Abstract
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–covariate (G–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–C correlation, but only weak evidence for G–C interaction. In contrast, G–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–covariate (R–C) interaction. We also show that the residual variances estimated by standard additive models can be inflated in the presence of G–C and/or R–C interactions. We conclude that it is essential to correctly account for both interaction and correlation in complex trait analyses.
Highlights
The genomics era has brought useful tools to dissect the genetic architecture of complex traits
The genomic era has brought useful tools to dissect the genetic architecture of complex traits, where genetic variance and covariance can be estimated based on genome-wide single nucleotide polymorphisms (SNPs) genotyped in large-scale population samples
We propose an extension of the wholegenome reaction norm model (RNM) that can estimate G–C interactions, where covariates can be continuous phenotypes of traits correlated with the response
Summary
The genomics era has brought useful tools to dissect the genetic architecture of complex traits. The relationship between smoking and BMI is a good example for a complex association which can be best modelled using a framework that can account both for genotype–covariate correlation and interaction (GCCI) Both correlation (‘association’) and interaction (‘effect modification’) are fundamental in biology[8,9,10], but it is critical to distinguish between them because their biological mechanisms differ, as do their implications. The genomic era has brought useful tools to dissect the genetic architecture of complex traits, where genetic variance and covariance can be estimated based on genome-wide single nucleotide polymorphisms (SNPs) genotyped in large-scale population samples. The increased availability of sufficiently powered data sets, with information on measured genetic and non-genetic risk factors, motivates the need to develop appropriate statistical tools for GCCI analysis. This can be modelled by introducing dependence between the phenotype and the covariate, where the covariate represents the phenotype of the modulating trait, with both phenotypes having shared genetic and environmental components
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