Hostname: page-component-cd9895bd7-gbm5v Total loading time: 0 Render date: 2024-12-27T07:21:57.340Z Has data issue: false hasContentIssue false

Analysis of Longitudinal Twin Data. Basic Model and Applications to Physical Growth Measures

Published online by Cambridge University Press:  01 August 2014

Ronald S. Wilson*
Affiliation:
University of Louisville School of Medicine, Louisville, Kentucky
*
Louisville Twin Study, PO Box 35260, Louisville, KY 40232

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

A formal model is presented for the analysis of longitudinal twin data, based on the underlying analysis-of-variance model for repeated measures. The model is developed in terms of the expected values for the variance components representing twin concordance, and the derivation is provided for computing within-pair (intraclass) correlations, and for estimating the percent of variance explained by each component. The procedures are illustrated with physical growth data extending from birth to six years, and concordance estimates are obtained for average size and for the pattern of spurts and lags in growth. A test of significance is also described for comparing monozygotic twins with dizygotic twins. The procedures are particularly useful for assessing chronogenetic influences on development, especially whether the episodes of acceleration and lag occur in parallel for genetically matched twins. The model may be employed with psychological data also.

Type
Research Article
Copyright
Copyright © The International Society for Twin Studies 1979

References

REFERENCES

1.Bayley, N 1956: Individual patterns of development. Child Dev 27:4574.Google Scholar
2.Christian, JC, Kang, KW, Norton, JA 1974: Choice of an estimate of genetic variance from twin data. Am J Hum Genet 26:154161.Google ScholarPubMed
3.Dwyer, JH 1974: Analysis of variance and the magnitude of effects: A general approach. Psychol Bull 81:731737.Google Scholar
4.Eaves, LJ, Last, K, Martin, NG, Jinks, JL (1977): A progressive approach to non-additivity and genotype-environmental covariance in the analysis of human differences. Br J Math Stat Psychol 30:142.Google Scholar
5.Haggard, EA (1958): Intraclass correlation and the analysis of variance. Dryden Press, New York.Google Scholar
6.Jinks, JI, Fulker, DW 1970: Comparison of the biometrical genetical, MAVA, and classical approaches to the analysis of human behavior. Psychol Bull 73:311349.Google Scholar
7.Kempthorne, O, Osborne, RH (1961): The interpretation of twin data. Am J Hum Genet 13:320339.Google ScholarPubMed
8.Tanner, JM (1970): Physical growth. In Mussen, PH (ed): Carmichael's manual of child psychology, Vol 1. Wiley, New York.Google Scholar
9.Wilson, RS (1968): Autonomic research with twins: Methods of analysis. In Vandenberg, SG (ed): Progress in human behavior genetics. Johns Hopkins, Baltimore.Google Scholar
10.Wilson, RS 1972: Twins: Early mental development. Science 175:914917.Google Scholar
11.Wilson, RS (1974): CARDIVAR: The statistical analysis of heart rate data. Psychophysiology 11: 7785.Google Scholar
12.Wilson, RS 1975: Analysis of developmental data: Comparison among alternative methods. Dev Psychol 11:676680.Google Scholar
13.Wilson, RS (1977): Mental development in twins. In Oliverio, A (ed): Genetics, environment, and intelligence. Elsevier/North Holland, Amsterdam.Google Scholar
14.Wilson, RS (1979): Twin growth: Initial deficit, recovery, and trends in concordance from birth to nine years. Ann Hum Biol, in press.Google Scholar
15.Winer, BJ (1962): Statstical principles in experimental design. McGraw-Hill, New York.Google Scholar