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Combining samples for genetic association is standard practice in human genetic analysis of complex traits, but is rarely undertaken in rodent genetics. Here, using 23 phenotypes and genotypes from two independent laboratories, we obtained a sample size of 3,076 commercially available outbred mice and identified 70 loci, more than double the number of loci identified in the component studies. Fine-mapping in the combined sample reduced the number of likely causal variants, with a median reduction in set size of 51%, and indicated novel gene associations, including Pnpo, Ttll6 and GM11545 with bone mineral density, and Psmb9 with weight. However replication at a nominal threshold of 0.05 between the two component studies was surprisingly low, with less than a third of loci identified in one study replicated in the second. In addition to overestimates in the effect size in the discovery sample (Winner’s Curse), we also found that heterogeneity between studies explained the poor replication, but the contribution of these two factors varied among traits. Available methods to control Winner’s Curse were contingent on the power of the discovery sample, and depending on the method used, both overestimated and underestimated the true effect. Leveraging these observations we integrated information about replication rates, confounding, and Winner’s Curse corrected estimates of power to assign variants to one of four confidence levels. Our approach addresses concerns about reproducibility, and demonstrates how to obtain robust results from mapping complex traits in any genome-wide association study.

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. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07890-x.

Low bone mass and an increased risk of fracture are predictors of osteoporosis. Individuals who share the same bone-mineral density (BMD) vary in their fracture risk, suggesting that microstructural architecture is an important determinant of skeletal strength. Here, we utilized the rich diversity of the Collaborative Cross mice to identify putative causal genes that contribute to the risk of fractures. Using microcomputed tomography, we examined key structural features that pertain to bone quality in the femoral cortical and trabecular compartments of male and female mice. We estimated the broad-sense heritability to be 50–60% for all examined traits, and we identified five quantitative trait loci (QTL) significantly associated with six traits. We refined each QTL by combining information inferred from the ancestry of the mice, ranging from RNA-Seq data and published literature to shortlist candidate genes. We found strong evidence for new candidate genes, particularly Rhbdf2, whose close association with the trabecular bone volume fraction and number was strongly suggested by our analyses. We confirmed our findings with mRNA expression assays of Rhbdf2 in extreme-phenotype mice, and by phenotyping bones of Rhbdf2 knockout mice. Our results indicate that Rhbdf2 plays a decisive role in bone mass accrual and microarchitecture.

Mott et al. show that association between a quantitative trait and genotype can be performed using data that has been transformed by first rotating it in a high-dimensional space. The resulting..., Sharing human genotype and phenotype data is essential to discover otherwise inaccessible genetic associations, but is a challenge because of privacy concerns. Here, we present a method of homomorphic encryption that obscures individuals’ genotypes and phenotypes, and is suited to quantitative genetic association analysis. Encrypted ciphertext and unencrypted plaintext are analytically interchangeable. The encryption uses a high-dimensional random linear orthogonal transformation key that leaves the likelihood of quantitative trait data unchanged under a linear model with normally distributed errors. It also preserves linkage disequilibrium between genetic variants and associations between variants and phenotypes. It scrambles relationships between individuals: encrypted genotype dosages closely resemble Gaussian deviates, and can be replaced by quantiles from a Gaussian with negligible effects on accuracy. Likelihood-based inferences are unaffected by orthogonal encryption. These include linear mixed models to control for unequal relatedness between individuals, heritability estimation, and including covariates when testing association. Orthogonal transformations can be applied in a modular fashion for multiparty federated mega-analyses where the parties first agree to share a common set of genotype sites and covariates prior to encryption. Each then privately encrypts and shares their own ciphertext, and analyses all parties’ ciphertexts. In the absence of private variants, or knowledge of the key, we show that it is infeasible to decrypt ciphertext using existing brute-force or noise-reduction attacks. We present the method as a challenge to the community to determine its security.

Respiratory infection caused by P. aeruginosa is one of the most critical health burdens worldwide. People affected by P. aeruginosa infection include patients with a weakened immune system, such as those with cystic fibrosis (CF) genetic disease or non-CF bronchiectasis. Disease outcomes range from fatal pneumonia to chronic life-threatening infection and inflammation leading to the progressive deterioration of pulmonary function. The development of these respiratory infections is mediated by multiple causes. However, the genetic factors underlying infection susceptibility are poorly known and difficult to predict. Our study employed novel approaches and improved mouse disease models to identify genetic modifiers that affect the severity of P. aeruginosa lung infection. We identified candidate genes to enhance our understanding of P. aeruginosa infection in humans and provide a proof of concept that could be exploited for other human pathologies mediated by bacterial infection., Human genetics influence a range of pathological and clinical phenotypes in respiratory infections; however, the contributions of disease modifiers remain underappreciated. We exploited the Collaborative Cross (CC) mouse genetic-reference population to map genetic modifiers that affect the severity of Pseudomonas aeruginosa lung infection. Screening for P. aeruginosa respiratory infection in a cohort of 39 CC lines exhibits distinct disease phenotypes ranging from complete resistance to lethal disease. Based on major changes in the survival times, a quantitative-trait locus (QTL) was mapped on murine chromosome 3 to the genomic interval of Mb 110.4 to 120.5. Within this locus, composed of 31 protein-coding genes, two candidate genes, namely, dihydropyrimidine dehydrogenase (Dpyd) and sphingosine-1-phosphate receptor 1 (S1pr1), were identified according to the level of genome-wide significance and disease gene prioritization. Functional validation of the S1pr1 gene by pharmacological targeting in C57BL/6NCrl mice confirmed its relevance in P. aeruginosa pathophysiology. However, in a cohort of Canadian patients with cystic fibrosis (CF) disease, regional genetic-association analysis of the syntenic human locus on chromosome 1 (Mb 97.0 to 105.0) identified two single-nucleotide polymorphisms (rs10875080 and rs11582736) annotated to the Dpyd gene that were significantly associated with age at first P. aeruginosa infection. Thus, there is evidence that both genes might be implicated in this disease. Our results demonstrate that the discovery of murine modifier loci may generate information that is relevant to human disease progression.

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. This work was funded by the Operational Program ”Competitiveness, Entrepreneurship and Innovation 2014–2020″ (co-funded by the European Regional Development Fund) and managed by the General Secretariat of Research and Technology, Ministry Of Education, Research & Religious Affairs, under the project” Innovative Nanopharmaceuticals: Targeting Breast CancerStem Cells by a Novel Combination of Epigenetic and Anticancer Drugs with Gene Therapy (INNOCENT)” (7th Joint Translational Call - 2016, European Innovative Research and Technological Development Projects In Nanomedicine) of the ERA-NET EuroNanoMed II and by “BioS: Digital Skills on Computational Biology”, ΕRASMUS+, EACEA/04/2017, “KA2: Cooperation for innovation and the exchange of good practices - Sector Skills Alliances” for the development of the bioinformatic analysis pipeline, Hendrech and Eiran Gotwert Fund for studying diabetes, Wellcome Trust grants 085906/Z/ 08/Z, 075491/Z/04 and 090532/Z/09/Z; for financially supporting the initiation, breeding, genotyping and maintaining the CC mouse colony at TAU, core funding by Tel-Aviv University (TAU) for supporting staff at Iraqi’s lab, Israeli Science foundation (ISF) grants 429/09 and 1085/18, Binational Science Foundation (BSF) grant number 2015077 and German Israeli Science Foundation (GIF) grant I-63-410.20-2017 for performing the mouse assessment for T2D and technical staff, and European Sequencing and Genotyping Infrastructure (ESGI) consortium grant for conducting the RNAseq analysis.Ilona Binenbaum’s PhD thesis was supported by a scholarship from the State Scholarship Foundation in Greece (IKY) (Operational Program “Human Resources Development - Education and Lifelong Learning” Partnership Agreement (PA) 2014–2020)

Meiotic recombination ensures the faithful segregation of chromosomes and influences patterns of genetic diversity. Morgan et al. used genotype data.. Meiotic recombination is an essential feature of sexual reproduction that ensures faithful segregation of chromosomes and redistributes genetic variants in populations. Multiparent populations such as the Diversity Outbred (DO) mouse stock accumulate large numbers of crossover (CO) events between founder haplotypes, and thus present a unique opportunity to study the role of genetic variation in shaping the recombination landscape. We obtained high-density genotype data from 6886 DO mice, and localized 2.2 million CO events to intervals with a median size of 28 kb. The resulting sex-averaged genetic map of the DO population is highly concordant with large-scale (order 10 Mb) features of previously reported genetic maps for mouse. To examine fine-scale (order 10 kb) patterns of recombination in the DO, we overlaid putative recombination hotspots onto our CO intervals. We found that CO intervals are enriched in hotspots compared to the genomic background. However, as many as 26% of CO intervals do not overlap any putative hotspots, suggesting that our understanding of hotspots is incomplete. We also identified coldspots encompassing 329 Mb, or 12% of observable genome, in which there is little or no recombination. In contrast to hotspots, which are a few kilobases in size, and widely scattered throughout the genome, coldspots have a median size of 2.1 Mb and are spatially clustered. Coldspots are strongly associated with copy-number variant (CNV) regions, especially multi-allelic clusters, identified from whole-genome sequencing of 228 DO mice. Genes in these regions have reduced expression, and epigenetic features of closed chromatin in male germ cells, which suggests that CNVs may repress recombination by altering chromatin structure in meiosis. Our findings demonstrate how multiparent populations, by bridging the gap between large-scale and fine-scale genetic mapping, can reveal new features of the recombination landscape.

Hepatic gene expression is known to differ between healthy and type 2 diabetes conditions. Identifying these variations will provide better knowledge to the development of gene-targeted therapies. The aim of this study is to assess diet-induced hepatic gene expression of susceptible versus resistant CC lines to T2D development. Next-generation RNA-sequencing was performed for 84 livers of diabetic and non-diabetic mice of 41 different CC lines (both sexes) following 12 weeks on high-fat diet (42% fat). Data analysis revealed significant variations of hepatic gene expression in diabetic versus non-diabetic mice with significant sex effect, where 601 genes were differentially expressed (DE) in overall population (males and females), 718 genes in female mice, and 599 genes in male mice. Top prioritized DE candidate genes were Lepr, Ins2, Mb, Ckm, Mrap2, and Ckmt2 for the overall population; for females-only group were Hdc, Serpina12, Socs1, Socs2, and Mb, while for males-only group were Serpine1, Mb, Ren1, Slc4a1, and Atp2a1. Data analysis for sex differences revealed 193 DE genes in health (Top: Lepr, Cav1, Socs2, Abcg2, and Col5a3), and 389 genes DE between diabetic females versus males (Top: Lepr, Clps, Ins2, Cav1, and Mrap2). Furthermore, integrating gene expression results with previously published QTL, we identified significant variants mapped at chromosomes at positions 36–49 Mb, 62–71 Mb, and 79–99 Mb, on chromosomes 9, 11, and 12, respectively. Our findings emphasize the complexity of T2D development and that significantly controlled by host complex genetic factors. As well, we demonstrate the significant sex differences between males and females during health and increasing to extent levels during disease/diabetes. Altogether, opening the venue for further studies targets the discovery of effective sex-specific and personalized preventions and therapies.

Background Animal models for neurodevelopmental disorders (NDD) generally rely on a single genetic mutation on a fixed genetic background. Recent human genetic studies however indicate that a clinical diagnosis with ASDAutism Spectrum Disorder (ASD) is almost always associated with multiple genetic fore- and background changes. The translational value of animal model studies would be greatly enhanced if genetic insults could be studied in a more quantitative framework across genetic backgrounds. Methods We used the Collaborative Cross (CC), a novel mouse genetic reference population, to investigate the quantitative genetic architecture of mouse behavioral phenotypes commonly used in animal models for NDD. Results Classical tests of social recognition and grooming phenotypes appeared insufficient for quantitative studies due to genetic dilution and limited heritability. In contrast, digging, locomotor activity, and stereotyped exploratory patterns were characterized by continuous distribution across our CC sample and also mapped to quantitative trait loci containing genes associated with corresponding phenotypes in human populations. Conclusions These findings show that the CC can move animal model studies beyond comparative single gene-single background designs, and point out which type of behavioral phenotypes are most suitable to quantify the effect of developmental etiologies across multiple genetic backgrounds. Electronic supplementary material The online version of this article (10.1186/s13229-018-0252-2) contains supplementary material, which is available to authorized users.

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

Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in the western world, with spectrum from simple steatosis to non-alcoholic steatohepatitis, which can progress to cirrhosis. NAFLD developments are known to be affected by host genetic background. Herein we emphasize the power of collaborative cross (CC) mouse for dissecting this complex trait and revealing quantitative trait loci (QTL) controlling hepatic fat accumulation in mice. 168 female and 338 male mice from 24 and 37 CC lines, respectively, of 18-20 weeks old, maintained on standard rodent diet, since weaning. Hepatic fat content was assessed, using dual DEXA scan in the liver. Using the available high-density genotype markers of the CC line, QTL mapping associated with percentage liver fat accumulation was performed. Our results revealed significant fatty liver accumulation QTL that were specifically, mapped in females. Two significant QTLs on chromosomes 17 and 18, with genomic intervals 3 and 2 Mb, respectively, were mapped. A third QTL, with a less significant P value, was mapped to chromosome 4, with genomic interval of 2 Mb. These QTLs were named Flal1-Flal3, referring to Fatty Liver Accumulation Locus 1-3, for the QTLs on chromosomes 17, 18, and 4, respectively. Unfortunately, no QTL was mapped with males. Searching the mouse genome database suggested several candidate genes involved in hepatic fat accumulation. Our results show that susceptibility to hepatic fat accumulations is a complex trait, controlled by multiple genetic factors in female mice, but not in male.

Two bottlenecks impeding the genetic analysis of complex traits in rodents are access to mapping populations able to deliver gene-level mapping resolution and the need for population-specific genotyping arrays and haplotype reference panels. Here we combine low-coverage (0.15×) sequencing with a new method to impute the ancestral haplotype space in 1,887 commercially available outbred mice. We mapped 156 unique quantitative trait loci for 92 phenotypes at a 5% false discovery rate. Gene-level mapping resolution was achieved at about one-fifth of the loci, implicating Unc13c and Pgc1a at loci for the quality of sleep, Adarb2 for home cage activity, Rtkn2 for intensity of reaction to startle, Bmp2 for wound healing, Il15 and Id2 for several T cell measures and Prkca for bone mineral content. These findings have implications for diverse areas of mammalian biology and demonstrate how genome-wide association studies can be extended via low-coverage sequencing to species with highly recombinant outbred populations.

Inexpensive genotyping methods are essential to modern genomics. Here we present QUILT, which performs diploid genotype imputation using low-coverage whole-genome sequence data. QUILT employs Gibbs sampling to partition reads into maternal and paternal sets, facilitating rapid haploid imputation using large reference panels. We show this partitioning to be accurate over many megabases, enabling highly accurate imputation close to theoretical limits and outperforming existing methods. Moreover, QUILT can impute accurately using diverse technologies, including long reads from Oxford Nanopore Technologies, and a new form of low-cost barcoded Illumina sequencing called haplotagging, with the latter showing improved accuracy at low coverages. Relative to DNA genotyping microarrays, QUILT offers improved accuracy at reduced cost, particularly for diverse populations that are traditionally underserved in modern genomic analyses, with accuracy nearly doubling at rare SNPs. Finally, QUILT can accurately impute (four-digit) human leukocyte antigen types, the first such method from low-coverage sequence data.

Periodontitis is one of the most common inflammatory human diseases with a strong genetic component. Due to the limited sample size of available periodontitis cohorts and the underlying trait heterogeneity, genome-wide association studies (GWASs) of chronic periodontitis (CP) have largely been unsuccessful in identifying common susceptibility factors. A combination of quantitative trait loci (QTL) mapping in mice with association studies in humans has the potential to discover novel risk loci. To this end, we assessed alveolar bone loss in response to experimental periodontal infection in 25 lines (286 mice) from the Collaborative Cross (CC) mouse population using micro-computed tomography (µCT) analysis. The orthologous human chromosomal regions of the significant QTL were analyzed for association using imputed genotype data (OmniExpress BeadChip arrays) derived from case-control samples of aggressive periodontitis (AgP; 896 cases, 7,104 controls) and chronic periodontitis (CP; 2,746 cases, 1,864 controls) of northwest European and European American descent, respectively. In the mouse genome, QTL mapping revealed 2 significant loci (-log P = 5.3; false discovery rate = 0.06) on chromosomes 1 ( Perio3) and 14 ( Perio4). The mapping resolution ranged from ~1.5 to 3 Mb. Perio3 overlaps with a previously reported QTL associated with residual bone volume in F2 cross and includes the murine gene Ccdc121. Its human orthologue showed previously a nominal significant association with CP in humans. Use of variation data from the genomes of the CC founder strains further refined the QTL and suggested 7 candidate genes ( CAPN8, DUSP23, PCDH17, SNORA17, PCDH9, LECT1, and LECT2). We found no evidence of association of these candidates with the human orthologues. In conclusion, the CC populations enabled mapping of confined QTL that confer susceptibility to alveolar bone loss in mice and larger human phenotype-genotype samples and additional expression data from gingival tissues are likely required to identify true positive signals.

We report full-length draft de novo genome assemblies for 16 widely used inbred mouse strains and find extensive strain-specific haplotype variation. We identify and characterize 2,567 regions on the current mouse reference genome exhibiting the greatest sequence diversity. These regions are enriched for genes involved in pathogen defence and immunity and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. We used these genomes to improve the mouse reference genome, resulting in the completion of 10 new gene structures. Also, 62 new coding loci were added to the reference genome annotation. These genomes identified a large, previously unannotated, gene (Efcab3-like) encoding 5,874 amino acids. Mutant Efcab3-like mice display anomalies in multiple brain regions, suggesting a possible role for this gene in the regulation of brain development. Medical Research Council and the Wellcome Trust

Tameness is a major behavioral factor for domestication, and can be divided into two potential components: motivation to approach humans (active tameness) and reluctance to avoid humans (passive tameness). We identified genetic loci for active tameness through selective breeding, selection mapping, and association analysis. In previous work using laboratory and wild mouse strains, we found that laboratory strains were predominantly selected for passive tameness but not active tameness during their domestication. To identify genetic regions associated with active tameness, we applied selective breeding over 9 generations for contacting, a behavioural parameter strongly associated with active tameness. The prerequisite for successful selective breeding is high genetic variation in the target population, so we established and used a novel resource, wild-derived heterogeneous stock (WHS) mice from eight wild strains. The mice had genetic variations not present in other outbred mouse populations. Selective breeding of the WHS mice increased the contacting level through the generations. Selection mapping was applied to the selected population using a simulation based on a non-selection model and inferred haplotype data derived from single-nucleotide polymorphisms. We found a genomic signature for selection on chromosome 11 containing two closely linked loci.

The role of sex in biomedical studies has often been overlooked, despite evidence of sexually dimorphic effects in some biological studies. Here, we used high-throughput phenotype data from 14,250 wildtype and 40,192 mutant mice (representing 2,186 knockout lines), analysed for up to 234 traits, and found a large proportion of mammalian traits both in wildtype and mutants are influenced by sex. This result has implications for interpreting disease phenotypes in animal models and humans., Systemic dissection of sexually dimorphic phenotypes in mice is lacking. Here, Karp and the International Mouse Phenotype Consortium show that approximately 10% of qualitative traits and 56% of quantitative traits in mice as measured in laboratory setting are sexually dimorphic.

Most biological traits of human importance are complex in nature; their manifestation controlled by the cumulative effect of many genetic factors interacting with one another and with the individual's life history. Because of this, mouse genetic reference populations (GRPs) consisting of collections of inbred lines or recombinant inbred lines (RIL) derived from crosses between inbred lines are of particular value in analysis of complex traits, since massive amounts of data can be accumulated on the individual lines. However, existing mouse GRPs are derived from inbred lines that share a common history, resulting in limited genetic diversity, and reduced mapping precision due to long-range gametic disequilibrium. To overcome these limitations, the Collaborative Cross (CC) a genetically highly diverse collection of mouse RIL was established. The CC, now in advanced stages of development, will eventually consist of about 500 RIL derived from reciprocal crosses of eight divergent founder strains of mice, including three wild subspecies. Previous studies have shown that the CC indeed contains enormous diversity at the DNA level, that founder haplotypes are inherited in expected frequency, and that long-range gametic disequilibrium is not present. We here present data, primarily from our own laboratory, documenting extensive genetic variation among CC lines as expressed in broad-sense heritability (H2) and by the well-known "coefficient of genetic variation," demonstrating the ability of the CC resource to provide unprecedented mapping precision leading to identification of strong candidate genes. © 2014 Springer Science+Business Media.

Heterogeneous Stock (HS) populations allow for fine-resolution genetic mapping of a variety of complex traits. HS mice and rats were created from breeding together eight inbred strains, followed by maintaining the colony in a manner that minimizes inbreeding. After 50 or more generations of breeding, the resulting animals' chromosomes represent a genetic mosaic of the founders' haplotypes, with the average distance between recombination events in the centiMorgan range. This allows for genetic mapping to only a few Mb, a much smaller region than what can be identified using traditional F2 intercross or backcross mapping strategies. HS animals have been used to fine-map a variety of complex traits including anxiety and fear behaviors, diabetes, asthma, and heart disease, among others. Once a quantitative trait locus (QTL) has been identified, founder sequence and expression analysis can be used to identify underlying causal genes. In the following review, we provide an overview of how HS rats and mice have been used to identify genetic loci, and in some cases the causal genes, underlying complex traits. We discuss the creation and breeding strategies for both HS rats and mice. We then discuss the statistical analyses used to identify genetic loci, as well as strategies to identify causal genes underlying these loci. We end the chapter by discussing limitations faced when using HS populations, including several statistical challenges that have not been fully resolved.

The genes involved in conferring susceptibility to anxiety remain obscure. We developed a new method to identify genes at quantitative trait loci (QTLs) in a population of heterogeneous stock mice descended from known progenitor strains. QTLs were partitioned into intervals that can be summarized by a single phylogenetic tree among progenitors and intervals tested for consistency with alleles influencing anxiety at each QTL. By searching for common Gene Ontology functions in candidate genes positioned within those intervals, we identified actin depolymerizing factors (ADFs), including cofilin-1 (Cfl1), as genes involved in regulating anxiety in mice. There was no enrichment for function in the totality of genes under each QTL, indicating the importance of phylogenetic filtering. We confirmed experimentally that forebrain-specific inactivation of Cfl1 decreased anxiety in knockout mice. Our results indicate that similarity of function of mammalian genes can be used to recognize key genetic regulators of anxiety and potentially of other emotional behaviours., Author Summary Thousands of small effect loci are believed to contribute to behavioural variation in mammals. Their abundance and small size frustrate gene identification and make it difficult to know which among them are central to the responsible biological mechanisms. Using imputed genome sequences from 2,000 outbred mice and by testing for an enrichment of functional annotations, we identify 167 candidate genes involved in anxiety. Unexpectedly, annotations implicate actin depolymerizing factors (ADFs), including cofilin-1 (Cfl1), as being involved with the expression of anxiety phenotypes in mice. We confirmed that forebrain-specific inactivation of Cfl1 decreased anxiety in knockout 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., Author Summary Regulatory sites of the genome affect gene expression and complex traits, including disease susceptibility. Variable regulatory sites are potentially interesting because they are a likely cause of phenotypic variation, providing a bridge between sequence and transcriptional variation. In this paper we identify regions of the genome where DNA is not wrapped up in chromatin (hence potentially regulatory) in eight inbred strains of mice. We compare sites that vary among strains and compare them to non-variable sites. We show that more than half of variable sites cannot be attributed to local sequence variation. Functional consequences (in terms of readily detectable changes in gene expression) are associated with less than 10% of variable DNase I hypersensitive sites. We show that variable sites are enriched for sequence variants contributing to complex traits in mice.

We report on the progress of a project funded by the Wellcome Trust to produce over 100 recombinant inbred mouse lines as part of the Collaborative Cross (CC) genetic reference panel. These new strains of mice are being derived from a set of eight genetically diverse founders. The genomes of the finished strains will be mosaics of the founder strains’ genomes with a high density of independent recombination breakpoints. The CC mice will be available for distribution free of any intellectual property constraints to serve as a community resource for systems genetics studies.

The use of hybridisation of synthetic oligonucleotides to cDNAs under high stringency to characterise gene sequences has been demonstrated by a number of groups. We have used two cDNA libraries of 9 and 12 day mouse embryos (24 133 and 34 783 clones respectively) in a pilot study to characterise expressed genes by hybridisation with 110 hybridisation probes. We have identified 33 369 clusters of cDNA clones, that ranged in representation from 1 to 487 copies (0.7%). 737 were assigned to known rodent genes, and a further 13 845 showed significant homologies. A total of 404 clusters were identified as significantly differentially represented (P < 0.01) between the two cDNA libraries. This study demonstrates the utility of the fingerprinting approach for the generation of comparative gene expression profiles through the analysis of cDNAs derived from different biological materials.

International audience; Background: Array comparative genomic hybridization (aCGH) to detect copy number variants (CNVs) in mammalian genomes has led to a growing awareness of the potential importance of this category of sequence variation as a cause of phenotypic variation. Yet there are large discrepancies between studies, so that the extent of the genome affected by CNVs is unknown. We combined molecular and aCGH analyses of CNVs in inbred mouse strains to investigate this question. Principal Findings: Using a 2.1 million probe array we identified 1,477 deletions and 499 gains in 7 inbred mouse strains. Molecular characterization indicated that approximately one third of the CNVs detected by the array were false positives and we estimate the false negative rate to be more than 50%. We show that low concordance between studies is largely due to the molecular nature of CNVs, many of which consist of a series of smaller deletions and gains interspersed by regions where the DNA copy number is normal. Conclusions: Our results indicate that CNVs detected by arrays may be the coincidental co-localization of smaller CNVs, whose presence is more likely to perturb an aCGH hybridization profile than the effect of an isolated, small, copy number alteration. Our findings help explain the hitherto unexplored discrepancies between array-based studies of copy number variation in the mouse genome.

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Background The prevalence of Type 2 Diabetes (T2D) mellitus in the past decades, has reached epidemic proportions. Several lines of evidence support the role of genetic variation in the pathogenesis of T2D and insulin resistance. Elucidating these factors could contribute to developing new medical treatments and tools to identify those most at risk. The aim of this study was to characterize the phenotypic response of the Collaborative Cross (CC) mouse genetic resource population to high-fat diet (HFD) induced T2D-like disease to evluate its suitability for this purpose. Results We studied 683 mice of 21 different lines of the CC population. Of these, 265 mice (149 males and 116 females) were challenged by HFD (42 % fat); and 384 mice (239 males and145 females) of 17 of the 21 lines were reared as control group on standard Chow diet (18 % fat). Briefly, 8 week old mice were maintained on HFD until 20 weeks of age, and subsequently assessed by intraperitoneal glucose tolerance test (IPGTT). Biweekly body weight (BW), body length (BL), waist circumstance (WC), and body mass index (BMI) were measured. On statistical analysis, trait measurements taken at 20 weeks of age showed significant sex by diet interaction across the different lines and traits. Consequently, males and females were analyzed, separately. Differences among lines were analyzed by ANOVA and shown to be significant (P

International audience; We report genome sequences of 17 inbred strains of laboratory mice and identify almost ten times more variants than previously known. We use these genomes to explore the phylogenetic history of the laboratory mouse and to examine the functional consequences of allele-specific variation on transcript abundance, revealing that at least 12% of transcripts show a significant tissue-specific expression bias. By identifying candidate functional variants at 718 quantitative trait loci we show that the molecular nature of functional variants and their position relative to genes vary according to the effect size of the locus. These sequences provide a starting point for a new era in the functional analysis of a key model organism.

This review deals with the pharmacological properties of an alkylated monosaccharide mimetic, N-butyldeoxynojirimycin (NB-DNJ). This compound is of pharmacogenetic interest because one of its biological effects in mice – impairment of spermatogenesis, leading to male infertility – depends greatly on the genetic background of the animal. In susceptible mice, administration of NB-DNJ perturbs the formation of an organelle, the acrosome, in early post-meiotic male germ cells. In all recipient mice, irrespective of reproductive phenotype, NB-DNJ has a similar biochemical effect: inhibition of the glucosylceramidase β-glucosidase 2 and subsequent elevation of glucosylceramide, a glycosphingolipid. The questions that we now need to address are: how can glucosylceramide specifically affect early acrosome formation, and why is this contingent on genetic factors? Here we discuss relevant aspects of reproductive biology, the metabolism and cell biology of sphingolipids, and complex trait analysis; we also present a speculative model that takes our observations into account.

International audience; Background: Exploiting synteny between mouse and human disease loci has been proposed as a cost-effective method for the identification of human susceptibility genes. Here we explore its utility in an analysis of a human personality trait, neuroticism, which can be modeled in mice by tests of emotionality. We investigated a mouse emotionality locus on chromosome 1 that contains no annotated genes but abuts four regulators of G protein signaling, one of which (rgs2) has been previously identified as a quantitative trait gene for emotionality. This locus is syntenic with a human region that has been consistently implicated in the genetic aetiology of neuroticism. Methods: The functional candidacy of 29 murine sequence variants was tested by a combination of gel shift and transient transfection assays. Murine sequences that contained functional variants and exhibited significant cross-species conservation were prioritized for investigation in humans. Genetic association with neuroticism was tested in 1869 high and 2032 low unrelated individuals scored for neuroticism, selected from the extremes of 88,141 people from southwest England. Results: Fifteen sequence variants contributed to variation in the expression of rgs18, the gene lying at the edge of the quantitative trait loci (QTL) interval. There was no evidence of association between neuroticism and single nucleotide polymorphisms (SNPs) lying in the human regions homologous to those of mouse functional variants. One SNP, rs6428058, in a region of sequence conservation 644 kb upstream of RGS18, showed significant association (p ϭ .000631). Conclusions: It is unlikely that a single variant is responsible for the mouse emotionality locus on chromosome 1. This level of underlying genetic complexity means that although cross-species QTL concordance may be invaluable for the identification of human disease loci, it is unlikely to be as informative in the identification of human disease-causing variants.

Motivation: Advances in high-throughput genotyping have made it possible to carry out genome-wide association studies using very high densities of genetic markers. This has led to the problem of the storage, management, quality control, presentation and interpretation of results. In order to achieve a successful outcome, it may be necessary to analyse the data in different ways and compare the results with genome annotations and other genome scans.Results: We created GSCANDB, a database for genome scan data, using a MySQL backend and Perl-CGI web interface. It displays genome scans of multiple phenotypes analysed in different ways and projected onto genome annotations derived from EnsMart. The current version is optimized for analysis of mouse data, but is customizable to other species.Availability: Source code and example data are available under the GPL, in versions tailored to either human or mouse association studies, from http://gscan.well.ox.ac.uk/software.Contact: Richard.Mott@well.ox.ac.ukSupplementary information: The GSCANDB database of mouse genome scans is accessible from http://gscan.well.ox.ac.uk.

Summary Control over the number of mtDNA molecules per cell appears to be tightly regulated, but the mechanisms involved are largely unknown. Reversible alterations in the amount of mtDNA occur in response to stress suggesting that control over the amount of mtDNA is involved in stress-related diseases including major depressive disorder (MDD). Using low-coverage sequence data from 10,442 Chinese women to compute the normalized numbers of reads mapping to the mitochondrial genome as a proxy for the amount of mtDNA, we identified two loci that contribute to mtDNA levels: one within the TFAM gene on chromosome 10 (rs11006126, p value = 8.73 × 10−28, variance explained = 1.90%) and one over the CDK6 gene on chromosome 7 (rs445, p value = 6.03 × 10−16, variance explained = 0.50%). Both loci replicated in an independent cohort. CDK6 is thus a new molecule involved in the control of mtDNA. We identify increased rates of heteroplasmy in women with MDD, and show from an experimental paradigm using mice that the increase is likely due to stress. Furthermore, at least one heteroplasmic variant is significantly associated with changes in the amount of mtDNA (position 513, p value = 3.27 × 10−9, variance explained = 0.48%) suggesting site-specific heteroplasmy as a possible link between stress and increase in amount of mtDNA. These findings indicate the involvement of mitochondrial genome copy number and sequence in an organism’s response to stress., Highlights • Loci near TFAM and CDK6 contribute to variation in the amount of mtDNA • Mutations accumulate in the mtDNA of cases of major depression • Animal experiments show that heteroplasmy can be induced by chronic stress • Amount of mtDNA is associated with site-specific heteroplasmy, Having previously shown that cases of major depressive disorder have increased mtDNA, that exposure to stress is likely responsible, and that the changes in amount of mtDNA are reversible, Cai et al. investigate in this paper the genetic control and consequences of this change and how this relates to major depressive disorder.

International audience; Here we present a strategy to determine the genetic basis of variance in complex phenotypes that arise from natural, as opposed to induced, genetic variation in mice. We show that a commercially available strain of outbred mice, MF1, can be treated as an ultrafine mosaic of standard inbred strains and accordingly used to dissect a known quantitative trait locus influencing anxiety. We also show that this locus can be subdivided into three regions, one of which contains Rgs2, which encodes a regulator of G protein signaling. We then use quantitative complementation to show that Rgs2 is a quantitative trait gene. This combined genetic and functional approach should be applicable to the analysis of any quantitative trait.

International audience; Investigation of sequence variation in common inbred mouse strains has revealed a segmented pattern in which regions of high and low variant density are intermixed. Furthermore, it has been suggested that allelic strain distribution patterns also occur in well defined blocks and consequently could be used to map quantitative trait loci (QTL) in comparisons between inbred strains. We report a detailed analysis of polymorphism distribution in multiple inbred mouse strains over a 4.8-megabase region containing a QTL influencing anxiety. Our analysis indicates that it is only partly true that the genomes of inbred strains exist as a patchwork of segments of sequence identity and difference. We show that the definition of haplotype blocks is not robust and that methods for QTL mapping may fail if they assume a simple block-like structure.

The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact potygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

We compared strategies to fine-map Quantitative Trait Loci (QTL) in mice with heterogeneous stocks (HS). We showed that a panel of about 100 Recombinant Inbred Lines (RIL) derived from an HS, and which we called an RIHS, was ideally suited to fine-map QTL to very high resolution, without the cost of additional genotyping. We also investigated a strategy based on in vitro fertilization of large numbers of F(1) offspring of HS males crossed with an inbred line (IVHS). This method required some additional genotyping but avoided the breeding delays and costs associated with the construction of an RI panel. We showed that QTL detection was higher by using RIHS than with IVHS and that it was independent of the number of RI lines, provided the total number of animals phenotyped was constant. However, fine-mapping accuracy was slightly better with IVHS. We also investigated the effects of varying the number of HS generations and using multiallelic microsatellites instead of SNPs. We found that quite modest generation times of 10-20 generations were optimal. Microsatellites were superior to SNPs only when the generation time was 30 or more and when the markers were widely spaced.

Background Klebsiella pneumoniae (Kp) is a bacterium causing severe pneumonia in immunocompromised hosts and is often associated with sepsis. With the rise of antibiotic resistant bacteria, there is a need for new effective and affordable control methods; understanding the genetic architecture of susceptibility to Kp will help in their development. We performed the first quantitative trait locus (QTL) mapping study of host susceptibility to Kp infection in immunocompetent Collaborative Cross mice (CC). We challenged 328 mice from 73 CC lines intraperitoneally with 104 colony forming units of Kp strain K2. Survival and body weight were monitored for 15 days post challenge. 48 of the CC lines were genotyped with 170,000 SNPs, with which we mapped QTLs. Results CC lines differed significantly (P

We want to discuss how recent dramatic progress in whole-genome association analysis (WGA) applied to human case-control studies will affect complex trait analysis in the mouse. At first sight it might now seem that one can identify the genetic determinants of human complex disease directly. However, the picture is less straightforward, for although a significant number of genes have been identified and replicated across different human WGA studies, in most diseases the genetic variation segregating at these genes explains only a small fraction of cases that should be accounted for by genetic causes. The question remains whether the missing genetic signal is from common variants in other genes but with very small phenotypic effect, or is caused by rare variants in the same genes, or a combination of both. In either case, to continue making progress directly with the human WGA methodology it will be necessary to increase sample sizes significantly (Zeggini et al. 2008). Where does this leave mouse complex genetics? Of course, as the readers of Mammalian Genome will appreciate, working with mice does have certain advantages in that it is possible to design and control experiments and take detailed measurements in a way that is impossible with humans. Nevertheless, the mouse complex genetics community must address three key issues in order to contribute to our understanding of human biology and disease. The first of these is mapping resolution. We need to establish suitable mouse populations in which high-resolution mapping is the norm. The small extent of linkage disequilibrium (LD) in most human populations means that a positive signal in a human WGA is localized to a few tens of kilobases, usually the span a single gene. Assuming that the functional genetic variant acts on the nearest gene (which is not always the case), then human WGA delivers single-gene resolution. In contrast, a detected QTL in an F2 intercross between two inbred laboratory mouse strains that explains 5% of the phenotypic variance will be mapped into a 95% confidence interval of approximately 30 Mb, containing approximately 300 genes. In some cases, combining information from multiple F2 crosses with the haplotype map of the mouse genome can refine QTL localization (Hitzemann et al. 2002; Li et al. 2005). Several other strategies have been proposed to solve this problem, by using populations of mice with more recombinants, and consequently steeper LD decay profiles. Heterogeneous stocks (HS) are descended from eight known inbred strain of mice that are outcrossed using a rotational breeding scheme for many generations until the genomes are relatively fine-grained mosaics of the founder haplotypes; mapping resolution of 2–3Mb is obtainable (Mott et al. 2000; Talbot et al. 1999; Valdar et al. 2006). However, despite being much more accurate than an F2 cross, this is still too crude for single-gene mapping for which we require mapping resolution of about 100 kb. Commercial mouse breeders maintain very large genetically heterogeneous outbred populations, some of which are suitable for complex trait analysis. As proof of principle, Yalcin et al. (2004) showed how one such population, MF1, could be used to fine-map a QTL for behavior down to the gene Rgs2. Our group is now in the process of evaluating the genetic variability and LD profile of several commercially available outbred populations. Preliminary data suggest that there are some populations that have suitable properties but that others are not useful, having been recently rederived from a small number of animals and therefore containing extensive LD. There is also one tantalizing study of outbred wild mice that suggests they have an LD structure similar to that of humans (Laurie et al. 2007). The disadvantage of working with outbreds is that each animal is unique so that it is impossible to perform repeat experiments on the same genetic background, which may be necessary; for example, in a study to measure gene expression changes during development. Furthermore, the cost of high-density genotyping required limits the size of experiments. In contrast, inbred strains of mice permit these types of studies, need only be genotyped once, and there is a synergy in accumulating data from different experiments on standardized genetic backgrounds. There has been considerable debate over the direct use of the standard laboratory inbred strains for WGA. The main point of contention is that the number of independent inbred strains available is limited (for example, the mouse phenome database http://www.phenome.jax.org/pub-cgi/phenome/mpdcgi uses less than 40 priority strains, and even these strains share haplotypes to a considerable degree). Although the method may work for major QTLs explaining over 50% of the phenotypic variance, results are mixed for complex traits (Payseur and Place 2007) where QTLs of small effect are lost among false-positive signals in a genome scan. On the other hand, the sharp LD decay profile of the inbred strains is very attractive, so that if one can be sure that a QTL is segregating in a region (for example, from an F2 cross), then an analysis of inbred strains across the region may identify the gene in some cases. In addition, there is a considerable saving in genotyping costs. This discussion suggests there is a strong case for designing and constructing a large population of inbred lines that contains a high density of recombinants and where the lines are independent, in the sense that they do not share any recombination events. This was the motivation behind the Collaborative Cross (CC) (Churchill et al. 2004; Threadgill et al. 2002). Currently, over 400 CC recombinant inbred lines are being bred at Oak Ridge National Laboratary, USA, and Tel Aviv University, Israel, in a collaboration funded by the U.S. Department of Energy (DOE), The Ellison Foundation, National Institutes of Health (NIH), and The Wellcome Trust. The lines are descended from eight genetically diverse founder strains (including three wild-derived strains) and are currently between generation 6 and 10 of inbreeding. They will be fully inbred in about 4 years but are already sufficiently advanced that a pilot project is planned to assess their use for QTL mapping. The CC is almost ideal, except that the expected QTL mapping resolution is about 1 Mb (Valdar et al. 2005)—not quite single gene. The second issue is that the haplotype structure of the classical laboratory strains of mice is not ideal for complex trait analysis. To make best use of the mouse, we need complete genome sequences of the common laboratory strains. Already we know from partial resequencing of 16 strains that the so-called classical strains share only a fraction of the genetic variation segregating in wild-derived strains (Frazer et al. 2007; Yang et al. 2007) so there should be fewer QTLs in a study solely using classical strains. Moreover, their genomes are not independent; the pattern of haplotype sharing is not random across the genome, causing the problem of false-positive QTLs alluded to above. By contrast, the haplotype structure of the CC is necessarily random, very rare variants should not exist, and the founding strains contain more genetic variation than is present in some human populations (Roberts et al. 2007). We do not yet know much about the haplotype structure or origins of commercial outbreds. The third issue is how we should relate discoveries made in the mouse to human disease. It should be reiterated that many studies in mice are not feasible in humans, such as the elucidation of gene networks from most tissues and developmental stages. As it is extremely unlikely that identical causative polymorphisms will be segregating in both species, we should not necessarily expect the same genes to be identified in WGA, although there are examples where this is the case, such as cancer modifiers common to mice and humans (Ruivenkamp et al. 2002). Nevertheless, we should expect the same pathways to be implicated (Emilsson et al. 2008). However, at present the functional annotation of both species is incomplete. Therefore, alongside the development of suitable mapping populations, we need comprehensive annotation of the gene networks in the mouse, and how they vary during development, between tissues and between genetic backgrounds. There is not space here to say more except that it will require a concerted international effort, integrating and extending existing online resources.

Background Periodontal infection (Periodontitis) is a chronic inflammatory disease, which results in the breakdown of the supporting tissues of the teeth. Previous epidemiological studies have suggested that resistance to chronic periodontitis is controlled to some extent by genetic factors of the host. The aim of this study was to determine the phenotypic response of inbred and Collaborative Cross (CC) mouse populations to periodontal bacterial challenge, using an experimental periodontitis model. In this model, mice are co-infected with Porphyromonas gingivalis and Fusobacterium nucleatum, bacterial strains associated with human periodontal disease. Six weeks following the infection, the maxillary jaws were harvested and analyzed for alveolar bone loss relative to uninfected controls, using computerized microtomography (microCT). Initially, four commercial inbred mouse strains were examined to calibrate the procedure and test for gender effects. Subsequently, we applied the same protocol to 23 lines (at inbreeding generations 10–18) from the newly developed mouse genetic reference population, the Collaborative Cross (CC) to determine heritability and genetic variation of control bone volume prior to infection (CBV, naïve bone volume around the teeth of uninfected mice), and residual bone volume (RBV, bone volume after infection) and loss of bone volume (LBV, the difference between CBV and RBV) following infection. Results BALB/CJ mice were highly susceptible (P Conclusions The moderate heritability values indicate that the variation in host susceptibility to the disease is controlled to an appreciable extent by genetic factors. These results strongly support the possibility of using the Collaborative Cross, as well as developing dedicated F2 (resistant x susceptible inbred strains) resource populations, for future dissection of genetic factors in periodontitis.

Progress in complex trait mapping in mice has been accelerated by the development of new populations suited to high-resolution mapping and by statistical methodologies that control for population structure. When combined with newly acquired catalogs of sequence variation in inbred strains, the genetic architecture of these new populations makes it possible to dissect complex traits down to the level of single variants. These analyses have shown not only that complex traits are caused by multiple contributing loci but also that each locus is likely due to the combined effects of multiple causal DNA variants. In combination with new rapid methods for producing transgenic mice that make it efficient to test candidate genes and variants, these advances significantly enhance the mouse genetics toolbox for dissecting quantitative traits.

Membre INSERM du Rat Genome Sequencing and Mapping Consortium: Sophie Calderari (INSERM, UMRS872, Paris France); International audience; 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.

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.

Host susceptibility to periodontal infection is controlled by genetic factors. As a step toward identifying and cloning these factors, we generated an A/J x BALB/cJ F2 mouse resource population. A genome-wide search for Quantitative Trait Loci (QTL) associated with periodontitis was performed. We aimed to quantify the phenotypic response of the progenies to periodontitis by microCT analysis, to perform a genome-wide search for QTL associated with periodontitis, and, finally, to suggest candidate genes for periodontitis. We were able to produce 408 F2 mice. All mice were co-infected with Porphyromonas gingivalis and Fusobacterium nucleatum bacteria. Six weeks following infection, alveolar bone loss was quantified by computerized tomography (microCT) technology. We found normal distribution of the phenotype, with 2 highly significant QTL on chromosomes 5 and 3. A third significant QTL was found on chromosome 1. Candidate genes were suggested, such as Toll-like receptors (TLR) 1 and 6, chemokines, and bone-remodeling genes (enamelin, ameloblastin, and amelotin). This report shows that periodontitis in mice is a polygenic trait with highly significant mapped QTL.

A significant challenge of in-vivo studies is the identification of phenotypes with a method that is robust and reliable. The challenge arises from practical issues that lead to experimental designs which are not ideal. Breeding issues, particularly in the presence of fertility or fecundity problems, frequently lead to data being collected in multiple batches. This problem is acute in high throughput phenotyping programs. In addition, in a high throughput environment operational issues lead to controls not being measured on the same day as knockouts. We highlight how application of traditional methods, such as a Student's t-Test or a 2-way ANOVA, in these situations give flawed results and should not be used. We explore the use of mixed models using worked examples from Sanger Mouse Genome Project focusing on Dual-Energy X-Ray Absorptiometry data for the analysis of mouse knockout data and compare to a reference range approach. We show that mixed model analysis is more sensitive and less prone to artefacts allowing the discovery of subtle quantitative phenotypes essential for correlating a gene's function to human disease. We demonstrate how a mixed model approach has the additional advantage of being able to include covariates, such as body weight, to separate effect of genotype from these covariates. This is a particular issue in knockout studies, where body weight is a common phenotype and will enhance the precision of assigning phenotypes and the subsequent selection of lines for secondary phenotyping. The use of mixed models with in-vivo studies has value not only in improving the quality and sensitivity of the data analysis but also ethically as a method suitable for small batches which reduces the breeding burden of a colony. This will reduce the use of animals, increase throughput, and decrease cost whilst improving the quality and depth of knowledge gained.

During a meeting of the SYSGENET working group 'Bioinformatics', currently available software tools and databases for systems genetics in mice were reviewed and the needs for future developments discussed. The group evaluated interoperability and performed initial feasibility studies. To aid future compatibility of software and exchange of already developed software modules, a strong recommendation was made by the group to integrate HAPPY and R/qtl analysis toolboxes, GeneNetwork and XGAP database platforms, and TIQS and xQTL processing platforms. R should be used as the principal computer language for QTL data analysis in all platforms and a 'cloud' should be used for software dissemination to the community. Furthermore, the working group recommended that all data models and software source code should be made visible in public repositories to allow a coordinated effort on the use of common data structures and file formats.

The Collaborative Cross (CC) is a panel of recombinant inbred lines derived from eight genetically diverse laboratory inbred strains. Recently, the genetic architecture of the CC population was reported based on the genotype of a single male per line, and other publications reported incompletely inbred CC mice that have been used to map a variety of traits. The three breeding sites, in the US, Israel, and Australia, are actively collaborating to accelerate the inbreeding process through marker-assisted inbreeding and to expedite community access of CC lines deemed to have reached defined thresholds of inbreeding. Plans are now being developed to provide access to this novel genetic reference population through distribution centers. Here we provide a description of the distribution efforts by the University of North Carolina Systems Genetics Core, Tel Aviv University, Israel and the University of Western Australia.

International audience; 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.

The Collaborative Cross (CC) is a genetic reference panel of recombinant inbred lines of mice, designed for the dissection of complex traits and gene networks. Each line is independently descended from eight genetically diverse founder strains such that the genomes of the CC lines, once fully inbred, are fine-grained homozygous mosaics of the founder haplotypes. We present an analysis of 120 CC lines, from a cohort of the CC bred at Tel Aviv University in collaboration with the University of Oxford, which at the time of this study were between the sixth and 12th generations of inbreeding and substantially homozygous at 170,000 SNPs. We show how CC genomes decompose into mosaics, and we identify loci that carry a deficiency or excess of a founder, many being deficient for the wild-derived strains WSB/EiJ and PWK/PhJ. We phenotyped 371 mice from 66 CC lines for a susceptibility to Aspergillus fumigatus infection. The survival time after infection varied significantly between CC lines. Quantitative trait locus (QTL) mapping identified genome-wide significant QTLs on chromosomes 2, 3, 8, 10 (two QTLs), 15, and 18. Simulations show that QTL mapping resolution (the median distance between the QTL peak and true location) varied between 0.47 and 1.18 Mb. Most of the QTLs involved contrasts between wild-derived founder strains and therefore would not segregate between classical inbred strains. Use of variation data from the genomes of the CC founder strains refined these QTLs further and suggested several candidate genes. These results support the use of the CC for dissecting complex traits. © 2011 by Cold Spring Harbor Laboratory Press.

Genome-wide association studies using commercially available outbred mice can detect genes involved in phenotypes of biomedical interest. Useful populations need high-frequency alleles to ensure high power to detect quantitative trait loci (QTLs), low linkage disequilibrium between markers to obtain accurate mapping resolution, and an absence of population structure to prevent false positive associations. We surveyed 66 colonies for inbreeding, genetic diversity, and linkage disequilibrium, and we demonstrate that some have haplotype blocks of less than 100 Kb, enabling gene-level mapping resolution. The same alleles contribute to variation in different colonies, so that when mapping progress stalls in one, another can be used in its stead. Colonies are genetically diverse: 45% of the total genetic variation is attributable to differences between colonies. However, quantitative differences in allele frequencies, rather than the existence of private alleles, are responsible for these population differences. The colonies derive from a limited pool of ancestral haplotypes resembling those found in inbred strains: over 95% of sequence variants segregating in outbred populations are found in inbred strains. Consequently it is possible to impute the sequence of any mouse from a dense SNP map combined with inbred strain sequence data, which opens up the possibility of cataloguing and testing all variants for association, a situation that has so far eluded studies in completely outbred populations. We demonstrate the colonies' potential by identifying a deletion in the promoter of H2-Ea as the molecular change that strongly contributes to setting the ratio of CD4+ and CD8+ lymphocytes., Author Summary We show that commercially available mice are a resource for detecting single genes by genome-wide association. We surveyed 66 populations and identified those with properties conducive to high-resolution mapping. Importantly, we show that the same alleles contribute to variation in different colonies, so that when mapping progress stalls in one colony, another can be used in its stead. As a proof of principle, we detect the same QTL in different colonies influencing CD4+/CD8+ ratios and refine this mapping to the gene level. We show that a deletion in the promoter of H2-Ea is the molecular change that strongly contributes to setting the ratio of CD4+ and CD8+ lymphocytes. Our results make it possible for geneticists to make informed choices on the use of colonies for genome-wide association studies of complex traits in mice.

A proportion of the genetic variants underlying complex phenotypes do so through their effects on gene expression, so an important challenge in complex trait analysis is to discover the genetic basis for the variation in transcript abundance. So far, the potential of mapping both quantitative trait loci (QTLs) and expression quantitative trait loci (eQTLs) in rodents has been limited by the low mapping resolution inherent in crosses between inbred strains. We provide a megabase resolution map of thousands of eQTLs in hippocampus, lung, and liver samples from heterogeneous stock (HS) mice in which 843 QTLs have also been mapped at megabase resolution. We exploit dense mouse SNP data to show that artifacts due to allele-specific hybridization occur in ∼30% of the cis-acting eQTLs and, by comparison with exon expression data, we show that alternative splicing of the 3′ end of the genes accounts for cis-acting eQTLs. Approximately one third of cis-acting eQTLs and one half of trans-acting eQTLs are tissue specific. We have created an important systems biology resource for the genetic analysis of complex traits in a key model organism.

A number of authors have demonstrated that the schistosomicidal compound, Praziquantel (Pzq), depends for its action upon the immune status of the host (Sabah et al. 1985; Brindley & Sher, 1987; Doenhoff et al. 1987). We have attempted to define the synergistic interaction between immuno- and chemotherapy further, using the murine irradiated vaccine model of schistosomiasis mansoni. In vaccinated mice, resistance operates in the skin but not the lungs; drug targeted towards lung-stage worms exacerbates lung-phase immunity, however, as depicted by the increased number and size of inflammatory reactions in the pulmonary tissues. Parasites are often found trapped within such foci. In the present investigation, light and ultrastructural studies have been utilized to examine the nature and extent of damage inflicted upon lung-stage larvae recovered from day 6 Pzq-treated vaccinated mice. Such studies have revealed that damage involves muscle disorganization, internal disruption and occasionally, loss of the tegument; in the latter case, cells are often seen attached to the denuded lung worms. To identify the crucial cellular effector cell(s) involved in the synergy between immuno- and chemotherapy, cell depletion studies have been performed in vivo. It would appear from these experiments that eosinophils or lymphocytes rather than neutrophils or macrophages are important effector cells in this synergy. Histological studies argue in favour of eosinophils being the key effector cells.