Brain Expression Quantitative Trait Loci Research Articles

M97 - TRANSCRIPTIONAL SIGNATURES OF CHILDHOOD ONSET SCHIZOPHRENIA IN HIPSC-DERIVED NPCS AND NEURONS ARE CONCORDANT WITH SIGNATURES FROM POST MORTEM ADULT BRAINS

Background Large-scale Genome Wide Association Studies (GWAS) revealed that Schizophrenia (SZ) risk reflects both rare and common variants. Whereas highly penetrant rare 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. Childhood-Onset-SZ (COS) patients have both a higher rate of rare SZ-associated Copy Number Variations (CNVs), and also stronger common SZ polygenic risk scores. Methods We developed a case/control hiPSC cohort (12 individuals with COS and 10 controls) with which to conduct a comprehensive evaluation of global gene expression in hiPSC-derived neural progenitor cells (NPCs) and neurons. Results Although we identified many sources of variation across our 94 RNAseq samples, this was partially addressed by establishing a rigorous series of bioinformatic practices to reduce and assess the variance inherent in this approach. We report that the donor-specific signal is enriched for post- mortem brain Expression Quantitative Trait Loci (eQTLs), but also that there is a significant correlation between the case/control differential expression observed in our hiPSC-derived COS neurons and the CommonMind Consortium post-mortem study. While the issue of small sample size is shared between post-mortem and hiPSC-based studies, and may be exacerbated in hiPSC-based experiments through the variability that arises as a result of the reprogramming and neural differentiation processes, we find that the common variation effects predicted in GWAS and detected in much larger post-mortem analyses are reflected in hiPSC-neurons. Discussion We predict a growing convergence of common variation findings between hiPSC and post-mortem studies as both approaches expand to larger cohort sizes.

IMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes

BackgroundA range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches.MethodsLeveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE.ResultsiMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available.ConclusioniMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.

Comprehensive integrative analyses identify GLT8D1 and CSNK2B as schizophrenia risk genes

Recent genome-wide association studies (GWAS) have identified multiple risk loci that show strong associations with schizophrenia. However, pinpointing the potential causal genes at the reported loci remains a major challenge. Here we identify candidate causal genes for schizophrenia using an integrative genomic approach. Sherlock integrative analysis shows that ALMS1, GLT8D1, and CSNK2B are schizophrenia risk genes, which are validated using independent brain expression quantitative trait loci (eQTL) data and integrative analysis method (SMR). Consistently, gene expression analysis in schizophrenia cases and controls further supports the potential role of these three genes in the pathogenesis of schizophrenia. Finally, we show that GLT8D1 and CSNK2B knockdown promote the proliferation and inhibit the differentiation abilities of neural stem cells, and alter morphology and synaptic transmission of neurons. These convergent lines of evidence suggest that the ALMS1, CSNK2B, and GLT8D1 genes may be involved in pathophysiology of schizophrenia.

Identification of Genetic Loci Jointly Influencing Schizophrenia Risk and the Cognitive Traits of Verbal-Numerical Reasoning, Reaction Time, and General Cognitive Function

ImportanceSchizophrenia is associated with widespread cognitive impairments. Although cognitive deficits are one of the factors most strongly associated with functional outcome in schizophrenia, current treatment strategies largely fail to ameliorate these impairments. To develop more efficient treatment strategies in patients with schizophrenia, a better understanding of the pathogenesis of these cognitive deficits is needed. Accumulating evidence indicates that genetic risk of schizophrenia may contribute to cognitive dysfunction.ObjectiveTo identify genomic regions jointly influencing schizophrenia and the cognitive domains of reaction time and verbal-numerical reasoning, as well as general cognitive function, a phenotype that captures the shared variation in performance across cognitive domains.Design, Setting, and ParticipantsCombining data from genome-wide association studies from multiple phenotypes using conditional false discovery rate analysis provides increased power to discover genetic variants and could elucidate shared molecular genetic mechanisms. Data from the following genome-wide association studies, published from July 24, 2014, to January 17, 2017, were combined: schizophrenia in the Psychiatric Genomics Consortium cohort (n = 79 757 [cases, 34 486; controls, 45 271]); verbal-numerical reasoning (n = 36 035) and reaction time (n = 111 483) in the UK Biobank cohort; and general cognitive function in CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) (n = 53 949) and COGENT (Cognitive Genomics Consortium) (n = 27 888). Main Outcomes and MeasuresGenetic loci identified by conditional false discovery rate analysis. Brain messenger RNA expression and brain expression quantitative trait locus functionality were determined.ResultsAmong the participants in the genome-wide association studies, 21 loci jointly influencing schizophrenia and cognitive traits were identified: 2 loci shared between schizophrenia and verbal-numerical reasoning, 6 loci shared between schizophrenia and reaction time, and 14 loci shared between schizophrenia and general cognitive function. One locus was shared between schizophrenia and 2 cognitive traits and represented the strongest shared signal detected (nearest gene TCF20; chromosome 22q13.2), and was shared between schizophrenia (z score, 5.01; P = 5.53 × 10−7), general cognitive function (z score, –4.43; P = 9.42 × 10−6), and verbal-numerical reasoning (z score, –5.43; P = 5.64 × 10−8). For 18 loci, schizophrenia risk alleles were associated with poorer cognitive performance. The implicated genes are expressed in the developmental and adult human brain. Replicable expression quantitative trait locus functionality was identified for 4 loci in the adult human brain.Conclusions and RelevanceThe discovered loci improve the understanding of the common genetic basis underlying schizophrenia and cognitive function, suggesting novel molecular genetic mechanisms.

ASD restricted and repetitive behaviors associated at 17q21.33: genes prioritized by expression in fetal brains.

Autism Spectrum Disorder (ASD) is a behaviorally defined condition that manifests in infancy or early childhood as deficits in communication skills and social interactions. Often, restricted and repetitive behaviors (RRBs) accompany this disorder. ASD is polygenic and genetically complex, so we hypothesized that focusing analyses on intermediate core component phenotypes, such as RRBs, can reduce genetic heterogeneity and improve statistical power. Applying this approach, we mined Caucasian GWAS data from two of the largest ASD family cohorts, the Autism Genetics Resource Exchange (AGRE) and Autism Genome Project (AGP). Of the twelve RRBs measured by the Autism Diagnostic Interview-Revised (ADI-R), seven were found to be significantly familial and substantially variable, and hence, were tested for genome-wide association in 3 104 ASD affected children from 2 045 families. Using a stringent significance threshold (p < 7.1×10-9), GWAS in the AGP revealed an association between ‘the degree of the repetitive use of objects or interest in parts of objects’ and rs2898883 (p < 6.8×10-9), which resides within the sixth intron of PHB. To identify the candidate target genes of the associated SNPs at that locus, we applied chromosome conformation studies in developing brains and implicated three additional genes: SLC35B1, CALCOCO2 and DLX3. Gene expression, brain imaging, and fetal brain eQTL studies prioritize SLC35B1 and PHB. These analyses indicate that GWAS of single heritable features of genetically complex disorders followed by chromosome conformation studies in relevant tissues can be successful in revealing novel risk genes for single core features of ASD.

Accumulating evidence for a role of TCF7L2 variants in bipolar disorder with elevated body mass index.

Bipolar disorder (BD) is a complex disease associated with various hereditary traits, including a higher body mass index (BMI). In a prior genome-wide association study, we found that BMI modified the association of rs12772424 - a common variant in the gene encoding transcription factor 7-like 2 (TCF7L2) - with risk for BD. TCF7L2 is a transcription factor in the canonical Wnt pathway, involved in multiple disorders, including diabetes, cancer and psychiatric conditions. Here, using an independent sample, we evaluated 26 TCF7L2 single nucleotide polymorphisms (SNPs) to explore further the association of BD with the TCF7L2-BMI interaction. Using a sample of 662 BD cases and 616 controls, we conducted SNP-level and gene-level tests to assess the evidence for an association between BD and the interaction of BMI and genetic variation in TCF7L2. We also explored the potential mechanism behind the detected associations using human brain expression quantitative trait loci (eQTL) analysis. The analysis provided independent evidence of an rs12772424-BMI interaction (p = 0.011). Furthermore, while overall there was no evidence for SNP marginal effects on BD, the TCF7L2-BMI interaction was significant at the gene level (p = 0.042), with seven of the 26 SNPs showing SNP-BMI interaction effects with p < 0.05. The strongest evidence of interaction was observed for rs7895307 (p = 0.006). TCF7L2 expression showed a significant enrichment of association with the expression of other genes in the Wnt canonical pathway. The current study provides further evidence suggesting that TCF7L2 involvement in BD risk may be regulated by BMI. Detailed, prospective assessment of BMI, comorbidity, and other possible contributing factors is necessary to explain fully the mechanisms underlying this association.

Alzheimer's Disease Risk Polymorphisms Regulate Gene Expression in the ZCWPW1 and the CELF1 Loci.

Late onset Alzheimer’s disease (LOAD) is a genetically complex and clinically heterogeneous disease. Recent large-scale genome wide association studies (GWAS) have identified more than twenty loci that modify risk for AD. Despite the identification of these loci, little progress has been made in identifying the functional variants that explain the association with AD risk. Thus, we sought to determine whether the novel LOAD GWAS single nucleotide polymorphisms (SNPs) alter expression of LOAD GWAS genes and whether expression of these genes is altered in AD brains. The majority of LOAD GWAS SNPs occur in gene dense regions under large linkage disequilibrium (LD) blocks, making it unclear which gene(s) are modified by the SNP. Thus, we tested for brain expression quantitative trait loci (eQTLs) between LOAD GWAS SNPs and SNPs in high LD with the LOAD GWAS SNPs in all of the genes within the GWAS loci. We found a significant eQTL between rs1476679 and PILRB and GATS, which occurs within the ZCWPW1 locus. PILRB and GATS expression levels, within the ZCWPW1 locus, were also associated with AD status. Rs7120548 was associated with MTCH2 expression, which occurs within the CELF1 locus. Additionally, expression of several genes within the CELF1 locus, including MTCH2, were highly correlated with one another and were associated with AD status. We further demonstrate that PILRB, as well as other genes within the GWAS loci, are most highly expressed in microglia. These findings together with the function of PILRB as a DAP12 receptor supports the critical role of microglia and neuroinflammation in AD risk.

Genome-wide DNA methylation profiling in the superior temporal gyrus reveals epigenetic signatures associated with Alzheimer's disease.

BackgroundAlzheimer’s disease affects ~13 % of people in the United States 65 years and older, making it the most common neurodegenerative disorder. Recent work has identified roles for environmental, genetic, and epigenetic factors in Alzheimer’s disease risk.MethodsWe performed a genome-wide screen of DNA methylation using the Illumina Infinium HumanMethylation450 platform on bulk tissue samples from the superior temporal gyrus of patients with Alzheimer’s disease and non-demented controls. We paired a sliding window approach with multivariate linear regression to characterize Alzheimer’s disease-associated differentially methylated regions (DMRs).ResultsWe identified 479 DMRs exhibiting a strong bias for hypermethylated changes, a subset of which were independently associated with aging. DMR intervals overlapped 475 RefSeq genes enriched for gene ontology categories with relevant roles in neuron function and development, as well as cellular metabolism, and included genes reported in Alzheimer’s disease genome-wide and epigenome-wide association studies. DMRs were enriched for brain-specific histone signatures and for binding motifs of transcription factors with roles in the brain and Alzheimer’s disease pathology. Notably, hypermethylated DMRs preferentially overlapped poised promoter regions, marked by H3K27me3 and H3K4me3, previously shown to co-localize with aging-associated hypermethylation. Finally, the integration of DMR-associated single nucleotide polymorphisms with Alzheimer’s disease genome-wide association study risk loci and brain expression quantitative trait loci highlights multiple potential DMRs of interest for further functional analysis.ConclusionWe have characterized changes in DNA methylation in the superior temporal gyrus of patients with Alzheimer’s disease, highlighting novel loci that facilitate better characterization of pathways and mechanisms underlying Alzheimer’s disease pathogenesis, and improve our understanding of epigenetic signatures that may contribute to the development of disease.Electronic supplementary materialThe online version of this article (doi:10.1186/s13073-015-0258-8) contains supplementary material, which is available to authorized users.

Systematic Integration of Brain eQTL and GWAS Identifies ZNF323 as a Novel Schizophrenia Risk Gene and Suggests Recent Positive Selection Based on Compensatory Advantage on Pulmonary Function.

Genome-wide association studies have identified multiple risk variants and loci that show robust association with schizophrenia. Nevertheless, it remains unclear how these variants confer risk to schizophrenia. In addition, the driving force that maintains the schizophrenia risk variants in human gene pool is poorly understood. To investigate whether expression-associated genetic variants contribute to schizophrenia susceptibility, we systematically integrated brain expression quantitative trait loci and genome-wide association data of schizophrenia using Sherlock, a Bayesian statistical framework. Our analyses identified ZNF323 as a schizophrenia risk gene (P = 2.22×10(-6)). Subsequent analyses confirmed the association of the ZNF323 and its expression-associated single nucleotide polymorphism rs1150711 in independent samples (gene-expression: P = 1.40×10(-6); single-marker meta-analysis in the combined discovery and replication sample comprising 44123 individuals: P = 6.85×10(-10)). We found that the ZNF323 was significantly downregulated in hippocampus and frontal cortex of schizophrenia patients (P = .0038 and P = .0233, respectively). Evidence for pleiotropic effects was detected (association of rs1150711 with lung function and gene expression of ZNF323 in lung: P = 6.62×10(-5) and P = 9.00×10(-5), respectively) with the risk allele (T allele) for schizophrenia acting as protective allele for lung function. Subsequent population genetics analyses suggest that the risk allele (T) of rs1150711 might have undergone recent positive selection in human population. Our findings suggest that the ZNF323 is a schizophrenia susceptibility gene whose expression may influence schizophrenia risk. Our study also illustrates a possible mechanism for maintaining schizophrenia risk variants in the human gene pool.

A meta-analysis of gene expression quantitative trait loci in brain.

Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10−9), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.

Frontotemporal dementia and its subtypes: a genome-wide association study

Frontotemporal dementia (FTD) is a complex disorder characterised by a broad range of clinical manifestations, differential pathological signatures, and genetic variability. Mutations in three genes-MAPT, GRN, and C9orf72--have been associated with FTD. We sought to identify novel genetic risk loci associated with the disorder. We did a two-stage genome-wide association study on clinical FTD, analysing samples from 3526 patients with FTD and 9402 healthy controls. To reduce genetic heterogeneity, all participants were of European ancestry. In the discovery phase (samples from 2154 patients with FTD and 4308 controls), we did separate association analyses for each FTD subtype (behavioural variant FTD, semantic dementia, progressive non-fluent aphasia, and FTD overlapping with motor neuron disease [FTD-MND]), followed by a meta-analysis of the entire dataset. We carried forward replication of the novel suggestive loci in an independent sample series (samples from 1372 patients and 5094 controls) and then did joint phase and brain expression and methylation quantitative trait loci analyses for the associated (p<5 × 10(-8)) single-nucleotide polymorphisms. We identified novel associations exceeding the genome-wide significance threshold (p<5 × 10(-8)). Combined (joint) analyses of discovery and replication phases showed genome-wide significant association at 6p21.3, HLA locus (immune system), for rs9268877 (p=1·05 × 10(-8); odds ratio=1·204 [95% CI 1·11-1·30]), rs9268856 (p=5·51 × 10(-9); 0·809 [0·76-0·86]) and rs1980493 (p value=1·57 × 10(-8), 0·775 [0·69-0·86]) in the entire cohort. We also identified a potential novel locus at 11q14, encompassing RAB38/CTSC (the transcripts of which are related to lysosomal biology), for the behavioural FTD subtype for which joint analyses showed suggestive association for rs302668 (p=2·44 × 10(-7); 0·814 [0·71-0·92]). Analysis of expression and methylation quantitative trait loci data suggested that these loci might affect expression and methylation in cis. Our findings suggest that immune system processes (link to 6p21.3) and possibly lysosomal and autophagy pathways (link to 11q14) are potentially involved in FTD. Our findings need to be replicated to better define the association of the newly identified loci with disease and to shed light on the pathomechanisms contributing to FTD. The National Institute of Neurological Disorders and Stroke and National Institute on Aging, the Wellcome/MRC Centre on Parkinson's disease, Alzheimer's Research UK, and Texas Tech University Health Sciences Center.

Brain expression genome-wide association study (eGWAS) identifies human disease-associated variants.

Genetic variants that modify brain gene expression may also influence risk for human diseases. We measured expression levels of 24,526 transcripts in brain samples from the cerebellum and temporal cortex of autopsied subjects with Alzheimer's disease (AD, cerebellar n = 197, temporal cortex n = 202) and with other brain pathologies (non–AD, cerebellar n = 177, temporal cortex n = 197). We conducted an expression genome-wide association study (eGWAS) using 213,528 cisSNPs within ±100 kb of the tested transcripts. We identified 2,980 cerebellar cisSNP/transcript level associations (2,596 unique cisSNPs) significant in both ADs and non–ADs (q<0.05, p = 7.70×10−5–1.67×10−82). Of these, 2,089 were also significant in the temporal cortex (p = 1.85×10−5–1.70×10−141). The top cerebellar cisSNPs had 2.4-fold enrichment for human disease-associated variants (p<10−6). We identified novel cisSNP/transcript associations for human disease-associated variants, including progressive supranuclear palsy SLCO1A2/rs11568563, Parkinson's disease (PD) MMRN1/rs6532197, Paget's disease OPTN/rs1561570; and we confirmed others, including PD MAPT/rs242557, systemic lupus erythematosus and ulcerative colitis IRF5/rs4728142, and type 1 diabetes mellitus RPS26/rs1701704. In our eGWAS, there was 2.9–3.3 fold enrichment (p<10−6) of significant cisSNPs with suggestive AD–risk association (p<10−3) in the Alzheimer's Disease Genetics Consortium GWAS. These results demonstrate the significant contributions of genetic factors to human brain gene expression, which are reliably detected across different brain regions and pathologies. The significant enrichment of brain cisSNPs among disease-associated variants advocates gene expression changes as a mechanism for many central nervous system (CNS) and non–CNS diseases. Combined assessment of expression and disease GWAS may provide complementary information in discovery of human disease variants with functional implications. Our findings have implications for the design and interpretation of eGWAS in general and the use of brain expression quantitative trait loci in the study of human disease genetics.

Loci nominally associated with autism from genome-wide analysis show enrichment of brain expression quantitative trait loci but not lymphoblastoid cell line expression quantitative trait loci

BackgroundAutism spectrum disorder is a severe early onset neurodevelopmental disorder with high heritability but significant heterogeneity. Traditional genome-wide approaches to test for an association of common variants with autism susceptibility risk have met with limited success. However, novel methods to identify moderate risk alleles in attainable sample sizes are now gaining momentum.MethodsIn this study, we utilized publically available genome-wide association study data from the Autism Genome Project and annotated the results (P <0.001) for expression quantitative trait loci present in the parietal lobe (GSE35977), cerebellum (GSE35974) and lymphoblastoid cell lines (GSE7761). We then performed a test of enrichment by comparing these results to simulated data conditioned on minor allele frequency to generate an empirical P-value indicating statistically significant enrichment of expression quantitative trait loci in top results from the autism genome-wide association study.ResultsOur findings show a global enrichment of brain expression quantitative trait loci, but not lymphoblastoid cell line expression quantitative trait loci, among top single nucleotide polymorphisms from an autism genome-wide association study. Additionally, the data implicates individual genes SLC25A12, PANX1 and PANX2 as well as pathways previously implicated in autism.ConclusionsThese findings provide supportive rationale for the use of annotation-based approaches to genome-wide association studies.

Brain expression quantitative trait locus mapping informs genetic studies of psychiatric diseases

Genome-wide association study (GWAS) can be used to identify genes that increase the risk of psychiatric diseases. However, much of the disease heritability is still unexplained, suggesting that there are genes to be discovered. Functional annotation of the genetic variants may increase the power of GWAS to identify disease genes, by providing prior information that can be used in Bayesian analysis or in reducing the number of tests. Expression quantitative trait loci (eQTLs) are genomic loci that regulate gene expression. Genetic mapping of eQTLs can help reveal novel functional effects of thousands of single nucleotide polymorphisms (SNPs). The present review mainly focused on the current knowledge on brain eQTL mapping, and discussed some major methodological issues and their possible solutions. The frequently ignored problems of batch effects, covariates, and multiple testing were emphasized, since they can lead to false positives and false negatives. The future application of eQTL data in GWAS analysis was also discussed.