Practical Issues in Screening and Variable Selection in Genome-Wide Association Analysis. (January 2014)

• Record Type: Journal Article
• Authors: Hong, Sungyeon; Kim, Yongkang; Park, Taesung
• Is Part Of: Cancer informatics. Volume 13(2014)Supplement 7
• Publication Date: 2014-01
• Subjects: genome-wide association study -- the Korea Association Resource (KARE) -- the Age-Related Eye Disease Study (AREDS) -- penalized regression -- variable selection; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.4137/CIN.S16350
• Languages: English
• ISSNs: 1176-9351

Abstract

Variable selection methods play an important role in high-dimensional statistical modeling and analysis. Computational cost and estimation accuracy are the two main concerns for statistical inference from ultrahigh-dimensional data. In particular, genome-wide association studies (GWAS), which focus on identifying single nucleotide polymorphisms (SNPs) associated with a disease of interest, have produced ultrahigh-dimensional data. Numerous methods have been proposed to handle GWAS data. Most statistical methods have adopted a two-stage approach: pre-screening for dimensional reduction and variable selection to identify causal SNPs. The pre-screening step selects SNPs in terms of their P -values or the absolute values of the regression coefficients in single SNP analysis. Penalized regressions, such as the ridge, lasso, adaptive lasso, and elastic-net regressions, are commonly used for the variable selection step. In this paper, we investigate which combination of pre-screening method and penalized regression performs best on a quantitative phenotype using two real GWAS datasets.