High-Diversity Mouse Populations for Complex Traits
Authors: Michael C. Saul, Vivek M. Philip, Laura G. Reinholdt, Elissa J. Chesler
Journal: Trends in Genetics (2019)

High-diversity mouse populations with known and reproducible genetic variation make complex trait genetics tractable in a mammalian system.Together, these populations are a valuable integrated and scalable tool for discovery genetics in complex trait studies.The Collaborative Cross (CC), its founders, and the heterozygous CC-RIX derived from crosses of the CC strains are a fully reproducible population for exact genome-matched correlational and controlled studies.The Diversity Outbred (DO) population displays high genetic and phenotypic variability and enables precise genetic mapping.Cross-species genomic analysis of mouse-derived results allows comparative and translational applications. Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species. Contemporary mouse genetic reference populations are a powerful platform to discover complex disease mechanisms. Advanced high-diversity mouse populations include the Collaborative Cross (CC) strains, Diversity Outbred (DO) stock, and their isogenic founder strains. When used in systems genetics and integrative genomics analyses, these populations efficiently harnesses known genetic variation for precise and contextualized identification of complex disease mechanisms. Extensive genetic, genomic, and phenotypic data are already available for these high-diversity mouse populations and a growing suite of data analysis tools have been developed to support research on diverse mice. This integrated resource can be used to discover and evaluate disease mechanisms relevant across species. Complex diseases present compelling biomedical challenges that can be studied using human and nonhuman animal genetics. Although advances in human genetics have identified loci for many heritable complex diseases [1.Geschwind D.H. Flint J. Genetics and genomics of psychiatric disease.Science. 2015; 349: 1489-1494Crossref PubMed Scopus (122) Google Scholar, 2.Okada Y. et al.Meta-analysis identifies multiple loci associated with kidney function-related traits in East Asian populations.Nat. Genet. 2012; 44: 904-909Crossref PubMed Scopus (138) Google Scholar, 3.Ehret G.B. et al.The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.Nat. Genet. 2016; 48: 1171-1184Crossref PubMed Scopus (272) Google Scholar, 4.Baranov V.S. et al.Systems genetics view of endometriosis: a common complex disorder.Eur. J. Obstet. Gynecol. Reprod. Biol. 2015; 185: 59-65Abstract Full Text Full Text PDF PubMed Google Scholar, 5.Locke A.E. et al.Genetic studies of body mass index yield new insights for obesity biology.Nature. 2015; 518: 197-206Crossref PubMed Scopus (1264) Google Scholar, 6.Raj T. et al.Genetic architecture of age-related cognitive decline in African Americans.Neurol. Genet. 2017; 3e125Crossref PubMed Scopus (5) Google Scholar, 7.Marioni R.E. et al.GWAS on family history of Alzheimer's disease.Transl. Psychiatry. 2018; 8: 99Crossref PubMed Scopus (7) Google Scholar, 8.Karaderi T. et al.Insights into the genetic susceptibility to type 2 diabetes from genome-wide association studies of obesity-related traits.Curr. Diabetes Rep. 2015; 15: 83Crossref PubMed Google Scholar], there are several well-known limitations of genome-wide association studies (GWASs). (i) For many human disease loci, the biological mechanism of action is unknown. (ii) When little is known about a locus, the path from genetic association to clinically actionable targets is unclear. (iii) Disease process or developmental trajectory is not always obvious from a causal genetic variant. (iv) GWAS results may not generalize across human subpopulations. (v) Medical records and participant phenotyping is often incomplete, imprecise, and retrospective, whereas model organism phenotyping can include in-depth, prospective, and standardized measures. (vi) Sample size requirements are formidable in human GWASs. (vii) Power is insufficient to study interacting genetic loci. (viii) Studies of genetic interaction with development, environment, and sex are largely intractable. Further, heterogeneous diseases like psychiatric disorders manifest with overlapping symptoms, intertwined disease trajectories [9.Arnedo J. et al.Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies.Am. J. Psychiatry. 2015; 172: 139-153Crossref PubMed Scopus (15) Google Scholar], and complex genetic regulation [9.Arnedo J. et al.Uncovering the hidden risk architecture of the schizophrenias: confirmation in three independent genome-wide association studies.Am. J. Psychiatry. 2015; 172: 139-153Crossref PubMed Scopus (15) Google Scholar, 10.Zuk O. et al.Searching for missing heritability: designing rare variant association studies.Proc. Natl. Acad. Sci. U. S. A. 2014; 111: E455-E464Crossref PubMed Scopus (0) Google Scholar]. In summary, the SNP-to-disease association model underlying GWASs cannot readily capture complex disease biology without further biological context. These challenges are often tractable with discovery genetics in model organisms. Human genetic studies are essential to identify causal loci for human disease. However, there is growing recognition that these studies merely point to etiology and mechanisms rather than therapeutic interventions. Further, these findings are often specific to the population under investigation, so precise causal information is of limited general utility. Model organisms simplify the discovery of molecular networks associated with disease phenotypes in temporal and anatomical context. Thus, when a human variant is found, nonhuman animal studies may connect it to a relevant biological process – and often to a druggable target. When the goal is to elucidate biological mechanisms of disease for diagnosis and intervention, consilience between human and nonhuman animal genetics provides an efficient and inexpensive approach to biological discovery and clinical translation [11.Nadeau J.H. Auwerx J. The virtuous cycle of human genetics and mouse models in drug discovery.Nat. Rev. Drug Discov. 2019; 18: 255-272Crossref PubMed Scopus (0) Google Scholar]. While consilience is rare at the variant level, it often manifests at the level of orthologous genes, pathways, and molecular networks. The mouse remains the predominant resource for disease-related research [12.Perrin S. Preclinical research: make mouse studies work.Nature. 2014; 507: 423-425Crossref PubMed Scopus (244) Google Scholar, 13.Gould S.E. et al.Translational value of mouse models in oncology drug development.Nat. Med. 2015; 21: 431-439Crossref PubMed Scopus (106) Google Scholar, 14.Justice M.J. Dhillon P. Using the mouse to model human disease: increasing validity and reproducibility.Dis. Model. Mech. 2016; 9: 101-103Crossref PubMed Scopus (0) Google Scholar, 15.Sukoff Rizzo S.J. Crawley J.N. Behavioral phenotyping assays for genetic mouse models of neurodevelopmental, neurodegenerative, and psychiatric disorders.Annu. Rev. Anim. Biol. 2017; 5: 371-389Crossref PubMed Scopus (0) Google Scholar]. Recent advances in sequencing, computation, and gene editing that have advanced human genetics have also greatly augmented the capabilities of mouse genetics, resulting in new tools and resources for mechanistic discovery and translation. Much biomedical research has utilized C57BL/6 mice and outbred stocks (see Glossary) such as Hsd:ICR, and Swiss Webster mice. Inbred mouse strains such as C57BL/6 promise experimental rigor and reproducibility [16.Festing M.F. Evidence should trump intuition by preferring inbred strains to outbred stocks in preclinical research.ILAR J. 2014; 55: 399-404Crossref PubMed Scopus (8) Google Scholar]. Individuals of the same sex from within ten generations of properly maintained isogenic stocks have essentially identical genomes. Modern colony maintenance further reduces the accumulation of spontaneous mutations [17.Taft R.A. et al.Know thy mouse.Trends Genet. 2006; 22: 649-653Abstract Full Text Full Text PDF PubMed Scopus (88) Google Scholar, 18.Sarsani V.K. et al.The genome of C57BL/6J "Eve", the mother of the laboratory mouse genome reference strain.bioRxiv. 2019Crossref Google Scholar]. Standardized genomes allow direct comparison of individuals from isogenic stocks across laboratories separated by years and continents. As a consequence of genome standardization, inbred mice are widely used in biomedical science. Unfortunately, standardization impedes many important research applications. Generalizability is limited with results gleaned from a single strain. Further, individuals from inbred strains exhibit variability within and across laboratories, attributable to limited diversity in stabilizing mechanisms in response to environmental variation [19.Tuttle A.H. et al.Comparing phenotypic variation between inbred and outbred mice.Nat. Methods. 2018; 15: 994-996Crossref PubMed Scopus (4) Google Scholar]. Naturally occurring genetic diversity of mice provides allelic variation for biological discovery within a well-characterized, easily manipulated experimental system. Findings from genetically diverse populations are more likely to generalize across species or strains. Early experimental crosses and other populations derived from inbred mouse strains harnessed the diversity of laboratory mice [20.Biggers J.D. Claringbold P. Why use inbred lines?.Nature. 1954; 174: 596-597Crossref PubMed Scopus (0) Google Scholar] and have been used to detect complex trait mechanisms in behavioral science, immunology, and physiology [21.Chesler E.J. Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research.Mamm. Genome. 2014; 25: 3-11Crossref PubMed Scopus (33) Google Scholar]. Using quantitative trait locus (QTL) mapping and genetic correlation in mouse populations, researchers have detected variants in genes orthologous to candidates from human GWASs [22.Attie A.D. et al.How mice are indispensable for understanding obesity and diabetes genetics.Curr. Opin. Endocrinol. Diabetes Obes. 2017; 24: 83-91Crossref PubMed Scopus (8) Google Scholar]. However, early QTL mapping studies reported broad regions containing many genes and variants that were difficult to prioritize due to limited contemporaneous genetic and genomic resources. One advance was the development of systems genetics approaches, which integrate QTL mapping with genome-wide molecular phenotyping to discover, characterize, and contextualize molecular and phenotypic network variation across biological scale. These studies often used genetic reference populations such as the BXD recombinant inbred strains to integrate data across experiments. Over the past two decades, community-based efforts to improve mouse resources gave rise to three new advanced mouse populations. The Complex Trait Consortium (CTC), a group dedicated to producing new genetics tools to study complex traits in diverse populations, proposed the first of these resources in the early 2000s [23.Threadgill D.W. et al.Genetic dissection of complex and quantitative traits: from fantasy to reality via a community effort.Mamm. Genome. 2002; 13: 175-178Crossref PubMed Scopus (0) Google Scholar]. The CTC sought to improve mapping precision and systems genetics power. The resulting mouse populations – the CC, the DO, and their founder inbred strains – provide complimentary genetic reagents for the study of complex traits. They comprise an integrated resource harboring a substantial pool of shared genetic variation. That variation is randomized throughout the genome in the CC and DO populations. The eight founder strains capture approximately 90% of the genetic diversity seen in the Mus musculus species [24.Roberts A. et al.The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm. Genome. 2007; 18: 473-481Crossref PubMed Scopus (144) Google Scholar]. These strains are A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/HlLtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ (Figure 1A ). Five are common laboratory strains descendent from fancy mice of the M. musculus domesticus subspecies. The remaining strains are wild-derived inbred representatives of three M musculus subspecies: the European domesticus, the north Asian musculus, and the South Asian castaneus. Wild mice display traits that were bred out of domesticated mice [21.Chesler E.J. Out of the bottleneck: the Diversity Outcross and Collaborative Cross mouse populations in behavioral genetics research.Mamm. Genome. 2014; 25: 3-11Crossref PubMed Scopus (33) Google Scholar, 25.Wahlsten D. et al.A rating scale for wildness and ease of handling laboratory mice: results for 21 inbred strains tested in two laboratories.Genes Brain Behav. 2003; 2: 71-79Crossref PubMed Scopus (0) Google Scholar] while providing genetic diversity intrinsic to the mouse species [24.Roberts A. et al.The polymorphism architecture of mouse genetic resources elucidated using genome-wide resequencing data: implications for QTL discovery and systems genetics.Mamm. Genome. 2007; 18: 473-481Crossref PubMed Scopus (144) Google Scholar]. The DO and CC genetic reference populations are recombinant populations systematically derived from the founders. Randomized breeding design greatly reduces population structure effects that limit mapping resolution in collections of extant inbred strains. The CTC intended to breed a panel of 1000 recombinant inbred strains [26.Williams R.W. Churchill G.A. The Collaborative Cross: Rationale, Implementation, and Costs. NIH, 2004Google Scholar, 27.Vogel G. Scientists dream of 1001 complex mice.Science. 2003; 301: 456-457Crossref PubMed Scopus (0) Google Scholar, 28.Churchill G.A. et al.The Collaborative Cross, a community resource for the genetic analysis of complex traits.Nat. Genet. 2004; 36: 1133-1137Crossref PubMed Scopus (626) Google Scholar] to obtain a reproducible mapping population. The CTC worked to provide the highest-diversity resource achievable. However, there were some challenges. When the founders were chosen, only limited genotypic data were available. While the committee that chose the founder strains made efforts to maximize genetic variation, additional consideration was given to disease susceptibility, particularly in cancer and diabetes research. The CTC simulated many designs to introduce high diversity with randomization. In the absence of genotypic information, an eight-founder design was attractive, but a simpler four-founder design would have had a higher, 25% minor allele frequency for strain-specific variants and could have simplified breeding logistics. Furthermore, such a design allows all possible breeding configurations to be performed, whereas only a fraction of some 80 000 possible matings could be sampled in the CC, requiring extensive efforts to randomize and balance mitochondrial and sex chromosome composition [29.Chesler E.J. et al.The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics.Mamm. Genome. 2008; 19: 382-389Crossref PubMed Scopus (163) Google Scholar]. Three international breeding sites produced hundreds of incipient CC funnels [29.Chesler E.J. et al.The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics.Mamm. Genome. 2008; 19: 382-389Crossref PubMed Scopus (163) Google Scholar, 30.Morahan G. et al.Establishment of "The Gene Mine": a resource for rapid identification of complex trait genes.Mamm. Genome. 2008; 19: 390-393Crossref PubMed Scopus (55) Google Scholar, 31.Iraqi F.A. et al.The Collaborative Cross, developing a resource for mammalian systems genetics: a status report of the Wellcome Trust cohort.Mamm. Genome. 2008; 19: 379-381Crossref PubMed Scopus (61) Google Scholar] and a wide variety of insights into mammalian genetics [32.Philip V.M. et al.Genetic analysis in the Collaborative Cross breeding population.Genome Res. 2011; 21: 1223-1238Crossref PubMed Scopus (0) Google Scholar, 33.Aylor D.L. et al.Genetic analysis of complex traits in the emerging Collaborative Cross.Genome Res. 2011; 21: 1213-1222Crossref PubMed Scopus (211) Google Scholar, 34.Durrant C. et al.Collaborative Cross mice and their power to map host susceptibility to Aspergillus fumigatus infection.Genome Res. 2011; 21: 1239-1248Crossref PubMed Scopus (85) Google Scholar], but inbreeding depression, infertility, and other factors led to a high rate of attrition [35.Shorter J.R. et al.Male infertility is responsible for nearly half of the extinction observed in the mouse Collaborative Cross.Genetics. 2017; 206: 557-572Crossref PubMed Scopus (14) Google Scholar]. Approximately 50 finished CC strains are publicly available today, capturing a representative cross-section of founder haplotypes [36.Srivastava A. et al.Genomes of the mouse Collaborative Cross.Genetics. 2017; 206: 537-556Crossref PubMed Scopus (20) Google Scholar] and providing adequate power for genetic correlation studies. Their genetic equidistance makes them a suitable replacement for the nonuniformly related extant inbred strain collection that comprise the original Mouse Phenome Panel [37.Paigen K. Eppig J.T. A mouse phenome project.Mamm. Genome. 2000; 11: 715-717Crossref PubMed Google Scholar] and the Hybrid Mouse Diversity Panel [38.Ghazalpour A. et al.Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits.Mamm. Genome. 2012; 23: 680-692Crossref PubMed Scopus (58) Google Scholar]. The DO was initiated as an ultrahigh-precision mapping population [39.Svenson K.L. et al.High-resolution genetic mapping using the mouse Diversity Outbred population.Genetics. 2012; 190: 437-447Crossref PubMed Scopus (237) Google Scholar]. Intercrossing the incipient CC lines results in recombination to reduce linkage disequilibrium (LD) blocks to their biological limits. The result is a heterogeneous stock whose genetics derive from an equal contribution of each of the eight founder strains in a random configuration. This population is maintained primarily through pseudorandom matings, although interventions have been performed to maintain variation across the genome [40.Chesler E.J. et al.Diversity Outbred mice at 21: maintaining allelic variation in the face of selection.G3 (Bethesda). 2016; 6: 3893-3902Crossref PubMed Scopus (14) Google Scholar]. Historically, heterogeneous stock populations including the HS/Ibg and HS/Npt were developed as selection base populations, but work in the late 1990s described their use as a resource for fine mapping of complex traits [41.Mott R.M. et al.A method for fine mapping quantitative trait loci in outbred animal stocks.Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 12649-12654Crossref PubMed Scopus (302) Google Scholar, 42.Talbott C.J. et al.High-resolution mapping of quantitative trait loci in outbred mice.Nat. Genet. 1999; 21: 305-308Crossref PubMed Scopus (195) Google Scholar], leading to a large mapping study of many complex traits [43.Valdar W. et al.Genome-wide genetic association of complex traits in heterogeneous stock mice.Nat. Genet. 2006; 38: 879-887Crossref PubMed Scopus (388) Google Scholar]. This extremely fine mapping can also be performed in other advanced intercross lines [44.Darvasi A. Soller M. Advanced intercross lines, an experimental population for fine genetic mapping.Genetics. 1995; 141: 1199-1207Crossref PubMed Google Scholar]. With an estimated 45 million segregating polymorphisms, the CC and DO populations have more genetic diversity than observed across the human population [45.Keane T.M. et al.Mouse genomic variation and its effect on phenotypes and gene regulation.Nature. 2011; 477: 289-294Crossref PubMed Scopus (737) Google Scholar]. This may lead to complex patterns of trait regulation at the population level, manifested as high-sample-size requirements likely to be attributable to epistatic interactions that stabilize trait variation, but has the benefit of providing detectable genetic variation in every gene and pathway, facilitating the discovery of biological mechanisms for virtually any complex phenotype. The advanced-diversity populations comprise an integrated resource to discover and explore biological mechanisms underlying complex disease-related traits. Many experimental applications are possible. Four modes of complex trait discovery are widely used: (i) establishing trait heritability; (ii) evaluating relationships among traits to test for shared mechanisms; (iii) identifying model strains for focused mechanistic studies; and (iv) mapping the genetic basis of trait variation. Each mouse population is best suited to particular applications (Figure 2, Key Figure), but their shared set of polymorphisms provides extensive opportunities for data integration. Molecular, genetic, genomic, and phenotypic resources are available: fully sequenced genomes from the Sanger Mouse Genomes project can be accessed at the Mouse Genome Database (MGD) and trait data are deposited in the Mouse Phenome Database (MPD) [46.Grubb S.C. et al.Mouse phenome database.Nucleic Acids Res. 2013; 42: D825-D834Crossref PubMed Scopus (0) Google Scholar]. Analytical tools tailored to common applications of multiparent populations are available (Box 1). Because of their common genetics, studies performed in these mice can be integrated with existing molecular, genetic, and disease-relevant work (Figure 3).Box 1Software and Analysis ToolsA growing analytical toolkit facilitates work with advanced mouse populations. A recent review discussed these resources in great depth [110.Morgan A.P. Welsh C.E. Informatics resources for the Collaborative Cross and related mouse populations.Mamm. Genome. 2015; 26: 521-539Crossref PubMed Scopus (16) Google Scholar]. Most of the software packages available are free and open-source.An advantage of isogenic founder strains is their known and reproducible underlying genotypes. The sequenced reference mouse strain is one founder, the C57BL/6J mouse strain. The Mouse Genomes Project led by the Sanger Institute has sequenced and assembled the genomes of the other seven founder strains. Their website includes useful tools to query genetic variation among the founder strains, allowing researchers to quickly identify variants likely to drive biological mechanisms (available at https://www.sanger.ac.uk/science/data/mouse-genomes-project). Visualization facilities for genome features are available using the MGI Multiple Genome Viewer (available at http://www.informatics.jax.org/mgv). The genomes of many extant CC mice have been both genotyped and sequenced; their genotypes were released on the UNC Systems Genetics website (available at http://csbio.unc.edu/CCstatus/CCGenomes).For DO animals, individual genotyping is required for any genetic mapping studies. At present, the most recent Mouse Universal Genotyping Array (GigaMUGA) platform is the predominant whole-genome genotyping resource used for DO mice. Genotyping using these arrays can be purchased as a service from the Neogen Corporation (available at http://www.neogen.com/genomics). HaploQA is web-based software for the interpretation of MUGA-derived microarrays (available at http://haploqa.jax.org). Genotyping by RNA sequencing (GBRS) is a potentially promising avenue to high-resolution genotyping that also produces relevant molecular phenotyping data for systems genetics work. The software package GBRS is presently under development for use with advanced mouse populations (available at https://gbrs.readthedocs.io/en/latest) and can be used for alignment and transcript abundance estimation as well as for genotyping.Sequencing experiments with these populations typically involve the reconstruction of each founder genome and annotation, lifting over length variations introduced by short insertions and deletions. The g2g tools software package performs these tasks (available at https://github.com/churchill-lab/g2gtools). Allele-specific expression patterns for RNA-seq can be elucidated using EMASE (available at https://emase.readthedocs.io/en/latest).Typically, a statistical model used for QTL mapping in DO will model phenotype as a function of genotype and a number of other factors [39.Svenson K.L. et al.High-resolution genetic mapping using the mouse Diversity Outbred population.Genetics. 2012; 190: 437-447Crossref PubMed Scopus (237) Google Scholar]. DO mice display 36 genotype states (eight homozygous and 28 heterozygous diplotypes) compared with two or three in traditional mapping crosses. To infer these states, a hidden Markov model (HMM) on genotype array data represents predicted diplotypes as probabilistic estimates of each diplotype state [111.Broman K.W. et al.R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multiparent populations.Genetics. 2019; 211: 495-502Crossref PubMed Scopus (3) Google Scholar]. A typical QTL mapping model regresses the trait on estimated diplotype probabilities for each of the eight founder alleles, a reduced form of the more complex 36-state diplotype model that assumes heterozygotes are intermediates of homozygous states for any given trait. In standard modeling notation, this model is:yi=βssi+βggi+∑j=18βjgij+λi+ϵi,[I] where yi is the ith animal's phenotype, βssi is the effect of sex for the ith animal, βggi is the effect of an additive grouping covariate for the ith animal, βjgij is the effect of founder allele probability for allele j in the ith animal, λi is the polygenic random effect of the ith animal, and ϵi is the error term. Statistical software was developed to operationalize this model for R, including R/DOQTL (https://bioconductor.org/packages/release/bioc/html/DOQTL.html) and qtl2 (https://kbroman.org/qtl2).Although this model does not capture dominance, it has been effective in mapping complex traits. A critical component of the regression model, a random-effects kinship matrix derived from pairwise diplotype probabilities, captures relatedness among subjects to increase power. Traits can also be adjusted by regression against nuisance factors, using the residuals for mapping, although this approach may suppress genetic signal [112.Darlington R.B. Smulders T.V. Problems with residual analysis.Anim. Behav. 2001; 62: 599-602Crossref Google Scholar]. These models produce LOD scores for genotypes across the genome, and genome-wide statistical significance is tested via permutation excluding the kinship matrix (kinship violates the assumption of exchangeability) [113.Churchill G. Doerge R. Naive application of permutation testing leads to inflated type I error rates.Genetics. 2008; 178: 609-610Crossref PubMed Scopus (37) Google Scholar, 114.Cheng R. Palmer A.A. A simulation study of permutation, bootstrap, and gene dropping for assessing statistical significance in the case of unequal relatedness.Genetics. 2013; 193: 1015-1018Crossref PubMed Scopus (21) Google Scholar]. A 95% Bayesian credible interval describes positional confidence intervals [115.Sen Ś. Churchill G.A. A statistical framework for quantitative trait mapping.Genetics. 2001; 159: 371-387Crossref PubMed Google Scholar] often as narrow as 2 Mb, underscoring the precision of mapping in the DO. Within a QTL, a two-state SNP association model improves precision.An advantage to using diversity mice is the reusability of data resources when the researcher deposits them in a database. For DO, CC, and founders datasets, the Mouse Phenome Database (MPD) (https://phenome.jax.org/) includes measures readily useable for initial exploration, trait correlation, and discovery genetics. A repository of DO QTL studies is maintained at the Jackson Laboratory (https://dodb.jax.org). For various two-parent mouse crosses such as the BXD, GeneNetwork has produced an impressive suite of resources to deposit, analyze, and integrate systems genetics datasets (http://genenetwork.org).To integrate, compare, and contrast mouse and human data, GeneWeaver facilitates the integration of heterogeneous genome-scale datasets collected across multiple species (https://geneweaver.org). Knowledge-guided exploration of discoveries from gene sets generated in multiple species can be performed in KnowEnG (https://knoweng.org). Cross-species integration applications such as the Monarch Initiative [108.Mungall C.J. et al.The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.Nucleic Acids Res. 2017; 45: D712-D722Crossref PubMed Scopus (0) Google Scholar] aim to bridge genomic and phenotypic annotations between commonly used species using orthology information.Last, technologies change quickly. The Jackson Laboratory Genetic Diversity Initiative provides up-to-date information on these mouse populations (available at https://www.jax.org/research-and-faculty/genetic-diversity-initiative).Figure 3Multiple Diversity Mouse Resources Can Be Used in Separate Experiments to Dissect a Single Biological Question at Multiple Levels.Show full captionIn this case, multiple published experiments deposited in the Mouse Phenome Data