Iterative Hard Thresholding for Model Selection in Genome-Wide Association Studies

TL;DR

This paper introduces an iterative hard thresholding algorithm for GWAS that improves model selection accuracy and computational efficiency, enabling analysis on standard desktop computers.

Contribution

The paper presents a novel IHT algorithm tailored for GWAS, enhancing model selection accuracy and computational feasibility compared to existing penalized regression methods.

Findings

IHT reduces false positives and negatives in GWAS.

IHT is computationally competitive with penalized regression methods.

Parallel implementation allows analysis on commodity hardware.

Abstract

A genome-wide association study (GWAS) correlates marker variation with trait variation in a sample of individuals. Each study subject is genotyped at a multitude of SNPs (single nucleotide polymorphisms) spanning the genome. Here we assume that subjects are unrelated and collected at random and that trait values are normally distributed or transformed to normality. Over the past decade, researchers have been remarkably successful in applying GWAS analysis to hundreds of traits. The massive amount of data produced in these studies present unique computational challenges. Penalized regression with LASSO or MCP penalties is capable of selecting a handful of associated SNPs from millions of potential SNPs. Unfortunately, model selection can be corrupted by false positives and false negatives, obscuring the genetic underpinning of a trait. This paper introduces the iterative hard thresholding (IHT) algorithm to the GWAS analysis of continuous traits. Our parallel implementation of IHT accommodates SNP genotype compression and exploits multiple CPU cores and graphics processing units (GPUs). This allows statistical geneticists to leverage commodity desktop computers in GWAS analysis and to avoid supercomputing. We evaluate IHT performance on both simulated and real GWAS data and conclude that it reduces false positive and false negative rates while remaining competitive in computational time with penalized regression. Source code is freely available at https://github.com/klkeys/IHT.jl.