2025 Sequencing Strategies for Population and Cancer Epidemiology Studies (SeqSPACE) : Submission #7

Abstract Summary Purpose: While previous studies have assessed associations between rare genetic variation and cancer, they have mostly 1) examined a limited number of cancer types and 2) included only European genetic ancestry individuals. The generation sequencing data by large population-based biobanks enables more comprehensive examinations of the relationships between rare genetic variants and multiple cancers across populations. We assessed associations between rare genetic variants and over 70 cancer types using sequencing data from 431,961 and 297,508 multi-ancestry participants from the UK Biobank (UKB) and All of Us Research Program (AoU) cohorts, respectively. Methods: We used three approaches to characterize the influences of rare coding variants on risk for various cancers. First, we used generalized linear mixed models to conduct gene-based burden tests and single variant tests for over 70 individual cancers and groups of cancers in the full multi-ancestry UKB and AoU cohorts. Second, we estimated the contributions of rare coding variants to breast, colorectal, and prostate cancer heritability in genetic ancestry-stratified groups in the UKB and AoU cohorts using methods that generate both aggregate and gene-level heritability estimates. Third, we developed polygenic scores (PGS) that integrated existing common variant PGS and rare variant effects for breast, colorectal, and prostate cancers and evaluated the performance of these PGS across genetic ancestry groups in the UKB and AoU cohorts. Results: We identified 15 genes (ASXL1, ATM, BRCA1, BRCA2, CDKN2A, CHEK2, DNMT3A, MLH1, PALB2, POT1, PPM1D, RTEL1, SAMHD1, TET2, and TP53) that showed associations across a range of the individual cancer types tested. We estimated small heritability contributions from rare variants that were enriched in known cancer risk genes. Conclusions: Our results suggest that utilizing the expansive sequencing and cancer diagnosis data in large population-based biobanks could expand our understanding of the role of rare genetic variants in cancer risk.