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Estimating additive genetic variance can be inaccurate in related individuals. Nonadditive genetic effects combined with population structure can inflate variance estimates in mixed models.

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Area of Science:

  • Quantitative genetics
  • Population genetics
  • Statistical genetics

Background:

  • Mixed models are standard for estimating additive genetic variance using genetic similarity matrices from dense markers.
  • A key assumption is that nonadditive genetic effects only increase residual variance, not additive variance estimates.
  • This assumption holds for unrelated individuals but not for related populations.

Discussion:

  • Genetic relatedness combined with population structure can bias additive genetic variance estimates.
  • Epistatic interactions (nonadditive effects) interacting with population structure are identified as the cause of inflation.
  • This challenges the universal applicability of standard mixed-model assumptions in natural populations.

Key Insights:

  • Additive genetic variance estimates can be inflated in natural populations with genetic relatedness.
  • The inflation arises from the interplay between population structure and epistatic genetic interactions.
  • Standard mixed-model assumptions may not hold when genetic relatedness is present.

Outlook:

  • Revising mixed-model methodologies to account for population structure and epistasis is crucial.
  • Future research should focus on developing robust methods for variance component estimation in structured populations.
  • Accurate estimation of additive genetic variance is vital for understanding evolutionary potential and implementing breeding programs.