Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations
Combining a standard polygenic risk score (PRS) as a feature in a machine learning model increases the percentage variance explained for those traits, helping to account for non-linearities or interaction effects in genetics-based prediction models.
Bibliographic Details
| Published in: | Communications Biology |
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| Main Authors: |
Michael Elgart,
Genevieve Lyons,
Santiago Romero-Brufau,
Nuzulul Kurniansyah,
Jennifer A. Brody,
Xiuqing Guo,
Henry J. Lin,
Laura Raffield,
Yan Gao,
Han Chen,
Paul de Vries,
Donald M. Lloyd-Jones,
Leslie A. Lange,
Gina M. Peloso,
Myriam Fornage,
Jerome I. Rotter,
Stephen S. Rich,
Alanna C. Morrison,
Bruce M. Psaty,
Daniel Levy,
Susan Redline,
the NHLBI’s Trans-Omics in Precision Medicine (TOPMed) Consortium,
Tamar Sofer |
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| Format: | Article
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| Language: | English |
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| Published: |
Nature Portfolio
2022-08-01
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| Online Access: | https://doi.org/10.1038/s42003-022-03812-z
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