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

Author

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  • Guiyan Ni

    (University of South Australia Cancer Research Institute, University of South Australia
    University of New England)

  • 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
    Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health
    South Australian Health and Medical Research Institute)

  • Naomi R. Wray

    (University of Queensland
    University of Queensland)

  • S. Hong Lee

    (University of South Australia Cancer Research Institute, University of South Australia
    University of New England
    University of Queensland)

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.

Suggested Citation

  • Guiyan Ni & Julius Werf & Xuan Zhou & Elina Hyppönen & Naomi R. Wray & S. Hong Lee, 2019. "Genotype–covariate correlation and interaction disentangled by a whole-genome multivariate reaction norm model," Nature Communications, Nature, vol. 10(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10128-w
    DOI: 10.1038/s41467-019-10128-w
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    Cited by:

    1. Matteo Di Scipio & Mohammad Khan & Shihong Mao & Michael Chong & Conor Judge & Nazia Pathan & Nicolas Perrot & Walter Nelson & Ricky Lali & Shuang Di & Robert Morton & Jeremy Petch & Guillaume Paré, 2023. "A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    2. Kathryn E. Kemper & Julia Sidorenko & Huanwei Wang & Ben J. Hayes & Naomi R. Wray & Loic Yengo & Matthew C. Keller & Michael Goddard & Peter M. Visscher, 2024. "Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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