Cancer Epidemiology Biomarkers & PreventionGenetic & Genomic MedicinePeer-reviewed journal article
Genetic analysis of functional rare germline variants across 9 cancer types from the DiscovEHR study
Manu ShivakumariD, Jason E. MilleriD, Venkata Ramesh Dasari, Yanfei ZhangiD, Ming Ta Michael LeeiD, David J. CareyiD, Radhika P. GogoiiD, Dokyoon KimiD
Abstract
Rare variants play an essential role in the etiology of cancer and characterizing rare germline variants that impact the risk of cancer is an ongoing challenge. We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning pathogenic and likely pathogenic variants in 5,538 cancer patients and 7,286 matched controls in a discovery set and 1,991 cancer patients and 2,504 matched controls in a validation set across nine cancer types. We discovered 87 genes and 106 pathways significantly associated with cancer (Bonferroni-corrected P < 0.05) out of which seven genes and 26 pathways replicated from the validation set (suggestive threshold P < 0.05). Further, four genes and 21 pathways were discovered to be associated with multiple cancers (Bonferroni-corrected P < 0.05). Additionally, we identified 13 genes and two pathways associated with survival outcome across seven cancers (Bonferroni-corrected P < 0.05), where two genes, PCDHB8 and DCHS2, were also associated with survival outcome in TCGA data. In summary, we conducted one of the largest pan-cancer association studies using germline data derived from a single hospital system to find novel predisposition genes and pathways associated with nine cancers. Our results can inform future guidelines for germline genetic testing in cancer, which will be helpful in screening for cancer high-risk patients. This work adds to the knowledge base and progress being made in precision medicine.
- organism: human
- assay: whole-exome sequencing
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 0
- FWCI
- 0.00
- Field percentile
- 10th percentile
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