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A genome-wide association study (GWAS) correlates marker variation with traitvariation in a sample of individuals. Each study subject is genotyped at amultitude of SNPs (single nucleotide polymorphisms) spanning the genome. Herewe assume that subjects are unrelated and collected at random and that traitvalues are normally distributed or transformed to normality. Over the pastdecade, researchers have been remarkably successful in applying GWAS analysisto hundreds of traits. The massive amount of data produced in these studiespresent unique computational challenges. Penalized regression with LASSO or MCPpenalties is capable of selecting a handful of associated SNPs from millions ofpotential SNPs. Unfortunately, model selection can be corrupted by falsepositives and false negatives, obscuring the genetic underpinning of a trait.This paper introduces the iterative hard thresholding (IHT) algorithm to theGWAS analysis of continuous traits. Our parallel implementation of IHTaccommodates SNP genotype compression and exploits multiple CPU cores andgraphics processing units (GPUs). This allows statistical geneticists toleverage commodity desktop computers in GWAS analysis and to avoidsupercomputing. We evaluate IHT performance on both simulated and real GWASdata and conclude that it reduces false positive and false negative rates whileremaining competitive in computational time with penalized regression. Sourcecode is freely available at https://github.com/klkeys/IHT.jl.

Iterative hard thresholding for model selection in genome‐wide association studies