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Activity Number: 499
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #319145 View Presentation
Title: Genome-Wide Association Studies Using a Penalized Moving-Window Regression
Author(s): Minli Bao* and Kai Wang
Companies: University of Iowa and University of Iowa
Keywords: Genetic association ; Feature selection ; Penalized regression ; LASSO ; Linkage disequilibrium

Genome-wide association studies (GWAS) have played an important role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and noise at non-causal variants. In an effort to overcome these difficulties, Liu et al. (2010) proposed a regularized regression method that penalizes on the difference of signal strength between two consecutive single-nucleotide polymorphisms (SNPs). We provides a generalization to this method so that more adjacent SNPs can be considered. The choice of optimal number of markers is studied. Simulation studies indicate that this penalized moving window regression method provides improved true positive findings. The practical utility of the proposed method is demonstrated by applying it to Genetic Analysis Workshop 16 rheumatoid arthritis GWAS data.

Authors who are presenting talks have a * after their name.

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