Featured Publications
Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk
Rajarajan P, Borrman T, Liao W, Schrode N, Flaherty E, Casiño C, Powell S, Yashaswini C, LaMarca EA, Kassim B, Javidfar B, Espeso-Gil S, Li A, Won H, Geschwind DH, Ho SM, MacDonald M, Hoffman GE, Roussos P, Zhang B, Hahn CG, Weng Z, Brennand KJ, Akbarian S. Neuron-specific signatures in the chromosomal connectome associated with schizophrenia risk. Science 2018, 362 PMID: 30545851, PMCID: PMC6408958, DOI: 10.1126/science.aat4311.Peer-Reviewed Original ResearchMeSH KeywordsBrainCells, CulturedChromatinChromatin Assembly and DisassemblyChromosomes, HumanConnectomeEpigenesis, GeneticGene Expression Regulation, DevelopmentalGenetic Predisposition to DiseaseGenome, HumanGenome-Wide Association StudyHumansMaleNeural Stem CellsNeurogenesisNeurogliaNeuronsNucleic Acid ConformationProtein Interaction MapsProteomicsRiskSchizophreniaTranscription, GeneticTranscriptomeConceptsCoordinated transcriptional regulationThree-dimensional genomeSpatial genome organizationChromosomal contact mapsNeural progenitor cellsSchizophrenia risk variantsGenome organizationChromatin remodelingChromosomal conformationTranscriptional regulationProteomic interactionsDevelopmental remodelingHeritable riskGlial differentiationRisk variantsContact mapsProgenitor cellsVariant sequencesGenesConformation changeNeuronal connectivitySchizophrenia riskSequenceNeuropsychiatric diseasesDistal targets
2024
Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain
Wen C, Margolis M, Dai R, Zhang P, Przytycki P, Vo D, Bhattacharya A, Matoba N, Tang M, Jiao C, Kim M, Tsai E, Hoh C, Aygün N, Walker R, Chatzinakos C, Clarke D, Pratt H, Peters M, Gerstein M, Daskalakis N, Weng Z, Jaffe A, Kleinman J, Hyde T, Weinberger D, Bray N, Sestan N, Geschwind D, Roeder K, Gusev A, Pasaniuc B, Stein J, Love M, Pollard K, Liu C, Gandal M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clement A, Collado-Torres L, Cooper G, Crawford G, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gaynor S, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Haroutunian V, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Hoffman G, Huang Y, Huuki-Myers L, Hwang A, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollen A, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Roussos P, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Voloudakis G, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Cross-ancestry atlas of gene, isoform, and splicing regulation in the developing human brain. Science 2024, 384: eadh0829. PMID: 38781368, DOI: 10.1126/science.adh0829.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSplicing quantitative trait lociQuantitative trait lociSplicing regulationCross-ancestryTrait lociAssociation studiesRegulatory elementsCellular contextHuman brainTranscriptome regulationCoexpression networkRisk genesAutism spectrum disorderGenesCellular heterogeneityComprehensive landscapeSpectrum disorderIsoformsSplicingIncreased cellular heterogeneityLociNeuronal maturationRegulationSingle-cell genomics and regulatory networks for 388 human brains
Emani P, Liu J, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee C, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken T, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard J, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman G, Huang A, Jiang Y, Jin T, Jorstad N, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran J, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan A, Riesenmy T, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini K, Wamsley B, Wang G, Xia Y, Xiao S, Yang A, Zheng S, Gandal M, Lee D, Lein E, Roussos P, Sestan N, Weng Z, White K, Won H, Girgenti M, Zhang J, Wang D, Geschwind D, Gerstein M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Brennand K, Capauto D, Champagne F, Chatzinakos C, Chen H, Cheng L, Chess A, Chien J, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Davila-Velderrain J, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duong D, Eagles N, Edelstein J, Galani K, Girdhar K, Goes F, Greenleaf W, Guo H, Guo Q, Hadas Y, Hallmayer J, Han X, Haroutunian V, He C, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hyde T, Iatrou A, Inoue F, Jajoo A, Jiang L, Jin P, Jops C, Jourdon A, Kellis M, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Li J, Li M, Lin X, Liu S, Liu C, Loupe J, Lu D, Ma L, Mariani J, Martinowich K, Maynard K, Myers R, Micallef C, Mikhailova T, Ming G, Mohammadi S, Monte E, Montgomery K, Mukamel E, Nairn A, Nemeroff C, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Pinto D, Pochareddy S, Pollard K, Pollen A, Przytycki P, Purmann C, Qin Z, Qu P, Raj T, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Ruzicka W, Sanders S, Schneider J, Scuderi S, Sebra R, Seyfried N, Shao Z, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Subburaju S, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Voloudakis G, Wang T, Wang S, Wang Y, Wei Y, Weimer A, Weinberger D, Wen C, Whalen S, Willsey A, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang Y, Ziffra R, Zeier Z, Zintel T. Single-cell genomics and regulatory networks for 388 human brains. Science 2024, 384: eadi5199. PMID: 38781369, PMCID: PMC11365579, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchConceptsSingle-cell genomicsSingle-cell expression quantitative trait locusExpression quantitative trait lociDrug targetsQuantitative trait lociPopulation-level variationSingle-cell expressionCell typesDisease-risk genesTrait lociGene familyRegulatory networksGene expressionCell-typeMultiomics datasetsSingle-nucleiGenomeGenesCellular changesHeterogeneous tissuesExpressionCellsChromatinLociMultiomics
2023
Multi-omic profiling of the developing human cerebral cortex at the single-cell level
Zhu K, Bendl J, Rahman S, Vicari J, Coleman C, Clarence T, Latouche O, Tsankova N, Li A, Brennand K, Lee D, Yuan G, Fullard J, Roussos P. Multi-omic profiling of the developing human cerebral cortex at the single-cell level. Science Advances 2023, 9: eadg3754. PMID: 37824614, PMCID: PMC10569714, DOI: 10.1126/sciadv.adg3754.Peer-Reviewed Original ResearchConceptsCis-regulatory elementsChromatin accessibilityGene expressionPseudotime trajectory analysisNeuronal lineage commitmentMulti-omics profilingSingle-cell levelSpecific genetic lociDevelopmental time pointsChromatin structureType-specific domainsLineage determinationCellular complexityLineage commitmentNeuropsychiatric traitsComplex regulationGenetic lociSpatiotemporal activityDynamic changesCritical roleExpressionSpatiotemporal alterationsCell compositionCritical stageNeuropsychiatric diseasesLineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome
Rahman S, Dong P, Apontes P, Fernando M, Kosoy R, Townsley K, Girdhar K, Bendl J, Shao Z, Misir R, Tsankova N, Kleopoulos S, Brennand K, Fullard J, Roussos P. Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome. Nucleic Acids Research 2023, 51: 11142-11161. PMID: 37811875, PMCID: PMC10639075, DOI: 10.1093/nar/gkad798.Peer-Reviewed Original ResearchConceptsChromatin interactomeNeural developmentSpecific gene expressionEnhancer-promoter loopsDistinct cell typesGenome compartmentalizationRepressive compartmentGenome architectureFine-scale changesGenome structureChromatin loopsGWAS lociTAD boundariesTranscriptional inactivationActive promotersGene expressionInteractomeGenomeCell typesComplex organDisease mechanismsHuman brainAdult prefrontal cortexAdult human brainNeurodevelopmental processesActivity-Dependent Transcriptional Program in NGN2+ Neurons Enriched for Genetic Risk for Brain-Related Disorders
Ma Y, Bendl J, Hartley B, Fullard J, Abdelaal R, Ho S, Kosoy R, Gochman P, Rapoport J, Hoffman G, Brennand K, Roussos P. Activity-Dependent Transcriptional Program in NGN2+ Neurons Enriched for Genetic Risk for Brain-Related Disorders. Biological Psychiatry 2023, 95: 187-198. PMID: 37454787, PMCID: PMC10787819, DOI: 10.1016/j.biopsych.2023.07.003.Peer-Reviewed Original ResearchConceptsTranscriptional programsBrain-related disordersGlutamatergic neuronsGene coexpression network analysisSignificant heritability enrichmentsEnhancer-promoter interactionsCoexpression network analysisDisease-associated genesExpression of genesLarge-scale geneticMultiomics data integrationChromatin accessibilityEpigenomic changesHeritability enrichmentGenetic regulationRegulatory elementsMultiple genesSequence variationGene expressionAxon guidanceGenetic riskPotassium chloride-induced depolarizationActivity-dependent changesDepolarization-induced changesGenesThe functional and evolutionary impacts of human-specific deletions in conserved elements
Xue J, Mackay-Smith A, Mouri K, Garcia M, Dong M, Akers J, Noble M, Li X, Lindblad-Toh K, Karlsson E, Noonan J, Capellini T, Brennand K, Tewhey R, Sabeti P, Reilly S, Andrews G, Armstrong J, Bianchi M, Birren B, Bredemeyer K, Breit A, Christmas M, Clawson H, Damas J, Di Palma F, Diekhans M, Dong M, Eizirik E, Fan K, Fanter C, Foley N, Forsberg-Nilsson K, Garcia C, Gatesy J, Gazal S, Genereux D, Goodman L, Grimshaw J, Halsey M, Harris A, Hickey G, Hiller M, Hindle A, Hubley R, Hughes G, Johnson J, Juan D, Kaplow I, Karlsson E, Keough K, Kirilenko B, Koepfli K, Korstian J, Kowalczyk A, Kozyrev S, Lawler A, Lawless C, Lehmann T, Levesque D, Lewin H, Li X, Lind A, Lindblad-Toh K, Mackay-Smith A, Marinescu V, Marques-Bonet T, Mason V, Meadows J, Meyer W, Moore J, Moreira L, Moreno-Santillan D, Morrill K, Muntané G, Murphy W, Navarro A, Nweeia M, Ortmann S, Osmanski A, Paten B, Paulat N, Pfenning A, Phan B, Pollard K, Pratt H, Ray D, Reilly S, Rosen J, Ruf I, Ryan L, Ryder O, Sabeti P, Schäffer D, Serres A, Shapiro B, Smit A, Springer M, Srinivasan C, Steiner C, Storer J, Sullivan K, Sullivan P, Sundström E, Supple M, Swofford R, Talbot J, Teeling E, Turner-Maier J, Valenzuela A, Wagner F, Wallerman O, Wang C, Wang J, Weng Z, Wilder A, Wirthlin M, Xue J, Zhang X. The functional and evolutionary impacts of human-specific deletions in conserved elements. Science 2023, 380: eabn2253. PMID: 37104592, PMCID: PMC10202372, DOI: 10.1126/science.abn2253.Peer-Reviewed Original ResearchConceptsHuman-specific deletionHuman phenotypic traitsParallel reporterEvolutionary impactDevelopmental genesPhenotypic traitsEvolutionary mechanismsGenomic sequencesNew traitsTranscriptomic datasetsSequence altersRegulatory functionsCell typesRegulatory activityRich resourceDeletionSynaptic functionTraitsBrain developmentGenesSpeciesReporterHumansSequenceExpressionIntegrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings
Hicks E, Seah C, Cote A, Marchese S, Brennand K, Nestler E, Girgenti M, Huckins L. Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings. Translational Psychiatry 2023, 13: 129. PMID: 37076454, PMCID: PMC10115809, DOI: 10.1038/s41398-023-02412-7.Peer-Reviewed Original ResearchConceptsBioinformatics approachTranscriptomic dataBrain transcriptomeGenome-wide analysisDynamic transcriptional landscapeBrain gene expression dataGene expression dataTranscriptional landscapeTranscriptomic studiesIntegrating GeneticExpression dataPhenotypic signaturesGenomic driversTranscriptomeMajor depressive disorderValuable resourceRecent findingsEnvironmental influencesTranscriptomicsDepressive disorderGeneticsMultiple approachesPathophysiology of depressionSignaturesDysregulationPandemic city: Village-in-a-dish unlocks dynamic genetic effects in the brain
Seah C, Brennand K. Pandemic city: Village-in-a-dish unlocks dynamic genetic effects in the brain. Cell Stem Cell 2023, 30: 239-241. PMID: 36868190, DOI: 10.1016/j.stem.2023.02.002.Peer-Reviewed Original Research
2022
Population-level variation in enhancer expression identifies disease mechanisms in the human brain
Dong P, Hoffman G, Apontes P, Bendl J, Rahman S, Fernando M, Zeng B, Vicari J, Zhang W, Girdhar K, Townsley K, Misir R, Brennand K, Haroutunian V, Voloudakis G, Fullard J, Roussos P. Population-level variation in enhancer expression identifies disease mechanisms in the human brain. Nature Genetics 2022, 54: 1493-1503. PMID: 36163279, PMCID: PMC9547946, DOI: 10.1038/s41588-022-01170-4.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociPopulation-level variationTranscriptome-wide association studyQuantitative trait lociSpecific transcriptomeTrait lociTrait heritabilitySpecific transcriptionEnhancer functionGenetic mechanismsTarget genesAssociation studiesDisease locusNeuropsychiatric diseasesRisk variantsGenesRobust expressionTranscriptomeFunctional interpretationDisease mechanismsEnhancerDiseased statesLociHuman brainBrain samples
2021
Fitness selection of hyperfusogenic measles virus F proteins associated with neuropathogenic phenotypes
Ikegame S, Hashiguchi T, Hung C, Dobrindt K, Brennand K, Takeda M, Lee B. Fitness selection of hyperfusogenic measles virus F proteins associated with neuropathogenic phenotypes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2021, 118: e2026027118. PMID: 33903248, PMCID: PMC8106313, DOI: 10.1073/pnas.2026027118.Peer-Reviewed Original ResearchConceptsF mutantsMeasles inclusion body encephalitisBSR-T7 cellsMeasles virus F proteinReceptor-binding proteinVirus F proteinGenomic contextFitness advantageWild-type MeVRegulatory domainHyperfusogenic phenotypePrimary human neuronsMutant libraryPoint mutantsMutantsFitness selectionMeV receptorsF phenotypeInclusion body encephalitisNeuropathogenic phenotypeFitness landscapeChronic latent infectionFusion geneF proteinHuman neuronsMolecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets
Neff R, Wang M, Vatansever S, Guo L, Ming C, Wang Q, Wang E, Horgusluoglu-Moloch E, Song W, Li A, Castranio E, Julia T, Ho L, Goate A, Fossati V, Noggle S, Gandy S, Ehrlich M, Katsel P, Schadt E, Cai D, Brennand K, Haroutunian V, Zhang B. Molecular subtyping of Alzheimer’s disease using RNA sequencing data reveals novel mechanisms and targets. Science Advances 2021, 7: eabb5398. PMID: 33523961, PMCID: PMC7787497, DOI: 10.1126/sciadv.abb5398.Peer-Reviewed Original ResearchConceptsAlzheimer's diseaseMouse modelAD mouse modelDiverse pathophysiologic mechanismsTau-mediated neurodegenerationMajor molecular subtypesSpecific mouse modelsPathophysiologic mechanismsHuman trialsMolecular subtypesImmune activityHeterogeneous diseaseAD cohortAD subtypesBrain regionsSynaptic signalingMolecular subtypingSubtype heterogeneityDiseaseCommon formPrecision medicineMultiscale network analysisDevastating diseaseMolecular heterogeneitySubtypes
2020
Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease
Wang M, Li A, Sekiya M, Beckmann ND, Quan X, Schrode N, Fernando MB, Yu A, Zhu L, Cao J, Lyu L, Horgusluoglu E, Wang Q, Guo L, Wang YS, Neff R, Song WM, Wang E, Shen Q, Zhou X, Ming C, Ho SM, Vatansever S, Kaniskan HÜ, Jin J, Zhou MM, Ando K, Ho L, Slesinger PA, Yue Z, Zhu J, Katsel P, Gandy S, Ehrlich ME, Fossati V, Noggle S, Cai D, Haroutunian V, Iijima KM, Schadt E, Brennand KJ, Zhang B. Transformative Network Modeling of Multi-omics Data Reveals Detailed Circuits, Key Regulators, and Potential Therapeutics for Alzheimer’s Disease. Neuron 2020, 109: 257-272.e14. PMID: 33238137, PMCID: PMC7855384, DOI: 10.1016/j.neuron.2020.11.002.Peer-Reviewed Original ResearchConceptsLate-onset Alzheimer's diseaseAlzheimer's diseaseKey regulatorPluripotent stem cell-derived neuronsRNAi-based knockdownStem cell-derived neuronsNovel therapeutic targetNext-generation therapeutic agentsCell-derived neuronsKey brain regionsIntegrative network analysisMulti-omics dataComplex molecular interactionsMulti-omics profilingNCH-51Neuronal impairmentGene subnetworksDisease-related processesCortical areasTherapeutic targetDrosophila modelNeuropathological phenotypeBrain regionsTherapeutic agentsMolecular mechanismsMassively parallel techniques for cataloguing the regulome of the human brain
Townsley KG, Brennand KJ, Huckins LM. Massively parallel techniques for cataloguing the regulome of the human brain. Nature Neuroscience 2020, 23: 1509-1521. PMID: 33199899, PMCID: PMC8018778, DOI: 10.1038/s41593-020-00740-1.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsRegulatory elementsTarget genesParallel reporter assaysPutative regulatory elementsNon-coding regionsDisease-associated lociSpecific expression patternsCandidate risk lociPluripotent stem cellsHigh-throughput assaysRelevant molecular pathwaysTranscriptional responseRegulatory architectureRisk lociExpression patternsReporter assaysComplex brain disordersMolecular pathwaysRegulomeStem cellsRisk architectureGenetic riskGenesLociGenetic diagnosisParsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome
Powell SK, O'Shea C, Brennand KJ, Akbarian S. Parsing the Functional Impact of Noncoding Genetic Variants in the Brain Epigenome. Biological Psychiatry 2020, 89: 65-75. PMID: 33131715, PMCID: PMC7718420, DOI: 10.1016/j.biopsych.2020.06.033.Peer-Reviewed Original ResearchConceptsGenetic variantsDisease-associated genetic variationProtein-coding lociRisk-associated genetic variantsGene regulatory lociThousands of variantsFunctional impactRare genetic variantsEpigenomic mappingRegulatory lociBrain epigenomeGenetic variationDNA sequencesNoncoding variantsGene expressionIntegrative analysisEpigenomic architectureMolecular pathwaysPsychiatric geneticsFunctional readoutRisk variantsLociVariantsHighlight findingsEpigenomeFunctional annotation of rare structural variation in the human brain
Han L, Zhao X, Benton ML, Perumal T, Collins RL, Hoffman GE, Johnson JS, Sloofman L, Wang HZ, Stone MR, Brennand K, Brand H, Sieberts S, Marenco S, Peters M, Lipska B, Roussos P, Capra J, Talkowski M, Ruderfer D. Functional annotation of rare structural variation in the human brain. Nature Communications 2020, 11: 2990. PMID: 32533064, PMCID: PMC7293301, DOI: 10.1038/s41467-020-16736-1.Peer-Reviewed Original ResearchA computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles
Sey NYA, Hu B, Mah W, Fauni H, McAfee JC, Rajarajan P, Brennand KJ, Akbarian S, Won H. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nature Neuroscience 2020, 23: 583-593. PMID: 32152537, PMCID: PMC7131892, DOI: 10.1038/s41593-020-0603-0.Peer-Reviewed Original ResearchConceptsChromatin interaction profilesH-MAGMARisk genesMost risk variantsGenome-wide association studiesCell typesGene regulatory relationshipsRelevant target genesCell-type specificitySingle nucleotide polymorphism associationsBrain cell typesDisease-relevant tissuesInteraction profilesGenomic annotationsNearest geneTarget genesRegulatory relationshipsAssociation studiesBiological pathwaysGenesRisk variantsDevelopmental windowBiological mechanismsNeurodegenerative disordersHuman brain tissue
2019
CRISPR-based functional evaluation of schizophrenia risk variants
Rajarajan P, Flaherty E, Akbarian S, Brennand KJ. CRISPR-based functional evaluation of schizophrenia risk variants. Schizophrenia Research 2019, 217: 26-36. PMID: 31277978, PMCID: PMC6939156, DOI: 10.1016/j.schres.2019.06.017.Peer-Reviewed Original ResearchConceptsSchizophrenia-associated variantsPluripotent stem cellsCRISPR genome engineeringSchizophrenia risk variantsCellular functionsGenome engineeringGenomic studiesSchizophrenia lociList of variantsGene expressionPatient-specific humanGenotype dataRisk variantsStem cellsFunctional impactCommon variantsCRISPRPost-mortem brain tissueRecent findingsVariantsNeuropsychiatric diseasesPoint of convergenceGenetic riskLociSpecific effects
2018
GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis
Kajiwara Y, Wang E, Wang M, Sin WC, Brennand KJ, Schadt E, Naus CC, Buxbaum J, Zhang B. GJA1 (connexin43) is a key regulator of Alzheimer’s disease pathogenesis. Acta Neuropathologica Communications 2018, 6: 144. PMID: 30577786, PMCID: PMC6303945, DOI: 10.1186/s40478-018-0642-x.Peer-Reviewed Original ResearchConceptsPost-mortem Alzheimer's diseaseAlzheimer's diseaseTop key driverRNA sequencing analysisDisease pathogenesisProteomic datasetsKey regulatorNormal control brainsGJA1 expressionAlzheimer's disease (AD) pathogenesisApoE protein levelsPromising pharmacological targetSequencing analysisGJA1Wildtype astrocytesWildtype neuronsAβ metabolismAβ phagocytosisProtein levelsControl brainsAD pathogenesisAD amyloidPharmacological targetsAstrocytesCognitive function
2017
Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains
Hoffman GE, Hartley BJ, Flaherty E, Ladran I, Gochman P, Ruderfer DM, Stahl EA, Rapoport J, Sklar P, Brennand KJ. Transcriptional signatures of schizophrenia in hiPSC-derived NPCs and neurons are concordant with post-mortem adult brains. Nature Communications 2017, 8: 2225. PMID: 29263384, PMCID: PMC5738408, DOI: 10.1038/s41467-017-02330-5.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAntigens, SurfaceAutopsyBrainCase-Control StudiesChildDNA Copy Number VariationsFemaleHumansInduced Pluripotent Stem CellsLinear ModelsMaleNanog Homeobox ProteinNestinNeural Stem CellsNeuronsOctamer Transcription Factor-3ProteoglycansRNA, MessengerSchizophreniaSequence Analysis, RNASOXB1 Transcription FactorsStage-Specific Embryonic AntigensSynapsinsTranscriptomeYoung Adult