Abstract:
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The vast majority of variants associated with human complex diseases are located in the non-coding regions of the genome and predispose disease risk through regulating their target genes. It is a great challenge, however, to pinpoint their target genes, as the targets can be megabases away from the risk variants. We developed an integrative framework to integrate genomic and epigenomic data across multiple tissues, along with a central rationale that disease risk genes often converge to a small number of function modules/pathways, to systematically identify target genes and pathways underlying the genetics of complex diseases. We tackled the computational challenges via a Gibbs sampling strategy to effectively incorporate both genomics data and the prior knowledge embeded in gene-gene functional networks. We applied the framework to schizophrenia associated variants to probabilistically identified risk genes, providing new insights into the genetics mechanisms of schizophrenia as well as potential drug targets.
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