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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Linear mixed model for heritability estimation that explicitly addresses environmental variation.

David Heckerman1, Deepti Gurdasani2, Carl Kadie3

  • 1Microsoft Research, Los Angeles, CA 90024; heckerma@microsoft.com.

Proceedings of the National Academy of Sciences of the United States of America
|July 7, 2016
PubMed
Summary
This summary is machine-generated.

Standard linear mixed models (LMMs) can inflate heritability estimates. A more general LMM incorporating genomic and spatial environmental effects reduces this inflation, resolving some "missing heritability".

Keywords:
Gaussian radial basis functionenvironmentheritability estimationlinear mixed modelmodel misspecification

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Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Genomics

Background:

  • Linear mixed models (LMMs) are standard for estimating heritability.
  • Standard LMMs can lead to inflated heritability estimates.
  • The "missing heritability" problem suggests a gap between estimated and explained genetic variance.

Purpose of the Study:

  • To develop and validate a more general LMM to reduce heritability inflation.
  • To investigate the impact of incorporating genomic and spatial environmental effects.
  • To address the "missing heritability" phenomenon by re-evaluating heritability estimates.

Main Methods:

  • Utilized a generalized LMM with two random effects: one for genomic variants (identity-by-descent) and one for spatial location (Gaussian radial basis function).
  • Applied the model to simulated data and a Ugandan cohort (4,778 individuals) across 34 diverse phenotypes.
  • Employed accurately phased genome-wide data for genomic relationship estimation.

Main Results:

  • The generalized LMM yielded lower narrow-sense heritability estimates compared to standard LMMs across both simulated and real-world data.
  • The reduction in heritability suggests that a portion of previously "missing" heritability was an artifact of inflated estimates.
  • The model effectively integrated genomic and spatial environmental factors.

Conclusions:

  • The proposed generalized LMM offers a more accurate estimation of heritability by accounting for both genetic and environmental influences.
  • This approach helps to resolve the "missing heritability" issue by correcting for overestimation in standard models.
  • The findings have implications for understanding the genetic architecture of complex traits.