Penalized multimarker vs. single-marker regression methods for genome-wide association studies of quantitative traits.

ABSTRACT: The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single-marker association methods. As an alternative to single-marker analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of penalized regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by false discovery rate (FDR) control, and assess their performance in comparison with SMA. PR methods were compared with SMA, using realistically simulated GWAS data with a continuous phenotype and real data. Based on these comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for

SUBMITTER: Yi H 

PROVIDER: S-EPMC4286685 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature