CommonMind Consortium provides transcriptomic and epigenomic data for Schizophrenia and Bipolar Disorder.
- Authors
- Hoffman, Gabriel E; Bendl, Jaroslav; Voloudakis, Georgios; Montgomery, Kelsey S; Sloofman, Laura; Wang, Ying-Chih; Shah, Hardik R; Hauberg, Mads E; Johnson, Jessica S; Girdhar, Kiran; Song, Lingyun; Fullard, John F; Kramer, Robin; Hahn, Chang-Gyu; Gur, Raquel; Marenco, Stefano; Lipska, Barbara K; Lewis, David A; Haroutunian, Vahram; Hemby, Scott; Sullivan, Patrick; Akbarian, Schahram; Chess, Andrew; Buxbaum, Joseph D; Crawford, Greg E; Domenici, Enrico; Devlin, Bernie; Sieberts, Solveig K; Peters, Mette A; Roussos, Panos
- Year
- 2019
- Journal
- Scientific data
- PMID
- 31551426
- DOI
- 10.1038/s41597-019-0183-6
- PMCID
- PMC6760149
Schizophrenia and bipolar disorder are serious mental illnesses that affect more than 2% of adults. While large-scale genetics studies have identified genomic regions associated with disease risk, less is known about the molecular mechanisms by which risk alleles with small effects lead to schizophrenia and bipolar disorder. In order to fill this gap between genetics and disease phenotype, we have undertaken a multi-cohort genomics study of postmortem brains from controls, individuals with schizophrenia and bipolar disorder. Here we present a public resource of functional genomic data from the dorsolateral prefrontal cortex (DLPFC; Brodmann areas 9 and 46) of 986 individuals from 4 separate brain banks, including 353 diagnosed with schizophrenia and 120 with bipolar disorder. The genomic data include RNA-seq and SNP genotypes on 980 individuals, and ATAC-seq on 269 individuals, of which 264 are a subset of individuals with RNA-seq. We have performed extensive preprocessing and quality control on these data so that the research community can take advantage of this public resource available on the Synapse platform at http://CommonMind.org .
RNA-seq quality control metrics stratified by disease status. All samples from the 4 brain banks are shown.
Integrated quality control of RNA-seq data. (a) Principal components analysis of log2 CPM values from RNA-seq across 4 brain banks. Brain bank, age of death, and diagnosis are indicated in the legend. (b) Plot of log2 CPM expression of UTY gene from chrY against XIST gene from chrX in order to validate reported sex.
Sex check of ATAC-seq samples. (a) Heterozygosity rate of chromosome X genotype calls outside pseudoautosomal regions. (b) Read counts in OCRs on chromosome Y outside the pseudoautosomal region. (c,d) The read counts of OCRs adjacent to XIST and FIRRE genes.
Quality control metrics for ATAC-seq samples. Histograms of (a) fraction of uniquely mapped reads (mean 0.919, sd Β±0.010), (b) fraction of mitochondrial chromosome reads (mean 0.089, sd Β±0.022), (c) mean insert sizes of pair-end reads (mean 288, sd Β±29), (d) mean GC content (mean 0.418, sd Β±0.012), (e) number of called peaks (mean 14,810, sd Β±8,979), (f) mean coverage (mean 2.473, sd Β±0.642), (g) normalized strand cross-correlation coefficient (mean 1.054, sd Β±0.020), (h) relative strand correlation coefficient (mean 0.991, sd Β±0.084)) and (i) fraction of fragments in peaks (FRiP) (mean 0.151, sd Β±0.027).
Summary of ATAC-seq data. (a) Genomic annotation of consensus OCRs (OCRs within 3 kb of a transcription start site were considered as promoter OCRs). (b) Clustering of the individual samples (n = 269) by chromatin accessibility in consensus OCRs using multidimensional scaling.
Quality control of genotype data. Genotype QC for sex (a,b) and ancestry inference (c,d) for MSSM-Penn-Pitt (a,c) and HBCC (b,d). (a,b) F statistic from plinkβs check-sex function, plotted by reported sex. Following data QC there is 100% concordance between reported sex and inferred sex based on F statistic for both MSSM-Penn-Pitt (a) and HBCC (b). (c,d) The first two principal components (PC) of genetic ancestry as inferred by GEMTOOLs. For both MSSM-Penn-Pitt (c) and HBCC (d) we see good concordance between reported ethnicity and genetic background clusters inferred by GEMTOOLs.
Assessing sample concordance using genetic variants. Estimating contamination using Chipmix (x-axis) and Freemix (y-axis) output from VerifyBamID, on RNA-seq and genotyping data for HBCC and MSSM-Penn-Pitt cohorts. Each point is an RNA-seq sample and is colored according to whether the sample was accepted, excluded or rescued. Box in lower left-hand corner indicates criteria for a sample to be accepted if samples match the expected individual. Box in lower right indicates samples that were rescued by re-labeling to the proper individual. We note that this figure included samples there were excluded because of other filters.
| Name | Type |
|---|---|
| 32 unique animals local | cohort |
| 981 individuals local | cohort |
| acute neurological insults local | phenotype |
| affective disorders | phenotype |
| Agilent 2100 Bioanalyzer | drug |
| Agilent 4200 TapeStation local | drug |
| Allegheny County Office of the Medical Examiner local | cohort |
| Alzheimerβs disease | phenotype |
| anoxia local | phenotype |
| anterior commissure | anatomy |
| ATAC-seq | drug |
| ATAC-seq BAM files local | drug |
| ATAC-seq BigWig per sample local | drug |
| ATAC-seq consensus BigWig local | drug |
| ATAC-seq consensus peaks local | drug |
| ATAC-seq fastq local | drug |
| ATAC-seq peaks per sample local | drug |
| ATAC-seq read count matrix local | drug |
| basal ganglia | anatomy |
| bipolar disorder | phenotype |
| brain bank | cohort |
| Brodmann area 9 | anatomy |
| caQTL local | variant |
| cerebellar tissue | anatomy |
| cerebral hemispheres | anatomy |
| chloroform | drug |
| Clinical local | phenotype |
| clozapine | drug |
| CMC_HBCC local | cohort |
| CMC_HBCC study local | cohort |
| CMCMSSM local | cohort |
| CMC study local | cohort |
| Cohort_269_samples local | cohort |
| CommonMind Consortium | cohort |
| CommonMind.org local | cohort |
| control | cohort |
| control individuals | cohort |
| controls (n=127) local | cohort |
| dlPFC | anatomy |
| DNA | drug |
| dorsolateral prefrontal cortex | anatomy |
| dry ice | drug |
| Eagle2 | drug |
| ENCODE project | cohort |
| eQTLGen Consortium | cohort |
| final dataset of 606 samples local | cohort |
| FIRRE local | gene |
| Fromer et al.9 local | cohort |
| Fromer gene signature local | phenotype |
| fruit treats local | drug |
| genetic variants | cohort |
| Genotypes Imputed - CMC/CMC_HBCC local | drug |
| Genotypes QCd - CMC_HBCC study local | drug |
| Genotypes QCd - CMC study local | drug |
| GRCh38 | gene |
| haloperidol | drug |
| HBCC local | cohort |
| HBCC cohort local | cohort |
| HBCC samples local | cohort |
| Head injury | phenotype |
| hg19 | drug |
| high dose haloperidol local | drug |
| HRC local | drug |
| Human1M-Duo local | drug |
| HumanHap650Y local | drug |
| HumanOmni5M-Quad local | drug |
| Icahn School of Medicine at Mount Sinai | cohort |
| Illumina Bead Array Reader, iScan local | drug |
| Illumina flow cell local | drug |
| Illumina Genome Studio local | drug |
| Illumina genotyping | drug |
| Illumina HiSeq 2000 | drug |
| Illumina Hiseq 2500 | drug |
| Illumina Hiseq 4000 | drug |
| Illumina Infinium HumanOmniExpressExome 8 v 1.1b chip local | drug |
| Illumina Quad Bead Chips local | drug |
| KAPA RiboErase local | drug |
| KAPA Stranded RNA-seq Kit local | drug |
| Klinefelterβs syndrome local | phenotype |
| low dose haloperidol local | drug |
| medial frontal cortex | anatomy |
| Michigan Imputation Server | drug |
| monkeys | cohort |
| mood disorder other than bipolar local | phenotype |
| MSSM local | cohort |
| MSSM-Penn-Pitt cohort local | cohort |
| MSSM/Penn/Pitt cohorts local | cohort |
| Mt. Sinai Brain Repository local | cohort |
| NanoDrop 1000 local | drug |
| NCT03092687 local | cohort |
| negative-strand library preparation local | drug |
| neuropsychiatric disorders | phenotype |
| New York Genome Center local | cohort |
| NIMH-HBCC local | cohort |
| NIMH HBCC brain bank local | cohort |
| NIMH Repository and Genomics Resources local | cohort |
| non-coding RNA | drug |
| non-psychiatric controls local | cohort |
| non-stranded library preparation local | drug |
| Other | phenotype |
| Other brain banks local | cohort |
| Parkinsonβs disease | phenotype |
| peanut butter local | drug |
| Penn local | cohort |
| Penn Alzheimerβs Disease Core Center prospective collection local | cohort |
| Pilgrim Psychiatric Center local | cohort |
| Pitt local | cohort |
| Pitt case/control pairs local | cohort |
| Pitt control samples local | cohort |
| Pitt matched case/control pairs local | cohort |
| Pitt samples local | cohort |
| Plink | drug |
| polyadenylated coding RNA local | drug |
| powdered sugar local | drug |
| prefrontal cortex | anatomy |
| principal sulcus local | anatomy |
| psychiatric disorders | phenotype |
| QIAamp DNA mini Kit local | drug |
| Qiagen DNeasy Blood and Tissue Kit | drug |
| Qiagen RNeasy kit local | drug |
| Qubit | drug |
| race/ethnicity | phenotype |
| Rhesus macaque cohort local | cohort |
| Rhesus macaque RNA-seq BAM files local | drug |
| Rhesus macaque RNA-seq expression quantifications local | drug |
| Rhesus macaque RNA-seq files with remaining unmapped reads local | drug |
| Rhesus macaques drug response experiments local | cohort |
| Ribozero Magnetic Gold kit local | drug |
| RNA | drug |
| RNA integrity number (RIN) | phenotype |
| RNA-seq | drug |
| RNA-seq BAM files local | drug |
| RNA-seq expression data local | drug |
| RNA-seq expression quantifications local | drug |
| RNA-seq FASTQ files with remaining unmapped reads local | drug |
| RNeasy kit local | drug |
| rostral pole local | anatomy |
| rRNA | drug |
| samples | cohort |
| samples removed post sequencing local | cohort |
| samples with RIN <5.5 local | cohort |
| samples with RIN >=5.5 local | cohort |
| schizoaffective disorder | phenotype |
| schizophrenia | phenotype |
| schizophrenia cases (n=126) local | cohort |
| sequencing library local | drug |
| sequencing set local | cohort |
| Serious mental illnesses local | phenotype |
| sex | phenotype |
| SNP | cohort |
| strokes local | phenotype |
| substance use | phenotype |
| Suffolk County Medical Examinerβs Office local | cohort |
| Synapse26 local | cohort |
| Synapse platform local | drug |
| Thermo Scientific NanoDrop local | drug |
| tissue | anatomy |
| Tissue Lyser local | drug |
| Tn5 transposase | drug |
| total RNA | drug |
| Trizol | drug |
| TruSeq RNA Sample Preparation Kit v2 local | drug |
| University of Pittsburgh brain bank local | cohort |
| Untreated monkeys local | cohort |
| UTY local | gene |
| Veteran Affairs Medical Centers local | cohort |
| XIST | gene |
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In this knowledge base
| Title | Year | PMID |
|---|---|---|
| RNA alternative splicing impacts the risk for alcohol use disorder. | 2023 | 37217680 |
External
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| Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization. | Tambets R et al. | β | 2024 | β |
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| Expansion of Schizophrenia Gene Network Knowledge Using Machine Learning Selected Signals From Dorsolateral Prefrontal Cortex and Amygdala RNA-seq Data. | Liu Y et al. | β | 2022 | β |
| Identifying enhancer properties associated with genetic risk for complex traits using regulome-wide association studies. | Casella AM et al. | β | 2022 | β |
| Neural Transcriptomic Analysis of Sex Differences in Autism Spectrum Disorder: Current Insights and Future Directions. | Kissel LT et al. | β | 2022 | β |
| Oxytocin receptor expression patterns in the human brain across development. | Rokicki J et al. | β | 2022 | β |
| Population-level variation in enhancer expression identifies disease mechanisms in the human brain. | Dong P et al. | β | 2022 | β |
| Powerful and robust inference of complex phenotypes' causal genes with dependent expression quantitative loci by a median-based Mendelian randomization. | Jiang L et al. | β | 2022 | β |
| Powerful eQTL mapping through low-coverage RNA sequencing. | Schwarz T et al. | β | 2022 | β |
| Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders. | Truong TTT et al. | β | 2022 | β |
| Sex Differences in Molecular Rhythms in the Human Cortex. | Logan RW et al. | β | 2022 | β |
| Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia. | Hoffman GE et al. | β | 2022 | β |
| Statistical and functional convergence of common and rare genetic influences on autism at chromosome 16p. | Weiner DJ et al. | β | 2022 | β |
| Upper cortical layer-driven network impairment in schizophrenia. | Batiuk MY et al. | β | 2022 | β |
| What genes are differentially expressed in individuals with schizophrenia? A systematic review. | Merikangas AK et al. | β | 2022 | β |
| Advances toward precision medicine for bipolar disorder: mechanisms & molecules. | Haggarty SJ et al. | β | 2021 | β |
| A pipeline for RNA-seq based eQTL analysis with automated quality control procedures. | Wang T et al. | β | 2021 | β |
| Chromatin accessibility in neuropsychiatric disorders. | Egervari G | β | 2021 | β |
| CoExp: A Web Tool for the Exploitation of Co-expression Networks. | GarcΓa-Ruiz S et al. | β | 2021 | β |
| Emerging Methods and Resources for BiologicalΒ Interrogation of Neuropsychiatric Polygenic Signal. | Uffelmann E et al. | β | 2021 | β |
| Epigenetic Modifications in Schizophrenia and Related Disorders: Molecular Scars of Environmental Exposures and Source of Phenotypic Variability. | Richetto J et al. | β | 2021 | β |
| Evaluation of Genotype-Based Gene Expression Model Performance: A Cross-Framework and Cross-Dataset Study. | Tavares V et al. | β | 2021 | β |
| Gene-environment correlations and causal effects of childhood maltreatment on physical and mental health: a genetically informed approach. | Warrier V et al. | β | 2021 | β |
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