Schizophrenia Genome-wide Association Studies Research Articles

Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights.

Genome-wide association studies (GWAS) have identified over 100 risk loci for schizophrenia, but the causal mechanisms remain largely unknown. We performed a transcriptome-wide association study (TWAS) integrating a schizophrenia GWAS of 79,845 individuals from the Psychiatric Genomics Consortium with expression data from brain, blood, and adipose tissues across 3,693 primarily control individuals. We identified 157 TWAS significant genes, of which 35 did not overlap a known GWAS locus. 42/157 genes were associated to specific chromatin features measured in independent samples, highlighting potential regulatory targets for follow-up. Suppression of one identified susceptibility gene, MAPK3, in zebrafish showed a significant effect on neurodevelopmental phenotypes. Expression and splicing from brain captured the majority of the TWAS effect across all genes. This large-scale connection of associations to target genes, tissues, and regulatory features is an essential step in moving towards a mechanistic understanding of GWAS.

T189. PEPTIDE SHARING BETWEEN SCHIZOPHRENIA-RELATED PROTEINS AND THE INFLUENZA A VIRUS MAY OFFER A WINDOW INTO THE IMMUNE AETIOLOGY OF PSYCHOTIC DISORDERS

BackgroundSchizophrenia is a complex disorder in which infection and immune mechanisms are thought to play a role. Epidemiological and ecological studies have implicated influenza infection in particular and it is possible that cross-reactivity, or molecular mimicry, between the influenza virus and brain proteins underlies this association. Proteins might share amino acid sequences, which could thus provide the basis for an autoimmune response that targets endogenous proteins. This study is the first to characterise sequence alignment between schizophrenia-related brain proteins and the proteome of the influenza A virus, and comparing it with sequence alignment in proteins not implicated in schizophrenia.MethodsThe software Peptide Match Service (https://research.bioinformatics.udel.edu/peptidematch/index.jsp; Protein Information Resource, University of Delaware and Georgetown University Medical Center) was used to obtain sequence alignments between protein sequences. A case-control study design was used to compare schizophrenia-related proteins to proteins not involved in schizophrenia. Schizophrenia-related proteins were operationalised as proteins found significant in the Psychiatric Genomics Consortium schizophrenia genome-wide association studies (GWAS). The control group consisted of null proteins (p-value > .75) in the GWAS. Null proteins were also selected to represent genes expressed in tissues other than central nervous system tissues. Both groups were equalised for the total amino acid count. Perfect pentapeptide matches (i.e. 5 amino acids) in proteins and the influenza proteome were explored.ResultsThere was a link between schizophrenia-related (GWAS-significant) proteins and presence of perfect matches between proteins and the influenza proteins polymerase acidic protein (χ2 (1) = 5.284, p = .022, two-sided) and RNA-directed RNA polymerase catalytic subunit (χ2 (1) = 6.132, p = .013, two-sided). Pentapeptide-sharing was found to be highly significant between schizophrenia-related proteins and the hemagglutinin precursor (χ2 (1) = 17.723, p = .000026, two-sided). There was no significant difference (p > .05) between schizophrenia-related proteins and proteins not implicated in schizophrenia (GWAS-null proteins) in the frequency of proteins having perfect matches with the influenza A proteins PB2-S1, polymerase basic protein 2, matrix protein 1 and 2, and neuraminidase. However, the result for matrix protein 1 approached statistical significance (χ2 (1) = 3.319, p = .068, two-sided).DiscussionWe find evidence to suggest there is significant overlap between the linear structures of proteins involved in schizophrenia and those integral to the influenza virus. Future research should establish the biological relevance of this finding, particularly regarding the antigenicity of the peptide sequences which we have identified. Extra studies should also go beyond sequences and address structural homologies. Future research could assess whether an immune reaction against particular schizophrenia-related proteins is a plausible mechanism contributing to psychotic disorders. Also, exploring peptide sharing in different influenza strains could offer insights into links between influenza pandemics, maternal infection, and psychosis. Elucidating peptide sharing might have implications for schizophrenia risk management and safe influenza prevention.

Multilocus genetic profile in dopaminergic pathway modulates the striatum and working memory

Dopamine is critical in pathophysiology and therapy of schizophrenia. Many studies have reported altered dopaminergic activity in the dorsal but not ventral striatum in schizophrenia. Based on the largest genome-wide association study of schizophrenia to date, we calculated the polygenic risk score (PGRS) of each subject in a healthy general group, including all variations in the set of functionally related genes involved in dopamine neurotransmitter system. We aimed to test whether the genetic variations in the dopaminergic pathway that have been identified as associated with schizophrenia are related to the function of the striatum and to working memory. We found that a higher PGRS was significantly associated with impairment in working memory. Moreover, resting-state functional connectivity analysis revealed that as the polygenic risk score increased, the connections between left putamen and caudate and the default mode network grew stronger, while the connections with the fronto-parietal network grew weaker. Our findings may shed light on the biological mechanism underlying the “dopamine hypothesis” of schizophrenia and provide some implications regarding the polygenic effects on the dopaminergic activity in the risk for schizophrenia.

Schizophrenia-associated rs4702 G allele-specific downregulation of FURIN expression by miR-338-3p reduces BDNF production

Genome-wide association studies (GWAS) reveal numerous schizophrenia (SCZ)-associated single-nucleotide polymorphisms (SNPs); however, functional characterizations of the risk variants remain to be established. Using data from 108 SCZ GWAS loci, we performed systematic miRNA binding site screening of 128 SCZ-associated SNPs and found that 2 out of 3 SNPs located in the 3′UTR were predicted to alter 3 miRNAs' binding sites in 2 target genes. Of the identified SNPs, the most genome-wide significant SNP rs4702 (A/G) in the FURIN 3′UTR, previously identified as an SCZ-associated cis-expression quantitative trait loci (downregulated by the risk G allele), is located in the binding site of miR-338-3p in the presence of the risk G allele. Allele-specific downregulation of FURIN by miR-338-3p was validated with a luciferase reporter assay. Furthermore, we demonstrated that miR-338-3p-mediated FURIN inhibition reduced brain-derived neurotrophic factor (BDNF) maturation and secretion in human embryonic kidney 293T cells. Our data reveal that schizophrenia-associated rs4702 G allele-specific downregulation of FURIN by miR-338-3p reduces mature BDNF production. These data help elucidate the mechanism of genetic predisposition toward schizophrenia or other neurodevelopmental diseases.

Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection.

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights. We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures. These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

Effects of MiR-137 genetic risk score on brain volume and cortical measures in patients with schizophrenia and controls.

Multiple genome-wide association studies of schizophrenia have implicated genetic variants within the gene encoding microRNA-137. As risk variants within or regulated by MIR137 have been implicated in memory performance, we investigated the additive effects of schizophrenia-associated risk variants in genes empirically regulated by MIR137 on brain regions associated with memory function. A polygenic risk score (PRS) was calculated (at a p = 0.05 threshold), using this empirically regulated MIR137 gene set, to investigate associations between this PRS and structural brain measures. These measures included total brain volume, cortical thickness, cortical surface area, and hippocampal volume, in a sample of 216 individuals consisting of healthy participants (n = 171) and patients with psychosis (n = 45). We did not observe a significant association between MIR137 PRS and these cortical thickness, surface area or hippocampal volume measures linked to memory function; a significant association between increasing PRS and decreasing total brain volume, independent of diagnosis status (R2 = 0.008, Beta = -0.09, p = 0.029), was observed. This did not survive correction for multiple testing. In conclusion, our study yielded only suggestive evidence that risk variants interacting with MIR137 impacts on cortical structure.

Integrating eQTL data with GWAS summary statistics in pathway-based analysis with application to schizophrenia.

Many genetic variants affect complex traits through gene expression, which can be exploited to boost statistical power and enhance interpretation in genome-wide association studies (GWASs) as demonstrated by the transcriptome-wide association study (TWAS) approach. Furthermore, due to polygenic inheritance, a complex trait is often affected by multiple genes with similar functions as annotated in gene pathways. Here, we extend TWAS from gene-based analysis to pathway-based analysis: we integrate public pathway collections, expression quantitative trait locus (eQTL) data and GWAS summary association statistics (or GWAS individual-level data) to identify gene pathways associated with complex traits. The basic idea is to weight the SNPs of the genes in a pathway based on their estimated cis-effects on gene expression, then adaptively test for association of the pathway with a GWAS trait by effectively aggregating possibly weak association signals across the genes in the pathway. The P values can be calculated analytically and thus fast. We applied our proposed test with the KEGG and GO pathways to two schizophrenia (SCZ) GWAS summary association data sets, denoted by SCZ1 and SCZ2 with about 20,000 and 150,000 subjects, respectively. Most of the significant pathways identified by analyzing the SCZ1 data were reproduced by the SCZ2 data. Importantly, we identified 15 novel pathways associated with SCZ, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS. The newly identified pathways may help us gain insights into the biological mechanism underlying SCZ. Our results showcase the power of incorporating gene expression information and gene functional annotations into pathway-based association testing for GWAS.

Schizophrenia polygenic risk score predicts mnemonic hippocampal activity.

The use of polygenic risk scores has become a practical translational approach to investigating the complex genetic architecture of schizophrenia, but the link between polygenic risk scores and pathophysiological components of this disorder has been the subject of limited research. We investigated in healthy volunteers whether schizophrenia polygenic risk score predicts hippocampal activity during simple memory encoding, which has been proposed as a risk-associated intermediate phenotype of schizophrenia. We analysed the relationship between polygenic risk scores and hippocampal activity in a discovery sample of 191 unrelated healthy volunteers from the USA and in two independent replication samples of 76 and 137 healthy unrelated participants from Europe and the USA, respectively. Polygenic risk scores for each individual were calculated as the sum of the imputation probability of reference alleles weighted by the natural log of odds ratio from the recent schizophrenia genome-wide association study. We examined hippocampal activity during simple memory encoding of novel visual stimuli assessed using blood oxygen level-dependent functional MRI. Polygenic risk scores were significantly associated with hippocampal activity in the discovery sample [P = 0.016, family-wise error (FWE) corrected within Anatomical Automatic Labeling (AAL) bilateral hippocampal-parahippocampal mask] and in both replication samples (P = 0.033, FWE corrected within AAL right posterior hippocampal-parahippocampal mask in Bari sample, and P = 0.002 uncorrected in the Duke Neurogenetics Study sample). The relationship between polygenic risk scores and hippocampal activity was consistently negative, i.e. lower hippocampal activity in individuals with higher polygenic risk scores, consistent with previous studies reporting decreased hippocampal-parahippocampal activity during declarative memory tasks in patients with schizophrenia and in their healthy siblings. Polygenic risk scores accounted for more than 8% of variance in hippocampal activity during memory encoding in discovery sample. We conclude that polygenic risk scores derived from the most recent schizophrenia genome-wide association study predict significant variability in hippocampal activity during memory encoding in healthy participants. Our findings validate mnemonic hippocampal activity as a genetic risk associated intermediate phenotype of schizophrenia, indicating that the aggregate neurobiological effect of schizophrenia risk alleles converges on this pattern of neural activity.awy004media15749593779001.

How can genetics help understand the relationship between cognitive dysfunction and schizophrenia?

Despite the consistent finding that cognitive dysfunction is a core characteristic of schizophrenia (SCZ), little is known about the underlying pathophysiology. Recent progress in human genetics, driven by large genome-wide association studies (GWAS), has provided new data about the genetic architecture of complex human traits, including cognition and SCZ. Novel analytical tools have provided unprecedented opportunities to leverage the large amount of information from GWAS. Here we review the latest findings related to genetic architecture and risk genes of SCZ and cognitive functions, and recent findings of overlapping genetic factors. The recent GWAS of SCZ implicate over 100 risk gene loci, each with a small effect. A similar genetic architecture seems to be present in cognitive domains, suggesting that these phenotypes are highly polygenic. Further, GWAS have revealed more than 20 gene loci associated with cognitive traits, including intelligence, general cognition (g-factor), reaction time and verbal-numerical reasoning. Several gene loci have been implicated in educational attainment, a proxy measure of cognitive function. Recently, overlapping gene loci were found between education and SCZ, and between SCZ and cognitive traits, suggesting common genetic risk between SCZ and cognitive dysfunction. Mathematical modeling of GWAS of cognition and SCZ indicate that only a fraction of the heritability is identified. The evidence suggests a polygenic architecture for SCZ and cognitive functions, and a large degree of shared genetic risk. This indicates novel molecular genetic mechanisms and strengthens the notion that SCZ is more likely a part of the normal distribution and not a separate entity.

Identification of shared genetic variants between schizophrenia and lung cancer

Epidemiology studies suggest associations between schizophrenia and cancer. However, the underlying genetic mechanisms are not well understood, and difficult to identify from epidemiological data. We investigated if there is a shared genetic architecture between schizophrenia and cancer, with the aim to identify specific overlapping genetic loci. First, we performed genome-wide enrichment analysis and second, we analyzed specific loci jointly associated with schizophrenia and cancer by the conjunction false discovery rate. We analyzed the largest genome-wide association studies of schizophrenia and lung, breast, prostate, ovary, and colon-rectum cancer including more than 220,000 subjects, and included genetic association with smoking behavior. Polygenic enrichment of associations with lung cancer was observed in schizophrenia, and weak enrichment for the remaining cancer sites. After excluding the major histocompatibility complex region, we identified three independent loci jointly associated with schizophrenia and lung cancer. The strongest association included nicotinic acetylcholine receptors and is an established pleiotropic locus shared between lung cancer and smoking. The two other loci were independent of genetic association with smoking. Functional analysis identified downstream pleiotropic effects on epigenetics and gene-expression in lung and brain tissue. These findings suggest that genetic factors may explain partly the observed epidemiological association of lung cancer and schizophrenia.

Semiparametric covariate-modulated local false discovery rate for genome-wide association studies

While genome-wide association studies (GWAS) have discovered thousands of risk loci for heritable disorders, so far even very large meta-analyses have recovered only a fraction of the heritability of most complex traits. Recent work utilizing variance components models has demonstrated that a larger fraction of the heritability of complex phenotypes is captured by the additive effects of SNPs than is evident only in loci surpassing genome-wide significance thresholds, typically set at a Bonferroni-inspired $p\le5\times10^{-8}$. Procedures that control false discovery rate can be more powerful, yet these are still under-powered to detect the majority of nonnull effects from GWAS. The current work proposes a novel Bayesian semiparametric two-group mixture model and develops a Markov Chain Monte Carlo (MCMC) algorithm for a covariate-modulated local false discovery rate (cmfdr). The probability of being nonnull depends on a set of covariates via a logistic function, and the nonnull distribution is approximated as a linear combination of B-spline densities, where the weight of each B-spline density depends on a multinomial function of the covariates. The proposed methods were motivated by work on a large meta-analysis of schizophrenia GWAS performed by the Psychiatric Genetics Consortium (PGC). We show that the new cmfdr model fits the PGC schizophrenia GWAS test statistics well, performing better than our previously proposed parametric gamma model for estimating the nonnull density and substantially improving power over usual fdr. Using loci declared significant at cmfdr $\le0.20$, we perform follow-up pathway analyses using the Kyoto Encyclopedia of Genes and Genomes (KEGG) Homo sapiens pathways database. We demonstrate that the increased yield from the cmfdr model results in an improved ability to test for pathways associated with schizophrenia compared to using those SNPs selected according to usual fdr.

Analysis of the joint effect of SNPs to identify independent loci and allelic heterogeneity in schizophrenia GWAS data

We have tested published methods for capturing allelic heterogeneity and identifying loci of joint effects to uncover more of the “hidden heritability” of schizophrenia (SCZ). We used two tools, cojo-GCTA and multi-SNP, to analyze meta-statistics from the latest genome-wide association study (GWAS) on SCZ by the Psychiatric Genomics Consortium (PGC). Stepwise regression on markers with p values <10−7 in cojo-GCTA identified 96 independent signals. Eighty-five passed the genome-wide significance threshold. Cross-validation of cojo-GCTA by CLUMP was 76%, i.e., 26 of the loci identified by the PGC using CLUMP were found to be dependent on another locus by cojo-GCTA. The overlap between cojo-GCTA and multi-SNP was better (up to 92%). Three markers reached genome-wide significance (5 × 10−8) in a joint effect model. In addition, two loci showed possible allelic heterogeneity within 1-Mb genomic regions, while CLUMP analysis had identified 16 such regions. Cojo-GCTA identified fewer independent loci than CLUMP and seems to be more conservative, probably because it accounts for long-range LD and interaction effects between markers. These findings also explain why fewer loci with possible allelic heterogeneity remained significant after cojo-GCTA analysis. With multi-SNP, 86 markers were selected at the threshold 10−7. Multi-SNP identifies fewer independent signals, due to splitting of the data and use of smaller samples. We recommend that cojo-GCTA and multi-SNP are used for post-GWAS analysis of all traits to call independent loci. We conclude that only a few loci in SCZ show joint effects or allelic heterogeneity, but this could be due to lack of power for that data set.

Association Between Schizophrenia-Related Polygenic Liability and the Occurrence and Level of Mood-Incongruent Psychotic Symptoms in Bipolar Disorder

Bipolar disorder (BD) overlaps schizophrenia in its clinical presentation and genetic liability. Alternative approaches to patient stratification beyond current diagnostic categories are needed to understand the underlying disease processes and mechanisms. To investigate the association between common-variant liability for schizophrenia, indexed by polygenic risk scores (PRSs), and psychotic presentations of BD. This case-control study in the United Kingdom used multinomial logistic regression to estimate differential PRS associations across categories of cases and controls. Participants included in the final analyses were 4436 cases of BD from the Bipolar Disorder Research Network. These cases were compared with the genotypic data for 4976 cases of schizophrenia and 9012 controls from the Type 1 Diabetes Genetics Consortium study and the Generation Scotland study. Data were collected between January 1, 2000, and December 31, 2013. Data analysis was conducted from March 1, 2016, to February 28, 2017. Standardized PRSs, calculated using alleles with an association threshold of P < .05 in the second Psychiatric Genomics Consortium genome-wide association study of schizophrenia, were adjusted for the first 10 population principal components and genotyping platforms. Multinomial logit models estimated PRS associations with BD stratified by Research Diagnostic Criteria subtypes of BD, by lifetime occurrence of psychosis, and by lifetime mood-incongruent psychotic features. Ordinal logistic regression examined PRS associations across levels of mood incongruence. Ratings were derived from the Schedules for Clinical Assessment in Neuropsychiatry interview and the Bipolar Affective Disorder Dimension Scale. Of the 4436 cases of BD, 2966 (67%) were female patients, and the mean (SD) age at interview was 46 [12] years. Across clinical phenotypes, there was an exposure-response gradient, with the strongest PRS association for schizophrenia (risk ratio [RR] = 1.94; 95% CI, 1.86-2.01), followed by schizoaffective BD (RR = 1.37; 95% CI, 1.22-1.54), bipolar I disorder subtype (RR = 1.30; 95% CI, 1.24-1.36), and bipolar II disorder subtype (RR = 1.04; 95% CI, 0.97-1.11). Within BD cases, there was an effect gradient, indexed by the nature of psychosis. Prominent mood-incongruent psychotic features had the strongest association (RR = 1.46; 95% CI, 1.36-1.57), followed by mood-congruent psychosis (RR = 1.24; 95% CI, 1.17-1.33) and BD with no history of psychosis (RR = 1.09; 95% CI, 1.04-1.15). For the first time to date, a study shows a polygenic-risk gradient across schizophrenia and BD, indexed by the occurrence and level of mood-incongruent psychotic symptoms.

Integrating genome-wide association study and expression quantitative trait locus study identifies multiple genes and gene sets associated with schizophrenia

Schizophrenia is a serious mental disease with high heritability. To better understand the genetic basis of schizophrenia, we conducted a large scale integrative analysis of genome-wide association study (GWAS) and expression quantitative trait loci (eQTLs) data. GWAS summary data was derived from a published GWAS of schizophrenia, containing 9394 schizophrenia patients and 12,462 healthy controls. The eQTLs dataset was obtained from an eQTLs meta-analysis of 5311 subjects, containing 923,021 cis-eQTLs for 14,329 genes and 4732 trans-eQTLs for 2612 genes. Genome-wide single gene expression association analysis was conducted by SMR software. The SMR analysis results were further subjected to gene set enrichment analysis (GSEA) to identify schizophrenia associated gene sets. SMR detected 49 genes significantly associated with schizophrenia. The top five significant genes were CRELD2 (p value=1.65×10−11), DIP2B (p value=3.94×10−11), ZDHHC18 (p value=1.52×10−10) and ZDHHC5 (p value=7.45×10−10), C11ORF75 (p value=3.70×10−9). GSEA identified 80 gene sets with p values <0.01. The top five significant gene sets were COWLING_MYCN_TARGETS (p value <0.001) and CHR16P11 (p value <0.001), ACTACCT_MIR196A_MIR196B (p value=0.002), CELLULAR_COMPONENT_DISASSEMBLY (p value=0.002) and GRAESSMANN_RESPONSE_TO_MC_AND_DOXORUBICIN_DN (p value=0.002). Our results provide useful information for revealing the genetic basis of schizophrenia.

Drug enrichment and discovery from schizophrenia genome-wide association results: an analysis and visualisation approach

Using successful genome-wide association results in psychiatry for drug repurposing is an ongoing challenge. Databases collecting drug targets and gene annotations are growing and can be harnessed to shed a new light on psychiatric disorders. We used genome-wide association study (GWAS) summary statistics from the Psychiatric Genetics Consortium (PGC) Schizophrenia working group to build a drug repositioning model for schizophrenia. As sample size increases, schizophrenia GWAS results show increasing enrichment for known antipsychotic drugs, selective calcium channel blockers, and antiepileptics. Each of these therapeutical classes targets different gene subnetworks. We identify 123 Bonferroni-significant druggable genes outside the MHC, and 128 FDR-significant biological pathways related to neurons, synapses, genic intolerance, membrane transport, epilepsy, and mental disorders. These results suggest that, in schizophrenia, current well-powered GWAS results can reliably detect known schizophrenia drugs and thus may hold considerable potential for the identification of new therapeutic leads. Moreover, antiepileptics and calcium channel blockers may provide repurposing opportunities. This study also reveals significant pathways in schizophrenia that were not identified previously, and provides a workflow for pathway analysis and drug repurposing using GWAS results.

No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Noncandidate Genes

BackgroundA recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not. MethodsThe present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia. ResultsAs a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of noncandidate genes. While a small subset of candidate genes did appear to be significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated noncandidate genes. ConclusionsThe history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.

Comparative genomic evidence for the involvement of schizophrenia risk genes in antipsychotic effects.

Genome-wide association studies (GWAS) for schizophrenia have identified over 100 loci encoding >500 genes. It is unclear whether any of these genes, other than dopamine receptor D2, are immediately relevant to antipsychotic effects or represent novel antipsychotic targets. We applied an in vivo molecular approach to this question by performing RNA sequencing of brain tissue from mice chronically treated with the antipsychotic haloperidol or vehicle. We observed significant enrichments of haloperidol-regulated genes in schizophrenia GWAS loci and in schizophrenia-associated biological pathways. Our findings provide empirical support for overlap between genetic variation underlying the pathophysiology of schizophrenia and the molecular effects of a prototypical antipsychotic.

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

BackgroundOver the past decade genome-wide association studies (GWAS) have been applied to aid in the understanding of the biology of traits. The success of this approach is governed by the underlying effect sizes carried by the true risk variants and the corresponding statistical power to observe such effects given the study design and sample size under investigation. Previous ASD GWAS have identified genome-wide significant (GWS) risk loci; however, these studies were of only of low statistical power to identify GWS loci at the lower effect sizes (odds ratio (OR) <1.15).MethodsWe conducted a large-scale coordinated international collaboration to combine independent genotyping data to improve the statistical power and aid in robust discovery of GWS loci. This study uses genome-wide genotyping data from a discovery sample (7387 ASD cases and 8567 controls) followed by meta-analysis of summary statistics from two replication sets (7783 ASD cases and 11359 controls; and 1369 ASD cases and 137308 controls).ResultsWe observe a GWS locus at 10q24.32 that overlaps several genes including PITX3, which encodes a transcription factor identified as playing a role in neuronal differentiation and CUEDC2 previously reported to be associated with social skills in an independent population cohort. We also observe overlap with regions previously implicated in schizophrenia which was further supported by a strong genetic correlation between these disorders (Rg = 0.23; P = 9 × 10−6). We further combined these Psychiatric Genomics Consortium (PGC) ASD GWAS data with the recent PGC schizophrenia GWAS to identify additional regions which may be important in a common neurodevelopmental phenotype and identified 12 novel GWS loci. These include loci previously implicated in ASD such as FOXP1 at 3p13, ATP2B2 at 3p25.3, and a ‘neurodevelopmental hub’ on chromosome 8p11.23.ConclusionsThis study is an important step in the ongoing endeavour to identify the loci which underpin the common variant signal in ASD. In addition to novel GWS loci, we have identified a significant genetic correlation with schizophrenia and association of ASD with several neurodevelopmental-related genes such as EXT1, ASTN2, MACROD2, and HDAC4.

Widespread Allelic Heterogeneity in Complex Traits

Recent successes in genome-wide association studies (GWASs) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AHand applied it to three GWASs and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4,152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4%-23% in eQTLs, 35% in GWASs of high-density lipoprotein (HDL), and 23% in GWASs of schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R2 = 0.85, p = 2.2× 10-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

Identification of shared risk loci and pathways for bipolar disorder and schizophrenia.

Bipolar disorder (BD) is a highly heritable neuropsychiatric disease characterized by recurrent episodes of mania and depression. BD shows substantial clinical and genetic overlap with other psychiatric disorders, in particular schizophrenia (SCZ). The genes underlying this etiological overlap remain largely unknown. A recent SCZ genome wide association study (GWAS) by the Psychiatric Genomics Consortium identified 128 independent genome-wide significant single nucleotide polymorphisms (SNPs). The present study investigated whether these SCZ-associated SNPs also contribute to BD development through the performance of association testing in a large BD GWAS dataset (9747 patients, 14278 controls). After re-imputation and correction for sample overlap, 22 of 107 investigated SCZ SNPs showed nominal association with BD. The number of shared SCZ-BD SNPs was significantly higher than expected (p = 1.46x10-8). This provides further evidence that SCZ-associated loci contribute to the development of BD. Two SNPs remained significant after Bonferroni correction. The most strongly associated SNP was located near TRANK1, which is a reported genome-wide significant risk gene for BD. Pathway analyses for all shared SCZ-BD SNPs revealed 25 nominally enriched gene-sets, which showed partial overlap in terms of the underlying genes. The enriched gene-sets included calcium- and glutamate signaling, neuropathic pain signaling in dorsal horn neurons, and calmodulin binding. The present data provide further insights into shared risk loci and disease-associated pathways for BD and SCZ. This may suggest new research directions for the treatment and prevention of these two major psychiatric disorders.