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

Abstract

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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.

Keywords

Binary disease dataFamily dataLogistic regressionLoglinear modelsRandom effectsTwin data
Type
Research Article
Information
Acta geneticae medicae et gemellologiae: twin research , Volume 39 , Issue 2 , April 1990 , pp. 173 - 180
Copyright
Copyright © The International Society for Twin Studies 1990

References

REFERENCES

1.Anderson, DA, Aitkin, M (1985): Variance component models with binary response: interviewer variability. J Ft Statist Soc B 47:203–210.Google Scholar
2.Bahadur, RR (1961): A representation of the joint distribution of responses to n dichotomous items. In Solomon, H. (ed): Studies in Item Analysis and Prediction, Stanford Mathematical Studies in the Social Sciences VI. Stanford, California: Stanford University Press.Google Scholar
3.Bonney, GE (1984): On the statistical determination of major gene mechanisms in continuous human traits: Regressive models. Am J Med Genet 18:731–749.CrossRefGoogle ScholarPubMed
4.Bonney, GE (1986): Regressive logistic models for familial disease and other binary traits. Biometrics 42:611–625.CrossRefGoogle ScholarPubMed
5.Elston, RC, Stewart, J (1971). A general model for the genetic analysis of pedigree data. Hum Hered 21:523–542.CrossRefGoogle ScholarPubMed
6.Elston, RC (1981). Segregation analysis. In (Harris, H., Hirschhorn, K., eds): Advances in Human Genetics, Vol. 11. New York: Plenum Press, Ch 2.Google Scholar
7.Fienberg, SE (1977): The Analysis of Cross-Classified Categorical Data. Cambridge, MA: MIT Press.Google Scholar
8.Hannah, MC, Hopper, JL, Mathews, JD (1983): Twin concordance for a binary trait. I. Statistical models illustrated with data on drinking status. Acta Genet Med Gemellol 332:127–137.Google Scholar
9.Hopper, JL, Mathews, JD (1982). Extensions to multivariate normal models for pedigree analysis. Ann Hum Genet 46:373–383.CrossRefGoogle ScholarPubMed
10.Hopper, JL, Hannah, MC, Mathews, JD (1984): Genetic Analysis Workshop II. Pedigree analysis of a binary trait without assuming a liability. Genet Epidemiol 1:183–188.CrossRefGoogle ScholarPubMed
11.Hopper, JL, Derrick, PL (1986): A log-linear model for binary pedigree data. Genet Epidemiol Supplement 1:73–82.Google Scholar
12.Hopper, JL, Judd, FK, Derrick, PL, Burrows, GD (1987): A family study of panic disorder. Genet Epidemiol 4:33–41.CrossRefGoogle ScholarPubMed
13.Hopper, JL (1989): Modelling sibship environment in the regressive logistic model for familial disease. Genet Epidemiol 6:235–240.Google Scholar
14.Hopper, JL, Judd, FK, Derrick, PL, Macaskill, GT, Burrows, GD (1990): A family study of panic disorder - Reanalysis using a regressive logistic model that incorporates a sibship environment. Genet Epidemiol (in press).Google Scholar
15.Hopper, JL, Hannah, MC, Macaskill, GT, Mathews, JD (1990): Twin concordance for a binary trait. III. A bivariate analysis of hayfever and asthma. Genet Epidemiol (in press).Google Scholar
16.Kramer, AA, Corey, L (1986). The offspring of twins as sampling units in pedigree analysis of congenital anomalies. Acta Genet Med Gemellol 35:35–48.Google ScholarPubMed
17.Lange, K, Westlake, J, Spence, MA (1976): Extensions to pedigree analysis. III. Variance components by the scoring method. Ann Hum Genet 46:373–383.Google Scholar
18.Lange, K, Boehnke, M, Weeks, D (1986): Programs for Pedigree Analysis. UCLA School of Biomathematics, Los Angeles.Google Scholar
19.Liang, KY, Zeger, SL (1986): Longitudinal data analysis using generalized linear models. Biometrika 73;13–22.CrossRefGoogle Scholar
20.Prentice, RL (1988): Correlated binary regression with covariates specific to each binary observation. Biometrics 44:1033–1048.CrossRefGoogle ScholarPubMed
21.Smith, C (1974): Concordance in twins: Methods and interpretation. Am J Hum Genet 26:454–466.Google ScholarPubMed