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Abstract Details
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Activity Number:
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586
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Type:
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Invited
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Date/Time:
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Thursday, August 2, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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| Abstract - #303871 |
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Title:
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Integration of Protein Binding Data in eQTL Mapping
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Author(s):
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Hao Wu*+
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Companies:
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Emory University
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Address:
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Department of Biostatistics and Bioinformatics, Atlanta, GA, ,
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Keywords:
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eQTL ;
ChIP-seq ;
data integration ;
hierarchical model
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Abstract:
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Expression-QTL (eQTL) mapping is a powerful approach to discover the genetic basis of gene expression variations. Although the approach has identified numerous genetic loci associated with gene expressions, the mechanisms for associations are often unknown. With advances of high-throughput technologies, massive amount of transcriptomics, genomics and epigenomics data have been generated and made available at public databases. It is desirable to incorporate these data in eQTL mapping to improve scientific discoveries.
We develop a general statistical framework to integrate genome-wide data from different sources in eQTL studies. The data integration is achieved by a Bayesian hierarchical model. The prior probability for a genetic marker being associated depends on its surrounding genomic and epigenomic contexts. Compared to existing algorithms, the new methods will provide more biologically meaningful results.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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