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 brainVariantsCross-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 maturationRegulation
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 studiesGenesBetter together: Non-additive interactions between schizophrenia risk genes
Deans P, Brennand K. Better together: Non-additive interactions between schizophrenia risk genes. Cell Genomics 2023, 3: 100403. PMID: 37719145, PMCID: PMC10504666, DOI: 10.1016/j.xgen.2023.100403.Peer-Reviewed Original ResearchConvergent 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
2020
A 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
2017
Evaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes
Ho S, Hartley B, Flaherty E, Rajarajan P, Abdelaal R, Obiorah I, Barretto N, Muhammad H, Phatnani H, Akbarian S, Brennand K. Evaluating Synthetic Activation and Repression of Neuropsychiatric-Related Genes in hiPSC-Derived NPCs, Neurons, and Astrocytes. Stem Cell Reports 2017, 9: 615-628. PMID: 28757163, PMCID: PMC5550013, DOI: 10.1016/j.stemcr.2017.06.012.Peer-Reviewed Original ResearchConceptsSynthetic activationRisk genesCell typesModulation of transcriptionNeuropsychiatric risk genesCommon single nucleotide variantsCas9 fusion proteinsEndogenous expression levelsNeural cell typesPluripotent stem cell-derived neural progenitor cellsRare copy number variationsCopy number variationsSingle nucleotide variantsNeural progenitor cellsGene functionFunctional annotationGenetic studiesGenesRisk variantsProgenitor cellsExpression levelsTranscriptionRepressionPositional effectsProtein