eLife assessment
The study presents a framework viewing gene-by-environment (GxE) effect estimation as a bias-variance tradeoff problem. The authors convincingly show that greater statistical power can be achieved in detecting GxE if an underlying model of polygenic GxE is assumed. This polygenic amplification model is a truly novel view with fundamental promise for the detection of GxE in genomic datasets. That said, at present the polygenic architecture investigation presented in the manuscript is somewhat limited to specific models and may not adequately build over the bias-variance tradeoff part of the manuscript. If the authors can show in their simulations that they can in principle detect more complex scenarios of amplification, then the strength of the paper would be enhanced.
