Identifying shared polygenic risk across cancers
- 1Department of Chronic Disease Epidemiology, Yale School of Public Health, New Haven, ConnecticutUnited States of America
- 2Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, United States of America
- 3Program of Computational Biology and Bioinformatics, Yale University, New Haven, ConnecticutUnited States of America
Abstract
Background: Shared genetic susceptibility across cancers has been reported but is generally modest at the genome-wide level. Whether such shared polygenic risk exhibits structured convergence at regional or functional levels remains unclear. We investigated shared genetic risk across cancers by integrating local genetic correlation analyses with cross-cancer polygenic risk score (PRS) associations. Methods: We estimated pairwise local genetic correlations across 16 specific cancers and one pan-cancer phenotype using SUPERGNOVA. Genome regions harboring multiple cancers with mutually correlated local genetic effects were annotated using genetic correlations with non-cancer phenotypes and associations from the GWAS Catalog. In parallel, cross-cancer PRS associations were evaluated, and significant cancer pairs were identified. Genome-wide PRSs for selected pairs were further decomposed into pleiotropy-informed and pathway-specific components to assess functional enrichment of shared polygenic risk. Results: Genome-wide genetic correlation analyses identified 20 significantly correlated cancer pairs, whereas local analyses revealed 82 regions with shared genetic signals across 66 cancer pairs. Five regions exhibited mutually correlated cancer clusters, with enrichment in functional domains such as inflammatory functions. Cross-cancer PRS analyses identified five cancer pairs with shared polygenic risk. Decomposition of PRSs indicated that these cross-cancer associations were enriched in specific pleiotropy groups and immune-related pathways rather than reflecting diffuse genome-wide overlap. Conclusion: Our findings demonstrate that although shared genetic susceptibility across cancers is limited at the genome-wide level, it becomes evident when examined at regional and polygenic scales. Integrating local genetic correlation and PRS decomposition analyses reveals structured patterns of shared genetic risk, providing a framework for investigating cross-cancer polygenic susceptibility. Author summary: Many cancers are known to share inherited genetic risk, but it remains unclear where this genetic overlap occurs and what biological mechanisms underlie it. Identifying these shared genetic influences may reveal common processes involved in cancer development and could ultimately improve cancer risk prediction and prevention. In this study, we analyzed genetic data from 24 specific cancer types and a pan-cancer phenotype to investigate shared inherited cancer risk. In addition to assessing genetic overlap across the genome, we examined specific genomic regions and groups of genes involved in related biological functions. We found that most cancers showed only limited shared genetic risk at the genome-wide level. However, many pairs of cancers shared genetic influences within particular genomic regions and biological pathways. These findings suggest that different cancers may arise through overlapping genetic mechanisms, even when their overall genetic similarity is low. Overall, this study provides a framework for identifying shared genetic susceptibility across cancer types and offers new insight into biological processes that contribute to cancer development, with potential implications for future cancer prevention and risk prediction.
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BibTeX
@article{hu2026identifying,
title = {{Identifying shared polygenic risk across cancers}},
author = {Jiaqi Hu and Maiyier Muheyati and Leqi Xu and Andrew DeWan and Hongyu Zhao},
journal = {PLOS Genetics},
year = {2026},
month = sep,
doi = {10.1371/journal.pgen.1012308},
url = {https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1012308},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Identifying shared polygenic risk across cancers
AU - Jiaqi Hu
AU - Maiyier Muheyati
AU - Leqi Xu
AU - Andrew DeWan
AU - Hongyu Zhao
PY - 2026
DA - 2026/09/18
JO - PLOS Genetics
DO - 10.1371/journal.pgen.1012308
UR - https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1012308
AB - Background: Shared genetic susceptibility across cancers has been reported but is generally modest at the genome-wide level. Whether such shared polygenic risk exhibits structured convergence at regional or functional levels remains unclear. We investigated shared genetic risk across cancers by integrating local genetic correlation analyses with cross-cancer polygenic risk score (PRS) associations. Methods: We estimated pairwise local genetic correlations across 16 specific cancers and one pan-cancer phenotype using SUPERGNOVA. Genome regions harboring multiple cancers with mutually correlated local genetic effects were annotated using genetic correlations with non-cancer phenotypes and associations from the GWAS Catalog. In parallel, cross-cancer PRS associations were evaluated, and significant cancer pairs were identified. Genome-wide PRSs for selected pairs were further decomposed into pleiotropy-informed and pathway-specific components to assess functional enrichment of shared polygenic risk. Results: Genome-wide genetic correlation analyses identified 20 significantly correlated cancer pairs, whereas local analyses revealed 82 regions with shared genetic signals across 66 cancer pairs. Five regions exhibited mutually correlated cancer clusters, with enrichment in functional domains such as inflammatory functions. Cross-cancer PRS analyses identified five cancer pairs with shared polygenic risk. Decomposition of PRSs indicated that these cross-cancer associations were enriched in specific pleiotropy groups and immune-related pathways rather than reflecting diffuse genome-wide overlap. Conclusion: Our findings demonstrate that although shared genetic susceptibility across cancers is limited at the genome-wide level, it becomes evident when examined at regional and polygenic scales. Integrating local genetic correlation and PRS decomposition analyses reveals structured patterns of shared genetic risk, providing a framework for investigating cross-cancer polygenic susceptibility. Author summary: Many cancers are known to share inherited genetic risk, but it remains unclear where this genetic overlap occurs and what biological mechanisms underlie it. Identifying these shared genetic influences may reveal common processes involved in cancer development and could ultimately improve cancer risk prediction and prevention. In this study, we analyzed genetic data from 24 specific cancer types and a pan-cancer phenotype to investigate shared inherited cancer risk. In addition to assessing genetic overlap across the genome, we examined specific genomic regions and groups of genes involved in related biological functions. We found that most cancers showed only limited shared genetic risk at the genome-wide level. However, many pairs of cancers shared genetic influences within particular genomic regions and biological pathways. These findings suggest that different cancers may arise through overlapping genetic mechanisms, even when their overall genetic similarity is low. Overall, this study provides a framework for identifying shared genetic susceptibility across cancer types and offers new insight into biological processes that contribute to cancer development, with potential implications for future cancer prevention and risk prediction.
SN - 1553-7404
ER -