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Title: Genotype-covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
Contributor(s): Ni, Guiyan  (author); Van Der Werf, Julius  (author)orcid ; Zhou, Xuan (author); Hypponen, Elina (author); Wray, Naomi R (author); Lee, S Hong  (author)
Publication Date: 2019
Early Online Version: 2019-05-20
Open Access: Yes
DOI: 10.1038/s41467-019-10128-w
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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.

Publication Type: Journal Article
Grant Details: NHMRC/1080157
Source of Publication: Nature Communications, 10(1), p. 1-15
Publisher: Nature Publishing Group
Place of Publication: United Kingdom
ISSN: 2041-1723
Fields of Research (FoR) 2020: 310207 Statistical and quantitative genetics
Socio-Economic Objective (SEO) 2020: 200104 Prevention of human diseases and conditions
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science

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