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Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations.
Monday,2022-10-10 20:51 America/Los_Angeles — ecterry
| Title | Non-linear machine learning models incorporating SNPs and PRS improve polygenic prediction in diverse human populations. |
| Publication Type | Journal Article |
| Year of Publication | 2022 |
| Authors | Elgart, M, Lyons, G, Romero-Brufau, S, Kurniansyah, N, Brody, JA, Guo, X, Lin, HJ, Raffield, L, Gao, Y, Chen, H, de Vries, P, Lloyd-Jones, DM, Lange, LA, Peloso, GM, Fornage, M, Rotter, JI, Rich, SS, Morrison, AC, Psaty, BM, Levy, D, Redline, S, Sofer, T |
| Corporate/Institutional Authors | NHLBI’s Trans-Omics in Precision Medicine (TOPMed) Consortium |
| Journal | Commun Biol |
| Volume | 5 |
| Issue | 1 |
| Pagination | 856 |
| Date Published | 2022 08 22 |
| ISSN | 2399-3642 |
| Keywords | Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Machine Learning, Multifactorial Inheritance, Polymorphism, Single Nucleotide |
| Abstract | <p>Polygenic risk scores (PRS) are commonly used to quantify the inherited susceptibility for a trait, yet they fail to account for non-linear and interaction effects between single nucleotide polymorphisms (SNPs). We address this via a machine learning approach, validated in nine complex phenotypes in a multi-ancestry population. We use an ensemble method of SNP selection followed by gradient boosted trees (XGBoost) to allow for non-linearities and interaction effects. We compare our results to the standard, linear PRS model developed using PRSice, LDpred2, and lassosum2. Combining a PRS as a feature in an XGBoost model results in a relative increase in the percentage variance explained compared to the standard linear PRS model by 22% for height, 27% for HDL cholesterol, 43% for body mass index, 50% for sleep duration, 58% for systolic blood pressure, 64% for total cholesterol, 66% for triglycerides, 77% for LDL cholesterol, and 100% for diastolic blood pressure. Multi-ancestry trained models perform similarly to specific racial/ethnic group trained models and are consistently superior to the standard linear PRS models. This work demonstrates an effective method to account for non-linearities and interaction effects in genetics-based prediction models.</p> |
| DOI | 10.1038/s42003-022-03812-z |
| Alternate Journal | Commun Biol |
| PubMed ID | 35995843 |
| PubMed Central ID | PMC9395509 |
| Grant List | R01 HL120393 / HL / NHLBI NIH HHS / United States U54 HG003067 / HG / NHGRI NIH HHS / United States U01 HL120393 / HL / NHLBI NIH HHS / United States R01 HL146860 / HL / NHLBI NIH HHS / United States HHSN268201800001C / HL / NHLBI NIH HHS / United States UM1 HG008898 / HG / NHGRI NIH HHS / United States R01 HL117626 / HL / NHLBI NIH HHS / United States U24 HG008956 / HG / NHGRI NIH HHS / United States HHSN268201100037C / HL / NHLBI NIH HHS / United States HHSN268201600032C / ES / NIEHS NIH HHS / United States R21 HL145425 / HL / NHLBI NIH HHS / United States R01 HL092577 / HL / NHLBI NIH HHS / United States R01 HL059367 / HL / NHLBI NIH HHS / United States U54 HG003273 / HG / NHGRI NIH HHS / United States R01 HL142711 / HL / NHLBI NIH HHS / United States R35 HL135818 / HL / NHLBI NIH HHS / United States R01 HL098433 / HL / NHLBI NIH HHS / United States HHSN268201500015C / HL / NHLBI NIH HHS / United States R21 AG070644 / AG / NIA NIH HHS / United States HHSN268201600033C / ES / NIEHS NIH HHS / United States HHSN268201500014C / HL / NHLBI NIH HHS / United States KL2 TR002490 / TR / NCATS NIH HHS / United States |
ePub date:
22/08