Recent substantive research on biometric analyses of twin and family data has used both a biometric path analysis model (PAM) and a biometric variance components model (VCM). Methodological research on these same topics have suggested benefits of using linear structural equation model algorithms (SEMA) as well as mixed effect multilevel algorithms (MEMA). To better understand the potential similarities and differences among these approaches we first highlight the algebraic equivalence between the standard biometric PAM and the corresponding biometric VCM models for family data. Second, we demonstrate how several SEMA programs based on either the PAM or VCM approach produce equivalent estimates for all phenotypic and biometric parameters. Third, we show how the biometric VCM approach (but not the PAM approach) can be easily programmed using current MEMA programs (e.g., SAS PROC MIXED). We then expand the scope of these different approaches to include measured covariates, observed variable interactions and multiple relatives within each family. MEMA software is compared to SEMA software for programming complex models, including the flexibility of data input, treatment of missing data, inclusion of covariates, and ease of accommodating varying numbers of observations (per family or individual).
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
J. L. Arbuckle W. Wothke (1999) Amos 4.0 User's Guide Smallwaters Corp Chicago, IL
R. D. Bock R. E. Bargmann (1966) ArticleTitleAnalysis of covariance structures Psychometrika 31 IssueID4 507–534 Occurrence Handle5232439
D. Boomsma P. C. M. Molenaar (1987) ArticleTitleConstrained maximum likelihood analysis of familial resemblance of twins and their families Acta Genet. Med. Gemelloi 36 29–39
A. S. Bryk S. W. Raudenbush (1992) Hierarchical Linear Models: Applications and Data Analysis Methods. SAGE Newbury Park, CA
R. B. Cattell (1953) ArticleTitleResearch designs in psychological genetics, with special reference to the multiple variance method Am. J. Human Genet. 5 76–93
R. B. Cattell (1960) ArticleTitleThe multiple abstract variance analysis equations and solutions: for nature–nurture research on continuous variables Psychol. Rev. 67 353–337 Occurrence Handle13691636
R. B. Cattell (1963) ArticleTitleThe interaction of hereditary and environmental influences Br. J. Stat. Psychol. 16 191–210
R. B. Cattell (1982) The Inheritance of Personality and Ability: Research Methods and Findings Academic Press New York
L. J. Eaves J. S. Gale (1974) ArticleTitleA method for analyzing the genetic basis of covariation Behav. Genet. 4 253–267 Occurrence Handle10.1007/BF01074158 Occurrence Handle4471972
L. J. Eaves K. A. Last P. A. Young N. G. Martin (1968) ArticleTitleModel fitting approaches to the analysis of human behavior Heredity 41 249–320
L. Eaves A. Erkanli (2003) ArticleTitleMarkov Monte Carlo approaches to analysis of genetic and environmental components of human development change and G × E interaction Behav. Genet. 33 IssueID3 279–299 Occurrence Handle10.1023/A:1023446524917 Occurrence Handle12837018
R. A. Fisher (1918) ArticleTitleThe correlations between relatives on the supposition of Mendelian inheritance Trans. Roy. Soc. Edinburgh 52 399–433
R. A. Fisher (1925) ArticleTitleThe resemblance between twins: a statistical examination of Lauterbach's measurements Genetics 10 569–579
H. Goldstein (1995) Multilevel Statistical Models, 2nd edn Oxford Press New York
G. Guo J. Wang (2002) ArticleTitleThe mixed or multilevel model for behavior genetic analysis Behav. Genet. 32 IssueID1 37–49 Occurrence Handle10.1023/A:1014455812027 Occurrence Handle11958541
N. D. Henderson (1975) Gene–evniroment interaction in human behavioral development K. W. Schaie V. E. Anderson G. E. McClearn J. Money (Eds) Developmental human behavior genetics: Nature-nurture redefined Lexington Books Lexington, MA 5–24
J. Jinks D. W. Fulker (1970) ArticleTitleA comparison of the biometrical genetics, MAVA, and classical approaches to the analysis of human behavior Psychol. Bull. 73 311–349 Occurrence Handle5528333
K. G. Jöreskog D. Sörbom (1999) LISREL 8.30: LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International Hillsdale, NJ
C. C. Li (1968) ArticleTitleFisher, Wright, and path coefficients Biometrics 24 471–483 Occurrence Handle5686301
J. C. Loehlin (1965) ArticleTitleSome methodological problems in Cattell's multiple abstract variance analysis Psychol. Rev. 72 156–161 Occurrence Handle14282673
J. C. Loehlin (1978) ArticleTitleHeredity-environment analyses of Jenck's IQ correlations Behav. Genet. 8 415–436 Occurrence Handle10.1007/BF01067938 Occurrence Handle570034
J. C. Loehlin (1979) Combining data from different groups in human behavior genetics J. R. Royce (Eds) Theoretical Advances in Behavior Genetics Sijthoff and Noordhoff Leiden, The Nethderlands 303–334
H. M. Maes M. C. Neale L. J. Eaves (1997) ArticleTitleGenetic and environmental factors in relative body weight and human adiposity Behav. Genet. 27 325–351 Occurrence Handle10.1023/A:1025635913927 Occurrence Handle9519560
N. G. Martin L. J. Eaves (1977) ArticleTitleThe genetical analysis of covariance structure Heredity 38 79–95 Occurrence Handle268313
N. G. Martin L. J. Eaves A. Heath (1987) ArticleTitleProspects for detecting genotype × environment interactions in twins with breast cancer Acta Genet. Med. Gemelloi. 36 5–20
K. Mather J. L. Jinks (1977) Introduction to Behavioral Genetics. Cornell University. Press Ithaca, NY
J. J. McArdle (1986) ArticleTitleLatent variable growth within behavior genetic models Behav. Genet. 16 IssueID1 163–200 Occurrence Handle10.1007/BF01065485 Occurrence Handle3707483
J. J. McArdle (1996) ArticleTitleA workshop on contemporary behavioral genetic models and methods Contemp. Psychol. 41 IssueID9 886–887
J. J. McArdle J. Connell H. H. Goldsmith (1980) ArticleTitleLatent variable approaches to measurement preliminary results from the study of behavioral style structure, longitudinal stability, and genetic influences Behav. Genet. 10 609
J. J. McArdle E. Ferrer-Caja F. Hamagami R. W. Woodcock (2002) ArticleTitleComparative longitudinal multilevel structural analyses of the growth and decline of multiple intellectual abilities over the life-span Develop. Psychol. 38 IssueID1 115–142 Occurrence Handle10.1037//0012-1649.38.1.115
J. J. McArdle H. H. Goldsmith (1990) ArticleTitleSome alternative structural equation models for multivariate biometric analyses Behav. Genet. 20 IssueID5 569–608 Occurrence Handle10.1007/BF01065873 Occurrence Handle2288547
J. J. McArdle F. Hamagami (1996) Multilevel models from a multiple group structural equation perspective G. Marcoulides R. Schumacker (Eds) Advanced Structural Equation Modeling Techniques Erlbaum Hillsdale, N. J. 89–124
J. J. McArdle F. Hamagami (2003) ArticleTitleStructural equation models for evaluating dynamic concepts within longitudinal twin analyses Behav. Genet. 33 IssueID3 137–159 Occurrence Handle10.1023/A:1022553901851 Occurrence Handle14574148
J. J. McArdle C. A. Prescott (1996) Contemporary models for the biometric genetic analysis of intellectual abilities D. P. Flanagan J. L. Genshaft P. L. Harrison (Eds) Contemporary Intellectual Assessment: Theories, Tests and Issues Guilford Press New York 403–436
J.J. McArdle C.A. Prescott F. Hamagami J.L. Horn (1998) ArticleTitleA contemporary method for developmental-genetic analyses of age changes in intellectual abilities Develop. Neuropsychol. 14 IssueID1 69–114
L. K. Muthén B. O. Muthén (2004) Mplus VERSION User's Guide Muthén and Muthén Los Angeles CA
Neale, M. C., Boker, S. M., Xie, G., and Maes, H. H. (2002). Mx: Statistical Modeling, 5th edn. Department of Psychiatry, Virginia Commonwealth University, P.O. Box 980126, Richmond, VA 23298.
M. C. Neale L. Cardon (1992) Methodology for Genetic Studies of Twins and Families. Kluwer Academic Dordrecht, The Netherlands
J. C. Pinherio D. M. Bates (2000) Mixed-Effects Models in S and S-PLUS Springer New York
D. A. Powers Y. Xie (2000) Statistical Methods for Categorical Data Analysis Academic Press New York
C. A. Prescott (2004) ArticleTitleUsing the Mplus computer program to estimate models for continuous and categorical data from twins Behav. Genet. 34 IssueID1 17–40 Occurrence Handle10.1023/B:BEGE.0000009474.97649.2f Occurrence Handle14739694
S. Purcell P. Sham (2002) ArticleTitleVariance components models for gene-environment interaction in twin analysis Twin Res. 5 IssueID6 554–571 Occurrence Handle10.1375/136905202762342026 Occurrence Handle12573187
SAS Institute (1999) SAS/STAT Software, Version 8 SAS Institute Cary NC
Skrondal, A., and Rabe-Hesketh, S. (2004). Generalized Latent Variable Modeling: Multilevel, Longitudinal and Structural Equation Models. Chapman and Hall/CRC.
S. Wright (1918) ArticleTitleOn the nature of size factors Genetics 5 367–374
S. Wright (1921) ArticleTitleCorrelation and causation J. Agricult. Res. 20 557–585
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
McArdle, J.J., Prescott, C.A. Mixed-Effects Variance Components Models for Biometric Family Analyses. Behav Genet 35, 631–652 (2005). https://doi.org/10.1007/s10519-005-2868-1
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/s10519-005-2868-1