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Updated: Sep 4, 2025

An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
Improving the computation efficiency of polygenic risk score modeling: faster in Julia
Annika Faucon1, Julian Samaroo2, Tian Ge3
1Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
None:
To enable large-scale application of polygenic risk scores (PRSs) in a computationally efficient manner, we translate a widely used PRS construction method, PRS-continuous shrinkage, to the Julia programming language, PRS.jl. On nine different traits with varying genetic architectures, we demonstrate that PRS.jl maintains accuracy of prediction while decreasing the average runtime by 5.5×. Additional programmatic modifications improve usability and robustness. This freely available software substantially improves work flow and democratizes usage of PRSs by lowering the computational burden of the PRS-continuous shrinkage method.
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