Featured Publications
Synergistic effects of common schizophrenia risk variants
Schrode N, Ho SM, Yamamuro K, Dobbyn A, Huckins L, Matos MR, Cheng E, Deans PJM, Flaherty E, Barretto N, Topol A, Alganem K, Abadali S, Gregory J, Hoelzli E, Phatnani H, Singh V, Girish D, Aronow B, Mccullumsmith R, Hoffman GE, Stahl EA, Morishita H, Sklar P, Brennand KJ. Synergistic effects of common schizophrenia risk variants. Nature Genetics 2019, 51: 1475-1485. PMID: 31548722, PMCID: PMC6778520, DOI: 10.1038/s41588-019-0497-5.Peer-Reviewed Original ResearchMeSH KeywordsChloride ChannelsCRISPR-Cas SystemsFemaleFurinGene EditingGene Expression RegulationGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInduced Pluripotent Stem CellsMaleMonomeric Clathrin Assembly ProteinsPolymorphism, Single NucleotideQuantitative Trait LociSchizophreniaSNARE ProteinsConceptsExpression quantitative trait lociComplex genetic disorderEQTL genesCommon variantsQuantitative trait lociRisk variantsGene expression differencesPsychiatric disease riskCommon risk variantsPluripotent stem cellsSchizophrenia risk variantsGenetic disordersTrait lociGene perturbationsGenetic approachesExpression differencesGene editingStem cellsGeneralizable phenomenonSynaptic functionGenesVariantsCRISPRLociSpecific effectsNeuron-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-Wide Association StudyGenome, HumanHumansMaleNeural 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
Somatic mosaicism in schizophrenia brains reveals prenatal mutational processes
Maury E, Jones A, Seplyarskiy V, Nguyen T, Rosenbluh C, Bae T, Wang Y, Abyzov A, Khoshkhoo S, Chahine Y, Zhao S, Venkatesh S, Root E, Voloudakis G, Roussos P, Network B, Park P, Akbarian S, Brennand K, Reilly S, Lee E, Sunyaev S, Walsh C, Chess A. Somatic mosaicism in schizophrenia brains reveals prenatal mutational processes. Science 2024, 386: 217-224. PMID: 39388546, PMCID: PMC11490355, DOI: 10.1126/science.adq1456.Peer-Reviewed Original ResearchConceptsTranscription factor binding sitesWhole-genome sequencingOpen chromatinMutational processesSomatic mutationsFactor binding sitesSchizophrenia casesSchizophrenia risk genesSomatic mosaicismSomatic variantsRisk genesG mutationGene expressionGermline mutationsBinding sitesGenesMutationsIncreased somatic mutationsChromatinMosaic somatic mutationsPrenatal neurogenesisContext of schizophreniaBrain neuronsSchizophrenia brainVariantsRegulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease
Huang Y, Wang M, Ni H, Zhang J, Li A, Hu B, Junqueira Alves C, Wahane S, Rios de Anda M, Ho L, Li Y, Kang S, Neff R, Kostic A, Buxbaum J, Crary J, Brennand K, Zhang B, Zou H, Friedel R. Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease. Nature Neuroscience 2024, 27: 1489-1504. PMID: 38802590, PMCID: PMC11346591, DOI: 10.1038/s41593-024-01664-w.Peer-Reviewed Original ResearchPlexin-B1Alzheimer's diseaseGlial netsNetwork hub genesLate-onset ADPlaque-associated astrocytesPathophysiology of Alzheimer's diseaseMouse AD modelPlaque compactionNeuritic dystrophyHub genesGuidance receptorsTranscriptional changesAD modelAmyloid depositsAmyloidReducing neuroinflammationGlial cellsReactive astrocytesReceptor Plexin-B1Net activityGlial processesDeletionGenesCell distanceSingle-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 tissuesExpressionCellsChromatinLociMultiomicsCross-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 maturationRegulationDissecting the biology of feeding and eating disorders
Huckins L, Brennand K, Bulik C. Dissecting the biology of feeding and eating disorders. Trends In Molecular Medicine 2024, 30: 380-391. PMID: 38431502, DOI: 10.1016/j.molmed.2024.01.009.Peer-Reviewed Original ResearchGenome-wide association studiesVariants to genesGenes to pathwaysSignificant lociFunctional genomicsAssociation studiesGenetic relationshipsIntestinal microbiotaGenetic researchGenomeGenetic correlationsGenesMetabolic contributorsAnorexia nervosaEating disordersPathwayBiologyMetabolic outcomesRisk factorsLociMicrobiotaPhenomicsLethal illnessTraitsFeeding
2023
56. USING HIPSC-NEURONS AND CRISPR TO UNCOVER NON-ADDITIVE EFFECTS OF SCZ RISK GENES
Deans M, Seah C, Johnson J, García-González J, Townsley K, Cao E, Schrode N, Stahl E, O'Reilly P, Huckins L, Brennand K. 56. USING HIPSC-NEURONS AND CRISPR TO UNCOVER NON-ADDITIVE EFFECTS OF SCZ RISK GENES. European Neuropsychopharmacology 2023, 75: s86. DOI: 10.1016/j.euroneuro.2023.08.162.Peer-Reviewed Original ResearchSCZ risk genesNon-additive effectsRisk genesCombinatorial perturbationsTranscriptomic effectsFunctional roleRisk variantsGene expression changesBulk RNA-seqMultiple functional rolesSynaptic functionHigh-throughput imagingFunctional redundancyTranscriptional regulatorsRNA-seqCRISPR activationCellular phenotypesRNA interferenceEGenesGene expressionExpression changesHiPSC neuronsPolygenic risk scoresGenetic studiesGenesSTRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD
Seah C, Signer R, Young H, Kozik E, Rusielewicz T, Bader H, Xu C, de Pins A, Breen M, Paull D, Yehuda R, Girgenti M, Brennand K, Huckins L. STRESS IN A DISH: MODELING THE IMPACT OF COMMON GENETIC VARIATION ON STRESS RESPONSE IN HIPSC-DERIVED NEURONS IN PTSD. European Neuropsychopharmacology 2023, 75: s40. DOI: 10.1016/j.euroneuro.2023.08.081.Peer-Reviewed Original ResearchCommon genetic variationGenetic variationStress responseCell typesEQTL associationsTranscriptional stress responseGenomic risk lociTissue-specific mannerChIP-seq datasetsCell type deconvolutionCommon genetic variantsPost-mortem brainsGene expression signaturesHiPSC-derived neuronsTranscription factorsSuch lociCatalog genesRisk lociGenetic studiesExpression signaturesGenetic variantsRegulatory activityGenesEQTLsMechanistic understandingActivity-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 changesGenesContributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder
Mishra H, Wei H, Rohr K, Ko I, Nievergelt C, Maihofer A, Shilling P, Alda M, Berrettini W, Brennand K, Calabrese J, Coryell W, Frye M, Gage F, Gershon E, McInnis M, Nurnberger J, Oedegaard K, Zandi P, Kelsoe J, McCarthy M. Contributions of circadian clock genes to cell survival in fibroblast models of lithium-responsive bipolar disorder. European Neuropsychopharmacology 2023, 74: 1-14. PMID: 37126998, DOI: 10.1016/j.euroneuro.2023.04.009.Peer-Reviewed Original ResearchConceptsCell survival genesCircadian clockSurvival genesCell survivalCircadian clock genesCircadian rhythmGenetic variationClock genesKnockdown studiesCaspase activityCell deathMolecular pathwaysPrimary fibroblastsCellular modelGenesMouse fibroblastsFibroblast modelApoptosisStaurosporinePER1FibroblastsOpposite mannerLithium responsivenessDistinct patternsClockThe 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 developmentGenesSpeciesReporterHumansSequenceExpressionConvergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies
Liao C, Moyses-Oliveira M, De Esch C, Bhavsar R, Nuttle X, Li A, Yu A, Burt N, Erdin S, Fu J, Wang M, Morley T, Han L, Consortium C, Dion P, Rouleau G, Zhang B, Brennand K, Talkowski M, Ruderfer D. Convergent coexpression of autism-associated genes suggests some novel risk genes may not be detectable in large-scale genetic studies. Cell Genomics 2023, 3: 100277. PMID: 37082147, PMCID: PMC10112287, DOI: 10.1016/j.xgen.2023.100277.Peer-Reviewed Original ResearchRisk genesNovel risk genesProtein-altering variantsLarge-scale genetic studiesASD risk genesHeritable neurodevelopmental disorderAutism-associated genesCRISPR perturbationsConvergent genesNovel genesTranscriptional consequencesFunctional mutationsGenetic studiesCoexpression patternsDifferential expressionGenesHuman neuronsASD-associationHuman postmortem brainRare variationCoexpressionASD brainNeurodevelopmental disordersPostmortem brainsMutations
2022
Rescue of deficits by Brwd1 copy number restoration in the Ts65Dn mouse model of Down syndrome
Fulton S, Wenderski W, Lepack A, Eagle A, Fanutza T, Bastle R, Ramakrishnan A, Hays E, Neal A, Bendl J, Farrelly L, Al-Kachak A, Lyu Y, Cetin B, Chan J, Tran T, Neve R, Roper R, Brennand K, Roussos P, Schimenti J, Friedman A, Shen L, Blitzer R, Robison A, Crabtree G, Maze I. Rescue of deficits by Brwd1 copy number restoration in the Ts65Dn mouse model of Down syndrome. Nature Communications 2022, 13: 6384. PMID: 36289231, PMCID: PMC9606253, DOI: 10.1038/s41467-022-34200-0.Peer-Reviewed Original ResearchConceptsGene expressionChromatin accessibilityChromatin effectorsBAF chromatinGenetic basisTrisomic animalsIPS cellsBRWD1Chromosome 21Down syndromeHSA21Ts65Dn mouse modelCommon chromosomal conditionExpressionChromatinNormal neurodevelopmentChromosomal conditionHippocampal LTPMouse modelMistargetingGenesTrisomic miceCognitive deficitsEffectorsSyndromeThe three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease
Bendl J, Hauberg M, Girdhar K, Im E, Vicari J, Rahman S, Fernando M, Townsley K, Dong P, Misir R, Kleopoulos S, Reach S, Apontes P, Zeng B, Zhang W, Voloudakis G, Brennand K, Nixon R, Haroutunian V, Hoffman G, Fullard J, Roussos P. The three-dimensional landscape of cortical chromatin accessibility in Alzheimer’s disease. Nature Neuroscience 2022, 25: 1366-1378. PMID: 36171428, PMCID: PMC9581463, DOI: 10.1038/s41593-022-01166-7.Peer-Reviewed Original ResearchConceptsOpen chromatin regionsCis-regulatory domainsChromatin accessibilitySpecific enhancer-promoter interactionsTranscription factor regulatory networksEnhancer-promoter interactionsATAC-seq librariesChromatin regionsLysosomal genesNonneuronal nucleiRegulatory networksThree-dimensional structureGenomeThree-dimensional landscapeRegulatory effectsAlzheimer's diseaseCommunity-based analysisUSF2GenesDysregulationRepertoireTFAD casesLandscapeDomainPopulation-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 samplesA translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility
Voloudakis G, Vicari J, Venkatesh S, Hoffman G, Dobrindt K, Zhang W, Beckmann N, Higgins C, Argyriou S, Jiang S, Hoagland D, Gao L, Corvelo A, Cho K, Lee K, Bian J, Lee J, Iyengar S, Luoh S, Akbarian S, Striker R, Assimes T, Schadt E, Lynch J, Merad M, tenOever B, Charney A, Brennand K, Fullard J, Roussos P. A translational genomics approach identifies IL10RB as the top candidate gene target for COVID-19 susceptibility. Npj Genomic Medicine 2022, 7: 52. PMID: 36064543, PMCID: PMC9441828, DOI: 10.1038/s41525-022-00324-x.Peer-Reviewed Original ResearchCandidate gene targetsGene targetsTranslational genomics approachesHost susceptibilityGenomic approachesGenetic susceptibility variantsGenetic lociDruggable genesGene expressionMolecular pathwaysSusceptibility variantsCOVID-19 susceptibilityGenetic findingsApproach identifiesExpressionCOVID-19 patient bloodCritical next stepGenesLociOverexpressionTargetPathwaySusceptibilityIL10RBRecent effortsUsing Stem Cell Models to Explore the Genetics Underlying Psychiatric Disorders: Linking Risk Variants, Genes, and Biology in Brain Disease
Brennand K. Using Stem Cell Models to Explore the Genetics Underlying Psychiatric Disorders: Linking Risk Variants, Genes, and Biology in Brain Disease. American Journal Of Psychiatry 2022, 179: 322-328. PMID: 35491564, DOI: 10.1176/appi.ajp.20220235.Commentaries, Editorials and LettersConceptsRisk variantsFunctional genomic studiesCell typesDiverse cell typesPatient-specific variantsStem cell modelGenomic studiesSignificant lociStem cell-based approachesGenetic studiesExciting questionsCell-based approachesEngineering strategiesGenetic profileNovel therapeutic interventionsCell modelPluripotent stem cell-based approachesVariantsComplex interplayGenetic riskCRISPRGenesLociBiologyTherapeutic interventions
2021
Using the dCas9-KRAB system to repress gene expression in hiPSC-derived NGN2 neurons
Li A, Cartwright S, Yu A, Ho SM, Schrode N, Deans PJM, Matos MR, Garcia MF, Townsley KG, Zhang B, Brennand KJ. Using the dCas9-KRAB system to repress gene expression in hiPSC-derived NGN2 neurons. STAR Protocols 2021, 2: 100580. PMID: 34151300, PMCID: PMC8188621, DOI: 10.1016/j.xpro.2021.100580.Peer-Reviewed Original ResearchConceptsCRISPR inhibitionGene expressionDCas9-KRAB systemEndogenous gene expressionMultiple target genesGene repressionGene activationTarget genesGene manipulationFusion proteinComplete detailsPluripotent stemExpressionGlutamatergic neuronsRepressionGenesPhenotypicProteinStemNeuronsActivationBrain diseasesInhibition
2020
Massively 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 diagnosis