtl computations"""
+import os
+from typing import Dict, List, Union
+
+import numpy as np
+
+from flask import current_app
+
+from gn3.commands import compose_rqtl_cmd
+from gn3.computations.gemma import generate_hash_of_string
+from gn3.fs_helpers import get_hash_of_files
+
+def generate_rqtl_cmd(rqtl_wrapper_cmd: str,
+                      rqtl_wrapper_kwargs: Dict,
+                      rqtl_wrapper_bool_kwargs: list) -> Dict:
+    """Given the base rqtl_wrapper command and
+dict of keyword arguments, return the full rqtl_wrapper command and an
+output filename generated from a hash of the genotype and phenotype files
+
+    """
+
+    # Generate a hash from contents of the genotype and phenotype files
+    _hash = get_hash_of_files(
+        [v for k, v in rqtl_wrapper_kwargs.items() if k in ["g", "p"]])
+
+    # Append to hash a hash of keyword arguments
+    _hash += generate_hash_of_string(
+        ",".join([f"{k}:{v}" for k, v in rqtl_wrapper_kwargs.items() if k not in ["g", "p"]]))
+
+    # Append to hash a hash of boolean keyword arguments
+    _hash += generate_hash_of_string(
+        ",".join(rqtl_wrapper_bool_kwargs))
+
+    # Temporarily substitute forward-slashes in hash with underscores
+    _hash = _hash.replace("/", "_")
+
+    _output_filename = f"{_hash}-output.csv"
+    rqtl_wrapper_kwargs["filename"] = _output_filename
+
+    return {
+        "output_file":
+        _output_filename,
+        "rqtl_cmd":
+        compose_rqtl_cmd(rqtl_wrapper_cmd=rqtl_wrapper_cmd,
+                         rqtl_wrapper_kwargs=rqtl_wrapper_kwargs,
+                         rqtl_wrapper_bool_kwargs=rqtl_wrapper_bool_kwargs)
+    }
+
+
+def process_rqtl_output(file_name: str) -> List:
+    """Given an output file name, read in R/qtl results and return
+    a List of marker objects
+
+    """
+    marker_obs = []
+    # Later I should probably redo this using csv.read to avoid the
+    # awkwardness with removing quotes with [1:-1]
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", file_name), "r") as the_file:
+        for line in the_file:
+            line_items = line.split(",")
+            if line_items[1][1:-1] == "chr" or not line_items:
+                # Skip header line
+                continue
+
+            # Convert chr to int if possible
+            the_chr: Union[int, str]
+            try:
+                the_chr = int(line_items[1][1:-1])
+            except ValueError:
+                the_chr = line_items[1][1:-1]
+            this_marker = {
+                "name": line_items[0][1:-1],
+                "chr": the_chr,
+                "cM": float(line_items[2]),
+                "Mb": float(line_items[2]),
+                "lod_score": float(line_items[3])
+            }
+            marker_obs.append(this_marker)
+
+    return marker_obs
+
+
+def process_perm_output(file_name: str):
+    """Given base filename, read in R/qtl permutation output and calculate
+    suggestive and significant thresholds
+
+    """
+    perm_results = []
+    with open(os.path.join(current_app.config.get("TMPDIR", "/tmp"),
+                           "output", "PERM_" + file_name), "r") as the_file:
+        for i, line in enumerate(the_file):
+            if i == 0:
+                # Skip header line
+                continue
+
+            line_items = line.split(",")
+            perm_results.append(float(line_items[1]))
+
+    suggestive = np.percentile(np.array(perm_results), 67)
+    significant = np.percentile(np.array(perm_results), 95)
+
+    return perm_results, suggestive, significant
diff --git a/gn3/settings.py b/gn3/settings.py
index bde856d..770ba3d 100644
--- a/gn3/settings.py
+++ b/gn3/settings.py
@@ -6,6 +6,7 @@ import os
 BCRYPT_SALT = "$2b$12$mxLvu9XRLlIaaSeDxt8Sle"  # Change this!
 DATA_DIR = ""
 GEMMA_WRAPPER_CMD = os.environ.get("GEMMA_WRAPPER", "gemma-wrapper")
+RQTL_WRAPPER_CMD = os.environ.get("RQTL_WRAPPER")
 CACHEDIR = ""
 REDIS_URI = "redis://localhost:6379/0"
 REDIS_JOB_QUEUE = "GN3::job-queue"
diff --git a/mypy.ini b/mypy.ini
index 5e1af13..5d66812 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -3,6 +3,9 @@
 [mypy-scipy.*]
 ignore_missing_imports = True
 
+[mypy-numpy.*]
+ignore_missing_imports = True
+
 [mypy-MySQLdb.*]
 ignore_missing_imports = True
 
diff --git a/scripts/rqtl_wrapper.R b/scripts/rqtl_wrapper.R
new file mode 100644
index 0000000..f7e0406
--- /dev/null
+++ b/scripts/rqtl_wrapper.R
@@ -0,0 +1,229 @@
+library(optparse)
+library(qtl)
+library(stringi)
+library(stringr)
+
+tmp_dir = Sys.getenv("TMPDIR")
+
+option_list = list(
+  make_option(c("-g", "--geno"), type="character", help=".geno file containing a dataset's genotypes"),
+  make_option(c("-p", "--pheno"), type="character", help="File containing two columns - sample names and values"),
+  make_option(c("-c", "--addcovar"), action="store_true", default=NULL, help="Use covariates (included as extra columns in the phenotype input file)"),
+  make_option(c("--model"), type="character", default="normal", help="Mapping Model - Normal or Non-Parametric"),
+  make_option(c("--method"), type="character", default="hk", help="Mapping Method - hk (Haley Knott), ehk (Extended Haley Knott), mr (Marker Regression), em (Expectation-Maximization), imp (Imputation)"),
+  make_option(c("-i", "--interval"), action="store_true", default=NULL, help="Use interval mapping"),
+  make_option(c("--nperm"), type="integer", default=0, help="Number of permutations"),
+  make_option(c("--pstrata"), action="store_true", default=NULL, help="Use permutation strata (stored as final column/vector in phenotype input file)"),
+  make_option(c("-s", "--scale"), type="character", default="mb", help="Mapping scale - Megabases (Mb) or Centimorgans (cM)"),
+  make_option(c("--control"), type="character", default=NULL, help="Name of marker (contained in genotype file) to be used as a control"),
+  make_option(c("-o", "--outdir"), type="character", default=file.path(tmp_dir, "output"), help="Directory in which to write result file"),
+  make_option(c("-f", "--filename"), type="character", default=NULL, help="Name to use for result file"),
+  make_option(c("-v", "--verbose"), action="store_true", default=NULL, help="Show extra information")
+);
+
+opt_parser = OptionParser(option_list=option_list);
+opt = parse_args(opt_parser);
+
+verbose_print <- function(...){
+  if (!is.null(opt$verbose)) {
+    for(item in list(...)){
+      cat(item)
+    }
+    cat("\n")
+  }
+}
+
+if (is.null(opt$geno) || is.null(opt$pheno)){
+  print_help(opt_parser)
+  stop("Both a genotype and phenotype file must be provided.", call.=FALSE)
+}
+
+geno_file = opt$geno
+pheno_file = opt$pheno
+
+# Generate randomized filename for cross object
+cross_file = file.path(tmp_dir, "cross", paste(stri_rand_strings(1, 8), ".cross", sep = ""))
+
+trim <- function( x ) { gsub("(^[[:space:]]+|[[:space:]]+$)", "", x) }
+
+get_geno_code <- function(header, name = 'unk'){
+  mat = which(unlist(lapply(header,function(x){ length(grep(paste('@',name,sep=''), x)) })) == 1)
+  return(trim(strsplit(header[mat],':')[[1]][2]))
+}
+
+geno_to_csvr <- function(genotypes, trait_names, trait_vals, out, sex = NULL,
+                         mapping_scale = "Mb", verbose = FALSE){
+  # Assume a geno header is not longer than 40 lines
+  header = readLines(genotypes, 40)
+
+  # Major hack to skip the geno headers
+  toskip = which(unlist(lapply(header, function(x){ length(grep("Chr\t", x)) })) == 1) - 1
+
+  type <- get_geno_code(header, 'type')
+
+  # Get the genotype codes
+  if(type == '4-way'){
+    genocodes <- NULL
+  } else {
+    genocodes <- c(get_geno_code(header, 'mat'), get_geno_code(header, 'het'),
+                   get_geno_code(header, 'pat'))
+  }
+  genodata <- read.csv(genotypes, sep='\t', skip=toskip, header=TRUE,
+                       na.strings=get_geno_code(header,'unk'),
+                       colClasses='character', comment.char = '#')
+
+  verbose_print('Genodata:', toskip, " ", dim(genodata), genocodes, '\n')
+
+  # If there isn't a sex phenotype, treat all as males
+  if(is.null(sex)) sex <- rep('m', (ncol(genodata)-4))
+
+  cross_items = list()
+
+  # Add trait and covar phenotypes
+  for (i in 1:length(trait_names)){
+    cross_items[[i]] <- c(trait_names[i], '', '', unlist(trait_vals[[i]]))
+  }
+
+  # Sex phenotype for the mice
+  cross_items[[length(trait_names) + 1]] <- c('sex', '', '', sex)
+  # Genotypes
+  cross_items[[length(trait_names) + 2]] <- cbind(genodata[,c('Locus','Chr', mapping_scale)],
+                                                  genodata[, 5:ncol(genodata)])
+
+  out_csvr <- do.call(rbind, cross_items)
+
+  # Save it to a file
+  write.table(out_csvr, file=out, row.names=FALSE, col.names=FALSE, quote=FALSE, sep=',')
+
+  # Load the created cross file using R/qtl read.cross
+  if (type == '4-way') {
+    verbose_print('Loading in as 4-WAY\n')
+    cross = read.cross(file=out, 'csvr', genotypes=NULL, crosstype="4way")
+  } else if(type == 'f2') {
+    verbose_print('Loading in as F2\n')
+    cross = read.cross(file=out, 'csvr', genotypes=genocodes, crosstype="f2")
+  } else {
+    verbose_print('Loading in as normal\n')
+    cross = read.cross(file=out, 'csvr', genotypes=genocodes)
+  }
+  if (type == 'riset') {
+    # If its a RIL, convert to a RIL in R/qtl
+    verbose_print('Converting to RISELF\n')
+    cross <- convert2riself(cross)
+  }
+
+  return(cross)
+}
+
+create_marker_covars <- function(the_cross, control_marker){
+  #' Given a string of one or more marker names (comma separated), fetch
+  #' the markers' values from the genotypes and return them as vectors/a vector
+  #' of values
+
+  # In case spaces are added in the string of marker names
+  covariate_names <- strsplit(str_replace(control_marker, " ", ""), ",")
+
+  genotypes <- pull.geno(the_cross)
+  covariates_in_geno <- which(covariate_names %in% colnames(genotypes))
+  covariate_names <- covariate_names[covariates_in_geno]
+  marker_covars <- genotypes[, unlist(covariate_names)]
+
+  return(marker_covars)
+}
+
+# Get phenotype vector from input file
+df <- read.table(pheno_file, na.strings = "x", header=TRUE, check.names=FALSE)
+sample_names <- df$Sample
+trait_names <- colnames(df)[2:length(colnames(df))]
+
+# Since there will always only be one non-covar phenotype, its name will be in the first column
+pheno_name = unlist(trait_names)[1]
+
+trait_vals <- vector(mode = "list", length = length(trait_names))
+for (i in 1:length(trait_names)) {
+  this_trait <- trait_names[i]
+  this_vals <- df[this_trait]
+  trait_vals[[i]] <- this_vals
+
+  trait_names[i] = paste("T_", this_trait, sep = "")
+}
+
+verbose_print('Generating cross object\n')
+cross_object = geno_to_csvr(geno_file, trait_names, trait_vals, cross_file)
+
+# Calculate genotype probabilities
+if (!is.null(opt$interval)) {
+  verbose_print('Calculating genotype probabilities with interval mapping\n')
+  cross_object <- calc.genoprob(cross_object, step=5, stepwidth="max")
+} else {
+  verbose_print('Calculating genotype probabilities\n')
+  cross_object <- calc.genoprob(cross_object)
+}
+
+# Pull covariates out of cross object, if they exist
+covars = vector(mode = "list", length = length(trait_names) - 1)
+if (!is.null(opt$addcovar)) {
+  #If perm strata are being used, it'll be included as the final column in the phenotype file
+  if (!is.null(opt$pstrata)) {
+    covar_names = trait_names[3:length(trait_names) - 1]
+  } else {
+    covar_names = trait_names[3:length(trait_names)]
+  }
+  covars <- pull.pheno(cross_object, covar_names)
+}
+
+# Pull permutation strata out of cross object, if it is being used
+perm_strata = vector()
+if (!is.null(opt$pstrata)) {
+  strata_col = trait_names[length(trait_names)]
+  perm_strata <- pull.pheno(cross_object, strata_col)
+}
+
+# If a marker name is supplied as covariate, get its vector of values and add them as a covariate
+if (!is.null(opt$control)) {
+  marker_covars = create_marker_covars(cross_object, opt$control)
+  covars <- cbind(covars, marker_covars)
+}
+
+# Calculate permutations
+if (opt$nperm > 0) {
+  if (!is.null(opt$filename)){
+    perm_out_file = file.path(opt$outdir, paste("PERM_", opt$filename, sep = "" ))
+  } else {
+    perm_out_file = file.path(opt$outdir, paste(pheno_name, "_PERM_", stri_rand_strings(1, 8), sep = ""))
+  }
+
+  if (!is.null(opt$addcovar) || !is.null(opt$control)){
+    if (!is.null(opt$pstrata)) {
+      verbose_print('Running permutations with cofactors and strata\n')
+      perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+    } else {
+      verbose_print('Running permutations with cofactors\n')
+      perm_results = scanone(cross_object, pheno.col=1, addcovar=covars, n.perm=opt$nperm, model=opt$model, method=opt$method)
+    }
+  } else {
+    if (!is.null(opt$pstrata)) {
+      verbose_print('Running permutations with strata\n')
+      perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, perm.strata=perm_strata, model=opt$model, method=opt$method)
+    } else {
+      verbose_print('Running permutations\n')
+      perm_results = scanone(cross_object, pheno.col=1, n.perm=opt$nperm, model=opt$model, method=opt$method)
+    }
+  }
+  write.csv(perm_results, perm_out_file)
+}
+
+if (!is.null(opt$filename)){
+  out_file = file.path(opt$outdir, opt$filename)
+} else {
+  out_file = file.path(opt$outdir, paste(pheno_name, "_", stri_rand_strings(1, 8), sep = ""))
+}
+
+if (!is.null(opt$addcovar) || !is.null(opt$control)){
+  verbose_print('Running scanone with cofactors\n')
+  qtl_results = scanone(cross_object, pheno.col=1, addcovar=covars, model=opt$model, method=opt$method)
+} else {
+  verbose_print('Running scanone\n')
+  qtl_results = scanone(cross_object, pheno.col=1, model=opt$model, method=opt$method)
+}
+write.csv(qtl_results, out_file)
diff --git a/tests/unit/computations/test_rqtl.py b/tests/unit/computations/test_rqtl.py
new file mode 100644
index 0000000..955d0ab
--- /dev/null
+++ b/tests/unit/computations/test_rqtl.py
@@ -0,0 +1,42 @@
+"""Test cases for procedures defined in computations.rqtl"""
+import unittest
+
+from unittest import mock
+from gn3.computations.rqtl import generate_rqtl_cmd
+
+class TestRqtl(unittest.TestCase):
+    """Test cases for computations.rqtl module"""
+    @mock.patch("gn3.computations.rqtl.generate_hash_of_string")
+    @mock.patch("gn3.computations.rqtl.get_hash_of_files")
+    def test_generate_rqtl_command(self, mock_get_hash_files, mock_generate_hash_string):
+        """Test computing mapping results with R/qtl"""
+        mock_get_hash_files.return_value = "my-hash1"
+        mock_generate_hash_string.return_value = "my-hash2"
+
+        self.assertEqual(
+            generate_rqtl_cmd(rqtl_wrapper_cmd="rqtl-wrapper",
+                              rqtl_wrapper_kwargs={
+                                  "g": "genofile",
+                                  "p": "phenofile",
+                                  "model": "normal",
+                                  "method": "hk",
+                                  "nperm": 1000,
+                                  "scale": "Mb",
+                                  "control": "rs123456"
+                              },
+                              rqtl_wrapper_bool_kwargs=[
+                                  "addcovar",
+                                  "interval"
+                              ]), {
+                                  "output_file":
+                                  "my-hash1my-hash2my-hash2-output.csv",
+                                  "rqtl_cmd": (
+                                      "Rscript rqtl-wrapper "
+                                      "--g genofile --p phenofile "
+                                      "--model normal --method hk "
+                                      "--nperm 1000 --scale Mb "
+                                      "--control rs123456 "
+                                      "--filename my-hash1my-hash2my-hash2-output.csv "
+                                      "--addcovar --interval"
+                                  )
+                              })
diff --git a/tests/unit/test_commands.py b/tests/unit/test_commands.py
index aafb3a2..f36ba55 100644
--- a/tests/unit/test_commands.py
+++ b/tests/unit/test_commands.py
@@ -6,6 +6,7 @@ from datetime import datetime
 from typing import Callable
 from unittest import mock
 from gn3.commands import compose_gemma_cmd
+from gn3.commands import compose_rqtl_cmd
 from gn3.commands import queue_cmd
 from gn3.commands import run_cmd
 from gn3.exceptions import RedisConnectionError
@@ -53,6 +54,30 @@ class TestCommands(unittest.TestCase):
              "-p /tmp/gf13Ad0tRX/phenofile.txt"
              " -gk"))
 
+    def test_compose_rqtl_cmd(self):
+        """Test that the R/qtl cmd is composed correctly"""
+        self.assertEqual(
+            compose_rqtl_cmd(rqtl_wrapper_cmd="rqtl-wrapper",
+                             rqtl_wrapper_kwargs={
+                                 "g": "the_genofile",
+                                 "p": "the_phenofile",
+                                 "model": "np",
+                                 "method": "ehk",
+                                 "nperm": 2000,
+                                 "scale": "Mb",
+                                 "control": "rs234567"
+                             },
+                             rqtl_wrapper_bool_kwargs=[
+                                 "addcovar"
+                             ]),
+            ("Rscript rqtl-wrapper "
+             "--g the_genofile --p the_phenofile "
+             "--model np --method ehk "
+             "--nperm 2000 --scale Mb "
+             "--control rs234567 "
+             "--addcovar")
+        )
+
     def test_queue_cmd_exception_raised_when_redis_is_down(self):
         """Test that the correct error is raised when Redis is unavailable"""
         self.assertRaises(RedisConnectionError,