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A Bayesian mixed modeling approach for estimating heritability.

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This study estimates genome-wide DNA methylation heritability using a Bayesian approach. Results show significant heritability for many CpG sites and blood lipids, providing insights into genetic influences on these traits.

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Area of Science:

  • Genetics
  • Epigenetics
  • Biostatistics

Background:

  • DNA methylation heritability is not well understood.
  • Bayesian mixed models with integrated nested Laplace approximations (INLA) can model pedigree structures.
  • Estimating genome-wide heritability of DNA methylation and blood lipids is crucial.

Purpose of the Study:

  • To estimate genome-wide DNA methylation heritability (h²).
  • To estimate blood lipid heritability (h²).
  • To assess the heritability of treatment effects on blood lipids.

Main Methods:

  • Utilized data from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study.
  • Employed Bayesian mixed models with INLA for genome-wide heritability estimation.
  • Analyzed Infinium 450K CpG methylation and blood lipid data pre- and post-fenofibrate treatment in families.

Main Results:

  • Identified 425,791 CpG sites with non-zero heritability pre-treatment and 199,027 post-treatment.
  • Median DNA methylation heritability (h²) was 0.31 (pre) and 0.34 (post).
  • Significant heritability was found for triglycerides and high-density lipoprotein cholesterol, including treatment effects.

Conclusions:

  • Demonstrated the feasibility of a fully Bayesian genome-wide DNA methylation heritability estimation using INLA.
  • INLA provides uncertainty assessment and enables model selection for identifying CpGs with significant heritability.
  • The approach offers a robust method for understanding genetic contributions to DNA methylation and lipid traits.