Behavior Genetics

, Volume 24, Issue 3, pp 239–258 | Cite as

The power of the classical twin study to resolve variation in threshold traits

  • Michael C. Neale
  • Lindon J. Eaves
  • Kenneth S. Kendler


We explore the power of the twin study to resolve sources of familial resemblance when the data are measured at the binary or ordinal level. Four components of variance were examined: additive genetic, nonadditive genetic, and common and specific environment. Curves are presented to compare the power of the continuous case with those of threshold models corresponding to different prevalences in the population: 1, 5, 10, 25, and 50%. Approximately three times the sample size is needed for equivalent power to the continuous case when the threshold is at the optimal 50%, and this ratio increases to about 10 times when 10% are above threshold. Some power may be recovered by subdividing those above threshold to form three or more ordered classes, but power is determined largely by the lowest threshold. Non-random ascertainment of twins (i) through affected twins and examining their cotwins or (ii) through ascertainment of all pairs in which at least one twin is affected increases power. In most cases, strategy i is more efficient than strategy ii. Though powerful for the rarer disorders, these methods suffer the disadvantage that they rely on prior knowledge of the population prevalence. Furthermore, sampling from hospital cases may introduce biases, reducing their value. A useful approach may be to assess the population with a screening instrument; the power calculations indicate that sampling all concordant and half of the discordant pairs would be efficient, as along as the cost of screening is not too high.

Key Words

Twin study threshold traits variance power research design ascertainment sampling selection 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Agresti, A. (1990).Categorical Data Analysis, Wiley, New York.Google Scholar
  2. Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures.Br. J. Math. Stat. Psychol. 37:62–83.Google Scholar
  3. DeFries, J. C., and Fulker, D. W. (1985). Multiple regression analysis of twin data.Behav. Genet. 15:467–474.Google Scholar
  4. Eaves, L. J. (1969). The genetic analysis of continuous variation: A comparison of experimental designs applicable to human data.Br. J. Math. Stat. Psychol. 22:131–147.Google Scholar
  5. Eaves, L. J. (1972). Computer simulation of sample size and experimental design in human psychogenetics.Psychol. Bull. 77:144–152.Google Scholar
  6. Falconer, D. S. (1960).Quantitative Genetics, Oliver and Boyd, Edinburgh.Google Scholar
  7. Haynam, G. E., Govindarajulu, Z., and Leone, F. C. (1970). Tables of the cumulative non-central chi-square distribution. InSelected Tables in Mathematical Statistics, Vol. 1, American Mathematical Society, Providence, RI.Google Scholar
  8. Heath, A. C., and Eaves, L. J. (1985). Resolving the effects of phenotype and social background on mate selection.Behav. Genet. 15:15–30.Google Scholar
  9. IMSL (1987).IMSL User's Manual. Version 1.0, IMSL, Inc., Houston, TX.Google Scholar
  10. Jöreskog, K. G., and Sörbom, D. (1993).New Features in PRELIS 2, Chicago: Scientific Software International.Google Scholar
  11. Kendler, K. S., Heath, A. C., Neale, M. C., Kessler, R. C., and Eaves, L. J. (1992). A population-based twin study of alcoholism in women.JAMA 268:1877–1882.Google Scholar
  12. Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., and Eaves, L. J. (1993a). A longitudinal study of one-year prevalence of major depression in women.Arch. Gen. Psychiat. 50:853–862.Google Scholar
  13. Kendler, K. S., Neale, M. C., Kessler, R. C., Heath, A. C., and Eaves, L. J. (1993b). A test of the equal-environment assumption in twin studies of psychiatric illness.Behav. Genet. 23:21–27.Google Scholar
  14. Lee, S.-Y., and Leunt, K.-M. (1992). Estimation of multivariate polychoric and polyserial correlations with missing observations.Br. J. Math. Stat. Psychol. 45:225–238.Google Scholar
  15. Loehlin, J. C., and Nichols, R. C. (1976).Heredity, Environment, and Personality, University of Texas Press, Austin.Google Scholar
  16. Martin, N. G., and Eaves, L. J. (1977). The genetical analysis of covariance structure.Heredity,38:79–95.Google Scholar
  17. Martin, N. G., Eaves, L. J., Kearsey, M. J., and Davies, P. (1978). The power of the classical twin study.Heredity,40:97–116.Google Scholar
  18. McCulloch, C. E. (1993). Maximum likelihood variance components estimation for binary data.J. Am. Stat. Assoc. (in press).Google Scholar
  19. McGue, M., Pickens, R. W., and Svikis, D. S. (1992). Sex and age effects on the inheritance of alcohol problems: A twin study.J. Abnorm. Psychol. 101:3–17.Google Scholar
  20. Morton, N. E. (1983).Outline of Genetic Epidemiology, Karger, New York.Google Scholar
  21. NAG (1990).The NAG Fortran Library Manual, Mark 14, Numerical Algorithms Group, Oxford.Google Scholar
  22. Neale, M. C. (1991).Mx: Statistical Modeling, Department of Human Genetics, Medical College of Virginia, Richmond.Google Scholar
  23. Neale, M. C., and Cardon, L. R. (1992).Methodology for Genetic Studies of Twins and Families, Kluwer Academic, New York.Google Scholar
  24. Neale, M. C., and Eaves, L. J. (1993). Estimating and controlling for the effects of volunteer bias with pairs of relatives.Behav. Genet. 23:271–277.Google Scholar
  25. Neale, M. C., Rushton, J. P., and Fulker, D. W. (1986). The heritability of items from the eysenck personality questionnaire.Personal. Indiv. Diff. 7:771–779.Google Scholar
  26. Neale, M. C., Hewitt, J. K., Heath, A. C., and Eaves, L. J. (1989). The power of multivariate and categorical twin studies. Presented at the 6th International Congress of Twin Studies, Rome.Google Scholar
  27. Neale, M. C., Walters, E. E., Eaves, L. J., Kessler, R. C., Heath, A. C., and Kendler, K. S. (1994). The genetics of blood-injury fears and phobias: A population-based twin study.Neuropsychiatric Genetics (in press).Google Scholar
  28. Pearson, E. S., and Hartley, H. O. (1972).Biometrika Tables for Statistians, Vol. 2, Cambridge University Press, Cambridge.Google Scholar
  29. Pickens, R. W., Svikis, D. S., McGue, M., Lykken, D. T., Heston, L. L., and Clayton, P. J. (1991). Heterogeneity in the inheritance of alcoholism: A study of male and female twins.Arch. Gen. Psychiat. 48:19–28.Google Scholar
  30. Record, R. G., McKeown, T., and Edwards, J. H. (1970). An investigation of the difference in measured intelligence between twins and single births.Ann. Hum. Genet. 30:618–643.Google Scholar
  31. Reich, T., Rice, J., Cloninger, R., Wette, R., and James, J. (1979). The use of multiple thresholds and segregation analysis in analyzing the phenotypic heterogeniety of multifactorial traits.Ann. Hum. Genet. 42:371–389.Google Scholar
  32. SAS (1988),SAS/STAT User's Guide: Release 6.03, SAS Institute, Cary, NC.Google Scholar
  33. Sham, P., Neale, M. C., and Walters, E. (1994). Comparison of logistic regression and other methods for the analysis of binary data (submitted for publication).Google Scholar

Copyright information

© Plenum Publishing Corporation 1994

Authors and Affiliations

  • Michael C. Neale
    • 1
  • Lindon J. Eaves
    • 2
  • Kenneth S. Kendler
    • 1
    • 2
  1. 1.Department of PsychiatryMedical College of VirginiaRichmond
  2. 2.Department of Human GeneticsMedical College of VirgimaRichmond

Personalised recommendations