Twin studies and their implications for molecular genetic studies: endophenotypes integrate quantitative and molecular genetics in ADHD research

Abstract

Objective: To describe the utility of twin studies for attention-deficit/hyperactivity disorder (ADHD) research and demonstrate their potential for the identification of alternative phenotypes suitable for genomewide association, developmental risk assessment, treatment response, and intervention targets.

Method: Brief descriptions of the classic twin study and genetic association study methods are provided, with illustrative findings from ADHD research. Biometrical genetics refers to the statistical modeling of data gathered from one or more group of known biological relation; it was apparently coined by Francis Galton in the 1860s and led to the "Biometrical School" at the University of London. Twin studies use genetic correlations between pairs of relatives, derived using this theoretical framework, to parse the individual differences in a trait into latent (unmeasured) genetic and environmental influences. This method enables the estimation of heritability, i.e., the percentage of variance due to genetic influences. It is usually implemented with a method called structural equation modeling, which is a statistical technique for fitting models to data, typically using maximum likelihood estimation. Genetic association studies aim to identify those genetic variants that account for the heritability estimated in twin studies. Measurements other than those used for the clinical diagnosis of the disorder are popular phenotype choices in current ADHD research. It is argued that twin studies have great potential to refine phenotypes relevant to ADHD.

Results: Prior studies have consistently found that the majority of the variance in ADHD symptoms is due to genetic factors. To date, genomewide association studies of ADHD have not identified replicable associations that account for the heritable variation. Possibly, the application of genomewide association studies to these alternative phenotypic measurements will assist in identifying the pathways from genetic variants to ADHD.

Conclusion: Power to detect associations should be improved by the study of highly heritable endophenotypes for ADHD and by reducing the number of phenotypes to be considered. Therefore, twin studies are an important research tool in the development of endophenotypes, defined as alternative, more highly heritable traits that act at earlier stages of the pathway from genes to behavior. Although genetic variation in liability to ADHD is likely polygenic, the proposed approach should help to identify improved alternative measurements for genetic association studies.

FIGURE 1

Factor model for data collected from unrelated individuals. Three factors (F1 to F3) are hypothesized to generate covariance between three endophenotypes (End1 to End3) and four behavioral measurements (Beh4 to Beh7). Causal paths from the factors to the observed variables are drawn as single-direction arrows (e.g., l₁₁, l₇₃). All latent factors (F1 to F3) and residual variance components (RE1 to RB7) are specified to have unit variance. Variation in each observed measurement is thus partitioned into four components: that due to each of the factors and that due to residual variance (including measurement error) specific to each measurement.

FIGURE 2

Factor model for genetically informative data from relatives assessed on seven measures (M1 to M7). Variation in the latent factors (F1 to F3) and residual components is partitioned into additive genetic (A1 to A3 and ARM1 to ARM7), shared or common environment (C1 to C3 and CRM1 to CRM7), and specific environment (E1 to E3 and ERM1 to ERM7) components. The regressions on each of these sources are estimated as free parameters (l₁ to l₇, a₁ to a₃, c₁ to c₃, e₁ to e₃, arm₁ to arm₇, crm₁ to crm₇, erm₁ to erm₇). If estimates for a particular factor appear to be largely genetic (e.g., a₁ is large but c₁ and e₁ are small), it would be a good candidate for genomewide association studies. Beh = behavioral measurement; End = endophenotypes.