Multivariate genome-wide association analysis by iterative hard thresholding.

ABSTRACT:

Motivation

In a genome-wide association study, analyzing multiple correlated traits simultaneously is potentially superior to analyzing the traits one by one. Standard methods for multivariate genome-wide association study operate marker-by-marker and are computationally intensive.

Results

We present a sparsity constrained regression algorithm for multivariate genome-wide association study based on iterative hard thresholding and implement it in a convenient Julia package MendelIHT.jl. In simulation studies with up to 100 quantitative traits, iterative hard thresholding exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA's linear mixed models and mv-PLINK's canonical correlation analysis. On UK Biobank data with 470 228 v

SUBMITTER: Chu BB 

PROVIDER: S-EPMC10133532 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature