LOGISTIC-REGRESSION ANALYSIS OF TWIN DATA - ESTIMATION OF PARAMETERS OF THE MULTIFACTORIAL LIABILITY-THRESHOLD MODEL

We extend the DeFries-Fulker regression model for the analysis of quan titative twin data to cover binary traits and genetic dominance. In th e proposed logistic regression model, the cotwin's trait status, C, is the response variable, while the proband's trait status, P, is a pred ictor variable coded as k (affected) and 0 (unaffected). Additive gene tic effects are modeled by the predictor variable PII, which equals P for monozygotic (MZ) and P/2 for dizygotic (DZ) twins; and dominant ge netic effects, by PD, which equals P for MZ and P/4 for DZ twins. By s etting an appropriate scale for P (i.e., the value of k), the regressi on coefficients of P, PR, and PD are estimates of the proportions of v ariance in liability due to common family environment, additive geneti c effects, and dominant genetic effects, respectively, for a multifact orial liability-threshold model. This model was applied to data on lif etime depression from the Virginia Twin Registry and produced results similar to those from structural equation modeling.