Genotype–Environment Interactions in Microsatellite Stable/Microsatellite Instability-Low Colorectal Cancer: Results from a Genome-Wide Association Study
Bibliographic record
Abstract
BACKGROUND: Genome-wide association studies (GWAS) have led to the identification of a number of common susceptibility loci for colorectal cancer (CRC); however, none of these GWAS have considered gene-environment (G × E) interactions. Therefore, it is unclear whether current hits are modified by environmental exposures or whether there are additional hits whose effects are dependent on environmental exposures. METHODS: We conducted a systematic search for G × E interactions using genome wide data from the Colon Cancer Family Registry that included 1,191 cases of microsatellite stable (MSS) or microsatellite instability-low (MSI-L) CRC and 999 controls genotyped using either the Illumina Human1M or Human1M-Duo BeadChip. We tested for interactions between genotypes and 14 environmental factors using 3 methods: a traditional case-control test, a case-only test, and the recently proposed 2-step method by Murcray and colleagues. All potentially significant findings were replicated in the ARCTIC Study. RESULTS: No G × E interactions were identified that reached genome-wide significance by any of the 3 methods. When analyzing previously reported susceptibility loci, 7 significant G × E interactions were found at a 5% significance level. We investigated these 7 interactions in an independent sample and none of the interactions were replicated. CONCLUSIONS: Identifying G × E interactions will present challenges in a GWAS setting. Our power calculations illustrate the need for larger sample sizes; however, as CRC is a heterogeneous disease, a tradeoff between increasing sample size and heterogeneity needs to be considered. IMPACT: The results from this first genome-wide analysis of G × E in CRC identify several challenges, which may be addressed by large consortium efforts.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".