A key goal of biomedical research is to elucidate the complex network of gene interactions underlying complex traits such as common human diseases. Here we detail a multistep procedure for identifying potential key drivers of complex traits that integrates DNA-variation and gene-expression data with other complex trait data in segregating mouse populations. Ordering gene expression traits relative to one another and relative to other complex traits is achieved by systematically testing whether variations in DNA that lead to variations in relative transcript abundances statistically support an independent, causative or reactive function relative to the complex traits under consideration. We show that this approach can predict transcriptional responses to single gene-perturbation experiments using gene-expression data in the context of a segregating mouse population. We also demonstrate the utility of this approach by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.In the past few years, gene-expression microarrays and other general molecular profiling technologies have been applied to a wide range of biological problems and have contributed to discoveries about the complex network of biochemical processes underlying living Correspondence should be addressed to E.E.S. (eric_schadt@merck.com). Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. NIH Public Access Author ManuscriptNat Genet. Author manuscript; available in PMC 2010 March 18. Published in final edited form as:Nat Genet. 2005 July ; 37(7): 710-717. doi:10.1038/ng1589. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript systems 1 , common human diseases 2,3 and gene discovery and structure determination [4][5][6] . Microarrays have also helped to identify biomarkers 7 , disease subtypes 3,8,9 and mechanisms of toxicity 10 and, more recently, to elucidate the genetics of gene expression in human populations 11,12 and to reconstruct gene networks by integrating gene-expression and genetic data 13 . The use of molecular profiling technologies as tools to identify genes underlying common, polygenic diseases has been less successful. Hundreds or even thousands of genes whose expression changes are associated with disease traits have been identified, but determining which of the genes cause disease rather than respond to the disease state has proven difficult.Microarray data have recently been combined with other experimental approaches to facilitate identification of key mechanistic drivers of complex traits 3,[13][14][15][16][17] . One such technique involves treating relative transcript abundances as quantitative traits in segregating populations. In this method, chromosomal regions that control the level of expression of a particular gene are mapped as expression quantitative trait loci (eQTLs). Gene-expression QTLs that contain the gene encoding t...

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Cyclin-dependent kinases (Cdks) drive the cell cycle in all eukaryotic cells. In budding yeast, Cdk1 (Cdc28) expression is constant, but cyclin transcription, stability, and activity are regulated across the cell cycle (Miller and Cross 2001). These multiple levels of regulation result in the ordered appearance of different G1 (Cln)-and B-type (Clb) cyclins, which direct the phase-specific localization and/or substrate specificity of the kinase. There is a critical distinction between G1 phase and the rest of the cell cycle, in that G1 is expandable in response to the environment (Rupe 2002). The length of G1 is influenced by age, growth conditions, and the size of the cell (Hartwell and Unger 1977;Johnston et al. 1979). In contrast, once the cells exit G1, the length of the rest of the cycle is fairly constant (Jagadish and Carter 1977), even after severe nutrient limitation (Johnston et al. 1977). Accumulation of G1 cyclins (Clns) is rate-limiting for the G1 to S transition, and Clns are regulated at virtually every level (Wittenberg et al. 1990;Gallego et al. 1997;Polymenis and Schmidt 1997;MacKay et al. 2001;Newcomb et al. 2002). However, one of the great remaining mysteries is what triggers the rapid accumulation of Clns and causes the irreversible transition into S phase in the normal mitotic cycle.Entry into G1 requires that Clb kinase activity be eliminated (Zachariae and Nasmyth 1999). Clb kinase activity decays due to cessation of CLB transcription, targeted proteolysis of the Clbs by the anaphase-promoting complex (APC), and the M/G1-specific expression of an inhibitory subunit, Sic1, which inactivates Clb/Cdk complexes. Low Clb kinase activity allows the nuclear localization and assembly of Cdc6 and Mcm2-7 onto origin DNA to form the prereplication complexes (PRCs; Tye 1999). These PRC components are transcribed coordinately at the M/G1 boundary, and the assembly of this highly conserved complex sets the stage for DNA replication. Once the PRCs are formed, Clb kinases are required to initiate replication. This is brought about by the accumulation of Cln/Cdk complexes, which phosphorylate and promote the degradation of Sic1 (Schneider et al. 1996;Tyers 1996;Nash et al. 2001) and restore Clb kinase activity.Accumulation of the G1 cyclins requires the activation of Cln3/Cdk. This kinase is uniquely capable of activating two late G1-specific transcription complexes (SBF and MBF;Dirick et al. 1995;Stuart and Wittenberg 1996). Once activated, SBF and MBF cause a burst of transcription of the late G1 cyclins CLN1 and CLN2, and many other genes required for S phase. The burst of CLN1 and CLN2 transcription is delayed under conditions that prolong G1 (Sillje et al. 1997). This indicates that Cln3/Cdk and/or the transcription factors (SBF and MBF) are the likely targets of G1 regulation. Cold Spring Harbor Laboratory Press on May 9, 2018 -Published by genesdev.cshlp.org Downloaded from

We previously used high-density expression arrays to interrogate a genetic cross between strains C3H/HeJ and C57BL/6J and observed thousands of differences in gene expression between sexes. We now report analyses of the molecular basis of these sex differences and of the effects of sex on gene expression networks. We analyzed liver gene expression of hormone-treated gonadectomized mice as well as XX male and XY female mice. Differences in gene expression resulted in large part from acute effects of gonadal hormones acting in adulthood, and the effects of sex chromosomes, apart from hormones, were modest. We also determined whether there are sex differences in the organization of gene expression networks in adipose, liver, skeletal muscle, and brain tissue. Although coexpression networks of highly correlated genes were largely conserved between sexes, some exhibited striking sex dependence. We observed strong body fat and lipid correlations with sex-specific modules in adipose and liver as well as a sexually dimorphic network enriched for genes affected by gonadal hormones. Finally, our analyses identified chromosomal loci regulating sexually dimorphic networks. This study indicates that gonadal hormones play a strong role in sex differences in gene expression. In addition, it results in the identification of sex-specific gene coexpression networks related to genetic and metabolic traits.

The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here we propose a novel gene network reconstruction algorithm, derived from classic Bayesian network methods, that utilizes naturally occurring genetic variations as a source of perturbations to elucidate the network. This algorithm incorporates relative transcript abundance and genotypic data from segregating populations by employing a generalized scoring function of maximum likelihood commonly used in Bayesian network reconstruction problems. The utility of this novel algorithm is demonstrated via application to liver gene expression data from a segregating mouse population. We demonstrate that the network derived from these data using our novel network reconstruction algorithm is able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data.