  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.
