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Activity Number:
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300
- Gene-Gene and Gene-Environment Interactions
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Type:
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Contributed
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Date/Time:
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Tuesday, August 1, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #324935
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Title:
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Subset-Based Meta-Analysis That Exploits Gene-Environment Interactions for Genetic Association Discovery
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Author(s):
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Lu Xia* and Youfei Yu and Seunggeun Lee and Xiang Zhou and Bhramar Mukherjee
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Companies:
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University of Michigan and University of Michigan and University of Michigan and University of Michigan and Department of Biostatistics, University of Michigan, Ann Arbor
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Keywords:
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GWAS ;
Subset-based ;
Meta-analysis ;
Gene-environment interactions
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Abstract:
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Meta-analysis is commonly used for combining genome-wise association studies (GWASs) and increasing the power of detecting genetic associations. Classical methods for meta-analysis only screen for marginal effects and can lose power when heterogeneity, such as associations residing only in a subset of the studies or being in opposite directions, is present. We present a subset-based meta-analysis framework that generalizes the association analysis based on subsets (ASSET) and has the flexibility of incorporating gene-environment interactions into testing procedure. The proposed method can also identify the subset of studies that most likely bare true associations. Extensive simulation studies show that the proposed approach is superior to marginal effect methods in the presence of gene-environment interactions and maintains comparable power even in their absence. Our method is applied to examine the association between two candidate SNPs and type 2 diabetes when potentially modified by body mass index in the meta-analysis of six studies.
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Authors who are presenting talks have a * after their name.
