Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders 1 . They are heritable 2 , 3 and etiologically related 4 , 5 behaviors that have been resistant to gene discovery efforts 6 – 11 . In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

We examined the role of common genetic variation in schizophrenia in a genome-wide association study of substantial size: a stage 1 discovery sample of 21,856 individuals of European ancestry and a stage 2 replication sample of 29,839 independent subjects. The combined stage 1 and 2 analysis yielded genome-wide significant associations with schizophrenia for seven loci, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which have been previously implicated (6p21.32-p22.1 and 18q21.2). The strongest new finding (P = 1.6 × 10−11) was with rs1625579 within an intron of a putative primary transcript for MIR137 (microRNA 137), a known regulator of neuronal development. Four other schizophrenia loci achieving genome-wide significance contain predicted targets of MIR137, suggesting MIR137-mediated dysregulation as a previously unknown etiologic mechanism in schizophrenia. In a joint analysis with a bipolar disorder sample (16,374 affected individuals and 14,044 controls), three loci reached genome-wide significance: CACNA1C (rs4765905, P = 7.0 × 10−9), ANK3 (rs10994359, P = 2.5 × 10−8) and the ITIH3-ITIH4 region (rs2239547, P = 7.8 × 10−9).

Objective Gene-by-environment interaction (G×E) studies in psychiatry have typically been conducted using a candidate G×E (cG×E) approach, analogous to the candidate gene association approach used to test genetic main effects. Such cG×E research has received widespread attention and acclaim, yet cG×E findings remain controversial. The authors examined whether the many positive cG×E findings reported in the psychiatric literature were robust or if, in aggregate, cG×E findings were consistent with the existence of publication bias, low statistical power, and a high false discovery rate. Method The authors conducted analyses on data extracted from all published studies (103 studies) from the first decade (2000–2009) of cG×E research in psychiatry. Results Ninety-six percent of novel cG×E studies were significant compared with 27% of replication attempts. These findings are consistent with the existence of publication bias among novel cG×E studies, making cG×E hypotheses appear more robust than they actually are. There also appears to be publication bias among replication attempts because positive replication attempts had smaller average sample sizes than negative ones. Power calculations using observed sample sizes suggest that cG×E studies are underpowered. Low power along with the likely low prior probability of a given cG×E hypothesis being true suggests that most or even all positive cG×E findings represent type I errors. Conclusion In this new era of big data and small effects, a recalibration of views about “groundbreaking” findings is necessary. Well-powered direct replications deserve more attention than novel cG×E findings and indirect replications.

Inbreeding depression, which refers to reduced fitness among offspring of related parents, has traditionally been studied using pedigrees. In practice, pedigree information is difficult to obtain, potentially unreliable, and rarely assessed for inbreeding arising from common ancestors who lived more than a few generations ago. Recently, there has been excitement about using SNP data to estimate inbreeding (F) arising from distant common ancestors in apparently “outbred” populations. Statistical power to detect inbreeding depression using SNP data depends on the actual variation in inbreeding in a population, the accuracy of detecting that with marker data, the effect size, and the sample size. No one has yet investigated what variation in F is expected in SNP data as a function of population size, and it is unclear which estimate of F is optimal for detecting inbreeding depression. In the present study, we use theory, simulated genetic data, and real genetic data to find the optimal estimate of F, to quantify the likely variation in F in populations of various sizes, and to estimate the power to detect inbreeding depression. We find that F estimated from runs of homozygosity (Froh), which reflects shared ancestry of genetic haplotypes, retains variation in even large populations (e.g., SD = 0.5% when Ne = 10,000) and is likely to be the most powerful method of detecting inbreeding effects from among several alternative estimates of F. However, large samples (e.g., 12,000–65,000) will be required to detect inbreeding depression for likely effect sizes, and so studies using Froh to date have probably been underpowered.