title: Practical issues in screening and variable selection in genome-wide association analysis.
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.
authors: Department of Statistics, Seoul National University, Seoul, South Korea.
authors: Sungyeon
authors: S
authors: Hong
authors: Department of Statistics, Seoul National University, Seoul, South Korea.
authors: Yongkang
authors: Y
authors: Kim
authors: Department of Statistics, Seoul National University, Seoul, South Korea. ; Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea.
authors: Taesung
authors: T
authors: Park
keywords: genome-wide association study; penalized regression; the Age-Related Eye Disease Study (AREDS); the Korea Association Resource (KARE); variable selection
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