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

Guiyan Ni; Julius van der Werf; Xuan Zhou; Elina Hyppönen; Naomi R. Wray; S. Hong Lee

doi:10.1038/s41467-019-10128-w

Nature Communications (May 2019)

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

Guiyan Ni,
Julius van der Werf,
Xuan Zhou,
Elina Hyppönen,
Naomi R. Wray,
S. Hong Lee

Affiliations

Guiyan Ni: Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia
Julius van der Werf: School of Environmental and Rural Science, University of New England
Xuan Zhou: Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia
Elina Hyppönen: Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia
Naomi R. Wray: Institute for Molecular Bioscience, University of Queensland
S. Hong Lee: Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia

DOI: https://doi.org/10.1038/s41467-019-10128-w
Journal volume & issue: Vol. 10, no. 1
pp. 1 – 15

Abstract

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Complex traits are often influenced by genetic and non-genetic factors (such as environmental exposures), which are themselves interconnected. Here, the authors develop a method for disentangling genotype–covariate correlation and interaction, and investigate their effects on estimating statistical genetic parameters.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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