| Title: | Integrative Gene-Centric Analysis Reveals Cellular Pathways Associated with Heritable Breast Cancer Predisposition |
| Journal: | Cancers |
| Published: | 12 Dec 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41463216/ |
| DOI: | https://doi.org/10.3390/cancers17243969 |
| URL: | https://www.mdpi.com/2072-6694/17/24/3969/pdf |
Publication 17194
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Abstract
Background: Heritable breast cancer (BC) predisposition is strongly influenced by high-penetrance genes such as BRCA1 and BRCA2, but many moderate- and low-penetrance genes remain poorly characterized. Although over 100 loci have been reported, the causal genes often include false positives or uncertain associations. Methods: We applied a gene-centric, integrative approach to multi-ethnic genomic datasets, including the UK Biobank (UKB) and FinnGen (FG). We assessed consistency across multiple GWAS in Open Targets (OT) and additional complementary genetic association approaches, including ExPheWAS, TWAS, and PWAS. Collapsing variant-level effects to a gene-level view enhanced confidence and reaffirmed contributions from genes such as BRCA1, BRCA2, PALB2, CHEK2, and other DNA repair genes. Results: Using this integrative framework, we identified 38 high-confidence BC predisposition genes, including 8 previously reported drivers, 13 supported by multiple lines of evidence, and additional candidates (e.g., APOBEC3A, TNS1, PEX14) with emerging evidence. PWAS revealed several genes with potential recessive effects often missed by standard GWAS. Multi-cohort replication showed robust findings in European ancestry populations, while transferability to other populations was more limited. Conclusions: This work demonstrates the value of a gene-centric, integrative framework for prioritizing high-confidence BC predisposition genes, highlighting associated cellular pathways, and uncovering new candidates for further functional study, providing a reliable foundation for future research.</p>
4 Authors
- Roei Zucker
- Shirel Schreiber
- Amos Stern
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
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