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Most genome-wide association study (GWAS) analyses test the association between single-nucleotide polymorphisms (SNPs) and a single trait or outcome. While valuable second-step analyses of these associations (e.g., calculating genetic correlations between traits) are common, single-step multivariate analyses of GWAS data are rarely performed. This is unfortunate because multivariate analyses can reveal information which is irrevocably obscured in multi-step analysis. One simple example is the distinction between variance common to a set of measures, and variance specific to each. Neither GWAS of sum- or factor-scores, nor GWAS of the individual measures will deliver a clean picture of loci associated with each measure's specific variance. While multivariate GWAS opens up a broad new landscape of feasible and informative analyses, its adoption has been slow, likely due to the heavy computational demands and difficulties specifying models it requires. Here we describe GW-SEM 2.0, which is designed to simplify model specification and overcome the inherent computational challenges associated with multivariate GWAS. In addition, GW-SEM 2.0 allows users to accurately model ordinal items, which are common in behavioral and psychological research, within a GWAS context. This new release enhances computational efficiency, allows users to select the fit function that is appropriate for their analyses, expands compatibility with standard genomic data formats, and outputs results for seamless reading into other standard post-GWAS processing software. To demonstrate GW-SEM's utility, we conducted (1) a series of GWAS using three substance use frequency items from data in the UK Biobank, (2) a timing study for several predefined GWAS functions, and (3) a Type I Error rate study. Our multivariate GWAS analyses emphasize the utility of GW-SEM for identifying novel patterns of associations that vary considerably between genomic loci for specific substances, highlighting the importance of differentiating between substance-specific use behaviors and polysubstance use. The timing studies demonstrate that the analyses take a reasonable amount of time and show the cost of including additional items. The Type I Error rate study demonstrates that hypothesis tests for genetic associations with latent variable models follow the hypothesized uniform distribution. Taken together, we suggest that GW-SEM may provide substantially deeper insights into the underlying genomic architecture for multivariate behavioral and psychological systems than is currently possible with standard GWAS methods. The current release of GW-SEM 2.0 is available on CRAN (stable release) and GitHub (beta release), and tutorials are available on our github wiki (https://jpritikin.github.io/gwsem/). [ABSTRACT FROM AUTHOR]

Aims To examine the genetic and environmental contributions to the initiation of use and progression to more serious use of alcohol, cigarettes and marijuana during adolescence, and to examine the relationship between initiation and progression of substance use. Design The study used a twin-based design and a new theoretical model, the causal–common–contingent (CCC) model. This allows modelling of the relationship between initiation of use and progression to heavier use as a two-stage model and the examination of genetic and environmental influences on both stages, while taking into account their relationship. Participants The participants consisted of 1214 twin pairs (69% response rate) aged 11–19 years sampled from the UK population-based Cardiff Study of All Wales and North-west of England Twins (CaStANET). Measurements Data on adolescent initiation and progression to more serious use of alcohol, cigarettes and marijuana were obtained using self-report questionnaires. Findings Initiation of alcohol and progression to heavier alcohol use had separate but related underlying aetiologies. For cigarette and marijuana use the relation between initiation and progression to heavier use was stronger, suggesting greater overlap in aetiologies. For all three substances, environmental influences that make twins more similar (common environment) tended to be greater for initiation, while genetic influences were stronger for heavier use. Conclusions These findings have implications for policy decisions aimed at an adolescent and early adult age group. Specifically, these findings suggest that it may be more efficacious to focus alcohol interventions on risk factors for the development of heavier use rather than initiation of use. In contrast, interventions aimed at reducing the initiation of cigarettes and marijuana use may be more appropriate. [ABSTRACT FROM AUTHOR]

Abstract: Previous studies exploring the heritability of cannabis initiation have been carried out in the United States, Australia and United Kingdom. In the present study we assess cannabis initiation in The Netherlands, where the use of cannabis in small amounts is permitted. The sample included 3115 twins with a mean age of 27.4years (SD 4.7) who are registered with the Netherlands Twin Register (NTR). Individual differences in cannabis initiation showed moderate genetic influences (44%). The remaining variance was explained by environmental influences shared by twins (31%) and by unique environmental factors (24%). Compared to studies from other countries, these results suggest that the relative importance of genetic and environmental factors is not different in a country with a more liberal cannabis policy. [Copyright &y& Elsevier]

Background: To date, 60 genetic variants have been robustly associated with birthweight. It is unclear whether these associations represent the effect of an individual's own genotype on their birthweight, their mother's genotype, or both., Methods: We demonstrate how structural equation modelling (SEM) can be used to estimate both maternal and fetal effects when phenotype information is present for individuals in two generations and genotype information is available on the older individual. We conduct an extensive simulation study to assess the bias, power and type 1 error rates of the SEM and also apply the SEM to birthweight data in the UK Biobank study., Results: Unlike simple regression models, our approach is unbiased when there is both a maternal and a fetal effect. The method can be used when either the individual's own phenotype or the phenotype of their offspring is not available, and allows the inclusion of summary statistics from additional cohorts where raw data cannot be shared. We show that the type 1 error rate of the method is appropriate, and that there is substantial statistical power to detect a genetic variant that has a moderate effect on the phenotype and reasonable power to detect whether it is a fetal and/or a maternal effect. We also identify a subset of birthweight-associated single nucleotide polymorphisms (SNPs) that have opposing maternal and fetal effects in the UK Biobank., Conclusions: Our results show that SEM can be used to estimate parameters that would be difficult to quantify using simple statistical methods alone.