PLoS GeneticsGenetic & Genomic MedicinePeer-reviewed journal article
On Cross-ancestry Cancer Polygenic Risk Scores
Lars G. FritscheiD, Ying MaiD, Daiwei ZhangiD, Maxwell SalvatoreiD, Seunggeun LeeiD, Xiang ZhouiD, Bhramar MukherjeeiD
Abstract
Polygenic risk scores (PRS) can provide useful information for personalized risk stratification and disease risk assessment, especially when combined with non-genetic risk factors. However, their construction depends on the availability of summary statistics from genome-wide association studies (GWAS) independent from the target sample. For best compatibility, it was reported that GWAS and the target sample should match in terms of ancestries. Yet, GWAS, especially in the field of cancer, often lack diversity and are predominated by European ancestry. This bias is a limiting factor in PRS research. By using electronic health records and genetic data from the UK Biobank, we contrast the utility of breast and prostate cancer PRS derived from external European-ancestry-based GWAS across African, East Asian, European, and South Asian ancestry groups. We highlight differences in the PRS distributions of these groups that are amplified when PRS methods condense hundreds of thousands of variants into a single score. While European-GWAS-derived PRS were not directly transferrable across ancestries on an absolute scale, we establish their predictive potential when considering them separately within each group. For example, the top 10% of the breast cancer PRS distributions within each ancestry group each revealed significant enrichments of breast cancer cases compared to the bottom 90% (odds ratio of 2.81 [95%CI: 2.69,2.93] in European, 2.88 [1.85, 4.48] in African, 2.60 [1.25, 5.40] in East Asian, and 2.33 [1.55, 3.51] in South Asian individuals). Our findings highlight a compromise solution for PRS research to compensate for the lack of diversity in well-powered European GWAS efforts while recruitment of diverse participants in the field catches up.
- organism: human
- assay: GWAS summary statistics
- research focus: polygenic risk
- research focus: benchmarking
- research focus: GWAS / association mapping
- research focus: fine-mapping
- approach: statistical model
- Citations
- 54
- FWCI
- 7.06
- Field percentile
- top 2%
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