Skip to main content
SpringerLink
Log in

Using the Mplus Computer Program to Estimate Models for Continuous and Categorical Data from Twins

  • Published:
Behavior Genetics Aims and scope Submit manuscript

Abstract

Historically, the focus of behavior genetic research was to obtain estimates of the sources of familial resemblance for a single phenotype. Current research strategies have moved beyond heritability estimates to the search for physiological and behavioral mechanisms by which genetic risk is translated into individual differences in behavior and disease liability. Such research questions often require multivariate designs and complex analytic models, including the analysis of continuous and categorical dependent variables within the same model. Recent advances in computer software for categorical data analysis have increased the tools available for researchers in behavior genetics. This paper describes how to use the Mplus software program (Muthén and Muthén, 1998, 2002) for the analysis of data obtained from twins. Example analyses include two- and five-group twin models for univariate and bivariate continuous and categorical variables. Data on alcoholism and age at first drink drawn from the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders are used to illustrate how Mplus can be used to analyze multiple-category variables, recode and transform variables, select subgroups for analysis, handle subjects with incomplete data, include constraints to ensure non-negative loadings, include model covariates, model sex differences, and test alternative hypotheses about mediation of genetic risk by measured variables.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • American Psychiatric Association. (1994). Diagnostic and Statistical Manual of Mental Disorders (4th ed.). Washington, DC: American Psychiatric Association.

    Google Scholar 

  • Arbuckle, P., and Wothke, W. (1999). AMOS User's Guide, version 4. Chicago, IL: SPSS.

    Google Scholar 

  • Behrman, J. R., Taubman, P., and Wales, T. J. (1978). The roles of genetics and environment in the distribution of earnings. In Griliches, Z., Krelle, W., Krupp, H.-J., and Kyn, O. (eds.), Income Distribution and Economic Inequality (pp. 220-239). New York: J. Wiley and Sons.

    Google Scholar 

  • Bentler, P. M. (1989). EQS: Structural Equations Program Manual. Los Angeles, CA: BMDP Statistical Software.

    Google Scholar 

  • Boomsma, D. I., and Molenaar, P. C. (1986). Using LISREL to analyze genetic and environmental covariance structure. Behav. Genet. 16:237-250.

    Google Scholar 

  • Cantor, R. M., and Nance, W. E. (1981). An application of confirmatory factor analysis to individual finger ridge count data from the offspring of MZ twins. Am. J. Hum. Genet. 33:134A.

    Google Scholar 

  • Cantor, R. M. (1983). A multivariate analysis of ridge count data from the offspring of monozygotic twins. Acta Genet. Med. Gemellol 32:161-207.

    Google Scholar 

  • Cattell, R. B. (1960). The multiple abstract variance analysis equations and solutions: For nature-nurture research on continuous variables. Psych. Rev. 67:353-377.

    Google Scholar 

  • Eaves, L. J., Last, K. A., Young, P. A., and Martin, N. G. (1978). Model fitting approaches to the analysis of human behavior. Heredity 41:249-320.

    Google Scholar 

  • Edwards, J. H. (1969). Familial predisposition in man. Br. Med. Bull. 25:58-64.

    Google Scholar 

  • Falconer, D. S. (1965). The inheritance of liability to certain diseases, estimated from the incidence among relatives. Ann. Hum. Genet. 29:51-76.

    Google Scholar 

  • Fulker, D. W. (1973). A biometrical genetic approach to intelligence and schizophrenia. Soc. Biol. 20:266-275.

    Google Scholar 

  • Fulker, D. W. (1978). Multivariate extensions of a biometrical model of twin data. Prog. Clin. Biol. Res. 24A:217-236.

    Google Scholar 

  • Guo, G., and Wang, J. (2002). The mixed or multilevel model for behavior genetic analysis. Behav. Genet. 32:37-49.

    Google Scholar 

  • Hannah, M. C., Hopper, J. L., and Mathews, J. D. (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 

  • Jinks, J. L., and Fulker, D. W. (1970). A comparison of the biometrical genetical, MAVA and classical approaches to the analysis of human behaviour. Psychol. Bull. 73:311-349.

    Google Scholar 

  • Joreskog, K. G. (1973). Analysis of covariance structures. In Krishnarah, P. R. (ed.), Multivariate Analysis III: Proceedings of the Third International Symposium on Multivariate Analysis. New York: Academic Press.

    Google Scholar 

  • Joreskog, K. G., and Sorbom, D. (1977). LISREL: Analysis of Linear Structural Relationships by the Method of Maximum Likelihood. Chicago, IL: National Educational Resources.

    Google Scholar 

  • Joreskog, K. G., and Sorbom, D. (1986). PRELIS: A Preprocessor for LISREL. Mooresville, IN: Scientific Software.

    Google Scholar 

  • Kaprio, J., Sarna, S., and Koskenvuo, M. (1981). Multivariate logit analysis of concordance ratios for qualitative traits in twin studies. Acta Genet. Med. Gemellol. 30:267-274.

    Google Scholar 

  • Kendler, K. S., and Prescott, C. A. (1999). A population-based twin study of lifetime major depression in men and women. Arch. Gen. Psychiatry 56:39-44.

    Google Scholar 

  • Martin, N. G., and Eaves, L. J. (1977). The genetical analysis of covariance structure. Heredity 38:79-95.

    Google Scholar 

  • Mather, K., and Jinks, J. L. (1971). Biometrical Genetics (2nd ed.). London: Chapman and Hall.

    Google Scholar 

  • McArdle, J. J., Connell, J. P., and Goldsmith, H. H. (1980). Structural modeling of stability and genetic influences: Some results from a longitudinal study of behavioral style. Behav. Genet. 10:487.

    Google Scholar 

  • McArdle, J. J., and Goldsmith, H. H. (1990). Alternative common factor models for multivariate biometric analyses. Behav. Genet. 20:569-608.

    Google Scholar 

  • McArdle, J. J., and McDonald, R. P. (1984). Some algebraic properties of the reticular action model. Br. J. Math. Stat. Psych. 37:234-251.

    Google Scholar 

  • McArdle, J. J., and Prescott, C. A. (1996). Contemporary models for the biometric genetic analysis of intellectual abilities. In Flanagan, D. P., Genshaft, J. L., and Harrison P. L. (eds.), Intellectual Assessment: Contemporary and Emerging Theories, Tests and Issues (pp. 403-436). New York: Guilford Press.

    Google Scholar 

  • Meyer, J. M., Eaves, L. J., Heath, A. C., and Martin, N. G. (1991). Estimating genetic influences on the age-at-menarche: A survival analysis approach. Am. J. Med. Genet. 39:148-154.

    Google Scholar 

  • Muthén, B. O. (1988). LISCOMP: Analysis of Linear Structural Equations Using a Comprehensive Measurement Model (2nd ed.). Mooresville, IN: Scientific Software.

    Google Scholar 

  • Muthén, B., and Asparouhov, T. (2002). Latent variable analysis with categorical outcomes: Multiple-group and growth modeling in Mplus, version 5, December 9, 2002. www.statmodel.com/mplus/examples/webnote.html

  • Muthén, B., Shedden, K., and Spisic, D. (1999). General Latent Variable Mixture Modeling. Tech. Report. Santa Monica, CA: Muthén and Muthén.

    Google Scholar 

  • Muthén, L. K., and Muthén, B. O. (1998). Mplus User's Guide. Los Angeles, CA: Muthén and Muthén.

    Google Scholar 

  • Muthén, L. K., and Muthén, B. O. (2002). Mplus VERSION 2.12: Addendum to the Mplus user's Guide. Los Angeles, CA: Muthén and Muthén.

    Google Scholar 

  • Neale, M. C. (1991). Mx: Statistical Modeling, Department of Human Genetics. Richmond, VA: Virginia Commonwealth University.

    Google Scholar 

  • Neale, M. C., and Cardon, L. R. (1992). Methodology for Genetic studies of Twins and Families. Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  • Neale, M. C., Boker, S. M., Xie, G., and Maes, H. H. (2002). Mx: Statistical Modeling (5th ed.). Richmond, VA: Department of Psychiatry, Virginia Commonwealth University.

    Google Scholar 

  • Prescott, C. A. (1991). Clinical, Psychometric and Biometric Assessment of Schizophrenia: A Psychiatric Twin Study. University of Virginia. Unpublished Dissertation.

  • Prescott, C. A., Aggen, S. H., and Kendler, K. S. (1999). Sex differences in the sources of genetic liability to alcohol abuse and dependence in a population based sample of U.S. twins. Alcohol Clin. Exp. Res. 23:1136-1144.

    Google Scholar 

  • Prescott, C. A., Cross, R. J., Kuhn, J. W., Horn, J. L., and Kendler, K. S. (in press). Is risk for alcoholism mediated by individual differences in drinking motivations? Alcohol Clin. Exp. Res.Author: Please supply missing information.

  • Prescott, C. A., and Gottesman, I. I. (1990). Biometric prediction of genetic liability for schizophrenia. Behav. Genet. 20:742.

    Google Scholar 

  • Prescott, C. A., and Kendler, K. S. (1999a). Age at first drink and risk for alcoholism: A noncausal association. Alcohol Clin. Exp. Res. 23:101-107.

    Google Scholar 

  • Prescott, C. A., and Kendler, K. S. (1999b). Genetic and environmental contributions to alcohol abuse and dependence in a populationbased sample of male twins. Am. J. Psychiatry 156:34-40.

    Google Scholar 

  • Prescott, C. A., and Kendler, K. S. (2001). Associations between marital status and alcohol consumption in a longitudinal study of female twins. J. Stud. Alcohol 62:589-604.

    Google Scholar 

  • SAS Institute. (1999). SAS/STAT Software, version 8. Cary NC: SAS Institute.

    Google Scholar 

  • Smith, C. (1974). Concordance in twins: Methods and interpretation. Am. J. Hum. Genet. 6:454-466.

    Google Scholar 

  • Waller, N. G., and Muthén, B. O. (1992). Genetic Tobit factor analysis: Quantitative genetic modeling with censored data. Behav. Genet. 22:265-292.

    Google Scholar 

  • Yashin, A. I., Iachine, I. A., and Harris, J. R. (1999). Half of the variation in susceptibility to mortality is genetic: Findings from Swedish twin survival data. Behav. Genet. 29:11-19.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Virginia Commonwealth University, Richmond, Virginia, 23298-0126

    Carol A. Prescott

Authors
  1. Carol A. Prescott
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Prescott, C.A. Using the Mplus Computer Program to Estimate Models for Continuous and Categorical Data from Twins. Behav Genet 34, 17–40 (2004). https://doi.org/10.1023/B:BEGE.0000009474.97649.2f

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:BEGE.0000009474.97649.2f

Search

Navigation