{
"question": [
"Once a sperm combines with an egg, what determines how traits are passed on to the resulting lifeform? ",
"Why is genetic tracing matrilineal rather than patrilineal? ",
"How does one tell the difference between X and Y DNA, with respect to DNA tracing and determining QTLs?",
"what type of dataset is useful for qtl mapping analysis in genenetwork2? ",
"what are the bioinformatics tools for QTLs analysis?"
],
"answer": [
"The traits are determined by the combination of chromosomes from the sperm and egg during fertilization. Each parent contributes one set of 23 chromosomes, which include both dominant and recessive genes. These genes interact with each other and the environment, and sometimes by chance, to determine the traits of the offspring. The process of meiosis and recombination, or crossing over, also plays a crucial role in shuffling genetic material and creating genetic variation.",
"Genetic tracing is both matrilineal and patrilineal. Matrilineal tracing is done through mitochondrial DNA (mtDNA), which is passed from mother to all her children without any contribution from the father. Patrilineal tracing, on the other hand, is done through Y-DNA, which is passed from father to son. Both types of tracing provide different insights into an individual's ancestry.",
"The difference between X and Y DNA is not explicitly discussed in the context of determining QTLs in the provided text. However, in general, X and Y chromosomes are different in size and gene content, which can be identified through genetic sequencing. In the context of QTL studies, these differences could potentially influence the traits being studied. However, the specific process of how this is done in QTL mapping is not detailed in the provided text.",
"GeneNetwork2 utilizes datasets containing legacy SNP and transcriptome data for QTL mapping analysis. It also uses gene expression datasets from multiple brain regions and the entirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2.",
"The bioinformatics tools for QTL analysis include R/qtl, QTL cartographer, MapQTL, WebQTL, QTL IciMapping, eQTL Explorer, eQTL Viewer, FastMap, Lirnet, and xQTL workbench. Other tools built into resources include QTL Analyst, Semantic Gene Organizer, and various tools for Gene Ontology overrepresentation and pathway matching."
],
"contexts": [
[
"Selection could occur at multiple levels, from germ cell generation and propagation to fertilization and early embryonic growth.Chromosomal abnormalities, including aneuploidy, were found in 10-20% of spermatozoa and oocytes (20) and in the cleaved embryo, with a 21% rate of abnormalities in preimplantation embryos (21).These findings led to a model for natural selection against chromosome abnormalities (21).Selection extends to the end of gestation: Only approximately 30% of all conceptions result in a live birth, with more than half of aborted fetuses containing chromosomal abnormalities (22), a number likely to be an underestimate because of technological limitations in measuring all possible mutations.But even in the very small fraction of germ cell duos that survive this withering genome attack and result in a live birth, a number of severe de novo mutations will still be found (23).The data on gross chromosomal alterations suggest that overall, mutation frequency early in life is very high.The functional consequence, however, is limited because of selection.Somewhat surprisingly, this picture points toward an initial decline in genomic alterations, allowing the adult individual to acquire a somatic genome optimally equipped to provide function.",
"In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?",
"Crossing over-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes cross over one another.Crossing over results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.This process is also known as meiotic recombination.The reason for the rarity of these mutations is natural selection: If the mutations result in disorders that decrease health and reproductive fitness, they will eventually be eliminated from a population.In exceptional cases, mutations may cause both beneficial and detrimental consequences, resulting in opposing forces of positive selection and negative selection that may cause the mutations to be preserved at nonrare frequencies in a population.For example, the HbS mutation in the HBB gene (which produces the subunit of hemoglobin) causes sickle cell disease when present in both alleles, a detrimental consequence, but protects against malaria when present in 1 allele, a beneficial consequence, ensuring that the mutation persists in populations in areas of the world where malaria is endemic.Genes are passed from parents to offspring via the process of meiosis by which gametes, the egg cells in the mother and the sperm cells in the father, are generated.Ordinarily, each cell has 23 pairs of chromosomes; the gametes have 23 unpaired chromosomes.In meiosis, the 23 pairs are split so that each gamete receives 1 chromosome from each pair (Figures 8 and 9).Two gametes (egg and sperm) ultimately join into a single cell, the zygote, which has the full complement of 23 chromosome pairs restored.If all goes well, the zygote gives rise to a live offspring.Recombination (meiotic recombination)-The swapping of genetic material that occurs in the germline.During the formation of egg and sperm cells, also known as meiosis, paired chromosomes from each parent align so that similar DNA sequences from the paired chromosomes recombine with one another.Recombination results in a shuffling of genetic material and is an important cause of the genetic variation seen among offspring.Also known as crossing over.",
"In the generation of gametes, crossing over regularly occurs, and genetic information is swapped between members of a chromosome pair.That doesn't matter within inbred animals, because the swapped parts are identical.In an F 1 animal, however, the chromosomes of a particular pair are genetically different, one each having come from each parent.Each gamete produced will be unique, as will be each F 2 zygote formed by uniting of the gametes from two F 1 parents.An F 2 group thus provides for expression of some genetic variability.This variability is limited to the allelic differences existing between the parent strains of the F 1 s, so that another F 2 , derived from different inbred strains, will express different genetic differences.",
"In most plants and animals, sexis a necessary component of reproduction, and the question for evolutionary biologistsis why reproductive mechanisms have evolved that way. In one of the experimentsdescribed next, evolutionary geneticists have nevertheless devised a way to compareevolution with and without recombination in the obligately sexual fruit fly.Sex brings harmful alleles together into thesame genetic background, allowing selection to more efficiently purge them fromthe population and potentially producing some offspring that are fitter than eitherparent. However, the benefit of recombining deleterious mutations may depend on thenature of the epistatic interactions between them. The mutational deterministic hypothesis(Kondrashov 1988) depends partly on this epistasis.This disparity in investment is the basis for the twofold cost: asexualfemales hypothetically could transmit twice as many alleles at the same cost. In most plants and animals, mates tend to be unrelated, leading to outcrossing. Butsex usually also involves the basic process of physical recombination: the breakage andreunion of two different DNA or RNA molecules. Of these two processes, recombinationis clearly the more widespread feature of sexual reproduction. A variety of reproductivesystems, such as selfing and automixis, involve recombination but not outcrossing. Incontrast, relatively few reproductive systems have outcrossing without recombination.Longago, Wright (1931) noted that sex may destroy adaptation because a successful combination of characteristics is attained in individuals only to be broken up in the next generation by the mechanisms of meiosis itself. Similarly, if alleles at different loci werejointly responsible for the production of phenotypes, sex has the potential to break apartcoadapted gene complexes, as it moves alleles away from genetic backgrounds wherebeneficial epistatic interactions have evolved through natural selection. Why should sex therefore be so common, given the obvious costs?",
"Traditionally, it has been agreed that thenal sex of an individual (phenotypic sex)depends on two sequential processes: the sexdetermination system of the species and thegonad differentiation process (Valenzuela,2008). However, recently, these two seeminglydistinct processes are viewed as part of a general process leading to gonad formation andsex ratios (Sarre et al. , 2004; Quinn et al. , 2011;Uller and Helantera, 2011).However, we expect thatonly at this level, the most signicant contributions brought by integrating epigenetics will bemade. Concluding Remarks and FutureProspectsFish sex ratios are the result of a complex interaction between genetic, biochemical, and environmental interactions. The ultimate resultof these interactions at the individual level isgender: male or female. However, at the population level, the combination of sex determination and differentiation sets the sex ratio. Inturn, sex ratios dene the reproductive capacityof populations and, if sex growth dimorphismexists, also the growth characteristics, something very important in an aquaculture context.The inheritance of sex based on major sexfactors, also known as chromosomal sex determination, includes monofactorial and multifactorial SD mechanisms, with the presence of aFunctional Genomic Analysis of Sex Determination and Differentiation in Teleost Fish(A)ZygoteSex determinationEmbryosSex differentiationLarvaeJuvenilesSex changeAdultsTime(B)Majorsex factorsMinorsex factorsMonofactorialaPolyfactorialdbcEnvironmentalEnvironmentaldifferencesFigure 8.2 Sex determination and differentiationin sh. (A) The processes of sex determination,sex differentiation, and sex change are representedalong the timeline of development.",
"Obehav is, in turn, influenced by offspring genesand environment (Ogene and Oenvir respectively). Hence, indirect genetic effects (blue arrows)and direct genetic effects (red arrow) are important influencers of behaviour. B) Parentoffspring conflict theory predicts that parental resource investment and offspring solicitationbehaviours are influenced by the fitness benefit to a focal individual (O), cost to a socialpartner such as a sibling (S1 and S2) or parent (P), and by their coefficient of relatedness(black arrows). 42Figure 2: Genomic imprinting can result in divergent phenotypes from the samegenotype. A) A paternally imprinted gene, i.e. maternally expressed.",
"Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011).Therefore, the resulting phenotypic patterns lag a generationbehind the genetic transmission of the causal variants. The most well-studied parental genetic effectsare caused by deposition of maternal transcripts into the egg prior to fertilization, resulting indifferences in early embryonic development depending on the genotype of the mother. Certain geneshave also been shown to respond to maternal influence after birth through genetically definedmaternal behaviors (Weaver et al. , 2004).",
"It was believed by many that for each trait variant we should expect to find acorresponding genetic change, or gene for that trait. Through historical happenstance therelationship between genes and traits was set up and treated as if it were one-to-one. But theproduction of a trait involves not only genes, but also their interactions with each other and theenvironment, and chance."
],
[
"distinguishing prenatalfrom postnatal maternal effects, see below). Maternal effects canaccount for a large proportion of phenotypic variance, especiallyduring early life, and for some traits explain more variation thandirect genetic effects [33, 97, 99, 100, 102115]. However, maternal and offspring genotype are correlated (i.e. half their genes areshared), and in inbred lines they are fully confounded, thus separating the effects of their respective genotypes is difficult. To removethis confounding effect cross-fostering has been used, both in thelaboratory and in the field [119, 131].",
"Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.",
"Although autosomal SNPs are commonly used as genetic markers to infer ancestry or race/ethnicity membership, haploid such as mitochondria, Y-DNA, and X-lined markers are also important to provide separate stories of ancestry of individuals from paternal and maternal sides [42,43].Therefore, genetic structure created due to autosomal markers could be different from those of lineage markers (often influenced by political, social, and migration history of individuals/populations).mitochondrial DNA or mtDNA haploid is the maternally inherited mitochondrial genome (mtDNA) [44].All children inherit mtDNA from their mother, with no admixture from the father.Like Y-line DNA, mtDNA is passed intact from one generation to the next but through maternal line.a) Autosomal DNA (testing both sexes) markers: autosomal DNA tests utilize DNA from the 22 pairs of autosomal chromosomes.Autosomal DNA is inherited from both parents.Autosomal testing provides percentages of ethnicity using autosomal DNA SNP test (i.e., ancestry informative markers), and it is the most commonly used test to infer ancestry across diploid genome.b) Y-DNA or Y-SNPs (paternal line testing) markers: a haploid Y-DNA is the paternally inherited non-recombining portion of the Y chromosome, and it tests only for males.The Y-DNA testing tests the Y chromosome which is passed intact from father to son with no DNA from the mother.Y-DNA testing can then be used to trace direct paternal line.Y-DNA remains the same in each generation, allowing us to compare surname from different regions to see if we are from the same family.Y-line testing does not indicate anything about the contributions of the other ancestors in a family tree.In other words, you could be 3/4th Native American, with only the direct paternal line being European, and this test would tell you nothing at all about those other three Native lines.When testing the Y-chromosome, there are two types of tests, short tandem repeat (STR) and SNP markers.STR tests are best for recent ancestry while SNP tests tell about more ancient ancestry.c) Mitochondrial DNA (maternal line testing) markers:",
"Additional information about past breeding practices can be gleaned by quantifying the number of reproductive males and females in a population.This can be achieved by comparing levels of genetic diversity between sex chromosomes, autosomes and mtDNA 99 .In cattle, for example, gene flow from aurochs is evident in the autosomes but is absent in mtDNA 41 .This has been interpreted as a management strategy that may have involved allowing insemination of domesticated females by wild bulls 41,100 .In horses, a comparison of the levels of diversity of the Y chromosome and the autosomal chromosomes demonstrated that some cultures allowed fewer males to breed and instead selected specific stallion bloodlines 55 .This male-oriented breeding strategy was not practised by the Romans and only became increasingly prominent in the past 1,000 years as a result of the growing influence of Oriental stallions (Arabian, Persian and Turkmen) 101 .",
"Dr Ring: What makes the maternal gene so peculiar compared to the paternal?Dr Cookson: If you look in the epidemiologic sense, many studies show that there is increased risk of allergic disease if the mother is affected.However, very few studies have actually set out to test that formally and most of them might suffer from some sort of selection bias because the mother is more likely to be aware of her symptoms and feel guilty, and so on.It is very difficult to explain.Is it genomic imprinting, where the gene is only active when transmitted through the mother?I do not think all of these genes would be imprinted, though it is possible.It also seems that there are effects of the maternal phenotype.The maternal phenotype, if the mother is affected or unaffected, determines the strength of the maternal effect.Again, if a gene was imprinted, you would not expect maternal phenotype to be important.So, I think that this has something to do with maternal/fetal interaction, either through the placenta or shortly after birth.There is the issue of immune conflict between mother and child.At the same time, the mother is trying to prime the infant's immune system.",
"Genetic and Genomic Discovery Using Family StudiesIngrid B. Borecki, PhD; Michael A. Province, PhD G enetic studies traditionally have been performed on sets of related individuals, that is, families.Mendel's early studies in sweet peas (Pisum sativum) on the inheritance patterns of discrete traits from parents with specific mating types to offspring has shed light on the basic mechanisms of inheritance, including the fundamental laws of segregation of discrete factors (genes) from parents to offspring and the cosegregation of genes that are closely located on a chromosome (linkage).The distribution of traits within families exhibited mathematical segregation ratios in offspring from known mating types.These expected segregation ratios have been used as an important discovery tool in the study of human diseases in pedigrees, providing evidence for a multitude of single-gene disorders.Furthermore, in some cases, trait cosegregation with genetic markers with known positions provides mapping information that enables localization and, ultimately, identification of the relevant causative gene.",
"In fact, this idea has been pursued before in thecontext of signatures of reproductive isolation and shown to revealpatterns consistent with epistatic gene interactions that arise in theshape of Dobzhansky-Muller incompatibilities [10,11]. In contrast to the mouse data, the available human genotypeswere derived from outbred, ethnically distinct populations. In thiscase pairs of functionally interacting genes can be detectedfollowing a slightly different approach.",
"Fig. 3. Illustrations of the three CEU pedigrees (black) showing how genetic information from distant patrilineal relatives (arrow; red, patrilineal lines) can identify individuals.Filled squares represent sequenced individuals.To respect the privacy of these families, only abbreviated versions are presented.The sex of the CEU grandchildren was randomized.The numbers of grandchildren are not given.",
"DiscussionKinship and genetic driftAuthor ManuscriptThe expanded family of BXDs is a well powered resource for both forward and reversegenetic analyses of genome-to-phenome linkage. As this family has grown, relations amongindividual strains have become complex, requiring the use of linear mixed models (Arends etal. , 2010; Sul et al. , 2016; Zhou and Stephens, 2014) or nonparametric equivalents such asmixed random forests (Stephan et al. , 2015) that account for kinship, epoch, and othercofactors. The family has kinship at several levels.",
"When I was in high school, I remember often trying to match my friends to their parents at various school functions and being surprised at how easy this was.As human geneticists, in spite of the enormous advances being made in our field, we still cannot answer many of the everyday questions that we are asked, such as: \"Why does he look just like his mother? \"Max Perutz [1], in a recent editorial comment in the New Scientist entitled \"The Molecular Biology of the Future,\" suggested some questions, for, as he put it, \"an examination in some future century. \"Here are two of them: (1) \"The time has come\" the Walrus said, \"To talk of many things ...And why the sea is boiling hot And whether pigs have wings. \"Calculate the amount of genetic information this would require in megacricks.",
"Using genetic markers, the pattern of inheritance can be tracked throughfamilies. For example, by analyzing a marker linked to the eye color genein several generations, it is possible to determine from which grandparents achild has inherited its eye color alleles. More importantly, nding a markerlinked to a disease can lead to location of the faulty gene causing the disease. Finding the gene is very valuable in the search for the cure. The distance between two loci can be expressed either as physical or genetic distance.",
"Another way of avoiding stratification is to use family-based samples.This approach has several theoretical advantages: as well as being immune to stratification 114 , these samples can be used to determine whether an allele has different effects on disease when it is inherited maternally or paternally 115 , and DISCORDANT SIB designs [116][117][118] can control for the effects of shared environment.Furthermore, more complex family-based designs are possible 119 that might allow combined association and linkage analysis 120 , and family-based association tests have also been developed for quantitative traits [94][95][96][97][98] .However, pure sibship-based association studies are underpowered relative to case-control studies 107,116,117 , and the requirement for living parents might introduce an age-of-onset bias towards younger patients for diseases that usually arise late in life.Furthermore, family-based samples are often much more difficult to collect, particularly if larger pedigrees are sought.Finally, the most commonly used family-based design, the TRANSMISSION DISEQUILIBIRIUM TEST (TDT; see REF. 114) is susceptible to technical artefacts (see below).",
"There are also a number of companies that utilize ancestry informative markers (AIMs) and claim that they can provide accurate determinations of a person's ancestry.The problem with these services is their assumption that for all populations reliable genetic markers of high ancestry informative value exist.There is also a second assumption that the frequency of these markers has not changed through time.This may be true for persons of european descent, in areas that have not seen large population disruptions.however, it is doubtful that a reliable genetic marker panel can be produced for German or Lithuanian Jews, just as such a panel for Western or Central African regions that were impacted by the slave trade is less likely.To understand this sophistication requires training in evolutionary and population genetics.Unfortunately, many of the scientists working with these companies do not have adequate background in these disciplines.In general, American universities are not providing the majority of biology students training in these disciplines.Marocco (2000) reported that only 46 percent of the phD-granting public universities and 15 percent of the phD-granting private universities required evolution as a core course.Genetics is widely required as a core at the undergraduate level, but the topics of population and quantitative genetics are at the back of the major texts and the genetics courses are usually taught by molecular geneticists.Neither is evolution well covered in anthropology texts (White et al. 2009. )White and colleagues' ( 2009) study showed that these texts did not give a single accurate definition when the topic was present.Additionally, the definitions often changed when books were written for cultural versus physical anthropology and often changed within the same text.This means that even at the undergraduate level, the tools required to critically approach molecular reductionist thinking are not widely provided to students.Graduate curricula tend to be narrower than undergraduate training.Thus, the vast majority of scientists who go into human genetics, bioinformatics, computational biology, and genomics are not well prepared to address the complex interactions that account for the phenotypes we observe in modern societies.",
"To scrutinize the polygenic networks underlying complex diseases, however, mouse resourcesthat are optimized to study the actions of isolated genetic loci ona fixed background will be insufficient on their own. For example, predisposition to the metabolic syndrome is inherited ina non-Mendelian fashion stressing genetic heterogeneity andmultigenetic pathogenesis (Nandi et al. , 2004). With the reawakening as to the extraordinary genetic resources and phenotypicdiversity archived in extant inbred strains, however, a foundationis in place for tracking down these complex traits and quantitative trait loci (QTL).",
"Otherwise, tens of thousands or markers will appear significant inthe genome-wise association studies using up to one million geneticmarkers. Approaches to control for stratification include using ofself report of ancestry or genetically derived principle componentsin the analysis. For studies using inbred mouse lines, a cladogramwhich is a hierarchical grouping based on phylogenetic analysis ofstrain relatedness can be created to subdivide inbred strains intomore genetically homogenous subgroups.",
"These haplotype mosaics form the basis of geneticanalysis and data integration in the CC and DO. In contrastto natural or commercial outbred populations, the founderhaplotypes of these multiparental populations (and similarpopulations in other model organisms) are known and wellcharacterized by sequencing. This presents a tremendousadvantage in the search for causal variants of complextraits: provided a genomic segment in an experimentalanimal can be assigned to a founder haplotype using a fewtagging markers, the remaining known variants can beimputed with essentially complete certainty.",
"Although bilateral descent is the norm in Western societies, it is not universal and there is variation with cultural practices around lineage.In certain societies, individuals place greater importance on (and have greater knowledge about) one side of the family than another (unilineal descent).Thus, individuals in patrilineal groups trace relationships through males only so that your father's brother's children are members of your family, but not your father's sisters (Kottak, 2007).They are members of their husband's group or family.Efforts to create a family pedigree may be hampered if the participant is not familiar with her mother's relatives, but her mother's brother's children (her cousins) may be able to supplement her overall family history.Knowledge about the cultural system of unilineal descent avoids assuming the universality of bilateral descent.Cultural beliefs such as these also have implications in the conduct of genetic research in terms of confidentiality and autonomy (Benkendorf et al., 1997;Wertz, 1997).One cannot assume that the named proband is in a position to speak for the extended family in agreeing to participate in any genetic research (DudokdeWit et al., 1997).",
"In particular in polygynous species, a femalesoffspring may have different fathers and are thus more closely related through the maternalthan the paternal line. Therefore, any fitness cost to mothers, such as increased provisioningand care, affect maternally derived genes more strongly than paternally derived genes,leading to the silencing of the maternal copy (i.e. paternal expression) of genes that increaseresource transfer. 5. Coadaptation between offspring and maternal traitsThe genetics of the co-evolution of parental and offspring traits has been investigated usingquantitative genetics models and in several empirical studies (Agrawal et al.",
"Because of the small contribution, through the sperm, ofthe paternal transcriptome to the fertilized zygote, and because of the stronger maternal contributionto child rearing in most model organisms, parental effects are typically thought of as synonymous withmaternal effects, although true paternal effects are known to exist (Rando, 2012). Maternal effects have been shown to be important during embryonic development, leading todifferences in the birth weight of mice depending on the genotype of the mother (Cowley et al. ,1989; Wolf et al. , 2011)."
],
[
"Genetic mapping inmouse strains enhances the power of detecting modifier genes and identifying complexgenetic interactions. Genomewide quantitative trait locus (QTL) analysis, as described inmore detail below, represents a promising approach to detect genetic variants that areassociated with specific phenotypes and interact with each other. 16ACCEPTED MANUSCRIPTIn experimental crosses of two (inbred) strains the first generation (F1) ofoffsprings is genetically heterozygous but equal. Then in the next generation (F2) thePTstrain-specific genetic information is distributed across the genomes of their progeny andRIeach offspring is genetically unique.",
"Second, and perhaps moreimportant, is the difference in the size and types of thegenetic reference populations. In our previous study, wemapped the QTL with 36 F2 mice that were genotyped at82 markers. In the current study, by comparison, we wereable to map QTLs after examining 342 mice from 55 strainsthat were genotyped at approximately 4000 markers.",
"This contrast can be exploited to identify subregions that underlie the trans-QTLs [67]. SNPs were counted for all four pairs of parental haplotypesBvs D, B vs H, B vs C, and L vs Sand SNP profiles for the fourcrosses were compared (figure 6). Qrr1 is a highly polymorphicPLoS Genetics | www.plosgenetics.org8November 2008 | Volume 4 | Issue 11 | e1000260QTL Hotspot on Mouse Distal Chromosome 1Figure 5. QTL for aminoacyl-tRNA synthetases in distal Qrr1.",
"The traditional approach to QTL mapping is to usetwo strains that differ maximally in the phenotype asparental strains for genetic crosses, with the followingcaveats. QTL analysis based on a single cross will mostlikely reflect only a small portion of the net geneticvariation, and QTL detection will be limited to regionswhere the two progenitor strains have functional polymorphisms. Data from multiple crosses, or from an HS,will overcome this limitation and can also be used toreduce QTL intervals [5,30].",
"These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.",
"Furthermore, splicing QTLs(sQTLs) rather than eQTLs could comprise the molecular mechanism linking DNA variants with YFP53; thus, sQTL analysis could uncover genes that would not normally bedetected at the level of differential gene expression (DGE),53 and thus, a differentially181182Molecular-Genetic and Statistical Techniques for Behavioral and Neural ResearchFigure 8.5 Schematic for immediate, rapid ne mapping in select F2 recombinants of the RCC-F2cross. Top panel: Genome-wide signicant QTL (green trace; red dashed line signicance threshold;blue vertical lines Bayes credible interval).",
"Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].",
"Interval-specific haplotype analysisApproximately 97% of the genetic variation betweeninbred mouse strains is ancestral [22], so regions ofidentity by descent (IBD) between two strains used todetect a QTL are highly unlikely to contain the causalgenetic polymorphism underlying the QTL [28]. Forexample, a cross between C57BL/6J and A/J mice detectedwww.sciencedirect.coma blood pressure QTL on Chr 1 [7].",
"At present, the BXD panel is composed of 80 different strains that all have beenfully genotyped.26 Variation in any quantifiable trait can be associated with thesegregation of parental alleles, and linkage genetics can map this variation toquantitative trait loci (QTLs), thereby identifying the genomic region(s) affectingthat trait. An overview of the QTL mapping approach is depicted in Figure 2. Classical QTL analysis has permitted the identification of loci that areassociated with variation in HSC traits.",
"In general,linking genetic variation with trait variation identifies QTL and a significant linkage ofphenotype and genotype suggest that the DNA status helps to determine trait expression. As stated above, mouse QTL studies provide distinct advantages over human studiesin the examination of genetic causes of a quantitative trait (e.g. alcoholism), even in theabsence of specific hypotheses regarding its aetiology or candidate genes.The progenitor mouse strainsshould have sufficient variation for the traits of interest and they should be genetically diverseenough to enable genetic mapping (BENNETT et al. 2006; FLINT 2003; GRISEL 2000). Thesample size required for the identification of QTL depends largely on the effect size that aQTL contributes to phenotypes on interest. Inference about QTL can be made if one or moregenetic markers are over- or underrepresented in the analysed individuals. Genotyping isoften done by means of microsatellite markers, which contains mono, di-, tri-, ortetranucleotide tandem repeats flanked by specific sequences (Figure 4a).This comparison gives information about the reliability of the observed genotypeinformation: The more the marker locations differ between the two maps (which signifiesvariation in marker positions), the higher the possibility of genotyping errors. QTL mapping was done in several stages to identify loci acting individually and QTL thatinteracted, either additively or epistatically. To determine individually-acting QTL, a singleQTL genome scan was conducted with the function scanone.",
"Importantly, whereasthese studies required substantial labor, time, and resources, X-QTL is a quick and easyapproach to achieve a comparable level of genetic dissection. The levels of complexityobserved here (e.g. 14 loci explaining 70% of the genetic variance for 4-NQO resistance) arestill dramatically lower than those seen in for some human traits in GWAS (e.g. 40 lociexplaining 5% of the variance for height 2,5). One obvious explanation is the difference inexperimental designs (line crosses vs. population association studies), but differences ingenetic architectures among species and traits may also contribute.",
"The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.",
"The method uses two pieces of information: mapping data from crosses thatinvolve more than two inbred strains and sequence variants in the progenitor strains within the intervalcontaining a quantitative trait locus (QTL). By testing whether the strain distribution pattern in the progenitor strains is consistent with the observed genetic effect of the QTL we can assign a probability that anysequence variant is a quantitative trait nucleotide (QTN). It is not necessary to genotype the animals exceptat a skeleton of markers; the genotypes at all other polymorphisms are estimated by a multipoint analysis.",
"Genotyping all the individual progeny formarkers that show allelic variation between the parental strains (either single nucleotide polymorphisms or simple sequence repeats) will allow the detection of associations between trait values and marker genotype, and in this way demonstrate to whichset of markers a QTL is linked. To reduce the genotyping effort, selective genotypingof the individuals at the extremes of the phenotypic spectrum can be performed (20,23). Although these three approaches are in general considered to be the best to detect andmap QTL, they have several disadvantages for quantitative traits involving HSC.",
"So, how do you go about planning and performing a QTL study, and howdo you identify the responsible gene within a QTL that you have identified? Generally, one starts by performing a strain survey to find two parental inbredstrains that have a markedly different trait. One can now look up many differenttraits of inbred mice online at the Mouse Phenome Database (http://phenome. jax.org/pub-cgi/phenome/mpdcgi?rtn=docs/home). However, the trait you maywant to study may not be present in wild type mice, so you may want to crossa mutant (or genetically engineered) strain onto several inbred strains.QTL Theory and PlanningThe theory behind the most basic form of QTL mapping is based upon intercrossing two inbred strains. The mouse genome consists of 19 pairs of autosomes (non sex-determining chromosome) and the X and Y chromosomes. Inthe example shown in Fig. 18.1, we are intercrossing stain A (shown with ablack chromosome pair) with strain B (shown with a white chromosome pair). The initial F1 (filial generation 1) mice are true hybrids, with each individualFrom: Molecular Biomethods Handbook, 2nd Edition.",
"These candidate genes are then sequenced in the two parental inbredstrains looking for sequence dierences in coding or regulatory regions. After ne mapping the QTL interval and shortening the list of plausiblecandidate polymorphisms, the major challenge remains \u0001 proving denitivelywhich nucleotide polymorphism underlies the QTL. The most direct proofwould be replacing one strains allele with another strains allele (creating aFIG. 1. Intercross breeding strategy for mapping quantitative trait loci (QTLs). On the right, the parental, F1 hybrid, and intercross (F2) mousegenerations are depicted.",
"QTL mapping studies thenseek to detect the polymorphisms underlying the complex traits of interest byscanning for alleles that co-vary withthe traits. Similar experiments also can be conducted with special derivatives of inbredstrains known as recombinant inbred(RI) mice. These animals are derivedby cross-breeding two or more distinctparental strains (which often divergewidely for the trait of interest), followedby inbreeding of the offspring for severalgenerations (Bailey 1971). Given thecorrect breeding strategy, this method1This is an issue faced by GWASs researchers when classifyingsamples as cases or controls."
],
[
"The project also provides online analysis tools to allowidentification of correlations within its data set. GeneNetwork (http://www.genenetwork.org), encompassing WebQTL, is a database ofgenotypes and complex phenotypes ranging from gene expression to behaviour in standardinbred strains, and six panels of mouse recombinant inbred strains including the two largestsets (BXD and LXS) of approximately 80 strains each. Rat and Arabidopsis populations arealso represented. Approximately 1500 phenotypes spanning the 25 year history of thesestrains are incorporated in this public resource, many of which were retrieved from theliterature.",
"BioinformaticsAll of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mappingThe QTL mapping module of GeneNetwork was used to identifyQTLs for hippocampal morphometry and radial maze trait data. Thismodule enables interval mapping, composite interval mapping anda pairwise scan option to identify epistatic effects.",
"Thereare four options for QTL mapping on the GeneNetwork website: intervalmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkagemaps for the entire genome. The log of odds (LOD) score was used toassert that a causal relation exists between a chromosomal location and aphenotypic variant, such as Gsto1 expression variation.",
"Webqtl is an online database [110] of linked datasets, including genotype and expressiondata, covering multiple species including mouse, macaque monkey, rat, drosophila,arabidopsis, plants and humans [60]. While this tool cannot be used to calculate eQTLs, itcan be used to find and visualize eQTLs in different species, strains and tissues. It canperform single- and multiple-interval QTL mapping of up to 100 selected traits. Users canalso upload their own trait data for populations included in the database. It can also calculateand display trait-correlation matrices and network graphs (also for up to 100 traits).",
"Once the data is normalized appropriately (in our case, no normalization was required), the QTLcan be mapped. To do this, select the mapping tools drop down window (Figure 6). There arethree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhouand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to mapphenotypes with SNPs with a correction for kinship or any other covariate of interest. Thisability to account for covariates is highly useful, but also this increases the time taken forcomputations.",
"WebQTL is the primary module in the GeneNetwork online resource (www.genenetwork.org),and provides a powerful environment to analyzetraits controlled by genetic variants (Chesler et al. 2004; Wang et al. 2003). It includes data from many485Fig. 2. Complexity of eQTL data. The graph shows a threedimensional schematic view of the high dimensionality ofthe eQTL data set generated from the BXH/HXB RI strainpanel (Hubner et al 2005; unpublished).",
"QTL MAPPING AND QTG DISCOVERY IN THE RCCA variety of statistical methods and tools have been developed for QTL mapping andimplemented in free software for public use. These methods are well suited for simplebackcross and F2 RCC populations. R/qtl9,39 was developed for identication ofQTLs and higher order modeling. Another Web-based tool, GeneNetwork orWebQTL (GeneNetwork.org),40 was developed for QTL mapping and to exploreassociations between variants, molecular traits (e.g. , gene expression), and higher orderphenotypes (e.g. , behavior) and facilitate QTG identication.",
"This enables gene expressioncorrelation and interval mapping, candidate gene searches and multitrait analyses. Each exported dataset was subject to an interval mapping analysis,which uses GeneNetworks embedded MapManager software(Manly et al . 2001) to perform HaleyKnott regression. Empirical P values were derived using 1000 permutations using the incorporatedpermutation feature of WebQTL. The peak of each statisticallysignificant (P -value <0.05) or suggestive (P -value <0.63) (Lander& Kruglyak 1995) QTL was determined based on empirical P values (Doerge & Churchill 1996). A one-LOD drop-off was usedto determine the QTL confidence interval about each peak.",
"The peak linkage valueand position was databased in GeneNetwork and userscan rapidly retrieve and view these mapping results forany probe set. Any of the QTL maps can also be rapidlyregenerated using the same Haley-Knott methods, againusing functions imbedded in GeneNetwork. GeneNetwork also enable a search for epistatic interactions (pairscanning function) and composite interval mapping withcontrol for a single marker. Data quality controlWe used two simple but effective methods to confirmcorrect sample identification of all data entered intoGeneNetwork.",
"QTL analysisAll QTL mapping for phenotypes was performed using the WebQTL software module of the170GeneNetwork (www.genenetwork.org) [34]. Interval mapping to evaluate potential QTLs wascalculated from the likelihood ratio statistics (LRS) as the softwares default measurement ofthe association between differences in traits and differences in particular genotype markers. Another common measure score, the log of the odds (LOD) ratio, can be converted from theLRS (LRS/4.61). Suggestive and significant LRS values were determined by applying 1000175permutations.",
"Once the data is normalized appropriately (in our case, no normalization was required), the QTLcan be mapped. To do this, select the mapping tools drop down window (Figure 6). There arethree methods to choose from, GEMMA, Haley-Knott Regression, and R/qtl (Figure 6). Genomewide Efficient Mixed Model Analysis (GEMMA; github.com/genetics-statistics/GEMMA; (Zhouand Stephens, 2012) is a multivariate linear mixed model mapping tool that is used to mapphenotypes with SNPs with a correction for kinship or any other covariate of interest. Thisability to account for covariates is highly useful, but also this increases the time taken forcomputations.",
"Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared bymultiple crosses, genome-wide haplotype analysisrequires only phenotype information from many inbredstrains and can effectively narrow a QTL identified inonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mbusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and geneexpression comparisons are effective for focusing on afew strong candidate genes (Figure 7).",
"Unlike interval-specific haplotype analysis, which is most useful for narrowing a QTL shared bymultiple crosses, genome-wide haplotype analysisrequires only phenotype information from many inbredstrains and can effectively narrow a QTL identified inonly one experimental cross [36]. After narrowing the QTL to an interval that is !5 Mbusing these bioinformatics techniques or classical experimental methods, strain-specific sequence and geneexpression comparisons are effective for focusing on afew strong candidate genes (Figure 7).",
"We considered QTL intervals that achieved genome-widesignificance for one phenotype, and genome-wide suggestive forothers, as highest priority for candidate gene analysis. The January 2017 BXD genotype file was used4 . Updated linear mixed model mapping algorithms are nowavailable on GeneNetwork 25 (Sloan et al. , 2016), that account forkinship among strains. These new algorithms include GEMMA(Zhou and Stephens, 2012), pyLMM6 (Sul et al. , 2016), andR/qtl27 .",
"GeneNetwork and WebQTL are our groups first attempts to embrace thesenew opportunities (Wang et al. 2003) and to generatean appropriate research environment that combinesdata sets, statistical resources, and summaries offindingsa knowledgebase (www.genenetwork.org). Mapping traits will become far easier; cloning allelicvariants for molecular and cellular phenotypes willprogress from difficult to trivial as it already has formost cis-QTL with high LOD scores.",
"Next, we used GeneNetwork2, an online analysis tool and data repository containinglegacy SNP and transcriptome datasets to explore gene regulatory networks (Chesler et al. 2004; Mulligan et al. 2017). We conducted both eQTL and PheQTL-eQTL network analysis using several BXD RI gene expressiondatasets from multiple brain regions (datasets documented in Supplementary Information) and using theentirety of > 7,000 BXD Published Phenotypes deposited in GeneNetwork2 [BXDPublish; GN602].",
"Thereare four options for QTL mapping on the GeneNetwork website: intervalmapping, marker regression analysis, composite interval mapping, and pairscan analysis. In this case, interval mapping was used to compute linkagemaps for the entire genome. The log of odds (LOD) score was used toassert that a causal relation exists between a chromosomal location and aphenotypic variant, such as Gsto1 expression variation.",
"Genetic MappingIn this study we utilize GeneNetwork, a database containing phenotypes and genotypes,and also serves as an analysis engine for quantitative trait locus (QTL) mapping, geneticcorrelations, and phenome-wide association studies (PheWAS) (Sloan et al. , 2016; Mulligan etal. , 2017; Watson and Ashbrook, 2020). QTL analysis involves connecting phenotype data withgenotype data to examine genetic variation in traits controlled by multiple genes and theirinteraction with the environment (also called complex traits)(Lynch et al. , 1998; Myles andWayne, 2008; Goddard et al. , 2016).",
"Once the resulting record set of thequery is returned, it can be further restricted by selectingrelevant records based on attached annotations before forwarding it for further analysis. To map genetic loci associated with mRNA abundance ortrait phenotypes, any one of the three QTL mapping functions currently employed by GeneNetwork's WebQTLmodule can be used. These are 1. interval mapping, 2. single-marker regression, or 3. composite mapping [29,30].",
"genenetwork.org/) a set of 3795 markers. Linkage is reported withgenome-wide significance levels based on 2000 permutation tests. Two types of QTL mapping analysessimple mapping using the HaleyKnott regression equation, and composite interval mappingwereutilized in this study. Simple interval mapping was performed toillustrate the significance of any QTLs that regulate the TID. As asecondary analysis, composite interval mapping which controlled forthe influence of Tyrp1 was also performed with the goal of identifyingany secondary QTLs that may have been masked by the major QTL onChr 4."
],
[
"BioinformaticsAll of the genetic analyses were carried out in GeneNetwork, whichis an open source bioinformatics resource for systems genetics thatexists as both a repository for genetic, genomic and phenotypicdata together with a suite of statistical programs for data analysis that includes mapping and evaluating QTLs, examining phenotype/genotype correlations and building interaction networks. QTL mappingThe QTL mapping module of GeneNetwork was used to identifyQTLs for hippocampal morphometry and radial maze trait data. Thismodule enables interval mapping, composite interval mapping anda pairwise scan option to identify epistatic effects.",
"Below we detail several software tools thathave been used for eQTL analysis and provide a short description of the advantages andlimitations of each package, with a focus on the ease of use for the wider scientificcommunity (Table 1). Several of the packages were not specifically developed for eQTLanalysis, and thus our speed comparisons should be viewed in that context. However, suchpackages may already be used in individual laboratories, and therefore we include those that,in our opinion, may be scaled for eQTL analysis on data from genome-wide array platforms.",
"Other Useful QTL Mapping SoftwaresQTL Cartographer is a highly capable mapping programone that may beparticularly suitable for those with a background in UNIX and who are comfortable with advanced statistical analysis. The program is available for threeoperating systems at http://statgen.ncsu.edu/qtlcart/cartographer.html. Windows QTL Cartographer at http://statgen.ncsu.edu/qtlcart/ WQTLCart.htmis a command-line sibling and a relatively more user friendly version of QTLCartographer. This program includes a powerful graphic tool for presenting mapping results and can import and export data in a variety of formats and provide agraphical interface to QTL Cartographers features.",
"Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.",
"These relative phenotypevalues are then analyzed in the context of the mouse genotypeusing WebQTL tools available on www.genenetwork.com,which provides the QTL mapping for phenotypes of interest. The bioinformatics tools allow us to inspect the single nucleotide polymorphism density within the mapped loci and toexamine the genes within the loci in order to narrow down thenumber of candidate genes that should be further interrogated. The tools also allow us to identify interactive loci, throughwhich we can discover interactive pathways modulating themeasured phenotype.",
"ReviewTRENDS in Genetics Vol.21 No.12 December 2005Bioinformatics toolbox for narrowingrodent quantitative trait lociKeith DiPetrillo, Xiaosong Wang, Ioannis M. Stylianou and Beverly PaigenThe Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USAQuantitative trait locus (QTL) analysis is a powerfulmethod for localizing disease genes, but identifying thecausal gene remains difficult. Rodent models of diseasefacilitate QTL gene identification, and causal genesunderlying rodent QTL are often associated with thecorresponding human diseases.Recently developedbioinformatics methods, including comparativegenomics, combined cross analysis, interval-specificand genome-wide haplotype analysis, followed bysequence and expression analysis, each facilitated bypublic databases, provide new tools for narrowingrodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identification. IntroductionQuantitative trait locus (QTL) analysis is a method tolocalize chromosomal regions harboring genetic variantsthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1].Summary of bioinformatics tools for dissecting rodent QTLsBioinformatics toolComparative genomicsCombined cross analysisInterval-specific haplotypeanalysisGenome-wide haplotypeanalysisSequence comparisonExpression comparisonSummaryIdentifies regions of chromosomal synteny in QTLs that are concordant acrossspeciesRecodes genotype information from multiple crosses detecting a shared QTL intoone susceptibility and one resistance genotype to combine the crosses in a singleQTL analysisDetects regions of IBD within QTLs shared in multiple crossesAssociates conserved haplotype patterns across the genome with a phenotype ininbred strainsSearches strain-specific sequence databases for regulatory or coding polymorphisms within the QTL intervalSearches EST or microarray databases to identify genes expressed in an organ ofinterest or genes exhibiting differential expression between the strains of interestthe homologous regions in humans, which complicatesthis approach.",
"Recently developedbioinformatics methods, including comparativegenomics, combined cross analysis, interval-specificand genome-wide haplotype analysis, followed bysequence and expression analysis, each facilitated bypublic databases, provide new tools for narrowingrodent QTLs. Here we discuss each tool, illustrate itsapplication and generate a bioinformatics strategy fornarrowing QTLs. Combining these bioinformatics toolswith classical experimental methods should accelerateQTL gene identification. IntroductionQuantitative trait locus (QTL) analysis is a method tolocalize chromosomal regions harboring genetic variantsthat affect a continuously distributed, polygenic phenotype(including many common diseases) [1].ReviewTRENDS in Genetics Vol.21 No.12 December 2005Bioinformatics toolbox for narrowingrodent quantitative trait lociKeith DiPetrillo, Xiaosong Wang, Ioannis M. Stylianou and Beverly PaigenThe Jackson Laboratory, 600 Main St, Bar Harbor, ME 04609, USAQuantitative trait locus (QTL) analysis is a powerfulmethod for localizing disease genes, but identifying thecausal gene remains difficult. Rodent models of diseasefacilitate QTL gene identification, and causal genesunderlying rodent QTL are often associated with thecorresponding human diseases.Summary of bioinformatics tools for dissecting rodent QTLsBioinformatics toolComparative genomicsCombined cross analysisInterval-specific haplotypeanalysisGenome-wide haplotypeanalysisSequence comparisonExpression comparisonSummaryIdentifies regions of chromosomal synteny in QTLs that are concordant acrossspeciesRecodes genotype information from multiple crosses detecting a shared QTL intoone susceptibility and one resistance genotype to combine the crosses in a singleQTL analysisDetects regions of IBD within QTLs shared in multiple crossesAssociates conserved haplotype patterns across the genome with a phenotype ininbred strainsSearches strain-specific sequence databases for regulatory or coding polymorphisms within the QTL intervalSearches EST or microarray databases to identify genes expressed in an organ ofinterest or genes exhibiting differential expression between the strains of interestthe homologous regions in humans, which complicatesthis approach.",
"1 The234IntroductionModern high-throughput technologies generate large amounts of genomic, transcriptomic, proteomic and metabolomic data. However, existing open source web-based tools for QTL analysis, such as webQTL[358] and QTLNetwork [377], are not easily extendable to dierent settings and computationally scalable for whole genome analyses. xQTLworkbench makes it easy to analyse large and complex datasets usingstate-of-the-art QTL mapping tools and to apply these methods to millions of phenotypes using parallelized Big Data solutions [342].",
"Software developed towards facilitating mining ofgenetic expression and variant associations includeeQTL Explorer, eQTL Viewer, FastMap and Lirnet. Bioinformatics concepts relating to eQTL have beenreviewed in [116]. eQTL Explorer (http://web. bioinformatics.ic.ac.uk/eqtlexplorer/) [117] as anaddition to resources provided by previous softwareslike WebQTL [118] and QTL Express [119], enablesintegrated visualization using a Java graphicalinterfaces; extracts eQTL results from externalsources (multiple microarray experiments) andpresents them such that they can be compared amongeach other, and with the pQTL (protein expression)mapped to the genome. eQTL Viewer (http://statgen.",
"These relative phenotypevalues are then analyzed in the context of the mouse genotypeusing WebQTL tools available on www.genenetwork.com,which provides the QTL mapping for phenotypes of interest. The bioinformatics tools allow us to inspect the single nucleotide polymorphism density within the mapped loci and toexamine the genes within the loci in order to narrow down thenumber of candidate genes that should be further interrogated. The tools also allow us to identify interactive loci, throughwhich we can discover interactive pathways modulating themeasured phenotype.",
"Author ManuscriptPrevious studies have used bioinformatics analyses in conjunction with a specific set ofcriteria to narrow down the set of genes into those most likely to underlie the differentialresponse (Baker et al. , 2017, Cook et al. , 2015). In the present study, genes within thesignificant QTLs were identified using the online tools available at GeneNetwork.org. Thegene lists include expressed sequence tags and Riken clones.",
"Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.",
"Another database, WebQTL, provides multiple tools that, when used incombination, provide valuable insight into candidate gene lists (11). WebQTL isan online database with built in statistical tools that take advantage of the isogenicnature of recombinant inbred (RI) animals. It combines a large database ofcomplex traits collected using RI animals with built in software to perform QTLanalysis and produce correlations of traits (11). WebQTL has genotypicinformation on five different RI lines including those derived from the C57/B6and DBA inbred strains (BxDs) (11).",
"Tools for QTL analysis have been developed and released for researchers such asR/qtl, QTL cartographer, MapQTL, and WebQTL. Recently, Wang et al. (2012)developed a free software for QTL mapping called QTL IciMapping which constructsgenetic linkage maps and QTL analysis by simple interval mapping and inclusivecomposite interval mapping. QTL IciMapping is available for segregating and inbred9populations and nested association mapping populations. Unlike R/qtl, QTL IciMappingis not available for Unix/Linux. Running QTL IciMapping using a basic computer for thenumbers of SNPs identified from genotype-by-sequencing (GBS) is time-consuming.",
"Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries.",
"Built in to this resource are tools such as QTL Analyst to identify candidate genes and polymorphisms; literature correlation analysis by the latent semanticindexing tool, Semantic Gene Organizer (Homayouni et al. , 2005); and integratedanalysis with many large SNP sets, Gene Ontology overrepresentation and pathwaymatching (Zhang et al. , 2004), and annotation resources. By coupling flexible analytic tools with both molecular endophenotypes and higher-order phenotypic data,users can pursue a wealth of integrative systems genetics queries."
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