Activity Number:
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584
- Statistical Methods for Genetic Association Analysis
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Type:
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Contributed
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Date/Time:
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Wednesday, August 2, 2017 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #324925
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View Presentation
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Title:
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Detecting Marginal Weak but Jointly Informative Markers in Genome-Wide Association Studies
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Author(s):
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Yanming Li* and Hyokyoung (Grace) Hong and Kevin He and Jian Kang and Qingyi Wei and Yi Li
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Companies:
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and Michigan State University and University of Michiga and University of Michigan and Duke University and University of Michigan
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Keywords:
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GWAS ;
weak signal detection ;
MUJI ;
Heritability
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Abstract:
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We propose a covariance-insured screening method for detecting Marginal Weak but Jointly Informative (MUJI) markers in Genome-Wide Association Studies (GWAS) for the purpose of classification and regression, two major tasks often encountered in genetic studies. The proposed method provides an effective and efficient way for multi-polymorphism genome-wide scan for simultaneously detecting common/rare variants with weak marginal effects, which may explain significantly more disease heritability. Specifically, we will investigate (i) how to identify MUJI signals in both framework of classification and generalized linear models; (ii) how MUJI signals can help explain more disease heritability.
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Authors who are presenting talks have a * after their name.