Grammatical evolution decision trees for trio designs

Abstract

The detection of gene-gene and gene-interactions in genetic association studies is an important challenge in human genetics. The detection of such interactive models presents a difficult computational and statistical challenge, especially as advances in genotyping technology have rapidly expanded the number of potential genetic predictors in such studies. The scale of these studies makes exhaustive search approaches infeasible, inspiring the application of evolutionary computation algorithms to perform variable selection and build classification models. Recently, an application of grammatical evolution to evolve decision trees (GEDT) has been introduced for detecting interaction models. Initial results were promising, but the previous applications of GEDT have been limited to case-control studies with unrelated individuals. While this study design is popular in human genetics, other designs with related individuals offer distinct advantages. Specifically, a trio-based design (with genetic data for an affected individual and their parents collected) can be a powerful approach to mapping that is robust to population heterogeneity and other potential confounders. In the current study, we extend the GEDT approach to be able to handle trio data (trioGEDT), and demonstrate its potential in simulated data with gene-gene interactions that underlie disease risk.