Long-term memory (LTM) problems are associated with many psychiatric and neurological illnesses and are commonly measured using free and cued recall tasks. Although LTM has been linked with biologic mechanisms, the etiology of distinct LTM tasks is unknown. We studied LTM in 95 healthy female twin pairs identified through birth records in the state of Missouri. Performance on tasks of free recall of unrelated words, free and cued recall of categorized words, and the vocabulary section of the Wechsler Adult Intelligence Scale (WAIS-R) were examined using structural equation modeling. Additive genetic and unique environmental factors influenced LTM and intelligence. Free recall of unrelated and categorized words, and cued recall of categorized words, were moderately heritable (55%, 38%, and 37%). WAIS-R vocabulary score was highly heritable (77%). Controlling for verbal intelligence in multivariate analyses of recall, two components of genetic influence on LTM were found; one for all three recall scores and one for free and cued categorized word recall. Recall of unrelated and categorized words is influenced by different genetic and environmental factors indicating heterogeneity in LTM. Verbal intelligence is etiologically different from LTM indicating that these two abilities utilize different brain functions.

Incomplete data on trait values may bias estimates of genetic and environmental variance components obtained from twin analyses. If the nonresponse mechanism is ‘ignorable’ then methods such as full information maximum likelihood estimation will produce consistent variance component estimates. If, however, nonresponse is ‘nonignorable’, then the situation is more complicated. We demonstrate that a within-pair correlation of nonresponse, possibly different for monozygotic (MZ) and dizygotic (DZ) twins, may well be compatible with ‘ignorability’. By means of Monte Carlo simulation, we assess the potential bias in variance component estimates for different types of nonresponse mechanisms. The simulation results guide the interpretation of analyses of data on perceptual speed from the Swedish Adoption/Twin Study of Aging. The results suggest that the dramatic decrease in genetic influences on perceptual speed observed after 13 years of follow-up is not attributable solely to dropout from the study, and thus support the hypothesis that genetic influences on some cognitive abilities decrease with age in late life.

Recent studies have claimed to detect interaction between candidate genes and specific environmental factors (Genotype × Environment interaction, G × E) in susceptibility to psychiatric disorder. The objective of the present study was to examine possible artifacts that could explain widely publicized findings. The additive effects of candidate genes and measured environment on liability to disorder were simulated under a model that allowed for mixture of distributions in liability conditional on genotype and environment. Simulated liabilities were dichotomized at a threshold value to reflect diagnosis of disorder. Multiple blocks of simulated data were analyzed by standard statistical methods to test for the main effects and interactions of genes and environment on outcome. The main outcome of this study was simulated liabilities and diagnoses of major depression and antisocial behavior. Analysis of the dichotomized data by logistic regression frequently detected significant G × E interaction even though none was present for liability. There is therefore reason to question the biological significance of published findings.

Sex-limited expression of genetic or environmental factors occurs in two basic forms. First, the effects of a factor may be larger on one sex than on another, which is known as scalar sex limitation. Second, some factors may have an effect on one sex but not on the other, which is called nonscalar sex limitation. In the classical twin study, scalar sex-limited effects cause same-sex male and same-sex female twin correlations to differ. Nonscalar sex-limited effects would cause the correlations between opposite-sex pairs of relatives to be lower than would be expected from the correlations between relatives of the same sex. One approach to modeling such effects is to allow the genetic correlation between opposite-sex dizygotic twins to be less than one-half; another is to allow the common environment correlation for opposite-sex pairs to be less than unity. Extension of this approach to the multivariate case is not straightforward. Direct extension of the Cholesky decomposition such that each Cholesky factor is allowed to correlate less than one-half in opposite-sex pairs yields a model where the order of the variables can change the goodness-of-fit of the model. It is shown that similar problems exist with a variety of multivariate and longitudinal models, and in a variety of models of genotype × environment interaction. Several solutions to these problems are described.

This article discusses new latent variable techniques developed by the authors. As an illustration, a new factor mixture model is applied to the monozygotic–dizygotic twin analysis of binary items measuring alcohol-use disorder. In this model, heritability is simultaneously studied with respect to latent class membership and within-class severity dimensions. Different latent classes of individuals are allowed to have different heritability for the severity dimensions. The factor mixture approach appears to have great potential for the genetic analyses of heterogeneous populations. Generalizations for longitudinal data are also outlined.

When using maximum likelihood methods to estimate genetic and environmental components of (co)variance, it is common to test hypotheses using likelihood ratio tests, since such tests have desirable asymptotic properties. In particular, the standard likelihood ratio test statistic is assumed asymptotically to follow a χ2 distribution with degrees of freedom equal to the number of parameters tested. Using the relationship between least squares and maximum likelihood estimators for balanced designs, it is shown why the asymptotic distribution of the likelihood ratio test for variance components does not follow a χ2 distribution with degrees of freedom equal to the number of parameters tested when the null hypothesis is true. Instead, the distribution of the likelihood ratio test is a mixture of χ2 distributions with different degrees of freedom. Implications for testing variance components in twin designs and for quantitative trait loci mapping are discussed. The appropriate distribution of the likelihood ratio test statistic should be used in hypothesis testing and model selection.

The Australian Twin Registry (ATR) has, since the late 1970s, enrolled more than 30,000 pairs of all zygosity types and ages willing to consider participation in approved research studies. Its core functions are the recruitment to, and maintenance of, an up-to-date database containing contact details and baseline information, and the management of fair and equitable access so as to enhance medical and scientific research. The ATR has facilitated more than 430 studies producing 525 peer-reviewed publications using a variety of designs including classic biometrical twin and twin family studies, co-twin control studies, intervention studies, longitudinal studies, and studies of issues relevant specifically to twins. The ATR is supported for 2004 to 2009 by an Australian National Health and Medical Research Council (NHMRC) Enabling Grant, a new form of funding which recognizes the importance of long-term support for shared national resources. New initiatives include: integration with the Western Australian Twin Child Health (WATCH) cohort and the new Western Australian Twin Registry (WATR); foundation of a cohort of mothers and their twin children recruited from the time of diagnosis of the multiple gestation (match); a national Twins Festival run in collaboration with the Australian Multiple Birth Association (AMBA); promotion of the ATR at medical conferences; and fostering an active network of researchers from a range of disciplines and providing financial support for new researchers to attend international twin research workshops. Consistent with its mission statement, the long-term goal of the ATR is to make twin studies a standard component of medical and scientific research.

Accurate determination of same-sex twin zygosity is important for medical, scientific and personal reasons. Determination may be based upon questionnaire data, blood group, enzyme isoforms and fetal membrane examination, but assignment of zygosity must ultimately be confirmed by genotypic data. Here methods are reviewed for calculating average probabilities of correctly concluding a twin pair is monozygotic, given they share the same genotypes across all loci for commonly utilized multiplex short tandem repeat (STR) kits. Numerous tools enabling convenient and accurate zygosity probability calculation may be accessed via the ZygProb homepage at http://genepi.qimr.edu.au/general/daleN/ZygProb/

Linkage analysis (either parametric or nonparametric) is commonly applied to identify chromosomal regions using related individuals affected by disease. In complex disease the incomplete relationship between phenotype and genotype can be modeled using a phenocopy parameter, the probability that an individual is affected given they do not carry the disease mutation of interest, and a nonpenetrance parameter, the probability that an individual is not affected given they do carry the disease mutation of interest. If the linkage phase between multiple markers and a putative disease locus is known, then haplotypes carrying the mutation can, in principle, be identified by comparing the chromosome segments that are shared identical-by-descent (IBD) across affected individuals. We consider here the effect of a nonzero phenocopy rate on the linkage peak and hence upon the identification of disease haplotypes that are shared IBD between affected individuals. We show, by theory and computer simulation, that in diseases for which there is a nonzero phenocopy rate, the chromosomal regions identified may not include the true disease locus. We utilize a LOD-1 confidence interval for a widely used nonparametric linkage statistic. We find that in small/moderate samples this confidence interval may be inappropriate. We give specific examples where the phenocopy rates are nonnegligible in some complex diseases. The success of further work to identify the causal mutations underlying the linkage peaks in these diseases will depend on researchers allowing for the presence of phenocopies by examining appropriately wide regions around the initial positive linkage finding.

Diagnosis of a major depressive episode by the Diagnostic and Statistical Manual of Mental Disorders of the American Psychiatric Association requires 5 out of 9 symptoms to be present. Therefore, individuals may differ in the specific symptoms they experience and reach a diagnosis of depression via different pathways. It has been suggested that depressed women more often report symptoms of sleep disturbance, appetite or weight disturbance, fatigue, feelings of guilt/worthlessness and psychomotor retardation than depressed men. In the current study, we investigate whether depressed men and women differ in the symptoms they report. Two samples were selected from a sample of Dutch and Australian twins and siblings. First, Dutch and Australian unrelated depressed individuals were selected. Second, a matched epidemiological sample was created consisting of opposite-sex twin and sibling pairs in which both members were depressed. No sex differences in prevalence rates for symptoms were found, with the exception of decreased weight in women in the sample of unrelated individuals. In general, the similarities in symptoms seem to far outweigh the differences in symptoms between men and women. This signifies that men and women are alike in their symptom profiles for major depression and genes for depression are probably expressed in the same way in the two sexes.

We investigated the extent to which cognitive dysfunction is shaped by genetic or environmental influences, and whether these factors differ in women and men. All members of the Swedish Twin Registry aged 65 and older were screened by telephone using the TELE, a brief cognitive assessment instrument (Gatz et al., 2002), and the Blessed Dementia Rating Scale (Blessed et al., 1968) from relatives of those who scored poorly on the TELE. Data were available for 4308 pairs where both members responded and 5070 pairs where only one member was alive and participated. To analyze all available data, we used a raw data method extended to ordinal data. As the prevalence of cognitive dysfunction increases with age, we incorporated age-adjusted thresholds. The best fitting model from biometric analyses indicated 35% of the variation in liability to cognitive dysfunction could be explained by heritable influences and the remaining 65% by nonfamilial environmental influences. Differences by gender were not significant. As this is a normative population including cognitively intact individuals, preclinical dementia cases and demented individuals, the relative magnitude of genetic and environmental effects is of particular interest in light of high heritabilities found for dementias such as Alzheimer's disease. The findings emphasize the extent to which research is needed to uncover nonfamilial environmental influences on cognitive dysfunction in later life.

A transmission disequilibrium test for quantitative traits which combines association and linkage analyses is currently available in several dedicated software packages. We describe how to implement such models in linear mixed model procedures that are available in widely used statistical packages such as SPSS. We also briefly mention a few extensions of the model that become naturally available once the model is implemented in such procedures.

Hostility has been shown to be a vulnerability marker for various health problems. The present study examined genetic and environmental contributions to individual differences in hostility in South Korean adolescent and young adult twins. Seven hundred and nineteen same- and opposite-sex twin pairs aged from 13 to 23 years completed a hostility scale. The scalar sex-limitation model was applied to the data. The best fitting model indicated that 34% of the total variation of hostility was attributable to genetic factors operating in a nonadditive manner. The remaining 66% of the variance was associated with nonshared environmental influences and measurement error. These findings were largely consistent with results from previous twin studies of personality based on Caucasian twins, rendering support for the pervasive influence of genetic non-additivity on human personality traits and the generalization of the heritability of personality across human populations.

The basis of most twin studies is the ascertainment of twins, often through twin registries, and determination of zygosity. The current rate of twin births in many industrialized countries implies that in the near future around 3% or more of individuals will be a twin. Hence, there are and will be a lot of twins around and many of those will not participate in twin studies. However, if large population-based samples are available that include appropriate identifiers, then twins can be detected and twin studies performed, even in the absence of zygosity information. We quantified the number of twin pairs that could be detected from a longitudinal survey in the Netherlands, which aims to answer questions about educational strategies and performance in primary education in the Netherlands. We detected 2865 twin pairs if we used a coded name identifier, date of birth, school, grade and year of survey, which is 2.01% of 284,945 pupils in five cohorts. Relaxing our selection criteria increased the number of apparent twin pairs identified, most of which are false positives due to chance matching of identification criteria. We show that the intraclass correlation on measured phenotypes can be used as a quality control measure for twin identification, and quantify the proportion of false negatives (true twin pairs not identified) due to missing data and data coding errors. We compared our estimated rate of twins in the sample to census data and estimate that with our most stringent selection criteria we detect more than 80% of all twin pairs in the sample. We conclude that the identification of twin pairs from large population-based samples is feasible, rapid and accurate if the appropriate identifiers are available, and that twin pairs from such sources are a valuable resource for studies to answer scientific question about twins versus nontwins and about genetic and environmental factors of twin resemblance.

The Western Australian Twin Register (WATR) was established in 1997 to study the health of all child multiples born in Western Australia (WA). The Register has until recently consisted of all multiples born in WA between 1980 and 1997. Using unique record linkage capacities available through the WA data linkage system, we have subsequently been able to identify all multiple births born in WA since 1974. New affiliations with the Australian Twin Registry and the WA Institute for Medical Research are further enabled by the use of the WA Genetic Epidemiology Resource — a high-end bioinformatics infrastructure that allows efficient management of health datasets and facilitates collaborative research capabilities. In addition to this infrastructure, funding provided by these institutions has allowed the extension of the WATR to include a greater number of WA multiples, including those born between 1974 and 1979, and from 1998 onwards. These resources are in the process of being enabled for national and international access.

Experimental studies have suggested biological factors as a possible explanation for gender disparities in perception of pain. Recently, heritability of liability to neck pain (NP) has been found to be statistically significantly larger in women compared to men. However, no studies have been conducted to determine whether the sex differences in heritability of NP are due to sex-specific genetic factors. Data on lifetime prevalence of NP from a populationbased cross-sectional survey of 33,794 Danish twins were collected and age-stratified univariate biometrical modeling using sex-limitation models was performed based on 10,605 dizygotic (DZ) twins of opposite sex to estimate the qualitative sex differences. In a full sex-limitation model the genetic component in females were higher than in males, but the genetic and the shared environmental correlations were equal to what is normally assumed between same-sex DZ twins. A ‘no-sex-effects’ model showed the overall best model fit which confirms absence of sex-related gene interaction. The age-stratified sex-limitation models showed similar results. Thus, there is no evidence for a sex-specific genetic influence in the liability of heritability of NP.

The objective of this study was to estimate the magnitude of genetic and environmental influences to variation in adolescent neuroticism as a function of age and sex. Neuroticism was assessed using the Amsterdamse Biografische Vragenlijst (ABV): a self-report personality instrument similar in content to the Eysenck Personality Questionnaire. Genetic modeling procedures, including age as modifier, were fitted to the total sample of 3301 Dutch adolescent twins aged 12 to 17 years (mean age 15.5). Significant influences of additive genetic factors (.59, 95% confidence intervals [CI] .54–.63) and unshared environmental factors (.41, 95% CI .37–.45) were found. Our data did not support a role of shared environment. Results showed that different genes may influence variation in neuroticism between girls and boys. No interaction was found between the variance components and age. Results generally support prior findings in adults and young children that neuroticism is influenced principally by additive genetic and unique environmental factors. The magnitude of the genetic component appears higher in the present sample of adolescents than in most studies of adults. The present study suggests that, in adolescence, different genes are expressed in boys and girls.

Maximum likelihood estimation techniques are widely used in twin and family studies, but soon reach computational boundaries when applied to highly complex models (e.g., models including gene-by-environment interaction and gene–environment correlation, item response theory measurement models, repeated measures, longitudinal structures, extended pedigrees). Markov Chain Monte Carlo (MCMC) algorithms are very well suited to fit complex models with hierarchically structured data. This article introduces the key concepts of Bayesian inference and MCMC parameter estimation and provides a number of scripts describing relatively simple models to be estimated by the freely obtainable BUGS software. In addition, inference using BUGS is illustrated using a data set on follicle-stimulating hormone and luteinizing hormone levels with repeated measures. The examples provided can serve as stepping stones for more complicated models, tailored to the specific needs of the individual researcher.