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Abstract Details
Activity Number:
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57
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
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Sponsor:
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National Institute on Drug Abuse-NIH
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Abstract - #300212 |
Title:
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Graph-Based Interaction Association Mapping in Genome-Wide Studies
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Author(s):
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Yu Zhang*+
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Companies:
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Penn State University
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Address:
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325 Thomas, University Park, PA, 16802,
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Keywords:
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Association Mapping ;
Epistasis ;
Graph model ;
Bayesian method
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Abstract:
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Genomewide association studies are becoming increasingly important due to the advance in high-throughput sequencing technologies. In addition to detecting marginal associations, it is also of interests to identify multi-marker interaction associations. Mapping from an astronomical number of possible interactions in the genome is a daunting task both computationally and statistically. For high-density markers, the problem is further complicated by the complex dependence between markers. We introduce a graph-based Bayesian model for large-scale interaction association mapping. Compared with existing methods, our method has two features. 1) We identify complex gene-gene interaction graphs associated with the disease. 2) We design better models to account for the dependence in high-density markers. We use simulation and real data examples to demonstrate the performance of our method.
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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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