A Generalized Defries–Fulker Regression Framework for the Analysis of Twin Data
 Laura C. Lazzeroni,
 Amrita Ray
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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, predictionbased 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.
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 Title
 A Generalized Defries–Fulker Regression Framework for the Analysis of Twin Data
 Journal

Behavior Genetics
Volume 43, Issue 1 , pp 8596
 Cover Date
 20130101
 DOI
 10.1007/s1051901295737
 Print ISSN
 00018244
 Online ISSN
 15733297
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Variance components
 Heritability
 Mixed effects
 Family resemblance
 Recurrence risk
 Authors

 Laura C. Lazzeroni ^{(1)}
 Amrita Ray ^{(1)}
 Author Affiliations

 1. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, 401 Quarry Road, Stanford, CA, 943055723, USA