DF-analyses of heritability with double-entry twin data: asymptotic standard errors and efficient estimation

Area of Science:

• Quantitative genetics
• Behavioral genetics
• Statistical genetics

Background:

• DeFries Fulker (DF) regression is a key method for estimating genetic and environmental influences in twin studies.
• Accurate estimation of heritability and shared environmental influences requires robust statistical methods.
• Previous DF-regression analyses may lack sufficient statistical power for detecting effects.

Purpose of the Study:

• To establish the asymptotic distribution of DeFries Fulker (1985) regression estimates for heritability and shared environmental influences using double-entry twin data.
• To provide a method for increasing statistical power in twin analyses.
• To introduce an efficient DF-analysis for improved precision with additional covariates.

Main Methods:

• Derivation of the asymptotic distribution for DF-regression coefficients.
• Development of a formula for estimating the covariance matrix of DF-regression coefficients.
• Application of the method to simulated and real-world Danish twin data.

Main Results:

• The asymptotic distribution of DF-regression estimates is established for double-entry twin data.
• A simple formula for the covariance matrix of DF-regression coefficients is provided.
• The proposed method significantly increases statistical power in twin analyses.
• An 'efficient DF-analysis' offers more precise estimates when incorporating additional covariates.

Conclusions:

• The established asymptotic distribution provides a theoretical foundation for DF-regression in twin studies.
• The developed methods enhance the statistical power and precision of heritability and environmental influence estimates.
• This approach offers a valuable tool for researchers in behavioral and quantitative genetics.