Twin Methodology

Abstract

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.