Improving the computation efficiency of polygenic risk score modeling: faster in Julia
Life Science Alliance, ISSN: 2575-1077, Vol: 5, Issue: 12
2022
- 1Citations
- 11Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations1
- Citation Indexes1
- Captures11
- Readers11
- 11
Article Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85134556760&origin=inward; http://dx.doi.org/10.26508/lsa.202201382; http://www.ncbi.nlm.nih.gov/pubmed/35851544; https://www.life-science-alliance.org/lookup/doi/10.26508/lsa.202201382; https://dx.doi.org/10.26508/lsa.202201382; https://www.life-science-alliance.org/content/5/12/e202201382
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