Does accounting for gene-environment interactions help uncover association between rare variants and complex diseases?

Polygenic Panels Predicting the Susceptibility of Multiple Upper Aerodigestive Tract Cancer in Oral Cancer Patients

Head and neck cancer was closely related with habitual use of cigarette and alcohol. Those cancer patients are susceptible to develop multiple primary tumors (MPTs). In this study, we utilized the single nucleotide polymorphisms (SNPs) array (Affymetrix Axion Genome-Wide TWB 2.0 Array Plate) to investigate patients' risks of developing multiple primary cancers. We recruited 712 male head and neck cancer patients between Mar 1996 and Feb 2017. Two hundred and eighty-six patients (40.2%) had MPTs and 426 (59.8%) had single cancer. Four hundred and twelve normal controls were also recruited. A list of seventeen factors was extracted and ten factors were demonstrated to increase the risks of multiple primary cancers (alcohol drinking, rs118169127, rs149089400, rs76367287, rs61401220, rs141057871, rs7129229, older age, rs3760265, rs9554264; all were p value < 0.05). Polygenic scoring model was built and the area under curve to predict the risk developing MPTs is 0.906. Alcohol drinking, among the seventeen factors, was the most important risk factor to develop MPT in upper aerodigestive tract (OR: 7.071, 95% C.I.: 2.134-23.434). For those with high score in polygenic model, routine screening of upper digestive tract including laryngoscope and esophagoscope is suggested to detect new primaries early.

Update on the State of the Science for Analytical Methods for Gene-Environment Interactions

The analysis of gene-environment interaction (G×E) may hold the key for further understanding the etiology of many complex traits. The current availability of high-volume genetic data, the wide range in types of environmental data that can be measured, and the formation of consortiums of multiple studies provide new opportunities to identify G×E but also new analytical challenges. In this article, we summarize several statistical approaches that can be used to test for G×E in a genome-wide association study. These include traditional models of G×E in a case-control or quantitative trait study as well as alternative approaches that can provide substantially greater power. The latest methods for analyzing G×E with gene sets and with data in a consortium setting are summarized, as are issues that arise due to the complexity of environmental data. We provide some speculation on why detecting G×E in a genome-wide association study has thus far been difficult. We conclude with a description of software programs that can be used to implement most of the methods described in the paper.

Are SNP-Smoking Association Studies Needed in Controls? DNA Repair Gene Polymorphisms and Smoking Intensity

Variations in tobacco-related cancers, incidence and prevalence reflect differences in tobacco consumption in addition to genetic factors. Besides, genes related to lung cancer risk could be related to smoking behavior. Polymorphisms altering DNA repair capacity may lead to synergistic effects with tobacco carcinogen-induced lung cancer risk. Common problems in genetic association studies, such as presence of gene-by-environment (G x E) correlation in the population, may reduce the validity of these designs. The main purpose of this study was to evaluate the independence assumption for selected SNPs and smoking behaviour in a cohort of 320 healthy Spanish smokers. We found an association between the wild type alleles of XRCC3 Thr241Met or KLC3 Lys751Gln and greater smoking intensity (OR = 12.98, 95% CI = 2.86–58.82 and OR=16.90, 95% CI=2.09-142.8; respectively). Although preliminary, the results of our study provide evidence that genetic variations in DNA-repair genes may influence both smoking habits and the development of lung cancer. Population-specific G x E studies should be carried out when genetic and environmental factors interact to cause the disease.

Incorporating gene-environment interaction in testing for association with rare genetic variants

OBJECTIVES

The incorporation of gene-environment interactions could improve the ability to detect genetic associations with complex traits. For common genetic variants, single-marker interaction tests and joint tests of genetic main effects and gene-environment interaction have been well-established and used to identify novel association loci for complex diseases and continuous traits. For rare genetic variants, however, single-marker tests are severely underpowered due to the low minor allele frequency, and only a few gene-environment interaction tests have been developed. We aimed at developing powerful and computationally efficient tests for gene-environment interaction with rare variants.

METHODS

In this paper, we propose interaction and joint tests for testing gene-environment interaction of rare genetic variants. Our approach is a generalization of existing gene-environment interaction tests for multiple genetic variants under certain conditions.

RESULTS

We show in our simulation studies that our interaction and joint tests have correct type I errors, and that the joint test is a powerful approach for testing genetic association, allowing for gene-environment interaction. We also illustrate our approach in a real data example from the Framingham Heart Study.

CONCLUSION

Our approach can be applied to both binary and continuous traits, it is powerful and computationally efficient.