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Innovations in the Statistical Analysis of Twin Studies

Published online by Cambridge University Press:  01 August 2014

J.L. Hopper ,
P.L. Derrick  and
C.A. Clifford
J.L. Hopper*
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
P.L. Derrick
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
C.A. Clifford
Affiliation:
Faculty of Medicine Epidemiology Unit, University of Melbourne, Carlton, Victoria, Australia
*
The University of Melbourne, Faculty of Medicine Epidemiology Unit, 151 Barry Street, Carlton, Victoria 3053, Australia

Abstract

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Advances in computer technology have made possible a greater sophistication in the statistical analysis of pedigree data, however this is not necessarily manifest by fitting more comprehensive causative models. Planned twin and family studies measure numerous explanatory variables, including perhaps genetic and DNA marker information status on all pedigree members, and the cohabitation of all pairs of individuals. A statistical analysis should examine the contribution of these measured factors on individual means, and in explaining the variation and covariation between individuals, concurrently with the postulated effect of unmeasured factors such as polygenes. We present two models that meet this requirement: the Multivariate Normal Model for Pedigree Analysis for quantitative traits, and a Log-Linear Model for Binary Pedigree Data. For both models, important issues are examination of fit, detection of outlier pedigrees and outlier individuals, and critical examination of the model assumptions. Procedures for fulfilling these needs and examples of modelling are discussed.

Keywords

Binary dataLog-linear modelsLogistic regressionMaximum likelihoodMultivariate normalityPedigree analysis
Type
Research Article
Information
Acta geneticae medicae et gemellologiae: twin research , Volume 36 , Issue 1 , January 1987 , pp. 21 - 27
Copyright
Copyright © The International Society for Twin Studies 1987

References

REFERENCES

1.Boerwinkle, E, Chakraborty, R, Sing, CF (1986): The use of measured genotype information in the analysis of quantitative phenotypes in man. I. Models and analytical methods. Ann Hum Genet 50:181–194.CrossRefGoogle Scholar
2.Eaves, LJ, Long, J, Heath, AC (1986): A theory of developmental change in quantitative phenotypes applied to cognitive development. Behav Genet 16:143–162.CrossRefGoogle ScholarPubMed
3.Elston, RC, Stewart, J (1971): A genetic model for the genetic analysis of pedigree data. Hum Hered 21:523–542.CrossRefGoogle ScholarPubMed
4.Clifford, CA, Hopper, JL, Fulker, DW, Murray, RM (1984): A genetic and environmental analysis of a twin family study of alcohol use, anxiety, and depression. Genet Epidemiol 1:63–79.Google Scholar
5.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 32:127–137.Google Scholar
6.Hopper, JL, Culross, PR (1983): Covariation between family members as a function of cohabitation history. Behav Genet 13:459–471.CrossRefGoogle ScholarPubMed
7.Hopper, JL, Derrick, PL (1986): A log-linear model for binary pedigree data. In: Genetic Analysis Workshop IV. New York: Alan R Liss.Google Scholar
8.Hopper, JL, Hannah, MC, Mathews, JD (1984): Genetic Analysis Workshop II: Pedigree analysis of a binary trait without assuming underlying liability. Genet Epidemiol 1:183–188.Google Scholar
9.Hopper, JL, Hannah, MC, Mathews, JD (1986): Twin concordance for a binary trait. III. A bivariate analysis of hayfever and asthma. Genet Epidemiol (submitted).Google Scholar
10.Hopper, JL, Judd, FK, Derrick, PL, Burrows, GD (1986): A family study of panic disorder. Genet Epidemiol 4:33–41.Google Scholar
11.Hopper, JL, Mathews, JD (1982): Extensions to multivariate normal models for pedigree analysis. Ann Hum Genet 46:373–383.Google Scholar
12.Hopper, JL, Mathews, JD (1983): Extensions to multivariate normal models for pedigree analysis. II. Modeling the effect of shared environment in blood lead levels. Am J Epidemiol 117:344–355.Google Scholar
13.Hopper, JL, Tait, BD, Propert, DN, Mathews, JD (1982): Genetic analysis of systolic blood pressure in Melbourne families. Clin Exp Pharmacol Physiol 9:247–252.Google Scholar
14.Hopper, JL, Speed, TP (1986): Testing of assumptions in the simplest variance component model. Biometriks (submitted).Google Scholar
15.Kaplan, EB, Elston, RC (1972): A subroutine package for maximum likelihood estimation (MAX-LIK). Institute of Statistics Mimeo Series No. 823 (revised 03 1978).Google Scholar
16.Kendler, KS, Heath, A, Martin, NG, Eaves, LJ (1986): Symptoms of anxiety and depression in a volunteer twin population. Arch Gen Psychiatry 43:213–221.Google Scholar
17.Lange, K (1986): Cohabitation, convergence, and environmental covariances. Am J Med Genet 24:483–491.Google Scholar
18.Lange, K, Boehnke, M (1983): Extensions to pedigree analysis. IV. Covariance components models for multivariate traits. Am J Med Genet 14:513–524.Google Scholar
19.Lange, K, Boehnke, M, Weeks, D (1986): Programs for Pedigree Analysis. Los Angeles: Department of Biomathematics, UCLA School of Medicine.Google Scholar
20.Lange, K, Westlake, J, Spence, MA (1976): Extensions to pedigree analysis. III. Variance components by the scoring method. Ann Hum Genet 39:485–491.CrossRefGoogle ScholarPubMed
21.Mardia, KV (1974): Applications of some measures of multivariate skewness and kurtosis in testing normality and robustness studies. Sankhya Ser B 36:115–128.Google Scholar
22.Martin, NG, Clark, P, Ofulue, AF, Eaves, LJ, Corey, LA, Nance, WE (1986): Does the Pi polymorphism alone control alpha-1-antitripsin expression? Am J Hum Genet 40:267–277.Google Scholar
23.Mathews, JD, Hopper, JL (1982): Age-dependent means and variances of quantitative traits studied over pedigrees. In: Proceedings of the Workshop on Methods for the Analysis of Family Data. University of Melbourne, pp 129–131.Google Scholar
24.Moll, PP, Powsner, R, Sing, CF (1979): Analysis of genetic and environmental sources of variation in serum cholesterol in Tecumseh, Michigan. V. Variance components estimated from pedigrees. Ann Hum Genet 42:343–354.Google Scholar
25.Province, MA, Rao, DC (1985): Path analysis of family resemblance with temporal trends: Applications to height, weight and Quetelet index in Northeastern Brazil. Am Hum Genet 37:178–192.Google Scholar
26.Rao, DC, McGue, M, Wette, R, Glueck, CJ (1984): Path analysis in genetic epidemiology. In Chakravarti, (ed): Human Population Genetics: The Pittsburgh Symposium. Stroudsburg, PA: Hutchinson Ross.Google Scholar
27.Rice, J, Reich, T (1985): Familial analysis of qualitative traits under multifactorial inheritance. Genet Epidemiol 2:301–315.Google Scholar
28.Self, SG, Prentice, RL (1986): Incorporating random effects into multivariate relative risks regression models. In Moolgavakar, SH, Prentice, RL (eds): Modern Statistical Methods in Chronic Diseases Epidemiology. New York: Wiley, pp. 167–177.Google Scholar
29.Smith, C (1970): Heritability of liability and concordance in monozygous twins. Ann Hum Genet 34:85–91.Google Scholar
30.Thomas, DC, Langholz, B, Mack, T, Deapen, D, Floderus-Myrhed, B (1986): Bivariate lifetable models for analysis of gene-environment interaction in twins. Paper presented at the Fifth Int Congr on Twin Studies, Amsterdam.Google Scholar