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CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses

Author

Listed:
  • Xuan Zhou

    (University of South Australia
    University of South Australia
    South Australian Health and Medical Research Institute)

  • Hae Kyung Im

    (The University of Chicago)

  • S. Hong Lee

    (University of South Australia
    University of South Australia
    South Australian Health and Medical Research Institute)

Abstract

As a key variance partitioning tool, linear mixed models (LMMs) using genome-based restricted maximum likelihood (GREML) allow both fixed and random effects. Classic LMMs assume independence between random effects, which can be violated, causing bias. Here we introduce a generalized GREML, named CORE GREML, that explicitly estimates the covariance between random effects. Using extensive simulations, we show that CORE GREML outperforms the conventional GREML, providing variance and covariance estimates free from bias due to correlated random effects. Applying CORE GREML to UK Biobank data, we find, for example, that the transcriptome, imputed using genotype data, explains a significant proportion of phenotypic variance for height (0.15, p-value = 1.5e-283), and that these transcriptomic effects correlate with the genomic effects (genome-transcriptome correlation = 0.35, p-value = 1.2e-14). We conclude that the covariance between random effects is a key parameter for estimation, especially when partitioning phenotypic variance by multi-omics layers.

Suggested Citation

  • Xuan Zhou & Hae Kyung Im & S. Hong Lee, 2020. "CORE GREML for estimating covariance between random effects in linear mixed models for complex trait analyses," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18085-5
    DOI: 10.1038/s41467-020-18085-5
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    Cited by:

    1. Md. Moksedul Momin & Jisu Shin & Soohyun Lee & Buu Truong & Beben Benyamin & S. Hong Lee, 2023. "A method for an unbiased estimate of cross-ancestry genetic correlation using individual-level data," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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