On the causal interpretation of heritability from a structural causal modeling perspective

Area of Science:

• Quantitative genetics
• Causal inference
• Biostatistics

Background:

• Traditional heritability estimates using analysis of variance (ANOVA) face significant criticism regarding their causal interpretation.
• The standard ANOVA model's assumptions are often violated by gene-environment interactions and covariation, rendering causal interpretations unwarranted.

Purpose of the Study:

• To re-evaluate the causal meaning of heritability from a structural causal modeling (SCM) perspective.
• To address limitations of ANOVA in estimating heritability, particularly in the presence of gene-environment interactions.

Main Methods:

• Application of structural causal modeling (SCM) to analyze heritability.
• Comparison of SCM with traditional ANOVA methods for causal interpretation.

Main Results:

• SCM clarifies that heritability in the standard model represents the causal effect of removing genotypic differences on phenotypic variance.
• ANOVA inaccurately estimates heritability when gene-environment interactions or covariation are present.
• SCM can accurately determine the causal effect of genotypes on phenotypic variance under specific interventions.

Conclusions:

• SCM provides a systematic causal interpretation of heritability, supplementing ANOVA estimates.
• SCM offers a more substantial causal analysis of genetic causation, capable of addressing individual-level questions and complex interactions.