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Realized Genome Sharing in Heritability Estimation Using Random Effects Models.

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|March 24, 2019
PubMed
Summary
This summary is machine-generated.

We derived formulas for heritability estimation, showing that incorrect kinship measures can bias results. Mis-specified genetic correlation matrices, particularly using the GRM in population studies, may explain the missing heritability problem.

Keywords:
asymptotic biaskinshipmissing heritabilitymodel mis-specificationrandom effects

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Area of Science:

  • Quantitative genetics
  • Statistical genetics

Background:

  • Heritability estimation is crucial for understanding genetic contributions to traits.
  • Random effects models are commonly used, but their accuracy depends on correct model specification.
  • The "missing heritability" problem highlights discrepancies between estimated and actual genetic influence.

Purpose of the Study:

  • To provide formulas for the limiting distribution of the maximum likelihood estimate in a two-component random effects model for heritability.
  • To assess the impact of using incorrect kinship measures on heritability estimation.
  • To investigate how model mis-specification, particularly of the genetic correlation matrix, contributes to the missing heritability problem.

Main Methods:

  • Derivation of formulas for the limiting distribution of the maximum likelihood estimate.
  • Theoretical analysis of asymptotic sampling variance and bias.
  • Simulation study using a population-based design and the genomic relationship matrix (GRM).

Main Results:

  • Formulas are provided for heritability estimation, valid even with incorrect kinship measures.
  • Asymptotic sampling variance depends on study design and variation in the kinship measure.
  • Mis-specified correlation matrices lead to asymptotic bias, influenced by the difference between fitted and true matrices.
  • Estimating heritability with population-based designs and the GRM may exacerbate the missing heritability issue.

Conclusions:

  • The accuracy of heritability estimates is sensitive to the correct specification of kinship and genetic correlation matrices.
  • Mis-specification, particularly using the GRM in population studies, can lead to biased heritability estimates and contribute to the missing heritability problem.
  • These findings have implications for designing genetic studies and interpreting heritability estimates.