Multivariate analysis of genome-wide data to identify potential pleiotropic genes for five major psychiatric disorders using MetaCCA.
ABSTRACT: BACKGROUND:Genome-wide association studies have been extensively applied in identifying SNP associated with major psychiatric disorders. However, the SNPs identified by the prevailing univariate approach only explain a small percentage of the genetic variance of traits, and the extensive data have shown the major psychiatric disorders have common biological mechanisms and the overlapping pathophysiological pathways. METHODS:We applied the genetic pleiotropy-informed metaCCA method on summary statistics data from the Psychiatric Genomics Consortium Cross-Disorder Group to examine the overlapping genetic relations between the five major psychiatric disorders. Furthermore, to refine all genes, we performed gene-based association analyses for the five disorders respectively using VEGAS2. Gene enrichment analysis was applied to explore the potential functional significance of the identified genes. RESULTS:After metaCCA analysis, 1147 SNPs reached the Bonferroni corrected threshold (p?
Project description:Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility locus discovery, this univariate approach has limitations in detecting complex genotype-phenotype correlations. Multivariate analysis is essential to identify shared genetic risk factors acting through common biological mechanisms of autoimmune/autoinflammatory diseases. In this study, GWAS summary statistics, including 41,274 single nucleotide polymorphisms (SNPs) located in 11,516 gene regions, were analyzed to identify shared variants of seven autoimmune/autoinflammatory diseases using the metaCCA method. Gene-based association analysis was used to refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein interaction network analysis were applied to explore the potential biological functions of the identified genes. A total of 4,962 SNPs (P < 1.21 × 10-6) and 1,044 pleotropic genes (P < 4.34 × 10-6) were identified by metaCCA analysis. By screening the results of gene-based P-values, we identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic genes that achieved statistical significance in the metaCCA analysis and were also associated with at least one autoimmune/autoinflammatory in the VEGAS2 analysis. Using the metaCCA method, we identified novel variants associated with complex diseases incorporating different GWAS datasets. Our analysis may provide insights for the development of common therapeutic approaches for autoimmune/autoinflammatory diseases based on the pleiotropic genes and common mechanisms identified.
Project description:Asthma, hay fever and eczema are three comorbid diseases with high prevalence and heritability. Their common genetic architectures have not been well-elucidated. In this study, we first conducted a linkage disequilibrium score regression analysis to confirm the strong genetic correlations between asthma, hay fever and eczema. We then integrated three distinct association analyses (metaCCA multi-trait association analysis, MAGMA genome-wide and MetaXcan transcriptome-wide gene-based tests) to identify shared risk genes based on the large-scale GWAS results in the GeneATLAS database. MetaCCA can detect pleiotropic genes associated with these three diseases jointly. MAGMA and MetaXcan were performed separately to identify candidate risk genes for each of the three diseases. We finally identified 150 shared risk genes, in which 60 genes are novel. Functional enrichment analysis revealed that the shared risk genes are enriched in inflammatory bowel disease, T cells differentiation and other related biological pathways. Our work may provide help on treatment of asthma, hay fever and eczema in clinical applications.
Project description:AIMS:Although converging evidence from experimental and epidemiological studies indicates Alzheimer's disease (AD) and ischemic stroke (IS) are related, the genetic basis underlying their links is less well characterized. Traditional SNP-based genome-wide association studies (GWAS) have failed to uncover shared susceptibility variants of AD and IS. Therefore, this study was designed to investigate whether pleiotropic genes existed between AD and IS to account for their phenotypic association, although this was not reported in previous studies. METHODS:Taking advantage of large-scale GWAS summary statistics of AD (17,008 AD cases and 37,154 controls) and IS (10,307 IS cases and 19,326 controls), we performed gene-based analysis implemented in VEGAS2 and Fisher's meta-analysis of the set of overlapped genes of nominal significance in both diseases. Subsequently, gene expression analysis in AD- or IS-associated expression datasets was conducted to explore the transcriptional alterations of pleiotropic genes identified. RESULTS:16 AD-IS pleiotropic genes surpassed the cutoff for Bonferroni-corrected significance. Notably, MS4A4A and TREM2, two established AD-susceptibility genes showed remarkable alterations in the spleens and brains afflicted by IS, respectively. Among the prioritized genes identified by virtue of literature-based knowledge, most are immune-relevant genes (EPHA1, MS4A4A, UBE2L3 and TREM2), implicating crucial roles of the immune system in the pathogenesis of AD and IS. CONCLUSIONS:The observation that AD and IS had shared disease-associated genes offered mechanistic insights into their common pathogenesis, predominantly involving the immune system. More importantly, our findings have important implications for future research directions, which are encouraged to verify the involvement of these candidates in AD and IS and interpret the exact molecular mechanisms of action.
Project description:Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestory. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.SNPs at four loci surpassed the cutoff for genome-wide significance (p<5×10(-8)) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.National Institute of Mental Health.
Project description:The complex aetiology of schizophrenia is postulated to share components with other psychiatric disorders. We investigated pleiotropy amongst the common variant genomics of schizophrenia and seven other psychiatric disorders using a multimarker association test. Transcriptomic imputation was then leveraged to investigate the functional significance of variation mapped to these genes, prioritising several interesting functional candidates. Gene-based analysis of common variation revealed 67 schizophrenia-associated genes shared with other psychiatric phenotypes, including bipolar disorder, major depressive disorder, ADHD and autism-spectrum disorder. In addition, we uncovered 78 genes significantly enriched with common variant associations for schizophrenia that were not linked to any of these seven disorders (P?>?0.05). Multivariable gene-set association suggested that common variation enrichment within biologically constrained genes observed for schizophrenia also occurs across several psychiatric phenotypes. Pairwise meta-analysis of schizophrenia and each psychiatric phenotype was implemented and identified 330 significantly associated genes (PMeta?<?2.7?×?10-6) that were only nominally associated with each disorder individually (P?<?0.05). These analyses consolidate the overlap between the genomic architecture of schizophrenia and other psychiatric disorders, uncovering several candidate pleiotropic genes which warrant further investigation.
Project description:Results from Genome-Wide Association Studies (GWAS) have shown that complex diseases are often affected by many genetic variants with small or moderate effects. Identifications of these risk variants remain a very challenging problem. There is a need to develop more powerful statistical methods to leverage available information to improve upon traditional approaches that focus on a single GWAS dataset without incorporating additional data. In this paper, we propose a novel statistical approach, GPA (Genetic analysis incorporating Pleiotropy and Annotation), to increase statistical power to identify risk variants through joint analysis of multiple GWAS data sets and annotation information because: (1) accumulating evidence suggests that different complex diseases share common risk bases, i.e., pleiotropy; and (2) functionally annotated variants have been consistently demonstrated to be enriched among GWAS hits. GPA can integrate multiple GWAS datasets and functional annotations to seek association signals, and it can also perform hypothesis testing to test the presence of pleiotropy and enrichment of functional annotation. Statistical inference of the model parameters and SNP ranking is achieved through an EM algorithm that can handle genome-wide markers efficiently. When we applied GPA to jointly analyze five psychiatric disorders with annotation information, not only did GPA identify many weak signals missed by the traditional single phenotype analysis, but it also revealed relationships in the genetic architecture of these disorders. Using our hypothesis testing framework, statistically significant pleiotropic effects were detected among these psychiatric disorders, and the markers annotated in the central nervous system genes and eQTLs from the Genotype-Tissue Expression (GTEx) database were significantly enriched. We also applied GPA to a bladder cancer GWAS data set with the ENCODE DNase-seq data from 125 cell lines. GPA was able to detect cell lines that are biologically more relevant to bladder cancer. The R implementation of GPA is currently available at http://dongjunchung.github.io/GPA/.
Project description:Depression and alcohol dependence (AD) are common psychiatric disorders that often co-occur. Both disorders are genetically influenced, with heritability estimates in the range of 35-60%. In addition, evidence from twin studies suggests that AD and depression are genetically correlated. Herein, we report results from a genome-wide association study of a comorbid phenotype, in which cases meet the Diagnostic and Statistical Manual of Mental Disorders-IV symptom threshold for major depressive symptomatology and the Diagnostic and Statistical Manual of Mental Disorders-IV criteria for AD.Samples (N=467 cases and N=407 controls) were of European-American descent and were genotyped using the Illumina Human 1M BeadChip array.Although no single-nucleotide polymorphism (SNP) meets genome-wide significance criteria, we identified 10 markers with P values less than 1 × 10(-5), seven of which are located in known genes, which have not been previously implicated in either disorder. Genes harboring SNPs yielding P values less than 1 × 10(-5) are functionally enriched for a number of gene ontology categories, notably several related to glutamatergic function. Investigation of expression localization using online resources suggests that these genes are expressed across a variety of tissues, including behaviorally relevant brain regions. Genes that have been previously associated with depression, AD, or other addiction-related phenotypes - such as CDH13, CSMD2, GRID1, and HTR1B - were implicated by nominally significant SNPs. Finally, the degree of overlap of significant SNPs between a comorbid phenotype and an AD-only phenotype is modest.These results underscore the complex genomic influences on psychiatric phenotypes and suggest that a comorbid phenotype is partially influenced by genetic variants that do not affect AD alone.
Project description:Disturbed sleep and disrupted circadian rhythms are a common feature of psychiatric disorders, and many groups have postulated an association between genetic variants in circadian clock genes and psychiatric disorders. Using summary data from the association analyses of the Psychiatric Genomics Consortia (PGC) for schizophrenia, bipolar disorder and major depressive disorder, we evaluated the evidence that common SNPs in genes encoding components of the molecular clock influence risk to psychiatric disorders. Initially, gene-based and SNP P-values were analyzed for 21 core circadian genes. Subsequently, an expanded list of genes linked to control of circadian rhythms was analyzed. After correcting for multiple comparisons, none of the circadian genes were significantly associated with any of the three disorders. Several genes previously implicated in the etiology of psychiatric disorders harbored no SNPs significant at the nominal level of P?<?0.05, and none of the the variants identified in candidate studies of clock genes that were included in the PGC datasets were significant after correction for multiple testing. There was no evidence of an enrichment of associations in genes linked to control of circadian rhythms in human cells. Our results suggest that genes encoding components of the molecular clock are not good candidates for harboring common variants that increase risk to bipolar disorder, schizophrenia, or major depressive disorder.
Project description:Individuals with a history of child abuse are at high risk for depression, anxiety disorders, aggressive behavior, and substance use problems. The goal of this paper is to review studies of the genetics of these stress-related psychiatric disorders. An informative subset of studies that examined candidate gene by environment (GxE) predictors of these psychiatric problems in individuals maltreated as children is reviewed, together with extant genome wide association studies (GWAS). Emerging findings on epigenetic changes associated with adverse early experiences are also reviewed. Meta-analytic support and replicated findings are evident for several genetic risk factors; however, extant research suggests the effects are pleiotropic. Genetic factors are not associated with distinct psychiatric disorders, but rather diverse clinical phenotypes. Research also suggests adverse early life experiences are associated with changes in gene expression of multiple known candidate genes, genes involved in DNA transcription and translation, and genes necessary for brain circuitry development, with changes in gene expression reported in key brain structures implicated in the pathophysiology of psychiatric and substance use disorders. The finding of pleiotropy highlights the value of using the Research Domain Criteria (RDoC) framework in future studies of the genetics of stress-related psychiatric disorders, and not trying simply to link genes to multifaceted clinical syndromes, but to more limited phenotypes that map onto distinct neural circuits. Emerging work in the field of epigenetics also suggests that translational studies that integrate numerous unbiased genome-wide approaches will help to further unravel the genetics of stress-related psychiatric disorders.
Project description:BACKGROUND:Mounting evidence shows genetic overlap between multiple psychiatric disorders. However, the biological underpinnings of shared risk for psychiatric disorders are not yet fully uncovered. The identification of underlying biological mechanisms is crucial for the progress in the treatment of these disorders. METHODS:We applied gene-set analysis including 7372 gene sets, and 53 tissue-type specific gene-expression profiles to identify sets of genes that are involved in the etiology of multiple psychiatric disorders. We included genome-wide meta-association data of the five psychiatric disorders schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorder, and attention-deficit/hyperactivity disorder. The total dataset contained 159 219 cases and 262 481 controls. RESULTS:We identified 19 gene sets that were significantly associated with the five psychiatric disorders combined, of which we excluded five sets because their associations were likely driven by schizophrenia only. Conditional analyses showed independent effects of several gene sets that in particular relate to the synapse. In addition, we found independent effects of gene expression levels in the cerebellum and frontal cortex. CONCLUSIONS:We obtained novel evidence for shared biological mechanisms that act across psychiatric disorders and we showed that several gene sets that have been related to individual disorders play a role in a broader range of psychiatric disorders.