Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

In 2013, analyses by the PGC’s Cross-Disorder Group identified loci with pleiotropic effects across five disorders: autism spectrum disorder (ASD), ADHD, SCZ, BIP, and MD in a sample comprising 33,332 cases and 27,888 controls (). In the current study, we examined pleiotropic effects in a greatly expanded dataset, encompassing 232,964 cases and 494,162 controls, that included three additional psychiatric disorders: Tourette syndrome (TS), obsessive-compulsive disorder (OCD), and anorexia nervosa (AN). We address four major questions regarding the shared genetic basis of these eight disorders: (1) Can we identify a shared genetic structure within the broad range of these clinically distinct psychiatric disorders? (2) Can we detect additional loci associated with risk for multiple disorders (pleiotropic loci)? (3) Do some of these risk loci have opposite allelic effects across disorders? and (4) Can we identify functional features of the pleiotropic loci that could account for their broad effects on psychopathology?

Psychiatric disorders affect more than 25% of the population in any given year and are a leading cause of worldwide disability (). The substantial influence of genetic variation on risk for a broad range of psychiatric disorders has been established by both twin and, more recently, large-scale genomic studies (). Psychiatric disorders are highly polygenic, with a large proportion of heritability contributed by common variation. Many risk loci have emerged from genome-wide association studies (GWAS) of, among others, schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), and attention-deficit/hyperactivity disorder (ADHD) from the Psychiatric Genomics Consortium (PGC) and other efforts (). These studies have revealed a surprising degree of genetic overlap among psychiatric disorders (). Elucidating the extent and biological significance of cross-disorder genetic influences has implications for psychiatric nosology, drug development, and risk prediction. In addition, characterizing the functional genomics of cross-phenotype genetic effects may reveal fundamental properties of pleiotropic loci that differentiate them from disorder-specific loci and help identify targets for diagnostics and therapeutics.

GBD 2016 Disease and Injury Incidence and Prevalence Collaborators Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

GWAS catalog data for the 109 pleiotropic risk loci showed enrichment of implicated genes in a range of brain-related traits ( Table S7 .2). As expected, the associated traits included SCZ, BIP, and ASD. In addition, the pleiotropic risk loci were enriched among genes previously associated with neuroticism (corrected enrichment p = 5.28 × 10; GRIK3, CTNND1, DRD2, RGS6, RBFOX1, ZNF804A, L3MBTL2, CHADL, RANGAP1, RSRC1, GRM3), cognitive ability (corrected p = 7.15 × 10; PTPRF, NEGR1, ELOVL3, SORCS3, DCC, CACNA1I), and night sleep phenotypes (corrected p = 1.86 × 10; PBX1, NPAS3, RGS6, GRIN2A, MYO18A, TIAF1, CNTN4, PPP2R2B, TENM2, CSMD1). We also found significant enrichment of pleiotropic risk genes in multiple measures of body mass index (BMI), supporting previous studies suggesting a shared etiologic basis between a range of neuropsychiatric disorders and obesity ().

To explore the genetic relationship of cross-disorder genetic risk with other traits, we treated this 8-disorder GWAS meta-analysis as a single “cross-disorder phenotype.” We applied LDSC to estimate SNP heritability (h) and genetic correlations with other phenotypes, using block jackknife-based standard errors to estimate statistical significance. The estimated hof the cross-disorder phenotype was 0.146 (SE 0.0058; observed scale). Using data for 25 brain-related traits selected from LDHub (), we found significant genetic correlations of the cross-disorder phenotype with seven traits (at a FDR-corrected p value threshold 0.002): never/ever smoking status, years of education, neuroticism, subjective well-being, and three sleep-related phenotypes (chronotype, insomnia, and excessive daytime sleepiness) ( Table S7 .1).

Previous studies of model organisms using gene knock-out experiments suggested that pleiotropic risk loci may undergo stronger selection than non-pleiotropic loci (). However, we found no evidence that pleiotropic risk variants are under stronger evolutionary constraints ( Table S6 .4). Various comparative genomics resources, including PhyloP (), PhastCons (), and GERP++ (), showed our top loci to have similar properties regardless of the extent of pleiotropy. Neither did we find differences between disorder-specific lead SNPs and pleiotropic SNPs with respect to their minor allele frequencies, average heterozygosity, or predicted allele ages (). Pleiotropic and non-pleiotropic SNPs also did not differ in terms of the distance to nearest genes, distance to splicing sites, chromosome compositions, and predicted functional consequences of non-coding regulatory elements.

Genome of the Netherlands Consortium Deleterious alleles in the human genome are on average younger than neutral alleles of the same frequency.

Enrichment analyses using the genes preferentially expressed in specific cortical regions suggested that pleiotropic loci were over-represented among genes expressed in the frontal cortex, while non-pleiotropic loci were enriched in the occipital cortex (FDR q<0.05; Figure 5 C). Cell-type-specific analysis indicated that genes implicated in pleiotropic loci were mainly expressed in neurons (FDR q<0.05) but not in glial cell types. Further, enrichment of pleiotropic loci in neuronal cells was also associated with the degree of pleiotropy, as highlighted in Figure 5 D.

Next, we compared spatio-temporal gene-expression patterns for the 109 pleiotropic risk loci and the 37 disorder-specific loci using post-mortem brain data. On average, disorder-specific and pleiotropic risk loci showed a similar level of gene expression in both prenatal and postnatal development after multiple testing correction (t test p > 0.025 x10 Figure S4 ). During prenatal development, non-pleiotropic loci (mainly SCZ-associated) showed peak expression in the first trimester, after which expression rapidly decreased, while pleiotropic genes associated with only 2 disorders (“pleiotropy=2”; 60 loci) and those associated with more than 2 (“pleiotropy>2,” 49 loci) showed peak expression around the second trimester ( Figure 5 ). After birth, all three groups showed gradually increasing gene expression until adulthood. Expression levels were associated with the degree of pleiotropy, with the pleiotropy > 2 group showing higher gene expression than either the pleiotropy = 2 group (t test p < 2.10 × 10) or non-pleiotropic risk loci (t test p < 2.2 × 10).

Average normalized gene expression in fetal and adult post-mortem brain tissue for pleiotropic (109) and non-pleiotropic (37) loci were plotted. Disorder-specific and pleiotropic risk loci showed a similar level of gene expression in prenatal and postnatal development after multiple testing correction (t test p > 0.025).

In contrast to the differences in neuronal development and neuronal signaling pathways, pleiotropic and non-pleiotropic risk loci shared several characteristics related to genomic function. For instance, gene-set enrichment analyses indicated that both pleiotropic and non-pleiotropic risk loci were enriched for genes involved in the regulation of synaptic plasticity, neurotransmission, and synaptic cellular components. More than 41% of the genes associated with our genome-wide significant loci, both pleiotropic and non-pleiotropic, were intolerant of loss of function mutations (pLI score ≥ 0.9); this is highly unlikely to occur by chance (Fisher’s exact p = 4.90 × 10 −8 ). This finding was consistent when examining pleiotropic (p = 2.85 × 10 −11 ) and non-pleiotropic risk loci (p = 1.56 × 10 −3 ) separately.

Gene-set enrichment analyses using Gene Ontology data suggested involvement of pleiotropic risk loci in neurodevelopmental processes ( Table S6 .1). The 109 pleiotropic risk loci were enriched for genes involved in neurogenesis (gene-set enrichment p = 9.67 × 10), regulation of nervous system development (p = 3.41 × 10), and neuron differentiation (p = 3.30 × 10), while enrichment of these gene-sets was not seen for the 37 disorder-specific risk loci (adjusted enrichment p > 0.05; Table S6 .2). Pleiotropic risk loci also showed enrichment of genes involved in specific neurotransmitter-related pathways–glutamate receptor signaling (p = 2.45 × 10) and voltage-gated calcium channel complex (p = 5.72 × 10)–while non-pleiotropic risk loci, which were predominantly SCZ-associated, were over-represented among acetylcholine receptor genes (p = 7.25 × 10). Analysis of cortical gene expression data also suggested enrichment of pleiotropic risk genes in cortical glutamatergic neurons through layers 2-6 ( Table S6 .3), further supporting the shared role of glutamate receptor signaling in the pathogenesis of diverse neuropsychiatric disorders.

We conducted a series of bioinformatic analyses that examined whether loci with shared risk effects on multiple neuropsychiatric disorders had characteristic features that distinguished them from non-pleiotropic risk loci. First, we annotated the functional characteristics of 146 lead SNPs using various public data sources ( STAR Methods Table S4 ). Overall, they showed significant enrichment of genes expressed in the brain (beta = 0.123, SE = 0.0109, enrichment p = 1.22 × 10) and pituitary (beta = 0.0916, SE = 0.0136, p = 8.74 × 10), but not in the other Genotype-Tissue Expression (GTEx) tissues. ( Table S5 .1; Figure 5 . A separate analysis of 109 pleiotropic risk loci also showed specific enrichment of genes expressed in multiple brain tissues (p = 1.55 × 10 Table S5 .2), while disorder-specific loci showed nominally enriched brain gene expression in the cortex (p = 2.14 × 10 Table S5 .3).

(D) Genes mapped to 146 risk loci show higher expression values in neurons and oligodendrocytes, with much higher neuronal specificity for pleiotropic loci. Single cell-type specific expression profiles (Darmanis et al., 2015) were used to measure scaled expression of risk loci associatd with three distinct pleiotropy groups.

(C) In the adult cortex, genes mapped to pleiotropic loci were enriched for frontal cortex specific genes, while genes mapped to non-pleiotropic loci are enriched for occipical cortex specific genes.

(B) Brain developmental expression trajectory displayed for the three groups of genes based on () The 146 genome-wide significant loci from the cross-disorder meta analysis were clustered into three groups based on predicted disorder-specific associations: (1) no-pleiotopy; (2) pleiotropy = 2; and (3) pleiotropy > 2. The “no-pleiotropy” group included 37 loci that showed a single-disorder-specific association, while the “pleiotropy=2” and “pleiotropy>2” groups included 60 and 49 loci that were associated with two and more than two disorders, respectively.

(A) GTEX tissue-specific enrichment results for 146 risk loci associated with at least one of eight neuropsychiatric disorders. GTEX tissues were classified as 9 distinct categories, of which the brain tissues were colored in blue. The dotted red line indicates a statistically significant p value after conducting Bonferroni correction for multiple testing. Psychiatric disorder-associated loci show significant enrichment in genes expressed in pituitary and all brain tissues.

Our prior cross-disorder meta-analysis of five psychiatric disorders () found no evidence of SNPs with antagonistic effects on two or more disorders. Here, we examined whether any variants with meta-analysis p ≤ 1 × 10had opposite directional effects between disorders ( STAR Methods ). After adjusting for having examined 206 loci across eight disorders (q < 0.001), we identified 11 loci with evidence of opposite directional effects on two or more disorders ( Figure 4 Table S3 .3). The disorder configuration of opposite directional effects varied for the 11 loci, including three loci with opposite directional effects on SCZ and MD (rs301805, rs1933802, rs3806843), two loci between SCZ and ASD (rs9329221, rs2921036), and one locus with opposite directional effects on the two mood disorders, BIP and MD (rs75595651). Notably, all of the six loci involving SCZ and BIP exhibited the same directional effect on the two disorders (P< 0.05), in line with their strong genome-wide genetic correlation.

The radius of each wedge corresponds to the absolute values of the Z-scores (log(Odds ratios)/SE) obtained from association tests of the SNP for eight disorders. The color indicates whether the examined SNP carries risk (red) or protective effects (green) for each disorder. The dotted line around the center indicates statistically significant SNP effects that account for multiple testing of 206 SNPs at the q-value of 0.001.

Of the 109 pleiotropic loci, 76 were identified in the GWAS of individual disorders, while the remaining 33 are novel. The most pleiotropic among these novel loci was a region downstream of NOX4 (NADPH Oxidase 4) that was associated with SCZ, BIP, MD, ASD, and AN (rs117956829; P= 1.82 × 10 Figure 3 C). Brain Hi-C data () detected a direct interaction of the cross-disorder association region with NOX4 in both adult and fetal brain (interaction p = 3.2 × 10and 9.3 × 10, respectively). As a member of the NOX family genes that encode subunits of NADPH oxidase, NOX4 is a major source of superoxide production in human brain and a promoter of neural stem cell growth ().

The second most pleiotropic locus in our analysis was identified in an intron of RBFOX1 (RNA Binding Fox-1 Homolog 1) on 16p13.3 (lead SNP rs7193263; P= 5.59 × 10). The lead SNP showed association with all of the disorders except AN ( Figure 3 B). RBFOX1 (also called A2BP1) encodes a splicing regulator mainly expressed in neurons and known to target several genes important to neuronal development, including NMDA receptor 1 and voltage-gated calcium channels (). Knockdown and silencing of RBFOX1 during mouse corticogenesis impairs neuronal migration and synapse formation (), implying its pivotal role in early cortical maturation. In contrast to DCC, however, developmental gene-expression of RBFOX1 showed gradually increasing gene expression throughout the prenatal period ( Figure S3 ). Animal models and association studies have implicated RBFOX1 in aggressive behaviors, a trait observed in several of the disorders in our analysis ().

Of the 109 risk loci with shared effects, the 18q21.2 region surrounding SNP rs8084351 at the netrin 1 receptor gene DCC featured the most pleiotropic association (P= 4.26 × 10 Figure 3 A). This region showed association with all eight psychiatric disorders, and has been previously associated with both MD and neuroticism (). The signal in our meta-analysis colocalizes with brain eQTLs for DCC (eQTL association FDR q = 2.27 × 10), supporting DCC as a plausible candidate gene ( Figure S2 ). The product of DCC plays a key role in guiding axonal growth during neurodevelopment and serves as a master regulator of midline crossing and white matter projections (). Gene expression data indicate that DCC expression peaks during early prenatal development ( Figure S3 ).

Gene expression trajectories from a transcriptome atlas of post-mortem brain tissue across development are plotted for four top loci, DCC, RBFOX1, NOX4 and BRAF in six different brain tissue types. AMY = amygdala; MD = mediodorsal nucleus of the thalamus; CBC = cerebellar cortex; NCX = neocortex; HIP = hippocampus; STR = striatum.

For each locus, disorder-specific effects of the index SNP are shown using ForestPMPlot. The first panel is the forest plot, displaying disorder-specific association p value, log odds ratios (ORs), and standard errors of the SNP. The meta-analysis p value and the corresponding summary statistic are displayed on the top and the bottom of the forest plot, respectively. The second panel is the PM-plot in which x axis represents the m-value, the posterior probability that the effect exists in each disorder, and the y axis represents the disorder-specific association p value as -log 10 (p value). Disorders are depicted as a dot whose size represents the sample size of individual GWAS. Disorders with estimated m-values of at least 0.9 are colored in red, while those with m-values less than 0.9 are marked in green.

SNP ID, location, prioritized candidate gene, disorder-specific m-values for 23 most pleiotropic loci. The number of disorders with high confidence association (m-values0.9) is shown in the last column. Evidence for candidate gene mapping include: g (gene containing index SNP); fg (credible SNP gene); q (brain cis-eQTLs); h (hi-C interacting gene based on FUMA); hf (hi-C-based interaction between associated SNP and target gene in the fetal brain from); ha (hi-C-based interaction in the adult brain from); and tss (transcription start sites). At most two candidate genes are listed here. Full list of associated gene information is available in Table S3 .1.

Within these 136 loci, multi-SNP-based conditional analysis () identified 10 additional SNPs with independent associations, resulting in a total of 146 independent lead SNPs ( Table S3 .1). To provide a quantitative estimate of the best fit configuration of cross-disorder genotype-phenotype relationships, we estimated the posterior probability of association (referred to as the m-value) with each disorder using a Bayesian statistical framework () ( STAR Methods Table S3 .2) As recommended (), an m value threshold of 0.9 was used to predict with high confidence that a particular SNP was associated with a given disorder. Also, m values of < 0.1 were taken as strong evidence against association. Plots of the SNP p value versus m value for all 146 lead SNPs are shown in Data S2 . Nearly 75% (109/146) of the genome-wide significant SNPs were pleiotropic (i.e., associated with more than one disorder). As expected, configurations of disease association reflected the differences in the statistical power and genetic correlations between the samples ( Figure S1 ). Of the 109 pleiotropic loci, 83% and 72% involved SCZ and BIP, respectively. MD, which had the largest case-control sample, was associated with 48% of the pleiotropic loci (N = 52/109). Despite the relatively small sample size, ASD was implicated in 36% of the pleiotropic loci. Most of the ASD associations co-occurred with SCZ and BIP. The other disorders, ADHD, TS, OCD, and AN featured associations in 16%, 14%, 11%, and 7% of the pleiotropic loci, respectively. Of the single-disorder-specific loci, 81% and 16% were associated with SCZ and MD, respectively.

Power to detect associations across pairs of disorders was plotted with the number of cross-disorder associations identified in the current meta-analysis. For each pair of disorders, power was estimated using the number of cases and heritability for each disorder, as well as the genetic correlation between the disorders. In general, as power increased, so did the number of identified SNPs.

The factor structure described above is based on average effects across the genome, but does not address more fine-grained cross-disorder effects at the level of genomic regions or individual loci. To identify genetic loci with shared risk, we performed a meta-analysis of the eight neuropsychiatric disorders using a fixed-effects-based method () that accounts for the differences in sample sizes, existence of subset-specific effects, and overlapping subjects across datasets ( STAR Methods ). The standardized genomic inflation factor was close to one, suggesting no inflation of test statistics due to confounding (λ= 1.005; Figure 2 A). We identified 136 LD-independent regions with genome-wide significant association (P≤ 5 × 10). Due to the extensive LD at the major histocompatibility complex (MHC) region (chromosome 6 region at 25–35 Mb), we considered multiple signals present there as one locus. 101 of the 136 (74.3%) significantly associated regions overlapped with previously reported genome-wide significant regions from at least one individual disorder, while 35 loci (25.7%) represented novel genome-wide significant associations. Simulation analyses confirmed that the number of pleiotropic loci we identified exceeds chance expectation given the sample size and genetic correlations among the eight disorders (p < 9.9 × 10 Data S1 .5; for further details, see STAR Methods ).

(B) Gene prioritization strategies for significantly associated loci. Candidate genes were mapped on each locus if the index SNP and credible SNPs reside within a protein-coding gene, are eQTL markers of the gene in the brain tissue, or interact with promoter regions of the gene based on brain Hi-C data. (C) Manhattan plot displaying the cross-disorder meta-analysis results highlighting candidate genes mapped to top pleiotropic regions. When multiple genes were mapped to the same locus, genes encompassing the index SNP or genes with the largest number of evidences were displayed for clarity. Candidate genes that have not previously implicated in individual disorder GWAS are marked with an asterisk.

(A) Quantile-quantile (QQ) plot displaying the observed meta-analysis statistics versus the expected statistics under the null model of no associations in the -log 10 (p value) scale. Although a marked departure is notable between the two statistics, the estimated lambda 1000 and the estimated LD Score regression intercept indicate that the observed inflation is mainly due to polygenic signals rather than major confounding factors including population stratification.

We modeled the genome-wide joint architecture of the eight neuropsychiatric disorders using an exploratory factor analysis (EFA) (), followed by genomic structural equation modeling (SEM) () ( STAR Methods Figure 1 C). EFA identified three correlated factors, which together explained 51% of the genetic variation in the eight neuropsychiatric disorders ( Table S2 .2). The first factor consisted primarily of disorders characterized by compulsive/perfectionistic behaviors, specifically AN, OCD, and, more weakly, TS. The second factor was characterized by mood and psychotic disorders (MD, BIP, and SCZ), and the third factor by three early-onset neurodevelopmental disorders (ASD, ADHD, TS) as well as MD. Similar to our EFA results, hierarchical clustering analyses also identified three sub-groups among the eight disorders ( Data S1 .1). Based on extensive follow-up analyses, this genetic correlational structure does not appear to be biased by sample overlap or sample size differences among the eight disorders ( Data S1 .2-1.4).

After standardized and uniform quality control, additive logistic regression analyses were performed on individual disorders ( STAR Methods ). 6,786,993 SNPs were common across all datasets and were retained for further study. Using the summary statistics of these SNPs, we first estimated pairwise genetic correlations among the eight disorders using linkage disequilibrium (LD) score regression analyses () ( STAR Methods Figure 1 A; Table S2 .1). The results were broadly concordant with previous estimates (). The genetic correlation was highest between SCZ and BIP (r= 0.70 ± 0.02), followed by OCD and AN (r= 0.50 ± 0.12). Interestingly, based on genome-wide genetic correlations, MD was closely correlated with ASD (r= 0.45 ± 0.04) and ADHD (r= 0.44 ± 0.03), two childhood-onset disorders. Despite variation in magnitude, significant genetic correlations were apparent for most pairs of disorders, suggesting a complex, higher-order genetic structure underlying psychopathology ( Figure 1 B).

(C) Based on the results of an exploratory factor analysis of the genetic correlation matrix produced from multivariable LD-score regression, a confirmatory factor model with three correlated genetic factors was specified using Genomic SEM and estimated with the weighted least-squares algorithm. In this solution, each common genetic factor (i.e., F1 g, F2 g , F3 g ) represents variation in genetic liability that is shared across the disorders that load on it. These common factors are specified so as to account for the genetic covariation among the psychiatric disorders. For example, F1 g represents shared genetic liability among disorders characterized by compulsive behaviors (AN, OCD and TS). One-headed arrows connecting the common genetic factors to the individual disorders represent standardized loadings, which can be interpreted as coefficients from a regression of the true genetic liability for the disorder on the common factor. Two-headed arrows connecting the three factors to one another represent their correlations. Two-headed arrows connecting the genetic components of the individual psychiatric disorders to themselves represent residual genetic variances and correspond to the proportion of heritable variation in liability to each individual psychiatric disorder that is unexplained by the three factors. Standardized parameters are depicted with their standard errors in parentheses. Paths labeled 1 with no standard errors reported are fixed parameters, which are used for scaling.

(B) SNP-based genetic correlations between eight disorders were depicted using an in-directed graph to reveal complex genetic relationships. Only significant genetic correlations after Bonferroni correction in (A) were displayed. Each node represents a disorder, with edges indicating the strength of the pairwise correlations. The width of the edges increases, while the length decreases, with the absolute values of r g .

(A) SNP-based genetic correlations (r g ) were estimated between eight neuropsychiatric disorders using LDSC. The size of the circles scales with the significance of the p values. The darker the color, the larger the magnitude of r g . Star sign ( ∗ ) indicates statistical significance after Bonferroni correction.

We analyzed genome-wide single nucleotide polymorphism (SNP) data for eight neuropsychiatric disorders using a combined sample of 232,964 cases and 494,162 controls ( Table 1 Table S1 ). The eight disorders included AN () ASD (), ADHD (), BIP (), MD (), OCD (), TS (), and SCZ (). All study participants were of self-identified European ancestry, which was supported by principal component analysis of genome-wide data.

The number of cases and controls used in the meta-analysis of the present study. The numbers may differ from those reported in the original publications because our study included only European ancestry subjects to avoid potential confounding due to ancestral heterogeneity across distinct disorder studies. SNP heritability was estimated from the GWAS summary statistics using LD score regression.

Tourette Association of America International Consortium for Genetics, the Gilles de la Tourette GWAS Replication Initiative, the Tourette International Collaborative Genetics Study, and the Psychiatric Genomics Consortium Tourette Syndrome Working Group Interrogating the Genetic Determinants of Tourette’s Syndrome and Other Tic Disorders Through Genome-Wide Association Studies.

Tourette Association of America International Consortium for Genetics, the Gilles de la Tourette GWAS Replication Initiative, the Tourette International Collaborative Genetics Study, and the Psychiatric Genomics Consortium Tourette Syndrome Working Group Interrogating the Genetic Determinants of Tourette’s Syndrome and Other Tic Disorders Through Genome-Wide Association Studies.

Discussion

In the largest cross-disorder GWAS meta-analysis of neuropsychiatric disorders to date, comprising more than 725,000 cases and controls across eight disorders, we identified 146 LD-independent lead SNPs associated with at least one disorder, including 35 novel loci. Of these, 109 loci were found to affect two or more disorders, although characterization of this pleiotropy is partly dependent on per-disorder sample size. Our results provide five major insights into the shared genetic basis of psychiatric disorders.

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Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Third, we identified a set of loci that have opposite effects on risk of psychiatric disorders. Notably, these included loci with opposing effects on pairs of disorders that are genetically correlated and have common clinical features. For example, a SNP within MRSA was associated with opposing effects on two neurodevelopmental disorders (ASD and SCZ), and a variant within KIAA1109 had opposite directional effects on major mood disorders (BIP and MD) ( Table S3 .3). These results underscore the complexity of genetic relationships among related disorders and suggest that overall genetic correlations may obscure a more complex set of genetic relationships at the level of specific loci and pathways, as seen in immune-mediated diseases (). This heterogeneity of effects between genetically correlated disorders is also consistent with a recent analysis that revealed loci contributing to biological differences between BIP and SCZ and found polygenic risk score associations with specific symptom dimensions (). A complete picture of cross-phenotype genetic relationships will require understanding both same and opposite directional effects. In addition, to the extent that pleiotropic loci may reveal targets for drug discovery, opposite directional effects on psychiatric disorders could help anticipate problematic off-target effects.

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et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Fourth, we found extensive evidence that neurodevelopmental effects underlie the cross-disorder genetics of mental illness. In addition to DCC, a link between pleiotropy and genetic effects on neurodevelopment was also seen for other top loci in our analysis, including RBFOX1, BRAF, and KDM7A, all of which have been shown in prior research to influence aspects of nervous system development. Gene enrichment analyses showed that pleiotropic loci were distinguished from disorder-specific loci by their involvement in neurodevelopmental pathways including neurogenesis, regulation of nervous system development, and neuron differentiation. These results are consistent with those of a smaller recent analysis in the population-based Danish iPSYCH cohort (comprising 46,008 cases and 19,526 controls across six neuropsychiatric disorders) (). In that analysis, consistent with the present findings, functional genomic characterization of cross-disorder loci implicated fetal neurodevelopmental processes, with greater prenatal than postnatal expression. In addition, SORCS3 emerged as a genome-wide significant cross-disorder locus in both studies. However, other specific loci, cell types, and pathways implicated in the iPSYCH analysis differed from those identified in our study. In supplementary analyses, we did not find evidence of significant overrepresentation of genes related to pleiotropic SNPs identified here among previously defined genomic disorder regions or genes associated with neurodevelopmental disorders from rare variant studies (including ASD, intellectual disability, and developmental delay) () ( Data S3 .1–3.3).

Fifth, our analyses of spatiotemporal gene expression profiles revealed that pleiotropic loci are enriched among genes expressed in neuronal cell types, particularly in frontal or prefrontal regions. They also demonstrated a distinctive feature of genes related to pleiotropic loci: compared with disorder-specific loci, they are on average expressed at higher levels both prenatally and postnatally ( Figure 5 ). More specifically, single-disorder (mainly SCZ) loci were related to genes that were preferentially expressed in the first fetal trimester followed by a decline over the prenatal period and then relatively stable levels postnatally. In contrast, average expression of genes related to pleiotropic loci peaked in the second trimester and remained overexpressed throughout the lifespan. When dividing the pleiotropic loci into bins of those associated with two disorders (mainly SCZ and BIP) versus three or more disorders, we observed a consistent gradient of greater expression associated with broader pleiotropy. These results are based on average expression profiles, and not all individual gene expression patterns follow this pattern.

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Faraone S.V.

Glatt S.J.

Kendler K.S. Psychiatric genetics and the structure of psychopathology. Overall, our results identify a range of pleiotropic effects among loci associated with psychiatric disorders. Consistent with prior research (), we found substantial pairwise genetic correlations across child- and adult-onset disorders and extended these findings by demonstrating clusters of genetically-related disorders. These results augment a substantial body of research demonstrating that genetic influences on psychopathology do not map cleanly onto the clinical nosology instantiated in the DSM or ICD () Using a range of bioinformatic and functional genomic analyses, we find that loci with pleiotropic effects are distinguished by their involvement in early neurodevelopment and increased expression beginning in the second trimester of fetal development and persisting throughout adulthood. Apart from this, however, pleiotropic loci were similar to non-pleiotropic loci across a range of other functional features, including intolerance to loss-of-function mutations, evidence of selection, minor allele frequencies, and genomic position relative to functional elements.

Taken together, the analyses presented here suggest that genetic influences on psychiatric disorders comprise at least two general classes of loci. The first comprises a set of genes that confer relatively broad liability to psychiatric disorders by acting on early neurodevelopment and the establishment of brain circuitry. These pleiotropic genes, on average, begin to come online by the second trimester of fetal development and exhibit differentially high expression thereafter. The expression and differentiation of this generalized genetic risk into discrete psychiatric syndromes (e.g., ASD, BIP, AN) may then involve direct and/or interactive effects of additional sets of common and rare loci and environmental factors, possibly mediated by epigenetic effects, that shape phenotypic expression via effects on brain structure/function and behavior. Further research will be needed to clarify the nature of such effects.

Solovieff et al., 2013 Solovieff N.

Cotsapas C.

Lee P.H.

Purcell S.M.

Smoller J.W. Pleiotropy in complex traits: challenges and strategies. Zhu et al., 2018 Zhu Z.

Anttila V.

Smoller J.W.

Lee P.H. Statistical power and utility of meta-analysis methods for cross-phenotype genome-wide association studies. Our results should be interpreted in light of several limitations. First, while our dataset is the largest genome-wide cross-disorder analysis to date, data available for individual disorders varied substantially—from a minimum of 9,725 cases and controls for OCD to 461,134 cases and controls for MD. This imbalance of sample size may have limited our power to detect pleiotropic effects on underrepresented disorders. The future availability of larger samples will improve power for detection of cross-disorder effects. Second, it is possible that comorbidity among disorders contributed to apparent pleiotropy; we found, however, that fewer than 2% of cases overlapped between disorder datasets (excluding 23andMe data) and we adjusted for sample overlap in meta-analysis. Third, the method we applied to detect cross-phenotype association, which combines an all-subsets fixed-effects GWAS meta-analysis with a Bayesian method for evaluating the best-fit configuration of genotype-phenotype associations, is one of several approaches (). However, we have previously shown that this method outperforms a range of alternatives for detecting pleiotropy under various settings (). Fourth, our designation of loci as pleiotropic versus non-pleiotropic loci refers only to their observed effects on the eight target brain disorders. Thus, some of the “non-pleiotropic” loci may have additional effects on psychiatric phenotypes that were not included in our meta-analysis and/or on non-psychiatric phenotypes. Fifth, our functional genomic analyses were constrained by the limitations of existing resources (e.g., spatiotemporal gene expression data resources). Our work underscores the need for more comprehensive functional data including single cell transcriptomic and epigenomic profiles across development and brain tissues. Lastly, we included only individuals of European ancestry to avoid potential confounding due to ancestral heterogeneity across distinct disorder studies. Similar efforts are needed to examine these questions in other populations.

In sum, in a large-scale cross-disorder genome-wide meta-analysis, we identified three genetic factors underlying the genetic basis of eight psychiatric disorders. We also identified 109 genomic loci with pleiotropic effects, of which 33 had not previously been associated with any of the individual disorders. In addition, we identified 11 loci with opposing directional effects on two or more psychiatric disorders. These results highlight disparities between our clinically-defined classification of psychiatric disorders and underlying biology. Future research is warranted to determine whether more genetically-defined influences on cross-diagnostic traits or subtypes may inform a biologically-informed reconceptualization of psychiatric nosology. Finally, we found that genes associated with multiple psychiatric disorders are disproportionately associated with biological pathways related to neurodevelopment and exhibit distinctive gene expression patterns, with enhanced expression beginning in the second prenatal trimester and persistently elevated expression relative to less pleiotropic genes. Therapeutic modulation of pleiotropic gene products could have broad-spectrum effects on psychopathology.