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Activity Number:
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489
- Methods Development for Mediation and Interaction in Post-GWAS Data
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 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 #328399
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Presentation
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Title:
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Pleiotropy Informed Adaptive Association Test of Multiple Traits Using GWAS Summary Data
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Author(s):
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Maria Masotti* and Baolin Wu and Bin Guo
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Companies:
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University of Minnesota and University of Minnesota and University of Minnesota
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Keywords:
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GWAS;
Pleiotropy;
Adaptive association test
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Abstract:
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Disease related variants identified by GWAS only explain a part of the trait variation for most traits. Many genetic variants with small effect sizes remain to be discovered. Since many GWAS are conducted in deeply-phenotyped cohorts including many correlated and well-characterized outcomes, which can improve the power to detect novel variants if properly analyzed, we aim to develop powerful pleiotropy informed adaptive association test methods across multiple traits for GWAS that only need publicly available summary association statistics. We first develop a pleiotropy test, which has powerful performance for truly pleiotropic variants but is sensitive to the pleiotropy assumption. We then develop a pleiotropy informed adaptive test that has robust and powerful performance under various genetic models. We develop accurate and efficient numerical algorithms to compute the analytical p-value for the proposed adaptive test without the need of resampling or permutation. We illustrate the utility of proposed methods through an application to a joint association test of GWAS meta-analysis summary data for several glycemic traits.
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Authors who are presenting talks have a * after their name.
