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.

Summary Background Variation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50–70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder. Methods To conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations. Findings We identified two genome-wide significant loci: a novel chromosome 7 locus ( FOXP2 , lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07–1·15, p=1·84 × 10 −9 ) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2 , lead SNP rs4732724; OR 0·89, 95% CI 0·86–0·93, p=6·46 × 10 −9 ). Cannabis use disorder and cannabis use were genetically correlated ( r g 0·50, p=1·50 × 10 −21 ), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia. Interpretation These findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder. Funding National Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Serv...

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In science, multiple measures of the same constructs can be useful, but they are unlikely to all be equally valid indicators. In psychological assessment, the many popular personality inventories available in the marketplace also may be useful, but their comparative validity has long remained unassessed. This is the first comprehensive comparison of 11 such multiscale instruments against each of three types of criteria: clusters of behavioral acts, descriptions by knowledgeable informants, and clinical indicators potentially associated with various types of psychopathology. Using 1,000 bootstrap resampling analyses from a sample of roughly 700 adult research participants, we assess the relative predictability of each criterion and the comparative validity of each inventory. Although there was a wide range of criterion predictability, most inventories exhibited quite similar cross-validities when averaged across all three types of criteria. On the other hand, there were important differences between inventories in their predictive capabilities for particular criteria. We discuss the factors that lead to differential validity across predictors and criteria.

To provide novel insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing up to 4,503 OD cases, 4,173 opioid-exposed controls, and 32,500 opioid-unexposed controls. Among the variants identified, rs9291211 was associated with OE (a comparison of exposed vs. unexposed controls; z=-5.39, p=7.2×10 -8 ). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N>360,000) found association of this variant with propensity to use dietary supplements (p=1.68×10 -8 ). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (z=4.69, p=10 -6 ), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (z=5.55, p=2.9×10 -8 ) and a significant association with musculoskeletal disorders in the UK Biobank (p=4.88×10 -7 ). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (N=466,571) was positively associated with OD (OD cases vs. unexposed controls, p=8.1×10 -5 ; OD cases vs. exposed controls, p=0.054) and OE (exposed controls vs. unexposed controls, p=3.6×10 -5 ). A PRS based on a GWAS of neuroticism (N=390,278) was positively associated with OD (OD cases vs. unexposed controls, p=3.2×10 -5 ; OD cases vs. exposed controls, p=0.002) but not with OE (p=0.671). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls (exposed vs. unexposed) in studies of addiction.