Genotype–covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model
Guiyan Ni,
Julius Werf,
Xuan Zhou,
Elina Hyppönen,
Naomi R. Wray and
S. Hong Lee ()
Additional contact information
Guiyan Ni: University of South Australia Cancer Research Institute, University of South Australia
Julius Werf: University of New England
Xuan Zhou: University of South Australia Cancer Research Institute, University of South Australia
Elina Hyppönen: University of South Australia Cancer Research Institute, University of South Australia
Naomi R. Wray: University of Queensland
S. Hong Lee: University of South Australia Cancer Research Institute, University of South Australia
Nature Communications, 2019, vol. 10, issue 1, 1-15
Abstract:
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.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (2)
Downloads: (external link)
https://www.nature.com/articles/s41467-019-10128-w Abstract (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10128-w
Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/
DOI: 10.1038/s41467-019-10128-w
Access Statistics for this article
Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie
More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().