title: Biometrical modeling of twin and family data using standard mixed model software.
abstract: Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S-PLUS are available at the Biometrics website.
authors.affiliation: Graduate School of Education & Graduate Group in Biostatistics, University of California, Berkeley, CA 94720, USA. sophiarh@berkeley.edu
authors.forename: S
authors.initials: S
authors.lastname: Rabe-Hesketh
authors.forename: A
authors.initials: A
authors.lastname: Skrondal
authors.forename: H K
authors.initials: HK
authors.lastname: Gjessing
mesh_terms: D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D005190:Family; D006801:Humans; D008954:Models, Biological; D015233:Models, Statistical; D012984:Software; D014427:Twins