Iterative hard thresholding in genome-wide association studies: Generalized linear models, prior weights, and double sparsity.

ABSTRACT:

Background

Consecutive testing of single nucleotide polymorphisms (SNPs) is usually employed to identify genetic variants associated with complex traits. Ideally one should model all covariates in unison, but most existing analysis methods for genome-wide association studies (GWAS) perform only univariate regression.

Results

We extend and efficiently implement iterative hard thresholding (IHT) for multiple regression, treating all SNPs simultaneously. Our extensions accommodate generalized linear models, prior information on genetic variants, and grouping of variants. In our simulations, IHT recovers up to 30% more true predictors than SNP-by-SNP association testing and exhibits a 2-3 orders of magnitude decrease in false-positive rates compared with lasso regression. We als

SUBMITTER: Chu BB 

PROVIDER: S-EPMC7268817 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature