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Context: Circulating concentrations of free triiodothyronine (fT3), free thyroxine (fT4), and thyrotropin (TSH) are partly heritable traits. Recent studies have advanced knowledge of their genetic architecture. Epigenetic modifications, such as DNA methylation (DNAm), may be important in pituitary-thyroid axis regulation and action, but data are limited., Objective: To identify novel associations between fT3, fT4, and TSH and differentially methylated positions (DMPs) in the genome in subjects from 2 Australian cohorts., Method: We performed an epigenome-wide association study (EWAS) of thyroid function parameters and DNAm using participants from: Brisbane Systems Genetics Study (median age 14.2 years, n = 563) and the Raine Study (median age 17.0 years, n = 863). Plasma fT3, fT4, and TSH were measured by immunoassay. DNAm levels in blood were assessed using Illumina HumanMethylation450 BeadChip arrays. Analyses employed generalized linear mixed models to test association between DNAm and thyroid function parameters. Data from the 2 cohorts were meta-analyzed., Results: We identified 2 DMPs with epigenome-wide significant (P < 2.4E-7) associations with TSH and 6 with fT3, including cg00049440 in KLF9 (P = 2.88E-10) and cg04173586 in DOT1L (P = 2.09E-16), both genes known to be induced by fT3. All DMPs had a positive association between DNAm and TSH and a negative association between DNAm and fT3. There were no DMPs significantly associated with fT4. We identified 23 differentially methylated regions associated with fT3, fT4, or TSH., Conclusions: This study has demonstrated associations between blood-based DNAm and both fT3 and TSH. This may provide insight into mechanisms underlying thyroid hormone action and/or pituitary-thyroid axis function., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Dr Nick Martin has made enormous contributions to the field of behavior genetics over the past 50 years. Of his many seminal papers that have had a profound impact, we focus on his early work on the power of twin studies. He was among the first to recognize the importance of sample size calculation before conducting a study to ensure sufficient power to detect the effects of interest. The elegant approach he developed, based on the noncentral chi-squared distribution, has been adopted by subsequent researchers for other genetic study designs, and today remains a standard tool for power calculations in structural equation modeling and other areas of statistical analysis. The present brief article discusses the main aspects of his seminal paper, and how it led to subsequent developments, by him and others, as the field of behavior genetics evolved into the present era.

The extended twin model is a unique design in the genetic epidemiology toolbox that allows to simultaneously estimate multiple causes of variation such as genetic and cultural transmission, genotype-environment covariance and assortative mating, among others. Nick Martin has played a key role in the conception of the model, the collection of substantially large data sets to test the model, the application of the model to a range of phenotypes, the publication of the results including cross-cultural comparisons, the evaluation of bias and power of the design and the further elaborations of the model, such as the children-of-twins design.

This article describes Dr Nathan Gillespie's PhD training and supervision under Professor Nick Martin and their ongoing collaborations. Drs Gillespie and Martin have collaborated on numerous biometrical genetic analyses applied to cross-sectional and longitudinal twin data, combined molecular and phenotypic modeling, as well as genomewide meta-analyses of psychoactive substance use and misuse. Dr Gillespie remains an active collaborator with Professor Martin, including ongoing data collection, analysis and publications related to the Brisbane Longitudinal Twin Study.

Twin studies have shown that a substantial proportion of the variance for sleep variables is due to genetic factors. However, there is still considerable heterogeneity among research reports. Our main objectives were to: 1) Review the twin literature regarding sleep quality and duration, as well as their behavioural correlates; 2) Estimate the mean heritability of subjective sleep quality and sleep duration; 3) Assess heterogeneity among studies on these topics; and 4) Search for moderator variables. Two parallel meta-analyses were carried out for sleep quality and sleep duration. Seventeen articles were included in the meta-analysis. Mean MZ correlations were consistently higher than DZ correlations. A mean heritability of 0.31 (95% CI: 0.20, 0.41) was found for subjective sleep quality (range: 0-0.43) and 0.38 (95% CI: 0.16, 0.56) for sleep duration (range: 0-1). Heterogeneity indexes were significant for both sleep quality (I 2 = 98.77, p < .001) and sleep duration (I 2 = 99.73, p < .001). The high heterogeneity warrants further research considering possible moderators that may affect heritability., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The Chinese National Twin Registry (CNTR) currently includes data from 61 566 twin pair from 11 provinces or cities in China. Of these, 31 705, 15 060 and 13 531 pairs are monozygotic, same-sex dizygotic and opposite-sex dizygotic pairs, respectively, determined by opposite sex or intrapair similarity. Since its establishment in 2001, the CNTR has provided an important resource for analysing genetic and environmental influences on chronic diseases especially cardiovascular diseases. Recently, the CNTR has focused on collecting biologic specimens from disease-concordant or disease-discordant twin pairs or from twin pairs reared apart. More than 8000 pairs of these twins have been registered, and blood samples have been collected from more than 1500 pairs. In this review, we summarize the main findings from univariate and multivariate genetic effects analyses, gene-environment interaction studies, omics studies exploring DNA methylation and metabolomic markers associated with phenotypes. There remains further scope for CNTR research and data mining. The plan for future development of the CNTR is described. The CNTR welcomes worldwide collaboration., (© 2019 The Association for the Publication of the Journal of Internal Medicine.)

For many multivariate twin models, the numerical Type I error rates are lower than theoretically expected rates using a likelihood ratio test (LRT), which implies that the significance threshold for statistical hypothesis tests is more conservative than most twin researchers realize. This makes the numerical Type II error rates higher than theoretically expected. Furthermore, the discrepancy between the observed and expected error rates increases as more variables are included in the analysis and can have profound implications for hypothesis testing and statistical inference. In two simulation studies, we examine the Type I error rates for the Cholesky decomposition and Correlated Factors models. Both show markedly lower than nominal Type I error rates under the null hypothesis, a discrepancy that increases with the number of variables in the model. In addition, we observe slightly biased parameter estimates for the Cholesky decomposition and Correlated Factors models. By contrast, if the variance-covariance matrices for variance components are estimated directly (without constraints), the numerical Type I error rates are consistent with theoretical expectations and there is no bias in the parameter estimates regardless of the number of variables analyzed. We call this the direct symmetric approach. It appears that each model-implied boundary, whether explicit or implicit, increases the discrepancy between the numerical and theoretical Type I error rates by truncating the sampling distributions of the variance components and inducing bias in the parameters. The direct symmetric approach has several advantages over other multivariate twin models as it corrects the Type I error rate and parameter bias issues, is easy to implement in current software, and has fewer optimization problems. Implications for past and future research, and potential limitations associated with direct estimation of genetic and environmental covariance matrices are discussed.

Health-related behaviors are significant contributors to morbidity and mortality in the United States, yet evidence on the underlying causes of the vast within-population variation in behaviors is mixed. While many potential causes of health-related behaviors have been identified-such as schooling, genetics, and environments-little is known on how much of the variation across multiple behaviors is due to a common set of causes. We use three separate datasets on U.S. twins to investigate the degree to which multiple health-related behaviors correlate and can be explained by a common set of factors. We find that aside from smoking and drinking, most behaviors are not strongly correlated among individuals. Based on the results of both within-identical-twins regressions and multivariate behavioral genetics models, we find some evidence that schooling may be related to smoking but not to the covariation between multiple behaviors. Similarly, we find that a large fraction of the variance in each of the behaviors is consistent with genetic factors; however, we do not find strong evidence that a single common set of genes explains variation in multiple behaviors. We find, however, that a large portion of the correlation between smoking and heavy drinking is consistent with common, mostly childhood, environments. This suggests that the initiation and patterns of these two behaviors might arise from a common childhood origin. Research and policy to identify and modify this source may provide a strong way to reduce the population health burden of smoking and heavy drinking., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

The twin method refers to the use of data from same-sex identical and fraternal twins to estimate the genetic and environmental contributions to a trait or outcome. The standard twin method is the variance component twin method that estimates heritability, the fraction of variance attributed to additive genetic inheritance. The latent class twin method estimates two quantities that are easier to interpret than heritability: the genetic prevalence, which is the fraction of persons in the genetic susceptibility latent class, and the heritability fraction, which is the fraction of persons in the genetic susceptibility latent class with the trait or outcome. We extend the latent class twin method in three important ways. First, we incorporate an additive genetic model to broaden the sensitivity analysis beyond the original autosomal dominant and recessive genetic models. Second, we specify a separate survival model to simplify computations and improve convergence. Third, we show how to easily adjust for covariates by extending the method of propensity scores from a treatment difference to zygosity. Applying the latent class twin method to data on breast cancer among Nordic twins, we estimated a genetic prevalence of 1%, a result with important implications for breast cancer prevention research., (© 2016, The International Biometric Society.)

Random-effects models are often used in family-based genetic association studies to properly capture the within families relationships. In such models, the regression parameters have a conditional on the random effects interpretation and they measure, e.g., genetic effects for each family. Estimating parameters that can be used to make inferences at the population level is often more relevant than the family-specific effects, but not straightforward. This is mainly for two reasons: First the analysis of family data often requires high-dimensional random-effects vectors to properly model the familial relationships, for instance when members with a different degree of relationship are considered, such as trios, mix of monozygotic and dizygotic twins, etc. The second complication is the biased sampling design, such as the multiple cases families design, which is often employed to enrich the sample with genetic information. For these reasons deriving parameters with the desired marginal interpretation can be challenging. In this work we consider the marginalized mixed-effects models, we discuss challenges in applying them in ascertained family data and propose penalized maximum likelihood methodology to stabilize the parameter estimation by using external information on the disease prevalence or heritability. The performance of our methodology is evaluated via simulation and is illustrated on data from Rheumatoid Arthritis patients, where we estimate the marginal effect of HLA-DRB1*13 and shared epitope alleles across three different study designs and combine them using meta-analysis., (© 2015, The International Biometric Society.)

Accurately identifying interactions between genetic vulnerabilities and environmental factors is of critical importance for genetic research on health and behavior. In the previous work of Van Hulle et al. (Behavior Genetics, Vol. 43, 2013, pp. 71-84), we explored the operating characteristics for a set of biometric (e.g., twin) models of Rathouz et al. (Behavior Genetics, Vol. 38, 2008, pp. 301-315), for testing gene-by-measured environment interaction (GxM) in the presence of gene-by-measured environment correlation (rGM) where data followed the assumed distributional structure. Here we explore the effects that violating distributional assumptions have on the operating characteristics of these same models even when structural model assumptions are correct. We simulated N = 2,000 replicates of n = 1,000 twin pairs under a number of conditions. Non-normality was imposed on either the putative moderator or on the ultimate outcome by ordinalizing or censoring the data. We examined the empirical Type I error rates and compared Bayesian information criterion (BIC) values. In general, non-normality in the putative moderator had little impact on the Type I error rates or BIC comparisons. In contrast, non-normality in the outcome was often mistaken for or masked GxM, especially when the outcome data were censored.

Gene-based tests such as versatile gene-based association study (VEGAS) are commonly used following per-single nucleotide polymorphism (SNP) GWAS (genome-wide association studies) analysis. Two limitations of VEGAS were that the HapMap2 reference set was used to model the correlation between SNPs and only autosomal genes were considered. HapMap2 has now been superseded by the 1,000 Genomes reference set, and whereas early GWASs frequently ignored the X chromosome, it is now commonly included. Here we have developed VEGAS2, an extension that uses 1,000 Genomes data to model SNP correlations across the autosomes and chromosome X. VEGAS2 allows greater flexibility when defining gene boundaries. VEGAS2 offers both a user-friendly, web-based front end and a command line Linux version. The online version of VEGAS2 can be accessed through https://vegas2.qimrberghofer.edu.au/. The command line version can be downloaded from https://vegas2.qimrberghofer.edu.au/zVEGAS2offline.tgz. The command line version is developed in Perl, R and shell scripting languages; source code is available for further development.

There has been considerable interest in studying the magnitude and type of inheritance of specific diseases. This is typically derived from family or twin studies, where the basic idea is to compare the correlation for different pairs that share different amount of genes. We here consider data from the Danish twin registry and discuss how to define heritability for cancer occurrence. The key point is that this should be done taking censoring as well as competing risks due to e.g.  death into account. We describe the dependence between twins on the probability scale and show that various models can be used to achieve sensible estimates of the dependence within monozygotic and dizygotic twin pairs that may vary over time. These dependence measures can subsequently be decomposed into a genetic and environmental component using random effects models. We here present several novel models that in essence describe the association in terms of the concordance probability, i.e., the probability that both twins experience the event, in the competing risks setting. We also discuss how to deal with the left truncation present in the Nordic twin registries, due to sampling only of twin pairs where both twins are alive at the initiation of the registries.

It is commonly acknowledged that estimates of heritability from classical twin studies have many potential shortcomings. Despite this, in the post-GWAS era, these heritability estimates have come to be a continual source of interest and controversy. While the heritability estimates of a quantitative trait are subject to a number of biases, in this article we will argue that the standard statistical approach to estimating the heritability of a binary trait relies on some additional untestable assumptions which, if violated, can lead to badly biased estimates. The ACE liability threshold model assumes at its heart that each individual has an underlying liability or propensity to acquire the binary trait (e.g., disease), and that this unobservable liability is multivariate normally distributed. We investigated a number of different scenarios violating this assumption such as the existence of a single causal diallelic gene and the existence of a dichotomous exposure. For each scenario, we found that substantial asymptotic biases can occur, which no increase in sample size can remove. Asymptotic biases as much as four times larger than the true value were observed, and numerous cases also showed large negative biases. Additionally, regions of low bias occurred for specific parameter combinations. Using simulations, we also investigated the situation where all of the assumptions of the ACE liability model are met. We found that commonly used sample sizes can lead to biased heritability estimates. Thus, even if we are willing to accept the meaningfulness of the liability construct, heritability estimates under the ACE liability threshold model may not accurately reflect the heritability of this construct. The points made in this paper should be kept in mind when considering the meaningfulness of a reported heritability estimate for any specific disease.

While most outcomes may in part be genetically mediated, quantifying genetic heritability is a different matter. To explore data on twins and decompose the variation is a classical method to determine whether variation in outcomes, e.g. IQ or schooling, originate from genetic endowments or environmental factors. Despite some criticism, the model is still widely used. The critique is generally related to how estimates of heritability may encompass environmental mediation. This aspect is sometimes left implicit by authors even though its relevance for the interpretation is potentially profound. This short note is an appeal for clarity from authors when interpreting the magnitude of heritability estimates. It is demonstrated how disregarding existing theoretical contributions can easily lead to unnecessary misinterpretations and/or controversies. The key arguments are relevant also for estimates based on data of adopted children or from modern molecular genetics research., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Relatives provide the basic material for the study of inheritance of human disease. However, the methodologies for the estimation of heritability and the interpretation of the results have been controversial. The debate arises from the plethora of methods used, the validity of the methodological assumptions and the inconsistent and sometimes erroneous genetic interpretations made. We will discuss how to estimate disease heritability, how to interpret it, how biases in heritability estimates arise and how heritability relates to other measures of familial disease aggregation.

It is well known that the regular likelihood ratio test of a bounded parameter is not valid if the boundary value is being tested. This is the case for testing the null value of a scalar variance component. Although an adjusted test of variance component has been suggested to account for the effect of its lower bound of zero, no adjustment of its interval estimate has ever been proposed. If left unadjusted, the confidence interval of the variance may still contain zero when the adjusted test rejects the null hypothesis of a zero variance, leading to conflicting conclusions. In this research, we propose two ways to adjust the confidence interval of a parameter subject to a lower bound, one based on the Wald test and the other on the likelihood ratio test. Both are compatible to the adjusted test and parametrization-invariant. A simulation study and two examples are given in the framework of ACDE models in twin studies.

Twin studies allow us to estimate the relative contributions of nature and nurture to human phenotypes by comparing the resemblance of identical and fraternal twins. Variation in complex traits is a balance of genetic and environmental influences; these influences are typically estimated at a population level. However, what if the balance of nature and nurture varies depending on where we grow up? Here we use statistical and visual analysis of geocoded data from over 6700 families to show that genetic and environmental contributions to 45 childhood cognitive and behavioral phenotypes vary geographically in the United Kingdom. This has implications for detecting environmental exposures that may interact with the genetic influences on complex traits, and for the statistical power of samples recruited for genetic association studies. More broadly, our experience demonstrates the potential for collaborative exploratory visualization to act as a lingua franca for large-scale interdisciplinary research.

Twins, full siblings, and half-siblings are increasingly used as comparison groups in matched cohort and matched case-control studies. The "within-pair" estimates acquired through these comparisons are free from confounding from all factors that are shared by the siblings. This has made sibling comparisons popular in studying associations thought likely to suffer confounding from socioeconomic or genetic factors. Despite the wide application of these designs in epidemiology, they have received little scrutiny from a statistical or methodological standpoint. In this paper we show, analytically and through a series of simulations, that the standard interpretation of the models is subject to several limitations that are rarely acknowledged.Although within-pair estimates will not be confounded by factors shared by the siblings, such estimates are more severely biased by non-shared confounders than the unpaired estimate. If siblings are less similar with regard to confounders than to the exposure under study, the within-pair estimate will always be more biased than the ordinary unpaired estimate. Attenuation of associations due to random measurement error in exposure will also be higher in the within-pair estimate, leading within-pair associations to be weaker than corresponding unpaired associations, even in the absence of confounding. Implications for the interpretation of sibling comparison results are discussed.

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.

The classic twin model design has a wide application in human genetics. Under the assumption that nongenetic effects are shared to the same degree by monozygotic (MZ) and dizygotic (DZ) twin pairs, a test of the equality of casewise concordances between MZ and DZ twins provides a clue to the influence of genetic and environmental factors on a disease. The casewise concordance is the conditional probability that given that one member of a twin pair is affected, the other is also affected. When disease prevalence is low or cost-effectiveness is considered, collection of twin pairs by ascertainment for performing casewise concordance analysis is required. In this article, by defining an overall casewise concordance parameter, several likelihood-based tests, such as likelihood ratio test LR, score test Score, the usual Wald test Wald and an alternative Wald test WaldA are investigated for a test of the equality of concordances between ascertained MZ and DZ twin pairs under multinomial models. Simulation studies were conducted for data with small sample sizes. The results show that the type I error rates and power of LR and Score are stable only when the overall casewise concordances are not extremely small or large. The Wald has higher power performance in most cases but would slightly inflate type I error rates; the WaldA is the most robust and recommended approach., (Copyright © 2011 John Wiley & Sons, Ltd.)

Analysis of data from twin and family studies provides the foundation for studies of disease inheritance. The development of advanced theory and computational software for general linear models has generated considerable interest for using mixed-effect models to analyze twin and family data, as a computationally more convenient and theoretically more sound alternative to the classical structure equation modeling. Despite the long history of twin and family data analysis, some fundamental questions remain unanswered. We addressed two important issues. One is to determine the necessary and sufficient conditions for the identifiability in the mixed-effects models for twin and family data. The other is to derive the asymptotic distribution of the likelihood ratio test, which is novel due to the fact that the standard regularity conditions are not satisfied. We considered a series of specific yet important examples in which we demonstrated how to formulate mixed-effect models to appropriately reflect the data, and our key idea is the use of the Cholesky decomposition. Finally, we applied our method and theory to provide a more precise estimate of the heritability of two data sets than the previously reported estimate., (© 2011, The International Biometric Society.)

Researchers continue to pursue a better understanding of the symptoms, comorbidities, and causes of autism spectrum disorders. In this article we review more than 30 twin studies of autism spectrum disorders (ASDs) and autistic traits published in the last decade that have contributed to this endeavor. These twin studies have reported on the heritability of autism spectrum disorders and autistic traits in different populations and using different measurement and age groups. These studies have also stimulated debate and new hypotheses regarding why ASDs show substantial symptom heterogeneity, and what causes their comorbidity with intellectual disability, language delay, and other psychiatric disorders such as ADHD. These studies also reveal that the etiology of autism and autistic traits assessed in the general population is more similar than different, which contributes to the question of where the boundary lies between autism and typical development. Recent findings regarding molecular genetic and environmental causes of autism are discussed in the relation to these twin studies. Lastly, methodological assumptions of the twin design are given consideration, as well as issues of measurement. Future research directions are suggested to ensure that this decade is as productive as the last in attempting to disentangle the causes of autism spectrum disorders., (Copyright © 2011 Wiley-Liss, Inc.)

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.

Objective: The authors sought to clarify the structure of the genetic and environmental risk factors for 22 DSM-IV disorders: 12 common axis I disorders and all 10 axis II disorders., Method: The authors examined syndromal and subsyndromal axis I diagnoses and five categories reflecting number of endorsed criteria for axis II disorders in 2,111 personally interviewed young adult members of the Norwegian Institute of Public Health Twin Panel., Results: Four correlated genetic factors were identified: axis I internalizing, axis II internalizing, axis I externalizing, and axis II externalizing. Factors 1 and 2 and factors 3 and 4 were moderately correlated, supporting the importance of the internalizing-externalizing distinction. Five disorders had substantial loadings on two factors: borderline personality disorder (factors 3 and 4), somatoform disorder (factors 1 and 2), paranoid and dependent personality disorders (factors 2 and 4), and eating disorders (factors 1 and 4). Three correlated environmental factors were identified: axis II disorders, axis I internalizing disorders, and externalizing disorders versus anxiety disorders., Conclusions: Common axis I and II psychiatric disorders have a coherent underlying genetic structure that reflects two major dimensions: internalizing versus externalizing, and axis I versus axis II. The underlying structure of environmental influences is quite different. The organization of common psychiatric disorders into coherent groups results largely from genetic, not environmental, factors. These results should be interpreted in the context of unavoidable limitations of current statistical methods applied to this number of diagnostic categories.

Twins have been extensively used in economics, sociology, and behavioral genetics to investigate the role of genetic endowments on a broad range of social, demographic, and economic outcomes. However, the focus in these literatures has been distinct.: The economic literature has been primarily concerned with the need to control for unobserved endowments--including as an important subset, genetic endowments--in analyses that attempt to establish the impact of one variable, often schooling, on a variety of economic, demographic, and health outcomes. Behavioral genetic analyses have mostly been concerned with decomposing the variation in the outcomes of interest into genetic, shared environmental, and non-shared environmental components, with recent multivariate analyses investigating the contributions of genes and the environment to the correlation and causation between variables. Despite the fact that twins studies and the recognition of the role of endowments are central to both of these literatures, they have mostly evolved independently. In this paper we develop formally the relationship between the economic and behavioral genetic approaches to the analyses of twins, and we develop an integrative approach that combines the identification of causal effects, which dominates the economic literature, with the decomposition of variances and covariances into genetic and environmental factors that are the primary goal of behavioral genetic approaches. We apply this integrative ACE-beta approach to an illustrative investigation of the impact of schooling on several demographic outcomes such as fertility and nuptiality and health.

In this paper, we model multivariate time-to-event data by composite likelihood of pairwise frailty likelihoods and marginal hazards using natural cubic splines. Both right- and interval-censored data are considered. The suggested approach is applied on two types of family studies using the gamma- and stable frailty distribution: The first study is on adoption data where the association between survival in families of adopted children and their adoptive and biological parents is studied. The second study is a cross-sectional study of the occurrence of back and neck pain in twins, illustrating the methodology in the context of genetic epidemiology.

In 2004 the Netherlands Twin Register (NTR) started a large scale biological sample collection in twin families to create a resource for genetic studies on health, lifestyle and personality. Between January 2004 and July 2008, adult participants from NTR research projects were invited into the study. During a home visit between 7:00 and 10:00 am, fasting blood and morning urine samples were collected. Fertile women were bled on day 2-4 of the menstrual cycle, or in their pill-free week. Biological samples were collected for DNA isolation, gene expression studies, creation of cell lines and for biomarker assessment. At the time of blood sampling, additional phenotypic information concerning health, medication use, body composition and smoking was collected. Of the participants contacted, 69% participated. Blood and urine samples were collected in 9,530 participants (63% female, average age 44.4 (SD 15.5) years) from 3,477 families. Lipid profile, glucose, insulin, HbA1c, haematology, CRP, fibrinogen, liver enzymes and creatinine have been assessed. Longitudinal survey data on health, personality and lifestyle are currently available for 90% of all participants. Genome-wide SNP data are available for 3,524 participants, with additional genotyping ongoing. The NTR biobank, combined with the extensive phenotypic information available within the NTR, provides a valuable resource for the study of genetic determinants of individual differences in mental and physical health. It offers opportunities for DNA-based and gene expression studies as well as for future metabolomic and proteomic projects.

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.

The detection of genotyping errors, based on apparent Mendelian incompatibilities in a sample of sib-pairs, is a complicated problem. In the case of a single marker and unknown parental genotypes, all combinations of sib-pair genotypes are self-consistent. Moreover, the observed deviation from equilibrium genotype frequencies may result from genotyping errors as well as from the sample's stratification. This in turn, may profoundly affect the results of association and linkage analyses, and therefore an estimation of these factors should be done beforehand. Here we present several parametric models, and using likelihood ratio statistics, we suggest a method of combined analysis of genotyping errors and a sample stratification for randomly ascertained sib-pair single nucleotide polymorphism (SNP) data. Specifically, we implemented two models of genotyping errors in either heterozygotes or homozygotes, and two models of sample stratification resulting from either the presence of families of different ethnic origin (e.g., a population admixture) or from a different ethnic origin of the parents in the family (e.g., intermarriage). The power of this method was established by Monte Carlo data simulation. The results clearly suggest that the proposed method is most efficient for detecting genotyping errors in heterozygotes, a common error caused by incorrect SNP data interpretation. We also provide an example of its application to real data.

This article reviews recent developments in understanding the genetic etiology of obsessive-compulsive disorder (OCD). Family studies provide further support for the familial aggregation of OCD. Genome-wide linkage studies indicate that specific chromosomal regions are linked to OCD. Moreover, results from recent molecular genetic studies suggest that several candidate genes are associated with OCD. However, specific genes causing OCD have not been conclusively identified, and the molecular pathogenesis of the disorder has not been elucidated. The search for genes is complicated by the clinical and etiologic heterogeneity of OCD, as well as the possibility of gene-gene and gene-environment interactions. Despite this complexity, further refinement of the phenotype and developments in molecular and statistical genetics hold promise for further deepening our genetic understanding of OCD in the future.

We consider the problem of testing for a difference in the probability of success from matched binary pairs. Starting with three standard inexact tests, the nuisance parameter is first estimated and then the residual dependence is eliminated by maximization, producing what I call an E+M P-value. The E+M P-value based on McNemar's statistic is shown numerically to dominate previous suggestions, including partially maximized P-values as described in Berger and Sidik (2003, Statistical Methods in Medical Research 12, 91-108). The latter method, however, may have computational advantages for large samples.

The measurement of agreement of repeat rating is the usual method of assessing the reliability of categorical scales. Measurement of agreement is also important in genetic twin studies based on categorical scales. One of the most commonly used methods of analysis for both types of study is the kappa coefficient. For scales with more than two categories, one approach is to use a single summary kappa coefficient. While this may be sufficient for many studies, in some instances investigation of heterogeneity in the pattern of agreement may give additional insights as there may be greater agreement for some pairs of categories than for others. In this paper, kappa-type coefficients are used to model heterogeneity in the pattern of agreement. Constraints are added to the heterogeneous model to obtain simplified models. Procedures for estimation, confidence intervals, and inference for these coefficients are described for the case of two ratings per subject for a single sample and for the comparison of two independent samples. Formulae for sample size and power calculation are derived using the non-central chi-squared distribution. Two simulation studies are carried out to check the empirical test size and power. Methods are illustrated by two examples involving nominal scales with three categories.

Several structural brain abnormalities have been reported in patients with Attention Deficit Hyperactivity Disorder (ADHD). However, the etiology of these brain changes is still unclear. To investigate genetic and environmental influences on ADHD related neurobiological changes, we performed Voxel-Based Morphometry on MRI scans from monozygotic (MZ) twins selected from a large longitudinal population database to be highly concordant or highly discordant for ratings on the Child Behavior Checklist Attention Problem scale (CBCL-AP). Children scoring low on the CBCL-AP are at low risk for ADHD, whereas children scoring high on this scale are at high-risk for ADHD. Brain differences between concordant high-risk twin pairs and concordant low-risk twin pairs likely reflect the genetic risk for ADHD; brain differences between the low-risk and high-risk twins from discordant MZ twin pairs reflect the environmental risk for ADHD. A major difference between comparisons of high and low-risk twins from concordant pairs and high/low twins from discordant pairs was found for the prefrontal lobes. The concordant high-risk pairs showed volume loss in orbitofrontal subdivisions. High-risk members from the discordant twin pairs exhibited volume reduction in the right inferior dorsolateral prefontal cortex. In addition, the posterior corpus callosum was compromised in concordant high-risk pairs, only. Our findings indicate that inattention and hyperactivity symptoms are associated with anatomical abnormalities of a distributed action-attentional network. Different brain areas of this network appear to be affected in inattention/hyperactivity caused by genetic (i.e., high concordant MZ pairs) vs. environmental (i.e., high-low discordant MZ pairs) risk factors. These results provide clues that further our understanding of brain alterations in ADHD.

In this article we present the protocol of the Birmingham Registry for Twin Heritability Studies (BiRTHS), which aims to establish a long-term prospective twin registry with twins identified from the antenatal period and subjected to detailed follow-up. We plan to investigate the concordance in anthropometrics and early childhood phenotypes between 66 monozygotic and 154 dizygotic twin pairs in the first 2 years of recruitment. In this project we plan to determine the relative contributions of heritability and environment to fetal growth, birth size, growth in infancy and development up to 2 years of age in an ethnically mixed population. Twins will be assessed with the Griffitth's Mental Development Scales, which will enable us to obtain detailed information on development. As maternal depression may have an effect on the twins' neurodevelopment, the Edinburgh Postnatal Depression Scale will be used at various stages during pregnancy and after delivery to assess maternal depressive symptoms. The increasing prevalence of obesity in both adults and children has raised concerns about the effect of maternal obesity in pregnancy on fetal growth. The prospective study design gives us the opportunity to obtain data on maternal nutrition (reflected by body mass index) and ante- and postnatal growth and development of twins.

This article profiles the historical twin databases of the secondary education school attached to the Faculty of Education at the University of Tokyo. The school was established in 1948. Every year, about 50 pairs of twins of all sex and zygosity combinations and aged 11 to 12 years take an examination, and about 10 to 20 pairs are admitted based on the results. Three independent datasets exist: one for applicants (11-12 years), one for students (12-18 years), and one for graduates (18-72 years). These three historical databases and research perspectives are introduced herein.

The Swedish Young Male Twins Study is a population-based longitudinal twin study founded in 1997 through record-linkages of several national registers. Details on pregnancy and birth were obtained from the Swedish Medical Birth Register and used to identify 3566 male twins (1783 pairs) born in Sweden between 1973 and 1979 and resident in Sweden in 1997. A record-linkage was made between the Medical Birth Register and the Military Service Conscription Register for the years 1991 to 1999, providing information on body weight, height, blood pressure, muscle strength, cognitive ability of these twins at age 18 and 19 years. In 1998, 2002 and 2005 to 2006, the twins were surveyed on their zygosity, socioeconomic status, lifestyle factors (such as eating habits, physical activity, smoking habits, use of alcohol etc), height and weight. In 2002, additional information was collected on perceived body shape and size, and eating behavior, according to the Three-Factor Eating Questionnaire. In 2003, DNA via buccal mucosa was collected from a subset of the twins. Recent research using the Swedish Young Male Twins datasets has explored the relationships between fetal growth, body size and blood pressure in young adulthood, genetic and environmental contributions to eating behavior and physical activity, and relationships between diet and physical activity patterns with longitudinal changes in body mass index and attained waist circumference.

Since its start as a database of "possible twins", the Italian Twin Register has developed remarkably in terms of twin approach and recruitment, data-management tools, the cohorts enrolled, and the breadth of information gathered, making the Italian Twin Register a valuable resource for genetic epidemiological research. The Italian Twin Register is a random population of twins at both the national level and within targeted geographical areas or birth cohorts. Further, the Register is linked with disease records and has recently implemented a web-based method for volunteer twin recruitment specifically designed to promote the Register and to disseminate information on genetic epidemiology. To date, approximately 9000 twins have joined the Italian Twin Register, the majority of whom (approximately 70%) represent young adults aged 20 at time of enrollment. Although the total number of twins recruited to date is far below the expected figure initially predicted, the newly established standardized procedures guarantee an increase of around 2000 twins each year. Following the collaboration between the Italian Twin Register and the main Italian nonprofit association for blood donors, twin DNA sampling and storage has recently accelerated contributing to the large amount of phenotypic data collected. The Italian Twin Register is currently involved in both population and clinical based studies on various complex phenotypes and diseases, some conducted within large European consortia.

In the late 1980s The Netherlands Twin Register (NTR) was established by recruiting young twins and multiples at birth and by approaching adolescent and young adult twins through city councils. The Adult NTR (ANTR) includes twins, their parents, siblings, spouses and their adult offspring. The number of participants in the ANTR who take part in survey and / or laboratory studies is over 22,000 subjects. A special group of participants consists of sisters who are mothers of twins. In the Young NTR (YNTR), data on more than 50,000 young twins have been collected. Currently we are extending the YNTR by including siblings of twins. Participants in YNTR and ANTR have been phenotyped every 2 to 3 years in longitudinal survey studies, since 1986 and 1991 for the YNTR and ANTR, respectively. The resulting large population-based datasets are used for genetic epidemiological studies and also, for example, to advance phenotyping through the development of new syndrome scales based on existing items from other inventories. New research developments further include brain imaging studies in selected and unselected groups, clinical assessment of psychopathology through interviews, and cross-referencing the NTR database to other national databases. A large biobank enterprise is ongoing in the ANTR in which blood and urine samples are collected for genotyping, expression analysis, and metabolomics studies. In this paper we give an update on the YNTR and ANTR phenotyping and on the ongoing ANTR biobank studies.

From 1998 the population-based North of England Multiple Pregnancy Register (MPR) has collected data on all multiple pregnancies in the region from the earliest point of ascertainment in the pregnancy. This article describes the development of the MPR and the findings of the first 5 years of data collection. Mothers now give explicit consent for their inclusion with named data, in accordance with section 60 of the Health and Social Care Act 2001. During 1998 to 2002, 2310 twin pregnancies were registered, with an increasing twinning rate of 13.6 to 16.6 per 1000 maternities. Chorionicity ascertainment in twin maternities with at least one stillbirth or live birth has improved from 81% in 1998 to 91% in 2002. Before 24 weeks of gestation, 8.4% (359/4620) of fetuses were lost either spontaneously or as a result of termination of pregnancy. The perinatal mortality rate was much higher in monochorionic than dichorionic twins, mainly due to differences in stillbirth rates (49.0 vs. 11.5 per 1000 maternities respectively, risk rate = 4.2; 95% confidence intervals 2.7-6.6). The gestational-age-specific neonatal mortality rates were similar in twins and singletons, except in the group of term births (> or weeks' gestation) when compared by conventional gestational age categories. For stillbirths, the rates were even lower than in singletons in gestational age categories of less than 32 weeks. The register is an important resource of data on multiple pregnancies, which allows monitoring of trends in multiple birth rates and pregnancy losses and provides a unique opportunity for etiological and long-term follow-up studies.

More than 100 pairs of adult twins, reared apart and growing old separately, have participated in the Japanese Study of Adult Twins Reared Apart and Growing Old Separately since it began in 1974. The subjects are 161 pairs of adult twins born between 1910 and 1945 in Japan. The main focus of this study is to investigate the influences of environmental factors and life history on life satisfaction and psychological well-being in later adulthood. A mail survey has been conducted on these twins each year since 1974. To date, the Wechsler Adult Intelligent Scale, Maudsley Personality Inventory, Newgarten Life Satisfaction Index, comprehensive medical examinations and personal interviews have been conducted for 12 twin pairs residing in various areas of Japan.

This article is an updated review of the Pittsburgh Registry of Infant Multiplets including recruitment methods, data collection, and results of pilot studies conducted in this registry. The main goal of the registry is to study psychological development. The risk for behavior disorders including substance use disorders, as well as language development and dental health are among research targets. Pilot data on the heritability of minor physical anomalies and neuropsychological characteristics (Continuous Performance Test) are reported.

The Tokyo Twin Cohort Project (ToTCoP) is a large-scale longitudinal study of 5 years based on 1619 pairs of infant twins reared together. The purpose of the study is to construct a population-based twin registry in Japan and to investigate human growth and development and twin themselves. It covers behavioral, neurological, physical and environmental variables measured by questionnaire, home visiting and brain imaging technology. The full registry contains over 47,000 multiple births collected from the Basic Resident Register, and the targeted population is 3070 probable twins of 0 to 2 years old. Preliminary analysis of the entry questionnaire data showed no serious sampling biases. Descriptive statistics of parental characteristics (parental age, gestation age, parity and placentation, maternal weight, parenting stress) and children's characteristics (body size at birth, 4 and 10 months of age, milk consumption, and sleeping and social behavior) and their correlations, genetic and environmental contributions and correlations are reported.

The current article describes the creation and composition of the PennTwins Cohort and provides details on the demographic characteristics of the sample. The PennTwins Cohort was developed using a population-based method of ascertainment and currently has 9401 28- to 47-year-old twins, including 2225 confirmed twin pairs and 4951 twins whose co-twins have not yet registered. Zygosity data have been used to identify 919 monozygotic, 634 same-sex dizygotic, and 445 opposite-sex dizygotic pairs. GeoCode data on gender, age, and certain demographic characteristics were obtained for the addresses of all twins who were mailed invitations to be part of the cohort. Analysis of the available data show only very small differences between twins who are currently part of the PennTwins Cohort and potential twins who either did not respond to recruitment or who could not be located. Similarly, only very small demographic differences exist between twins from complete pairs and twins whose co-twins are not yet registered, and there are no differences across zygosity. Thus, despite a relatively low overall response rate (12% of all twins born in Pennsylvania from 1959-1978), there is no evidence that the sample differs in any meaningful respect from the larger population.

We describe subject recruitment from the University of Chile School-Age Children Twin Registry (REMEUCHI). The research aim of REMEUCHI is to quantify the impact of genetic and environmental factors on scholastic achievement in a multicausal approach. The Ministry of Education of Chile, in collaboration with the Registry Office, provided the list of possible twin pairs graduated from high school in 2004 in Chile's metropolitan region. From a population of 70,065 school-age children who had graduated from high school, 434 possible twin pairs were analyzed. Of these, 327 were twins reared together (75.3% of the 434 possible twins pairs) and born between 1986 and 1987 in Chile (mean age 18 years), and approximately 8% were not twins despite matches on full name and birth data. The rest of the possible twin pairs were probably twins reared apart, since one member of the pair had moved to study in another region of Chile. Zygosity was determined through questionnaires, maternal reports of twin similarities, and by the hospital records of the twins at the time of birth. Three hundred and twenty-seven pairs were identified, where monozygotic (MZ) and dizygotic (DZ) twins represented 46.8% and 53.2% of pairs, respectively, with a DZ/MZ ratio of 1.14. Considering same-sex MZ pairs, the percentage of female pairs was greater (55.6%) than male pairs (44.4%). When DZ pairs were analyzed, 47.7% were of opposite sex, 20.1% were male pairs and 32.2% female pairs. In Chile, these findings represent a baseline study to contribute to the establishment of a national twin registry in the future.

The University of Washington Twin Registry is a community-based registry of twins identified from the Washington State Department of Licensing. A fortuitous quirk in the Washington State drivers' license and identification number assignment, and collaborative Washington State laws, permitted us to build a statewide registry. Since obtaining approval from the Washington State Attorney General, the Washington State Department of Licensing has provided us with information on over 26,000 newly licensed twins, and we continue to receive computerized records on approximately 80 new twins per week. The University of Washington Twin Registry is assembled by mailing each twin a recruitment packet that includes an information sheet, brochure, brief survey, and nominal gift. Once both members of a twin pair have completed the packet, the pair is enrolled in the registry. As of June 2006, 2287 adult twin pairs have enrolled in the registry; about one half of these are female-female pairs. Among all twins, the average age is 32 years. Based on self-report, 52% of twins are monozygotic, 42% are dizygotic, and zygosity on 6% cannot yet be determined. We also have instituted a clinical protocol for collecting additional data and DNA from all twins participating in research studies requiring an in-person visit. The registry has established policies and procedures to protect the confidentiality of twin data and guidelines for the use of the registry by investigators. Plans for the further growth of the University of Washington Twin Registry and its use are discussed.