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 conducted a combined genome-wide association (GWAS) analysis of 7,481 individuals affected with bipolar disorder and 9,250 control individuals within the Psychiatric Genomewide Association Study Consortium Bipolar Disorder group (PGC-BD). We performed a replication study in which we tested 34 independent SNPs in 4,493 independent bipolar disorder cases and 42,542 independent controls and found strong evidence for replication. In the replication sample, 18 of 34 SNPs had P value < 0.05, and 31 of 34 SNPs had signals with the same direction of effect (P = 3.8 × 10−7). In the combined analysis of all 63,766 subjects (11,974 cases and 51,792 controls), genome-wide significant evidence for association was confirmed for CACNA1C and found for a novel gene ODZ4. In a combined analysis of non-overlapping schizophrenia and bipolar GWAS samples we observed strong evidence for association with SNPs in CACNA1C and in the region of NEK4/ITIH1,3,4. Pathway analysis identified a pathway comprised of subunits of calcium channels enriched in the bipolar disorder association intervals. The strength of the replication data implies that increasing samples sizes in bipolar disorder will confirm many additional loci.

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

To identify bipolar disorder (BD) genetic susceptibility factors, we conducted two genome-wide association (GWA) studies: one involving a sample of individuals of European ancestry (EA; n = 1001 cases; n = 1033 controls), and one involving a sample of individuals of African ancestry (AA; n = 345 cases; n = 670 controls). For the EA sample, single-nucleotide polymorphisms (SNPs) with the strongest statistical evidence for association included rs5907577 in an intergenic region at Xq27.1 (P = 1.6 Â 10 À6) and rs10193871 in NAP5 at 2q21.2 (P = 9.8 Â 10 À6). For the AA sample, SNPs with the strongest statistical evidence for association included rs2111504 in DPY19L3 at 19q13.11 (P = 1.5 Â 10 À6) and rs2769605 in NTRK2 at 9q21.33 (P = 4.5 Â 10 À5 ). We also investigated whether we could provide support for three regions previously associated with BD, and we showed that the ANK3 region replicates in our sample, along with some support for C15Orf53; other evidence implicates BD candidate genes such as SLITRK2. We also tested the hypothesis that BD susceptibility variants exhibit genetic background-dependent effects. SNPs with the strongest statistical evidence for genetic background effects included rs11208285 in ROR1 at 1p31.3 (P = 1.4 Â 10 À6 ), rs4657247 in RGS5 at 1q23.3 (P = 4.1 Â 10 À6 ), and rs7078071 in BTBD16 at 10q26.13 (P = 4.5 Â 10 À6). This study is the first to conduct GWA of BD in individuals of AA and suggests that genetic variations that contribute to BD may vary as a function of ancestry.

Major depressive disorder (MDD) is a common psychiatric illness with high levels of morbidity and mortality. Despite intensive research during the past several decades, the neurobiological basis and pathophysiology of depressive disorders remain unknown. Genetic factors play important roles in the development of MDD, as indicated by family, twin, and adoption studies, and may reveal important information about disease mechanisms. This article describes recent developments in the field of psychiatric genetics, with a focus on MDD. Early twin studies, linkage studies, and association studies are discussed. Recent findings from genome-wide association studies are reviewed and future directions discussed. Despite all efforts, thus far, no single genetic variation has been identified to increase the risk of depression substantially. Genetic variants are expected to have only small effects on overall disease risk, and multiple genetic factors in conjunction with environmental factors are likely necessary for the development of MDD. Future large-scale studies are needed to dissect this complex phenotype and to identify pathways involved in the etiology of MDD.

We previously demonstrated differential activation of the mesocorticolimbic reward circuitry in response to cigarette cues independent of withdrawal. Despite robust effects, we noted considerable individual variability in brain and subjective responses. As dopamine (DA) is critical for reward and its predictive signals, genetically driven variation in DA transmission may account for the observed differences. Evidence suggests that a variable number of tandem repeats (VNTRs) polymorphism in the DA transporter (DAT) SLC6A3 gene may influence DA transport. Brain and behavioral responses may be enhanced in probands carrying the 9-repeat allele. To test this hypothesis, perfusion fMR images were acquired during cue exposure in 19 smokers genotyped for the 40 bp VNTR polymorphism in the SLC6A3 gene. Contrasts between groups revealed that 9-repeat (9-repeats) had a greater response to smoking (vs nonsmoking) cues than smokers homozygous for the 10-repeat allele (10/10-repeats) bilaterally in the interconnected ventral striatal/pallidal/orbitofrontal cortex regions (VS/VP/OFC). Activity was increased in 9-repeats and decreased in 10/10-repeats in the VS/VP/OFC (p<0.001 for all analyses). Brain activity and craving was strongly correlated in 10/10-repeats in these regions and others (anterior cingulate, parahippocampal gyrus, and insula; r2 = 0.79–0.86, p<0.001 in all regions). Alternatively, there were no significant correlations between brain and behavior in 9-repeats. There were no differences in cigarette dependence, demographics, or resting baseline neural activity between groups. These results provide evidence that genetic variation in the DAT gene contributes to the neural and behavioral responses elicited by smoking cues.

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This review examines the genetic underpinnings of Alcohol Use Disorder (AUD), with an emphasis on GWAS approaches for identifying genetic risk variants. The most promising results associated with AUD and alcohol-related phenotypes have included SNPs of the alcohol metabolism genes ADH and ALDH.