A novel support vector regression approach for detecting gene-environment interactions and predicting trait values.

Abstract

Genome-wide association studies and genomic prediction are fundamental for investigating complex traits, but they have different objectives and are rarely unified within a shared analytical framework. Although machine learning has broadened the applicability of both lines of research, their combined use in detecting gene-environment interactions remains underexplored. This study presents a novel statistical framework, iSVR, that incorporates gene-environment interaction terms into a support vector regression model, enabling both modeling of interaction effects and their statistical testing. By formulating a score test based on M-estimation theory within this framework, the iSVR facilitates robust detection of gene-environment interactions while accommodating complex genotype-phenotype relationships. Extensive simulations demonstrate that the iSVR effectively controls the type I error rate and attains competitive or improved statistical power relative to existing methods under the investigated scenarios. Application to soybean and GAW19 datasets further highlights the iSVR's ability to accurately predict trait values and identify significant gene-environment interactions. Collectively, these findings illustrate that the unification of association testing and predictive modeling within a common statistical framework provides a powerful approach to characterize the gene-environment interaction landscapes underlying complex traits.