%0 Journal Article
%T Genotype每covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
%J -
%D 2019
%R https://doi.org/10.1038/s41467-019-10128-w
%X The genomics era has brought useful tools to dissect the genetic architecture of complex traits. Here we propose a multivariate reaction norm model (MRNM) to tackle genotype每covariate (G每C) correlation and interaction problems. We apply MRNM to the UK Biobank data in analysis of body mass index using smoking quantity as a covariate, finding a highly significant G每C correlation, but only weak evidence for G每C interaction. In contrast, G每C interaction estimates are inflated in existing methods. It is also notable that there is significant heterogeneity in the estimated residual variances (i.e., variances not attributable to factors in the model) across different covariate levels, i.e., residual每covariate (R每C) interaction. We also show that the residual variances estimated by standard additive models can be inflated in the presence of G每C and/or R每C interactions. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses
%U https://www.nature.com/articles/s41467-019-10128-w