Behavior Genetics

, Volume 40, Issue 3, pp 377–393 | Cite as

Are Extended Twin Family Designs Worth the Trouble? A Comparison of the Bias, Precision, and Accuracy of Parameters Estimated in Four Twin Family Models

  • Matthew C. KellerEmail author
  • Sarah E. Medland
  • Laramie E. Duncan
Original Research


The classical twin design (CTD) uses observed covariances from monozygotic and dizygotic twin pairs to infer the relative magnitudes of genetic and environmental causes of phenotypic variation. Despite its wide use, it is well known that the CTD can produce biased estimates if its stringent assumptions are not met. By modeling observed covariances of twins’ relatives in addition to twins themselves, extended twin family designs (ETFDs) require less stringent assumptions, can estimate many more parameters of interest, and should produce less biased estimates than the CTD. However, ETFDs are more complicated to use and interpret, and by attempting to estimate a large number of parameters, the precision of parameter estimates may suffer. This paper is a formal investigation into a simple question: Is it worthwhile to use more complex models such as ETFDs in behavioral genetics? In particular, we compare the bias, precision, and accuracy of estimates from the CTD and three increasingly complex ETFDs. We find the CTD does a decent job of estimating broad sense heritability, but CTD estimates of shared environmental effects and the relative importance of additive versus non-additive genetic variance can be biased, sometimes wildly so. Increasingly complex ETFDs, on the other hand, are more accurate and less sensitive to assumptions than simpler models. We conclude that researchers interested in characterizing the environment or the makeup of genetic variation should use ETFDs when possible.


Behavior genetics Model misspecification Extended twin family design Classical twin design Parameter indeterminacy 


  1. Carey G (2005) Cholesky problems. Behav Genet 35:653–665CrossRefPubMedGoogle Scholar
  2. Casela G, Berger RL (1990) Statistical inference. Wadsworth, BelmontGoogle Scholar
  3. Cloninger CR, Rice J, Reich T (1979) Multifactorial inheritance with cultural transmission and assortative mating II: a general model of combined polygenic and cultural inheritance. Am J Hum Genet 31:176–198PubMedGoogle Scholar
  4. Coventry WL, Keller MC (2005) Estimating the extent of parameter bias in the classical twin design: a comparison of parameter estimates from extended twin-family and classical twin designs. Twin Res Hum Genet 8:214–223CrossRefPubMedGoogle Scholar
  5. Crnokrak P, Roff DA (1995) Dominance variation: associations with selection and fitness. Heredity 75:530–540CrossRefGoogle Scholar
  6. Eaves LJ (1979) The use of twins in the analysis of assortative mating. Heredity 43:399–409CrossRefPubMedGoogle Scholar
  7. Eaves LJ (2009) Putting the ‘human’ back in genetics: modeling the extended kinship of twins. Twin Res Hum Genet 12:1–7CrossRefPubMedGoogle Scholar
  8. Eaves LJ, Last KA, Young PA, Martin NG (1978) Model-fitting approaches to the analysis of human behavior. Heredity 41:249–320CrossRefPubMedGoogle Scholar
  9. Eaves LJ, Eysenck HJ, Martin JM (eds) (1989) Genes, culture, and personality: an empirical approach. Academic Press, LondongGoogle Scholar
  10. Fisher RA (1918) The correlation between relatives on the supposition of Mendelian inheritance. Trans Roy Soc Edinb 52:399–433Google Scholar
  11. Fulker DW (1982) Extension of the classical twin method. In Bonné-Tamir B, Cohen T, Goodman RM (eds) Human genetics, part A: the unfolding genome (Progress in clinical and biological research 103A). Alan R Liss, New York, pp. 395–406Google Scholar
  12. Grayson DA (1989) Twins reared together: minimizing shared environmental effects. Behav Genet 19:593–604CrossRefPubMedGoogle Scholar
  13. Haldane JBS (1932) The causes of evolution. Princeton University Press, Princeton, N.J.Google Scholar
  14. Heath AC, Kendler KS, Eaves LJ, Markell D (1985) The resolution of cultural and biological inheritance: informativeness of different relationships. Behav Genet 15:439–465CrossRefPubMedGoogle Scholar
  15. Hill WG, Goddard ME, Visscher PM (2008) Data and theory point to mainly additive genetic variance for complex traits. PLos Genet 4:1–10CrossRefGoogle Scholar
  16. Keller, M. C. (2007). PedEvolve: a simulator of genetically informative data implemented in R. Annual meeting of the behavior genetics association, AmsterdamGoogle Scholar
  17. Keller MC, Coventry WL (2005) Quantifying and addressing parameter indeterminacy in the classical twin design. Twin Res Hum Genet 8:201–213CrossRefPubMedGoogle Scholar
  18. Keller MC, Medland SE, Duncan LE, Hatemi PK, Neale MC, Maes HMM et al (2009) Modeling extended twin family data I: description of the cascade model. Twin Res Hum Genet 12:8–18CrossRefPubMedGoogle Scholar
  19. Maes HMM, Neale MC, Medland SE, Keller MC, Martin NG, Heath AC et al (2009) Flexible Mx specifications of various extended twin kinship designs. Twin Res Hum Genet 12:26–34CrossRefPubMedGoogle Scholar
  20. Martin NG, Boomsma DI, Machin G (1997) A twin-pronged attach on complex traits. Nat Genet 17:387–392CrossRefPubMedGoogle Scholar
  21. Medland SE, Keller MC (2009) Modeling extended twin family data II: power associated with different family structures. Twin Res Hum Genet 12:19–25CrossRefPubMedGoogle Scholar
  22. Miller G, Todd PM (1998) Mate choice turns cognitive. Trends Cogn Sci 2:190–198CrossRefGoogle Scholar
  23. Nance WE, Corey LA (1976) Genetic models for the analysis of data from the families of identical twins. Genetics 83:811–826PubMedGoogle Scholar
  24. Neale MC (1999) MX: statistical modelling, 5th edn. Department of Psychiatry, Richmond, VAGoogle Scholar
  25. Neale MC, Fulker DW (1984) A bivariate path analysis of fear data on twins and their parents. Acta Genetica Medica Gemellol (Roma) 33:273–286Google Scholar
  26. Operario D, Tschann J, Flores E, Bridges M (2006) Brief report: associations of parental warmth, peer support, and gender with adolescent emotional distress. J Adolesc 29(2):299–305CrossRefPubMedGoogle Scholar
  27. Plomin R, DeFries JC, McClearn GE, McGuffin P (2001) Behavioral genetics, 4th edn. Worth Publishers, New YorkGoogle Scholar
  28. Posthuma D, Boomsma DI (2000) A note on the statistical power in extended twin designs. Behav Genet 30:147–158CrossRefPubMedGoogle Scholar
  29. R Core Development Team (2009) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  30. Reynolds CA, Baker LA, Pedersen NL (2000) Multivariate models of mixed assortment: phenotypic assortment and social homogamy for education and fluid ability. Behav Genet 30(6):455–476CrossRefPubMedGoogle Scholar
  31. Thiessen DD, Gregg B (1980) Human assortative mating and genetic equilibrium: an evolutionary perspective. Ethol Sociobiol 1:111–140CrossRefGoogle Scholar
  32. Truett KR, Eaves LJ, Walters EE, Heath AC, Hewitt JK, Meyer JM et al (1994) A model system for analysis of family resemblance in extended kinships of twins. Behav Genet 24:35–49CrossRefPubMedGoogle Scholar
  33. Wahlberg P (2009) Chicken genomics-linkage and QTL mapping. Digital comprehensive summaries of Uppsala. Dissertations from the Faculty of MedicineGoogle Scholar
  34. Wright S (1929) Fisher’s theory of dominance. Am Nat 63:274–279CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Matthew C. Keller
    • 1
    • 2
    • 4
  • Sarah E. Medland
    • 3
  • Laramie E. Duncan
    • 1
    • 2
  1. 1.Department of Psychology and NeuroscienceUniversity of ColoradoBoulderUSA
  2. 2.Institute for Behavioral GeneticsUniversity of ColoradoBoulderUSA
  3. 3.Queensland Institute for Medical ResearchBrisbaneAustralia
  4. 4.Department of Psychology and NeuroscienceBoulderUSA

Personalised recommendations