| 1. |
- Chen, Hongjie, et al.
(author)
-
Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals
- 2021
-
In: Human Genetics and Genomics Advances. - : Cell Press. - 2666-2477. ; 2:3
-
Journal article (peer-reviewed)abstract
- Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.
|
|
| 2. |
- Feng, Helian, et al.
(author)
-
Cross-cancer cross-tissue Transcriptome-wide Association Study (TWAS) of 11 cancers identifies 56 novel genes
- 2020
-
In: Genetic Epidemiology. - : John Wiley & Sons. - 0741-0395 .- 1098-2272. ; 44:5, s. 481-481
-
Journal article (other academic/artistic)abstract
- Though heterogeneous, multiple tumor types share hallmark mechanisms. Thus, identifying genes associated with multiple cancer types may shed light on general oncogenic mechanisms and identify genes missed in single‐cancer analyses. TWAS have been successful in testing whether genetically‐predicted tissue‐specific gene expression is associated with cancer risk. Although cross‐cancer genome‐wide association studies (GWAS) analyses have been performed previously, no cross‐cancer TWAS has been conducted to date. Here, we implement a pipeline to perform cross‐cancer, cross‐tissue TWAS analysis. We use newly‐developed multi‐trait TWAS test statistics to integrate the TWAS results for association between 11 separated cancers and predicted gene expression in 43 GTEx tissues, including a “sum” test and a “variance components” test, analogous to fixed‐ and random‐effects meta‐analyses. We then integrated the results across different tissues using the Aggregated Cauchy Association Test (ACAT) combined test.A total of 403 genes were significantly associated with at least one cancer type for at least one tissue; 96 additional genes were identified when combining test results across cancers; and 35 additional genes when further combining test results across tissue. Among these significant genes, 70 were not near previously‐published GWAS index variants. 14 of the 70 novel genes were identified from the single‐cancer single‐tissue test; an additional 43 were identified with the cross‐cancer test; and another 13 were identified when further combined across tissues. The newly identified genes, including RBBP8 and TP53BP , are involved in chromatin structure, tumorigenesis, apoptosis, transcriptional regulation, DNA repair, immune system, oxidative damage and cell‐cycle, proliferation, progression, shape, structure, and migration.
|
|
