| Title: | Genetic association studies of alterations in protein function expose recessive effects on cancer predisposition |
| Journal: | Scientific Reports |
| Published: | 21 Jul 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34290314/ |
| DOI: | https://doi.org/10.1038/s41598-021-94252-y |
| URL: | https://www.nature.com/articles/s41598-021-94252-y.pdf |
| Citations: | 21 (16 in last 2 years) as of 8 Aug 2024 |
Publication 7458
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Abstract
The characterization of germline genetic variation affecting cancer risk, known as cancer predisposition, is fundamental to preventive and personalized medicine. Studies of genetic cancer predisposition typically identify significant genomic regions based on family-based cohorts or genome-wide association studies (GWAS). However, the results of such studies rarely provide biological insight or functional interpretation. In this study, we conducted a comprehensive analysis of cancer predisposition in the UK Biobank cohort using a new gene-based method for detecting protein-coding genes that are functionally interpretable. Specifically, we conducted proteome-wide association studies (PWAS) to identify genetic associations mediated by alterations to protein function. With PWAS, we identified 110 significant gene-cancer associations in 70 unique genomic regions across nine cancer types and pan-cancer. In 48 of the 110 PWAS associations (44%), estimated gene damage is associated with reduced rather than elevated cancer risk, suggesting a protective effect. Together with standard GWAS, we implicated 145 unique genomic loci with cancer risk. While most of these genomic regions are supported by external evidence, our results also highlight many novel loci. Based on the capacity of PWAS to detect non-additive genetic effects, we found that 46% of the PWAS-significant cancer regions exhibited exclusive recessive inheritance. These results highlight the importance of recessive genetic effects, without relying on familial studies. Finally, we show that many of the detected genes exert substantial cancer risk in the studied cohort determined by a quantitative functional description, suggesting their relevance for diagnosis and genetic consulting.3 Authors
- Nadav Brandes
- Nathan Linial
- Michal Linial
1 Application
| Application ID | Title |
|---|---|
| 26664 | Novel machine-learning framework for improved inference in GWAS |
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