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Quantitative Genetics and Complex Traits

Manual: General · Subject: Genetics

Explore the statistical architecture of polygenic inheritance, heritability, gene-by-environment interaction, and prediction.

From single genes to trait architecture

Phenotypic variance decomposition

For a trait PP, a useful starting model is VP=VG+VE+2 Cov(G,E)V_P = V_G + V_E + 2\,\mathrm{Cov}(G,E), with genetic variance often decomposed into additive, dominance, and epistatic components. The additive component is central for prediction and response to selection.

Key terms in quantitative genetics

Heritability
The proportion of phenotypic variance attributable to genetic variance in a specified population and environment.
Narrow-sense heritability
The fraction due to additive genetic variance, often written h2h^2.
GWAS
A genome-wide scan for statistical association between variants and traits.
Polygenic score
An aggregate predictor formed by weighting many variants by estimated effects.

Gene-by-environment interaction

The effect of a genotype can depend on environment, and environmental sensitivity can itself be heritable. This means that genetic effect sizes are not universal constants; they are often conditional on ancestry, age, sex, tissue, diet, pathogens, and social context.

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Interpretive caution

Heritability is not immutability. A highly heritable trait can still be strongly modified by environment, and a low-heritability trait can still have major individual genetic causes.

Single-gene versus complex-trait analysis

Single-gene traits

  • Often large effect sizes
  • Mendelian or near-Mendelian segregation
  • Family-based mapping is powerful

Complex traits

  • Many variants of small effect
  • Strong environmental and demographic confounding
  • Requires large cohorts and careful statistical correction

Which quantity is most directly relevant for short-term response to selection?

Why can a polygenic score transfer poorly across populations?

Designing a GWAS interpretation pipeline

  1. 1

    Confirm phenotype definition and study design.

  2. 2

    Control for population structure, relatedness, and batch effects.

  3. 3

    Prioritize association peaks with fine-mapping and functional annotation.

  4. 4

    Test causality with perturbation and replication in independent cohorts.

Which statement is most accurate about heritability?