PT  - JOURNAL ARTICLE
AU  - Annika Faucon
AU  - Julian Samaroo
AU  - Tian Ge
AU  - Lea K. Davis
AU  - Ran Tao
AU  - Nancy J. Cox
AU  - Megan M. Shuey
TI  - Improving the computation efficiency of polygenic risk score modeling: Faster in Julia
AID  - 10.1101/2021.12.27.474263
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.12.27.474263
4099  - http://biorxiv.org/content/early/2021/12/27/2021.12.27.474263.short
4100  - http://biorxiv.org/content/early/2021/12/27/2021.12.27.474263.full
AB  - To enable large-scale application of polygenic risk scores in a computationally efficient manner we translate a widely used polygenic risk score construction method, Polygenic Risk Score – Continuous Shrinkage (PRS-CS), to the Julia programing language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average run time by 5.5x. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes utilization of polygenic risk scores by lowering the computational burden of the PRS-CS method.Competing Interest StatementThe authors have declared no competing interest.