Abstract 4579: Variants in long non-coding RNAs are associated with epithelial ovarian cancer risk in a pooled analysis of three genome-wide association studies.
Bibliographic record
Abstract
Abstract Since most GWAS hits fall in non-coding regions of the genome and long non-coding RNAs (lncRNAs) are emerging as drivers of carcinogenesis, we hypothesized that SNPs in lncRNAs influence epithelial ovarian cancer (EOC) risk. A comprehensive lncRNA database (lncRNA db) that contained 104 human lncRNAs when downloaded in November 2011 was used to identify 1,737 variants in 63 unique lncRNAs that were represented in three genome-wide association studies from North America, the United Kingdom, and Poland (3,995 EOC cases and 3,277 controls of European background). SNPs with MAF <5% were excluded, and missing genotypes were inferred with Mach using the HapMap CEU population. Unconditional logistic regression treating alternate alleles as an ordinal variable was used to evaluate individual SNP-EOC risk associations. Subgroup analysis was conducted for serous adenocarcinomas (N= 2,066), the main histologic subtype of EOC. Models were adjusted for study and European ancestry using the first two principal components (PCs). We reduced correlated signals (r2 ≥ 0.8) within each lncRNA and kept the most statistically significant variants based on Wald tests, resulting in 651 SNPs for further analyses. Fisher's product method was used to aggregate evidence of multiple variants within each lncRNA using 10,000 permutations. To adjust for multiple comparisons, a false discovery rate (FDR) of 10% was used. A replication phase was carried out for top-ranked SNPs from an additional 9,854 EOC cases (5,802 serous) and 17,633 controls genotyped through an international effort known as the Collaborative Oncological Gene-Environment study (COGS). Four lncRNAs, WT1-AS (p = 0.005, n = 2 SNPs), Hoxa11as (p = 0.013, n = 2), AK082072 (p = 0.016, n = 4) and H19 (p = 0.020, n = 2), were significantly associated with EOC risk among all histologies (FDR ≤ 10%). The SNP in WT1-AS most significantly associated with EOC risk was rs3809061 (T>C) (minor allele frequency= 36%, odds ratio (OR) = 0.91, CI: 0.84-0.97, p = 0.008). Among serous cases, rs3809061 was also protective (OR = 0.92, p = 0.04). These findings were replicated by a correlated SNP rs2301250 (r2 = 0.84) evaluated in COGS among serous cases (OR = 0.94, CI: 0.90-0.99, p = 0.009) and less significant among all histologies (OR = 0.96, CI: 0.93-1.00, p = 0.054). We further interrogated its functional role by performing an expression quantitative trait locus analysis on WTI-AS mRNA expression using the Cancer Genome Atlas (TCGA) data (N = 462 serous cases). WT1-AS expression was significantly higher among carriers of the variant allele (compared to TT homozygotes) using the Mann-Whitney U test (p = 0.001). WT1-AS is the antisense and possible regulator of WT1, a tumor suppressor gene reported to be prognostic in advanced EOC. These findings implicate germline lncRNA variants associated with EOC risk that merit further investigation. Citation Format: Yian Ann Chen, Zhihua Chen, Jennifer Permuth-Wey, Ya-Yu Tsai, Hui-Yi Lin, Xiaotao Qu, Kate Lawrenson, David Fenstermacher, Catherine M. Phelan, Alvaro Monteiro, Simon A. Gayther, Steven A. Narod, Rebecca Sutphen, Michael J. Birrer, Nicolas Wentzensen, Joellen M. Schildkraut, Ellen L. Goode, Paul Pharoah, Thomas Sellers. Variants in long non-coding RNAs are associated with epithelial ovarian cancer risk in a pooled analysis of three genome-wide association studies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4579. doi:10.1158/1538-7445.AM2013-4579
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".