AbstractGenetic variation in response to the environment is fundamental in biology and has been described as genotype-by-environment interaction (GxE), reaction norm or phenotypic plasticity. In the genomic era, there has been increasing interest in estimating GxE, using genome-wide SNPs, e.g. a whole-genome reaction norm model (RNM) that can estimate unbiased genome-wide GxE. However, the existing approach is computationally demanding and infeasible to handle large-scale biobank data. Here we introduce GxEsum, a model for estimating GxE based on GWAS summary statistics, which can be applied to a large sample size. In simulations, we show that GxEsum can control type I error rate and produce unbiased estimates in general. We apply GxEsum to UK Biobank to estimate genome-wide GxE for BMI and hypertension, and find that the computational efficiency of GxEsum is thousands of times higher than existing whole-genome GxE methods such as RNM. Because of its computational efficiency, GxEsum can achieve a higher precision (i.e. power) from a larger sample size. As the scale of available resources has been increased, GxEsum may be an efficient tool to estimate GxE that can be applied to large-scale data across multiple complex traits and diseases.
Response to mass selection when the genotype by environment interaction is modelled as a linear reaction norm
