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Activity Number: 390 - Challenges in Whole-Genome Sequence Analysis: Experiences and Approaches in the TOPMed Project
Type: Invited
Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
Sponsor: WNAR
Abstract #322370
Title: Integrating Multi-Omics Data to Identify Target Genes for Complex Traits Through a Gibbs Sampling Strategy
Author(s): Bingshan Li* and Quan Wang and Ying Ji and Rui Chen and Qiang Wei and Xue Zhong and Hai Yang
Companies: Vanderbilt University and Vanderbilt University and Vanderbilt University and Vanderbilt University and Vanderbilt University and Vanderbilt University and Vanderbilt University
Keywords: Statistical genomics ; Integrative genomics ; genetics of complex traits ; Gibbs sampler
Abstract:

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.


Authors who are presenting talks have a * after their name.

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