Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model

Title
Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
Publication Date
2019
Author(s)
Ni, Guiyan
Van Der Werf, Julius
( author )
OrcID: https://orcid.org/0000-0003-2512-1696
Email: jvanderw@une.edu.au
UNE Id une-id:jvanderw
Zhou, Xuan
Hypponen, Elina
Wray, Naomi R
Lee, S Hong
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
Nature Publishing Group
Place of publication
United Kingdom
DOI
10.1038/s41467-019-10128-w
UNE publication id
une:1959.11/51478
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.

Link
Citation
Nature Communications, 10(1), p. 1-15
ISSN
2041-1723
Pubmed ID
31110177
Start page
1
End page
15
Rights
Attribution 4.0 International

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