The aim of this study was to determine whether identity-by-descent (IBD) information for affected sib pairs (ASPs) can be used to select a sample of cases for a genetic case-control study which will provide more power for detecting association with loci in a known linkage region. By modeling the expected frequency of the disease allele in ASPs showing IBD sharing of 0, 1, or 2 alleles, and considering additive, recessive, and dominant disease models, we show that cases selected from IBD 2 families are best for this purpose, followed by those selected from IBD 1 families; least useful are cases selected from IBD 0 families.

Using behavioral genetic analyses, we investigated and present a possible relationship between adolescent alcohol use and six domains of common problem behaviors in a community-based sample of 633 twin pairs who were under the legal drinking age of 21 (mean age = 15.0 years). The underlying etiology of the six problem behavioral domains, classified as conduct problems, hyperactivity, school problems, low self-esteem, neuroticism, and social withdrawal, was previously described (Siewert et al., 2003) as two heritable and genetically distinct dimensions of problem behavior. We took the two best-fitting models from that study (one that proposed a generalized behavior problem factor along with an internalizing behavior factor, and one that proposed an externalizing behavior factor along with an internalizing behavior factor) and extended the analyses in this study to include an index of alcohol use. Our results suggest that there is a strong genetic relationship between adolescent alcohol use and a broad spectrum of both externalizing and internalizing behavioral problems. The individual who seems to be at risk for either generalized or specifically externalizing behavioral problems is also at risk for adolescent alcohol use. However, the individual who exhibits internalizing problem behaviors appears to be protected from adolescent alcohol use. We propose that adolescent alcohol consumption needs to be understood in the context of these genetically influenced externalizing and internalizing propensities.

Amulticategory item-response theory model was developed to characterize developmental changes in three items relating to the assessment of puberty in adolescent twin girls and boys. The model allowed for the fixed effects of age on probability of endorsing the responses and for the random effects of individual differences on the timing of pubertal changes relative to chronological age. In girls, the model was applied three-wave data on twin pairs (N = 414 female monozygotic [MZ] and 197 female dizygotic [DZ] pairs) and female twins from boy–girl pairs (N = 300 twins) from the Virginia Twin Study of Adolescent Behavioral Development. In boys, the data comprised 318 MZ and 185 DZ pairs and 297 male twins from boy–girl pairs. A total of 3172 and 2790 individual twin assessments were available in girls and boys, respectively, spanning ages 8–17 years. The availability of twin data allows the contributions of genes, the shared environment and individual unique environmental experiences to be resolved in the relative timing of pubertal changes. Parameters of the mixed model including fixed effects of age and random effects of genes and environment were estimated by Markov Chain Monte Carlo simulations using the BUGS algorithm for Gibbs sampling. The estimated standard deviation of random differences in the timing of puberty relative to age was 0.96 years in girls and 1.01 years in boys. The estimated intraclass correlations for the relative timing of pubertal changes were 0.99 +/−0.01 in MZ girls, 0.52 +/−0.02 in DZ girls, 0.88 +/−0.04 in MZ boys and 0.44+/−0.02 in DZ boys, indicating a very large contribution of genetic factors to the relative timing of pubertal change in both sexes. Additive genetic factors account for an estimated 96.3+/−3.3% of the total variance in random effects in girls and 88.0+/−3.6% in boys. Shared environmental influences account for 3.6+/−3.4% in girls and 0% in boys. In girls, nonshared environmental effects explain 0.1+/−0.1% of the total residual variance. The comparable figure in boys is 12.0+/−;3.6%.

We compared the accuracy of genotyping for DNA extracted from lymphocytes to that of DNA amplified from buccal epithelial cells. Amplification was via a rolling circle/φ29 DNA polymerase commercial kit. Paired buccal and lymphocyte DNA samples were available from 30 individuals. All samples were genotyped for 12 SNPs, 5 microsatellites and 2 VNTRs. The accuracy of genotyping (no-call proportions, reproducibility, and concordance) was similar for DNA from lymphocytes in comparison to amplified DNA from buccal samples. If used with caution, these data suggest that rolling-circle whole-genome amplification can be used to increase the DNA mass available for large-scale genotyping projects based on DNA from buccal cells.

The aim of the present study was to examine the contribution of genetic and environmental factors to depressive symptoms among older women. The participants were 102 monozygotic and 115 dizygotic female twin pairs aged 64 to 76 years. Depressive symptoms were assessed by the Center for the Epidemiologic Studies Depression Scale. The contribution of genetic and environmental effects was estimated for the constructed depressiveness factor and for the subscales which were depressed mood, psychomotor retardation, lack of wellbeing and interpersonal difficulties. Of the variance in depressiveness, shared environmental influences accounted for 39% and nonshared environmental influences 61%. For the subscales, 24% to 62% of the variance was explained by individual, and 13% to 23% by shared, environmental factors. Lack of well-being had its own moderate additive genetic effect explaining 30% of the variance. This study showed that in older women predominantly environmental factors underlay individual differences in depressiveness; however, the factors varied to some extent between dimensions measured by the subscales.

Nonresponse occurs when individuals either have no chance of being included in a study (noncoverage), refuse to take part (unit nonresponse), or fail to give complete information (item nonresponse). The purpose of this article is to test the possible biasing effects of nonresponse on the results of behavioral-genetic studies. Simulations and a real data ‘natural’ experiment were used to determine the impact of nonresponse on estimates of additive genetic and environmental effects. The simulations used realistic twin-pair correlations and models of nonresponse derived from prior research. The real data ‘natural experiment’ used data from a nationally representative birth-cohort twin study (E-Risk Study) and compared model results from families who had responded to a mail survey to those from all study cases. Results showed that the primary influence of nonresponse was to attenuate the effect of the shared environment and to inflate estimates of non-shared environment and additive genetic effects. At high levels of nonresponse a spurious nonadditive genetic effect (suggesting genetic dominance) was also found. Study nonresponse was shown to have the potential to bias the findings of behavioral-genetic research. Design and analysis methods that can be used to alleviate this potentially important biasing effect in behavioral-genetic studies are discussed in light of these findings.

The relative stability and magnitude of genetic and environmental effects underlying major dimensions of adolescent personality across time were investigated. The Junior Eysenck Personality Questionnaire was administered to over 540 twin pairs at ages 12, 14 and 16 years. Their personality scores were analyzed using genetic simplex modeling which explicitly took into account the longitudinal nature of the data. With the exception of the dimension lie, multivariate model fitting results revealed that familial aggregation was entirely explained by additive genetic effects. Results from simplex model fitting suggest that large proportions of the additive genetic variance observed at ages 14 and 16 years could be explained by genetic effects present at the age of 12 years. There was also evidence for smaller but significant genetic innovations at 14 and 16 years of age for male and female neuroticism, at 14 years for male extraversion, at 14 and 16 years for female psychoticism, and at 14 years for male psychoticism.

In humans, in contrast to animals, the genetic influences on infidelity are unclear. We report here a large study of over 1600 unselected United Kingdom female twin pairs who confidentially reported previous episodes of infidelity and total lifetime number of sexual partners, as well as attitudes towards infidelity. Our findings demonstrate that infidelity and number of sexual partners are both under moderate genetic influence (41% and 38% heritable, respectively) and the genetic correlation between these two traits is strong (47%). Conversely, attitudes towards infidelity are driven by shared and unique environmental, but not genetic, influences. A genome-wide linkage scan identified three suggestive but nonsignificant linkage areas associated with infidelity and number of sexual partners on chromosomes 3, 7 and 20 with a maximum LOD score of 2.46. We were unsuccessful in associating infidelity or number of sexual partners with a locus implicated in other mammals' sexual behavior, the vasopressin receptor gene. Nonetheless, our findings on the heritabil-ity of sexual infidelity and number of sexual partners provide support for certain evolutionary theories of human sexual behavior, as well as justifying further genetic and molecular research in this domain.

Statistical power of the classical twin design was revisited. The approximate sampling variances of a least-squares estimate of the heritability in a univariate analysis and estimate of the genetic correlation coefficient in a bivariate analysis were derived analytically for the ACE model. Statistical power to detect additive genetic variation under the ACE model was derived analytically for least-squares, goodness-of-fit and maximum likelihood-based test statistics. The noncentrality parameter for the likelihood ratio test statistic is shown to be a simple function of the MZ and DZ intraclass correlation coefficients and the proportion of MZ and DZ twin pairs in the sample. All theoretical results were validated using simulation. The derived expressions can be used to calculate power of the classical twin design in a simple and rapid manner.

Currently, mapping genes for complex human traits relies on two complementary approaches, linkage and association analyses. Both suffer from several methodological and theoretical limitations, which can considerably increase the type-1 error rate and reduce the power to map human quantitative trait loci (QTL). This review focuses on linkage methods for QTL mapping. It summarizes the most common linkage statistics used, namely Haseman–Elston-based methods, variance components, and statistics that condition on trait values. Methods developed more recently that accommodate the X-chromosome, parental imprinting and allelic association in linkage analysis are also summarized. The type-I error rate and power of these methods are discussed. Finally, rough guidelines are provided to help guide the choice of linkage statistics.

Genetic and environmental influences on variance in phenotypic traits may be estimated with normal theory Maximum Likelihood (ML). However, when the assumption of multivariate normality is not met, this method may result in biased parameter estimates and incorrect likelihood ratio tests. We simulated multivariate normal distributed twin data under the assumption of three different genetic models. Genetic model fitting was performed in six data sets: multivariate normal data, discrete uncensored data, censored data, square root transformed censored data, normal scores of censored data, and categorical data. Estimates were obtained with normal theory ML (data sets 1–5) and with categorical data analysis (data set 6). Statistical power was examined by fitting reduced models to the data. When fitting an ACE model to censored data, an unbiased estimate of the additive genetic effect was obtained. However, the common environmental effect was underestimated and the unique environmental effect was overestimated. Transformations did not remove this bias. When fitting an ADE model, the additive genetic effect was underestimated while the dominant and unique environmental effects were overestimated. In all models, the correct parameter estimates were recovered with categorical data analysis. However, with categorical data analysis, the statistical power decreased. The analysis of L-shaped distributed data with normal theory ML results in biased parameter estimates. Unbiased parameter estimates are obtained with categorical data analysis, but the power decreases.

It is shown that maximum likelihood estimation of variance components from twin data can be parameterized in the framework of linear mixed models. Standard statistical packages can be used to analyze univariate or multivariate data for simple models such as the ACE and CE models. Furthermore, specialized variance component estimation software that can handle pedigree data and user-defined covariance structures can be used to analyze multivariate data for simple and complex models, including those where dominance and/or QTL effects are fitted. The linear mixed model framework is particularly useful for analyzing multiple traits in extended (twin) families with a large number of random effects.

People are intensely interested in factors underlying earning power and what can be done to enhance it. A recent study using monozygotic male twins adds to knowledge in this area. Based on data from 136 twin pairs, the investigators favored a correlational hypothesis, namely that simply being married raises salaries by 19 to 26%. Following a review of this research, summaries of twin studies presented at the biennial meeting of the International Society for Human Ethology in Ghent, Belgium (July 27–30, 2004) and at the annual meeting of the Human Behavior and Evolution Society in Berlin, Germany (July 21–25, 2004) are presented. The final section of this article provides information of general interest to twin researchers, physicians and others working in twin-related fields.

An unusual case of uncertain twin paternity is presented. New research on inbred strains of mice may hold important clues for paternity testing of twins in the future. Next, four recent twin studies examining selected topics in autism, color perception, language development and high-order pregnancy risk are reviewed. Finally, several unusual twin-related situations are variously considered with respect to their research significance and practical applications.