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Activity Number:
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415
- Recent advancements in the analysis of large-scale GWAS
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 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 #318817
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Title:
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GWAS.BAYES: Bayesian stochastic search for GWAS analysis
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Author(s):
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Jacob Williams* and Marco May Ferreira and Tieming Ji
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Companies:
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Virginia Polytechnic Institute and State University and Virginia Tech Department of Statistics and University of Missouri
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Keywords:
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GWAS
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Abstract:
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We propose a new method of Bayesian Analysis for genome-wide association studies (GWAS) that selects significant single nucleotide polymorphisms (SNP) in two stages. The first stage fits as many linear mixed models as the number of SNPs, with each model containing one SNP as well as random effects formed from genetic similarities to account for population structure. The second stage takes the set of candidate significant SNPs from the first stage and performs a stochastic search through the model space with a genetic algorithm. Here the model again controls for population structure using random effects. The result is a list of models with their respective posterior probabilities. Compared to traditional GWAS methods, our novel method significantly reduces false positives. We illustrate our proposed Bayesian GWAS framework with an analysis of publicly available experimental data on root architecture remodeling of a model plant in response to salt stress.
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Authors who are presenting talks have a * after their name.
