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

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CORE GREML, a new method, accurately estimates covariance between random effects in linear mixed models (LMMs). This improves variance partitioning, revealing significant genetic and transcriptomic influences on traits like height.

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Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Bioinformatics

Background:

  • Linear mixed models (LMMs) with genome-based restricted maximum likelihood (GREML) are crucial for variance partitioning.
  • Conventional GREML assumes independence of random effects, which can lead to biased estimates when this assumption is violated.

Purpose of the Study:

  • Introduce CORE GREML, a generalized GREML method that estimates the covariance between random effects.
  • Evaluate CORE GREML's performance against conventional GREML using simulations and real-world data.
  • Assess the contribution of multi-omics data, specifically transcriptomic data, to phenotypic variance and its correlation with genomic effects.

Main Methods:

  • Developed CORE GREML, a novel statistical framework extending GREML to explicitly model covariance between random effects.
  • Conducted extensive simulations to compare CORE GREML with conventional GREML under various scenarios of correlated random effects.
  • Applied CORE GREML to UK Biobank data, integrating genotype and transcriptome data to analyze phenotypic variance for height.

Main Results:

  • CORE GREML provides unbiased variance and covariance estimates, outperforming conventional GREML when random effects are correlated.
  • Simulations demonstrate CORE GREML's superior accuracy in estimating variance components.
  • Analysis of UK Biobank data revealed significant transcriptomic contribution to height variance (0.15, p < 1.5e-283) and a notable correlation between genomic and transcriptomic effects (correlation = 0.35, p < 1.2e-14).

Conclusions:

  • Estimating the covariance between random effects is essential for accurate variance partitioning, particularly in multi-omics studies.
  • CORE GREML offers a robust approach for dissecting complex phenotypic variation by accounting for correlated random effects.
  • The findings highlight the importance of integrating multi-omics data and advanced statistical methods for a comprehensive understanding of trait heritability.