The Hybrid Mouse Diversity Panel: a resource for systems genetics analyses of metabolic and cardiovascular traits

The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions. The Hybrid Mouse Diversity Panel (HMDP) is a collection of approximately 100 well-characterized inbred strains of mice that can be used to analyze the genetic and environmental factors underlying complex traits. While not nearly as powerful for mapping genetic loci contributing to the traits as human genome-wide association studies, it has some important advantages. First, environmental factors can be controlled. Second, relevant tissues are accessible for global molecular phenotyping. Finally, because inbred strains are renewable, results from separate studies can be integrated. Thus far, the HMDP has been studied for traits relevant to obesity, diabetes, atherosclerosis, osteoporosis, heart failure, immune regulation, fatty liver disease, and host-gut microbiota interactions. High-throughput technologies have been used to examine the genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes of the mice under various environmental conditions. All of the published data are available and can be readily used to formulate hypotheses about genes, pathways and interactions. Common forms of cardiovascular and metabolic diseases are caused by the interactions of multiple genetic and environmental factors. The ability to interrogate the genomes of large numbers of individuals using high density genotyping and, more recently, next generation sequencing has enabled the identification of numerous loci robustly associated with many of the common disorders. However, efforts to extend these data to important biologic insights have progressed slowly. Human studies are often confounded by the difficulty of monitoring environmental factors and the inability to obtain relevant tissue samples for molecular analyses. To address these issues, we have developed the Hybrid Mouse Diversity Panel (HMDP), a collection of approximately 100 inbred strains of mice exhibiting substantial diversity of most cardiovascular and metabolic traits relevant to human disease (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar). The resource offers some important advantages for analysis of complex traits as compared with the traditional intercrosses between different mouse strains, including high-resolution association mapping and cumulative data. The HMDP strains have now been studied for a variety of metabolic and cardiovascular traits as well as various “omics” phenotypes (Table 1). The results have been collected in a database which can be searched and analyzed to identify novel disease genes, model biologic pathways, examine gene-by-environment, study host-gut microbiome relationships, and prioritize human genome-wide association study (GWAS) candidate genes.TABLE 1Clinical and molecular phenotypes studied in the HMDP resourceTraitDietPlasma lipidsC, HF, ATHAdiposityC, HF, ATHOsteoporosisCBlood cell levelsC, HF, ATHIRC, HF, ATHFatty liver diseaseHF, ATHHeart failure induced by isoproterenolISOAtherosclerosisATHDiabetic nephropathyCTranscript levelsLiverC, HF, ATHAdiposeC, HFAortaATHHippocampusCStriatumCSkeletal muscleHFHeartC, ISOProtein levels, liverCMetabolitesLiverCPlasmaHF, ATHGut microbiomeC, HF ATHDNA methylationCMice were maintained on chow (C), high-fat (HF), or atherogenic (ATH) diets or treated with ISO. Open table in a new tab Mice were maintained on chow (C), high-fat (HF), or atherogenic (ATH) diets or treated with ISO. We anticipate that this review will primarily be of interest to cardiometabolic investigators interested in using data from the HMDP to help guide their research. Therefore, at the end of the review, in the Database section, we have discussed the kinds of questions that can be addressed using the data. Also, because many cardiometabolic researchers may not be versed in genetics approaches, we have defined some of the terms and concepts used in this review in Table 2.TABLE 2Glossary of genetics terms used in this reviewTermDefinitionBiological scalesVarious levels in the flow of information from DNA to proteins to metabolites to cell structures to cell interactions.Cis-regulatory elementsRegions of DNA which regulate the transcription of genes, usually nearby, on the same DNA strand. Examples are promoters or enhancers.Congenic strainsStrains in which a small region of the genome from one strain has been placed, by repeated crossing, onto the genetic background of a second strain.CorrelationIn statistics, a measure of the strength and direction of a linear relationship between two variables. Usually measured as a correlation coefficient.eQTLA genetic locus that controls the levels of a transcript.GWASAn examination of common genetic variation across the genome designed to identify associations with traits such as common diseases. Typically, several hundred thousand SNPs are interrogated using microarray technologies.HaplotypesCombinations of alleles at genetic loci that are inherited together.HeritabilityAn estimate of the proportion of genetic variation in a population that is attributable to genetic variation among individuals.Inbred strainsStrains in which a set of naturally occurring genetic variations have been fixed by many generations of inbreeding.Linkage analysisAnalysis of the segregation patterns of alleles or loci in families or experimental crosses. Such analysis is commonly used to map genetic traits by testing whether a trait cosegregates with genetic markers whose chromosomal locations are known.LDIn population genetics, LD is the nonrandom association of alleles. For example, alleles of SNPs that reside near one another on a chromosome often occur in nonrandom combinations owing to infrequent recombination. LD should not be confused with genetic linkage, which occurs when genetic loci or alleles are inherited jointly, usually because they reside on the same chromosome.LD blocksRegions of high correlation across genetic markers, which results from their linkage in cis on a chromosome and thus infrequent recombination during meiosis. LD blocks are often demarcated by recombination hot spotsModulesIn the context of network modeling, groups of components that are tightly connected or correlated across a set of conditions, perturbations or genetic backgrounds.Natural genetic variationGenetic variation that is present in all populations as a result of mutations that occur in the germline; the frequencies of such mutations in populations are affected by selection and by random drift. This is in contrast with experimental variation that is introduced by techniques such as gene targeting and chemical mutagenesis.QTLA genetic locus that influences complex and usually continuous traits, such as blood pressure or cholesterol levels.RI strainsA set of inbred strains that is generally produced by crossing two parental inbred strains and then inbreeding random intercross progeny; they provide a permanent resource for examining the segregation of traits that differ between the parental strains.Systems geneticsA global analysis of the molecular factors that underlie variability in physiological or clinical phenotypes across individuals in a population. It considers not only the underlying genetic variation but also intermediate phenotypes such as gene expression, protein levels and metabolite levels, in addition to gene-by-gene and gene-by-environment interactions.Trans-regulatory factorsFactors which regulate the transcription of genes at a distance. Examples are transcription factors and microRNAs.LD, linkage disequilibrium. Open table in a new tab LD, linkage disequilibrium. The HMDP was developed as a systems genetics resource similar to recombinant inbred (RI) strain sets (2Andreux P.A. Williams E.G. Koutnikova H. Houtkooper R.H. Champy M.F. Henry H. Schoonjans K. Williams R.W. Auwerx J. Systems genetics of metabolism: the use of the BXD murine reference panel for multiscalar integration of traits.Cell. 2012; 150: 1287-1299Abstract Full Text Full Text PDF PubMed Scopus (157) Google Scholar, 3Williams E.G. Auwerx J. The convergence of systems and reductionist approaches in complex trait analysis.Cell. 2015; 162: 23-32Abstract Full Text Full Text PDF PubMed Scopus (46) Google Scholar) or chromosome substitution strains (4Nadeau J.H. Forejt J. Takada T. Shiroishi T. Chromosome substitution strains: gene discovery, functional analysis, and systems studies.Mamm. Genome. 2012; 23: 693-705Crossref PubMed Scopus (25) Google Scholar), but with the added advantage of high-resolution association mapping (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar). It consists of a set of 30 classic inbred strains chosen for diversity plus 70 or more RI strains derived primarily from strains C57BL/6J and DBA/2J (the BxD RI set) and A/J and C57BL/6J (the AxB and BxA RI sets). The classic strains provide mapping resolution, while the RI strains provide power. All of the chosen strains are commercially available from the Jackson Laboratory (www.jax.org) and all have been either sequenced (www.sanger.ac.uk/science/data/mouse-genomes-project) or densely genotyped (5Rau C.D. Parks B. Wang Y. Eskin E. Simecek P. Churchill G.A. Lusis A.J. High-density genotypes of inbred mouse strains: improved power and precision of association mapping.G3 (Bethesda). 2015; 5: 2021-2026Crossref PubMed Scopus (23) Google Scholar). In common with RI strains (6Toth L.A. Trammell R.A. Williams R.W. Mapping complex traits using families of recombinant inbred strains: an overview and example of mapping susceptibility to Candida albicans induced illness phenotypes.Pathog. Dis. 2014; 71: 234-248Crossref PubMed Scopus (4) Google Scholar), the HMDP resource is renewable in the sense that the inbred strains are permanent. This allows multiple mice of the same genotype to be studied, increasing the accuracy of the data that are collected, and results derived from different studies of the HMDP can be integrated. For example, transcriptomic data obtained in one study (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar) were used to interpret proteomic data (7Ghazalpour A. Bennett B. Petyuk V.A. Orozco L. Hagopian R. Mungrue I.N. Farber C.R. Sinsheimer J. Kang H.M. Furlotte N. et al.Comparative analysis of proteome and transcriptome variation in mouse.PLoS Genet. 2011; 7: e1001393Crossref PubMed Scopus (428) Google Scholar) and metabolic data (8Ghazalpour A. Bennett B.J. Shih D. Che N. Orozco L. Pan C. Hagopian R. He A. Kayne P. Yang W.P. et al.Genetic regulation of mouse liver metabolite levels.Mol. Syst. Biol. 2014; 10: 730Crossref PubMed Scopus (41) Google Scholar) obtained from a separate set of mice. The ability to perform high-resolution association mapping in the HMDP is based on the inclusion of about 30 “classic” inbred strains, which have undergone many generations of recombination since their origins from stocks of pet mice (9Silver, L. M., 1995. Mouse Genetics: Concepts and Applications. Oxford University Press, Oxford, UK.Google Scholar). This makes it possible to carry out association analysis much as in a human GWAS. Generally, it is possible to map complex traits to one to two megabase regions containing five to 20 genes or less using the HMDP, depending on the level of linkage disequilibrium and gene density of the region (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar). This resolution is at least an order of magnitude improved as compared with traditional linkage analysis. For example, Fig. 1 shows the mapping of a cis-expression quantitative trait locus (eQTL) in the HMDP and an F2 intercross. One important point to note is that because the classic inbred strains exhibit very significant population structure, it is essential that this is corrected to avoid false positive associations. This is conveniently accomplished using mixed model algorithms such as EMMA (10Kang H.M. Zaitlen N.A. Wade C.M. Kirby A. Heckerman D. Daly M.J. Eskin E. Efficient control of population structure in model organism association mapping.Genetics. 2008; 178: 1709-1723Crossref PubMed Scopus (1137) Google Scholar) or FaST-LMM (11Lippert C. Listgarten J. Liu Y. Kadie C.M. Davidson R.I. Heckerman D. FaST linear mixed models for genome-wide association studies.Nat. Methods. 2011; 8: 833-835Crossref PubMed Scopus (703) Google Scholar). These algorithms essentially perform a t-test for association while correcting for population structure using a kinship matrix based on genotypes. Genome-wide significance is determined using simulation, a Bonferroni correction, or a false discovery rate (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar, 12Parks B.W. Sallam T. Mehrabian M. Psychogios N. Hui S.T. Norheim F. Castellani L.W. Rau C.D. Pan C. Phun J. et al.Genetic architecture of insulin resistance in the mouse.Cell Metab. 2015; 21: 334-346Abstract Full Text Full Text PDF PubMed Scopus (138) Google Scholar). With only 100 inbred strains in the HMDP, mapping power is considerably limited as compared with large intercrosses between pairs of inbred strains or human GWASs with thousands of samples. Nevertheless, simulation studies suggest that there is reasonable power to map loci that explain 5% or more of the trait variance (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar). Because, as in humans, there are likely to be hundreds of loci that contribute to complex clinical traits, the mapping will generally detect only the handful of loci with strongest effects. Power can be increased by examining additional inbred and RI strains that have been genotyped (5Rau C.D. Parks B. Wang Y. Eskin E. Simecek P. Churchill G.A. Lusis A.J. High-density genotypes of inbred mouse strains: improved power and precision of association mapping.G3 (Bethesda). 2015; 5: 2021-2026Crossref PubMed Scopus (23) Google Scholar, 13Furlotte N.A. Kang E.Y. Van Nas A. Farber C.R. Lusis A.J. Eskin E. Increasing association mapping power and resolution in mouse genetic studies through the use of meta-analysis for structured populations.Genetics. 2012; 191: 959-967Crossref PubMed Scopus (9) Google Scholar), but for practical reasons most studies have been limited to about 100 strains. Power can also be considerably increased while retaining high resolution by performing meta-analysis that incorporates data from traditional crosses (14Kang E.Y. Han B. Furlotte N. Joo J.W. Shih D. Davis R.C. Lusis A.J. Eskin E. Meta-analysis identifies gene-by-environment interactions as demonstrated in a study of 4,965 mice.PLoS Genet. 2014; 10: e1004022Crossref PubMed Scopus (31) Google Scholar, 15Ohmen J. Kang E.Y. Li X. Joo J.W. Hormozdiari F. Zheng Q.Y. Davis R.C. Lusis A.J. Eskin E. Friedman R.A. Genome-wide association study for age-related hearing loss (AHL) in the mouse: a meta-analysis.J. Assoc. Res. Otolaryngol. 2014; 15: 335-352Crossref PubMed Scopus (26) Google Scholar). Molecular phenotypes, such as transcript levels, protein levels, and metabolite levels, are generally determined by a much smaller number of loci than clinical traits and there is adequate power to map at least the major loci affecting these. For example, using expression arrays to quantitate liver transcript levels, about 2,500 significant cis-expression quantitative trait loci (eQTLs) were detected in liver (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar), while about 5,000 cis-eQTLs were detected in cultured macrophages (16Orozco L.D. Bennett B.J. Farber C.R. Ghazalpour A. Pan C. Che N. Wen P. Qi H.X. Mutukulu A. Siemers N. et al.Unraveling inflammatory responses using systems genetics and gene-environment interactions in macrophages.Cell. 2012; 151: 658-670Abstract Full Text Full Text PDF PubMed Scopus (96) Google Scholar). The HMDP panel includes about 4,000,000 common SNPs, roughly similar to the number of common SNPs in human populations (17Park C.C. Gale G.D. de Jong S. Ghazalpour A. Bennett B.J. Farber C.R. Langfelder P. Lin A. Khan A.H. Eskin E. et al.Gene networks associated with conditional fear in mice identified using a systems genetics approach.BMC Syst. Biol. 2011; 5: 43Crossref PubMed Scopus (60) Google Scholar), and there is substantial variation of most clinical traits that have been examined, as discussed below. In contrast, the Collaborative Cross and the Diversity Outbred (18Iraqi F.A. Athamni H. Dorman A. Salymah Y. Tomlinson I. Nashif A. Shusterman A. Weiss E. Houri-Haddad Y. Mott R. et al.Heritability and coefficient of genetic variation analyses of phenotypic traits provide strong basis for high-resolution QTL mapping in the Collaborative Cross mouse genetic reference population.Mamm. Genome. 2014; 25: 109-119Crossref PubMed Scopus (33) Google Scholar) include “wild-derived” strains, which increase the diversity by an order of magnitude (17Park C.C. Gale G.D. de Jong S. Ghazalpour A. Bennett B.J. Farber C.R. Langfelder P. Lin A. Khan A.H. Eskin E. et al.Gene networks associated with conditional fear in mice identified using a systems genetics approach.BMC Syst. Biol. 2011; 5: 43Crossref PubMed Scopus (60) Google Scholar). While there will certainly be greater total variation of most complex traits in the Collaborative Cross, there will also be greater genetic complexity, potentially complicating genetic dissection. Among the HMDP mice, about 40% of genes exhibit significant cis-eQTLs in various tissues, and the vast majority of genes exhibit secondary (trans-regulated) genetic variation. If the mouse is to serve as a model of common metabolic and cardiovascular traits, it is important that the relevant pathways be conserved in the two species. One measure of such conservation is the degree of overlap between mouse and human GWAS data. Studies in the HMDP for osteoporosis (19Farber C.R. Bennett B.J. Orozco L. Zou W. Lira A. Kostem E. Kang H.M. Furlotte N. Berberyan A. Ghazalpour A. et al.Mouse genome-wide association and systems genetics identify Asxl2 as a regulator of bone mineral density and osteoclastogenesis.PLoS Genet. 2011; 7: e1002038Crossref PubMed Scopus (92) Google Scholar, 20Mesner L.D. Ray B. Hsu Y.H. Manichaikul A. Lum E. Bryda E.C. Rich S.S. Rosen C.J. Criqui M.H. Allison M. et al.Bicc1 is a genetic determinant of osteoblastogenesis and bone mineral density.J. Clin. Invest. 2014; 124: 2736-2749Crossref PubMed Scopus (42) Google Scholar), obesity (21Parks B.W. Nam E. Org E. Kostem E. Norheim F. Hui S.T. Pan C. Civelek M. Rau C.D. Bennett B.J. et al.Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.Cell Metab. 2013; 17: 141-152Abstract Full Text Full Text PDF PubMed Scopus (391) Google Scholar), blood cell levels (22Davis R.C. van Nas A. Bennett B. Orozco L. Pan C. Rau C.D. Eskin E. Lusis A.J. Genome-wide association mapping of blood cell traits in mice.Mamm. Genome. 2013; 24: 105-118Crossref PubMed Scopus (28) Google Scholar), and heart failure (23Rau C.D. Wang J. Avetisyan R. Romay M.C. Martin L. Ren S. Wang Y. Lusis A.J. Mapping genetic contributions to cardiac pathology induced by Beta-adrenergic stimulation in mice.Circ Cardiovasc Genet. 2015; 8: 40-49Crossref PubMed Scopus (53) Google Scholar) suggest that the overlap will be substantial. We discuss an example of pathway conservation in the section on fatty liver disease. The power of the HMDP for analysis of complex traits derives from the integration of genetics with global molecular phenotypes using “omics” technologies (Table 1). The natural variations found among the inbred strains of the HMDP directly perturb a substantial fraction of all genes, as judged by the number of genes exhibiting cis-eQTL or allele-specific expression (24Hasin-Brumshtein Y. Hormozdiari F. Martin L. van Nas A. Eskin E. Lusis A.J. Drake T.A. Allele-specific expression and eQTL analysis in mouse adipose tissue.BMC Genomics. 2014; 15: 471Crossref PubMed Scopus (38) Google Scholar, 25Lagarrigue S. Martin L. Hormozdiari F. Roux P.F. Pan C. van Nas A. Demeure O. Cantor R. Ghazalpour A. Eskin E. et al.Analysis of allele-specific expression in mouse liver by RNA-Seq: a comparison with Cis-eQTL identified using genetic linkage.Genetics. 2013; 195: 1157-1166Crossref PubMed Scopus (35) Google Scholar), and these, in turn, result in thousands of secondary perturbations. When the molecular and clinical traits are monitored together, relationships between them can be observed using mapping, correlation, and modeling [reviewed in (26Civelek M. Lusis A.J. Systems genetics approaches to understand complex traits.Nat. Rev. Genet. 2014; 15: 34-48Crossref PubMed Scopus (411) Google Scholar)]. This is the basis of “systems genetics.” Omics data can be analyzed using genetics in the same manner as other phenotypic traits. For example, variations in the levels of a transcript in a population can be treated as a quantitative trait and the genetic loci responsible can be mapped to regions of the genome using linkage or association analyses. Loci that reside near the genes whose transcripts are measured are likely to affect enhancer/promoter function and are thus often assumed to act in cis, while loci affecting expression of genes on other chromosomes or many megabases away on the same chromosome presumably act through diffusible factors and are thus assumed to act in trans. Such loci are termed eQTLs. Originally, individual transcript levels were quantitated in populations using hybridization or polymerase chain reaction amplification (27Machleder D. Ivandic B. Welch C. Castellani L. Reue K. Lusis A.J. Complex genetic control of HDL levels in mice in response to an atherogenic diet. Coordinate regulation of HDL levels and bile acid metabolism.J. Clin. Invest. 1997; 99: 1406-1419Crossref PubMed Scopus (123) Google Scholar), but with the advent of expression arrays and RNA-Seq, it became possible to map eQTLs globally (1Bennett B.J. Farber C.R. Orozco L. Kang H.M. Ghazalpour A. Siemers N. Neubauer M. Neuhaus I. Yordanova R. Guan B. et al.A high-resolution association mapping panel for the dissection of complex traits in mice.Genome Res. 2010; 20: 281-290Crossref PubMed Scopus (233) Google Scholar). Such studies have shown that genetic variations in gene expression are very common, affecting levels of thousands of genes in both human and mouse populations [reviewed in (26Civelek M. Lusis A.J. Systems genetics approaches to understand complex traits.Nat. Rev. Genet. 2014; 15: 34-48Crossref PubMed Scopus (411) Google Scholar, 28Montgomery S.B. Dermitzakis E.T. From expression QTLs to personalized transcriptomics.Nat. Rev. Genet. 2011; 12: 277-282Crossref PubMed Scopus (114) Google Scholar)]. Moreover, it appears that a large fraction (∼85%) of the variations for common disease traits result from variations in gene expression rather than from structural (protein coding) variation [for example, (29Brænne I. Civelek M. Vilne B. Di Narzo A. Johnson A.D. Zhao Y. Reiz B. Codoni V. Webb T.R. Foroughi Asl H. et al.Leducq Consortium CAD Genomics. Prediction of causal candidate genes in coronary artery disease loci.Arterioscler. Thromb. Vasc. Biol. 2015; 35: 2207-2217Crossref PubMed Scopus (67) Google Scholar)]. The levels of proteins and metabolites can also be quantitatively measured using high throughput technologies, and the loci controlling these can be similarly mapped to identify protein QTLs (pQTLs) or metabolite QTLs (7Ghazalpour A. Bennett B. Petyuk V.A. Orozco L. Hagopian R. Mungrue I.N. Farber C.R. Sinsheimer J. Kang H.M. Furlotte N. et al.Comparative analysis of proteome and transcriptome variation in mouse.PLoS Genet. 2011; 7: e1001393Crossref PubMed Scopus (428) Google Scholar, 8Ghazalpour A. Bennett B.J. Shih D. Che N. Orozco L. Pan C. Hagopian R. He A. Kayne P. Yang W.P. et al.Genetic regulation of mouse liver metabolite levels.Mol. Syst. Biol. 2014; 10: 730Crossref PubMed Scopus (41) Google Scholar). Whereas common disease traits are complex, influenced by tens or hundreds of loci, molecular traits tend to be much simpler. For example, cis-eQTLs often explain a large fraction of the variance of the transcript levels. A key aspect of the systems genetics approach is that molecular traits can thus constitute a bridge of sorts between DNA variation and clinical traits. An example of the application of such “vertical” omics is shown in Fig. 2. Several million sites of DNA methylation were identified in livers of the HMDP strains, using reduced representational bisulfite sequencing, and 22,000 sites that exhibited substantial genetic variation in methylation levels were selected. These were then tested for significant association with molecular traits, as quantitated by expression arrays, proteomics, and metabolomics, as well as clinical traits. The flow of biologic information is apparent at the “hotspot” loci where differences in DNA methylation at a single locus can be seen to influence the levels of multiple transcripts, proteins, and metabolites. As illustrated below, omics data can be used to identify candidate genes for clinical traits using correlation and causality testing (30Schadt E.E. Lamb J. Yang X. Zhu J. Edwards S. Guhathakurta D. Sieberts S.K. Monks S. Reitman M. Zhang C. et al.An integrative genomics approach to infer causal associations between gene expression and disease.Nat. Genet. 2005; 37: 710-717Crossref PubMed Scopus (811) Google Scholar, 31Aten J.E. Fuller T.F. Lusis A.J. Horvath S. Using genetic markers to orient the edges in quantitative trait networks: the NEO software.BMC Syst. Biol. 2008; 2: 34Crossref PubMed Scopus (112) Google Scholar, 32Lan H. Chen M. Flowers J.B. Yandell B.S. Stapleton D.S. Mata C.M. Mui E.T. Flowers M.T. Schueler K.L. Manly K.F. et al.Combined expression trait correlations and expression quantitative trait locus mapping.PLoS Genet. 2006; 2: e6Crossref PubMed Scopus (91) Google Scholar). Interactions between genes and their relationships to clinical traits can also be examined using enrichment analyses or network modeling (33Farber C.R. Systems-level analysis of genome-wide association data.G3 (Bethesda). 2013; 3: 119-129Crossref PubMed Scopus (48) Google Scholar, 34Rau C.D. Wisniewski N. Orozco L.D. Bennett B. Weiss J. Lusis A.J. Maximal information component analysis: a novel non-linear network analysis method.Front. Genet. 2013; 4: 28Crossref PubMed Scopus (19) Google Scholar). Finally, subclinical phenotypes can provide an additional useful “bridge” between molecular phenotypes and the more complex clinical traits; for example, Attie and Kebede studied insulin secretion by isolated pancreatic β cells as a subphenotype for diabetes (35Kebede M.A. Attie A.D. Insights into obesity and diabetes at the intersection of mouse and human genetics.Trends Endocrinol. Metab. 2014; 25: 493-501Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar). In the sections below, we discuss the various datasets that have been generated and provide examples of the types of analyses that have been performed. Bone mineral density (BMD), a trait relevant to osteoporosis, is highly heritable in mice. Farber and colleagues examined variation of BMD among the HMDP strains and, using association and network modeling, have uncovered several novel genes, some of which also influence BMD in humans (19Farber C.R. Bennett B.