You are here
The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup.
Friday,2025-02-07 14:22 America/Los_Angeles — ecterry
| Title | The expected polygenic risk score (ePRS) framework: an equitable metric for quantifying polygenetic risk via modeling of ancestral makeup. |
| Publication Type | Journal Article |
| Year of Publication | 2024 |
| Authors | Huang, Y-J, Kurniansyah, N, Goodman, MO, Spitzer, BW, Wang, J, Stilp, A, Laurie, C, de Vries, PS, Chen, H, Min, Y-I, Sims, M, Peloso, GM, Guo, X, Bis, JC, Brody, JA, Raffield, LM, Smith, JA, Zhao, W, Rotter, JI, Rich, SS, Redline, S, Fornage, M, Kaplan, R, Franceschini, N, Levy, D, Morrison, AC, Boerwinkle, E, Smith, NL, Kooperberg, C, Psaty, BM, Zöllner, S, Sofer, T |
| Corporate/Institutional Authors | Trans-Omics in Precision Medicine Consortium |
| Journal | medRxiv |
| Date Published | 2024 Dec 20 |
| Abstract | <p>Polygenic risk scores (PRSs) depend on genetic ancestry due to differences in allele frequencies between ancestral populations. This leads to implementation challenges in diverse populations. We propose a framework to calibrate PRS based on ancestral makeup. We define a metric called "expected PRS" (ePRS), the expected value of a PRS based on one's global or local admixture patterns. We further define the "residual PRS" (rPRS), measuring the deviation of the PRS from the ePRS. Simulation studies confirm that it suffices to adjust for ePRS to obtain nearly unbiased estimates of the PRS-outcome association without further adjusting for PCs. Using the TOPMed dataset, the estimated effect size of the rPRS adjusting for the ePRS is similar to the estimated effect of the PRS adjusting for genetic PCs. Similarly, we applied the ePRS framework to six cardiovascular-related traits in the All of Us dataset, and the results are consistent with those from the TOPMed analysis. The ePRS framework can protect from population stratification in association analysis and provide an equitable strategy to quantify genetic risk across diverse populations.</p> |
| DOI | 10.1101/2024.03.05.24303738 |
| Alternate Journal | medRxiv |
| PubMed ID | 39763564 |
| PubMed Central ID | PMC11702733 |
| Grant List | R56 HG013163 / HG / NHGRI NIH HHS / United States OT2 OD026556 / OD / NIH HHS / United States R01 HL139553 / HL / NHLBI NIH HHS / United States U2C OD023196 / OD / NIH HHS / United States OT2 OD025315 / OD / NIH HHS / United States OT2 OD026551 / OD / NIH HHS / United States U24 OD023121 / OD / NIH HHS / United States OT2 OD026549 / OD / NIH HHS / United States OT2 OD025337 / OD / NIH HHS / United States OT2 OD025277 / OD / NIH HHS / United States OT2 OD026555 / OD / NIH HHS / United States OT2 OD026550 / OD / NIH HHS / United States OT2 OD026553 / OD / NIH HHS / United States OT2 OD023205 / OD / NIH HHS / United States OT2 OD025276 / OD / NIH HHS / United States OT2 OD026557 / OD / NIH HHS / United States OT2 OD026554 / OD / NIH HHS / United States U24 OD023163 / OD / NIH HHS / United States R01 HG011031 / HG / NHGRI NIH HHS / United States OT2 OD023206 / OD / NIH HHS / United States R01 HL154385 / HL / NHLBI NIH HHS / United States U24 OD023176 / OD / NIH HHS / United States OT2 OD026548 / OD / NIH HHS / United States OT2 OD026552 / OD / NIH HHS / United States R01 HL161012 / HL / NHLBI NIH HHS / United States R01 HL142711 / HL / NHLBI NIH HHS / United States R01 AG080598 / AG / NIA NIH HHS / United States |
ePub date:
24/12