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- W3189598983 abstract "1 Abstract In 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 GEMMA ’s linear mixed models and mv-PLINK ’s 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." @default.
- W3189598983 created "2021-08-16" @default.
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- W3189598983 date "2021-08-06" @default.
- W3189598983 modified "2023-09-26" @default.
- W3189598983 title "Multivariate Genomewide Association Analysis by Iterative Hard Thresholding" @default.
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- W3189598983 doi "https://doi.org/10.1101/2021.08.04.455145" @default.
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