Twin Methodology

Abstract

The classic twin study is established as the ideal study design with which to investigate the relative importance of genetic and environmental factors to traits and diseases in human populations. Twin methodology is concerned with analysis techniques that have been developed to estimate and quantify the relative contribution of genes and environment to the disease or trait of interest. The current chapter gives an overview of the most important approaches. Both concordance measures and variance components approaches are discussed. The latter include analysis of variance (ANOVA), the DeFries–Fulker regression method and structural equation modelling (or path analysis). Structural equation models for twin data have recently been extended to incorporate and evaluate gene–environment interaction. Twin studies continue to play an important role in the postgenomic era, especially in the study of complex traits and diseases.

Key Concepts:

  • Most phenotypes show large individual differences (i.e. variance), the source of which may be genetic or environmental.

  • The classic twin study is the ideal study design to investigate the relative importance of genetic and environmental factors to traits and diseases in human populations.

  • Concordance measures express the similarity of twins for dichotomous traits such as the presence or absence of a disease.

  • Heritability is defined as the proportion of phenotypic variation that can be attributed to genetic variation.

  • Structural equation modelling is a standard tool in twin research and involves solving a series of simultaneous linear structural equations to estimate genetic and environmental parameters that best fit the observed twin variances and covariances.

  • Structural equation models for twin data have recently been extended to incorporate and evaluate gene–environment interaction.

  • Twin studies have often provided the first crucial proof of the importance of genes in the aetiology of a disease or trait, justifying further search for the underlying genes.

Keywords: concordance measures; heritability; DeFries–Fulker regression; structural equation modelling

Figure 1.

The path diagram for the simple twin model. This path diagram can be translated into linear structural equations (right), that is, they are equivalent representations of the same twin model. A number of conventions are used in path analysis and the representation of the path diagram. Observed variables for twin 1 and twin 2 are shown in squares; latent variables (or factors) are shown in circles. A single‐headed arrow indicates a direct influence of one variable on another, its value represented by a path coefficient (comparable to a factor loading). Double‐headed arrows between two variables indicate a correlation without any assumed directional relationship. Abbreviations: a, additive genetic factor loading; a2, additive genetic variance; A, additive genetic latent factor; c, shared (or common) environmental factor loading; c2, shared environmental variance; C, shared (or common) environmental latent factor; d, dominance genetic factor loading; d2, dominance genetic variance; D, dominance genetic latent factor; e, unique environmental factor loading; e2, unique environmental variance; E, unique environmental latent factor; P1, P2, phenotypic value of twin 1, twin 2; ra, additive genetic correlation (1 for MZ and 0.5 for DZ twins); rc, shared environmental correlation (1 for both MZ and DZ twins); rd, dominance genetic correlation (1 for MZ and 0.25 for DZ twins) and VP, total phenotypic variance.

Figure 2.

Partial path diagram for the basic gene–environment interaction model, shown for one twin only. The triangle represents the model for the means in which the effect of the moderator on the outcome variable is modelled. Abbreviations: A, additive genetic factor; B, linear effects of moderator on mean (forced entry); C, common environmental factor; D, dominant genetic factor; E, unique environmental factor; M, moderator; S, moderated component of D; T, moderated component of A; U, moderated component of C and V, moderated component of E.

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References

Christian JC and Williams CJ (2000) Comparison of analysis of variance and likelihood models of twin data analysis. In: Spector TD, Snieder H and MacGregor AJ (eds) Advances in Twin and Sib‐Pair Analysis, pp. 103–118. London: Greenwich Medical Media.

DeFries JC and Fulker DW (1985) Multiple regression analysis of twin data. Behavior Genetics 15: 467–473.

DeFries JC and Fulker DW (1988) Multiple regression analysis of twin data: etiology of deviant scores versus individual differences. Acta Geneticae Medicae et Gemellologiae (Roma) 37: 205–216.

Falconer DS (1989) Introduction to Quantitative Genetics, 3rd edn. Harlow, UK: Longman.

Hopper JL (1998) Heritability. In: Armitage P and Colton T (eds) Encyclopaedia of Biostatistics, pp. 1905–1906. New York: Wiley.

Kyvik KO (2000) Generalizability and assumptions of twin studies. In: Spector TD, Snieder H and MacGregor AJ (eds) Advances in Twin and Sib‐Pair Analysis, pp. 67–77. London: Greenwich Medical Media.

McCaffery JM, Snieder H, Dong Y and de Geus JCN (2007) Genetics in psychosomatic medicine: research designs and statistical approaches. Psychosomatic Medicine 69: 206–216.

Neale MC and Cardon LR (1992) Methodology for Genetic Studies of Twins and Families. Dordrecht, The Netherlands: Kluwer Academic Publishers.

Purcell S (2002) Variance components models for gene‐environment interaction in twin analysis. Twin Research 5: 554–571.

Snieder H (2000) Path analysis of age related disease traits. In: Spector TD, Snieder H and MacGregor AJ (eds) Advances in Twin and Sib‐Pair Analysis, pp. 119–130. London: Greenwich Medical Media.

Witte JS, Carlin JB and Hopper JL (1999) Likelihood‐based approach to estimating twin concordance for dichotomous traits. Genetic Epidemiology 16: 290–304.

Further Reading

Boomsma D, Busjahn A and Peltonen L (2002) Classical twin studies and beyond. Nature Reviews. Genetics 3: 872–882.

MacGregor AJ, Snieder H, Schork N and Spector TD (2000) Twins. Novel uses to study complex traits and genetic diseases. Trends in Genetics 16: 131–134.

Martin N, Boomsma D and Machin G (1997) A twin‐pronged attack on complex traits. Nature Genetics 17: 387–392.

Plomin R, DeFries JC, McClearn GE and McGuffin P (2008) Behavioral Genetics, 5th edn. New York: Worth Publishers.

Snieder H, Boomsma DI and van Doornen LJP (1999) Dissecting the genetic architecture of lipids, lipoproteins and apolipoproteins: lessons from twin studies. Arteriosclerosis, Thrombosis, and Vascular Biology 19: 2826–2834.

Spector TD, Snieder H and MacGregor AJ (eds) (2000) Advances in Twin and Sib‐Pair Analysis. London: Greenwich Medical Media.

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Snieder, Harold, Wang, Xiaoling, and MacGregor, Alex J(Sep 2010) Twin Methodology. In: eLS. John Wiley & Sons Ltd, Chichester. http://www.els.net [doi: 10.1002/9780470015902.a0005421.pub2]