medRxiv : the preprint server for health sciences · 2025
Prioritizing context-specific genetic risk mechanisms in 11 solid cancers.
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Wu Xueyao, Kim Artem, Breeze Charles E, O'Mara Tracy A, Ramachandran Dhanya, Dörk Thilo, Koutros Stella, Rothman Nathaniel, Prokunina-Olsson Ludmila, Mancuso Nicholas, Lindström Sara, Kraft Peter
Аннотация
BACKGROUND: While genome-wide association studies (GWAS) have identified hundreds of cancer-associated genetic variants, the specific biological contexts where these variants exert their effects remain largely unknown. We aimed to prioritize context-specific genetic risk mechanisms for 11 solid cancers at both genome-wide and single-variant resolutions.
METHODS: We integrated cancer GWAS summary statistics from European ancestry samples (avg. n cases=47,856) with ~1,500 context-specific annotations representing candidate cis-regulatory elements. For genome-wide analysis, we applied CT-FM, a method that leverages heritability enrichment estimates and an annotation correlation matrix to select likely disease-relevant biological contexts. After identifying putative causal SNPs (PIP≥0.5) via functionally informed fine-mapping, we used CT-FM-SNP to identify relevant contexts for individual variants. A combined SNP-to-gene framework was applied to construct putative {regulatory SNP-context-gene-cancer} quadruplets.
RESULTS: Stratified LD score regression analysis identified 52 annotations with significant heritability enrichment (Bonferroni-corrected P≤0.05). CT-FM prioritized four high-confidence (PIP≥0.5) biological contexts: mammary luminal epithelial cells for breast cancer, a prostate cancer epithelial cell line (VCaP) for prostate cancer, and bulk tumor tissue contexts for colorectal and renal cancers. Variant-level analysis of hundreds of putatively causal SNPs corroborated these findings and identified additional high-confidence contexts for other malignancies, including estrogen receptor-negative breast cancer and bladder cancer. A total of 489 putative regulatory quadruplets were constructed, proposing specific molecular mechanisms underlying the observed GWAS signals.
CONCLUSION: These findings advance our understanding of genetic susceptibility to different cancers. Future work in larger, more diverse GWAS, coupled with more comprehensive annotation atlases, is essential to expand upon and validate our results.
METHODS: We integrated cancer GWAS summary statistics from European ancestry samples (avg. n cases=47,856) with ~1,500 context-specific annotations representing candidate cis-regulatory elements. For genome-wide analysis, we applied CT-FM, a method that leverages heritability enrichment estimates and an annotation correlation matrix to select likely disease-relevant biological contexts. After identifying putative causal SNPs (PIP≥0.5) via functionally informed fine-mapping, we used CT-FM-SNP to identify relevant contexts for individual variants. A combined SNP-to-gene framework was applied to construct putative {regulatory SNP-context-gene-cancer} quadruplets.
RESULTS: Stratified LD score regression analysis identified 52 annotations with significant heritability enrichment (Bonferroni-corrected P≤0.05). CT-FM prioritized four high-confidence (PIP≥0.5) biological contexts: mammary luminal epithelial cells for breast cancer, a prostate cancer epithelial cell line (VCaP) for prostate cancer, and bulk tumor tissue contexts for colorectal and renal cancers. Variant-level analysis of hundreds of putatively causal SNPs corroborated these findings and identified additional high-confidence contexts for other malignancies, including estrogen receptor-negative breast cancer and bladder cancer. A total of 489 putative regulatory quadruplets were constructed, proposing specific molecular mechanisms underlying the observed GWAS signals.
CONCLUSION: These findings advance our understanding of genetic susceptibility to different cancers. Future work in larger, more diverse GWAS, coupled with more comprehensive annotation atlases, is essential to expand upon and validate our results.