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

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.

journal_name

Nat Commun

journal_title

Nature communications

authors

Zhou X,Im HK,Lee SH

doi

10.1038/s41467-020-18085-5

subject

Has Abstract

pub_date

2020-08-21 00:00:00

pages

4208

issue

1

issn

2041-1723

pii

10.1038/s41467-020-18085-5

journal_volume

11

pub_type

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