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bioRxiv · 10.1101/2021.08.04.455145

Multivariate Genomewide Association Analysis with IHT

Chu, B. B.·Ko, S.·Zhou, J. J.·Zhou, H.·Sinsheimer, J. S.·Lange, K. L.
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Abstract

1In genome-wide association studies (GWAS), analyzing multiple correlated traits is potentially superior to conducting multiple univariate analyses. Standard methods for multivariate GWAS operate marker-by-marker and are computationally intensive. We present a penalized regression algorithm for multivariate GWAS based on iterative hard thresholding (IHT) and implement it in a convenient Julia package MendelIHT.jl (https://github.com/OpenMendel/MendelIHT.jl). In simulation studies with up to 100 traits, IHT exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMAs linear mixed models and mv-PLINKs canonical correlation analysis. On UK Biobank data, our IHT software completed a 3-trait joint analysis in 20 hours and an 18-trait joint analysis in 53 hours, requiring up to 80GB of computer memory. In short, our software enables geneticists to fit a single regression model that simultaneously considers the effect of all SNPs and dozens of traits.

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Chu, B. B., Ko, S., Zhou, J. J., Zhou, H., Sinsheimer, J. S., Lange, K. L.. 2021-08-06. Multivariate Genomewide Association Analysis with IHT. https://doi.org/10.1101/2021.08.04.455145

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