Skip to main content

A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics



Recent genome-wide association studies (GWASs) have revealed the polygenic nature of psychiatric disorders and discovered a few of single-nucleotide polymorphisms (SNPs) associated with multiple psychiatric disorders. However, the extent and pattern of pleiotropy among distinct psychiatric disorders remain not completely clear.


We analyzed 14 psychiatric disorders using summary statistics available from the largest GWASs by far. We first applied the cross-trait linkage disequilibrium score regression (LDSC) to estimate genetic correlation between disorders. Then, we performed a gene-based pleiotropy analysis by first aggregating a set of SNP-level associations into a single gene-level association signal using MAGMA. From a methodological perspective, we viewed the identification of pleiotropic associations across the entire genome as a high-dimensional problem of composite null hypothesis testing and utilized a novel method called PLACO for pleiotropy mapping. We ultimately implemented functional analysis for identified pleiotropic genes and used Mendelian randomization for detecting causal association between these disorders.


We confirmed extensive genetic correlation among psychiatric disorders, based on which these disorders can be grouped into three diverse categories. We detected a large number of pleiotropic genes including 5884 associations and 2424 unique genes and found that differentially expressed pleiotropic genes were significantly enriched in pancreas, liver, heart, and brain, and that the biological process of these genes was remarkably enriched in regulating neurodevelopment, neurogenesis, and neuron differentiation, offering substantial evidence supporting the validity of identified pleiotropic loci. We further demonstrated that among all the identified pleiotropic genes there were 342 unique ones linked with 6353 drugs with drug-gene interaction which can be classified into distinct types including inhibitor, agonist, blocker, antagonist, and modulator. We also revealed causal associations among psychiatric disorders, indicating that genetic overlap and causality commonly drove the observed co-existence of these disorders.


Our study is among the first large-scale effort to characterize gene-level pleiotropy among a greatly expanded set of psychiatric disorders and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Peer Review reports


In the past decade, twins studies and more recent genome-wide association studies (GWASs) have successfully identified a large amount of single-nucleotide polymorphisms (SNPs) that are robustly associated with diverse psychiatric disorders [1,2,3,4,5], revolutionizing our understanding of genetic architecture underlying these illnesses. Two major findings have been revealed. First, there exists strong evidence that psychiatric disorders are highly heritable and polygenic [1, 5,6,7,8,9,10,11], with the estimated heritability of 40–80% across a wide range of such disorders, which means that a large number of genes have weak effects and substantially contribute to disease risk. Second, a surprisingly high degree of genetic loci are found to exhibit significant effects on multiple clinically distinct psychiatric disorders [1, 3, 5,6,7, 11,12,13], a phenomenon which is well-known as pleiotropy and is also pervasively perceived for many other complex phenotypes [14,15,16].

Understanding the extent to which one psychiatric disorder shares similar genetic component with the others is critical for identifying the etiology of phenotypic relationships and can inform disease nosology and diagnostic practice and improve drug development [1, 3, 11,12,13, 17]. However, the shared genetic foundation among psychiatric disorders remains not fully understood and there also exist practical and statistical issues that need to be investigated further. First, previous studies only incorporated a small set of psychiatric disorders; thus, they cannot offer a systematic and complete viewpoint about the genetic connection among various disorders. Second, nearly all prior work focused mainly on SNP-level pleiotropy (Additional file 1: Table S1) [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51]; the power of detecting single SNP association signal is still limited because genetic variants typically have weak effect on phenotypes [52,53,54]. Moreover, causal interpretation of SNP-based pleiotropic associations is challenging as truly causal genetic variants are often hard to pinpoint due to linkage disequilibrium (LD) among SNPs. Third, prior work primarily examined genetic correlation between psychiatric disorders [55]. Genetic correlation only quantifies an overall genetic similarity across the entire genome [56], it cannot characterize detailed association pattern for individual genetic loci and an insignificant estimate does not necessarily suggest the absence of common genetic background. Fourth, to identify commonly associated loci, almost all prior studies employed pleiotropy-informed mixture methods such as co-localization test [57], cFDR [58], GPA [20], and iMAP [59]. These methods were generally developed from a Bayesian perspective, their type I error rate control at a given family-wise error rate (FWER) is however not well established because they prioritize associations at a much more liberal significance level [60].

To overcome these limitations, in this work we attempt to address several critical issues in pleiotropy mapping for psychiatric disorders. First, instead considering individual SNPs, we implemented a gene-centric pleiotropy analysis by analyzing a set of SNPs located within a gene collectively. To this aim, relying on summary statistics of psychiatric disorders, we first conducted MAGMA [61] to aggregate a group of SNP-level association signals into a single gene-level association signal, based on which our pleiotropy analysis was carried out. Second, we analyzed a total of 14 psychiatric disorders, much larger compared to prior work; thus, our analysis had the potential to offer a comprehensive insight into shared genetic component underlying distinct disorders. Third, from a methodological perspective, we viewed the identification of pleiotropic gene associations across the whole genome as a high-dimensional problem of composite null hypothesis testing [62], and applied a powerful method called PLACO for pleiotropy mapping [60]. To further resolve the horizontal or vertical pleiotropy, we ultimately evaluated the potentially causal association among distinct psychiatric disorders using Mendelian randomization (MR) methods [63,64,65,66,67,68],


Summary statistics for 14 psychiatric disorders

We analyzed 14 psychiatric disorders obtained from the Psychiatric Genomics Consortium (PGC) (Table 1). These disorders included anorexia nervosa (AN; N = 14,477) [69], anxiety disorder (AD; N = 17,526) [70], autism spectrum disorder (ASD; N = 46,350) [71], alcohol use disorder (AUD; N = 121,604) [72], obsessive-compulsive disorder (OCD; N = 9725) [73], bipolar disorder (BIP; N = 51,710) [74], schizophrenia (SCZ; N = 77,096) [75], posttraumatic stress disorder (PTSD; N = 174,659) [76], Tourette’s syndrome (TS; N = 14,307) [77], cannabis use (CU; N = 184,765) [78], major depression disorder (MDD; N = 480,359) [79], and attention-deficit/hyperactivity disorder (ADHD; N = 53,293) [80]. For all the disorders, we obtained their European-only summary statistics and performed stringent quality control: (i) excluded non-biallelic SNPs and those with strand-ambiguous alleles; (ii) excluded SNPs that had no rs label; (iii) removed duplicated SNPs or those not included in the 1000 Genomes Project or whose alleles did not match those there; (iv) excluded SNPs that were located within the region of major histocompatibility complex (chr6: 28.5–33.5 Mb) because of its complex LD structure [81]; (v) kept SNPs that had minor allele frequency (MAF) > 0.01.

Table 1 Summary information of 14 psychiatric disorders analyzed in this study

Estimate genetic correlation with LDSC

We first employed the cross-trait linkage disequilibrium score regression (LDSC) to assess the genetic correlation between two psychiatric disorders with genome-wide SNPs [81]. The LD score for every SNP was calculated based on genotypes of common SNPs (with MAF > 0.01 and the P value of the Hardy Weinberg equilibrium test> 1 × 10−5) with a 10 Mb window on 503 Europeans in the 1000 Genomes Project. Then, LDSC carried out a weighted linear model by regressing the product of Z-statistics of two traits on the LD score across all available genetic variants across the whole genome. Theoretically, the regression slope provides an unbiased estimate for genetic correlation even when overlapping individuals exist between the two GWASs. Based on estimated genetic correlations, we conducted a hierarchical cluster analysis for these disorders.

Gene-based pleiotropic analysis under composite null hypothesis

To detect pleiotropic genes, we first applied MAGMA [61] to aggregate a set of SNP-level associations into a single gene-level association signal relying on summary statistics. It needs to emphasize that we used MAGMA here because it had been demonstrated that this method was powerful and computationally efficient and can be easily implemented with user-friendly software [61]. When conducting MAGMA, we defined the set of SNPs as those located within a given gene in terms of the annotation file provided in VAGIS [82]. Then, the P value of each gene was obtained and converted immediately into Z statistic. The direction of Z statistic was determined by the sign of summation of the product of effect sizes and MAFs of all SNPs in each gene [83]. Finally, depending on these newly transformed Z statistics, we carried out the pleiotropy test via PLACO [60], which was recently developed for detecting SNP-level pleiotropy by borrowing the perspective of composite null hypothesis from high-dimensional mediation analysis [62, 83]. We here extended it to discover pleiotropic associations at the gene level. Prior simulations [60] and variance-component-based mediation analysis under composite null hypothesis [84] already implied the validity of such extension. In brief, PLACO examines one gene at a time with two sets of Z-statistics as input and proceeds by dividing the composite null hypothesis of pleiotropy into three sub-null scenarios: (i) H00: the gene is not associated neither of the two disorders. (ii) H10: the gene is associated with the first disorder but not the second. (iii) H01: the gene is not associated with the first disorder but the second. The alternative hypothesis (H11) is that the gene is related to both disorders, corresponding to pleiotropic association.

The P values of MAGMA and PLACO were corrected by false discovery rate (FDR). Besides PLACO, we also leveraged a likelihood-ratio-based test (LRT) method to examine the existence of an overall pleiotropy between two disorders [20]. Moreover, as an empirical comparison, we performed two additional naïve methods. First, after obtaining P values for each disorder with MAGMA, we directly employed the FDR procedure to separately detect significant genes in each pair and identified genes having pleiotropic effect as those shared by both disorders; we referred to it as the direct FDR method. Second, we aggregated the two P values of a pair of disorders into a single P value by taking the maximum value, based on which pleiotropic genes were identified via the FDR procedure; we referred to it as the maximum P value method.

For each pleiotropic gene detected by PLACO, we simply calculated Pearson’s correlation coefficient (r) of SNP effect sizes to evaluate the similarity of genetic influence. We also assessed the effect heterogeneity of each SNP located within a pleiotropic gene through Cochran’s Q test, with the P value of heterogeneity corrected via the Benjamini-Yekutieli method to take the local dependency of SNPs into consideration [85].

Functional analysis for pleiotropic genes

Afterwards, we performed differential expression analysis and gene set enrichment analysis for pleiotropic genes identified by PLACO using FUMA [86]. Gene expressions of 53 tissues were obtained from GTEx, and a total of 22,146 were finally considered. To obtain differentially expressed gene (DEG) sets for every tissue, expressions were first normalized and then analyzed with the two-sided Student’s t test for each gene in one tissue against all others. Genes with Bonferroni-corrected P < 0.05 and absolute log-fold change ≥ 0.58 were defined as a DEG set in a given tissue [87,88,89,90], indicating that expression levels of these genes in that tissue had larger discrepancy compared to those in others. Upregulated and downregulated genes were further distinguished in a tissue by taking the sign of t-score into account. Finally, pleiotropic genes were tested against those DEG sets by hyper-geometric tests to evaluate whether an overrepresentation existed in DEG sets for special tissues.

To assess an overrepresentation of biological functions in the gene set enrichment analysis, we examined these detected pleiotropic genes against gene sets obtained from MsigDB (i.e., hallmark gene sets, positional gene sets, curated gene sets, motif gene sets, computational gene sets, GO gene sets, oncogenic signatures, and immunologic signatures) and WikiPathways using hyper-geometric tests [86]. The correction for multiple comparisons was performed per data source of tested gene sets (e.g., canonical pathways, GO biological processes, and hallmark genes) using FDR. FUMA reported gene sets with adjusted P ≤ 0.05 and the number of genes that overlapped with the gene set > 1 by default. For all identified pleiotropic genes, we also investigated potential antagonistic or shared drug-gene interactions related to psychiatric disorders by exploring the DGIdb database [91, 92].

Causal association among psychiatric disorders inferred via MR

MR is a commonly used instrumental variable causal inference for investigating the exposure on outcome effect with exposure-associated SNPs serving as instruments [63,64,65,66,67,68] (Additional file 2: Fig. S1). It is worth emphasizing that MR would not only offer an in-depth insight into the causal connection between these disorders, but also would provide a meaningfully genetic interpretation regarding to the nature of comorbidity of these disorders by resolving the horizontal or mediated (or vertical) pleiotropy [1, 71, 80] (Additional file 2: Fig. S2). We here conducted two major MR analyses. First, we aimed to study whether a childhood-onset psychiatric disorder (e.g., ASD or ADHD) would causally affect adulthood-onset psychiatric disorders (e.g., AD, AN, AUD, OCD, BIP, SCZ, PTSD, TS, CU, and MDD). To this aim, we carried out a one-sided MR analysis with ASD or ADHD as the exposure and adulthood-onset psychiatric disorders as outcomes. Second, because the temporal ordering among adulthood-onset disorders is not completely definitive, we intended to explore whether adulthood-onset psychiatric disorders may causally impact with each other. To this goal, we implemented a bidirectional MR analysis with one adulthood-onset disorder as the exposure and the remaining as outcomes. Because of the existence of high genetic overlap among the three alcohol use disorders, we only analyzed AUDIT-C in the two MR analyses (actually, AUDIT-T and AUDIT-P generated very similar results).

We selected SNP instruments using the clumping procedure of PLINK following prior work [93, 94]. During the clumping selection, we set the LD and physical distance thresholds to be 0.001 and 10 Mb, respectively, with LD estimated using a reference panel of 503 individuals of European ancestry in the 1000 Genomes Project. More importantly, as some psychiatric disorders had only a few independent genome-wide significant genetic loci (P < 5 × 10−8), to obtain sufficient SNPs serving as candidate instruments for fair comparison across diverse disorders, we employed a relatively relaxed significance cutoff of 1 × 10−5 for choosing associated genetic variants as done in [78]. In practical MR analysis, smaller significance threshold was often applied when few SNPs were available for the exposure at a more stringent level [95,96,97]. This would certainly generate a larger set of instruments that can thus explain larger phenotypic variation for power improvement; however, it also increased the potential risk of horizontal pleiotropy (Additional file 2: Fig. S1). Therefore, to avoid such issue, we additionally conducted a conservative quality control on candidate instruments by filtering out SNPs that might be potentially associated with the disorder under analysis if the selected SNP instruments had a Bonferroni-corrected P < 0.05 for that disorder [67, 98,99,100]; doing this would also minimize the influence of linkage association on our MR results [101].

In our MR study, we primarily employed the inverse-variance weighted (IVW) methods to estimate the causal effect [102,103,104,105]. To assess the robustness of significant associations identified by the IVW approach, we further undertook two complementary sensitivity analyses: (i) the weighted median-based method which is appropriate when some SNP instrumental variables are likely invalid [106] and (ii) the MR-Egger regression for which the intercept can be used to evaluate the directional pleiotropy of instruments [104, 107].


Estimated cross-trait genetic correlation and cluster analysis

We first present the result of estimated cross-trait genetic correlation (Fig. 1A). It is shown more than half of (75.8% = 69/91) pairs of psychiatric disorders exhibit positive genetic correlation, with an average of 0.219 and individual correlation coefficients ranging from − 0.354 ± 0.146 between AUDIT-C and PTSD to 0.986 ± 0.002 between AUDIT-C and AUDIT-T. Approximately 64.8% of these genetic correlation estimates have P values< 0.05 and 44.0% are still significant after Bonferroni’s correction. It needs to highlight that the genetic correlation analysis for the three AUD traits might be biased because of overlapping samples although LDSC takes such issue into account [81].

Fig. 1
figure 1

A Estimated genetic correlation of 14 psychiatric disorders with the LDSC method. The color on the top triangle indicates the magnitude of the genetic correlation; the significance of genetic correlation in − log10(P value) is shown on the bottom triangle, with significant genetic correlations after Bonferroni correction marked with an asterisk. B Cluster analysis based on the estimated genetic correlation matrix produced from cross-trait LDSC for the 14 psychiatric disorders. C Number of pleiotropic genes (FDR < 0.05) discovered by PLACO based on de-correlated Z-statistics for the 14 psychiatric disorders

Moreover, in terms of the cluster analysis with estimated genetic correlations, these psychiatric disorders can be genetically divided into three major categories (Fig. 1B). In brief, the first category consists primarily of disorders characterized by compulsive behaviors (e.g., BIP, SCZ, ASD, AN, OCD, and TS); the second factor is characterized by substance behavioral traits (e.g., AUDIT-P, AUDIT-T, AUDIT-C, and CU); the third factor is mainly characterized by depression and stress behaviors (e.g., ADHD, PTSD, AD, and MDD). Overall, the genetic correlation analysis indicates the existence of substantial common genetic basis across diverse psychiatric disorders.

Shared associated genes for 14 psychiatric disorders

The direct FDR method discovers 0.18% of genes showing pleiotropic effect, the maximum P value method detects 0.05% of genes displaying pleiotropic impact (Additional file 2: Fig. S3), whereas PLACO identifies 0.31% of genes exhibiting pleiotropic influence. By leveraging LRT, we find strongly statistical evidence supporting shared genetic foundation underlying most of pairs (64.8%) of psychiatric disorders (Additional file 2: Fig. S4). Totally, there are 5884 associations (2424 unique genes) (FDR < 0.05) detected by PLACO across all pairs of psychiatric disorders (Fig. 1C and Additional file 3: Table S2). Among these disorders, we discover that SCZ shares the most pleiotropic associations with BIP (i.e., 738 shared genes), in line with the high genetic correlation between them (rg = 0.85 ± 0.02) and also consistent with previous observation that extensively common polygenic variation contributes to the risk of the two disorders [1, 9, 55, 108, 109]. Interestingly, SCZ also shares a large number of pleiotropic genes with the three AUD traits (i.e., 150 with AUDIT-C, 48 with AUDIT-P, and 194 with AUDIT-T) although their genetic correlations are not evidently high (rg = − 0.03 ± 0.02 with AUDIT-C, rg = 0.18 ± 0.03 with AUDIT-P, and rg = 0.02 ± 0.02 with AUDIT-T). Moreover, we find the number of identified pleiotropic genes is slightly positively correlated to the effective sample size (r = 0.181) and strongly positively correlated to the estimated heritability (r = 0.548) across all analyzed psychiatric disorders.

Correlation of effect sizes and heterogeneity of SNPs for pleiotropic genes

We show estimated Pearson’s correlation coefficients of SNP effect sizes for each pleiotropic gene in a pair of psychiatric disorders in Fig. 2A. It is found most of pleiotropic genes (76.2%) exhibit positively correlated genetic effects, with an average of r = 0.529 (Fig. 2B), indicating that the majority of these genes generally show consistent direction in genetic influence on psychiatric disorders. Particularly, 39.2% of pleiotropic genes display substantially positive correlation in genetic effect (|r| > 0.5) and 11.7% show very strongly positive correlation (|r| > 0.9). Nevertheless, 23.8% of pleiotropic genes display negatively correlated genetic effects, with an average of r = − 0.377 (Fig. 2B), which implies that diverse functional roles of these genes underlie the pathological mechanism of psychiatric disorders and that the overall genetic correlation described above might be underestimated the genetic overlap among these disorders. Note that, the antagonistic effect phenomenon is also widely observed in other traits such as immune-relevant diseases [108, 110, 111].

Fig. 2
figure 2

A Distribution of correlation coefficient of SNP effect sizes of pleiotropic genes detected by PLACO. B Number of pleiotropic genes having positive (the upper triangular) or negative (the lower triangular) correlation in SNP effect sizes on psychiatric disorders

On the other hand, in terms of the Cochran’s Q test, we discover on average 13.1% (ranging from 0% between AD and AN to 51.8% between AD and ADHD) of these SNPs show heterogeneous genetic effect (FDR < 0.05) (Additional file 2: Fig. S5A). As expected, the average proportion of heterogeneous SNPs across identified pleiotropic genes in a pair of psychiatric disorders is significantly inversely correlated to the cross-trait genetic correlation (r = − 0.32, P = 5.93 × 10−3; Additional file 2: Fig. S5B), suggesting the heterogeneity in genetic influence may partly explain the discrepancy of symptoms for these psychiatric disorders.

Pleiotropic genes associated with multiple psychiatric disorders

Among all these pleiotropic genes, 44.0% are associated with at least three psychiatric disorders (Fig. 3A and Additional file 4: Table S3). The numbers and distribution of pleiotropic genes shared across disorders are demonstrated in Fig. 3B. Particularly, LRRC37A4P and MIR2113 are the most top genes that are identified in 10 psychiatric disorders, followed by LINC00461, MIR9-2, ARHGAP27_2, ARL17A_2, CRHR1, KANSL1, KANSL1-AS1, LOC100507091, LOC644172_1, MAPT, MAPT-AS1, MGC57346, MIR5688, MSRA, NSFP1_1, PLEKHM1, SPPL2C, STH, and WNT9B, which are detected in 9 psychiatric disorders. MIR2113 (microRNA 2113), on chromosome 6q16 [112, 113], was reportedly related to bipolar disease through being involved in multiple biological pathways that regulated brain development and synaptic plasticity [114]. In terms of two prospective longitudinal cohort studies, the gene CRHR1 was suggested to exert a protective effect against adult depression among subjects who reported childhood maltreatment by consolidating memories of emotionally arousing experiences [115]. The deficiency of KANSL1 can lead to neuronal dysfunction by oxidative stress-induced autophagy [116] and psychiatric symptoms were relatively common in MAPT mutation non-carriers compared to the general population [117]. The gene MGC57346 was recently identified to be associated with neuroticism that was a common brain-related disorder [118]. In addition, WNT9B might be involved in the development of ASD via the WNT pathway [119]. Again, it is shown that these pleiotropic genes also exhibit antagonistic effects although they generally show similar genetic impacts in the same direction across psychiatric disorders (Fig. 3A).

Fig. 3
figure 3

A Several associated genes which are shared across psychiatric disorders and are identified to show pleiotropic effects on at least eight psychiatric disorders. Color indicates direction and strength of associations across disorders. B UpSet plot to illustrate the numbers (N > 15) and distribution of pleiotropic genes shared across psychiatric disorders and the number of pleiotropic genes in each psychiatric disorder

Enrichment analysis for identified pleiotropic genes

We here performed gene set enrichment analyses for all the 2424 unique pleiotropic genes identified by PLACO using FUMA. It is shown that the differentially expressed ones of these pleiotropic genes are significantly enriched in pancreas, liver, heart, and brain tissues in terms of expression level across the GTEx tissues, particularly for these downregulated pleiotropic genes (Fig. 4). The gene ontology (GO) enrichment analysis shows that the biological process (BP) of these pleiotropic genes is remarkably enriched in regulating neurodevelopment, such as cell differentiation (FDR = 7.27 × 10−36), neurogenesis (FDR = 4.60 × 10−32), and neuron differentiation (FDR = 1.08 × 10−26). For the GO cellular component (CC) terms, these genes are concentrated in chromosome (FDR = 2.32 × 10−31), neuron part (FDR = 2.29 × 10−27), and protein DNA complex (FDR = 3.27 × 10−25). For the molecular function (MF) categories, these genes are enriched in protein dimerization activity (FDR = 2.57 × 10−19), RNA binding (FDR = 5.62 × 10−18), and protein heterodimerization activity (FDR = 1.13 × 10−16). Meanwhile, the KEGG enrichment analysis shows that these genes are remarkably enriched in systemic lupus erythematosus (FDR = 3.77 × 10−22), MAPK signaling pathway (FDR = 1.67 × 10−4), and arrhythmogenic right ventricular cardiomyopathy ARVC (FDR = 3.88 × 10−4). The top 10 significant GO and KEGG pathways are shown in Fig. 5. Overall, these enrichment results further support the validity of these identified pleiotropic genes.

Fig. 4
figure 4

Enrichment of differentially expressed ones of all identified pleiotropic genes in terms of expression level across 54 GTEx tissues. P values are shown in the y-axis with a scale of − log10. The bars in orange represent significant enrichment with Bonferroni adjustment for multiple hypothesis testing

Fig. 5
figure 5

Top 10 significant types of pathways in terms of the GO and KEGG enrichment analyses. BP: biological process; CC: cellular component; MF: molecular function; KEGG: KEGG pathways

Investigation of drug-gene interactions for psychiatric disorders

It is demonstrated that among all the analyzed pleiotropic genes there are 342 unique ones which are associated with 6353 drugs showing drug-gene interaction with psychiatric disorders. However, only two genes (i.e., HSP90B1 and NCAM1) show the same directional effects across all the 14 psychiatric disorders and the others display opposite directional effects on two or more disorders (Additional file 5: Table S4). Particularly, CYP2D6 is the top gene related to 589 drugs, followed by VDR (460 drugs), KDM4A (442 drugs), MAPT (436 drugs), FEN1 (199 drugs), HTT (198 drugs), EGFR (178 drugs), DRD2 (177 drugs), and MAPK1 (161 drugs). These drugs can be classified into distinct types including inhibitor (11.2% = 710/6353), agonist (3.6% = 228/6353), blocker (3.4% = 214/6353), antagonist (3.3% = 208/6353), and modulator (1.1% = 68/6353). With regard to these discovered gene-drug interactions, some prior studies provided evidence supporting their link with psychiatric disorders. For instance, CYP2D6 played an important role in the efficacy of imipramine, clomipramine, nortriptyline, and fluoxetine in depressed patients [120,121,122]. As another example, it was shown that, by regulating the well-known tau protein in neuronal axons, MAPT interacted actively with astemizole and lansoprazole, two benzimidazole derivatives which were proven to have a great potential in the treatment of brain-related disorders [123, 124].

Estimated causal associations between psychiatric disorders

The number of SNP instruments used for psychiatric disorders ranges from 19 to 433, with a median of 66. On average, the selected SNPs explain 4.3% of phenotypic variance across all psychiatric disorders. The minimum F statistic is above 10 (from 21.5 to 26.7) [102], indicating that weak instrumental bias is less likely to occur. We then present the results of the two MR analyses. First, among all the 110 examined relationships (20 for childhood-onset psychiatric disorders and 90 for adulthood-onset psychiatric disorders), most of the estimated effect sizes (whether significant or not) are positive (75.5% = 83/110) (Fig. 6A), with an average of 0.052 (sd = 0.352), implying the occurrence of one psychiatric disorder can lead to a substantially increased risk of other particular psychiatric disorders. Totally, there are 43 significant causal associations (FDR < 0.05) (i.e., 9 for childhood-onset psychiatric disorders and 34 for adulthood-onset psychiatric disorders). As anticipated, these significant relationships show higher average effect size compared to non-significant ones (0.110 vs. 0.015).

Fig. 6
figure 6

Results of the Mendelian randomization analysis for psychiatric disorders. A Distribution of effect sizes across all pairs of psychiatric disorders estimated with the inverse-variance weighted method. B Estimated effect sizes and their 95% confidence intervals (CIs) for two childhood-onset psychiatric disorders (e.g., ASD or ADHD) on ten adulthood-onset psychiatric disorders. C Significant causal associations among ten adulthood-onset psychiatric disorders indicated by arrows (FDR < 0.05). The arrow to the right side indicates that the adulthood-onset psychiatric disorders across the diagonal line has a significant causal effect on the adulthood-onset psychiatric disorders on the column, vice versa for the arrow to the left side; the two-sided arrow indicate that the two disorders have a significant causal effect on each other. The color indicates the direction of the estimated causal effect no matter whether it is significant or not. The plus and minus signs indicate positive and negative effect sizes, respectively. The legend on the bottom left shows the count of diverse effect sizes in direction for all the pairwise relationships or only these significant associations for adulthood-onset psychiatric disorders

Second, we observe that the two childhood-onset psychiatric disorders can causally affect the development of psychiatric disorders in adulthood (Fig. 6B). For example, in terms of the estimated odds ratio (OR), both ADHD and ASD can similarly elevate the risk of CU (5.8% [95% confidence intervals (CIs) 2.6 ~ 8.9%] and 5.3% [95%CIs 1.2 ~ 9.6%] increase, respectively) and PTSD (11.5% [95%CIs 6.1 ~ 17.1%] and 11.4% [95%CIs 3.8 ~ 19.6%] increase, respectively). However, compared to ASD, ADHD seems to result in a higher risk for a much wider spectrum of adulthood-onset psychiatric disorders, including an increased risk of 10.5% (95%CIs 4.5~16.8%) for AN, 7.3% (95%CIs 2.9~11.9%) for BIP, 15.7% (95%CIs 11.8~19.7%) for MDD, 7.1% (95%CIs 2.1~12.4%) for SCZ, and 23.9% (95%CIs 14.3~34.2%) for TS. In addition, despite being grouped into the same cluster (see Fig. 1B), ASD does not causally affect any other psychiatric disorders that belong to the same category; in contrast, as described above, ADHD causally impacts two psychiatric disorders (e.g., PTSD and MDD but not AD) which belong to the same group.

Third, the average effect size is 0.112 ± 0.555 across all the 34 significant associations for these adulthood-onset psychiatric disorders, indicating that adulthood-onset psychiatric disorders themselves often strongly promote the disease development and progression with each other. More than half of them (61.8% = 21/34) exhibit bidirectionally positive influences on each other, this value slightly increases up to 62.2% (=28/45) for all pairwise relationships among adulthood-onset psychiatric disorders (Fig. 6C). For example, SCZ leads to a 51.4% (95%CIs 47.0 ~ 55.8%) increased risk of BIP, which can in turn increase the risk of SCZ by 44.0% (95%CIs 37.8~50.5%); as another example, AN results in a 9.1% (95%CIs 5.3~13.1%) higher risk of MDD, which also increase the risk of AN by 44.8% (95%CIs 26.7~65.5%). Note that not all of the bidirectionally positive effect sizes are statistically significant; for instance, SCZ can lead to a 6.8% (95%CIs 3.8~9.8%) higher risk of CU, but the impact of CU on SCZ is not significant (FDR = 0.270), which is consistent with the observation in [78]. In addition, only a few examined relationships show opposite effects (2 for significant associations and 5 for all pairwise relationships). Particularly, there are two significant relationships showing bidirectionally negative impacts; that is, alcohol use disorder is inversely affected by PTSD (OR = 0.29, 95%CIs 0.14~0.58, FDR = 0.002) and TS (OR = 0.13, 95%CIs 0.03~0.30, FDR = 0.024). However, neither PTSD nor TS has a significant effect on alcohol use although both have a negative effect (β = − 2.8 × 10−3, 95%CIs − 8.0 × 10−3~2.0 × 10−3, and FDR = 0.416 for PTSD; β = − 1.1 × 10−3, 95%CIs − 4.0 × 10−3~2.0 × 10−3, and FDR = 0.683 for TS).

Finally, for these significant associations described above, other MR methods generate very consistent results (Additional file 6: Table S5). For example, the estimates of causal effects obtained via the weighted median method are highly similar with those estimated with the IVW method in magnitude and direction (r = 0.973, P = 6.90 × 10−28), and all remain significant (FDR < 0.05), indicating the robustness of the IVW MR result. However, in terms of Egger regression, the majority of these associations are no longer significant although most of them show the same directional effects. This observation can be anticipated as Egger regression was developed under more limited conditions and is generally conservative [104, 107]. Moreover, in terms of the result of MR-Egger regression, we can largely rule out the potential influence of horizontal pleiotropy for most of these identified causal associations (with one exception for the association between BIP to SCZ) (i.e., the MR-Egger intercept is not significantly different from zero) (Additional file 6: Table S5).


Summary of results in the present study

In the present work, we have carried out a systematic pleiotropy analysis for 14 psychiatric disorders, encompassing approximately 1.3 million cases and controls of European ancestry. Relying on GWAS summary statistics and applying a set of novel bioinformatics approaches, our analysis provides important insight into genetic background underlying these disorders. First of all, we reinforced the high heritability and the existence of widely common genetic component among psychiatric disorders at the whole genomic level [1, 3, 5,6,7, 11,12,13], which leads to the hypothesis that these disorders may be an extreme manifestation of continuous heritable traits, and also in part offers a reasonable explanation for the comorbidity observed in epidemiological studies [125, 126]. Although a few genetic correlations were non-significant, we cannot completely rule out the possibility that the null genetic correlations may be due to large estimation uncertainty for some psychiatric disorders because of small sample sizes (e.g., N is only 9725 for OCD).

Moreover, based on estimated genetic correlations, we found that these psychiatric disorders can be clustered into several distinct sub-groups, which, together with the high genetic overlap among these disorder, challenges the biological validity of existing diagnostic approaches that primarily rely on expert opinions, subjective description and experience of patients, and observational and syndromic systems of diagnosis and classification for psychiatric disorders [11]. This genetic overlap also offers a potentially alternative nosology informed by the similarity of disease genetic architecture underlying these disorders besides clinical manifestations [3, 11]. By carrying out the pleiotropy association analysis with PLACO [60], we detected a large number of potential pleiotropic genes for these psychiatric diseases. Furthermore, using the MR method we discovered, there existed a wide range of substantially causal associations between childhood-onset and adulthood-onset psychiatric disorders as well as among adulthood-onset psychiatric disorders, indicating that these disorders can actually cause each other and that genetic overlap and causality may commonly drive the observed co-existence of psychiatric disorders [11, 78].

Comparison our discoveries to prior studies

Different statistical perspective and pleiotropy test method

Compared to prior pleiotropy studies of psychiatric disorders that mainly focused on individual SNPs (Additional file 1: Table S1) [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51], our work has two pronounced features. First, we performed a gene-centric analysis based on a set of local SNPs rather than individual genetic variants. It is well known that gene is a more biologically meaningful functional unit in living organisms and a gene typically contains multiple association signals. Therefore, as an effective alternative analysis strategy, SNP-set analysis is in general more powerful than its counterpart of single-marker analysis due to the aggregation of multiple weak association signals and the reduced burden of multiple testing [127,128,129,130,131,132,133,134]. Second, we explicitly addressed the problem of pleiotropy identification from a statistical perspective of composite null hypothesis and applied PLACO to detect genes with pleiotropic effects [60]. Compared to previous methods whose error rate control for FWER was not well studied, PLACO was demonstrated to have well-calibrated error control and behaved better in power compared to other existing methods. Importantly, PLACO can be still valid even when overlapping subjects exist between diverse GWASs [60], which is not uncommon in large-scale meta-GWASs for phenotypic correlated traits. For example, it is shown there are about 2% of cases overlapped among these PGC GWASs for psychiatric disorders [1]. Note that, overlapping subjects can inflate test statistics of association signals [135,136,137]. Therefore, our pleiotropy analysis implemented with PLACO is less likely biased by overlapping subjects.

Comparison of estimated genetic correlation

It is worth highlighting that our estimates of genetic correlation based on summary-level data are largely consistent with those obtained with individual-level genotypes and phenotypes [55]. For example, both showed that SCZ and BIP shared a greatly high degree of genetic basis and revealed the existence of substantial genetic overlap between SCZ and MDD, SCZ and ASD, BIP and MDD, and MDD and ADHD. Nevertheless, opposite results are observed; for instance, we discovered a substantial positive genetic correlation between ASD and ADHD (rg = 0.40, P = 1.44 × 10−18), which is supported by the observation that the two disorders co-occur with each other [138], in contrast to a negative but non-significant value observed in prior work (rg = − 0.13, P = 0.13) [55]. Further, we discovered there existed a significantly positive genetic correlation between BIP and ADHD (rg = 0.17, P = 1.75 × 10−5), unlike the lack of genetic overlap between them (rg = 0.05, P = 0.31) discovered in prior study [55]. These evident distinctions imply the advantage and benefit of our analysis using much larger sample sizes for these psychiatric disorders.

Comparison of genetic cluster analysis

Similar to prior work [1], in our cluster analysis we also discovered that OCD and TS belonged to the same group, in line with substantial evidence that the two disorders overlap in many ways that suggest a much closer relationship. It was reported more than third of people with TS had OCD [139,140,141], leading to the difficulty of telling the difference between the two disorders. This finding is also consistent with the hypothesis that family history studies of comorbidity have found familial aggregation with TS, especially for early-onset OCD, and familial aggregation with other psychiatric disorders such as anxiety [142]. Furthermore, we found that two childhood-onset disorders (i.e., ADHD and ASD) were separately divided into two diverse groups; specifically, ADHD was shown to be genetically similar to PTSD, AD, and MDD, while ASD exhibited more genetic similarity with OCD, TS, BIP, SCZ, and AN, suggesting that the presentation of psychiatric symptoms have a broad spectrum ranging from childhood to adulthood and that the vulnerability for some of psychiatric disorders might begin early in the stage of neurodevelopment [143,144,145]. Note that, our cluster result may be not completely in agreement with the observation in clinical practice as it was generated based only on genetic similarity.

Comparison of gene enrichment analysis

Because of only using a small fraction of lead pleiotropic SNPs associated across psychiatric disorders, prior work only discovered significant enrichment of genes expressed in brain [1]. Besides brain, we additionally showed significant enrichment in pancreas, liver, and heart, offering more insights into the biological processes underlying these disorders. The relationship of psychiatric disorders with brain is well established [3, 144, 146,147,148,149], and their connection with liver is supported by the occurrence of various psychiatric syndromes among patents with liver-relevant diseases observed in prior literature [150, 151]. There also exists sufficient evidence supporting its link with liver. For example, the concept of cerebral intoxication by nitrogenous substances derived from the intestine partly accounts for portal-systemic encephalopathy and also offers a rational basis for effective therapy of liver disease [150]. Twin studies and molecular genetic studies further revealed substantial genetic correlation between coronary artery disease and psychiatric disorders such as schizophrenia, bipolar disorder, and major depressive disorder, and even suggested that both may actually cause one another [152]. Moreover, the animal experiment showed that brain-pancreas relative protein exposed to chronic unpredictable mild stress would induce depression in male rats [153], indicating pancreas is a tissue closely related to disease process of psychiatric disorders. As another evidence, it was demonstrated that nearly half of patients with pancreatic carcinoma had evident premorbid psychiatric symptoms [154], indicating their important indicator roles in the diagnosis of pancreatic carcinoma. Finally, both liver and pancreas are metabolism-related organs, implying that psychiatric disorders are associated with metabolic relevant biological process. This finding is consistent with the observation that persons with psychiatric disorders are at increased risk of developing metabolic syndromes, diabetes, and cardiovascular diseases, which has become one of the greatest challenges in psychiatric practice [155,156,157].

Important statistical and scientific implications of our findings

Our finding has important implications from both the statistical and scientific perspectives. First, the extensive genetic overlap among psychiatric disorders aids to discover potentially genetic connection that cannot be observed in a single phenotype study, it also promotes the ability to substantially boost statistical power and improve prediction accuracy in joint analysis by borrowing phenotypic correlation across psychiatric traits [13, 158], which means that more fruitful gains would be available by applying cost-effective pleiotropy-informed statistical approaches to mine existing and future data sources of psychiatric disorders. Second, advances in knowledge of common genetic architecture of psychiatric disorders are critical for developing novel genetically based therapeutic strategies. It is very likely that a target treatment designed for one disorder has a broader therapeutic role in other disorders, implying wider patients could possibly benefit from such research [11]. Third, among the pleiotropic associations, we emphasize the so-called antagonistic effect phenomenon that a specific gene may show strong associations with multiple psychiatric disorder but the directions of the genetic effect may be opposite in each other. This finding is particularly important for discovering molecular targets especially intending to repair mutations via genome-editing techniques such as the CRISPR-CAS system, since this might lead to unexpected genetic, and therefore phenotypic, side impacts [17]. Fourth, the positively causal associations between distinct psychiatric disorders offer important insight into the development of prevention and treatment strategies in the clinic. For example, much attention should be paid for a child with ADHD in order to avoid or decrease the risk of other adulthood-onset psychiatric disorders such as AN, BIP, and MDD.

Potential limitations

Finally, we highlight that our results should be interpreted in consideration with several limitations. First, although our results suggest that, although the comorbidity of psychiatric disorders is partly explained by common genetic components, the shared biological mechanism underlying psychiatric disorders is largely not clear [13, 78], the functional role of these pleiotropic genes remains completely unknown, and the causal mechanism among psychiatric disorders is not yet understood. Therefore, further experimental and methodological investigations are warranted. Second, because rare variants were not available in most GWAS summary data, in the present we only focused on SNPs with MAF > 0.01 and cannot detect shared rare SNPs. Exploring the pleiotropy for rare genetic loci underlying psychiatric disorders can certainly offer more in-depth understanding of shared genetic foundation between these disorders [159].

Third, reproducibility is a well-known important principle in evaluating the findings of association studies [160]; however, at this time, we are unaware of other similar large-scale European-only summary statistics that could be employed for replication of our association discoveries. Again, because of unavailability of relevant data, we primarily focused on our pleiotropy analysis in the European population; therefore, due to the trans-ethnic diversity of genetic architecture in many complex traits including psychiatric disorders [161], it is unclear whether our findings can be transportable to other ancestral groups such as East Asians.

Fourth, the substantial imbalance in sample sizes across these psychiatric disorders (ranging from 9725 for OCD to 480,359 for MDD) resulted in varying power, which might undermine our findings. As the power of identifying pleiotropic associations partly depends on sample size; therefore, larger samples are required for some disorders with small sizes. Fifth, we here only explored disorder-common genetic loci. Understanding disorder-specific associated genes is also equally important and has the potential to elucidate genetic difference between psychiatric disorders [162], to distinguish one particular psychiatric disorder from others and to shed light on the reason why a treatment is effective for only one disorder but is not for others.


To our knowledge, this study is among the first large-scale effort to characterize the gene-level pleiotropy among a greatly expanded set of psychiatric disorders, and provides important insight into shared genetic etiology underlying these disorders. The findings would inform psychiatric nosology, identify potential neurobiological mechanisms predisposing to specific clinical presentations, and pave the way to effective drug targets for clinical treatment.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.



Anxiety traits


Attention-deficit/hyperactivity trait


Anorexia nervosa


Autism spectrum trait


Alcohol use trait


Bipolar trait


Cannabis use


False discovery rate


Gene ontology


Genome-wide association study


Linkage disequilibrium


Linkage disequilibrium score regression


Minor allele frequency


Multi-marker analysis of genomic annotation


Major depression trait


Obsessive-compulsive disorder


Psychiatric Genomics Consortium


Pleiotropic analysis under composite null hypothesis


Posttraumatic stress trait




Single-nucleotide polymorphism


Tourette’s syndrome


  1. 1.

    Lee PH, Anttila V, Won H, Feng Y-CA, Rosenthal J, Zhu Z, et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell. 2019;179(7):1469–1482.e1411.

    Google Scholar 

  2. 2.

    Sullivan PF, Agrawal A, Bulik CM, Andreassen OA, Børglum AD, Breen G, et al. Psychiatric genomics: an update and an agenda. Am J Psychiatry. 2018;175(1):15–27.

    PubMed  Google Scholar 

  3. 3.

    van Os J, Linszen D, Reif A. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–9.

    Google Scholar 

  4. 4.

    Sullivan PF, Geschwind DH. Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders. Cell. 2019;177(1):162–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Sullivan PF, Daly MJ, O'Donovan M. Genetic architectures of psychiatric disorders: the emerging picture and its implications. Nat Rev Genet. 2012;13(8):537–51.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Polderman TJ, Benyamin B, De Leeuw CA, Sullivan PF, Van Bochoven A, Visscher PM, et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat Genet. 2015;47(7):702–9.

    CAS  PubMed  Google Scholar 

  7. 7.

    Smoller JW, Andreassen OA, Edenberg HJ, Faraone SV, Glatt SJ, Kendler KS. Psychiatric genetics and the structure of psychopathology. Mol Psychiatry. 2019;24(3):409–20.

    PubMed  Google Scholar 

  8. 8.

    Rees E, Owen MJ. Translating insights from neuropsychiatric genetics and genomics for precision psychiatry. Genome Med. 2020;12(1):43.

    PubMed  PubMed Central  Google Scholar 

  9. 9.

    Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature. 2009;460(7256):748–52.

    CAS  PubMed  Google Scholar 

  10. 10.

    Lee SH, DeCandia TR, Ripke S, Yang J, Sullivan PF, Goddard ME. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet. 2012;44(3):247–50.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    O'Donovan MC, Owen MJ. The implications of the shared genetics of psychiatric disorders. Nat Med. 2016;22(11):1214–9.

    CAS  PubMed  Google Scholar 

  12. 12.

    Maier RM, Visscher PM, Robinson MR, Wray NR. Embracing polygenicity: a review of methods and tools for psychiatric genetics research. Psychol Med. 2018;48(7):1055–67.

    CAS  PubMed  Google Scholar 

  13. 13.

    Martin J, Taylor MJ, Lichtenstein P. Assessing the evidence for shared genetic risks across psychiatric disorders and traits. Psychol Med. 2018;48(11):1759–74.

    PubMed  Google Scholar 

  14. 14.

    Sivakumaran S, Agakov F, Theodoratou E, Prendergast JG, Zgaga L, Manolio T, et al. Abundant pleiotropy in human complex diseases and traits. Am J Hum Genet. 2011;89(5):607–18.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14(7):483–95.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Watanabe K, Stringer S, Frei O, Umićević Mirkov M, de Leeuw C, Polderman TJC, et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat Genet. 2019;51(9):1339–48.

    CAS  PubMed  Google Scholar 

  17. 17.

    Gratten J, Visscher PM. Genetic pleiotropy in complex traits and diseases: implications for genomic medicine. Genome Med. 2016;8(1):78.

    PubMed  PubMed Central  Google Scholar 

  18. 18.

    Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR, et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 2013;9(4):e1003455.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Consortium C-DGotPG: Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet. 2013;381(9875):1371–79.

  20. 20.

    Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787.

    PubMed  PubMed Central  Google Scholar 

  21. 21.

    Lim CH, Zain SM, Reynolds GP, Zain MA, Roffeei SN, Zainal NZ, et al. Genetic association of LMAN2L gene in schizophrenia and bipolar disorder and its interaction with ANK3 gene polymorphism. Prog Neuro-Psychopharmacol Biol Psychiatry. 2014;54:157–62.

    CAS  Google Scholar 

  22. 22.

    Finseth PI, Sønderby IE, Djurovic S, Agartz I, Malt UF, Melle I, et al. Association analysis between suicidal behaviour and candidate genes of bipolar disorder and schizophrenia. J Affect Disord. 2014;163:110–14.

    CAS  PubMed  Google Scholar 

  23. 23.

    Cardno AG, Owen MJ. Genetic relationships between schizophrenia, bipolar disorder, and schizoaffective disorder. Schizophr Bull. 2014;40(3):504–15.

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Ruderfer DM, Fanous AH, Ripke S, McQuillin A, Amdur RL, Gejman PV, et al. Polygenic dissection of diagnosis and clinical dimensions of bipolar disorder and schizophrenia. Mol Psychiatry. 2014;19(9):1017–24.

    CAS  PubMed  Google Scholar 

  25. 25.

    Sumner JA, Duncan L, Ratanatharathorn A, Roberts AL, Koenen KC. PTSD has shared polygenic contributions with bipolar disorder and schizophrenia in women. Psychol Med. 2016;46(3):669–71.

    CAS  PubMed  Google Scholar 

  26. 26.

    Costas J, Carrera N, Alonso P, Gurriarán X, Segalàs C, Real E, et al. Exon-focused genome-wide association study of obsessive-compulsive disorder and shared polygenic risk with schizophrenia. Transl Psychiatry. 2016;6(3):e768.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Chen J, Bacanu S-A, Yu H, Zhao Z, Jia P, Kendler KS, et al. Genetic relationship between schizophrenia and nicotine dependence. Sci Rep. 2016;6(1):1–10.

    Google Scholar 

  28. 28.

    Chen X, Long F, Cai B, Chen X, Qin L, Chen G. A novel relationship for schizophrenia, bipolar, and major depressive disorder. Part 8: a hint from chromosome 8 high density association screen. Mol Neurobiol. 2017;54(8):5868–82.

    CAS  PubMed  Google Scholar 

  29. 29.

    Schork AJ, Won H, Appadurai V, Nudel R, Gandal M, Delaneau O, et al. A genome-wide association study for shared risk across major psychiatric disorders in a nation-wide birth cohort implicates fetal neurodevelopment as a key mediator. bioRxiv. 2017:240911.

  30. 30.

    Taylor MJ, Martin J, Lu Y, Brikell I, Lundström S, Larsson H, et al. Genetic evidence for shared risks across psychiatric disorders and related traits in a Swedish population twin sample. BioRxiv. 2017;234963.

  31. 31.

    Consortium TASDWGoTPG. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24. 32 and a significant overlap with schizophrenia. Mol Autism. 2017;8:1–17.

    Google Scholar 

  32. 32.

    Nivard MG, Gage SH, Hottenga JJ, Van Beijsterveldt CE, Abdellaoui A, Bartels M, et al. Genetic overlap between schizophrenia and developmental psychopathology: longitudinal and multivariate polygenic risk prediction of common psychiatric traits during development. Schizophr Bull. 2017;43(6):1197–207.

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Chung D, Kim HJ, Zhao H. graph-GPA: a graphical model for prioritizing GWAS results and investigating pleiotropic architecture. PLoS Comput Biol. 2017;13(2):e1005388.

    PubMed  PubMed Central  Google Scholar 

  34. 34.

    Stergiakouli E, Smith GD, Martin J, Skuse DH, Viechtbauer W, Ring SM, et al. Shared genetic influences between dimensional ASD and ADHD symptoms during child and adolescent development. Mol Autism. 2017;8(1):1–13.

    Google Scholar 

  35. 35.

    Foo JC, Streit F, Treutlein J, Ripke S, Witt SH, Strohmaier J, et al. Shared genetic etiology between alcohol dependence and major depressive disorder. Psychiatr Genet. 2018;28(4):66.

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Kaufmann T, van der Meer D, Doan NT, Schwarz E, Lund MJ, Agartz I, et al. Genetics of brain age suggest an overlap with common brain disorders. BioRxiv. 2018;303164.

  37. 37.

    Leonenko G, Di Florio A, Allardyce J, Forty L, Knott S, Jones L, et al. A data-driven investigation of relationships between bipolar psychotic symptoms and schizophrenia genome-wide significant genetic loci. Am J Med Genet B Neuropsychiatr Genet. 2018;177(4):468–75.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Aas M, Melle I, Bettella F, Djurovic S, Le Hellard S, Bjella T, et al. Psychotic patients who used cannabis frequently before illness onset have higher genetic predisposition to schizophrenia than those who did not. Psychol Med. 2018;48(1):43–9.

    CAS  PubMed  Google Scholar 

  39. 39.

    Pasman JA, Verweij KJ, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal effect of schizophrenia liability. Nat Neurosci. 2018;21(9):1161–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, Duncan L, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360(6395).

  41. 41.

    Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Pasman JA, Verweij KJ, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. Genome-wide association analysis of lifetime cannabis use (N = 184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use. bioRxiv. 2018:234294.

  43. 43.

    St Pourcain B, Robinson EB, Anttila V, Sullivan BB, Maller J, Golding J, et al. ASD and schizophrenia show distinct developmental profiles in common genetic overlap with population-based social communication difficulties. Mol Psychiatry. 2018;23(2):263–70.

    CAS  PubMed  Google Scholar 

  44. 44.

    Duncan LE, Ratanatharathorn A, Aiello AE, Almli LM, Amstadter AB, Ashley-Koch AE, et al. Largest GWAS of PTSD (N = 20 070) yields genetic overlap with schizophrenia and sex differences in heritability. Mol Psychiatry. 2018;23(3):666–73.

    CAS  PubMed  Google Scholar 

  45. 45.

    Schork A, Brown T, Hagler D, Thompson W, Chen CH, Dale A, et al. Polygenic risk for psychiatric disorders correlates with executive function in typical development. Genes Brain Behav. 2019;18(4):e12480.

    PubMed  Google Scholar 

  46. 46.

    Yilmaz Z, Halvorsen M, Bryois J, Yu D, Thornton LM, Zerwas S, et al. Examination of the shared genetic basis of anorexia nervosa and obsessive–compulsive disorder. Mol Psychiatry. 2020;25(9):2036–46.

    CAS  PubMed  Google Scholar 

  47. 47.

    Smeland OB, Frei O, Shadrin A, O’Connell K, Fan C-C, Bahrami S, et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet. 2020;139(1):85–94.

    CAS  PubMed  Google Scholar 

  48. 48.

    Bahrami S, Steen NE, Shadrin A, O’Connell K, Frei O, Bettella F, et al. Shared genetic loci between body mass index and major psychiatric disorders: a genome-wide association study. JAMA Psychiat. 2020;77(5):503–12.

    Google Scholar 

  49. 49.

    Ohi K, Otowa T, Shimada M, Sasaki T, Tanii H. Shared genetic etiology between anxiety disorders and psychiatric and related intermediate phenotypes. Psychol Med. 2020;50(4):692–704.

    PubMed  Google Scholar 

  50. 50.

    Peyrot WJ, Price AL. Identifying loci with different allele frequencies among cases of eight psychiatric disorders using CC-GWAS. Nat Genet. 2021;53(4):445–54.

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Muntané G, Farré X, Bosch E, Martorell L, Navarro A, Vilella E. The shared genetic architecture of schizophrenia, bipolar disorder and lifespan. Hum Genet. 2021;140(3):441–55.

    PubMed  Google Scholar 

  52. 52.

    Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A. 2009;106(23):9362–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 years of GWAS discovery: biology, function, and translation. Am J Hum Genet. 2017;101(1):5–22.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Zeng P, Zhao Y, Qian C, Zhang L, Zhang R, Gou J, et al. Statistical analysis for genome-wide association study. J Biomed Res. 2015;29(4):285.

    PubMed  Google Scholar 

  55. 55.

    Cross-Disorder Group of the Psychiatric Genomics Consortium. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet. 2013;45(9):984–94.

    PubMed Central  Google Scholar 

  56. 56.

    van Rheenen W, Peyrot WJ, Schork AJ, Lee SH, Wray NR. Genetic correlations of polygenic disease traits: from theory to practice. Nat Rev Genet. 2019;20(10):567–81.

    PubMed  Google Scholar 

  57. 57.

    Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383.

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Smeland OB, Frei O, Shadrin A, O'Connell K, Fan C-C, Bahrami S, et al. Discovery of shared genomic loci using the conditional false discovery rate approach. Hum Genet. 2020;139(1):85–94.

    CAS  PubMed  Google Scholar 

  59. 59.

    Zeng P, Hao X, Zhou X. Pleiotropic mapping and annotation selection in genome-wide association studies with penalized Gaussian mixture models. Bioinformatics. 2018;34(16):2797–807.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Ray D, Chatterjee N. A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between type 2 diabetes and prostate cancer. PLoS Genet. 2020;16(12):e1009218.

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219.

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: current successes and future challenges. Comput Struct Biotechnol J. 2021;19:3209–24.

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc. 1996;91(434):444–55.

    Google Scholar 

  64. 64.

    Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol. 2000;29(4):722–29.

    CAS  PubMed  Google Scholar 

  65. 65.

    Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.

    PubMed  Google Scholar 

  66. 66.

    Sheehan NA, Didelez V, Burton PR, Tobin MD. Mendelian randomisation and causal inference in observational epidemiology. PLoS Med. 2008;5(8):e177.

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Zeng P, Wang T, Zheng J, Zhou X. Causal association of type 2 diabetes with amyotrophic lateral sclerosis: new evidence from Mendelian randomization using GWAS summary statistics. BMC Med. 2019;17(1):225.

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Zeng P, Zhou X. Causal association between birth weight and adult diseases: evidence from a Mendelian randomization analysis. Front Genet. 2019;10:618.

  69. 69.

    Duncan L, Yilmaz Z, Gaspar H, Walters R, Goldstein J, Anttila V, et al. Significant locus and metabolic genetic correlations revealed in genome-wide association study of anorexia nervosa. Am J Psychiatry. 2017;174(9):850–8.

  70. 70.

    Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, et al. Meta-analysis of genome-wide association studies of anxiety disorders. Mol Psychiatry. 2016;21(10):1391–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431–44.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Sanchez-Roige S, Palmer AA, Fontanillas P, Elson SL, Adams MJ, Howard DM, et al. Genome-wide association study meta-analysis of the Alcohol Use Disorders Identification Test (AUDIT) in two population-based cohorts. Am J Psychiatry. 2019;176(2):107–18.

    PubMed  Google Scholar 

  73. 73.

    Arnold PD, Askland KD, Barlassina C, Bellodi L, Bienvenu OJ, Black D, et al. Revealing the complex genetic architecture of obsessive-compulsive disorder using meta-analysis. Mol Psychiatry. 2018;23(5):1181–8.

    CAS  Google Scholar 

  74. 74.

    Stahl EA, Breen G, Forstner AJ, McQuillin A, Ripke S, Trubetskoy V, et al. Genome-wide association study identifies 30 loci associated with bipolar disorder. Nat Genet. 2019;51(5):793–803.

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Zhang Q, Feitosa M, Borecki IB. Estimating and testing pleiotropy of single genetic variant for two quantitative traits. Genet Epidemiol. 2014;38(6):523–30.

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, et al. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nat Commun. 2019;10(1):4558.

    PubMed  PubMed Central  Google Scholar 

  77. 77.

    Yu D, Sul JH, Tsetsos F, Nawaz MS, Huang AY, Zelaya I, et al. Interrogating the genetic determinants of Tourette’s syndrome and other tic disorders through genome-wide association studies. Am J Psychiatry. 2019;176(3):217–27.

    PubMed  PubMed Central  Google Scholar 

  78. 78.

    Pasman JA, Verweij KJH, Gerring Z, Stringer S, Sanchez-Roige S, Treur JL, et al. GWAS of lifetime cannabis use reveals new risk loci, genetic overlap with psychiatric traits, and a causal influence of schizophrenia. Nat Neurosci. 2018;21(9):1161–70.

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50(5):668–81.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80.

    Demontis D, Walters RK, Martin J, Mattheisen M, Als TD, Agerbo E, et al. Discovery of the first genome-wide significant risk loci for attention deficit/hyperactivity disorder. Nat Genet. 2019;51(1):63–75.

    CAS  PubMed  Google Scholar 

  81. 81.

    Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, et al. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–41.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. 82.

    Liu JZ, McRae AF, Nyholt DR, Medland SE, Wray NR, Brown KM, et al. A versatile gene-based test for genome-wide association studies. Am J Hum Genet. 2010;87(1):139–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Huang Y-T. Genome-wide analyses of sparse mediation effects under composite null hypotheses. Ann Appl Stat. 2019;13(1):60–84.

    Google Scholar 

  84. 84.

    Huang Y-T. Variance component tests of multivariate mediation effects under composite null hypotheses. Biometrics. 2019;75(4):1191–204.

    PubMed  Google Scholar 

  85. 85.

    Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001:1165–88.

  86. 86.

    Watanabe K, Taskesen E, van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826.

    PubMed  PubMed Central  Google Scholar 

  87. 87.

    Koch M, Ilnytskyy Y, Golubov A, Metz L, Yong V, Kovalchuk O. Global transcriptome profiling of mild relapsing-remitting versus primary progressive multiple sclerosis. Eur J Neurol. 2018;25(4):651–8.

    CAS  PubMed  Google Scholar 

  88. 88.

    Boldanova T, Suslov A, Heim MH, Necsulea A. Transcriptional response to hepatitis C virus infection and interferon-alpha treatment in the human liver. EMBO Mol Med. 2017;9(6):816–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Baum M, Bielau S, Rittner N, Schmid K, Eggelbusch K, Dahms M, et al. Validation of a novel, fully integrated and flexible microarray benchtop facility for gene expression profiling. Nucleic Acids Res. 2003;31(23):e151.

    PubMed  PubMed Central  Google Scholar 

  90. 90.

    Khan MM, Poeckel D, Halavatyi A, Zukowska-Kasprzyk J, Stein F, Vappiani J, et al. An integrated multiomic and quantitative label-free microscopy-based approach to study pro-fibrotic signalling in ex vivo human precision-cut lung slices. Eur Respir J. 2021;58(1).

  91. 91.

    Freshour SL, Kiwala S, Cotto KC, Coffman AC, McMichael JF, Song JJ, et al. Integration of the Drug-Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 2021;49(D1):D1144–51.

    CAS  PubMed  Google Scholar 

  92. 92.

    Griffith M, Griffith OL, Coffman AC, Weible JV, McMichael JF, Spies NC, et al. DGIdb: mining the druggable genome. Nat Methods. 2013;10(12):1209–10.

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Noyce AJ, Kia DA, Hemani G, Nicolas A, Price TR, De Pablo-Fernandez E, et al. Estimating the causal influence of body mass index on risk of Parkinson disease: a Mendelian randomisation study. PLoS Med. 2017;14(6):e1002314.

    PubMed  PubMed Central  Google Scholar 

  94. 94.

    Zeng P, Zhou X. Causal effects of blood lipids on amyotrophic lateral sclerosis: a Mendelian randomization study. Hum Mol Genet. 2019;28(4):688–97.

    CAS  PubMed  Google Scholar 

  95. 95.

    Choi KW, Chen C-Y, Stein MB, Klimentidis YC, Wang M-J, Koenen KC, et al. Consortium ftMDDWGotPG: Assessment of bidirectional relationships between physical activity and depression among adults: a 2-sample Mendelian randomization studyassessment of bidirectional relationships between physical activity and depression among adultsassessment of bidirectional relationships between physical activity and depression among adults. JAMA Psychiat. 2019;76(4):399–408.

    Google Scholar 

  96. 96.

    Sanna S, van Zuydam NR, Mahajan A, Kurilshikov A, Vich Vila A, Vosa U, et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet. 2019;51(4):600-5.

  97. 97.

    Yang J, Yan B, Zhao B, Fan Y, He X, Yang L, et al. Assessing the causal effects of human serum metabolites on 5 major psychiatric disorders. Schizophr Bull. 2020;46(4):804-13.

  98. 98.

    Larsson SC, Burgess S, Michaëlsson K. Association of genetic variants related to serum calcium levels with coronary artery disease and myocardial infarction. JAMA. 2017;318(4):371–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99.

    Ahmad OS, Morris JA, Mujammami M, Forgetta V, Leong A, Li R, et al. A Mendelian randomization study of the effect of type-2 diabetes on coronary heart disease. Nat Commun. 2015;6(1):1-11.

  100. 100.

    Yu X, Wang T, Chen Y, Shen Z, Gao Y, Xiao L, et al. Alcohol drinking and amyotrophic lateral sclerosis: an instrumental variable causal inference. Ann Neurol. 2020;88(1):195–8.

    PubMed  Google Scholar 

  101. 101.

    Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7.

    CAS  PubMed  Google Scholar 

  102. 102.

    Burgess S, Small DS, Thompson SG. A review of instrumental variable estimators for Mendelian randomization. Stat Methods Med Res. 2017;26(5):2333–55.

    PubMed  Google Scholar 

  103. 103.

    Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol. 2016;45(6):1961–74.

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Yavorska OO, Burgess S. MendelianRandomization: an R package for performing Mendelian randomization analyses using summarized data. Int J Epidemiol. 2017;46(6):1734–9.

    PubMed  PubMed Central  Google Scholar 

  106. 106.

    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Burgess S, Thompson SG. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89.

    PubMed  PubMed Central  Google Scholar 

  108. 108.

    Lettre G, Rioux JD. Autoimmune diseases: insights from genome-wide association studies. Hum Mol Genet. 2008;17(R2):R116–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Ruderfer DM, Ripke S, McQuillin A, Boocock J, Stahl EA, Pavlides JMW, et al. Genomic dissection of bipolar disorder and schizophrenia, including 28 subphenotypes. Cell. 2018;173(7):1705–1715.e1716.

    CAS  PubMed Central  Google Scholar 

  110. 110.

    Baurecht H. Genome-wide comparative analysis of atopic dermatitis and psoriasis gives insight into opposing genetic mechanisms. Am J Hum Genet. 2015;96(1):104–20.

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Schmitt J, Schwarz K, Baurecht H, Hotze M, Fölster-Holst R, Rodríguez E, et al. Atopic dermatitis is associated with an increased risk for rheumatoid arthritis and inflammatory bowel disease, and a decreased risk for type 1 diabetes. J Allergy Clin Immunol. 2016;137(1):130–6.

    PubMed  Google Scholar 

  112. 112.

    Mühleisen TW, Leber M, Schulze TG, Strohmaier J, Degenhardt F, Treutlein J, et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nat Commun. 2014;5(1):3339.

    PubMed  Google Scholar 

  113. 113.

    Hou L, Bergen SE, Akula N, Song J, Hultman CM, Landén M, et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Hum Mol Genet. 2016;25(15):3383–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114.

    Reinbold CS, Forstner AJ, Hecker J, Fullerton JM, Hoffmann P, Hou L, et al. Analysis of the influence of microRNAs in lithium response in bipolar disorder. Frontiers. Psychiatry. 2018;9:207.

  115. 115.

    Polanczyk G, Caspi A, Williams B, Price TS, Danese A, Sugden K, et al. Protective effect of CRHR1 gene variants on the development of adult depression following childhood maltreatment: replication and extension. Arch Gen Psychiatry. 2009;66(9):978–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Linda K, Lewerissa E, Verboven AH, Gabriele M, Frega M, Gunnewiek TMK, et al. KANSL1 deficiency causes neuronal dysfunction by oxidative stress-induced autophagy. bioRxiv. 2020.

  117. 117.

    Cheran G, Silverman H, Manoochehri M, Goldman J, Lee S, Wu L, et al. Psychiatric symptoms in preclinical behavioural-variant frontotemporal dementia in MAPT mutation carriers. J Neurol Neurosurg Psychiatry. 2018;89(5):449–55.

    PubMed  Google Scholar 

  118. 118.

    Fan Q, Wang W, Hao J, He A, Wen Y, Guo X, et al. Integrating genome-wide association study and expression quantitative trait loci data identifies multiple genes and gene set associated with neuroticism. Prog Neuro-Psychopharmacol Biol Psychiatry. 2017;78:149–52.

    CAS  Google Scholar 

  119. 119.

    Levy D, Ronemus M, Yamrom B, Lee Y-h, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70(5):886–97.

  120. 120.

    Schenk P, Van Fessem M, Verploegh-Van Rij S, Mathot R, van Gelder T, Vulto A, et al. Association of graded allele-specific changes in CYP2D6 function with imipramine dose requirement in a large group of depressed patients. Mol Psychiatry. 2008;13(6):597–605.

    CAS  PubMed  Google Scholar 

  121. 121.

    Vandel P, Haffen E, Nezelof S, Broly F, Kantelip J, Sechter D. Clomipramine, fluoxetine and CYP2D6 metabolic capacity in depressed patients. Hum Psychopharmacol: Cli Exp. 2004;19(5):293–8.

    CAS  Google Scholar 

  122. 122.

    Murphy GM, Pollock BG, Kirshner MA, Pascoe N, Cheuk W, Mulsant BH, et al. CYP2D6 genotyping with oligonucleotide microarrays and nortriptyline concentrations in geriatric depression. Neuropsychopharmacology. 2001;25(5):737–43.

    CAS  PubMed  Google Scholar 

  123. 123.

    Maccioni RB, Cambiazo V. Role of microtubule-associated proteins in the control of microtubule assembly. Physiol Rev. 1995;75(4):835–64.

    CAS  PubMed  Google Scholar 

  124. 124.

    Rojo LE, Alzate-Morales J, Saavedra IN, Davies P, Maccioni RB. Selective interaction of lansoprazole and astemizole with tau polymers: potential new clinical use in diagnosis of Alzheimer's disease. J Alzheimers Dis. 2010;19(2):573–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125.

    Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, et al. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet. 2009;373(9659):234–9.

    CAS  PubMed  Google Scholar 

  126. 126.

    Craddock N, O'Donovan MC, Owen MJ. Genes for schizophrenia and bipolar disorder? Implications for psychiatric nosology Schizophr Bull. 2005;32(1):9–16.

    PubMed  Google Scholar 

  127. 127.

    Wu MC, Kraft P, Epstein MP, Taylor DM, Chanock SJ, Hunter DJ, et al. Powerful SNP-set analysis for case-control genome-wide association studies. Am J Hum Genet. 2010;86(6):929–42.

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Wu MC, Lee S, Cai T, Li Y, Boehnke M, Lin X. Rare-variant association testing for sequencing data with the sequence kernel association test. Am J Hum Genet. 2011;89(1):82–93.

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Lee S, Emond MJ, Bamshad MJ, Barnes KC, Rieder MJ, Nickerson DA, et al. Optimal unified approach for rare-variant association testing with application to small-sample case-control whole-exome sequencing studies. Am J Hum Genet. 2012;91(2):224–37.

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130.

    Schifano ED, Epstein MP, Bielak LF, Jhun MA, Kardia SLR, Peyser PA, et al. SNP set association analysis for familial data. Genet Epidemiol. 2012;36(8):797–810.

    PubMed  PubMed Central  Google Scholar 

  131. 131.

    Wang X, Lee S, Zhu X, Redline S, Lin X. GEE-based SNP set association test for continuous and discrete traits in family-based association studies. Genet Epidemiol. 2013;37(8):778–86.

    PubMed  PubMed Central  Google Scholar 

  132. 132.

    Wu MC, Maity A, Lee S, Simmons EM, Harmon QE, Lin X, et al. Kernel machine SNP-set testing under multiple candidate kernels. Genet Epidemiol. 2013;37(3):267–75.

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Lee S, Abecasis Gonçalo R, Boehnke M, Lin X. Rare-variant association analysis: study designs and statistical tests. Am J Hum Genet. 2014;95(1):5–23.

    CAS  PubMed  PubMed Central  Google Scholar 

  134. 134.

    Zeng P, Zhao Y, Liu J, Liu L, Zhang L, Wang T, et al. Likelihood ratio tests in rare variant detection for continuous phenotypes. Ann Hum Genet. 2014;78(5):320–32.

    PubMed  Google Scholar 

  135. 135.

    Han B, Duong D, Sul JH, de Bakker PIW, Eskin E, Raychaudhuri S. A general framework for meta-analyzing dependent studies with overlapping subjects in association mapping. Hum Mol Genet. 2016;25(9):1857–66.

    CAS  PubMed  PubMed Central  Google Scholar 

  136. 136.

    LeBlanc M, Zuber V, Thompson WK, Andreassen OA, Frigessi A, Andreassen BK. Psychiat Genomics C: A correction for sample overlap in genome-wide association studies in a polygenic pleiotropy-informed framework. BMC Genomics. 2018;19(1):1-15.

  137. 137.

    Lin D-Y, Sullivan PF. Meta-analysis of genome-wide association studies with overlapping subjects. Am J Hum Genet. 2009;85(6):862–72.

    CAS  PubMed  PubMed Central  Google Scholar 

  138. 138.

    Rommelse NNJ, Franke B, Geurts HM, Hartman CA, Buitelaar JK. Shared heritability of attention-deficit/hyperactivity disorder and autism spectrum disorder. Eur Child Adolesc Psychiatry. 2010;19(3):281–95.

    PubMed  PubMed Central  Google Scholar 

  139. 139.

    Scahill L, Bitsko R, Visser S, Blumberg S. Prevalence of diagnosed Tourette syndrome in persons aged 6-17 years-United States, 2007. Morb Mortal Wkly Rep. 2009;58(21):581–5.

    Google Scholar 

  140. 140.

    Kompoliti K, Goetz CG, Morrissey M, Leurgans S. Gilles de la Tourette syndrome: patient's knowledge and concern of adverse effects. Mov Disord. 2006;21(2):248–52.

    PubMed  Google Scholar 

  141. 141.

    Freeman RD, Fast DK, Burd L, Kerbeshian J, Robertson MM, Sandor P. An international perspective on Tourette syndrome: selected findings from 3500 individuals in 22 countries. Dev Med Child Neurol. 2000;42(7):436–47.

    CAS  PubMed  Google Scholar 

  142. 142.

    Bolton D, Rijsdijk F, O'CONNOR TG, Perrin S, Eley TC. Obsessive–compulsive disorder, tics and anxiety in 6-year-old twins. Psychol Med. 2007;37(1):39–48.

  143. 143.

    Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22(3):343–52.

    CAS  PubMed  PubMed Central  Google Scholar 

  144. 144.

    Schork AJ, Won H, Appadurai V, Nudel R, Gandal M, Delaneau O, et al. A genome-wide association study of shared risk across psychiatric disorders implicates gene regulation during fetal neurodevelopment. Nat Neurosci. 2019;22(3):353–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  145. 145.

    Davis LK. Common knowledge: shared genetics in psychiatry. Nat Neurosci. 2019;22(3):331–2.

    CAS  PubMed  Google Scholar 

  146. 146.

    Korologou-Linden R, Leyden GM, Relton CL, Richmond RC, Richardson TG. Multi-omics analyses of cognitive traits and psychiatric disorders highlights brain-dependent mechanisms. Hum Mol Genet. 2021.

  147. 147.

    O’Brien HE, Hannon E, Hill MJ, Toste CC, Robertson MJ, Morgan JE, et al. Expression quantitative trait loci in the developing human brain and their enrichment in neuropsychiatric disorders. Genome Biol. 2018;19(1):194.

    PubMed  PubMed Central  Google Scholar 

  148. 148.

    Smit DJA, Wright MJ, Meyers JL, Martin NG, Ho YYW, Malone SM, et al. Genome-wide association analysis links multiple psychiatric liability genes to oscillatory brain activity. Hum Brain Mapp. 2018;39(11):4183–95.

    PubMed  PubMed Central  Google Scholar 

  149. 149.

    Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359(6376):693–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150.

    Collis I, Lloyd G. Psychiatric aspects of liver disease. Br J Psychiatry. 1992;161(1):12–22.

    CAS  PubMed  Google Scholar 

  151. 151.

    Crone CC, Gabriel GM, DiMartini A. An overview of psychiatric issues in liver disease for the consultation-liaison psychiatrist. Psychosomatics. 2006;47(3):188–205.

    PubMed  Google Scholar 

  152. 152.

    De Hert M, Detraux J, Vancampfort D. The intriguing relationship between coronary heart disease and mental disorders. Dialogues Clin Neurosci. 2018;20(1):31–40.

    PubMed  PubMed Central  Google Scholar 

  153. 153.

    Lin Y-H, Liu A-H, Xu Y, Tie L, Yu H-M, Li X-J. Effect of chronic unpredictable mild stress on brain–pancreas relative protein in rat brain and pancreas. Behav Brain Res. 2005;165(1):63–71.

    CAS  PubMed  Google Scholar 

  154. 154.

    Fras I, Litin EM, Pearson JS. Comparison of psychiatric symptoms in carcinoma of the pancreas with those in some other intra-abdominal neoplasms. Am J Psychiatry. 1967;123(12):1553–62.

    CAS  PubMed  Google Scholar 

  155. 155.

    Casey DE. Metabolic issues and cardiovascular disease in patients with psychiatric disorders. Am J Med Suppl. 2005;118:15–22.

    Google Scholar 

  156. 156.

    Ferns G. Cause, consequence or coincidence: the relationship between psychiatric disease and metabolic syndrome. Transl Metab Syndr Res. 2018;1:23–38.

    Google Scholar 

  157. 157.

    Ho CS, Zhang MW, Mak A, Ho RC. Metabolic syndrome in psychiatry: advances in understanding and management. Adv Psychiatr Treat. 2014;20(2):101–12.

    Google Scholar 

  158. 158.

    Maier R, Moser G, Chen G-B, Ripke S, Coryell W, Potash JB, et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am J Hum Genet. 2015;96(2):283–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  159. 159.

    Gibson G. Rare and common variants: twenty arguments. Nat Rev Genet. 2012;13(2):135–45.

    CAS  PubMed  PubMed Central  Google Scholar 

  160. 160.

    McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, Ioannidis JPA, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–69.

    CAS  PubMed  Google Scholar 

  161. 161.

    Lu H, Wang T, Zhang J, Zhang S, Huang S, Zeng P. Evaluating marginal genetic correlation of associated loci for complex diseases and traits between European and East Asian populations. Hum Genet. 2021;140(9):1285–97.

    CAS  PubMed  Google Scholar 

  162. 162.

    Smeland OB, Frei O, Fan C-C, Shadrin A, Dale AM, Andreassen OA. The emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges. Psychiatr Genet. 2019;29(5):152–9.

    PubMed  Google Scholar 

Download references


We thank Psychiatric Genomics Consortium (PGC) for making the summary statistics of eight psychiatric disorders GWAS publicly available and are grateful of all the investigators and participants contributed to the studies. We are also very grateful to the editor and two referees for their insightful and constructive comments, which substantially improved our original manuscript. The data analyses in the present study were carried out with the high-performance computing cluster that was supported by the special central finance project of local universities for Xuzhou Medical University.


The research of Ping Zeng was supported in part by the National Natural Science Foundation of China (82173630 and 81402765), the Youth Foundation of Humanity and Social Science funded by Ministry of Education of China (18YJC910002), the Natural Science Foundation of Jiangsu Province of China (BK20181472), the China Postdoctoral Science Foundation (2018 M630607 and 2019 T120465), the QingLan Research Project of Jiangsu Province for Outstanding Young Teachers, the Six-Talent Peaks Project in Jiangsu Province of China (WSN-087), the Training Project for Youth Teams of Science and Technology Innovation at Xuzhou Medical University (TD202008), the Postdoctoral Science Foundation of Xuzhou Medical University, and the Statistical Science Research Project from National Bureau of Statistics of China (2014LY112). The research of Shuiping Huang was supported in part by the Social Development Project of Xuzhou City (KC19017). The research of Ting Wang was supported in part by the Social Development Project of Xuzhou City (KC20062).

Author information




PZ conceived the idea for the study. PZ obtained the genetic data and developed the study methods. PZ, JQ, ZS, and HL performed the data analyses. PZ, SH, TW, and HL interpreted the results of the data analyses. PZ and HL wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Ping Zeng.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Haojie Lu, Jiahao Qiao, and Zhonghe Shao are co-first authors.

Supplementary Information

Additional file 1: Table S1.

A selective overview of previous pleiotropy studies on psychiatric disorders.

Additional file 2: Figure S1-S5. Figure S1.

Graphical framework of the Mendelian randomization method using SNPs as instrumental variables for an exposure. A valid Mendelian randomization requires each of used SNP instruments satisfies three key model assumptions: (1) the relevance assumption, (2) the independence assumption, and (3) the exclusion restriction assumption. In the plot, solid or dotted arrow denotes the presence or absence of directional association. Figure S2. (A) Association of horizontal pleiotropy for causal genes, which can be examined by the composite-null based MAIUP method; (B) Association of mediated (or vertical) pleiotropy, which is also known as causality and can be examined by the Mendelian randomization method. Figure S3. (A) Number of associated genes (FDR < 0.05) discovered by the maximum P-value method between 14 psychiatric disorders; (B) Number of associated genes (FDR < 0.05) discovered by the direct FDR method between 14 psychiatric disorders. Figure S4. Result of the LRT method for examining the overall pleiotropy for each pair of the 14 psychiatric disorders. The P value is shown in the scale of -log10. Significant pleiotropy is marked with an asterisk after Bonferroni correction. Figure S5. (A) Proportion of heterogeneity in SNP genetic effects for pleiotropic genes across all significant pairs of the 14 psychiatric disorders. (B) Correlation of mean proportions of genetic effect heterogeneity of SNPs for pleiotropic genes in each pair of the 14 psychiatric disorders and their cross-trait genetic correlations. The estimated correlation coefficient and P value are shown on the top right.

Additional file 3: Table S2.

Pleiotropic genes for all pairs of 14 psychiatric disorders identified by PLACO (FDR < 0.05).

Additional file 4: Table S3

. List of all the unique pleiotropic genes and the number of psychiatric disorders which were affected by these genes.

Additional file 5: Table S4.

Unique genes with drug-gene interaction of directional effects on 14 psychiatric disorders.

Additional file 6: Table S5.

Results of sensitivity analyses for significantly causal associations between psychiatric disorders.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lu, H., Qiao, J., Shao, Z. et al. A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics. BMC Med 19, 314 (2021).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Psychiatric disorder
  • Pleiotropy
  • Genetic correlation
  • Gene-based association analysis
  • Genome-wide association study
  • Summary statistics
  • Pleiotropic analysis under composite null hypothesis
  • Mendelian randomization
  • Causal inference
  • Instrumental variable