title: A perspective on interaction effects in genetic association studies.
abstract: The identification of gene-gene and gene-environment interaction in human traits and diseases is an active area of research that generates high expectation, and most often lead to high disappointment. This is partly explained by a misunderstanding of the inherent characteristics of standard regression-based interaction analyses. Here, I revisit and untangle major theoretical aspects of interaction tests in the special case of linear regression; in particular, I discuss variables coding scheme, interpretation of effect estimate, statistical power, and estimation of variance explained in regard of various hypothetical interaction patterns. Linking this components it appears first that the simplest biological interaction models-in which the magnitude of a genetic effect depends on a common exposure-are among the most difficult to identify. Second, I highlight the demerit of the current strategy to evaluate the contribution of interaction effects to the variance of quantitative outcomes and argue for the use of new approaches to overcome this issue. Finally, I explore the advantages and limitations of multivariate interaction models, when testing for interaction between multiple SNPs and/or multiple exposures, over univariate approaches. Together, these new insights can be leveraged for future method development and to improve our understanding of the genetic architecture of multifactorial traits.
authors.affiliation: Department of Epidemiology, Harvard T.H. School of Public Health, Boston, Massachusetts, United States of America.
authors.forename: Hugues
authors.initials: H
authors.lastname: Aschard
mesh_terms: D015992:Body Mass Index; D015444:Exercise; D059647:Gene-Environment Interaction; D056726:Genetic Association Studies / Q000379:methods*; D006801:Humans; D016014:Linear Models; D008957:Models, Genetic; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D012044:Regression Analysis
keywords: GWAS; genetic risk score; interaction; joint test; multivariate analysis; power; pratt index; statistical method; variance explained