Abstract 4489: Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study
Bibliographic record
Abstract
Abstract To investigate the role of low frequency and rare genetic variation in colorectal cancer (CRC) susceptibility, the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) and the Colorectal Cancer Family Registry (CCFR) conducted whole genome sequencing and imputed into genome-wide association studies (GWAS) of 14,718 CRC cases and 12,186 controls. These data provide a unique opportunity to investigate rare variants, which contribute to the majority of the variation in the genome. To improve power for discovering rare CRC susceptibility variants (<1% MAF), Roadmap Epigenomics data were used to construct biologically relevant testing sets of enhancers, promoters and exons for gene-based association testing across the genome. Since enhancers exert their effects by impacting expression of target genes, we defined enhancer-gene networks by linking enhancer(s) to target gene expression using Roadmap chromatin state maps and gene expression. Variants in linked enhancers from digestive and immune tissues were aggregated together with variants in the promoter and non-synonymous coding variants in the target gene. We tested 9,884 variant sets for association with CRC risk using the Mixed effects Score Test (MiST). Our most significant findings are for acyl-Coenzyme A dehydrogenase, C-2 to C-3 short chain precursor-ACADS (p = 1×10−4), AlkB homologs, including AlkB homolog 1-ALKBH1 (p = 2×10−4), and SRA stem-loop interacting RNA binding protein-SLIRP (p = 2×10−4). We will replicate these findings within the Colorectal Cancer Transdisciplinary Study (CORECT), as well as additional samples currently genotyped in CCFR and GECCO (over 25,000 CRC cases and controls). Although the top findings are statistically non-significant in this initial dataset, each of these genes linked to molecular pathways implicated in CRC carcinogenesis (fatty acid metabolism, DNA/RNA repair, and Nuclear Receptor signaling pathway, which interacts with the Wnt, beta-catenin pathways to result in a diverse array of cellular effects including altered cellular adhesion, tissue morphogenesis, and oncogenesis). Our current findings suggest that although functional insight can improve power for novel discovery, even larger sample sizes and/or pathway-based analyses are necessary to understand the role of rare variants in CRC carcinogenesis. Citation Format: Stephanie A. Bien, Tabitha A. Harrison, Paul L. Auer, Flora Qu, Jeroen Huyghe, Barbara Banbury, Peyton Greenside, Goncalo R. Abecasis, Sonja I. Berndt, Stephane Bézieau, Hermann Brenner, Graham Casey, Andrew T. Chan, Jenny Chang-Claude, Sai Chen, Joshua D. Smith, Loic Le Marchand, Christopher Carlson, Polly A. Newcomb, Christian Fuchsberger, Marty L. Slattery, Hyun M. Kang, Emily White, John Potter, Steven J. Gallinger, Michael Hoffmeister, Stephen B. Gruber, Deborah A. Nickerson, Ulrike Peters, Anshul Kundaje, Li Hsu. Using functional data from Roadmap Epigenomics to inform analysis of rare variants linked to gene expression in a large colorectal cancer study. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4489.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".