Dominance is common in mammals and is associated with trans-acting gene expression and alternative splicing.

BACKGROUND: Dominance and other non-additive genetic effects arise from the interaction between alleles, and historically these phenomena play a major role in quantitative genetics. However, most genome-wide association studies (GWAS) assume alleles act additively. RESULTS: We systematically investigate both dominance-here representing any non-additive within-locus interaction-and additivity across 574 physiological and gene expression traits in three mammalian stocks: F2 intercross pigs, rat heterogeneous stock, and mice heterogeneous stock. Dominance accounts for about one quarter of heritable variance across all physiological traits in all species. Hematological and immunological traits exhibit the highest dominance variance, possibly reflecting balancing selection in response to pathogens. Although most quantitative trait loci (QTLs) are detectable as additive QTLs, we identify 154, 64, and 62 novel dominance QTLs in pigs, rats, and mice respectively that are undetectable as additive QTLs. Similarly, even though most cis-acting expression QTLs are additive, gene expression exhibits a large fraction of dominance variance, and trans-acting eQTLs are enriched for dominance. Genes causal for dominance physiological QTLs are less likely to be physically linked to their QTLs but instead act via trans-acting dominance eQTLs. In addition, thousands of eQTLs are associated with alternatively spliced isoforms with complex additive and dominant architectures in heterogeneous stock rats, suggesting a possible mechanism for dominance. CONCLUSIONS: Although heritability is predominantly additive, many mammalian genetic effects are dominant and likely arise through distinct mechanisms. It is therefore advantageous to consider both additive and dominance effects in GWAS to improve power and uncover causality.

Accounting for diet and age.

The diet and age of mice can modulate how different genetic variants impact body weight, demonstrating the need to take context into account when performing genetic studies.

Indirect Genetic Effects: A Cross-disciplinary Perspective on Empirical Studies.

Indirect genetic effects (IGE) occur when an individual's phenotype is influenced by genetic variation in conspecifics. Opportunities for IGE are ubiquitous, and, when present, IGE have profound implications for behavioral, evolutionary, agricultural, and biomedical genetics. Despite their importance, the empirical study of IGE lags behind the development of theory. In large part, this lag can be attributed to the fact that measuring IGE, and deconvoluting them from the direct genetic effects of an individual's own genotype, is subject to many potential pitfalls. In this Perspective, we describe current challenges that empiricists across all disciplines will encounter in measuring and understanding IGE. Using ideas and examples spanning evolutionary, agricultural, and biomedical genetics, we also describe potential solutions to these challenges, focusing on opportunities provided by recent advances in genomic, monitoring, and phenotyping technologies. We hope that this cross-disciplinary assessment will advance the goal of understanding the pervasive effects of conspecific interactions in biology.

Dissecting indirect genetic effects from peers in laboratory mice.

BACKGROUND: The phenotype of an individual can be affected not only by the individual's own genotypes, known as direct genetic effects (DGE), but also by genotypes of interacting partners, indirect genetic effects (IGE). IGE have been detected using polygenic models in multiple species, including laboratory mice and humans. However, the underlying mechanisms remain largely unknown. Genome-wide association studies of IGE (igeGWAS) can point to IGE genes, but have not yet been applied to non-familial IGE arising from "peers" and affecting biomedical phenotypes. In addition, the extent to which igeGWAS will identify loci not identified by dgeGWAS remains an open question. Finally, findings from igeGWAS have not been confirmed by experimental manipulation. RESULTS: We leverage a dataset of 170 behavioral, physiological, and morphological phenotypes measured in 1812 genetically heterogeneous laboratory mice to study IGE arising between same-sex, adult, unrelated mice housed in the same cage. We develop and apply methods for igeGWAS in this context and identify 24 significant IGE loci for 17 phenotypes (FDR < 10%). We observe no overlap between IGE loci and DGE loci for the same phenotype, which is consistent with the moderate genetic correlations between DGE and IGE for the same phenotype estimated using polygenic models. Finally, we fine-map seven significant IGE loci to individual genes and find supportive evidence in an experiment with a knockout model that Epha4 gives rise to IGE on stress-coping strategy and wound healing. CONCLUSIONS: Our results demonstrate the potential for igeGWAS to identify IGE genes and shed light into the mechanisms of peer influence.

Sex-dependent associations between addiction-related behaviors and the microbiome in outbred rats.

BACKGROUND: Multiple factors contribute to the etiology of addiction, including genetics, sex, and a number of addiction-related behavioral traits. One behavioral trait where individuals assign incentive salience to food stimuli ("sign-trackers", ST) are more impulsive compared to those that do not ("goal-trackers", GT), as well as more sensitive to drugs and drug stimuli. Furthermore, this GT/ST phenotype predicts differences in other behavioral measures. Recent studies have implicated the gut microbiota as a key regulator of brain and behavior, and have shown that many microbiota-associated changes occur in a sex-dependent manner. However, few studies have examined how the microbiome might influence addiction-related behaviors. To this end, we sought to determine if gut microbiome composition was correlated with addiction-related behaviors determined by the GT/ST phenotype. METHODS: Outbred male (N=101) and female (N=101) heterogeneous stock rats underwent a series of behavioral tests measuring impulsivity, attention, reward-learning, incentive salience, and locomotor response. Cecal microbiome composition was estimated using 16S rRNA gene amplicon sequencing. Behavior and microbiome were characterized and correlated with behavioral phenotypes. Robust sex differences were observed in both behavior and microbiome; further analyses were conducted within sex using the pre-established goal/sign-tracking (GT/ST) phenotype and partial least squares differential analysis (PLS-DA) clustered behavioral phenotype. RESULTS: Overall microbiome composition was not associated to the GT/ST phenotype. However, microbial alpha diversity was significantly decreased in female STs. On the other hand, a measure of impulsivity had many significant correlations to microbiome in both males and females. Several measures of impulsivity were correlated with the genus Barnesiella in females. Female STs had notable correlations between microbiome and attentional deficient. In both males and females, many measures were correlated with the bacterial families Ruminocococcaceae and Lachnospiraceae. CONCLUSIONS: These data demonstrate correlations between several addiction-related behaviors and the microbiome specific to sex.

A High-Resolution Genetic Map for the Laboratory Rat.

An accurate and high-resolution genetic map is critical for mapping complex traits, yet the resolution of the current rat genetic map is far lower than human and mouse, and has not been updated since the original Jensen-Seaman map in 2004. For the first time, we have refined the rat genetic map to sub-centimorgan (cM) resolution (<0.02 cM) by using 95,769 genetic markers and 870 informative meioses from a cohort of 528 heterogeneous stock (HS) rats. Global recombination rates in the revised sex-averaged map (0.66 cM/Mb) did not differ compared to the historical map (0.65 cM/Mb); however, substantial refinement was made to the localization of highly recombinant regions within the revised map. Also for the first time, sex-specific rat genetic maps were generated, which revealed both genomewide and fine-scale variation in recombination rates between male and female rats. Reanalysis of multiple quantitative trait loci (QTL) using the historical and refined rat genetic maps demonstrated marked changes to QTL localization, shape, and effect size. As a resource to the rat research community, we have provided revised centimorgan positions for all physical positions within the rat genome and commonly used genetic markers for trait mapping, including 44,828 SSLP markers and the RATDIV genotyping array. Collectively, this study provides a substantial improvement to the rat genetic map and an unprecedented resource for analysis of complex traits and recombination in the rat.

Identifying genes for neurobehavioural traits in rodents: progress and pitfalls.

Identifying genes and pathways that contribute to differences in neurobehavioural traits is a key goal in psychiatric research. Despite considerable success in identifying quantitative trait loci (QTLs) associated with behaviour in laboratory rodents, pinpointing the causal variants and genes is more challenging. For a long time, the main obstacle was the size of QTLs, which could encompass tens if not hundreds of genes. However, recent studies have exploited mouse and rat resources that allow mapping of phenotypes to narrow intervals, encompassing only a few genes. Here, we review these studies, showcase the rodent resources they have used and highlight the insights into neurobehavioural traits provided to date. We discuss what we see as the biggest challenge in the field - translating QTLs into biological knowledge by experimentally validating and functionally characterizing candidate genes - and propose that the CRISPR/Cas genome-editing system holds the key to overcoming this obstacle. Finally, we challenge traditional views on inbred versus outbred resources in the light of recent resource and technology developments.

Genetic Variation in the Social Environment Contributes to Health and Disease.

Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual's genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of "missing heritability" in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.

A multiple-phenotype imputation method for genetic studies.

Genetic association studies have yielded a wealth of biological discoveries. However, these studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the underlying complexity of the data sets. Joint genotype-phenotype analyses of complex, high-dimensional data sets represent an important way to move beyond simple genome-wide association studies (GWAS) with great potential. The move to high-dimensional phenotypes will raise many new statistical problems. Here we address the central issue of missing phenotypes in studies with any level of relatedness between samples. We propose a multiple-phenotype mixed model and use a computationally efficient variational Bayesian algorithm to fit the model. On a variety of simulated and real data sets from a range of organisms and trait types, we show that our method outperforms existing state-of-the-art methods from the statistics and machine learning literature and can boost signals of association.

Fine mapping of bone structure and strength QTLs in heterogeneous stock rat.

We previously demonstrated that skeletal structure and strength phenotypes vary considerably in heterogeneous stock (HS) rats. These phenotypes were found to be strongly heritable, suggesting that the HS rat model represents a unique genetic resource for dissecting the complex genetic etiology underlying bone fragility. The purpose of this study was to identify and localize genes associated with bone structure and strength phenotypes using 1524 adult male and female HS rats between 17 to 20 weeks of age. Structure measures included femur length, neck width, head width; femur and lumbar spine (L3-5) areas obtained by DXA; and cross-sectional areas (CSA) at the midshaft, distal femur and femoral neck, and the 5th lumbar vertebra measured by CT. In addition, measures of strength of the whole femur and femoral neck were obtained. Approximately 70,000 polymorphic SNPs distributed throughout the rat genome were selected for genotyping, with a mean linkage disequilibrium coefficient between neighboring SNPs of 0.95. Haplotypes were estimated across the entire genome for each rat using a multipoint haplotype reconstruction method, which calculates the probability of descent at each locus from each of the 8 HS founder strains. The haplotypes were then tested for association with each structure and strength phenotype via a mixed model with covariate adjustment. We identified quantitative trait loci (QTLs) for structure phenotypes on chromosomes 3, 8, 10, 12, 17 and 20, and QTLs for strength phenotypes on chromosomes 5, 10 and 11 that met a conservative genome-wide empiric significance threshold (FDR=5%; P<3×10(-6)). Importantly, most QTLs were localized to very narrow genomic regions (as small as 0.3 Mb and up to 3 Mb), each harboring a small set of candidate genes, both novel and previously shown to have roles in skeletal development and homeostasis.

Protease inhibitor 15, a candidate gene for abdominal aortic internal elastic lamina ruptures in the rat.

The inbred Brown Norway (BN) rat develops spontaneous ruptures of the internal elastic lamina (RIEL) of the abdominal aorta (AA) and iliac arteries. Prior studies with crosses of the BN/Orl RJ (susceptible) and LOU/M (resistant) showed the presence of a significant QTL on chromosome 5 and the production of congenic rats proved the involvement of this locus. In this study, we further dissected the above-mentioned QTL by creating a new panel of LOU.BN(chr5) congenic and subcongenic lines and reduced the locus to 5.2 Mb. Then we studied 1,002 heterogeneous stock (HS) rats, whose phenotyping revealed a low prevalence and high variability for RIEL. High-resolution mapping in the HS panel detected the major locus on chromosome 5 (log P > 35) and refined it to 1.4 Mb. Subsequently, RNA-seq analysis on AA of BN, congenics, and LOU revealed expression differences for only protease inhibitor 15 (Pi15) gene and a putative long intergenic noncoding RNA (lincRNA) within the linkage region. The high abundance of lincRNA with respect to reduced Pi15 expression, in conjunction with exertion of longitudinal strain, may be related to RIEL, indicating the potential importance of proteases in biological processes related to defective aortic internal elastic lamina structure. Similar mechanisms may be involved in aneurysm initiation in the human AA.

High-resolution genome screen for bone mineral density in heterogeneous stock rat.

We previously demonstrated that skeletal mass, structure, and biomechanical properties vary considerably in heterogeneous stock (HS) rat strains. In addition, we observed strong heritability for several of these skeletal phenotypes in the HS rat model, suggesting that it represents a unique genetic resource for dissecting the complex genetics underlying bone fragility. The purpose of this study was to identify and localize genes associated with bone mineral density in HS rats. We measured bone phenotypes from 1524 adult male and female HS rats between 17 and 20 weeks of age. Phenotypes included dual-energy X-ray absorptiometry (DXA) measurements for bone mineral content and areal bone mineral density (aBMD) for femur and lumbar spine (L3-L5), and volumetric BMD measurements by CT for the midshaft and distal femur, femur neck, and fifth lumbar vertebra (L5). A total of 70,000 polymorphic single-nucleotide polymorphisms (SNPs) distributed throughout the genome were selected from genotypes obtained from the Affymetrix rat custom SNPs array for the HS rat population. These SNPs spanned the HS rat genome with a mean linkage disequilibrium coefficient between neighboring SNPs of 0.95. Haplotypes were estimated across the entire genome for each rat using a multipoint haplotype reconstruction method, which calculates the probability of descent for each genotyped locus from each of the eight founder HS strains. The haplotypes were tested for association with each bone density phenotype via a mixed model with covariate adjustment. We identified quantitative trait loci (QTLs) for BMD phenotypes on chromosomes 2, 9, 10, and 13 meeting a conservative genomewide empiric significance threshold (false discovery rate [FDR] = 5%; p < 3 × 10(-6)). Importantly, most QTLs were localized to very small genomic regions (1-3 megabases [Mb]), allowing us to identify a narrow set of potential candidate genes including both novel genes and genes previously shown to have roles in skeletal development and homeostasis.

Natural polymorphisms in Tap2 influence negative selection and CD4∶CD8 lineage commitment in the rat.

Genetic variation in the major histocompatibility complex (MHC) affects CD4∶CD8 lineage commitment and MHC expression. However, the contribution of specific genes in this gene-dense region has not yet been resolved. Nor has it been established whether the same genes regulate MHC expression and T cell selection. Here, we assessed the impact of natural genetic variation on MHC expression and CD4∶CD8 lineage commitment using two genetic models in the rat. First, we mapped Quantitative Trait Loci (QTLs) associated with variation in MHC class I and II protein expression and the CD4∶CD8 T cell ratio in outbred Heterogeneous Stock rats. We identified 10 QTLs across the genome and found that QTLs for the individual traits colocalized within a region spanning the MHC. To identify the genes underlying these overlapping QTLs, we generated a large panel of MHC-recombinant congenic strains, and refined the QTLs to two adjacent intervals of ∼0.25 Mb in the MHC-I and II regions, respectively. An interaction between these intervals affected MHC class I expression as well as negative selection and lineage commitment of CD8 single-positive (SP) thymocytes. We mapped this effect to the transporter associated with antigen processing 2 (Tap2) in the MHC-II region and the classical MHC class I gene(s) (RT1-A) in the MHC-I region. This interaction was revealed by a recombination between RT1-A and Tap2, which occurred in 0.2% of the rats. Variants of Tap2 have previously been shown to influence the antigenicity of MHC class I molecules by altering the MHC class I ligandome. Our results show that a restricted peptide repertoire on MHC class I molecules leads to reduced negative selection of CD8SP cells. To our knowledge, this is the first study showing how a recombination between natural alleles of genes in the MHC influences lineage commitment of T cells.

The architecture of parent-of-origin effects in mice.

The number of imprinted genes in the mammalian genome is predicted to be small, yet we show here, in a survey of 97 traits measured in outbred mice, that most phenotypes display parent-of-origin effects that are partially confounded with family structure. To address this contradiction, using reciprocal F1 crosses, we investigated the effects of knocking out two nonimprinted candidate genes, Man1a2 and H2-ab1, that reside at nonimprinted loci but that show parent-of-origin effects. We show that expression of multiple genes becomes dysregulated in a sex-, tissue-, and parent-of-origin-dependent manner. We provide evidence that nonimprinted genes can generate parent-of-origin effects by interaction with imprinted loci and deduce that the importance of the number of imprinted genes is secondary to their interactions. We propose that this gene network effect may account for some of the missing heritability seen when comparing sibling-based to population-based studies of the phenotypic effects of genetic variants.

Genomes and phenomes of a population of outbred rats and its progenitors.

Finding genetic variants that contribute to phenotypic variation is one of the main challenges of modern genetics. We used an outbred population of rats (Heterogeneous Stock, HS) in a combined sequence-based and genetic mapping analysis to identify sequence variants and genes contributing to complex traits of biomedical relevance. Here we describe the sequences of the eight inbred progenitors of the HS and the variants that segregate between them. We report the genotyping of 1,407 HS rats, and the collection from 2,006 rats of 195 phenotypic measures that are relevant to models of anxiety, type 2 diabetes, hypertension and osteoporosis. We make available haplotype dosages for the 1,407 genotyped rats, since genetic mapping in the HS is best carried out by reconstructing each HS chromosome as a mosaic of the progenitor genomes. Finally, we have deposited an R object that makes it easy to incorporate our sequence data into any genetic study of HS rats. Our genetic data are available for both Rnor3.4 and Rnor5.0 rat assemblies.

Erratum: Genomes and phenomes of a population of outbred rats and its progenitors.

[This corrects the article DOI: 10.1038/sdata.2014.11.].

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.