Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics
Authors: Emily Swanzey, Callan O’Connor, Laura G. Reinholdt
Journal: Trends in Genetics (2020)

Genetically diverse mice, such as Diversity Outbred (DO) and Collaborative Cross (CC) panels, can be used to model human genetic diversity and map genetic modifiers underlying phenotypic variation at high resolution and under controlled environmental conditions.Cell lines derived from DO and CC mice can be used to study systems genetics in a variety of cell culture applications, to understand the influence of genetics on cell state transitions and cellular function in a variety of cell types and in toxicology and pharmacogenetic screening.DO and CC cell lines enable integrated in vitro and in vivo screening capabilities across the same genotypes for multidimensional systems genetics studies and validation.Integrative genomics facilitates the identification of orthologous genes and cellular pathways that regulate human diseases and traits. Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation. Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation. Connecting Complex Traits with Causal Genetic VariantsVariation in physiological and molecular phenotypes across individuals and organisms is a central evolutionary property that is, in part, determined by genetic variation. These traits can range from cellular morphology to behavior and disease susceptibility, and are typically influenced by multiple genes. The ability of genetic background to modulate the expression of genes implicated in purported Mendelian traits (see Glossary) suggests that few truly monogenic traits actually exist, and most are indeed genetically complex, with multiple variants of varying effect sizes contributing to a phenotype [1.Nadeau J.H. Modifier genes in mice and humans.Nat. Rev. Genet. 2001; 2: 165-174Crossref PubMed Scopus (435) Google Scholar, 2.Cooper D.N. et al.Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease.Hum. 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Genome-wide association studies (GWASs), for example, aim to correlate the frequency of SNPs with diseases or traits. While GWASs have been successful in identifying many loci associated with complex traitsi, the results from these studies often do not provide evidence for the specific underlying biological mechanisms. This is, in part, because most associations are found in regions with similarly correlated SNPs (i.e., linkage disequilibrium [5.Gallagher M.D. Chen-Plotkin A.S. The post-GWAS era: from association to function.Am. J. Hum. Genet. 2018; 102: 717-730Abstract Full Text Full Text PDF PubMed Scopus (156) Google Scholar]), making it challenging to pinpoint causal variants. In addition, many associated SNPs have been found in intergenic regions, suggesting the existence of underlying regulatory mechanisms that cannot be dissected with correlative studies alone. Finally, many common variants in the human population only subtly influence the trait of interest, and therefore large sample sizes are required to detect these small effects. Together, sample size requirements and the inability to functionally interrogate pathways in human cellular networks have limited further interrogation of GWAS loci.To bridge the gap between genotype and phenotype, approaches were needed to integrate molecular data with genotype and phenotype data. Systems genetics provided such a platform where multiple phenotypes, including cellular and molecular traits, can be collected from a genetic reference population (GRP) and associated with genetic variation using statistical genetics. With this approach, phenotype-regulating genomic regions, known as quantitative trait loci (QTLs), can be identified in a genetically diverse population with fully sequenced genomes and catalogs of phenotype data. Recent advances in the scalability of high-throughput sequencing technologies have enabled large-scale acquisition of transcriptomic, epigenetic, proteomic, and metabolomic data across different cell types, treatment groups, and phenotypes. With these molecular data, GRPs can be associated with specific omics signatures, providing a robust means of connecting traits with causal genetic variants and cellular network perturbations and revealing a deeper understanding of the molecular cause of interindividual variation.Cellular Models from GRPsThe power of systems genetics comes from the ability to integrate a multitude of cellular and molecular datasets for genetically diverse GRPs, enabling multidimensional analyses. In this framework, in vitro GRP models with renewable cell populations offer attractive and scalable platforms for systems genetics. A GRP with sufficient genetic diversity can be simulated in cell lines through mutagenic approaches such as chemical mutagenesis or CRISPR/Cas9-mediated perturbations, which have been used for genome-wide screening of essential genes [6.Shalem O. et al.Genome-scale CRISPR-Cas9 knockout screening in human cells.Science. 2014; 343: 84-87Crossref PubMed Scopus (2313) Google Scholar] and enhancer elements [7.Sanjana N.E. et al.High-resolution interrogation of functional elements in the noncoding genome.Science. 2016; 353: 1545-1549Crossref PubMed Google Scholar,8.Korkmaz G. et al.Functional genetic screens for enhancer elements in the human genome using CRISPR-Cas9.Nat. Biotechnol. 2016; 34: 192-198Crossref PubMed Scopus (211) Google Scholar] and for saturation mutagenesis of contiguous chromosomal regions for cis-regulatory element discovery [9.Diao Y. et al.A tiling-deletion-based genetic screen for cis-regulatory element identification in mammalian cells.Nat. Methods. 2017; 14: 629-635Crossref PubMed Scopus (92) Google Scholar]. Alternatively, cellular GRPs can be created by deriving cells directly from a genetically diverse population. While some advantages exist in the former approach, natural diversity encompasses the full spectrum of variant types found in populations (e.g., copy number, structural, and coevolved variants) and is the focus of this review.There are a variety of available model organism GRPs, and ultimately the choice of GRP depends not only on the ability of that organism to appropriately and economically model a particular disease but also on the diversity, strength, and haplotype structure of the alleles segregating in the population. Cellular GRPs can be created by deriving cell lines ('in vitro') or culturing primary cells ('ex vivo') directly from a genetically diverse population, thereby capturing the resident natural variation in those individuals [10.Panopoulos A.D. et al.iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types.Stem Cell Rep. 2017; 8: 1086-1100Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar, 11.Streeter I. et al.The Human-Induced Pluripotent Stem Cell Initiative – data resources for cellular genetics.Nucleic Acids Res. 2017; 45: D691-D697Crossref PubMed Scopus (0) Google Scholar, 12.Garbutt T.A. et al.Permissiveness to form pluripotent stem cells may be an evolutionarily derived characteristic in Mus musculus.Sci. 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In this review we discuss how these tools can be used to approximate human genetic variation in cellular systems to dissect network regulation and advance the field of systems genetics.Genetically Diverse Human Cell PanelsLarge-scale international human genome sequencing projects like the 1000 Genomes Project have produced collections of genetically diverse lymphoblastoid cell lines (Table 1) that have been used in a range of genetic studies, demonstrating the power of in vitro-generated molecular QTL data for fine mapping of cis-regulatory variation [14.Tehranchi A. et al.Fine-mapping cis-regulatory variants in diverse human populations.Elife. 2019; 8e39595Crossref PubMed Scopus (0) Google Scholar], improving GWAS resolution [15.Nicolae D.L. et al.Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.PLoS Genet. 2010; 6e1000888Crossref PubMed Scopus (782) Google Scholar,16.Calderon D. et al.Landscape of stimulation-responsive chromatin across diverse human immune cells.Nat. Genet. 2019; 51: 1494-1505Crossref PubMed Scopus (17) Google Scholar], and discovering genetic variation underlying differential G×E responses [15.Nicolae D.L. et al.Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS.PLoS Genet. 2010; 6e1000888Crossref PubMed Scopus (782) Google Scholar,17.Theusch E. et al.Genetic variants modulate gene expression statin response in human lymphoblastoid cell lines.BMC Genomics. 2020; 21: 555Crossref PubMed Scopus (0) Google Scholar]. These panels of cells have extensive genomic and molecular data that are publicly available [18.Zheng Z. et al.QTLbase: an integrative resource for quantitative trait loci across multiple human molecular phenotypes.Nucleic Acids Res. 2020; 48: D983-D991Crossref PubMed Scopus (4) Google Scholar]. Such resources demonstrate the power of cellular platforms for systems genetics in the human population but they are limited to essentially one cell type.Table 1Examples of Existing Systems Genetic Reference Populations and Cell Lines (Where Applicable) for In Vitro Applications.OrganismGeneticsPanelDerivationSystemRefsHumanHeterogeneousHipSciiPSC lines derived from ~150 rare disease and ~500 healthy donorshiPSCs[19.Carcamo-Orive I. et al.Analysis of transcriptional variability in a large human iPSC library reveals genetic and non-genetic determinants of heterogeneity.Cell Stem Cell. 2017; 20: 518-532.e519Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar, 20.DeBoever C. et al.Large-scale profiling reveals the influence of genetic variation on gene expression in human induced pluripotent stem cells.Cell Stem Cell. 2017; 20: 533-546.e537Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar, 21.Kilpinen H. et al.Common genetic variation drives molecular heterogeneity in human iPSCs.Nature. 2017; 546: 370-375Crossref PubMed Scopus (166) Google Scholar]HeterogeneousiPSCOREhiPSC lines derived from ~220 donors including matched familial and twin lineshiPSCs[10.Panopoulos A.D. et al.iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types.Stem Cell Rep. 2017; 8: 1086-1100Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar]HeterogeneousLymphoblastoid cell lines (LCLs)Derived from peripheral blood of donor populations (e.g., 1000 Genomes, HapMap Project, NHGRI Human Genetics Cell Repository)LCLs[103.1000 Genomes Project, Consortium et al.A global reference for human genetic variation.Nature. 2015; 526: 68-74Crossref PubMed Scopus (5283) Google Scholar], Coriell Cell RepositoryMouseInbred strainsHybrid Mouse Diversity Panel (HMDP), Mouse Phenome PanelCollections of diverse and well-characterized inbred strainsMouse embryonic fibroblasts (MEFs), limited iPSCs, limited mESCs[12.Garbutt T.A. et al.Permissiveness to form pluripotent stem cells may be an evolutionarily derived characteristic in Mus musculus.Sci. Rep. 2018; 8: 14706Crossref PubMed Scopus (0) Google Scholar,58.Suzuki O.T. et al.A cellular genetics approach identifies gene-drug interactions and pinpoints drug toxicity pathway nodes.Front. Genet. 2014; 5: 272Crossref PubMed Scopus (5) Google Scholar,104.Czechanski A. et al.Derivation and characterization of mouse embryonic stem cells from permissive and nonpermissive strains.Nat. Protoc. 2014; 9: 559-574Crossref PubMed Scopus (69) Google Scholar,105.Park S. et al.Genetic regulation of fibroblast activation and proliferation in cardiac fibrosis.Circulation. 2018; 138: 1224-1235Crossref PubMed Scopus (17) Google Scholar], The Jackson LaboratoryRI and RI advanced intercrossBXDStrains developed by crossing C57BL/6J and DBA/2J miceEx vivo only to date[106.Morse III, H.C. et al.Expression of xenotropic murine leukemia viruses as cell-surface gp70 in genetic crosses between strains DBA/2 and C57BL/6.J. Exp. Med. 1979; 149: 1183-1196Crossref PubMed Scopus (30) Google Scholar,107.Peirce J.L. et al.A new set of BXD recombinant inbred lines from advanced intercross populations in mice.BMC Genet. 2004; 5: 7Crossref PubMed Scopus (351) Google Scholar]RICCHighly diverse strains derived from an eight-way cross of A/J, C57BL/6J, 129S1/SvImJ, NOD/ShiLtJ, NZO/ HlLtJ, CAST/EiJ, PWK/PhJ, and WSB/EiJ miceEx vivo, limited mESCs, MEFs[13.Skelly D.A. et al.Mapping the effects of genetic variation on chromatin state and gene expression reveals loci that control ground state pluripotency.Cell Stem Cell. 2020; 27: 459-469.e8Abstract Full Text Full Text PDF PubMed Google Scholar,108.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 (717) Google Scholar,109.Graham J.B. et al.Extensive homeostatic T cell phenotypic variation within the Collaborative Cross.Cell Rep. 2017; 21: 2313-2325Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]OutbredDOPanel developed by random outcross matings of 160 CC mice and maintained by random matings that avoid sibling crossesEx vivo, mESCs[110.Svenson K.L. et al.High-resolution genetic mapping using the mouse Diversity Outbred population.Genetics. 2012; 190: 437-447Crossref PubMed Scopus (240) Google Scholar], Predictive BiologySaccharomyces cerevisiaeRISGRP-4XPanel developed from a four-way cross and then intercrossing for 12 generationsWhole organism[34.Cubillos F.A. et al.High-resolution mapping of complex traits with a four-parent advanced intercross yeast population.Genetics. 2013; 195: 1141-1155Crossref PubMed Scopus (0) Google Scholar]Outbred18F12Two populations developed from 18 genetically diverse founder strainsWhole organism[33.Linder R.A. et al.Two synthetic 18-way outcrossed populations of diploid budding yeast with utility for complex trait dissection.Genetics. 2020; 215: 323-342Crossref PubMed Scopus (0) Google Scholar] Open table in a new tab Pluripotent cell lines offer distinct advantages over primary cells because they can be differentiated into a potentially unlimited variety of cell types (Table 1) [19.Carcamo-Orive I. et al.Analysis of transcriptional variability in a large human iPSC library reveals genetic and non-genetic determinants of heterogeneity.Cell Stem Cell. 2017; 20: 518-532.e519Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar, 20.DeBoever C. et al.Large-scale profiling reveals the influence of genetic variation on gene expression in human induced pluripotent stem cells.Cell Stem Cell. 2017; 20: 533-546.e537Abstract Full Text Full Text PDF PubMed Scopus (68) Google Scholar, 21.Kilpinen H. et al.Common genetic variation drives molecular heterogeneity in human iPSCs.Nature. 2017; 546: 370-375Crossref PubMed Scopus (166) Google Scholar]. Both the NextGen Consortium and the Human Induced Pluripotent Stem Cell Initiative (HipSci) have been generating reference sets of human induced pluripotent stem cell (hiPSC) lines from healthy donors and patients with rare genetic diseases for a host of research purposes [10.Panopoulos A.D. et al.iPSCORE: a resource of 222 iPSC lines enabling functional characterization of genetic variation across a variety of cell types.Stem Cell Rep. 2017; 8: 1086-1100Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar,11.Streeter I. et al.The Human-Induced Pluripotent Stem Cell Initiative – data resources for cellular genetics.Nucleic Acids Res. 2017; 45: D691-D697Crossref PubMed Scopus (0) Google Scholar]. Recently, genetic mapping studies of molecular traits in these cellular GRPs have revealed the role of genetic variation in the regulation of intrachromosomal chromatin accessibility and gene expression in iPSCs [22.Young Greenwald W.W. et al.Chromatin co-accessibility is highly structured, spans entire chromosomes, and mediates long range regulatory genetic effects.bioRxiv. 2019; (Published online April 9, 2019. https://doi.org/10.1101/604371)Google Scholar]. Moreover, elegant 'village in a dish' approaches have been used to develop Census-seq, which is a genetic mapping approach that associates cellular phenotypes with individual genotypes cultured in pools [23.Mitchell J.M. et al.Mapping genetic effects on cellular phenotypes with "cell villages".bioRxiv. 2020; (Published online June 29, 2020. https://doi.org/10.1101/2020.06.29.174383)Google Scholar]. However, hurdles associated with population structure, limited genetic diversity in some panels, and heterogeneity in human environmental exposure must be carefully considered.Cell Panels from Model Organism GRPsModel organism GRPs have been precisely designed to offer increased statistical power (e.g., mouse [24.Gatti D.M. et al.Quantitative trait locus mapping methods for diversity outbred mice.G3 (Bethesda). 2014; 4: 1623-1633Crossref PubMed Scopus (89) Google Scholar, 25.Broman K.W. Haplotype probabilities in advanced intercross populations.G3 (Bethesda). 2012; 2: 199-202Crossref PubMed Scopus (16) Google Scholar, 26.Liu Y. et al.Joint analysis of strain and parent-of-origin effects for recombinant inbred intercrosses generated from multiparent populations with the Collaborative Cross as an example.G3 (Bethesda). 2018; 8: 599-605Crossref PubMed Scopus (1) Google Scholar], Drosophila [27.King E.G. et al.Genetic dissection of a model complex trait using the Drosophila Synthetic Population Resource.Genome Res. 2012; 22: 1558-1566Crossref PubMed Scopus (104) Google Scholar], yeast, see following text) and an opportunity for systems genetics discovery with in vivo correlation and validation capabilities. One of the pillars of model organism genetic mapping studies has been the analysis of pairwise crosses between two genetically divergent founder strains (Figure 1A ). With the use of large populations with segregating alleles, phenotypes can be mapped to genotypes. This approach has been particularly rewarding in yeast, where QTLs have been shown to explain over 70% of the narrow-sense heritability of traits [28.Ehrenreich I.M. et al.Dissection of genetically complex traits with extremely large pools of yeast segregants.Nature. 2010; 464: 1039-1042Crossref PubMed Scopus (257) Google Scholar, 29.Bloom J.S. et al.Finding the sources of missing heritability in a yeast cross.Nature. 2013; 494: 234-237Crossref PubMed Scopus (240) Google Scholar, 30.Bloom J.S. et al.Rare variants contribute disproportionately to quantitative trait variation in yeast.Elife. 2019; 8e49212Crossref PubMed Scopus (4) Google Scholar, 31.Bloom J.S. et al.Genetic interactions contribute less than additive effects to quantitative trait variation in yeast.Nat. Commun. 2015; 6: 8712Crossref PubMed Google Scholar, 32.Martens K. et al.Predicting quantitative traits from genome and phenome with near perfect accuracy.Nat. Commun. 2016; 7: 11512Crossref PubMed Scopus (0) Google Scholar, 33.Linder R.A. et al.Two synthetic 18-way outcrossed populations of diploid budding yeast with utility for complex trait dissection.Genetics. 2020; 215: 323-342Crossref PubMed Scopus (0) Google Scholar]. However, these panels offer extreme under-sampling of the functional variation that could segregate in natural populations. For this reason, multiparent populations (MPPs) have gained popularity in their ability to combine fine mapping resolution with increased genetic variation from the founders. MPP generation is accomplished by crossing several inbred founder strains to one another, thereby increasing genetic diversity, and then further introgression to capture the products of accumulating recombination events. These individuals can also be inbred to create a panel of recombinant inbred (RI) lines that are homozygous, reproducible genetic mosaics of the founder strains. Such MPP panels have now been generated for in vitro studies in Saccharomyces cerevisiae using four and 18 fully sequenced founder strains [33.Linder R.A. et al.Two synthetic 18-way outcrossed populations of diploid budding yeast with utility for complex trait dissection.Genetics. 2020; 215: 323-342Crossref PubMed Scopus (0) Google Scholar,34.Cubillos F.A. et al.High-resolution mapping of complex traits with a four-parent advanced intercross yeast population.Genetics. 2013; 195: 1141-1155Crossref PubMed Scopus (0) Google Scholar] (Table 1).Mouse GRPsGiven the difficulties in conducting research with human samples and the phenotypic limitations of yeast GRPs, mammalian model systems, such as mice, have been invaluable tools for cross-species comparison (Table 1 and Figure 1B). Studies have repeatedly shown considerable overlap between rodents and humans in the gene orthologs and pathways that contribute to complex traits, highlighting the translatability of these systems [35.Langley S.R. et al.Systems-level approaches reveal conservation of trans-regulated genes in the rat and genetic determinants of blood pressure in humans.Cardiovasc. Res. 2013; 97: 653-665Crossref PubMed Scopus (23) Google Scholar, 36.Justice M.J. Dhillon P. Using the mouse to model human disease: increasing validity and reproducibility.Dis. Model. 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The ability to continually regenerate these mice or comparable genotypes has enabled researchers to compile data across cell types and environmental conditions (reviewed by [39.Dunn A.R. et al.Gene-by-environment interactions in Alzheimer's disease and Parkinson's disease.Neurosci. Biobehav. Rev. 2019; 103: 73-80Crossref PubMed Scopus (0) Google Scholar]). The mouse systems genetics field has been generating such data for decades, primarily with genetically diverse inbred, consomic, and RI strains like the BXD mouse panel (Table 1), among other panels (reviewed in [40.Li H. Auwerx J. Mouse systems genetics as a prelude to precision medicine.Trends Genet. 2020; 36: 259-272Abstract Full Text Full Text PDF PubMed Scopus (1) Google Scholar]). These data are housed in databases such as GeneNetworkii and the Mouse Phenome Databaseiii where they can be used for further trait correlation and post hoc analyses. The BXD panel is an invaluable tool with moderate genetic variation that allows more facile discovery of causative variants with a range of effect sizes.While the BXD captures 13% of known genetic variation in mice, the more recently developed Collaborative Cross (CC) and Diversity Outbred (DO) MPP panels captures over 90% of the genetic variation in laboratory mice and with that, greater phenotypic diversity [41.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 (168) Google Scholar]. CC and DO panels were derived from eight inbred parental strains (Figure 1B) and capture approximately 45 million segregating polymorphisms [42.Keane T.M. et al.Mouse genomic variation and its effect on phenotypes and gene regulation.Nature. 2011; 477: 289-294Crossref PubMed Scopus (883) Google Scholar], representing ~1.0 million years of evolutionary divergence. This can be compared with ~30 years of divergence captured in a substrain reduced complexity cross (reviewed by [43.Bryant C.D. et al.Facilitating complex trait analysis via reduced complexity crosses.Trends Genet. 2020; 36: 549-562Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar]), ~100 years captured in a standard two-way RI cross involving common laboratory inbred strains of the same subspecies, and ~40 000 years of divergence captured in much of the human population after the great expansion [44.Garrigan D. et al.Inferring human population sizes, divergence times and rates of gene flow from mitochondrial, X and Y chromosome resequencing data.Genetics. 2007; 177: 2195-2207Crossref PubMed Scopus (56) Google Scholar,45.Henn B.M. et al.The great human expansion.Proc. Natl. Acad. Sci. U. S. A. 2012; 109: 17758-17764Crossref PubMed Scopus (0) Google Scholar]. The CC panel comprises RI strains designed to have controlled genomic randomization that is normally distributed across the genome, enabling high reproducibility for systems genetics studies (Table 1 and Figure 1B). The DO population was generated by random outcrossings of CC strains to create a population of mice with abundant and broadly distributed genetic diversity, high allelic heterozygosity, balanced founder allele frequencies, and accumulated recombination events that provide high resolution for genetic mapping (Table 1 and Figure 1B). The mating scheme used to generate and maintain the DO doubles the effective population size and minimizes the impact of selection and genetic drift on allele frequencies, increasing the statistical power of the DO panel and reducing the burden of sample size [46.Churchill G.A. et al.The Diversity Outbred mouse population.Mamm. Genome. 2012; 23: 713-718Crossref PubMed Scopus (191) Google Scholar,47.Rockman M.V. Kruglyak L. Breeding designs for recombinant inbred advanced intercross lines.Genetics. 2008; 179: 1069-1078Crossref PubMed Scopus (69) Google Scholar]. CC strains offer the advantage of replicable genomes, while every DO mouse is genetically unique and cannot be replicated at the genome level. However, selection of CC inbred founder strains based on genetic variation discovered in the derivative DO population allows the identification of replicable genotypes in specific regions of interest.While there is clear utility of these panels, in vitro application of genetically diverse cell systems remains a largely untapped resource due to a lack of available GRP cell panels and, until recently, lack of scalable genomic technologies. Increased availability of these resources will provide new opportunities to scale and integrate systems genetic studies in organisms that are amenable to in vivo validation and modeling of human disease (e.g., [13.Skelly D.A. et al.Mapping the effects of genetic variation on chromatin state and gene expression reveals loci that control ground state pluripotency.Cell Stem Cell. 2