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 effects
2021
Common Genetic Variation in Humans Impacts In Vitro Susceptibility to SARS-CoV-2 Infection
Dobrindt K, Hoagland DA, Seah C, Kassim B, O'Shea CP, Murphy A, Iskhakova M, Fernando MB, Powell SK, Deans PJM, Javidfar B, Peter C, Møller R, Uhl SA, Garcia MF, Kimura M, Iwasawa K, Crary JF, Kotton DN, Takebe T, Huckins LM, tenOever BR, Akbarian S, Brennand KJ. Common Genetic Variation in Humans Impacts In Vitro Susceptibility to SARS-CoV-2 Infection. Stem Cell Reports 2021, 16: 505-518. PMID: 33636110, PMCID: PMC7881728, DOI: 10.1016/j.stemcr.2021.02.010.Peer-Reviewed Original ResearchMeSH Keywords3' Untranslated RegionsAdolescentAdultAnimalsCell LineChlorocebus aethiopsClustered Regularly Interspaced Short Palindromic RepeatsCOVID-19FemaleFurinGenetic Predisposition to DiseaseHost-Pathogen InteractionsHumansInduced Pluripotent Stem CellsMaleNeuronsPeptide HydrolasesPolymorphism, Single NucleotideSARS-CoV-2Vero CellsConceptsSARS-CoV-2Clinical complicationsSARS-CoV-2 infectionCommon genetic variationHigh-risk individualsHost genetic variantsSignificant interindividual variabilityNeuron infectionUnderlying comorbiditiesViral loadHealthy individualsViral infectionClinical heterogeneityVitro SusceptibilityEtiologic agentHost responseInterindividual variabilityDiscovery of drugsInfectionHost geneticsHuman induced pluripotent stem cellsSingle nucleotide polymorphismsAntibody repertoireMore diseasesComplications
2020
Parsing 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 ResearchMeSH KeywordsBrainEpigenomeEpigenomicsGenome-Wide Association StudyHumansPolymorphism, Single NucleotideConceptsGenetic 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 findingsEpigenomeA 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 ResearchMeSH KeywordsBrainBrain DiseasesChromatinGenetic Predisposition to DiseaseGenomicsHumansPolymorphism, Single NucleotideRisk FactorsConceptsChromatin 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
2018
Chronotype and cellular circadian rhythms predict the clinical response to lithium maintenance treatment in patients with bipolar disorder
McCarthy MJ, Wei H, Nievergelt CM, Stautland A, Maihofer AX, Welsh DK, Shilling P, Alda M, Alliey-Rodriguez N, Anand A, Andreasson OA, Balaraman Y, Berrettini WH, Bertram H, Brennand KJ, Calabrese JR, Calkin CV, Claasen A, Conroy C, Coryell WH, Craig DW, D’Arcangelo N, Demodena A, Djurovic S, Feeder S, Fisher C, Frazier N, Frye MA, Gage FH, Gao K, Garnham J, Gershon ES, Glazer K, Goes F, Goto T, Harrington G, Jakobsen P, Kamali M, Karberg E, Kelly M, Leckband SG, Lohoff F, McInnis MG, Mondimore F, Morken G, Nurnberger JI, Obral S, Oedegaard KJ, Ortiz A, Ritchey M, Ryan K, Schinagle M, Schoeyen H, Schwebel C, Shaw M, Shekhtman T, Slaney C, Stapp E, Szelinger S, Tarwater B, Zandi PP, Kelsoe JR. Chronotype and cellular circadian rhythms predict the clinical response to lithium maintenance treatment in patients with bipolar disorder. Neuropsychopharmacology 2018, 44: 620-628. PMID: 30487653, PMCID: PMC6333516, DOI: 10.1038/s41386-018-0273-8.Peer-Reviewed Original ResearchConceptsBipolar disorderEffects of lithiumMaintenance treatmentBD patientsCircadian rhythmMinority of patientsLithium maintenance treatmentMood stabilizer treatmentSerious mood disorderCircadian rhythm abnormalitiesCircadian rhythm parametersClinical responseCircadian rhythm functionLithium monotherapyClinical trialsMood disordersRhythm abnormalitiesMood symptomsPharmacological effectsPatientsEvening chronotypeStabilizer treatmentCommon genetic variationRhythm parametersMonotherapy
2017
MEF2C transcription factor is associated with the genetic and epigenetic risk architecture of schizophrenia and improves cognition in mice
Mitchell A, Javidfar B, Pothula V, Ibi D, Shen E, Peter C, Bicks L, Fehr T, Jiang Y, Brennand K, Neve R, Gonzalez-Maeso J, Akbarian S. MEF2C transcription factor is associated with the genetic and epigenetic risk architecture of schizophrenia and improves cognition in mice. Molecular Psychiatry 2017, 23: 123-132. PMID: 28115742, PMCID: PMC5966823, DOI: 10.1038/mp.2016.254.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBrainChromatin ImmunoprecipitationCognition DisordersComputational BiologyDisease Models, AnimalEpigenomicsGene Expression RegulationGreen Fluorescent ProteinsHistonesMEF2 Transcription FactorsMiceMice, Inbred C57BLMice, KnockoutNerve Tissue ProteinsNeuronsPolymorphism, Single NucleotideSchizophreniaTransduction, GeneticConceptsTherapeutic potentialPrefrontal projection neuronsNeuron-specific promoterUnexplored therapeutic potentialProjection neuronsDrug challengeDisease casesRelated disordersRisk architecturePrefrontal cortexSchizophreniaSingle nucleotide polymorphismsCognitive performancePsychiatric Genomics ConsortiumNeuronal genomeH3K4 hypermethylationRisk lociCognitive enhancement
2016
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer M, Roussos P, Sieberts S, Johnson J, Kavanagh D, Perumal T, Ruderfer D, Oh E, Topol A, Shah H, Klei L, Kramer R, Pinto D, Gümüş Z, Cicek A, Dang K, Browne A, Lu C, Xie L, Readhead B, Stahl E, Xiao J, Parvizi M, Hamamsy T, Fullard J, Wang Y, Mahajan M, Derry J, Dudley J, Hemby S, Logsdon B, Talbot K, Raj T, Bennett D, De Jager P, Zhu J, Zhang B, Sullivan P, Chess A, Purcell S, Shinobu L, Mangravite L, Toyoshiba H, Gur R, Hahn C, Lewis D, Haroutunian V, Peters M, Lipska B, Buxbaum J, Schadt E, Hirai K, Roeder K, Brennand K, Katsanis N, Domenici E, Devlin B, Sklar P. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nature Neuroscience 2016, 19: 1442-1453. PMID: 27668389, PMCID: PMC5083142, DOI: 10.1038/nn.4399.Peer-Reviewed Original Research
2014
A Role for Noncoding Variation in Schizophrenia
Roussos P, Mitchell A, Voloudakis G, Fullard J, Pothula V, Tsang J, Stahl E, Georgakopoulos A, Ruderfer D, Charney A, Okada Y, Siminovitch K, Worthington J, Padyukov L, Klareskog L, Gregersen P, Plenge R, Raychaudhuri S, Fromer M, Purcell S, Brennand K, Robakis N, Schadt E, Akbarian S, Sklar P. A Role for Noncoding Variation in Schizophrenia. Cell Reports 2014, 9: 1417-1429. PMID: 25453756, PMCID: PMC4255904, DOI: 10.1016/j.celrep.2014.10.015.Peer-Reviewed Original ResearchMeSH KeywordsArthritis, RheumatoidCalcium Channels, L-TypeDatabases, GeneticDNA, IntergenicEnhancer Elements, GeneticGene Expression RegulationGenetic LociGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMolecular Sequence AnnotationOrgan SpecificityPolymorphism, Single NucleotidePromoter Regions, GeneticProtein BindingRisk FactorsSchizophreniaConceptsExpression quantitative trait lociGenome-wide significant lociCommon variant lociQuantitative trait lociPluripotent stem cell-derived neuronsDistal regulatory elementsStem cell-derived neuronsPotential physical interactionsCell-derived neuronsRegulatory element sequencesPotential functional roleGenome architectureChromosomal loopingTranscriptional regulationFunctional annotationTrait lociSignificant lociNoncoding SNPsRegulatory elementsNoncoding variationsRisk lociVariant lociUnknown functionFunctional linkElement sequences