Biology subjects

Sklar, P.

Publications and source records attributed to Sklar, P..

12 recordsLinked to original sources

bioRxiv2018

Comparative genetic architectures of schizophrenia in East Asian and European populations

Author summarySchizophrenia is a severe psychiatric disorder with a lifetime risk of about 1% world-wide. Most large schizophrenia genetic studies have studied people of primarily European ancestry, potentially missing important biological insights. Here we present a study of East Asian participants (22,778 schizophrenia cases and 35,362 controls), identifying 21 genome-wide significant schizophrenia associations in 19 genetic loci. Over the genome, the common genetic variants that confer risk for schizophrenia have highly similar effects in those of East Asian and European ancestry (rg=0.98), indicating for the first time that the genetic basis of schizophrenia and its biology are broadly shared across these world populations. A fixed-effect meta-analysis including individuals from East Asian and European ancestries revealed 208 genome-wide significant schizophrenia associations in 176 genetic loci (53 novel). Trans-ancestry fine-mapping more precisely isolated schizophrenia causal alleles in 70% of these loci. Despite consistent genetic effects across populations, polygenic risk models trained in one population have reduced performance in the other, highlighting the importance of including all major ancestral groups with sufficient sample size to ensure the findings have maximum relevance for all populations.

genetics↗

bioRxiv2018

Identifying tissues implicated in Anorexia Nervosa using Transcriptomic Imputation

Anorexia nervosa (AN) is a complex and serious eating disorder, occurring in ~1% of individuals. Despite having the highest mortality rate of any psychiatric disorder, little is known about the aetiology of AN, and few effective treatments exist.\n\nGlobal efforts to collect large sample sizes of individuals with AN have been highly successful, and a recent study consequently identified the first genome-wide significant locus involved in AN. This result, coupled with other recent studies and epidemiological evidence, suggest that previous characterizations of AN as a purely psychiatric disorder are over-simplified. Rather, both neurological and metabolic pathways may also be involved.\n\nIn order to elucidate more of the system-specific aetiology of AN, we applied transcriptomic imputation methods to 3,495 cases and 10,982 controls, collected by the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED). Transcriptomic Imputation (TI) methods approaches use machine-learning methods to impute tissue-specific gene expression from large genotype data using curated eQTL reference panels. These offer an exciting opportunity to compare gene associations across neurological and metabolic tissues. Here, we applied CommonMind Consortium (CMC) and GTEx-derived gene expression prediction models for 13 brain tissues and 12 tissues with potential metabolic involvement (adipose, adrenal gland, 2 colon, 3 esophagus, liver, pancreas, small intestine, spleen, stomach).\n\nWe identified 35 significant gene-tissue associations within the large chromosome 12 region described in the recent PGC-ED GWAS. We applied forward stepwise conditional analyses and FINEMAP to associations within this locus to identify putatively causal signals. We identified four independently associated genes; RPS26, C12orf49, SUOX, and RDH16. We also identified two further genome-wide significant gene-tissue associations, both in brain tissues; REEP5, in the dorso-lateral pre-frontal cortex (DLPFC; p=8.52x10-07), and CUL3, in the caudate basal ganglia (p=1.8x10-06). These genes are significantly enriched for associations with anthropometric phenotypes in the UK BioBank, as well as multiple psychiatric, addiction, and appetite/satiety pathways. Our results support a model of AN risk influenced by both metabolic and psychiatric factors.

genetics↗

bioRxiv2017

Gene expression imputation across multiple brain regions reveals schizophrenia risk throughout development.

Transcriptomic imputation approaches offer an opportunity to test associations between disease and gene expression in otherwise inaccessible tissues, such as brain, by combining eQTL reference panels with large-scale genotype data. These genic associations could elucidate signals in complex GWAS loci and may disentangle the role of different tissues in disease development. Here, we use the largest eQTL reference panel for the dorso-lateral pre-frontal cortex (DLPFC), collected by the CommonMind Consortium, to create a set of gene expression predictors and demonstrate their utility. We applied these predictors to 40,299 schizophrenia cases and 65,264 matched controls, constituting the largest transcriptomic imputation study of schizophrenia to date. We also computed predicted gene expression levels for 12 additional brain regions, using publicly available predictor models from GTEx. We identified 413 genic associations across 13 brain regions. Stepwise conditioning across the genes and tissues identified 71 associated genes (67 outside the MHC), with the majority of associations found in the DLPFC, and of which 14/67 genes did not fall within previously genome-wide significant loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple pathways associated with porphyric disorders. We investigated developmental expression patterns for all 67 non-MHC associated genes using BRAINSPAN, and identified groups of genes expressed specifically pre-natally or post-natally.

genetics↗

bioRxiv2017

Transcriptomic Imputation of Bipolar Disorder and Bipolar subtypes reveals 29 novel associated genes

Bipolar disorder is a complex neuropsychiatric disorder presenting with episodic mood disturbances. In this study we use a transcriptomic imputation approach to identify novel genes and pathways associated with bipolar disorder, as well as three diagnostically and genetically distinct subtypes. Transcriptomic imputation approaches leverage well-curated and publicly available eQTL reference panels to create gene-expression prediction models, which may then be applied to \"impute\" genetically regulated gene expression (GREX) in large GWAS datasets. By testing for association between phenotype and GREX, rather than genotype, we hope to identify more biologically interpretable associations, and thus elucidate more of the genetic architecture of bipolar disorder.\n\nWe applied GREX prediction models for 13 brain regions (derived from CommonMind Consortium and GTEx eQTL reference panels) to 21,488 bipolar cases and 54,303 matched controls, constituting the largest transcriptomic imputation study of bipolar disorder (BPD) to date. Additionally, we analyzed three specific BPD subtypes, including 14,938 individuals with subtype 1 (BD-I), 3,543 individuals with subtype 2 (BD-II), and 1,500 individuals with schizoaffective subtype (SAB).\n\nWe identified 125 gene-tissue associations with BPD, of which 53 represent independent associations after FINEMAP analysis. 29/53 associations were novel; i.e., did not lie within 1Mb of a locus identified in the recent PGC-BD GWAS. We identified 37 independent BD-I gene-tissue associations (10 novel), 2 BD-II associations, and 2 SAB associations. Our BPD, BD-I and BD-II associations were significantly more likely to be differentially expressed in post-mortem brain tissue of BPD, BD-I and BD-II cases than we might expect by chance. Together with our pathway analysis, our results support long-standing hypotheses about bipolar disorder risk, including a role for oxidative stress and mitochondrial dysfunction, the post-synaptic density, and an enrichment of circadian rhythm and clock genes within our results.

genetics↗

bioRxiv2017

Genetic validation of bipolar disorder identified by automated phenotyping using electronic health records

Bipolar disorder (BD) is a heritable mood disorder characterized by episodes of mania and depression. Although genomewide association studies (GWAS) have successfully identified genetic loci contributing to BD risk, sample size has become a rate-limiting obstacle to genetic discovery. Electronic health records (EHRs) represent a vast but relatively untapped resource for high-throughput phenotyping. As part of the International Cohort Collection for Bipolar Disorder (ICCBD), we previously validated automated EHR-based phenotyping algorithms for BD against in-person diagnostic interviews (Castro et al. 2015). Here, we establish the genetic validity of these phenotypes by determining their genetic correlation with traditionally-ascertained samples. Case and control algorithms were derived from structured and narrative text in the Partners Healthcare system comprising more than 4.6 million patients over 20 years. Genomewide genotype data for 3,330 BD cases and 3,952 controls of European ancestry were used to estimate SNP-based heritability (h2g) and genetic correlation(rg) between EHR-based phenotype definitions and traditionally-ascertained BD cases in GWAS by the ICCBD and Psychiatric Genomics Consortium (PGC) using LD score regression. We evaluated BD cases identified using 4 EHR-based algorithms: an NLP-based algorithm (95-NLP) and 3 rule-based algorithms using codified EHR with decreasing levels of stringency - \"coded-strict\", \"coded-broad\", and \"coded-broad based on a single clinical encounter\" (coded-broad-SV). The analytic sample comprised 862 95-NLP, 1,968 coded-strict, 2,581 coded-broad, 408 coded-broad-SV BD cases, and 3,952 controls. The estimated h2g were 0.24 (p=0.015), 0.09 (p=0.064), 0.13 (p=0.003), 0.00 (p=0.591) for 95-NLP, coded-strict, coded-broad and coded-broad-SV BD, respectively. The h2g for all EHR-based cases combined except coded-broad-SV (excluded due to 0 h2g) was 0.12 (p=0.004). These h2g were lower or similar to the h2g observed by the ICCBD+PGCBD (0.23, p=3.17E-80, total N=33,181). However, the rg between ICCBD+PGCBD and the EHR-based cases were high for 95-NLP (0.66, p=3.69x10-5), coded-strict (1.00, p=2.40x10-4), and coded-broad (0.74, p=8.11x10-7). The rg between EHR-based BDs ranged from 0.90 to 0.98. These results provide the first genetic validation of automated EHR-based phenotyping for BD and suggest that this approach identifies cases that are highly genetically correlated with those ascertained through conventional methods. High throughput phenotyping using the large data resources available in EHRs represents a viable method for accelerating psychiatric genetic research.

genetics↗

bioRxiv2017

Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with signatures from post mortem adult brains

Whereas highly penetrant variants have proven well-suited to human induced pluripotent stem cell (hiPSC)-based models, the power of hiPSC-based studies to resolve the much smaller effects of common variants within the size of cohorts that can be realistically assembled remains uncertain. In developing a large case/control schizophrenia (SZ) hiPSC-derived cohort of neural progenitor cells and neurons, we identified and accounted for a variety of technical and biological sources of variation. Reducing the stochastic effects of the differentiation process by correcting for cell type composition boosted the SZ signal in hiPSC-based models and increased the concordance with post mortem datasets. Because this concordance was strongest in hiPSC-neurons, it suggests that this cell type may better model genetic risk for SZ. We predict a growing convergence between hiPSC and post mortem studies as both approaches expand to larger cohort sizes. For studies of complex genetic disorders, to maximize the power of hiPSC cohorts currently feasible, in most cases and whenever possible, we recommend expanding the number of individuals even at the expense of the number of replicate hiPSC clones.

genetics↗

bioRxiv2017

Integrative analyses of splicing in the aging brain: role in susceptibility to Alzheimer’s Disease

We use deep sequencing to identify sources of variation in mRNA splicing in the dorsolateral prefrontal cortex (DLFPC) of 450 subjects from two prospective cohort studies of aging. Hundreds of aberrant pre-mRNA splicing events are reproducibly associated with Alzheimers Disease (AD). We also generate a catalog of splicing quantitative trait loci (sQTL) effects in the human cortex: splicing of 3,198 genes is influenced by genetic variation. sQTLs are enriched among those variants influencing DNA methylation and histone acetylation. In assessing known AD loci, we report that altered splicing is the mechanism for the effects of the PICALM, CLU, and PTK2B susceptibility alleles. Further, we leverage our sQTL catalog to identify genes whose aberrant splicing is associated with AD and mediated by genetics. This transcriptome-wide association study identified 21 genes with significant associations, many of which are found in AD GWAS loci, but 8 are in novel AD loci, including FUS, which is a known amyotrophic lateral sclerosis (ALS) gene. This highlights an intriguing shared genetic architecture that is further elaborated by the convergence of old and new AD genes in autophagy-lysosomal-related pathways already implicated in AD and other neurodegenerative diseases. Overall, this study of the aging brains transcriptome provides evidence that dysregulation of mRNA splicing is a feature of AD and is, in some genetically-driven cases, causal.

genomics↗

bioRxiv2017

Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2x10-6) while the inverse was not significant (19 regions, p=0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several significant correlations with: 1) SCZ subphenotypes: negative symptoms (SCZ, p=3.6x10-6) and manic symptoms (BD, p=2x10-5), 2) BD subphenotypes: psychotic features (SCZ p=1.2x10-10, BD p=5.3x10-5) and age of onset (SCZ p=7.9x10-4). Finally, we show that psychotic features in BD has significant SNP-heritability (h2 snp=0.15, SE=0.06), and a significant genetic correlation with SCZ (rg=0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p=0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.

genomics↗

bioRxiv2017

Genomewide association study identifies 30 loci associated with bipolar disorder

Bipolar disorder is a highly heritable psychiatric disorder that features episodes of mania and depression. We performed the largest genome-wide association study to date, including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 sentinel variants at loci with P<1x10-4 in an independent sample of 9,412 cases and 137,760 controls. In the combined analysis, 30 loci reached genome-wide significant evidence for association, of which 20 were novel. These significant loci contain genes encoding ion channels and neurotransmitter transporters (CACNA1C, GRIN2A, SCN2A, SLC4A1), synaptic components (RIMS1, ANK3), immune and energy metabolism components. Bipolar disorder type I (depressive and manic episodes; ~73% of our cases) is strongly genetically correlated with schizophrenia whereas bipolar disorder type II (depressive and hypomanic episodes; ~17% of our cases) is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for bipolar disorder.

genetics↗

bioRxiv2017

Evaluation Of Chromatin Accessibility In Prefrontal Cortex Of Schizophrenia Cases And Controls

Schizophrenia genome-wide association (GWA) studies have identified over 150 regions of the genome that are associated with disease risk, yet there is little evidence that coding mutations contribute to this disorder. To explore the mechanism of non-coding regulatory elements in schizophrenia, we performed ATAC-seq on adult prefrontal cortex brain samples from 135 individuals with schizophrenia and 137 controls, and identified 118,152 ATAC-seq peaks. These accessible chromatin regions in brain are highly enriched for SNP-heritability for schizophrenia (10.6 fold enrichment, P=2.4x10-4, second only to genomic regions conserved in Eutherian mammals) and replicated in an independent dataset (9.0 fold enrichment, P=2.7x10-4). This degree of enrichment of schizophrenia heritability was higher than in open chromatin found in 138 different cell and tissue types. Brain open chromatin regions that overlapped highly conserved regions exhibited an even higher degree of heritability enrichment, indicating that conservation can identify functional subsets within regulatory elements active in brain. However, we did not identify chromatin accessibility differences between schizophrenia cases and controls, nor did we find an interaction of chromatin QTLs with case-control status. This indicates that although causal variants map within regulatory elements, mechanisms other than differential chromatin may govern the contribution of regulatory element variation to schizophrenia risk. Our results strongly implicate gene regulatory processes involving open chromatin in the pathogenesis of schizophrenia, and suggest a strategy to understand the hundreds of common variants emerging from large genomic studies of complex brain diseases.

genomics↗

bioRxiv2017

Bayesian Integrated Analysis Of Multiple Types Of Rare Variants To Infer Risk Genes For Schizophrenia And Other Neurodevelopmental Disorders

BackgroundIntegrating rare variation from trio family and case/control studies has successfully implicated specific genes contributing to risk of neurodevelopmental disorders (NDDs) including autism spectrum disorders (ASD), intellectual disability (ID), developmental disorders (DD), and epilepsy (EPI). For schizophrenia (SCZ), however, while sets of genes have been implicated through study of rare variation, only two risk genes have been identified.\n\nMethodsWe used hierarchical Bayesian modeling of rare variant genetic architecture to estimate mean effect sizes and risk-gene proportions, analyzing the largest available collection of whole exome sequence (WES) data for schizophrenia (1,077 trios, 6,699 cases and 13,028 controls), and data for four NDDs (ASD, ID, DD, and EPI; total 10,792 trios, and 4,058 cases and controls).\n\nResultsFor SCZ, we estimate 1,551 risk genes, more risk genes and weaker effects than for NDDs. We provide power analyses to predict the number of risk gene discoveries as more data become available, demonstrating greater value of case-control over trio samples. We confirm and augment prior risk gene and gene set enrichment results for SCZ and NDDs. In particular, we detected 98 new DD risk genes at FDR < 0.05. Correlations of risk-gene posterior probabilities are high across four NDDs ({rho} > 0.55), but low between SCZ and the NDDs ({rho} < 0.3). In depth analysis of 288 NDD genes shows highly significant protein-protein interaction (PPI) network connectivity, and functionally distinct PPI subnetworks based on pathway enrichments, single-cell RNA-seq (scRNAseq) cell types and multi-region developmental brain RNA-seq.\n\nConclusionsWe have extended a pipeline used in ASD studies and applied it to infer rare genetic parameters for SCZ and four NDDs. We find many new DD risk genes, supported by gene set enrichment and PPI network connectivity analyses. We find greater similarity among NDDs than between NDDs and SCZ. NDD gene subnetworks are implicated in postnatally expressed presynaptic and postsynaptic genes, and for transcriptional and post-transcriptional gene regulation in prenatal neural progenitor and stem cells.

genomics↗

bioRxiv2017

Co-localization of Conditional eQTL and GWAS Signatures in Schizophrenia

Causal genes and variants within genome-wide association study (GWAS) loci can be identified by integrating GWAS statistics with expression quantitative trait loci (eQTL) and determining which SNPs underlie both GWAS and eQTL signals. Most analyses, however, consider only the marginal eQTL signal, rather than dissecting this signal into multiple independent eQTL for each gene. Here we show that analyzing conditional eQTL signatures, which could be important under specific cellular or temporal contexts, leads to improved fine mapping of GWAS associations. Using genotypes and gene expression levels from post-mortem human brain samples (N=467) reported by the CommonMind Consortium (CMC), we find that conditional eQTL are widespread; 63% of genes with primary eQTL also have conditional eQTL. In addition, genomic features associated with conditional eQTL are consistent with context specific (i.e. tissue, cell type, or developmental time point specific) regulation of gene expression. Integrating the Psychiatric Genomics Consortium schizophrenia (SCZ) GWAS and CMC conditional eQTL data reveals forty loci with strong evidence for co-localization (posterior probability >0.8), including six loci with co-localization of conditional eQTL. Our co-localization analyses support previously reported genes and identify novel genes for schizophrenia risk, and provide specific hypotheses for their functional follow-up.

genetics↗