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Incorporation of Twins in the Regressive Logistic Model for Pedigree Disease Data

Published online by Cambridge University Press:  01 August 2014

J.L. Hopper*
Faculty of Medicine Epidemiology Unit
J.B. Carlin
Department of Community Medicine, The University of Melbourne
G.T. Macaskill
Faculty of Medicine Epidemiology Unit
P.L. Derrick
Faculty of Medicine Epidemiology Unit
L.B. Flander
Department of Social and Preventive Medicine, Monash University
G.G. Giles
Anti-Cancer Council of Victoria, Australia
The University of Melbourne, Faculty of Medicine Epidemiology Unit, 151 Barry Street, Carlton, Victoria 3053, Australia


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Segregation and twin disease concordance analyses have assumed a theoretical underlying liability following a multivariate normal distribution. For reasons of computation, of incorporation of measured explanatory variables, and of testing of fit and assumptions, newer analytical methods are being developed. The regressive logistic model (RLM) relies on expressing the pedigree likelihood as a product of conditional probabilities, one for each individual. In addition to logistic regression modelling of measured epidemiological variables on disease prevalence, there is modelling of vertical transmission, of transmission of unmeasured genotypes and of sibship environment. This paper discusses methods for the analysis of binary traits in twins and in pedigrees. Some extensions to the RLM for pedigrees which include twins are proposed. These enable exploration of twin concordance in the context of the twins' common parenthood, the sibship similarities within the family, and the twins' similarity in age, sex, genes and environment.

Research Article
Copyright © The International Society for Twin Studies 1990



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