| Title: | FairPRS: adjusting for admixed populations in polygenic risk scores using invariant risk minimization. |
| Journal: | Biocomputing |
| Published: | 1 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36540977/ |
| Citations: | 4 (4 in last 2 years) as of 8 Aug 2024 |
Publication 8008
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Abstract
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.4 Authors
- Diego Machado Reyes
- Aritra Bose
- Ehud Karavani
- Laxmi Parida
Enabling scientific discoveries that improve human health