| Autor: |
Machado Reyes D; Biomedical Engineering Department, Rensselaer Polytechnic Institute, Troy, NY, USA., Bose A, Karavani E, Parida L |
| Jazyk: |
angličtina |
| Zdroj: |
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing [Pac Symp Biocomput] 2023; Vol. 28, pp. 198-208. |
| Abstrakt: |
Polygenic risk scores (PRS) are increasingly used to estimate the personal risk of a trait based on genetics. However, most genomic cohorts are of European populations, with a strong under-representation of non-European groups. Given that PRS poorly transport across racial groups, this has the potential to exacerbate health disparities if used in clinical care. Hence there is a need to generate PRS that perform comparably across ethnic groups. Borrowing from recent advancements in the domain adaption field of machine learning, we propose FairPRS - an Invariant Risk Minimization (IRM) approach for estimating fair PRS or debiasing a pre-computed PRS. We test our method on both a diverse set of synthetic data and real data from the UK Biobank. We show our method can create ancestry-invariant PRS distributions that are both racially unbiased and largely improve phenotype prediction. We hope that FairPRS will contribute to a fairer characterization of patients by genetics rather than by race. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
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