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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.002 |
| 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".