Skip to main content
Log in

Biometrical analysis of individual growth curves

  • Published:
Behavior Genetics Aims and scope Submit manuscript

Abstract

Longitudinal data for height (length) between birth and 2 years of age were examined for 690 Dutch Registry twin pairs. A two-stage analysis was performed, where individual growth curves were first fit to available data for each subject using a linear multiple regression procedure and estimated individual growth curve parameters were then subjected to multivariate biometrical analysis. Quadratic polynomial curves were found to adequately represent observed growth patterns for the majority of cases (median R2=.98). A specific scalar model of sex limitation best characterized individual variation in growth curve parameters. That is, there was significantly greater genetic variation for boys than for girls in both the predicted length and rate of growth at 1 year of age and the amount of deceleration in individual growth curves across age.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Akaike, H. (1987). Factor analysis and AIC.Psychometrika 52:317–332.

    Google Scholar 

  • Bentler, P. M., and Bonnet, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures.Psychol. Bull. 88:588–606.

    Google Scholar 

  • Cudeck, R., and Browne, M. W. (1983). Cross-validation of covariance structures.Multivar. Behav. Res. 18:147–167.

    Google Scholar 

  • heath, A. C., Neale, M. C., Hewitt, J. K., Eaves, L. J., and Fulker, D. W. (1989). Testing structural equation models for twin data using LISREL.Behav. Genet. 19:9–36.

    PubMed  Google Scholar 

  • Johnston, F. E., Wainer, H., Thissen, D., and MacVean, R. (1977). Hereditary and environmental determinants of growth in height in a longitudinal sample of children and youth of Guatemala and European ancestry.Am. J. Phys. Anthropol. 44:468–475.

    Google Scholar 

  • Joreskog, K. G., and Sorbom, D. (1989).LISREL VII 2nd ed., Scientific Software, Inc., Mooresville, IN.

    Google Scholar 

  • Loehlin, J. C. (1987).Latent Variable Models, Lawrence Erlbaum Associates, NJ.

    Google Scholar 

  • Neale, M. C., and Martin, N. G., (1989). The effects of age, sex and genotype on self-report drunkenness following a challenge dose of alcohol.Behav. Genet. 19:63–78.

    PubMed  Google Scholar 

  • Vandenberg, S. G., and Falkner, F. (1965). Hereditary factors in human growth.Hum. Biol. 37:357–365.

    PubMed  Google Scholar 

  • Vlietinck, R., Derom, R., Neale, M. C., Haes, H., van Loon, H., Derom, C., and Thiery, M. (1989). Genetic and environmental variation in the birth weight of twins.Behav. Genet. 19:151–161.

    PubMed  Google Scholar 

  • Welch, Q. B. (1970). A genetic interpretation of variation in human growth patterns.Behav. Genet. 1:157–167.

    PubMed  Google Scholar 

  • Willett, J. B. (1988). Questions and answers in the measurement of change. In Rothkopf, E. Z. (ed.),Review of Research in Education, Vol. 15 American Educational Research Association, Washington, DC.

    Google Scholar 

  • Wilson, R. S. (1984). Twins and chronogenetics: Correlated pathways of development.Acta Genet. Med. Gemellol. 33:149–157.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

This research was supported in part by a Helen Putnam Visiting Scholarship award to Laura Baker while at the Henry Murray Research Center at Radcliffe College.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Baker, L.A., Reynolds, C. & Phelps, E. Biometrical analysis of individual growth curves. Behav Genet 22, 253–264 (1992). https://doi.org/10.1007/BF01067005

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01067005

Key Words

Navigation