Improving the computation efficiency of polygenic risk score modeling: faster in Julia

Annika Faucon; Julian Samaroo; Tian Ge; Lea K Davis; Nancy J Cox; Ran Tao; Megan M Shuey

doi:10.26508/lsa.202201382

Improving the computation efficiency of polygenic risk score modeling: faster in Julia

Life Sci Alliance. 2022 Jul 18;5(12):e202201382. doi: 10.26508/lsa.202201382.

Authors

Annika Faucon¹, Julian Samaroo², Tian Ge³, Lea K Davis^{1

4}, Nancy J Cox^{1

4}, Ran Tao^{1

5}, Megan M Shuey^{6

4}

Affiliations

¹ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
² JuliaLab, Massachusetts Institute of Technology, Boston, MA, USA.
³ Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
⁴ Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
⁵ Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA megan.m.shuey.1@vumc.org.

Abstract

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Genome-Wide Association Study*
Multifactorial Inheritance* / genetics
Phenotype
Risk Factors
Software

Abstract

Publication types

MeSH terms

Grants and funding