Classical Twin Design Research Articles

HERITABILITY OF PREFERENCES FOR MULTIPLE CUES OF MATE QUALITY IN HUMANS

Human mate preferences have received a great deal of attention in recent decades because of their centrality to sexual selection, which is thought to play a substantial role in human evolution. Most of this attention has been on universal aspects of mate preferences, but variation between individuals is less understood. In particular, the relative contribution of genetic and environmental influences to variation in mate preferences is key to sexual selection models but has barely been investigated in humans, and results have been mixed in other species. Here, we used data from over 4000 mostly female twins who ranked the importance of 13 key traits in a potential partner. We used the classical twin design to partition variation in these preferences into that due to genes, family environment, and residual factors. In women, there was significant variability in the broad-sense heritability of individual trait preferences, with physical attractiveness the most heritable (29%) and housekeeping ability the least (5%). Over all the trait preferences combined, broad-sense heritabilities were highly significant in women and marginally significant in men, accounting for 20% and 19% of the variation, respectively; family environmental influences were much smaller. Heritability was a little higher in reproductive aged than in nonreproductive aged women, but the difference was not significant.

Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design

Considerable effort has been devoted to the analysis of genotype by environment (G × E) interactions in various phenotypic domains, such as cognitive abilities and personality. In many studies, environmental variables were observed (measured) variables. In case of an unmeasured environment, van der Sluis et al. (2006) proposed to study heteroscedasticity in the factor model using only MZ twin data. This method is closely related to the Jinks and Fulker (1970) test for G × E, but slightly more powerful. In this paper, we identify four challenges to the investigation of G × E in general, and specifically to the heteroscedasticity approaches of Jinks and Fulker and van der Sluis et al. We propose extensions of these approaches purported to solve these problems. These extensions comprise: (1) including DZ twin data, (2) modeling both A × E and A × C interactions; and (3) extending the univariate approach to a multivariate approach. By means of simulations, we study the power of the univariate method to detect the different G × E interactions in varying situations. In addition, we study how well we could distinguish between A × E, A × C, and C × E. We apply a multivariate version of the extended model to an empirical data set on cognitive abilities.

Reconsidering the heritability of intelligence in adulthood: taking assortative mating and cultural transmission into account.

Heritability estimates of general intelligence in adulthood generally range from 75 to 85%, with all heritability due to additive genetic influences, while genetic dominance and shared environmental factors are absent, or too small to be detected. These estimates are derived from studies based on the classical twin design and are based on the assumption of random mating. Yet, considerable positive assortative mating has been reported for general intelligence. Unmodeled assortative mating may lead to biased estimates of the relative magnitude of genetic and environmental factors. To investigate the effects of assortative mating on the estimates of the variance components of intelligence, we employed an extended twin-family design. Psychometric IQ data were available for adult monozygotic and dizygotic twins, their siblings, the partners of the twins and siblings, and either the parents or the adult offspring of the twins and siblings (N = 1314). Two underlying processes of assortment were considered: phenotypic assortment and social homogamy. The phenotypic assortment model was slightly preferred over the social homogamy model, suggesting that assortment for intelligence is mostly due to a selection of mates on similarity in intelligence. Under the preferred phenotypic assortment model, the variance of intelligence in adulthood was not only due to non-shared environmental (18%) and additive genetic factors (44%) but also to non-additive genetic factors (27%) and phenotypic assortment (11%).This non-additive nature of genetic influences on intelligence needs to be accommodated in future GWAS studies for intelligence.Electronic supplementary materialThe online version of this article (doi:10.1007/s10519-011-9507-9) contains supplementary material, which is available to authorized users.

The Complexity of Personality: Advantages of a Genetically Sensitive Multi-Group Design

Findings from many behavioral genetic studies utilizing the classical twin design suggest that genetic and non-shared environmental effects play a significant role in human personality traits. This study focuses on the methodological advantages of extending the sampling frame to include multiple dyads of relatives. We investigated the sensitivity of heritability estimates to the inclusion of sibling pairs, mother-child pairs and grandparent-grandchild pairs from the German Socio-Economic Panel Study in addition to a classical German twin sample consisting of monozygotic- and dizygotic twins. The resulting dataset contained 1.308 pairs, including 202 monozygotic and 147 dizygotic twin pairs, along with 419 sibling pairs, 438 mother-child dyads, and 102 grandparent-child dyads. This genetically sensitive multi-group design allowed the simultaneous testing of additive and non-additive genetic, common and specific environmental effects, including cultural transmission and twin-specific environmental influences. Using manifest and latent modeling of phenotypes (i.e., controlling for measurement error), we compare results from the extended sample with those from the twin sample alone and discuss implications for future research.

Increased Genetic Variance of BMI with a Higher Prevalence of Obesity

Background and objectivesThere is no doubt that the dramatic worldwide increase in obesity prevalence is due to changes in environmental factors. However, twin studies suggest that genetic differences are responsible for the major part of the variation in body mass index (BMI) and other measures of body fatness within populations. Several recent studies suggest that the genetic effects on adiposity may be stronger when combined with presumed risk factors for obesity. We tested the hypothesis that a higher prevalence of obesity and overweight and a higher BMI mean is associated with a larger genetic variation in BMI.MethodsThe data consisted of self-reported height and weight from two Danish twin surveys in 1994 and 2002. A total of 15,017 monozygotic and dizygotic twin pairs were divided into subgroups by year of birth (from 1931 through 1982) and sex. The genetic and environmental variance components of BMI were calculated for each subgroup using the classical twin design. Likewise, the prevalence of obesity, prevalence of overweight and the mean of the BMI distribution was calculated for each subgroup and tested as explanatory variables in a random effects meta-regression model with the square root of the additive genetic variance (equal to the standard deviation) as the dependent variable.ResultsThe size of additive genetic variation was positively and significantly associated with obesity prevalence (p = 0.001) and the mean of the BMI distribution (p = 0.015). The association with prevalence of overweight was positive but not statistically significant (p = 0.177).ConclusionThe results suggest that the genetic variation in BMI increases as the prevalence of obesity, prevalence of overweight and the BMI mean increases. The findings suggest that the genes related to body fatness are expressed more aggressively under the influence of an obesity-promoting environment.

Developing a Genetically Informative Measure of Alcohol Consumption Using Past-12-Month Indices

The goal of this study was to develop a factor score derived from measures of past-12-month alcohol consumption. Data were drawn from two studies-the Adult and Family Development Project (N = 734) and the Missouri Adolescent Female Twin Study (N = 3,787). Data on four indices of alcohol consumption (quantity, frequency, frequency of drinking to intoxication, and frequency of five or more drinks/day) were factor analyzed, and differences in factor loadings across gender, race/ethnicity, and study were tested. Correlations between these factors were computed across three assessments and between parent and offspring self-reports. Finally, using the classical twin design, variance in the past-12-month alcohol consumption factor was decomposed into additive genetic (A), shared environmental (C), and nonshared environmental (E) influences, and the extent to which these factors overlap with those influencing lifetime heaviest drinking were examined. Factor loadings across all groups were high (.69-.95), with some evidence for differing factor loadings across gender, race/ethnicity, and study. The across-wave correlations for the factor ranged from .22 to .62. The within-wave correlation between parental and offspring drinking was .25, suggesting the importance of familial influences, which genetic analyses attributed to both additive genetic (31%) and shared environmental (17%) factors. The overlap between genetic influences on past-12-month and lifetime heaviest drinking was 0.97. A factor score derived from past-12-month drinking measures is heritable and is largely influenced by those genetic factors that influence heaviest drinking, at least in young adults. It also shows moderate across-wave stability. This will allow for large- and small-scale genomic studies to use past-12-month drinking measures in data analysis of similar cohorts.

Genetic covariation underlying reading, language and related measures in a sample selected for specific language impairment.

Specific language impairment is a developmental language disorder characterized by failure to develop language normally in the absence of a specific cause. Previous twin studies have documented the heritability of reading and language measures as well as the genetic correlation between those measures. This paper presents results from an alternative to the classical twin designs by estimating heritability from extended pedigrees. These pedigrees were previously studied as part of series of molecular genetic studies of specific language impairment where the strongest genetic findings were with reading phenotypes rather than language despite selecting pedigrees based on language impairments. To explore the relationship between reading and language in these pedigrees, variance components estimates of heritability of reading and language measures were conducted showing general agreement with the twin literature, as were genetics correlations between reading and language. Phonological short-term memory, phonological awareness and auditory processing were evaluated as candidate mediators of the reading-language genetic correlations. Only phonological awareness showed significant genetic correlations with all reading measures and several language measures while phonological short-term memory and auditory processing did not.

Using Non-Normal SEM to Resolve the ACDE Model in the Classical Twin Design

One of the biggest problems in classical twin studies is that it cannot estimate additive genetic (A), non-additive genetic (D), shared environmental (C), and non-shared environmental (E) effects, simultaneously, because the model, referred to as the ACDE model, has negative degrees of freedom when using Structural Equation Modeling (SEM). Therefore, instead of the ACDE model, the ACE model or the ADE model is actually used. However, using the ACE or ADE models almost always leads to biased estimates. In the present paper, the univariate ACDE model is developed using non-normal Structural Equation Modeling (nnSEM). In SEM, (1st- and) 2nd-order moments, namely, (means and) covariances are used as information. However, nnSEM uses higher-order moments as well as (1st- and) 2nd-order moments. nnSEM has a number of advantages over SEM. One of which is that nnSEM can specify models that cannot be specified using SEM because of the negative degrees of freedom. Simulation studies have shown that the proposed method can decrease the biases. There are other factors that have possible effects on phenotypes, such as higher-order epistasis. Since the proposed method cannot estimate these effects, further research on developing a more exhaustive model is needed.

Pain reporting at different body sites is explained by a single underlying genetic factor

Objectives. Musculoskeletal pain is reported commonly; however, the extent to which pain in individual body areas reflects the severity of site-specific pathology or a more generalized propensity to feel pain is uncertain. We used a classical twin design to examine the pattern of pain reporting at different body sites among monozygotic (MZ) and dizygotic (DZ) twins to assess its heritability and to examine evidence for a common underlying propensity to report musculoskeletal pain.Methods. A well-characterized sample of female twins (TwinsUK cohort) was sent a questionnaire to determine their experience of pain in the neck and back, elbow, knee, thigh, hands or feet. The genetic contribution to pain reporting was assessed through univariate and multivariate analyses.Results. Pain was reported with a prevalence of 17–46%, depending on the anatomical site. Univariate analysis indicated an underlying heritability for pain reporting at all sites of 28–71%. Pain reporting at different sites was modestly but uniformly correlated; a single factor accounted for 95% of the overall variance in pain reporting. The correlation for scores on this factor was 0.46 in MZ twins and 0.23 in DZ twins, corresponding to a ‘pain reporting factor’ heritability of 46% (95% CI 40%, 52%).Conclusions. A single genetic factor underlies the propensity to report body pain at different musculoskeletal sites. These findings, which contrast with those for radiographic OA that is determined by genetic factors specific to each anatomical site, will inform the future search for therapeutic targets to treat pain in chronic degenerative diseases.

Dissecting complex phenotypes using the genomics of twins

Genetics in the post-genomic period is shifting from structural to functional genetics or genomics. Meanwhile, the use of twins is largely expanding from traditional heritability estimation for disease phenotypes to the study of both diseases and various molecular phenotypes, such as the regulatory phenotypes in functional genomics concerning gene expression and regulation, by engaging both classical twin design and marker-based gene mapping techniques in genetic epidemiology. New research designs have been proposed for making novel uses of twins in studying the molecular basis in the epigenetics of human diseases. Besides, twins not only serve as ideal samples for disease gene mapping using conventional genetic markers but also represent an excellent model for associating DNA copy number variations, a structural genetic marker, with human diseases. It is believed that, with the rapid development in biotechniques and new advances in bioinformatics, the unique samples of twins will make new contributions to our understanding of the nature and nurture in complex disease development and in human health. This paper aims at summarizing the new uses of twins in current genetic studies and suggesting novel proposes together with useful design and analytical strategies.

Classical Twin Design in Modern Pharmacogenomics Studies

Response to medication is highly variable, unpredictable and, at times, may be fatal. All drugs are more effective in certain groups of the population while showing no or minimal benefit in other groups. Although the current data on the subject are piecemeal, anecdotal evidence suggests that, in line with other common multifactorial traits, a myriad of genomic as well as environmental factors underpin population variability in drug response. Pharmacogenomics is the study of how variations in the human genome affect the variability in response to medication. Efforts to personalize treatment based on results from pharmacogenomics studies have the potential to increase efficacy, lower the overall cost of treatment, and decrease the incidence of adverse drug reactions, and are one of the major challenges of the modern era. The classical twin design has traditionally been used to assess the relative contribution of genetic and environmental factors to population variation in common, complex phenotypes, including drug response. Twins are not commonly regarded as providing the optimal design in genomic studies. However, we argue that, through their precise 'matching' for confounding variables (age, sex, cohort and common environmental effects), their amenability to numerous nonclassical study designs (genome-wide association studies or the role of epigenetic factors), and the availability of large, established registries worldwide, the twin model represents a flexible study design for systems-biology studies of drug response in humans. In this review, we describe the 'classical twin model' and its application in traditional pharmacogenetics studies, discuss the value of the twin design in the modern systems biology era, and highlight the potential of existing twin registries in formulating future strategies in pharmacogenomics research. We argue that the usefulness of this design goes beyond its traditional applications. Moreover, the flexibility of the model in concert with the amenability of large, established registries of twins worldwide to the collecting of new phenotypes will mean that the study of identical and nonidentical twins will play a considerable role in shaping our understanding of the important factors that underpin population variability in common, complex phenotypes, including response to medication.

Are extended twin family designs worth the trouble? A comparison of the bias, precision, and accuracy of parameters estimated in four twin family models.

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.

Genetic and environmental influences on the size of specific brain regions in midlife: The VETSA MRI study

The impact of genetic and environmental factors on human brain structure is of great importance for understanding normative cognitive and brain aging as well as neuropsychiatric disorders. However, most studies of genetic and environmental influences on human brain structure have either focused on global measures or have had samples that were too small for reliable estimates. Using the classical twin design, we assessed genetic, shared environmental, and individual-specific environmental influences on individual differences in the size of 96 brain regions of interest (ROIs). Participants were 474 middle-aged male twins (202 pairs; 70 unpaired) in the Vietnam Era Twin Study of Aging (VETSA). They were 51–59 years old, and were similar to U.S. men in their age range in terms of sociodemographic and health characteristics. We measured thickness of cortical ROIs and volume of other ROIs. On average, genetic influences accounted for approximately 70% of the variance in the volume of global, subcortical, and ventricular ROIs and approximately 45% of the variance in the thickness of cortical ROIs. There was greater variability in the heritability of cortical ROIs (0.00–0.75) as compared with subcortical and ventricular ROIs (0.48–0.85). The results did not indicate lateralized heritability differences or greater genetic influences on the size of regions underlying higher cognitive functions. The findings provide key information for imaging genetic studies and other studies of brain phenotypes and endophenotypes. Longitudinal analysis will be needed to determine whether the degree of genetic and environmental influences changes for different ROIs from midlife to later life.

Genetic and environmental influences on the co-morbidity between depression, panic disorder, agoraphobia, and social phobia: a twin study

Major depression (MD) and anxiety disorders such as panic disorder (PD), agoraphobia (AG), and social phobia (SP) are heritable and highly co-morbid. However, the relative importance of genetic and environmental etiology of the covariation between these disorders, particularly the relationship between PD and AG, is less clear. This study measured MD, PD, and AG in a population sample of 5,440 twin pairs and 1,245 single twins, about 45% of whom were also scored for SP. Prevalences, within individual co-morbidity and twin odds ratios for co-morbidity, are reported. A behavioral genetic analysis of the four disorders using the classical twin design was conducted. Odds ratios for MD, PD, AG, and SP in twins of individuals diagnosed with one of the four disorders were increased. Heritability estimates under a threshold-liability model for MD, PD, AG, and SP respectively were .33 (CI: 0.30-0.42), .38 (CI: 0.24-0.55), .48 (CI: 0.37-0.65), and .39 (CI: 0.16-0.65), with no evidence for any variance explained by the common environment shared by twins. We find that a common genetic factor explains a moderate proportion of variance in these four disorders. The genetic correlation between PD and AG was .83. MD, PD, AG, and SP strongly co-aggregate within families and common genetic factors explain a moderate proportion of variance in these four disorders. The high genetic correlation between PD and AG and the increased odds ratio for PD and AG in siblings of those with AG without PD suggests a common genetic etiology for PD and AG.

Childbirth Moderates the Genetic and Environmental Influences on BMI in Adult Twins

We report a study of the moderating role that the number of childbirths has on the genetic and environmental influences on BMI variation. We used a classical twin design with a sample of 704 adult female twins (334 monozygotic and 370 dizygotic). A gene-environment interaction (G x E) model was applied to estimate the moderating effects of childbearing. Results show that age and number of children exert a significant positive main effect on BMI. Furthermore, we found significant moderating effects of childbearing, with a larger number of children associated with an increased sensitivity to environmental factors.

Genetic Variation in Preferences for Giving and Risk Taking*

In this paper, we use the classical twin design to provide estimates of genetic and environmental influences on experimentally elicited preferences for risk and giving. Using standard methods from behavior genetics, we find strong prima facie evidence that these preferences are broadly heritable and our estimates suggest that genetic differences explain approximately twenty percent of individual variation. The results thus shed light on an important source of individual variation in preferences, a source that has hitherto been largely neglected in the economics literature.

Modeling Extended Twin Family Data I: Description of the Cascade Model

The classical twin design uses data on the variation of and covariation between monozygotic and dizygotic twins to infer underlying genetic and environmental causes of phenotypic variation in the population. By using data from additional relative classes, such as parents, extended twin family designs more comprehensively describe the causes of phenotypic variation. This article introduces an extension of previous extended twin family models, the Cascade model, which uses information on twins as well as their siblings, spouses, parents, and children to differentiate two genetic and six environmental sources of phenotypic variation. The Cascade also relaxes assumptions regarding mating and cultural transmission that existed in previous extended twin family designs. The estimation of additional parameters and relaxation of assumptions is potentially important, not only because it allows more fine-grained descriptions of the causes of phenotypic variation, but more importantly, because it can reduce the biases in parameter estimates that exist in earlier designs.

Political Science, Biometric Theory, and Twin Studies: A Methodological Introduction

As political scientists begin to incorporate biological influences as explanatory factors in political behavior, the need to present a methodological road map for utilizing biometric genetic theory and twin data is apparent. The classical twin design (CTD) remains the most popular design for initial examinations of the source of variance among social and political behaviors, and a vast majority of advanced variance components models as well as some molecular analyses are extensions of the CTD. Thus, it is appropriate to begin a series of works with the CTD and its most common variants. The CTD has strong roots in biometrical genetic theory and provides estimates of the correlations between observed traits of monozygotic and dizygotic twins in terms of underlying genetic and environmental influences. The majority of these analyses utilize SEMs of observed covariances for both twin types to assess the relative importance of these “latent” factors.

Political Science, Biometric Theory and Twin Studies: A Methodological Introduction

As political scientists begin to incorporate biological influences as explanatory factors in political behavior the need to present a methodological road map for utilizing biometric genetic theory and twin data specifically, is apparent. The classical twin design (CTD) remains the most popular design for initial epidemiological examinations of the source of variance among social and political behaviors, and a vast majority of advanced variance components models as well as many molecular analyses are largely extensions of the CTD. Thus, it is appropriate to begin a series of works with the CTD and its most common variants. The CTD has strong roots in biometrical genetic theory, and provides estimates of the correlations between observed traits of monozygotic and dizygotic twins in terms of underlying genetic and environmental influences. The majority of these analyses utilize structural equation models (SEM) of observed covariances for both twin types to assess the relative importance of these 'latent' factors. SEM estimates model parameters by minimizing a goodness-of-fit function between observed and predicted covariance matrices, usually by maximum-likelihood criterion.

Are there genetic influences on addiction: evidence from family, adoption and twin studies

In this exciting era of gene discovery, we review evidence from family, adoption and twin studies that examine the genetic basis for addiction. With a focus on the classical twin design that utilizes data on monozygotic and dizygotic twins, we discuss support in favor of heritable influences on alcohol, nicotine, cannabis and other illicit drug dependence. We review whether these genetic factors also influence earlier stages (e.g. experimentation) of the addictive process and whether there are genetic influences specific to each psychoactive substance. Converging evidence from these studies supports the role of moderate to high genetic influences on addiction with estimates ranging from 0.30 to 0.70. The changing role of these heritable factors as a function of gender, age and cultural characteristics is also discussed. We highlight the importance of the interplay between genes and the environment as it relates to risk for addiction and the utility of the children-of-twins design for emerging studies of gene-environment interaction is presented. Despite the advances being made by low-cost high-throughput whole genome association assays, we posit that information garnered from twin studies, especially extended twin designs with power to examine gene-environment interactions, will continue to form the foundation for genomic research.