The widely used statistical methods test interactions for a single phenotype (e.g. Mendelian, oligogenic, multifactorial). However, we often observe pleotropic genetic interaction effects (i.e. one gene expression predominates over another, hierarchy).
The simultaneous gene-gene (GxG) interaction analysis of multiple complementary traits will increase statistical power to detect GxG interactions. Although GxG interactions play an important role in uncovering the genetic structure of complex traits, the statistical methods for detecting GxG interactions in multiple phenotypes remain less developed–– owing obviouslt to its potential complexity.
In the attached article, authors extend the functional regression model from single variate, to multivariate, for simultaneous GxG interaction analysis of multiple correlated phenotypes. They have conducted large-scale simulations in order to evaluate Type I error rates––for testing interaction between two genes with multiple phenotypes and to compare statistical power with traditional multivariate pair-wise interaction analysis and single-trait interaction analysis by a single variate functional regression model. To further evaluate performance, the Multiple-Function Regression for interaction analysis is applied to five phenotypes of exome sequence data from NHLBI’s Exome Sequencing Project (ESP) to detect pleiotropic GxG interactions. Intriguingly, the authors found 267 pairs of genes that formed a genetic interaction network and showed significant evidence of interactions influencing these five traits.
PLoS Genet 2o16; 12: e1005965.