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.

Pan-European study of genotypes and phenotypes in the Arabidopsis relative Cardamine hirsuta reveals how adaptation, demography, and development shape diversity patterns.

We study natural DNA polymorphisms and associated phenotypes in the Arabidopsis relative Cardamine hirsuta. We observed strong genetic differentiation among several ancestry groups and broader distribution of Iberian relict strains in European C. hirsuta compared to Arabidopsis. We found synchronization between vegetative and reproductive development and a pervasive role for heterochronic pathways in shaping C. hirsuta natural variation. A single, fast-cycling ChFRIGIDA allele evolved adaptively allowing range expansion from glacial refugia, unlike Arabidopsis where multiple FRIGIDA haplotypes were involved. The Azores islands, where Arabidopsis is scarce, are a hotspot for C. hirsuta diversity. We identified a quantitative trait locus (QTL) in the heterochronic SPL9 transcription factor as a determinant of an Azorean morphotype. This QTL shows evidence for positive selection, and its distribution mirrors a climate gradient that broadly shaped the Azorean flora. Overall, we establish a framework to explore how the interplay of adaptation, demography, and development shaped diversity patterns of 2 related plant species.

Mapping novel QTL and fine mapping of previously identified QTL associated with glucose tolerance using the collaborative cross mice.

A chronic metabolic illness, type 2 diabetes (T2D) is a polygenic and multifactorial complicated disease. With an estimated 463 million persons aged 20 to 79 having diabetes, the number is expected to rise to 700 million by 2045, creating a significant worldwide health burden. Polygenic variants of diabetes are influenced by environmental variables. T2D is regarded as a silent illness that can advance for years before being diagnosed. Finding genetic markers for T2D and metabolic syndrome in groups with similar environmental exposure is therefore essential to understanding the mechanism of such complex characteristic illnesses. So herein, we demonstrated the exclusive use of the collaborative cross (CC) mouse reference population to identify novel quantitative trait loci (QTL) and, subsequently, suggested genes associated with host glucose tolerance in response to a high-fat diet. In this study, we used 539 mice from 60 different CC lines. The diabetogenic effect in response to high-fat dietary challenge was measured by the three-hour intraperitoneal glucose tolerance test (IPGTT) test after 12 weeks of dietary challenge. Data analysis was performed using a statistical software package IBM SPSS Statistic 23. Afterward, blood glucose concentration at the specific and between different time points during the IPGTT assay and the total area under the curve (AUC0-180) of the glucose clearance was computed and utilized as a marker for the presence and severity of diabetes. The observed AUC0-180 averages for males and females were 51,267.5 and 36,537.5 mg/dL, respectively, representing a 1.4-fold difference in favor of females with lower AUC0-180 indicating adequate glucose clearance. The AUC0-180 mean differences between the sexes within each specific CC line varied widely within the CC population. A total of 46 QTL associated with the different studied phenotypes, designated as T2DSL and its number, for Type 2 Diabetes Specific Locus and its number, were identified during our study, among which 19 QTL were not previously mapped. The genomic interval of the remaining 27 QTL previously reported, were fine mapped in our study. The genomic positions of 40 of the mapped QTL overlapped (clustered) on 11 different peaks or close genomic positions, while the remaining 6 QTL were unique. Further, our study showed a complex pattern of haplotype effects of the founders, with the wild-derived strains (mainly PWK) playing a significant role in the increase of AUC values.

Transcriptome-wide analyses of adipose tissue in outbred rats reveal genetic regulatory mechanisms relevant for human obesity.

Transcriptomic analysis in metabolically active tissues allows a systems genetics approach to identify causal genes and networks involved in metabolic disease. Outbred heterogeneous stock (HS) rats are used for genetic mapping of complex traits, but to-date, a systems genetics analysis of metabolic tissues has not been done. We investigated whether adiposity-associated genes and gene coexpression networks in outbred heterogeneous stock (HS) rats overlap those found in humans. We analyzed RNAseq data from adipose tissue of 415 male HS rats, correlated these transcripts with body weight (BW) and compared transcriptome signatures to two human cohorts: the "African American Genetics of Metabolism and Expression" and "Metabolic Syndrome in Men." We used weighted gene coexpression network analysis to identify adiposity-associated gene networks and mediation analysis to identify genes under genetic control whose expression drives adiposity. We identified 554 orthologous "consensus genes" whose expression correlates with BW in the rat and with body mass index (BMI) in both human cohorts. Consensus genes fell within eight coexpressed networks and were enriched for genes involved in immune system function, cell growth, extracellular matrix organization, and lipid metabolic processes. We identified 19 consensus genes for which genetic variation may influence BW via their expression, including those involved in lipolysis (e.g., Hcar1), inflammation (e.g., Rgs1), adipogenesis (e.g., Tmem120b), or no previously known role in obesity (e.g., St14 and Ms4a6a). Strong concordance between HS rat and human BW/BMI associated transcripts demonstrates translational utility of the rat model, while identification of novel genes expands our knowledge of the genetics underlying obesity.

Analysis of historical selection in winter wheat.

KEY MESSAGE: Modeling of the distribution of allele frequency over year of variety release identifies major loci involved in historical breeding of winter wheat. Winter wheat is a major crop with a rich selection history in the modern era of crop breeding. Genetic gains across economically important traits like yield have been well characterized and are the major force driving its production. Winter wheat is also an excellent model for analyzing historical genetic selection. As a proof of concept, we analyze two major collections of winter wheat varieties that were bred in Western Europe from 1916 to 2010, namely the Triticeae Genome (TG) and WAGTAIL panels, which include 333 and 403 varieties, respectively. We develop and apply a selection mapping approach, Regression of Alleles on Years (RALLY), in these panels, as well as in simulated populations. RALLY maps loci under sustained historical selection by using a simple logistic model to regress allele counts on years of variety release. To control for drift-induced allele frequency change, we develop a hybrid approach of genomic control and delta control. Within the TG panel, we identify 22 significant RALLY quantitative selection loci (QSLs) and estimate the local heritabilities for 12 traits across these QSLs. By correlating predicted marker effects with RALLY regression estimates, we show that alleles whose frequencies have increased over time are heavily biased toward conferring positive yield effect, but negative effects in flowering time, lodging, plant height and grain protein content. Altogether, our results (1) demonstrate the use of RALLY to identify selected genomic regions while controlling for drift, and (2) reveal key patterns in the historical selection in winter wheat and guide its future breeding.

Genome-wide sexually antagonistic variants reveal long-standing constraints on sexual dimorphism in fruit flies.

The evolution of sexual dimorphism is constrained by a shared genome, leading to 'sexual antagonism', in which different alleles at given loci are favoured by selection in males and females. Despite its wide taxonomic incidence, we know little about the identity, genomic location, and evolutionary dynamics of antagonistic genetic variants. To address these deficits, we use sex-specific fitness data from 202 fully sequenced hemiclonal Drosophila melanogaster fly lines to perform a genome-wide association study (GWAS) of sexual antagonism. We identify approximately 230 chromosomal clusters of candidate antagonistic single nucleotide polymorphisms (SNPs). In contradiction to classic theory, we find no clear evidence that the X chromosome is a hot spot for sexually antagonistic variation. Characterising antagonistic SNPs functionally, we find a large excess of missense variants but little enrichment in terms of gene function. We also assess the evolutionary persistence of antagonistic variants by examining extant polymorphism in wild D. melanogaster populations and closely related species. Remarkably, antagonistic variants are associated with multiple signatures of balancing selection across the D. melanogaster distribution range and in their sister species D. simulans, indicating widespread and evolutionarily persistent (about 1 million years) genomic constraints on the evolution of sexual dimorphism. Based on our results, we propose that antagonistic variation accumulates because of constraints on the resolution of sexual conflict over protein coding sequences, thus contributing to the long-term maintenance of heritable fitness variation.

Genetic mapping of novel modifiers for ApcMin induced intestinal polyps' development using the genetic architecture power of the collaborative cross mice.

BACKGROUND: Familial adenomatous polyposis is an inherited genetic disease, characterized by colorectal polyps. It is caused by inactivating mutations in the Adenomatous polyposis coli (Apc) gene. Mice carrying a nonsense mutation in the Apc gene at R850, which is designated ApcMin/+ (Multiple intestinal neoplasia), develop intestinal adenomas. Several genetic modifier loci of Min (Mom) were previously mapped, but so far, most of the underlying genes have not been identified. To identify novel modifier loci associated with ApcMin/+, we performed quantitative trait loci (QTL) analysis for polyp development using 49 F1 crosses between different Collaborative Cross (CC) lines and C57BL/6 J-ApcMin/+mice. The CC population is a genetic reference panel of recombinant inbred lines, each line independently descended from eight genetically diverse founder strains. C57BL/6 J-ApcMin/+ males were mated with females from 49 CC lines. F1 offspring were terminated at 23 weeks and polyp counts from three sub-regions (SB1-3) of small intestinal and colon were recorded. RESULTS: The number of polyps in all these sub-regions and colon varied significantly between the different CC lines. At 95% genome-wide significance, we mapped nine novel QTL for variation in polyp number, with distinct QTL associated with each intestinal sub-region. QTL confidence intervals varied in width between 2.63-17.79 Mb. We extracted all genes in the mapped QTL at 90 and 95% CI levels using the BioInfoMiner online platform to extract, significantly enriched pathways and key linker genes, that act as regulatory and orchestrators of the phenotypic landscape associated with the ApcMin/+ mutation. CONCLUSIONS: Genomic structure of the CC lines has allowed us to identify novel modifiers and confirmed some of the previously mapped modifiers. Key genes involved mainly in metabolic and immunological processes were identified. Future steps in this analysis will be to identify regulatory elements - and possible epistatic effects - located in the mapped QTL.

Genome-wide association coupled gene to gene interaction studies unveil novel epistatic targets among major effect loci impacting rice grain chalkiness.

Rice varieties whose quality is graded as excellent have a lower percent grain chalkiness (PGC) of two per cent and below with higher whole grain yields upon milling, leading to higher economic returns for farmers. We have conducted a genome-wide association study (GWAS) using a combined population panel of indica and japonica rice varieties, and identified a total of 746 single nucleotide polymorphisms (SNPs) that were strongly associated with the chalk phenotype, covered 78 Quantitative Trait Loci (QTL) regions. Among them, 21 were high-value QTLs, as they explained at least 10 % of the phenotypic variance for PGC. A combined epistasis and GWAS was applied to dissect the genetics of the complex chalkiness trait, and its regulatory cascades were validated using gene regulatory networks. Promising novel epistatic interactions were found between the loci of chromosomes 6 (PGC6.1) and 7 (PGC7.8) that contributed to lower PGC. Based on haplotype mining only a few modern rice varieties confounded with a lower chalkiness, and they possess several PGC QTLs. The importance of PGC6.1 was validated through multi-parent advanced generation intercrosses and several low-chalk lines possessing superior haplotypes were identified. The results of this investigation have deciphered the underlying genetic networks that can reduce PGC to 2%, and will thus support future breeding programs to improve the grain quality of elite genetic material with high-yielding potentials.

ADDO: a comprehensive toolkit to detect, classify and visualize additive and non-additive quantitative trait loci.

MOTIVATION: During the past decade, genome-wide association studies (GWAS) have been used to map quantitative trait loci (QTLs) underlying complex traits. However, most GWAS focus on additive genetic effects while ignoring non-additive effects, on the assumption that most QTL act additively. Consequently, QTLs driven by dominance and other non-additive effects could be overlooked. RESULTS: We developed ADDO, a highly efficient tool to detect, classify and visualize QTLs with additive and non-additive effects. ADDO implements a mixed-model transformation to control for population structure and unequal relatedness that accounts for both additive and dominant genetic covariance among individuals, and decomposes single-nucleotide polymorphism effects as either additive, partial dominant, dominant or over-dominant. A matrix multiplication approach is used to accelerate the computation: a genome scan on 13 million markers from 900 individuals takes about 5 h with 10 CPUs. Analysis of simulated data confirms ADDO's performance on traits with different additive and dominance genetic variance components. We showed two real examples in outbred rat where ADDO identified significant dominant QTL that were not detectable by an additive model. ADDO provides a systematic pipeline to characterize additive and non-additive QTL in whole genome sequence data, which complements current mainstream GWAS software for additive genetic effects. AVAILABILITY AND IMPLEMENTATION: ADDO is customizable and convenient to install and provides extensive analytics and visualizations. The package is freely available online at https://github.com/LeileiCui/ADDO. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Recovery of novel association loci in Arabidopsis thaliana and Drosophila melanogaster through leveraging INDELs association and integrated burden test.

Short insertions, deletions (INDELs) and larger structural variants have been increasingly employed in genetic association studies, but few improvements over SNP-based association have been reported. In order to understand why this might be the case, we analysed two publicly available datasets and observed that 63% of INDELs called in A. thaliana and 64% in D. melanogaster populations are misrepresented as multiple alleles with different functional annotations, i.e. where the same underlying variant is represented by inconsistent alignments leading to different variant calls. To address this issue, we have developed the software Irisas to reclassify and re-annotate these variants, which we then used for single-locus tests of association. We also integrated them to predict the functional impact of SNPs, INDELs, and structural variants for burden testing. Using both approaches, we re-analysed the genetic architecture of complex traits in A. thaliana and D. melanogaster. Heritability analysis using SNPs alone explained on average 27% and 19% of phenotypic variance for A. thaliana and D. melanogaster respectively. Our method explained an additional 11% and 3%, respectively. We also identified novel trait loci that previous SNP-based association studies failed to map, and which contain established candidate genes. Our study shows the value of the association test with INDELs and integrating multiple types of variants in association studies in plants and animals.

Container-aided integrative QTL and RNA-seq analysis of Collaborative Cross mice supports distinct sex-oriented molecular modes of response in obesity.

BACKGROUND: The Collaborative Cross (CC) mouse population is a valuable resource to study the genetic basis of complex traits, such as obesity. Although the development of obesity is influenced by environmental factors, underlying genetic mechanisms play a crucial role in the response to these factors. The interplay between the genetic background and the gene expression pattern can provide further insight into this response, but we lack robust and easily reproducible workflows to integrate genomic and transcriptomic information in the CC mouse population. RESULTS: We established an automated and reproducible integrative workflow to analyse complex traits in the CC mouse genetic reference panel at the genomic and transcriptomic levels. We implemented the analytical workflow to assess the underlying genetic mechanisms of host susceptibility to diet induced obesity and integrated these results with diet induced changes in the hepatic gene expression of susceptible and resistant mice. Hepatic gene expression differs significantly between obese and non-obese mice, with a significant sex effect, where male and female mice exhibit different responses and coping mechanisms. CONCLUSION: Integration of the data showed that different genes but similar pathways are involved in the genetic susceptibility and disturbed in diet induced obesity. Genetic mechanisms underlying susceptibility to high-fat diet induced obesity are different in female and male mice. The clear distinction we observed in the systemic response to the high-fat diet challenge and to obesity between male and female mice points to the need for further research into distinct sex-related mechanisms in metabolic disease.

Multi-parent populations in crops: a toolbox integrating genomics and genetic mapping with breeding.

Crop populations derived from experimental crosses enable the genetic dissection of complex traits and support modern plant breeding. Among these, multi-parent populations now play a central role. By mixing and recombining the genomes of multiple founders, multi-parent populations combine many commonly sought beneficial properties of genetic mapping populations. For example, they have high power and resolution for mapping quantitative trait loci, high genetic diversity and minimal population structure. Many multi-parent populations have been constructed in crop species, and their inbred germplasm and associated phenotypic and genotypic data serve as enduring resources. Their utility has grown from being a tool for mapping quantitative trait loci to a means of providing germplasm for breeding programmes. Genomics approaches, including de novo genome assemblies and gene annotations for the population founders, have allowed the imputation of rich sequence information into the descendent population, expanding the breadth of research and breeding applications of multi-parent populations. Here, we report recent successes from crop multi-parent populations in crops. We also propose an ideal genotypic, phenotypic and germplasm 'package' that multi-parent populations should feature to optimise their use as powerful community resources for crop research, development and breeding.

Toppar: an interactive browser for viewing association study results.

Summary: Data integration and visualization help geneticists make sense of large amounts of data. To help facilitate interpretation of genetic association data we developed Toppar, a customizable visualization tool that stores results from association studies and enables browsing over multiple results, by combining features from existing tools and linking to appropriate external databases. Availability and implementation: Detailed information on Toppar's features and functionality are on our website http://mccarthy.well.ox.ac.uk/toppar/docs along with instructions on how to download, install and run Toppar. Our online version of Toppar is accessible from the website and can be test-driven using Firefox, Safari or Chrome on sub-sets of publicly available genome-wide association study anthropometric waist and body mass index data (Locke et al., 2015; Shungin et al., 2015) from the Genetic Investigation of ANthropometric Traits consortium. Contact: totajuliusd@gmail.com.

Identification of new loci involved in the host susceptibility to Salmonella Typhimurium in collaborative cross mice.

BACKGROUND: Salmonella is a Gram-negative bacterium causing a wide range of clinical syndromes ranging from typhoid fever to diarrheic disease. Non-typhoidal Salmonella (NTS) serovars infect humans and animals, causing important health burden in the world. Susceptibility to salmonellosis varies between individuals under the control of host genes, as demonstrated by the identification of over 20 genetic loci in various mouse crosses. We have investigated the host response to S. Typhimurium infection in 35 Collaborative Cross (CC) strains, a genetic population which involves wild-derived strains that had not been previously assessed. RESULTS: One hundred and forty-eight mice from 35 CC strains were challenged intravenously with 1000 colony-forming units (CFUs) of S. Typhimurium. Bacterial load was measured in spleen and liver at day 4 post-infection. CC strains differed significantly (P < 0.0001) in spleen and liver bacterial loads, while sex and age had no effect. Two significant quantitative trait loci (QTLs) on chromosomes 8 and 10 and one suggestive QTL on chromosome 1 were found for spleen bacterial load, while two suggestive QTLs on chromosomes 6 and 17 were found for liver bacterial load. These QTLs are caused by distinct allelic patterns, principally involving alleles originating from the wild-derived founders. Using sequence variations between the eight CC founder strains combined with database mining for expression in target organs and known immune phenotypes, we were able to refine the QTLs intervals and establish a list of the most promising candidate genes. Furthermore, we identified one strain, CC042/GeniUnc (CC042), as highly susceptible to S. Typhimurium infection. CONCLUSIONS: By exploring a broader genetic variation, the Collaborative Cross population has revealed novel loci of resistance to Salmonella Typhimurium. It also led to the identification of CC042 as an extremely susceptible strain.

Weighted Burden Analysis of Exome-Sequenced Case-Control Sample Implicates Synaptic Genes in Schizophrenia Aetiology.

A previous study of exome-sequenced schizophrenia cases and controls reported an excess of singleton, gene-disruptive variants among cases, concentrated in particular gene sets. The dataset included a number of subjects with a substantial Finnish contribution to ancestry. We have reanalysed the same dataset after removal of these subjects and we have also included non-singleton variants of all types using a weighted burden test which assigns higher weights to variants predicted to have a greater effect on protein function. We investigated the same 31 gene sets as previously and also 1454 GO gene sets. The reduced dataset consisted of 4225 cases and 5834 controls. No individual variants or genes were significantly enriched in cases but 13 out of the 31 gene sets were significant after Bonferroni correction and the "FMRP targets" set produced a signed log p value (SLP) of 7.1. The gene within this set with the highest SLP, equal to 3.4, was FYN, which codes for a tyrosine kinase which phosphorylates glutamate metabotropic receptors and ionotropic NMDA receptors, thus modulating their trafficking, subcellular distribution and function. In the most recent GWAS of schizophrenia it was identified as a "prioritized candidate gene". Two of the subunits of the NMDA receptor which are substrates of FYN are coded for by GRIN1 (SLP = 1.7) and GRIN2B (SLP = 2.1). Of note, for some sets there was a substantial enrichment of non-singleton variants. Of 1454 GO gene sets, three were significant after Bonferroni correction. Identifying specific genes and variants will depend on genotyping them in larger samples and/or demonstrating that they cosegregate with illness within pedigrees.

Applying the ARRIVE Guidelines to an In Vivo Database.

The Animal Research: Reporting of In Vivo Experiments (ARRIVE) guidelines were developed to address the lack of reproducibility in biomedical animal studies and improve the communication of research findings. While intended to guide the preparation of peer-reviewed manuscripts, the principles of transparent reporting are also fundamental for in vivo databases. Here, we describe the benefits and challenges of applying the guidelines for the International Mouse Phenotyping Consortium (IMPC), whose goal is to produce and phenotype 20,000 knockout mouse strains in a reproducible manner across ten research centres. In addition to ensuring the transparency and reproducibility of the IMPC, the solutions to the challenges of applying the ARRIVE guidelines in the context of IMPC will provide a resource to help guide similar initiatives in the future.

Environmentally responsive genome-wide accumulation of de novo Arabidopsis thaliana mutations and epimutations.

Evolution is fueled by phenotypic diversity, which is in turn due to underlying heritable genetic (and potentially epigenetic) variation. While environmental factors are well known to influence the accumulation of novel variation in microorganisms and human cancer cells, the extent to which the natural environment influences the accumulation of novel variation in plants is relatively unknown. Here we use whole-genome and whole-methylome sequencing to test if a specific environmental stress (high-salinity soil) changes the frequency and molecular profile of accumulated mutations and epimutations (changes in cytosine methylation status) in mutation accumulation (MA) lineages of Arabidopsis thaliana. We first show that stressed lineages accumulate ∼100% more mutations, and that these mutations exhibit a distinctive molecular mutational spectrum (specific increases in relative frequency of transversion and insertion/deletion [indel] mutations). We next show that stressed lineages accumulate ∼45% more differentially methylated cytosine positions (DMPs) at CG sites (CG-DMPs) than controls, and also show that while many (∼75%) of these CG-DMPs are inherited, some can be lost in subsequent generations. Finally, we show that stress-associated CG-DMPs arise more frequently in genic than in nongenic regions of the genome. We suggest that commonly encountered natural environmental stresses can accelerate the accumulation and change the profiles of novel inherited variants in plants. Our findings are significant because stress exposure is common among plants in the wild, and they suggest that environmental factors may significantly alter the rates and patterns of incidence of the inherited novel variants that fuel plant evolution.

Glucose tolerance female-specific QTL mapped in collaborative cross mice.

Type-2 diabetes (T2D) is a complex metabolic disease characterized by impaired glucose tolerance. Despite environmental high risk factors, host genetic background is a strong component of T2D development. Herein, novel highly genetically diverse strains of collaborative cross (CC) lines from mice were assessed to map quantitative trait loci (QTL) associated with variations of glucose-tolerance response. In total, 501 mice of 58 CC lines were maintained on high-fat (42 % fat) diet for 12 weeks. Thereafter, an intraperitoneal glucose tolerance test (IPGTT) was performed for 180 min. Subsequently, the values of Area under curve for the glucose at zero and 180 min (AUC0-180), were measured, and used for QTL mapping. Heritability and coefficient of variations in glucose tolerance (CVg) were calculated. One-way analysis of variation was significant (P < 0.001) for AUC0-180 between the CC lines as well between both sexes. Despite Significant variations for both sexes, QTL analysis was significant, only for females, reporting a significant female-sex-dependent QTL (~2.5 Mbp) associated with IPGTT AUC0-180 trait, located on Chromosome 8 (32-34.5 Mbp, containing 51 genes). Gene browse revealed QTL for body weight/size, genes involved in immune system, and two main protein-coding genes involved in the Glucose homeostasis, Mboat4 and Leprotl1. Heritability and coefficient of genetic variance (CVg) were 0.49 and 0.31 for females, while for males, these values 0.34 and 0.22, respectively. Our findings demonstrate the roles of genetic factors controlling glucose tolerance, which significantly differ between sexes requiring independent studies for females and males toward T2D prevention and therapy.

Sequence-based characterization of structural variation in the mouse genome.

Structural variation is widespread in mammalian genomes and is an important cause of disease, but just how abundant and important structural variants (SVs) are in shaping phenotypic variation remains unclear. Without knowing how many SVs there are, and how they arise, it is difficult to discover what they do. Combining experimental with automated analyses, we identified 711,920 SVs at 281,243 sites in the genomes of thirteen classical and four wild-derived inbred mouse strains. The majority of SVs are less than 1 kilobase in size and 98% are deletions or insertions. The breakpoints of 160,000 SVs were mapped to base pair resolution, allowing us to infer that insertion of retrotransposons causes more than half of SVs. Yet, despite their prevalence, SVs are less likely than other sequence variants to cause gene expression or quantitative phenotypic variation. We identified 24 SVs that disrupt coding exons, acting as rare variants of large effect on gene function. One-third of the genes so affected have immunological functions.