Life Science AllianceGeneticsPeer-reviewed journal article
Improving the computation efficiency of polygenic risk score modeling: Faster in Julia
Annika B. FauconiD, Julian Samaroo, Tian GeiD, Lea Karatheodoris DavisiD, Ran TaoiD, Nancy Jean CoxiD, Megan M. ShueyiD
Abstract
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.
- assay: GWAS summary statistics
- research focus: polygenic risk
- research focus: variant effect prediction
- research focus: benchmarking
- research focus: software / tool
- approach: statistical model
- Citations
- 1
- FWCI
- 0.15
- Field percentile
- 43th percentile
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