OpenMx is free, full–featured, open source, structural equation modeling (SEM) software. OpenMx runs within the R statistical programming environment on Windows, Mac OS–X, and Linux computers. The rationale for developing OpenMx is discussed along with the philosophy behind the user interface. The OpenMx data structures are introduced — these novel structures define the user interface framework and provide new opportunities for model specification. Two short example scripts for the specification and fitting of a confirmatory factor model are next presented. We end with an abbreviated list of modeling applications available in OpenMx 1.0 and a discussion of directions for future development.

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly-written CSOLNP. Entire new methodologies such as Item Factor analysis (IRT) and State-space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

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Consistent but indirect evidence has implicated genetic factors in smoking behavior1,2. We report meta-analyses of several smoking phenotypes within cohorts of the Tobacco and Genetics Consortium (n = 74,053). We also partnered with the European Network of Genetic and Genomic Epidemiology (ENGAGE) and Oxford-GlaxoSmithKline (Ox-GSK) consortia to follow up the 15 most significant regions (n > 140,000). We identified three loci associated with number of cigarettes smoked per day. The strongest association was a synonymous 15q25 SNP in the nicotinic receptor gene CHRNA3 (rs1051730[A], β = 1.03, standard error (s.e.) = 0.053, P = 2.8 × 10−73). Two 10q25 SNPs (rs1329650[G], β = 0.367, s.e. = 0.059, P = 5.7 × 10−10; and rs1028936[A], β = 0.446, s.e. = 0.074, P = 1.3 × 10−9) and one 9q13 SNP in EGLN2 (rs3733829[G], β = 0.333, s.e. = 0.058, P = 1.0 × 10−8) also exceeded genome-wide significance for cigarettes per day. For smoking initiation, eight SNPs exceeded genome-wide significance, with the strongest association at a nonsynonymous SNP in BDNF on chromosome 11 (rs6265[C], odds ratio (OR) = 1.06, 95% confidence interval (Cl) 1.04–1.08, P = 1.8 × 10−8). One SNP located near DBH on chromosome 9 (rs3025343[G], OR = 1.12, 95% Cl 1.08–1.18, P = 3.6 × 10−8) was significantly associated with smoking cessation.

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest GWAS to date of DSM-IV diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case/control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, N = 46,568; African; N = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; p = 9.8E-13) and African ancestries (rs2066702; p = 2.2E-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, ADHD, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and non-pathological drinking behaviors.

Little is known about the contribution of genetic and environmental factors to risk for juvenile psychopathology. The Virginia Twin Study of Adolescent Behavioral Development allows these contributions to be estimated. A population-based, unselected sample of 1412 Caucasian twin pairs aged 8-16 years was ascertained through Virginia schools. Assessment of the children involved semi-structured face-to-face interviews with both twins and both parents using the Child and Adolescent Psychiatric Assessment (CAPA). Self-report questionnaires were also completed by parents, children, and teachers. Measures assessed DSM-III-R symptoms of Attention Deficit Hyperactivity Disorder (ADHD). Conduct Disorder, Oppositional Defiant Disorder, Overanxious Disorder, Separation Anxiety, and Depressive Disorder. Factorially derived questionnaire scales were also extracted. Scores were normalized and standardized by age and sex. Maximum likelihood methods were used to estimate contributions of additive and nonadditive genetic effects, the shared and unique environment, and sibling imitation or contrast effects. Estimates were tested for heterogeneity over sexes. Generally, monozygotic (MZ) twins correlated more highly than dizygotic (DZ) twins, parental ratings more than child ratings, and questionnaire scales more highly than interviews. DZ correlations were very low for measures of ADHD and DZ variances were greater than MZ variances for these variables. Correlations sometimes differed between sexes but those for boy-girl pairs were usually similar to those for like-sex pairs. Most of the measures showed small to moderate additive genetic effects and moderate to large effects of the unique individual environment. Measures of ADHD and related constructs showed marked sibling contrast effects. Some measures of oppositional behavior and conduct disorder showed shared environmental effects. There were marked sex differences in the genetic contribution to separation anxiety, otherwise similar genetic effects appear to be expressed in boys and girls. Effects of rater biases on the genetic analysis are considered. The study supports a widespread influence of genetic factors on risk to adolescent psychopathology and suggests that the contribution of different types of social influence may vary consistently across domains of measurement.

This study replicates a recent report of a genotype-environment interaction that predicts individual variation in risk for antisocial behavior in boys.

We review the literature on the familial resemblance of body mass index (BMI) and other adiposity measures and find strikingly convergent results for a variety of relationships. Results from twin studies suggest that genetic factors explain 50 to 90% of the variance in BMI. Family studies generally report estimates of parent-offspring and sibling correlations in agreement with heritabilities of 20 to 80%. Data from adoption studies are consistent with genetic factors accounting for 20 to 60% of the variation in BMI. Based on data from more than 25,000 twin pairs and 50,000 biological and adoptive family members, the weighted mean correlations are .74 for MZ twins, .32 for DZ twins, .25 for siblings, .19 for parent-offspring pairs, .06 for adoptive relatives, and .12 for spouses. Advantages and disadvantages of twin, family, and adoption studies are reviewed. Data from the Virginia 30,000, including twins and their parents, siblings, spouses, and children, were analyzed using a structural equation model (Stealth) which estimates additive and dominance genetic variance, cultural transmission, assortative mating, nonparental shared environment, and special twin and MZ twin environmental variance. Genetic factors explained 67% of the variance in males and females, of which half is due to dominance. A small proportion of the genetic variance was attributed to the consequences of assortative mating. The remainder of the variance is accounted for by unique environmental factors, of which 7% is correlated across twins. No evidence was found for a special MZ twin environment, thereby supporting the equal environment assumption. These results are consistent with other studies in suggesting that genetic factors play a significant role in the causes of individual differences in relative body weight and human adiposity.