Review of Statistical Methods for Gene-Environment Interaction Analysis
Purpose of Reviews
Complex diseases are caused by a combination of genetic and environmental factors, creating a challenge for understanding the disease mechanisms. Understanding the interplay between genes and environmental factors is important, as genes do not operate in isolation but rather in complex networks and pathways influenced by environmental factors. The advent of new technologies has made a massive amount of genetic data available, and various statistical methods have been developed to analyze genetic data and to identify interactions between genes and the environment, i.e., gene-environment (G-E) interactions.
In this review article, we introduce various statistical methods for identifying G-E interactions using case-control designs. We review a range of disease risk models for modeling the joint effects of genetic and environmental factors such as multiplicative and additive models. We then introduce various inference methods under these disease risk models, which include a standard prospective likelihood, case-only designs, a retrospective likelihood that exploits a gene-environment independence assumption to boost power, and an empirical Bayes type approach that uses the independence assumption in a data-adaptive way. Several tests for detecting genetic associations in the presence of G-E interactions are also introduced, which include a joint test and a maximum score test that provides a unified approach by integrating a class of disease risk models to maximize over a class of score tests.
There are several challenges of G-E interaction analysis that include replication issues. While more powerful statistical methods for detecting interactions are helpful, ultimately studies with larger sample sizes are needed to identify interactions through consortium-based studies to achieve adequate power for G-E analysis.
KeywordsGene-environment interaction GxE interaction Complex diseases Gene-environment independence Retrospective likelihood Empirical Bayes type estimator
Compliance with Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
- 4.• Hutter CM, Mechanic LE, Chatterjee N, Kraft P, Gillanders EM. Gene-environment interactions in cancer epidemiology: a National Cancer Institute Think Tank Report. Genet Epidemiol. 2013;37(7):643–57. Summarizes contemporary analytic methods for G × E interactions, provides an overview of motivation for performing G × E analysis, and discusses key considerations for analysis in case-control or nested case-control studies, and comments on interpretation of G × E interactions. https://doi.org/10.1002/gepi.21756.CrossRefPubMedPubMedCentralGoogle Scholar
- 5.• Maas P, Barrdahl M, Joshi AD, Auer PL, Gaudet MM, Milne RL, et al. Breast cancer risk from modifiable and nonmodifiable risk factors among white women in the United States. JAMA Oncol. 2016;2(10):1295–302. Evaluates combined risk stratification utility of common low penetrant single nucleotide polymorphisms (SNPs) and epidemiologic risk factors for breast cancer. Their model for absolute risk of breast cancer including SNPs can provide stratification for the population of white women in the United States and also can identify subsets of the population at an elevated risk that would benefit most from risk-reduction strategies based on altering modifiable factors. https://doi.org/10.1001/jamaoncol.2016.1025.CrossRefPubMedPubMedCentralGoogle Scholar
- 10.Han SS, Rosenberg PS, Ghosh A, Landi MT, Caporaso NE, Chatterjee N. An exposure-weighted score test for genetic associations integrating environmental risk factors. Biometrics. 2015.Google Scholar
- 12.• Figueroa JD, Han SS, Garcia-Closas M, Baris D, Jacobs EJ, Kogevinas M, et al. Genome-wide interaction study of smoking and bladder cancer risk. Carcinogenesis. 2014:bgu064. Conducted a genome-wide interaction study of smoking for bladder cancer risk by applying both multiplicative and additive interactions based on a prospective likelihood and a retrospective likelihood. They identified 10 significant SNPs that interact with smoking status (ever versus never smokers) for bladder cancer; these included rs1711973 that had an increased risk (OR=1.34; 95% confidence interval (CI): 1.2–1.5) among never smokers (multiplicative interaction P = 6.38E-06) and rs12216499 that had a reduced risk (OR=0.75; CI: 0.67–0.84) for ever-smokers (additive interaction P= 1.41E-06).Google Scholar
- 13.Joshi AD, Lindström S, Hüsing A, Barrdahl M, VanderWeele TJ, Campa D, et al. Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the breast and prostate cancer cohort consortium. Am J Epidemiol. 2014;180(10):1018–27. https://doi.org/10.1093/aje/kwu214.CrossRefPubMedPubMedCentralGoogle Scholar
- 16.Freedman BI, Langefeld CD, Lu L, Divers J, Comeau ME, Kopp JB, et al. Differential effects of MYH9 and APOL1 risk variants on FRMD3 association with diabetic ESRD in African Americans. PLoS Genet. 2011;7(6):e1002150. https://doi.org/10.1371/journal.pgen.1002150.CrossRefPubMedPubMedCentralGoogle Scholar
- 19.Umbach DM, Weinberg CR. Designing and analysing case control studies to exploit independence of genotype and exposure. Stat Med. 1997;16(15):1731–43. https://doi.org/10.1002/(SICI)1097-0258(19970815)16:15<1731::AID-SIM595>3.0.CO;2-S.CrossRefPubMedGoogle Scholar
- 21.• Han SS, Rosenberg PS, Garcia-Closas M, Figueroa JD, Silverman D, Chanock SJ, et al. Likelihood ratio test for detecting gene (G)-environment (E) interactions under an additive risk model exploiting GE independence for case-control data. Am J Epidemiol. 2012;176(11):1060–7. Developed a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption based on a retrospective likelihood. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study. https://doi.org/10.1093/aje/kws166.CrossRefPubMedPubMedCentralGoogle Scholar
- 22.•• Mukherjee B, Chatterjee N. Exploiting gene environment independence for analysis of case–control studies: an empirical Bayes type shrinkage estimator to trade off between bias and efficiency. Biometrics. 2008;64(3):685–94. Proposed a novel empirical Bayes-type shrinkage estimator to analyze case–control data that can relax the gene-environment independence assumption in a data-adaptive fashion. They also described a general approach for deriving the new shrinkage estimator and its variance within the retrospective maximum-likelihood framework developed by Chatterjee and Carroll (2005). Both simulated and real data examples suggested that the proposed estimator strikes a balance between bias and efficiency depending on the true nature of the gene-environment association and the sample size for a given study. https://doi.org/10.1111/j.1541-0420.2007.00953.x.CrossRefPubMedGoogle Scholar
- 24.Liu G, Lee S, Lee AW, Wu AH, Bandera EV, Jensen A, et al. Robust tests for additive gene-environment interaction in case-control studies using gene-environment independence. Am J Epidemiol. 2017.Google Scholar
- 25.• Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007;63(2):111–9. Present a joint test of marginal association and gene-environment interaction for case-control data. They compared the power and sample size requirements of this joint test to other analyses: the marginal test of genetic association, the standard test for gene-environment interaction based on logistic regression, and the case-only test for interaction that exploits gene-environment independence. https://doi.org/10.1159/000099183.CrossRefPubMedGoogle Scholar
- 26.• Hamza TH, Chen H, Hill-Burns EM, Rhodes SL, Montimurro J, Kay DM, et al. Genome-wide gene-environment study identifies glutamate receptor gene GRIN2A as a Parkinson's disease modifier gene via interaction with coffee. PLoS Genet. 2011;7(8):e1002237. Conducted a genome-wide joint test for gene x coffee interaction for Parkinson’s disease and identified a novel susceptibility locus in the GRIN2A gene. In the gene, the T allele of the SNP rs4998386 is associated with a reduced risk among heavy coffee drinkers, whereas this variant has a minimal effect among light coffee drinkers. https://doi.org/10.1371/journal.pgen.1002237.CrossRefPubMedPubMedCentralGoogle Scholar
- 39.Chatterjee N, Shi J, García-Closas M. Developing and evaluating polygenic risk prediction models for stratified disease prevention. Nat Rev Genet. 2016.Google Scholar