Incidence and severity of prostate cancer (PrCa) substantially varies across ancestries. American men of African ancestry (AA) are more likely to be diagnosed with and die from PrCa than the those of European ancestry (EA). Published polygenic risk scores for developing prostate cancer, even those based on multi-ancestry genome-wide association studies, do not address population-specific genetic mechanisms underlying PrCa risk in men of African ancestry. Specifically, the role of non-coding regulatory polymorphisms in driving inter-ancestry variation in PrCa has not been sufficiently explored. Here, by employing a sequence-based deep learning model of prostate regulatory enhancers, we identified ~2,000 SNPs with higher alternate allele frequency in AA men that potentially affect enhancer function associated with PrCa susceptibility, as supported by our experimental validation. The identified enhancer SNPs (eSNPs) may influence PrCa development through two complementary mechanisms: 1) the alternate allele that increase enhancer activity result in immune suppression and telomere elongation, and 2) the alternate alleles that decrease enhancer activity, lead to de-differentiation and inhibition of apoptosis. Notably, the eSNPs tend to disrupt the binding of known prostate transcription factors including FOX, AR and HOX families. Lastly, the identified eSNPs can be combined into a polygenic risk score that adds value to current GWAS-based risk variants in assessing PrCa risk in independent cohorts.

The authors have declared no competing interest.

This research was funded by U.S. National Cancer Institute grant 1-ZIA-BC011979-02 and 1-ZIA-BC011973-04.

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The study used ONLY openly available human data that were originally located at: https://gdc.cancer.gov/about-data/publications/pancanatlas https://xenabrowser.net/ https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v8.p2 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000882.v1.p1 https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000838.v1.p1

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