Kristen Brennand, PhD
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Co-director, Science Fellows Program
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Co-director, Science Fellows Program
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Appointments
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Co-director, Science Fellows Program
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Elizabeth Mears and House Jameson Professor of Psychiatry
Co-director, Science Fellows Program
Biography
Kristen Brennand, PhD is the Elizabeth Mears and House Jameson Professor of Psychiatry and Professor of Genetics at Yale University School of Medicine. She first established her independent laboratory in the Pamela Sklar Division of Psychiatric Genomics at the Icahn School of Medicine at Mount Sinai in 2012, after having completed post-doctoral training at the Salk Institute for Biological Studies and PhD studies at Harvard University. Dr. Brennand’s research combines expertise in genomic engineering, neuroscience, and stem cells, to identify the mechanisms that underlie brain disease. Her focus lies in resolving the convergence of, and complex interplay between, the many risk variants linked to disease, towards the goal of facilitating the clinical translation of genetic findings. Dr. Brennand’s work is funded by the National Institutes of Health, the New York Stem Cell Foundation, the Brain Research Foundation, and the Brain and Behavior Research Foundation.
Appointments
Psychiatry
ProfessorPrimaryGenetics
ProfessorSecondary
Other Departments & Organizations
- Center for Brain & Mind Health
- Center for RNA Science and Medicine
- Genetics
- Interdepartmental Neuroscience Program
- Molecular Cell Biology, Genetics and Development
- Molecular Medicine, Pharmacology, and Physiology
- Neural Disorders
- Neuroscience Research Training Program (NRTP)
- Neuroscience Track
- Program in Translational Biomedicine (PTB)
- Psychiatry
- Wu Tsai Institute
- Yale Center for Genomic Health
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale Stem Cell Center
Education & Training
- Post-doctoral scientist
- Salk Institute for Biological Studies (2012)
- PhD
- Harvard University, Molecular and Cellular Biology (2008)
- BSc (Hon)
- University of Calgary, Biology (2002)
Research
Overview
Each person’s distinct genetic, epigenetic, and environmental risk profile predisposes them to some phenotypes and confers resilience to others. My laboratory seeks to decode highly complex genetic insights into medically actionable information, better connecting the expanding list of genetic loci associated with human disease to pathophysiology. Our goal is to improve diagnostics, predict clinical trajectories, and identify pre-symptomatic points of therapeutic intervention.
Towards this, we employ a functional genomics approach that integrates stem cell models and genome engineering to resolve the impact of patient-specific variants across cell types, genetic backgrounds, and environmental conditions. Individually small risk effects combine to yield much larger impacts in aggregate, but the interactions between the myriad variants remain undetermined. Is there a “tipping point” between health and disease? Can ameliorative early interventions “untip” genetic disease risk? We seek to uncover disease-associated interactions within and between the cell types of the brain, querying the impacts of complex genetic risk within increasingly sophisticated neuronal circuits. Thus, we strive to translate risk “variants to genes”, “genes to pathways”, and “pathways to circuits”, revealing the convergent, additive, and synergistic relationships between risk factors within and between the cell types of the brain.
Even for highly penetrant mutations, a spectrum of phenotypes exist. We hope to understand the genetic, cellular, and environmental contexts that buffer genetic risk, and in doing so, develop interventions to help individuals achieve their greatest phenotypic potential. Thus, the variable penetrance of risk variants can be reframed as phenotypic resilience, evidence of biological “cures” capable of limiting, modifying, or preventing disease in individuals with otherwise high genetic predispositions. Such insights could identify therapeutics tailored to an individual’s specific risk profile, and so springboard the development of novel, personalized approaches to treat disease.
Medical Subject Headings (MeSH)
ORCID
0000-0003-0993-5956- View Lab Website
Brennand Lab
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Laura Marianne Huckins, PhD
Alexander Urban, PhD
Alexej Abyzov, PhD
Mark Gerstein, PhD
Matthew State, MD, PhD
Nenad Sestan, MD, PhD
Neurodegenerative Diseases
Publications
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 ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH 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 targetsNeuronal impact of patient-specific aberrant NRXN1α splicing
Flaherty E, Zhu S, Barretto N, Cheng E, Deans PJM, Fernando MB, Schrode N, Francoeur N, Antoine A, Alganem K, Halpern M, Deikus G, Shah H, Fitzgerald M, Ladran I, Gochman P, Rapoport J, Tsankova NM, McCullumsmith R, Hoffman GE, Sebra R, Fang G, Brennand KJ. Neuronal impact of patient-specific aberrant NRXN1α splicing. Nature Genetics 2019, 51: 1679-1690. PMID: 31784728, PMCID: PMC7451045, DOI: 10.1038/s41588-019-0539-z.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAlternative SplicingAnimalsAutism Spectrum DisorderBipolar DisorderCalcium-Binding ProteinsCase-Control StudiesDepressive Disorder, MajorFemaleGene ExpressionHeterozygoteHumansInduced Pluripotent Stem CellsMaleMiceNeural Cell Adhesion MoleculesProtein IsoformsSchizophreniaSequence DeletionModeling gene × environment interactions in PTSD using human neurons reveals diagnosis-specific glucocorticoid-induced gene expression
Seah C, Breen M, Rusielewicz T, Bader H, Xu C, Hunter C, McCarthy B, Deans P, Chattopadhyay M, Goldberg J, Desarnaud F, Makotkine I, Flory J, Bierer L, Staniskyte M, Noggle S, Huckins L, Paull D, Brennand K, Yehuda R. Modeling gene × environment interactions in PTSD using human neurons reveals diagnosis-specific glucocorticoid-induced gene expression. Nature Neuroscience 2022, 25: 1434-1445. PMID: 36266471, PMCID: PMC9630117, DOI: 10.1038/s41593-022-01161-y.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPost-traumatic stress disorderPeripheral blood mononuclear cellsGlucocorticoid-induced changesGlucocorticoid-induced gene expressionBlood mononuclear cellsIndividual clinical outcomesEnvironmental risk factorsHuman postmortem brainGlucocorticoid hypersensitivityClinical outcomesGlutamatergic neuronsMononuclear cellsRisk factorsHydrocortisone exposureSevere traumaPostmortem brainsHuman neuronsGlucocorticoid responseInduced neuronsStress disorderNeuronsNew therapeuticsGene expressionGene × environment interactionsCombat veteransModelling schizophrenia using human induced pluripotent stem cells
Brennand K, Simone A, Jou J, Gelboin-Burkhart C, Tran N, Sangar S, Li Y, Mu Y, Chen G, Yu D, McCarthy S, Sebat J, Gage F. Modelling schizophrenia using human induced pluripotent stem cells. Nature 2011, 473: 221-225. PMID: 21490598, PMCID: PMC3392969, DOI: 10.1038/nature09915.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsMeSH KeywordsAdolescentAdultAntipsychotic AgentsCell DifferentiationCells, CulturedCellular ReprogrammingChildDisks Large Homolog 4 ProteinFemaleFibroblastsGene Expression ProfilingGene Expression RegulationHumansIntracellular Signaling Peptides and ProteinsLoxapineMaleMembrane ProteinsModels, BiologicalNeuritesNeuronsPhenotypePluripotent Stem CellsReceptors, GlutamateSchizophreniaYoung Adult
2024
Regulation 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, DOI: 10.1038/s41593-024-01664-w.Peer-Reviewed Original ResearchCitationsAltmetricConceptsPlexin-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, DOI: 10.1126/science.adi5199.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsSingle-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 tissuesExpressionCellsChromatinLociMultiomicsMassively parallel characterization of regulatory elements in the developing human cortex
Deng C, Whalen S, Steyert M, Ziffra R, Przytycki P, Inoue F, Pereira D, Capauto D, Norton S, Vaccarino F, Pollen A, Nowakowski T, Ahituv N, Pollard K, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Bendl J, Berretta S, Bharadwaj R, Bhattacharya A, 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, Clarke D, 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, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Fullard J, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, 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, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kellis M, Khullar S, Kleinman J, 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 C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, 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, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, 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, Sestan N, Seyfried N, Shao Z, Shedd N, 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, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, 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 P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Massively parallel characterization of regulatory elements in the developing human cortex. Science 2024, 384: eadh0559. PMID: 38781390, DOI: 10.1126/science.adh0559.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGene regulatory elementsRegulatory elementsRegulation of enhancer activityCharacterization of regulatory elementsCis-regulatory activityNeuronal developmentPrimary cellsEnhanced activityGene regulationHuman neuronal developmentNucleotide changesEnhancer sequencesSequence basisUpstream regulatorComprehensive catalogHuman cellsDeveloping cortexSequenceVariantsOrganoidsCellsCerebral organoidsCortexHuman cortexNucleotideSingle-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka W, Mohammadi S, Fullard J, Davila-Velderrain J, Subburaju S, Tso D, Hourihan M, Jiang S, Lee H, Bendl J, Voloudakis G, Haroutunian V, Hoffman G, Roussos P, Kellis M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, 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, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kleinman J, 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 C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, 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, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, 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 P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384: eadg5136. PMID: 38781388, DOI: 10.1126/science.adg5136.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenetic risk factorsRisk factorsTranscriptional changesHeterogeneity of schizophreniaNeuronal cell statesSchizophrenia pathophysiologySingle-cell dissectionExcitatory neuronsEffective therapySchizophrenia transcriptomicsCortical cytoarchitectureSingle-cell atlasGenomic variantsCell groupsHuman prefrontal cortexMolecular pathwaysSchizophreniaTranscriptional alterationsTranscriptomic changesPrefrontal cortexCell statesAlterationsTherapyPathophysiologyDissectionCross-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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenome-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 maturationRegulation
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- June 05, 2024
Four Faculty Invited to Speak at Cold Spring Harbor's 2024 Schizophrenia and Related Disorders Course
- October 02, 2023Source: NPR-WSHU
A New Study Shows How Certain Genes Can Cause Brain Disorders Like Autism
- September 19, 2023
Investigators Launch Study Aimed at Accelerating Understanding of Bipolar Disorder
- August 14, 2023Source: Two Yale PhD Students Awarded Prestigious HHMI Gilliam Fellowships
Two BBS PhD Students Awarded Prestigious HHMI Gilliam Fellowships
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