Abstract 4831: Additive and multiplicative gene-environment interactions for colorectal cancer risk.
Bibliographic record
Abstract
Abstract Background: Genetic and environmental factors influence colorectal cancer (CRC) risk. Previous studies have provided additional etiologic insight by examining multiplicative gene-environment interactions for individual susceptibility loci. However, individual loci confer only modest risks, which may limit statistical power and clinical significance. A genetic risk score comprising known CRC loci may provide a more comprehensive assessment of risk. Further, there is a paucity of data on the role of additive gene-environment interactions, which may have greater public health implications than multiplicative interactions. We thus evaluated both additive and multiplicative interactions between a genetic risk score and 15 key environmental factors on risk of CRC. Methods: We conducted an analysis of 10,491 CRC cases and 10,725 controls of European ancestry in 7 cohort and 6 case-control studies participating in the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO) or Colon Cancer Family Registry (CCFR). To provide a global measure of genetic predisposition, information across multiple risk variants was combined by calculating a genetic risk score based on 24 polymorphisms near 21 genetic loci identified in previous genome-wide association studies. For the genetic score and environmental factors, the reference category corresponded to that associated with lower CRC risk. We tested for additive interactions by estimating relative excess risk due to interactions (RERIs) using logistic regression; for multiplicative interactions we used an empirical-Bayes approach. Nominal P values ≤ 0.05 were considered statistically significant. Results: After adjustment for age, sex, center, study, principal components, and total energy, as appropriate, we observed super-additive gene-environment interactions for CRC risk between the genetic risk score and body mass index (RERI=0.15; 95% CI: 0.00-0.31), ever smoking (RERI=0.14; 95% CI: 0.00-0.28), pack-years of smoking (RERI=0.23; 95% CI: 0.05-0.41), postmenopausal hormone therapy use (RERI=0.38; 95% CI: 0.17-0.59), and intake of processed meat (RERI=0.23; 95% CI: 0.06-0.40). Of the 15 environmental risk factors, 12 showed RERIs > 0 – suggesting an overall trend toward super-additive interactions for these factors. In addition, we observed evidence of sub-multiplicative interactions for use of aspirin and non-steroidal anti-inflammatory drugs. There were no other statistically significant multiplicative interactions. Conclusions: We observed evidence for super-additive effects of genetic predisposition and environmental risk factors on risk of CRC. Our findings suggest that certain environmental risk factors have stronger effects on absolute risk among individuals with higher genetic risk of CRC. If confirmed in future studies, these results may have implications for targeted prevention strategies. Citation Format: Mengmeng Du, Sonja I. Berndt, Hermann Brenner, Bette J. Caan, Peter T. Campbell, Graham Casey, Andrew Chan, Jenny Chang-Claude, Stephen J. Chanock, Nilanjan Chatterjee, David V. Conti, David Duggan, Jane C. Figueiredo, Steven Gallinger, Jian Gong, Robert W. Haile, Tabitha A. Harrison, Richard B. Hayes, Michael Hoffmeister, John L. Hopper, Li Hsu, Thomas J. Hudson, Carolyn M. Hutter, Eric J. Jacobs, Mark A. Jenkins, Shuo Jiao, Laurence N. Kolonel, Peter Kraft, Loic Le Marchand, Mathieu Lemire, Yi Lin, Noralane M. Lindor, Polly A. Newcomb, John D. Potter, Robert E. Schoen, Fredrick R. Schumacher, Daniela Seminara, Martha L. Slattery, Stephen N. Thibodeau, Cornelia M. Ulrich, Aung Ko Win, Emily White, Brent W. Zanke, Ulrike Peters. Additive and multiplicative gene-environment interactions for colorectal cancer risk. [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 4831. doi:10.1158/1538-7445.AM2013-4831
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".