2025
spVelo: RNA velocity inference for multi-batch spatial transcriptomics data
Long W, Liu T, Xue L, Zhao H. spVelo: RNA velocity inference for multi-batch spatial transcriptomics data. Genome Biology 2025, 26: 239. PMID: 40790237, PMCID: PMC12337411, DOI: 10.1186/s13059-025-03701-8.Peer-Reviewed Original ResearchConceptsSpatial transcriptomics dataTranscriptome dataGene regulatory network inferenceRegulatory network inferenceVelocity inferenceComplex tissue organizationTranscriptional dynamicsRNA velocityNetwork inferenceSpatial transcriptomicsMarker identificationRNATissue organizationDownstream applicationsBiological mechanismsTranscriptomeGenesCommunication inferenceGenomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes
Sierant M, Jin S, Bilguvar K, Morton S, Dong W, Jiang W, Lu Z, Li B, López-Giráldez F, Tikhonova I, Zeng X, Lu Q, Choi J, Zhang J, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Sedore S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, King E, Wagner M, Srivastava D, Shen Y, Bernstein D, Porter G, Newburger J, Seidman J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Chung W, Gelb B, Seidman C, Brueckner M, Lifton R. Genomic analysis of 11,555 probands identifies 60 dominant congenital heart disease genes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2420343122. PMID: 40127276, PMCID: PMC12002227, DOI: 10.1073/pnas.2420343122.Peer-Reviewed Original ResearchConceptsCongenital heart disease genesCongenital heart diseaseDamaging variantsMissense variantsAnalyzing de novo mutationsCHD probandsEpidermal growth factor (EGF)-like domainsNeurodevelopmental delayLoss of function variantsParent-offspring triosSyndromic congenital heart diseaseHeart disease genesDisease genesGenomic analysisCongenital heart disease subtypesAssociated with neurodevelopmental delayTetralogy of FallotFunctional variantsIncomplete penetranceCHD phenotypesGenesAssociated with developmentGenetic testingMolecular diagnosticsExtracardiac abnormalitiesRecessive genetic contribution to congenital heart disease in 5,424 probands
Dong W, Jin S, Sierant M, Lu Z, Li B, Lu Q, Morton S, Zhang J, López-Giráldez F, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, Cnota J, Wagner M, Srivastava D, Bernstein D, Porter G, Newburger J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Seidman J, Chung W, Gelb B, Seidman C, Lifton R, Brueckner M. Recessive genetic contribution to congenital heart disease in 5,424 probands. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419992122. PMID: 40030011, PMCID: PMC11912448, DOI: 10.1073/pnas.2419992122.Peer-Reviewed Original ResearchConceptsRecessive genotypeCHD probandsCongenital heart diseaseAssociated with laterality defectsGene-based analysisAnalyzed whole-exome sequencingLeft-sided congenital heart diseaseWhole-exome sequencingCongenital heart disease phenotypeAshkenazi Jewish probandsOffspring of consanguineous unionsSingle-cell transcriptomicsCHD geneExome sequencingMouse notochordSecreted proteinsConsanguineous familyFounder variantGenesSignificant enrichmentLaterality phenotypesHeart diseaseProbandsAbnormal contractile functionConsanguineous unions
2024
CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis
Liu T, Long W, Cao Z, Wang Y, He C, Zhang L, Strittmatter S, Zhao H. CosGeneGate selects multi-functional and credible biomarkers for single-cell analysis. Briefings In Bioinformatics 2024, 26: bbae626. PMID: 39592241, PMCID: PMC11596696, DOI: 10.1093/bib/bbae626.Peer-Reviewed Original ResearchQuantifying constraint in the human mitochondrial genome
Lake N, Ma K, Liu W, Battle S, Laricchia K, Tiao G, Puiu D, Ng K, Cohen J, Compton A, Cowie S, Christodoulou J, Thorburn D, Zhao H, Arking D, Sunyaev S, Lek M. Quantifying constraint in the human mitochondrial genome. Nature 2024, 635: 390-397. PMID: 39415008, PMCID: PMC11646341, DOI: 10.1038/s41586-024-08048-x.Peer-Reviewed Original ResearchMitochondrial genomeDeleterious variationMtDNA mutator modelHuman mitochondrial genomeGenome Aggregation DatabaseMtDNA variationMtDNA variantsMitochondrial DNANoncoding regionsMitochondrial proteinsRRNA geneGenetic variationMtDNAThree-dimensional structureMutation modelPathogenic variationDisease relevanceAggregation DatabaseGenomeLarge-scale population datasetRRNAConstrained sitesGenesTRNAPopulation datasetsStatistical Inference of Cell-Type Proportions Estimated from Bulk Expression Data
Cai B, Zhang J, Li H, Su C, Zhao H. Statistical Inference of Cell-Type Proportions Estimated from Bulk Expression Data. Journal Of The American Statistical Association 2024, 119: 2521-2532. PMID: 40241938, PMCID: PMC12002418, DOI: 10.1080/01621459.2024.2382435.Peer-Reviewed Original ResearchCell-type proportionsEstimates cell-type proportionsGene expression dataCell type-specific analysisCell type-specificPost-mortem brain samplesGTEx projectExpression dataDownstream analysisGene expressionAlzheimer's disease patientsCell typesSubject-specific covariatesAccurate identificationBrain samplesStatistical inferenceSampling distributionSimulation studySupplementary materialsROSMAPGTExTranscriptionGenesEstimated proportionAlzheimerDNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism
Xiao C, Peng G, Conneely K, Zhao H, Felger J, Wommack E, Higgins K, Shin D, Saba N, Bruner D, Miller A. DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism. Molecular Psychiatry 2024, 30: 76-83. PMID: 38977918, DOI: 10.1038/s41380-024-02652-z.Peer-Reviewed Original ResearchGene expressionMethylation lociAssociated with gene expressionHead and neck cancerDNA methylation profilesProtein markersLipid metabolismInvolvement of genesIllumina MethylationEPICDNA methylationRelevant gene expressionEpigenetic modificationsExpression pairsInflammatory markersInflammatory responseLociHead and neck cancer patientsAssociated with inflammatory markersGenesDNAMarkers of inflammationAssociated with fatigueExpressionMethylationPost-radiotherapyDecoding transcriptomic signatures of cysteine string protein alpha–mediated synapse maintenance
Wang N, Zhu B, Allnutt M, Grijalva R, Zhao H, Chandra S. Decoding transcriptomic signatures of cysteine string protein alpha–mediated synapse maintenance. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320064121. PMID: 38833477, PMCID: PMC11181078, DOI: 10.1073/pnas.2320064121.Peer-Reviewed Original ResearchConceptsSynapse maintenanceTranscriptional changesSynaptogenic adhesion moleculesGene ontology analysisKO miceKO brainMaintenance in vivoCell-cell interactionsGlial cellsSingle-nucleus transcriptomesOntology analysisCspADifferential expressionNeuron-glia interactionsAutophagy-related genesProtein AGenesCell typesNeurodegenerative diseasesInhibitory synapsesLittermate controlsSynaptic pathwaysAdhesion moleculesGlial responseSynapseGenome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
Nievergelt C, Maihofer A, Atkinson E, Chen C, Choi K, Coleman J, Daskalakis N, Duncan L, Polimanti R, Aaronson C, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Austin S, Avdibegoviç E, Babić D, Bacanu S, Baker D, Batzler A, Beckham J, Belangero S, Benjet C, Bergner C, Bierer L, Biernacka J, Bierut L, Bisson J, Boks M, Bolger E, Brandolino A, Breen G, Bressan R, Bryant R, Bustamante A, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum A, Børte S, Cahn L, Calabrese J, Caldas-de-Almeida J, Chatzinakos C, Cheema S, Clouston S, Colodro-Conde L, Coombes B, Cruz-Fuentes C, Dale A, Dalvie S, Davis L, Deckert J, Delahanty D, Dennis M, Desarnaud F, DiPietro C, Disner S, Docherty A, Domschke K, Dyb G, Kulenović A, Edenberg H, Evans A, Fabbri C, Fani N, Farrer L, Feder A, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Goleva S, Gordon S, Goçi A, Grasser L, Guindalini C, Haas M, Hagenaars S, Hauser M, Heath A, Hemmings S, Hesselbrock V, Hickie I, Hogan K, Hougaard D, Huang H, Huckins L, Hveem K, Jakovljević M, Javanbakht A, Jenkins G, Johnson J, Jones I, Jovanovic T, Karstoft K, Kaufman M, Kennedy J, Kessler R, Khan A, Kimbrel N, King A, Koen N, Kotov R, Kranzler H, Krebs K, Kremen W, Kuan P, Lawford B, Lebois L, Lehto K, Levey D, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lu Y, Luft B, Lupton M, Luykx J, Makotkine I, Maples-Keller J, Marchese S, Marmar C, Martin N, Martínez-Levy G, McAloney K, McFarlane A, McLaughlin K, McLean S, Medland S, Mehta D, Meyers J, Michopoulos V, Mikita E, Milani L, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Mufford M, Nelson E, Nordentoft M, Norman S, Nugent N, O’Donnell M, Orcutt H, Pan P, Panizzon M, Pathak G, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Porjesz B, Powers A, Qin X, Ratanatharathorn A, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Runz H, Rutten B, de Viteri S, Salum G, Sampson L, Sanchez S, Santoro M, Seah C, Seedat S, Seng J, Shabalin A, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stensland S, Stevens J, Sumner J, Teicher M, Thompson W, Tiwari A, Trapido E, Uddin M, Ursano R, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt F, Werge T, Williams M, Williamson D, Winsvold B, Winternitz S, Wolf C, Wolf E, Xia Y, Xiong Y, Yehuda R, Young K, Young R, Zai C, Zai G, Zervas M, Zhao H, Zoellner L, Zwart J, deRoon-Cassini T, van Rooij S, van den Heuvel L, Stein M, Ressler K, Koenen K. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nature Genetics 2024, 56: 792-808. PMID: 38637617, PMCID: PMC11396662, DOI: 10.1038/s41588-024-01707-9.Peer-Reviewed Original ResearchConceptsMeta-analysis of genome-wide association studiesGenome-wide significant lociMulti-ancestry meta-analysisGenome-wide association analysisGenome-wide association studiesIndividuals of European ancestryPotential causal genesNative American ancestryMulti-omics approachPost-traumatic stress disorderAdmixed individualsSignificant lociRisk lociCausal genesAssociation studiesAssociation analysisFunctional genesTranscription factorsGenetic studiesAmerican ancestryEuropean ancestryAxon guidanceSynaptic structureLociGenesStatistical methods for assessing the effects of de novo variants on birth defects
Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H. Statistical methods for assessing the effects of de novo variants on birth defects. Human Genomics 2024, 18: 25. PMID: 38486307, PMCID: PMC10938830, DOI: 10.1186/s40246-024-00590-z.Peer-Reviewed Original ResearchConceptsDe novo variantsAnalyzed de novo variantsDevelopment of next-generation sequencing technologiesNext-generation sequencing technologiesSequencing technologiesImprove statistical powerGenetic heterogeneitySequenced samplesStatistical powerBirth defectsDiseased individualsLow occurrenceCongenital heart diseaseVariantsGenesDeleterious effectsSequenceGeneral workflowStatistical methods
2023
eQTL studies: from bulk tissues to single cells
Zhang J, Zhao H. eQTL studies: from bulk tissues to single cells. Journal Of Genetics And Genomics 2023, 50: 925-933. PMID: 37207929, PMCID: PMC10656365, DOI: 10.1016/j.jgg.2023.05.003.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociBulk tissueIdentification of eQTLContext-dependent gene regulationCell typesQuantitative trait lociMost eQTL studiesSingle cellsComplex traitsGene regulationEQTL studiesFunctional genesTrait lociSpecific genesChromosomal regionsDynamic regulationGene expressionBiological processesDifferent tissuesGenetic variantsExpression levelsDisease mechanismsGenesRegulationRecent studies
2022
SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure
Tang D, Park S, Zhao H. SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology 2022, 23: 129. PMID: 35706040, PMCID: PMC9199219, DOI: 10.1186/s13059-022-02688-w.Peer-Reviewed Original ResearchConceptsCell type-specific gene expressionType-specific gene expressionCell type proportionsDifferential expression analysisCell type-specific gene expression profilesExpression analysisGene expressionSingle-cell RNA-seq dataRNA-seq dataGene differential expression analysisGene expression profilesType proportionsExpression profilesExpressionGenesCells
2020
Leveraging functional annotation to identify genes associated with complex diseases
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Lu Q, Zhao H. Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology 2020, 16: e1008315. PMID: 33137096, PMCID: PMC7660930, DOI: 10.1371/journal.pcbi.1008315.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociComplex traitsNovel lociIdentification of eQTLGene expressionTranscriptome-wide association study methodLinkage disequilibriumQuantitative trait lociAssociation study methodsCombined Annotation Dependent Depletion (CADD) scoresAnnotation-dependent depletion scoreExpression levelsDisease-associated genesEpigenetic annotationsEpigenetic informationFunctional annotationTrait lociGenetic variationGenesPrevious GWASLociGenetic effectsTraitsComplex diseasesGWASGenome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortiumStatistical Methods in Genome-Wide Association Studies
Sun N, Zhao H. Statistical Methods in Genome-Wide Association Studies. Annual Review Of Biomedical Data Science 2020, 3: 1-24. DOI: 10.1146/annurev-biodatasci-030320-041026.Peer-Reviewed Original ResearchGenome-wide association studiesAssociation studiesTraits of interestGenetic architectureIdentification of variantsGWAS dataStatistical methodologyStatistical challengesGenetic risk prediction modelsGenetic markersStatistical methodsHuman diseasesPhenotype informationGenetic variantsTraitsGenotype informationScientific goalsRecent progressGenesVariantsTens of thousandsHundreds of thousandsPrediction modelPathwayThousands
2019
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesDisease genesAssociation studiesGenome-wide significant lociAfrican-ancestry analysesNon-coding RNAsGenetic risk lociParkinson's disease genesEuropean ancestry populationsNovel genesSignificant lociGenetic variationSpecific lociRisk lociAdditional lociLociAncestry populationsCommon variantsHeritability estimatesGenesGWASRNABiologySNPsPARK2O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASE
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Zhao H. O2‐10‐03: LEVERAGING TISSUE SPECIFIC GENE EXPRESSION REGULATION TO IDENTIFY GENES ASSOCIATED WITH ALZHEIMER'S DISEASE. Alzheimer's & Dementia 2019, 15: p564-p565. DOI: 10.1016/j.jalz.2019.06.4507.Peer-Reviewed Original ResearchA statistical framework for cross-tissue transcriptome-wide association analysis
Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, Yu Z, Li B, Gu J, Muchnik S, Shi Y, Kunkle BW, Mukherjee S, Natarajan P, Naj A, Kuzma A, Zhao Y, Crane PK, Lu H, Zhao H. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature Genetics 2019, 51: 568-576. PMID: 30804563, PMCID: PMC6788740, DOI: 10.1038/s41588-019-0345-7.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association analysisAssociation analysisGene-trait associationsGene expression dataGene expression levelsGenetic architectureComplex traitsMore genesGene expressionSingle tissueExpression dataAssociation resultsExpression levelsPowerful approachImputation modelHuman tissuesImputation accuracyGenotypesStatistical frameworkTissueGenesKey componentTraitsPowerful metricExpression
2018
Integrative functional genomic analysis of human brain development and neuropsychiatric risks
Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, Sunkin SM, Li Z, Shin Y, Zhu Y, Sousa AMM, Werling DM, Kitchen RR, Kang HJ, Pletikos M, Choi J, Muchnik S, Xu X, Wang D, Lorente-Galdos B, Liu S, Giusti-Rodríguez P, Won H, de Leeuw C, Pardiñas AF, Hu M, Jin F, Li Y, Owen M, O’Donovan M, Walters J, Posthuma D, Reimers M, Levitt P, Weinberger D, Hyde T, Kleinman J, Geschwind D, Hawrylycz M, State M, Sanders S, Sullivan P, Gerstein M, Lein E, Knowles J, Sestan N, Willsey A, Oldre A, Szafer A, Camarena A, Cherskov A, Charney A, Abyzov A, Kozlenkov A, Safi A, Jones A, Ashley-Koch A, Ebbert A, Price A, Sekijima A, Kefi A, Bernard A, Amiri A, Sboner A, Clark A, Jaffe A, Tebbenkamp A, Sodt A, Guillozet-Bongaarts A, Nairn A, Carey A, Huttner A, Chervenak A, Szekely A, Shieh A, Harmanci A, Lipska B, Carlyle B, Gregor B, Kassim B, Sheppard B, Bichsel C, Hahn C, Lee C, Chen C, Kuan C, Dang C, Lau C, Cuhaciyan C, Armoskus C, Mason C, Liu C, Slaughterbeck C, Bennet C, Pinto D, Polioudakis D, Franjic D, Miller D, Bertagnolli D, Lewis D, Feng D, Sandman D, Clarke D, Williams D, DelValle D, Fitzgerald D, Shen E, Flatow E, Zharovsky E, Burke E, Olson E, Fulfs E, Mattei E, Hadjimichael E, Deelman E, Navarro F, Wu F, Lee F, Cheng F, Goes F, Vaccarino F, Liu F, Hoffman G, Gürsoy G, Gee G, Mehta G, Coppola G, Giase G, Sedmak G, Johnson G, Wray G, Crawford G, Gu G, van Bakel H, Witt H, Yoon H, Pratt H, Zhao H, Glass I, Huey J, Arnold J, Noonan J, Bendl J, Jochim J, Goldy J, Herstein J, Wiseman J, Miller J, Mariani J, Stoll J, Moore J, Szatkiewicz J, Leng J, Zhang J, Parente J, Rozowsky J, Fullard J, Hohmann J, Morris J, Phillips J, Warrell J, Shin J, An J, Belmont J, Nyhus J, Pendergraft J, Bryois J, Roll K, Grennan K, Aiona K, White K, Aldinger K, Smith K, Girdhar K, Brouner K, Mangravite L, Brown L, Collado-Torres L, Cheng L, Gourley L, Song L, Ubieta L, Habegger L, Ng L, Hauberg M, Onorati M, Webster M, Kundakovic M, Skarica M, Reimers M, Johnson M, Chen M, Garrett M, Sarreal M, Reding M, Gu M, Peters M, Fisher M, Gandal M, Purcaro M, Smith M, Brown M, Shibata M, Brown M, Xu M, Yang M, Ray M, Shapovalova N, Francoeur N, Sjoquist N, Mastan N, Kaur N, Parikshak N, Mosqueda N, Ngo N, Dee N, Ivanov N, Devillers O, Roussos P, Parker P, Manser P, Wohnoutka P, Farnham P, Zandi P, Emani P, Dalley R, Mayani R, Tao R, Gittin R, Straub R, Lifton R, Jacobov R, Howard R, Park R, Dai R, Abramowicz S, Akbarian S, Schreiner S, Ma S, Parry S, Shapouri S, Weissman S, Caldejon S, Mane S, Ding S, Scuderi S, Dracheva S, Butler S, Lisgo S, Rhie S, Lindsay S, Datta S, Souaiaia T, Roychowdhury T, Gomez T, Naluai-Cecchini T, Beach T, Goodman T, Gao T, Dolbeare T, Fliss T, Reddy T, Chen T, Hyde T, Brunetti T, Lemon T, Desta T, Borrman T, Haroutunian V, Spitsyna V, Swarup V, Shi X, Jiang Y, Xia Y, Chen Y, Jiang Y, Wang Y, Chae Y, Yang Y, Kim Y, Riley Z, Krsnik Z, Deng Z, Weng Z, Lin Z, Li Z. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 2018, 362 PMID: 30545854, PMCID: PMC6413317, DOI: 10.1126/science.aat7615.Peer-Reviewed Original ResearchConceptsIntegrative functional genomic analysisFunctional genomic analysisCell typesGene coexpression modulesDistinct cell typesCell type-specific dynamicsGenomic basisEpigenomic reorganizationEpigenomic landscapeEpigenomic regulationGenomic analysisCoexpression modulesIntegrative analysisHuman brain developmentFetal transitionHuman neurodevelopmentGenetic associationCellular compositionNeuropsychiatric riskBrain developmentNeurodevelopmental processesGenesTraitsPostnatal developmentNeuropsychiatric disorders
2015
Gene-based and pathway-based genome-wide association study of alcohol dependence
Lingjun Z, ZHANG CK, SAYWARD FG, CHEUNG KH, Kesheng W, KRYSTAL JH, Hongyu Z, Xingguang L. Gene-based and pathway-based genome-wide association study of alcohol dependence. General Psychiatry 2015, 27: 111-118. PMID: 26120261, PMCID: PMC4466852, DOI: 10.11919/j.issn.1002-0829.215031.Peer-Reviewed Original ResearchGenome-wide association studiesRisk genesAssociation studiesBiological signaling processesPXN geneGene pathwaysSignaling processesGlycan degradationInteraction pathwayGenetic markersTransporter pathwaysGenesDiscovery samplePathwayReplication sampleAfrican American casesRisk pathwaysMultiple testingBonferroni correctionNew evidence
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