A Generalized Defries–Fulker Regression Framework for the Analysis of Twin Data
Twin studies compare the similarity between monozygotic twins to that between dizygotic twins in order to investigate the relative contributions of latent genetic and environmental factors influencing a phenotype. Statistical methods for twin data include likelihood estimation and Defries–Fulker regression. We propose a new generalization of the Defries–Fulker model that fully incorporates the effects of observed covariates on both members of a twin pair and is robust to violations of the Normality assumption. A simulation study demonstrates that the method is competitive with likelihood analysis. The Defries–Fulker strategy yields new insight into the parameter space of the twin model and provides a novel, prediction-based interpretation of twin study results that unifies continuous and binary traits. Due to the simplicity of its structure, extensions of the model have the potential to encompass generalized linear models, censored and truncated data; and gene by environment interactions.
KeywordsVariance components Heritability Mixed effects Family resemblance Recurrence risk
The authors thank Drs. Heping Zhang and Vineet Bhandari for access to the BPD data. We also thank Joe Rodgers and an anonymous reviewer for many helpful suggestions. This work was supported by NIH grants R01MH086135, R01DA023063 and R01MH083972 and the Clinical Science Research & Development Service of the Department of Veterans Affairs (A Cooperative Studies Program-Wide DNA Bank, CSP#478).
Conflict of interest
The authors have no conflicts of interest to declare.
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