Despite advances in molecular and statistical genetics, identifying specific genes that contribute to the pathogenesis of common diseases has been challenging. This is due, at least in part, to the extensive genetic and phenotypic heterogeneity that characterize these diseases, the importance of non-genetic (e.g., environmental) factors that are rarely taken into account, and sample sizes that are often under powered to detect the likely modest effects of disease susceptibility genes. Here, we propose to integrate mapping and genome-wide expression profiling in order to find genes or regulatory regions that contribute to variability in susceptibility to and severity of cardiovascular diseases (CVD). To overcome the challenges described above, we propose to study the genetic basis for variation in physiological quantitative traits (QTs) that are associated with CVD susceptibility in a founder population with a remarkably uniform environment. By mapping genes for disease- associated physiological QTs, we will indirectly identify genes that influence susceptibility to or severity of the disease. Specifically, we plan to focus on four CVD-associated QTs, including a marker of general inflammation, for which associated genomic regions were previously identified in the Hutterites, a founder population of European descent. In order to hone in on the most promising candidate genes that underlie variation in these QTs, we will integrate several complementary approaches: (i) Use expression profiling in lymphoblastoid cell lines (LBL) from the Hutterites to identify candidate genes whose expression is associated with one or more of the QTs and that lie in genomic regions previously identified as linked to the QTs. (ii) Use a novel multi-species microarrays to compare expression profiles across primates and identify genes whose expression levels in the human liver, kidney, lymphocytes and heart evolved under natural selection and which lie in regions previously linked with the QTs, or whose expression is associated with variation in the QTs. (iii) Use an eQTL approach to map the genetic variants that influence the expression levels of the candidate genes identified in the first two approaches.
We propose a unique combination of genomics, evolutionary analyses of gene regulation, and genetic mapping to identify a set of genes that underlie variation in quantitative traits associated with cardiovascular diseases, including systolic blood pressure and a marker of general inflammation.
|Tung, Po-Yuan; Blischak, John D; Hsiao, Chiaowen Joyce et al. (2017) Batch effects and the effective design of single-cell gene expression studies. Sci Rep 7:39921|
|Cusanovich, Darren A; Caliskan, Minal; Billstrand, Christine et al. (2016) Integrated analyses of gene expression and genetic association studies in a founder population. Hum Mol Genet 25:2104-2112|
|Burrows, Courtney K; Banovich, Nicholas E; Pavlovic, Bryan J et al. (2016) Genetic Variation, Not Cell Type of Origin, Underlies the Majority of Identifiable Regulatory Differences in iPSCs. PLoS Genet 12:e1005793|
|Thomas, Samantha M; Kagan, Courtney; Pavlovic, Bryan J et al. (2015) Reprogramming LCLs to iPSCs Results in Recovery of Donor-Specific Gene Expression Signature. PLoS Genet 11:e1005216|
|Zhou, Xiang; Stephens, Matthew (2014) Efficient multivariate linear mixed model algorithms for genome-wide association studies. Nat Methods 11:407-9|
|Zhou, Xiang; Carbonetto, Peter; Stephens, Matthew (2013) Polygenic modeling with bayesian sparse linear mixed models. PLoS Genet 9:e1003264|
|Mizrahi-Man, Orna; Davenport, Emily R; Gilad, Yoav (2013) Taxonomic classification of bacterial 16S rRNA genes using short sequencing reads: evaluation of effective study designs. PLoS One 8:e53608|
|Gaffney, Daniel J; Veyrieras, Jean-Baptiste; Degner, Jacob F et al. (2012) Dissecting the regulatory architecture of gene expression QTLs. Genome Biol 13:R7|
|Zhou, Xiang; Stephens, Matthew (2012) Genome-wide efficient mixed-model analysis for association studies. Nat Genet 44:821-4|
|Cusanovich, Darren A; Billstrand, Christine; Zhou, Xiang et al. (2012) The combination of a genome-wide association study of lymphocyte count and analysis of gene expression data reveals novel asthma candidate genes. Hum Mol Genet 21:2111-23|
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