Identification of Novel Loci and New Risk Variant in Known Loci for Colorectal Cancer Risk in East Asians
Bibliographic record
Abstract
Abstract Background: Risk variants identified so far for colorectal cancer explain only a small proportion of familial risk of this cancer, particularly in Asians. Methods: We performed a genome-wide association study (GWAS) of colorectal cancer in East Asians, including 23,572 colorectal cancer cases and 48,700 controls. To identify novel risk loci, we selected 60 promising risk variants for replication using data from 58,131 colorectal cancer cases and 67,347 controls of European descent. To identify additional risk variants in known colorectal cancer loci, we performed conditional analyses in East Asians. Results: An indel variant, rs67052019 at 1p13.3, was found to be associated with colorectal cancer risk at P = 3.9 × 10–8 in Asians (OR per allele deletion = 1.13, 95% confidence interval = 1.08–1.18). This association was replicated in European descendants using a variant (rs2938616) in complete linkage disequilibrium with rs67052019 (P = 7.7 × 10–3). Of the remaining 59 variants, 12 showed an association at P < 0.05 in the European-ancestry study, including rs11108175 and rs9634162 at P < 5 × 10−8 and two variants with an association near the genome-wide significance level (rs60911071, P = 5.8 × 10−8; rs62558833, P = 7.5 × 10−8) in the combined analyses of Asian- and European-ancestry data. In addition, using data from East Asians, we identified 13 new risk variants at 11 loci reported from previous GWAS. Conclusions: In this large GWAS, we identified three novel risk loci and two highly suggestive loci for colorectal cancer risk and provided evidence for potential roles of multiple genes and pathways in the etiology of colorectal cancer. In addition, we showed that additional risk variants exist in many colorectal cancer risk loci identified previously. Impact: Our study provides novel data to improve the understanding of the genetic basis for colorectal cancer risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".