Variation In Complex Traits Research Articles

An ultra-high density bin-map for rapid QTL mapping for tassel and ear architecture in a large F₂ maize population.

BackgroundUnderstanding genetic control of tassel and ear architecture in maize (Zea mays L. ssp. mays) is important due to their relationship with grain yield. High resolution QTL mapping is critical for understanding the underlying molecular basis of phenotypic variation. Advanced populations, such as recombinant inbred lines, have been broadly adopted for QTL mapping; however, construction of large advanced generation crop populations is time-consuming and costly. The rapidly declining cost of genotyping due to recent advances in next-generation sequencing technologies has generated new possibilities for QTL mapping using large early generation populations.ResultsA set of 708 F2 progeny derived from inbreds Chang7-2 and 787 were generated and genotyped by whole genome low-coverage genotyping-by-sequencing method (average 0.04×). A genetic map containing 6,533 bin-markers was constructed based on the parental SNPs and a sliding-window method, spanning a total genetic distance of 1,396 cM. The high quality and accuracy of this map was validated by the identification of two well-studied genes, r1, a qualitative trait locus for color of silk (chromosome 10) and ba1 for tassel branch number (chromosome 3). Three traits of tassel and ear architecture were evaluated in this population, a total of 10 QTL were detected using a permutation-based-significance threshold, seven of which overlapped with reported QTL. Three genes (GRMZM2G316366, GRMZM2G492156 and GRMZM5G805008) encoding MADS-box domain proteins and a BTB/POZ domain protein were located in the small intervals of qTBN5 and qTBN7 (~800 Kb and 1.6 Mb in length, respectively) and may be involved in patterning of tassel architecture. The small physical intervals of most QTL indicate high-resolution mapping is obtainable with this method.ConclusionsWe constructed an ultra-high-dentisy linkage map for the large early generation population in maize. Our study provides an efficient approach for fast detection of quantitative loci responsible for complex trait variation with high accuracy, thus helping to dissect the underlying molecular basis of phenotypic variation and accelerate improvement of crop breeding in a cost-effective fashion.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-433) contains supplementary material, which is available to authorized users.

Novel risk loci for rheumatoid arthritis in Han Chinese and congruence with risk variants in Europeans.

To investigate differences in genetic risk factors for rheumatoid arthritis (RA) in Han Chinese as compared with Europeans. A genome-wide association study was conducted in China with 952 patients and 943 controls, and 32 variants were followed up in 2,132 patients and 2,553 controls. A transpopulation meta-analysis with results from a large European RA study was also performed to compare the genetic architecture across the 2 ethnic remote populations. Three non-major histocompatibility complex (non-MHC) loci were identified at the genome-wide significance level, the effect sizes of which were larger in anti-citrullinated protein antibody (ACPA)-positive patients than in ACPA-negative patients. These included 2 novel variants, rs12617656, located in an intron of DPP4 (odds ratio [OR] 1.56, P = 1.6 × 10(-21) ), and rs12379034, located in the coding region of CDK5RAP2 (OR 1.49, P = 1.1 × 10(-16) ), as well as a variant at the known CCR6 locus, rs1854853 (OR 0.71, P = 6.5 × 10(-15) ). The analysis of ACPA-positive patients versus ACPA-negative patients revealed that rs12617656 at the DPP4 locus showed a strong interaction effect with ACPAs (P = 5.3 × 10(-18) ), and such an interaction was also observed for rs7748270 at the MHC locus (P = 5.9 × 10(-8) ). The transpopulation meta-analysis showed genome-wide overlap and enrichment in association signals across the 2 populations, as confirmed by prediction analysis. This study has expanded the list of alleles that confer risk of RA, provided new insight into the pathogenesis of RA, and added empirical evidence to the emerging polygenic nature of complex trait variation driven by common genetic variants.

Statistical power to detect genetic (co)variance of complex traits using SNP data in unrelated samples.

We have recently developed analysis methods (GREML) to estimate the genetic variance of a complex trait/disease and the genetic correlation between two complex traits/diseases using genome-wide single nucleotide polymorphism (SNP) data in unrelated individuals. Here we use analytical derivations and simulations to quantify the sampling variance of the estimate of the proportion of phenotypic variance captured by all SNPs for quantitative traits and case-control studies. We also derive the approximate sampling variance of the estimate of a genetic correlation in a bivariate analysis, when two complex traits are either measured on the same or different individuals. We show that the sampling variance is inversely proportional to the number of pairwise contrasts in the analysis and to the variance in SNP-derived genetic relationships. For bivariate analysis, the sampling variance of the genetic correlation additionally depends on the harmonic mean of the proportion of variance explained by the SNPs for the two traits and the genetic correlation between the traits, and depends on the phenotypic correlation when the traits are measured on the same individuals. We provide an online tool for calculating the power of detecting genetic (co)variation using genome-wide SNP data. The new theory and online tool will be helpful to plan experimental designs to estimate the missing heritability that has not yet been fully revealed through genome-wide association studies, and to estimate the genetic overlap between complex traits (diseases) in particular when the traits (diseases) are not measured on the same samples.

Modelling effects of candidate genes on complex traits as variables over time

In this study, changes in gene effects for milk production traits were analysed over time. Such changes can be expected by investigating daily milk production yields, which increase during the early phase of lactation and then decrease. Moreover, additive polygenic effects on milk production traits estimated in other studies differed throughout the 305 days of lactation, clearly indicating changes in the genetic determination of milk production throughout this period. Our study focused on particular candidate genes known to affect milk production traits and on the estimation of potential changes in the magnitude of their effects over time. With two independent data sets from Holstein-Friesian and Jersey breeds, we show that the effects of the DGAT1:p.Lys232Ala polymorphism on fat and protein content in milk change during lactation. The other candidate genes considered in this study (leptin receptor, leptin and butyrophilin, subfamily 1, member A1) exhibited effects that vary across time, but these could be observed in only one of the breeds. Longitudinal modelling of SNP effects enables more precise description of the genetic background underlying the variation of complex traits. A gene that changes the magnitude or even the sign of its effect cannot be detected by a time-averaged model. This was particularly evident when analysing the effect of butyrophilin, missed by many previous studies, which considered butyrophilin's effect as constant over time.

Improving the accuracy of whole genome prediction for complex traits using the results of genome wide association studies.

Utilizing the whole genomic variation of complex traits to predict the yet-to-be observed phenotypes or unobserved genetic values via whole genome prediction (WGP) and to infer the underlying genetic architecture via genome wide association study (GWAS) is an interesting and fast developing area in the context of human disease studies as well as in animal and plant breeding. Though thousands of significant loci for several species were detected via GWAS in the past decade, they were not used directly to improve WGP due to lack of proper models. Here, we propose a generalized way of building trait-specific genomic relationship matrices which can exploit GWAS results in WGP via a best linear unbiased prediction (BLUP) model for which we suggest the name BLUP|GA. Results from two illustrative examples show that using already existing GWAS results from public databases in BLUP|GA improved the accuracy of WGP for two out of the three model traits in a dairy cattle data set, and for nine out of the 11 traits in a rice diversity data set, compared to the reference methods GBLUP and BayesB. While BLUP|GA outperforms BayesB, its required computing time is comparable to GBLUP. Further simulation results suggest that accounting for publicly available GWAS results is potentially more useful for WGP utilizing smaller data sets and/or traits of low heritability, depending on the genetic architecture of the trait under consideration. To our knowledge, this is the first study incorporating public GWAS results formally into the standard GBLUP model and we think that the BLUP|GA approach deserves further investigations in animal breeding, plant breeding as well as human genetics.

Explaining additional genetic variation in complex traits

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of complex traits, discovering >6000 variants associated with >500 quantitative traits and common complex diseases in humans. The associations identified so far represent only a fraction of those that influence phenotype, because there are likely to be many variants across the entire frequency spectrum, each of which influences multiple traits, with only a small average contribution to the phenotypic variance. This presents a considerable challenge to further dissection of the remaining unexplained genetic variance within populations, which limits our ability to predict disease risk, identify new drug targets, improve and maintain food sources, and understand natural diversity. This challenge will be met within the current framework through larger sample size, better phenotyping, including recording of nongenetic risk factors, focused study designs, and an integration of multiple sources of phenotypic and genetic information. The current evidence supports the application of quantitative genetic approaches, and we argue that one should retain simpler theories until simplicity can be traded for greater explanatory power.

Regional heritability advanced complex trait analysis for GPU and traditional parallel architectures

Quantification of the contribution of genetic variation to phenotypic variation for complex traits becomes increasingly computationally demanding with increasing numbers of single-nucleotide polymorphisms and individuals. To meet the challenges in making feasible large-scale studies, we present the REgional heritability advanced complex trait analysis software. Adapted from advanced complex trait analysis (and, in turn, genome-wide complex trait analysis), it is tailored to exploit the parallelism present in modern traditional and graphics processing unit (GPU)-accelerated machines, from workstations to supercomputers. We adapt the genetic relationship matrix estimation algorithm to remove limitations on memory, allowing the analysis of large datasets. We build on this to develop a version of the code able to efficiently exploit GPU-accelerated systems for both the genetic relationship matrix and REstricted maximum likelihood (REML) parts of the analysis, offering substantial speedup over the traditional central processing unit version. We develop the ability to analyze multiple small regions of the genome across multiple compute nodes in parallel, following the 'regional heritability' approach. We demonstrate the new software using 1024 GPUs in parallel on one of the world's fastest supercomputers. The code is freely available at http://www.epcc.ed.ac.uk/software-products CONTACT: a.gray@ed.ac.uk.

Weighted Interaction SNP Hub (WISH) network method for building genetic networks for complex diseases and traits using whole genome genotype data.

BackgroundHigh-throughput genotype (HTG) data has been used primarily in genome-wide association (GWA) studies; however, GWA results explain only a limited part of the complete genetic variation of traits. In systems genetics, network approaches have been shown to be able to identify pathways and their underlying causal genes to unravel the biological and genetic background of complex diseases and traits, e.g., the Weighted Gene Co-expression Network Analysis (WGCNA) method based on microarray gene expression data. The main objective of this study was to develop a scale-free weighted genetic interaction network method using whole genome HTG data in order to detect biologically relevant pathways and potential genetic biomarkers for complex diseases and traits.ResultsWe developed the Weighted Interaction SNP Hub (WISH) network method that uses HTG data to detect genome-wide interactions between single nucleotide polymorphism (SNPs) and its relationship with complex traits. Data dimensionality reduction was achieved by selecting SNPs based on its: 1) degree of genome-wide significance and 2) degree of genetic variation in a population. Network construction was based on pairwise Pearson's correlation between SNP genotypes or the epistatic interaction effect between SNP pairs. To identify modules the Topological Overlap Measure (TOM) was calculated, reflecting the degree of overlap in shared neighbours between SNP pairs. Modules, clusters of highly interconnected SNPs, were defined using a tree-cutting algorithm on the SNP dendrogram created from the dissimilarity TOM (1-TOM). Modules were selected for functional annotation based on their association with the trait of interest, defined by the Genome-wide Module Association Test (GMAT). We successfully tested the established WISH network method using simulated and real SNP interaction data and GWA study results for carcass weight in a pig resource population; this resulted in detecting modules and key functional and biological pathways related to carcass weight.ConclusionsWe developed the WISH network method which is a novel 'systems genetics' approach to study genetic networks underlying complex trait variation. The WISH network method reduces data dimensionality and statistical complexity in associating genotypes with phenotypes in GWA studies and enables researchers to identify biologically relevant pathways and potential genetic biomarkers for any complex trait of interest.

Development of a Nasonia vitripennis outbred laboratory population for genetic analysis

The parasitoid wasp genus Nasonia has rapidly become a genetic model system for developmental and evolutionary biology. The release of its genome sequence led to the development of high-resolution genomic tools, for both interspecific and intraspecific research, which has resulted in great advances in understanding Nasonia biology. To further advance the utility of Nasonia vitripennis as a genetic model system and to be able to fully exploit the advantages of its fully sequenced and annotated genome, we developed a genetically variable and well-characterized experimental population. In this study, we describe the establishment of the genetically diverse HVRx laboratory population from strains collected from the field in the Netherlands. We established a maintenance method that retains genetic variation over generations of culturing in the laboratory. As a characterization of its genetic composition, we provide data on the standing genetic variation and estimate the effective population size (Ne) by microsatellite analysis. A genome-wide description of polymorphism is provided through pooled resequencing, which yielded 417 331 high-quality SNPs spanning all five Nasonia chromosomes. The HVRx population and its characterization are freely available as a community resource for investigators seeking to elucidate the genetic basis of complex trait variation using the Nasonia model system.

A unified GMDR method for detecting gene-gene interactions in family and unrelated samples with application to nicotine dependence.

Gene-gene and gene-environment interactions govern a substantial portion of the variation in complex traits and diseases. In convention, a set of either unrelated or family samples are used in detection of such interactions; even when both kinds of data are available, the unrelated and the family samples are analyzed separately, potentially leading to loss in statistical power. In this report, to detect gene-gene interactions we propose a generalized multifactor dimensionality reduction method that unifies analyses of nuclear families and unrelated subjects within the same statistical framework. We used principal components as genetic background controls against population stratification, and when sibling data are included, within-family control were used to correct for potential spurious association at the tested loci. Through comprehensive simulations, we demonstrate that the proposed method can remarkably increase power by pooling unrelated and offspring's samples together as compared with individual analysis strategies and the Fisher's combining p value method while it retains a controlled type I error rate in the presence of population structure. In application to a real dataset, we detected one significant tetragenic interaction among CHRNA4, CHRNB2, BDNF, and NTRK2 associated with nicotine dependence in the Study of Addiction: Genetics and Environment sample, suggesting the biological role of these genes in nicotine dependence development.

Estimation and partition of heritability in human populations using whole-genome analysis methods.

Understanding genetic variation of complex traits in human populations has moved from the quantification of the resemblance between close relatives to the dissection of genetic variation into the contributions of individual genomic loci. However, major questions remain unanswered: How much phenotypic variation is genetic; how much of the genetic variation is additive and can be explained by fitting all genetic variants simultaneously in one model, and what is the joint distribution of effect size and allele frequency at causal variants? We review and compare three whole-genome analysis methods that use mixed linear models (MLMs) to estimate genetic variation. In all methods, genetic variation is estimated from the relationship between close or distant relatives on the basis of pedigree information and/or single nucleotide polymorphisms (SNPs). We discuss theory, estimation procedures, bias, and precision of each method and review recent advances in the dissection of genetic variation of complex traits in human populations. By using genome-wide data, it is now established that SNPs in total account for far more of the genetic variation than the statistically highly significant SNPs that have been detected in genome-wide association studies. All SNPs together, however, do not account for all of the genetic variance estimated by pedigree-based methods. We explain possible reasons for this remaining "missing heritability."

Genomic imprinting and parent-of-origin effects on complex traits

Parent-of-origin effects occur when the phenotypic effect of an allele depends on whether it is inherited from the mother or the father. Several phenomena can cause parent-of-origin effects, but the best characterized is parent-of-origin-dependent gene expression associated with genomic imprinting. The development of new mapping approaches applied to the growing abundance of genomic data has demonstrated that imprinted genes can be important contributors to complex trait variation. Therefore, to understand the genetic architecture and evolution of complex traits, including complex diseases and traits of agricultural importance, it is crucial to account for these parent-of-origin effects. Here, we discuss patterns of phenotypic variation associated with imprinting, evidence supporting its role in complex trait variation and approaches for identifying its molecular signatures.

Properties of Local Interactions and Their Potential Value in Complementing Genome-Wide Association Studies

Local interactions between neighbouring SNPs are hypothesized to be able to capture variants missing from genome-wide association studies (GWAS) via haplotype effects but have not been thoroughly explored. We have used a new high-throughput analysis tool to probe this underexplored area through full pair-wise genome scans and conventional GWAS in diastolic and systolic blood pressure and six metabolic traits in the Northern Finland Birth Cohort 1966 (NFBC1966) and the Atherosclerosis Risk in Communities study cohort (ARIC). Genome-wide significant interactions were detected in ARIC for systolic blood pressure between PLEKHA7 (a known GWAS locus for blood pressure) and GPR180 (which plays a role in vascular remodelling), and also for triglycerides as local interactions within the 11q23.3 region (replicated significantly in NFBC1966), which notably harbours several loci (BUD13, ZNF259 and APOA5) contributing to triglyceride levels. Tests of the local interactions within the 11q23.3 region conditional on the top GWAS signal suggested the presence of two independent functional variants, each with supportive evidence for their roles in gene regulation. Local interactions captured 9 additional GWAS loci identified in this study (3 significantly replicated) and 73 from previous GWAS (24 in the eight traits and 49 in related traits). We conclude that the detection of local interactions requires adequate SNP coverage of the genome and that such interactions are only likely to be detectable between SNPs in low linkage disequilibrium. Analysing local interactions is a potentially valuable complement to GWAS and can provide new insights into the biology underlying variation in complex traits.

Abundance and Distribution of Transposable Elements in Two Drosophila QTL Mapping Resources

Here we present computational machinery to efficiently and accurately identify transposable element (TE) insertions in 146 next-generation sequenced inbred strains of Drosophila melanogaster. The panel of lines we use in our study is composed of strains from a pair of genetic mapping resources: the Drosophila Genetic Reference Panel (DGRP) and the Drosophila Synthetic Population Resource (DSPR). We identified 23,087 TE insertions in these lines, of which 83.3% are found in only one line. There are marked differences in the distribution of elements over the genome, with TEs found at higher densities on the X chromosome, and in regions of low recombination. We also identified many more TEs per base pair of intronic sequence and fewer TEs per base pair of exonic sequence than expected if TEs are located at random locations in the euchromatic genome. There was substantial variation in TE load across genes. For example, the paralogs derailed and derailed-2 show a significant difference in the number of TE insertions, potentially reflecting differences in the selection acting on these loci. When considering TE families, we find a very weak effect of gene family size on TE insertions per gene, indicating that as gene family size increases the number of TE insertions in a given gene within that family also increases. TEs are known to be associated with certain phenotypes, and our data will allow investigators using the DGRP and DSPR to assess the functional role of TE insertions in complex trait variation more generally. Notably, because most TEs are very rare and often private to a single line, causative TEs resulting in phenotypic differences among individuals may typically fail to replicate across mapping panels since individual elements are unlikely to segregate in both panels. Our data suggest that “burden tests” that test for the effect of TEs as a class may be more fruitful.

Genes of the RNASE5 pathway contain SNP associated with milk production traits in dairy cattle

BackgroundIdentification of the processes and mutations responsible for the large genetic variation in milk production among dairy cattle has proved challenging. One approach is to identify a biological process potentially involved in milk production and to determine the genetic influence of all the genes included in the process or pathway. Angiogenin encoded by angiogenin, ribonuclease, RNase A family 5 (RNASE5) is relatively abundant in milk, and has been shown to regulate protein synthesis and act as a growth factor in epithelial cells in vitro. However, little is known about the role of angiogenin in the mammary gland or if the polymorphisms present in the bovine RNASE5 gene are associated with lactation and milk production traits in dairy cattle. Given the high economic value of increased protein in milk, we have tested the hypothesis that RNASE5 or genes in the RNASE5 pathway are associated with milk production traits. First, we constructed a “RNASE5 pathway” based on upstream and downstream interacting genes reported in the literature. We then tested SNP in close proximity to the genes of this pathway for association with milk production traits in a large dairy cattle dataset.ResultsThe constructed RNASE5 pathway consisted of 11 genes. Association analysis between SNP in 1 Mb regions surrounding these genes and milk production traits revealed that more SNP than expected by chance were associated with milk protein percent (P < 0.05 significance). There was no significant association with other traits such as milk fat content or fertility.ConclusionsThese results support a role for the RNASE5 pathway in milk production, specifically milk protein percent, and indicate that polymorphisms in or near these genes explain a proportion of the variation for this trait. This method provides a novel way of understanding the underlying biology of lactation with implications for milk production and can be applied to any pathway or gene set to test whether they are responsible for the variation of complex traits.

Partitioning of genetic variation across the genome using multimarker methods in a wild bird population

The underlying basis of genetic variation in quantitative traits, in terms of the number of causal variants and the size of their effects, is largely unknown in natural populations. The expectation is that complex quantitative trait variation is attributable to many, possibly interacting, causal variants, whose effects may depend upon the sex, age and the environment in which they are expressed. A recently developed methodology in animal breeding derives a value of relatedness among individuals from high-density genomic marker data, to estimate additive genetic variance within livestock populations. Here, we adapt and test the effectiveness of these methods to partition genetic variation for complex traits across genomic regions within ecological study populations where individuals have varying degrees of relatedness. We then apply this approach for the first time to a natural population and demonstrate that genetic variation in wing length in the great tit (Parus major) reflects contributions from multiple genomic regions. We show that a polygenic additive mode of gene action best describes the patterns observed, and we find no evidence of dosage compensation for the sex chromosome. Our results suggest that most of the genomic regions that influence wing length have the same effects in both sexes. We found a limited amount of genetic variance in males that is attributed to regions that have no effects in females, which could facilitate the sexual dimorphism observed for this trait. Although this exploratory work focuses on one complex trait, the methodology is generally applicable to any trait for any laboratory or wild population, paving the way for investigating sex-, age- and environment-specific genetic effects and thus the underlying genetic architecture of phenotype in biological study systems.

Selection on QTL and complex traits in complex environments.

Understanding genetic variation for complex traits in heterogeneous environments is a fundamental problem in biology. In this issue of Molecular Ecology, Fournier-Level et al. (2013) analyse quantitative trait loci (QTL)influencing ecologically important phenotypes in mapping populations of Arabidopsis thaliana grown in four habitats across its native European range. They used causal modelling to quantify the selective consequences of life history and morphological traits and QTL on components of fitness. They found phenology QTL colocalizing with known flowering time genes as well as novel loci. Most QTL influenced fitness via life history and size traits, rather than QTL having direct effects on fitness.Comparison of phenotypes among environments found no evidence for genetic trade-offs for phenology or growth traits, but genetic trade-offs for fitness resulted because flowering time had opposite fitness effects in different environments. These changes in QTL effects and selective consequences may maintain genetic variation among populations.

Causes and Consequences of Chromatin Variation between Inbred Mice

Variation at regulatory elements, identified through hypersensitivity to digestion by DNase I, is believed to contribute to variation in complex traits, but the extent and consequences of this variation are poorly characterized. Analysis of terminally differentiated erythroblasts in eight inbred strains of mice identified reproducible variation at approximately 6% of DNase I hypersensitive sites (DHS). Only 30% of such variable DHS contain a sequence variant predictive of site variation. Nevertheless, sequence variants within variable DHS are more likely to be associated with complex traits than those in non-variant DHS, and variants associated with complex traits preferentially occur in variable DHS. Changes at a small proportion (less than 10%) of variable DHS are associated with changes in nearby transcriptional activity. Our results show that whilst DNA sequence variation is not the major determinant of variation in open chromatin, where such variants exist they are likely to be causal for complex traits.

PGMRA: a web server for (phenotype x genotype) many-to-many relation analysis in GWAS

It has been proposed that single nucleotide polymorphisms (SNPs) discovered by genome-wide association studies (GWAS) account for only a small fraction of the genetic variation of complex traits in human population. The remaining unexplained variance or missing heritability is thought to be due to marginal effects of many loci with small effects and has eluded attempts to identify its sources. Combination of different studies appears to resolve in part this problem. However, neither individual GWAS nor meta-analytic combinations thereof are helpful for disclosing which genetic variants contribute to explain a particular phenotype. Here, we propose that most of the missing heritability is latent in the GWAS data, which conceals intermediate phenotypes. To uncover such latent information, we propose the PGMRA server that introduces phenomics—the full set of phenotype features of an individual—to identify SNP-set structures in a broader sense, i.e. causally cohesive genotype–phenotype relations. These relations are agnostically identified (without considering disease status of the subjects) and organized in an interpretable fashion. Then, by incorporating a posteriori the subject status within each relation, we can establish the risk surface of a disease in an unbiased mode. This approach complements—instead of replaces—current analysis methods. The server is publically available at http://phop.ugr.es/fenogeno.

Combined sequence-based and genetic mapping analysis of complex traits in outbred rats

Genetic mapping on fully sequenced individuals is transforming understanding of the relationship between molecular variation and variation in complex traits. Here we report a combined sequence and genetic mapping analysis in outbred rats that maps 355 quantitative trait loci for 122 phenotypes. We identify 35 causal genes involved in 31 phenotypes, implicating new genes in models of anxiety, heart disease and multiple sclerosis. The relationship between sequence and genetic variation is unexpectedly complex: at approximately 40% of quantitative trait loci, a single sequence variant cannot account for the phenotypic effect. Using comparable sequence and mapping data from mice, we show that the extent and spatial pattern of variation in inbred rats differ substantially from those of inbred mice and that the genetic variants in orthologous genes rarely contribute to the same phenotype in both species.