| Title: | Epigenetic germline variants predict cancer prognosis and risk and distribute uniquely in topologically associating domains |
| Journal: | F1000Research |
| Published: | 1 Sep 2023 |
| DOI: | https://doi.org/10.12688/f1000research.139476.1 |
| URL: | https://f1000research.com/articles/12-1083/pdf |
Publication 9654
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Abstract
Background: Methylation quantitative trait loci (meQTLs) associate with different levels of local DNA methylation in cancers. Here, we investigated whether the distribution of cancer meQTLs reflected functional organization of the genome in the form of chromatin topologically associated domains (TADs) and evaluated whether cancer meQTLs near known driver genes have the potential to influence cancer risk or progression. Methods: Published cancer meQTLs were analyzed according to their location in transcriptionally active or inactive TADs and TAD boundary regions. Cancer meQTLs near known cancer genes were analyzed for association with cancer risk in the UKBioBank and prognosis in The Cancer Genome Atlas (TCGA). Results: In TAD boundary regions, the density of cancer meQTLs was higher near inactive TADs. Furthermore, we observed an enrichment of cancer meQTLs in active TADs near tumor suppressors, whereas there was a depletion of such meQTLs near oncogenes. Several meQTLs were associated with cancer risk in the UKBioBank, and we were able to reproduce breast cancer risk associations in the DRIVE cohort. Survival analysis in TCGA implicated a number of meQTLs in 13 tumor types. In 10 of these, polygenic cancer meQTL scores were associated with increased hazard in a CoxPH analysis. Risk and survival-associated meQTLs tended to affect cancer genes involved in DNA damage repair and cellular adhesion and reproduced cancer-specific associations reported in prior literature. Conclusions: This study provides evidence that genetic variants that influence local DNA methylation are affected by chromatin structure and can impact tumor evolution. </p>
5 Authors
- Shervin Goudarzi
- Meghana Pagadala
- Adam Klie
- James V Talwar
- Hannah Carter
1 Application
| Application ID | Title |
|---|---|
| 37671 | Genetic Association Analyses of Complex Traits and Methods Development |
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