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Illicit substance use is dangerous in both acute and chronic forms, frequently resulting in lethal poisoning, addiction, and other negative consequences. Similar to research in other psychiatric conditions, whose ultimate goal is to enable effective prevention and treatment, studies in substance use are focused on factors elevating the risk for the disorder. The rapid growth of the substance use problem despite the effort invested in fighting it, however, suggests the need in changing the research approach. Instead of attempting to identify risk factors, whose neutralization is often infeasible if not impossible, it may be more promising to systematically reverse the perspective to the factors enhancing the aspect of liability to disorder that shares the same dimension but is opposite to risk, that is, resistance to substance use. Resistance factors, which enable the majority of the population to remain unaffected despite the ubiquity of psychoactive substances, may be more amenable to translation. While the resistance aspect of liability is symmetric to risk, the resistance approach requires substantial changes in sampling (high-resistance rather than high-risk) and using quantitative indices of liability. This article provides an overview and a practical approach to research in resistance to substance use/addiction, currently implemented in a NIH-funded project. The project benefits from unique opportunities afforded by the data originating from two longitudinal twin studies, the Virginia Twin Study of Adolescent and Behavioral Development and the Minnesota Twin Family Study. The methodology described is also applicable to other psychiatric disorders.
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.
Structural equation modeling (SEM) is an important research tool, both for path-based model specification (common in the social sciences) and also for matrix-based models (in heavy use in behavior genetics). We developed umx to give more immediate access, relatively concise syntax and helpful defaults for users in these two broad disciplines. umx supports development, modification and comparison of models, as well as both graphical and tabular outputs. The second major focus of umx, behavior genetic models, is supported via functions implementing standard multigroup twin models. These functions support raw and covariance data, including joint ordinal data, and give solutions for ACE models, including support for covariates, common- and independent-pathway models, and gene × environment interaction models. A tutorial site and question forum are also available.
Despite established clinical associations among major depression (MD), alcohol dependence (AD), and alcohol consumption (AC), the nature of the causal relationship between them is not completely understood. We leveraged genome-wide data from the Psychiatric Genomics Consortium (PGC) and UK Biobank to test for the presence of shared genetic mechanisms and causal relationships among MD, AD, and AC.
Linkage disequilibrium score regression and Mendelian randomization (MR) were performed using genome-wide data from the PGC (MD: 135 458 cases and 344 901 controls; AD: 10 206 cases and 28 480 controls) and UK Biobank (AC-frequency: 438 308 individuals; AC-quantity: 307 098 individuals).
Positive genetic correlation was observed between MD and AD (rgMD−AD = + 0.47, P = 6.6 × 10−10). AC-quantity showed positive genetic correlation with both AD (rgAD−AC quantity = + 0.75, P = 1.8 × 10−14) and MD (rgMD−AC quantity = + 0.14, P = 2.9 × 10−7), while there was negative correlation of AC-frequency with MD (rgMD−AC frequency = −0.17, P = 1.5 × 10−10) and a non-significant result with AD. MR analyses confirmed the presence of pleiotropy among these four traits. However, the MD-AD results reflect a mediated-pleiotropy mechanism (i.e. causal relationship) with an effect of MD on AD (beta = 0.28, P = 1.29 × 10−6). There was no evidence for reverse causation.
This study supports a causal role for genetic liability of MD on AD based on genetic datasets including thousands of individuals. Understanding mechanisms underlying MD-AD comorbidity addresses important public health concerns and has the potential to facilitate prevention and intervention efforts.
Background: Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent–offspring transmission and their contribution to the variability explained by genetic and/or environmental factors. Methods: We examined the role of genetic and environmental factors in individual differences for smoking initiation (SI) using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission, while also estimating the regression of the prevalence of SI on age. A dichotomous lifetime ‘ever’ smoking measure was obtained from twins and relatives in the ‘Virginia 30,000’ sample and the ‘Australian 25,000’. Results: Results demonstrate that both genetic and environmental factors play a significant role in the liability to SI. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission, and resulting genotype-environment covariance. Age regression of the prevalence of SI was significant. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent–offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (1) age × gene interaction, and (2) social homogamy. Neither of the mechanism provided a significantly better explanation of the data. Conclusions: This study showed significant heritability, partly due to assortment, and significant effects of primarily non-parental shared environment on liability to SI.
Drinking alcohol is a normal behavior in many societies, and prior studies have demonstrated it has both genetic and environmental sources of variation. Using two very large samples of twins and their first-degree relatives (Australia ≈ 20,000 individuals from 8,019 families; Virginia ≈ 23,000 from 6,042 families), we examine whether there are differences: (1) in the genetic and environmental factors that influence four interrelated drinking behaviors (quantity, frequency, age of initiation, and number of drinks in the last week), (2) between the twin-only design and the extended twin design, and (3) the Australian and Virginia samples. We find that while drinking behaviors are interrelated, there are substantial differences in the genetic and environmental architectures across phenotypes. Specifically, drinking quantity, frequency, and number of drinks in the past week have large broad genetic variance components, and smaller but significant environmental variance components, while age of onset is driven exclusively by environmental factors. Further, the twin-only design and the extended twin design come to similar conclusions regarding broad-sense heritability and environmental transmission, but the extended twin models provide a more nuanced perspective. Finally, we find a high level of similarity between the Australian and Virginian samples, especially for the genetic factors. The observed differences, when present, tend to be at the environmental level. Implications for the extended twin model and future directions are discussed.
Whether monozygotic (MZ) and dizygotic (DZ) twins differ from each other in a variety of phenotypes is important for genetic twin modeling and for inferences made from twin studies in general. We analyzed whether there were differences in individual, maternal and paternal education between MZ and DZ twins in a large pooled dataset. Information was gathered on individual education for 218,362 adult twins from 27 twin cohorts (53% females; 39% MZ twins), and on maternal and paternal education for 147,315 and 143,056 twins respectively, from 28 twin cohorts (52% females; 38% MZ twins). Together, we had information on individual or parental education from 42 twin cohorts representing 19 countries. The original education classifications were transformed to education years and analyzed using linear regression models. Overall, MZ males had 0.26 (95% CI [0.21, 0.31]) years and MZ females 0.17 (95% CI [0.12, 0.21]) years longer education than DZ twins. The zygosity difference became smaller in more recent birth cohorts for both males and females. Parental education was somewhat longer for fathers of DZ twins in cohorts born in 1990–1999 (0.16 years, 95% CI [0.08, 0.25]) and 2000 or later (0.11 years, 95% CI [0.00, 0.22]), compared with fathers of MZ twins. The results show that the years of both individual and parental education are largely similar in MZ and DZ twins. We suggest that the socio-economic differences between MZ and DZ twins are so small that inferences based upon genetic modeling of twin data are not affected.
We introduce the optimizer CSOLNP, which is a C++ implementation of the R package RSOLNP (Ghalanos & Theussl, 2012, Rsolnp: General non-linear optimization using augmented Lagrange multiplier method. R package version, 1) alongside some improvements. CSOLNP solves non-linearly constrained optimization problems using a Sequential Quadratic Programming (SQP) algorithm. CSOLNP, NPSOL (a very popular implementation of SQP method in FORTRAN (Gill et al., 1986, User's guide for NPSOL (version 4.0): A Fortran package for nonlinear programming (No. SOL-86-2). Stanford, CA: Stanford University Systems Optimization Laboratory), and SLSQP (another SQP implementation available as part of the NLOPT collection (Johnson, 2014, The NLopt nonlinear-optimization package. Retrieved from http://ab-initio.mit.edu/nlopt)) are three optimizers available in OpenMx package. These optimizers are compared in terms of runtimes, final objective values, and memory consumption. A Monte Carlo analysis of the performance of the optimizers was performed on ordinal and continuous models with five variables and one or two factors. While the relative difference between the objective values is less than 0.5%, CSOLNP is in general faster than NPSOL and SLSQP for ordinal analysis. As for continuous data, none of the optimizers performs consistently faster than the others. In terms of memory usage, we used Valgrind's heap profiler tool, called Massif, on one-factor threshold models. CSOLNP and NPSOL consume the same amount of memory, while SLSQP uses 71 MB more memory than the other two optimizers.
Background: Shared experiences within families play an important role in the initiation of cigarette use among adolescents. Behavioral genetic studies using various samples have implicated that the shared environment that twins experience is an important source of influence on whether adolescents initiate cigarette use. Whether the special twin environment, in addition to the shared environment, contributes significantly to making twin siblings more similar in cigarette initiation, and whether the influence of the special twin environment persists into adulthood, is less clear. Methods: Data for this study came from the National Longitudinal Survey of Adolescent Health. Twin, full-, and half-sibling pairs between the ages of 12 and 33 were separated into three age groups, with about 3,000 individuals in each age group. The proportion of variance in cigarette use initiation explained by genetic, shared, special twin, and unique environmental factors were examined. Results: The results of separate age-moderated univariate variance decomposition models indicate that the special twin environment does not significantly contribute to the variance in cigarette use initiation in adolescence or young adulthood. Conclusion: Factors shared by individuals in a family, but that are not specific to being a twin, are important in determining whether adolescents will initiate the use of cigarettes.
The relationship between the genetic and environmental risk factors for alcohol use disorders (AUD) detected in Swedish medical, pharmacy, and criminal registries has not been hitherto examined. Prior twin studies have varied with regard to the detection of shared environmental effects and sex differences in the etiology of AUD. In this report, structural equation modeling in OpenMx was applied to (1) the three types of alcohol registration in a population-based sample of male–male twins and reared-together full and half siblings (total 208,810 pairs), and (2) AUD, as a single diagnosis, in male–male, female–female, and opposite-sex (OS) twins and reared-together full and half siblings (total 787,916 pairs). An independent pathway model fit best to the three forms of registration and indicated that between 70% and 92% of the genetic and 63% and 98% of the shared environmental effects were shared in common with the remainder unique to each form of AUD registration. Criminal registration had the largest proportion of unique genetic and environmental factors. The best fit model for AUD estimated the heritability to be 22% and 57%, respectively, in females and males. Both shared (12% vs. 6%) and special twin environment (29% vs. 2%) were substantially more important in females versus males. In conclusion, AUD ascertained from medical, pharmacy, and criminal Swedish registries largely share the same genetic and environmental risk factors. Large sex differences in the etiology of AUD were seen in this sample, with substantially stronger familial environmental and weaker genetic effects in females versus males.
A trend toward greater body size in dizygotic (DZ) than in monozygotic (MZ) twins has been suggested by some but not all studies, and this difference may also vary by age. We analyzed zygosity differences in mean values and variances of height and body mass index (BMI) among male and female twins from infancy to old age. Data were derived from an international database of 54 twin cohorts participating in the COllaborative project of Development of Anthropometrical measures in Twins (CODATwins), and included 842,951 height and BMI measurements from twins aged 1 to 102 years. The results showed that DZ twins were consistently taller than MZ twins, with differences of up to 2.0 cm in childhood and adolescence and up to 0.9 cm in adulthood. Similarly, a greater mean BMI of up to 0.3 kg/m2 in childhood and adolescence and up to 0.2 kg/m2 in adulthood was observed in DZ twins, although the pattern was less consistent. DZ twins presented up to 1.7% greater height and 1.9% greater BMI than MZ twins; these percentage differences were largest in middle and late childhood and decreased with age in both sexes. The variance of height was similar in MZ and DZ twins at most ages. In contrast, the variance of BMI was significantly higher in DZ than in MZ twins, particularly in childhood. In conclusion, DZ twins were generally taller and had greater BMI than MZ twins, but the differences decreased with age in both sexes.
Background: Few studies examining the genetic architecture of cigarette smoking have focused on adolescents or examined developmental changes in additive genetic, shared environment, and unique environmental influences on liability to initiate cigarette smoking and quantity of cigarettes smoked. The aim of this study was to add to the literature on liability to initiate and use cigarettes during adolescence using a nationally representative sample. Method: Data for this study came from adolescent and young adult twin pairs (aged 14–33 years) from the National Longitudinal Study of Adolescent to Adult Health. We ran a series of developmental causal–contingent–common pathway models to examine whether additive genetic, shared, and unique environmental influences on liability to the initiation of cigarette use are shared with those on smoking quantity, and whether their contributions change across development. Results: We found evidence for a developmental shift in genetic and shared environmental contributions to cigarette use. Early in adolescence, genetic and environmental influences work independently on liability to cigarette smoking initiation and quantity of cigarettes smoked, but liability to these behaviors becomes correlated as individuals age into young adulthood. Conclusions: These findings provide insight into the causal processes underlying the liability to smoke cigarettes. With age, there is greater overlap in the genetic and environmental factors that influence the initiation of cigarette smoking and quantity of cigarettes smoked.
The public health burden of alcohol is unevenly distributed across the life course, with levels of use, abuse, and dependence increasing across adolescence and peaking in early adulthood. Here, we leverage this temporal patterning to search for common genetic variants predicting developmental trajectories of alcohol consumption. Comparable psychiatric evaluations measuring alcohol consumption were collected in three longitudinal community samples (N = 2,126, obs = 12,166). Consumption-repeated measurements spanning adolescence and early adulthood were analyzed using linear mixed models, estimating individual consumption trajectories, which were then tested for association with Illumina 660W-Quad genotype data (866,099 SNPs after imputation and QC). Association results were combined across samples using standard meta-analysis methods. Four meta-analysis associations satisfied our pre-determined genome-wide significance criterion (FDR < 0.1) and six others met our ‘suggestive’ criterion (FDR <0.2). Genome-wide significant associations were highly biological plausible, including associations within GABA transporter 1, SLC6A1 (solute carrier family 6, member 1), and exonic hits in LOC100129340 (mitofusin-1-like). Pathway analyses elaborated single marker results, indicating significant enriched associations to intuitive biological mechanisms, including neurotransmission, xenobiotic pharmacodynamics, and nuclear hormone receptors (NHR). These findings underscore the value of combining longitudinal behavioral data and genome-wide genotype information in order to study developmental patterns and improve statistical power in genomic studies.
For over 100 years, the genetics of human anthropometric traits has attracted scientific interest. In particular, height and body mass index (BMI, calculated as kg/m2) have been under intensive genetic research. However, it is still largely unknown whether and how heritability estimates vary between human populations. Opportunities to address this question have increased recently because of the establishment of many new twin cohorts and the increasing accumulation of data in established twin cohorts. We started a new research project to analyze systematically (1) the variation of heritability estimates of height, BMI and their trajectories over the life course between birth cohorts, ethnicities and countries, and (2) to study the effects of birth-related factors, education and smoking on these anthropometric traits and whether these effects vary between twin cohorts. We identified 67 twin projects, including both monozygotic (MZ) and dizygotic (DZ) twins, using various sources. We asked for individual level data on height and weight including repeated measurements, birth related traits, background variables, education and smoking. By the end of 2014, 48 projects participated. Together, we have 893,458 height and weight measures (52% females) from 434,723 twin individuals, including 201,192 complete twin pairs (40% monozygotic, 40% same-sex dizygotic and 20% opposite-sex dizygotic) representing 22 countries. This project demonstrates that large-scale international twin studies are feasible and can promote the use of existing data for novel research purposes.
Background: Prior twin and adoption studies have demonstrated the importance of both genetic and shared environmental factors in the etiology of criminal behavior (CB). However, despite substantial interest in life-course theories of CB, few genetically informative studies have examined CB in a developmental context. Method: In 69,767 male–male twin pairs and full-sibling pairs with ≤ 2 years’ difference in age, born 1958–1976 and ascertained from the Swedish Twin and Population Registries, we obtained information on all criminal convictions from 1973 to 2011 from the Swedish Crime Register. We fitted a Cholesky structural model, using the OpenMx package, to CB in these pairs over three age periods: 15–19, 20–24, and 25–29. Results: The Cholesky model had two main genetic factors. The first began at ages 15–19 and declined in importance over development. The second started at ages 20–24 and was stable over time. Only one major shared environmental factor was seen, beginning at ages 15–19. Heritability for CB declined from ages 15–29, as did shared environmental effects, although at a slower rate. Conclusions: Genetic risk factors for CB in males are developmentally dynamic, demonstrating both innovation and attenuation. These results are consistent with theories of adolescent-limited and life-course persistent CB subtypes. Heritability for CB did not increase over time as might be predicted from active gene-environmental correlation. However, consistent with expectation, the proportion of variability explained by shared environmental effects declined slightly as individuals aged and moved away from their original homes and neighborhoods.
Little is known regarding the underlying relationship between smoking initiation and current quantity smoked during adolescence into young adulthood. It is possible that the influences of genetic and environmental factors on this relationship vary across sex and age. To investigate this further, the current study applied a common causal contingency model to data from a Virginia-based twin study to determine: (1) if the same genetic and environmental factors are contributing to smoking initiation and current quantity smoked; (2) whether the magnitude of genetic and environmental factor contributions are the same across adolescence and young adulthood; and (3) if qualitative and quantitative differences in the sources of variance between males and females exist. Study results found no qualitative or quantitative sex differences in the relationship between smoking initiation and current quantity smoked, though relative contributions of genetic and environmental factors changed across adolescence and young adulthood. More specifically, smoking initiation and current quantity smoked remain separate constructs until young adulthood, when liabilities are correlated. Smoking initiation is explained by genetic, shared, and unique environmental factors in early adolescence and by genetic and unique environmental factors in young adulthood; while current quantity smoked is explained by shared environmental and unique environmental factors until young adulthood, when genetic and unique environmental factors play a larger role.
Aim: In this study, we introduce the first twin study in Turkey, focusing on smoking behavior, and laying the foundation to register all twins born in Turkey for research purposes. Using Turkish twins will contribute to our understanding of health problems in the context of cultural differences. Materials and methods: We assessed 309 twin pairs (339 males and 279 females) aged between 15 and 45 years living in the Kırıkkale and Ankara regions of Turkey, and administered a health and lifestyle interview that included questions about smoking status and smoking history. We analyzed the data using descriptive statistics, t-tests, chi-square tests, and bivariate and multivariate clustered logistic regression. In addition, we fit bivariate Structural Equation Models (SEM) to determine contributions of latent genetic and environmental factors to smoking outcomes in this sample. Results: One hundred seventy-eight participants (28.8%) were identified as smokers, smoking every day for a month or longer, of whom 79.2% were males and 20.8% were females. Mean values for number of cigarettes per day and the Fagerstrom Test of Nicotine Dependence (FTND; Fagerstrom, 1978) score were higher in males than in females, and age of onset was earlier in males. There was a significant positive correlation between the FTND score and number of cigarettes smoked per day, and a significant negative correlation between both variables and age at onset of smoking. Our study showed that gender, presence of a smoking twin in the family, age, alcohol use, marital status, daily sports activities, and feeling moody all played a significant role in smoking behavior among twins. The twin analysis suggested that 79.5% of the liability to FTND was influenced by genetic factors and 20.5% by unique environment, while familial resemblance for smoking initiation was best explained by common environmental factors. Conclusions: Marked differences in the prevalence of smoking behavior in men versus women were observed for the Turkish population. Genetic analyses showed that common environmental factors primarily contributed to smoking initiation, while genetic factors explained a greater proportion of variance in liability to nicotine dependence. Our study shows higher heritability estimate of the FTND scores and higher shared environmental influence on smoking initiation for both males and females than reported in previous studies.
The importance of including developmental and environmental measures in genetic studies of human pathology is widely acknowledged, but few empirical studies have been published. Barriers include the need for longitudinal studies that cover relevant developmental stages and for samples large enough to deal with the challenge of testing gene–environment–development interaction. A solution to some of these problems is to bring together existing data sets that have the necessary characteristics. As part of the National Institute on Drug Abuse-funded Gene-Environment-Development Initiative, our goal is to identify exactly which genes, which environments, and which developmental transitions together predict the development of drug use and misuse. Four data sets were used of which common characteristics include (1) general population samples, including males and females; (2) repeated measures across adolescence and young adulthood; (3) assessment of nicotine, alcohol, and cannabis use and addiction; (4) measures of family and environmental risk; and (5) consent for genotyping DNA from blood or saliva. After quality controls, 2,962 individuals provided over 15,000 total observations. In the first gene–environment analyses, of alcohol misuse and stressful life events, some significant gene–environment and gene–development effects were identified. We conclude that in some circumstances, already collected data sets can be combined for gene–environment and gene–development analyses. This greatly reduces the cost and time needed for this type of research. However, care must be taken to ensure careful matching across studies and variables.
Political sophistication is a concept that encompasses political reasoning, the coherence of people's issue attitudes, and their knowledge of political processes. To what extent is political sophistication affected by genes and environments? Do these distinct but related measures of sophistication share a common genetic structure? We analyze survey data collected from participants in the Minnesota Twin Registry to estimate influences of genes and environments on variables used to measure political sophistication. Additive genetic factors explain 48–76% of the variation in educational attainment, political interest, and political knowledge, while dominance genetics influence 28% of the variance of ideological consistency. Multivariate analyses show that, although these measures share common genetic and unique environmental factors to a modest extent, much of the variance is explained by specific genetic and unique environmental factors. Ideological consistency appears to be mostly distinct from the other measures, as it is strongly accounted for by unique environmental influences.
The purpose of the present study was to examine genetic and environmental contributions to individual differences in maximal isometric, concentric and eccentric muscle strength and muscle cross-sectional area (MCSA) of the elbow flexors. A generality versus specificity hypothesis was explored to test whether the 4 strength variables share a genetic component or common factors in the environment or whether the genetic/environmental factors are specific for each strength variable. The 4 variables under study were measured in 25 monozygotic and 16 dizygotic male Caucasian twin pairs (22.4 ± 3.7 years). The multivariate genetic analyses showed that all 4 variables shared a genetic and environmental component, which accounted for 43% and 6% in MCSA (h2 = 81%), 47% and 20% in eccentric (h2 = 65%), 58% and 4% in isometric (h2 = 70%) and 32% and 1% in concentric strength (h2 = 32%) respectively. The remaining variation was accounted for by contraction type specific and muscle cross-sectional area specific genetic and environmental effects, which accounted for 38% and 14% in MCSA, 18% and 15% in eccentric, 12% and 26% in isometric and 0% and 67% in concentric strength respectively. This exploratory multivariate study suggests shared pleiotropic gene action for MCSA, eccentric, isometric and concentric strength, with a moderate to high genetic contribution to the variability of these characteristics.