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 inferencescMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
Wang G, Zhao J, Lin Y, Liu T, Zhao Y, Zhao H. scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links. Nature Communications 2025, 16: 4994. PMID: 40442129, PMCID: PMC12122792, DOI: 10.1038/s41467-025-60333-z.Peer-Reviewed Original ResearchConceptsDeep learning frameworkSingle-cell multi-omics researchSingle-cell multi-omics dataLearning frameworkMulti-omics dataGenerative adversarial networkSingle-cell technologiesData alignmentSingle-cell resolutionMulti-omics researchDownstream analysisCellular statesOmics datasetsAdversarial networkNeural networkProteomic profilingCorrelated featuresBiological informationOmics perspectiveDiverse datasetsFeature topologyDisease mechanismsCell embeddingData resourcesRelationship inferenceA multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder
Wang J, Liu Y, Li H, Nguyen T, Soto-Vargas J, Wilson R, Wang W, Lam T, Zhang C, Lin C, Lewis D, Glausier J, Holtzheimer P, Friedman M, Williams K, Picciotto M, Nairn A, Krystal J, Duman R, Young K, Zhao H, Girgenti M. A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder. Genome Medicine 2025, 17: 43. PMID: 40301990, PMCID: PMC12042318, DOI: 10.1186/s13073-025-01473-1.Peer-Reviewed Original ResearchConceptsPost-traumatic stress disorderDorsolateral prefrontal cortexPsychiatric disordersAutism spectrum disorderPrefrontal cortexDepressive disorderStress disorderGamma-aminobutyric acidGenome-wide association studiesPTSD brainsGenome-wide measurementsStudies of postmortem brainsSubgenual prefrontal cortexDisabling psychiatric disorderMultiple psychiatric disordersPrefrontal cortical areasPTSD casesHuman brain studiesBrain regionsSpectrum disorderGABAergic processesPostmortem brainsMDDProtein co-expression modulesProteomic profilingThe left amygdala is genetically sexually-dimorphic: multi-omics analysis of structural MRI volumes
Gui Y, Zhou G, Cui S, Li H, Lu H, Zhao H. The left amygdala is genetically sexually-dimorphic: multi-omics analysis of structural MRI volumes. Translational Psychiatry 2025, 15: 17. PMID: 39843917, PMCID: PMC11754786, DOI: 10.1038/s41398-025-03223-8.Peer-Reviewed Original ResearchConceptsLeft amygdala volumePolygenic risk scoresLeft amygdalaSex differencesBrain volumeMental disordersAmygdala volumeBrain anatomyEffect of polygenic risk scoresStudy of sex differencesExamined sex differencesPsychiatric Genomics ConsortiumMechanisms of sex differencesSex-specific genetic correlationsGenetic correlation analysisAmygdalaStructural MRI volumesSexually-dimorphicGenetic correlationsBrainDisordersRNA-seq dataGenomics ConsortiumCell-type compositionKnowledge of genetic basisThe human and non-human primate developmental GTEx projects
Bell T, Blanchard T, Hernandez R, Linn R, Taylor D, VonDran M, Ahooyi T, Beitra D, Bernieh A, Delaney M, Faith M, Fattahi E, Footer D, Gilbert M, Guambaña S, Gulino S, Hanson J, Hattrell E, Heinemann C, Kreeb J, Leino D, Mcdevitt L, Palmieri A, Pfeiffer M, Pryhuber G, Rossi C, Rasool I, Roberts R, Salehi A, Savannah E, Stachowicz K, Stokes D, Suplee L, Van Hoose P, Wilkins B, Williams-Taylor S, Zhang S, Ardlie K, Getz G, Lappalainen T, Montgomery S, Aguet F, Anderson L, Bernstein B, Choudhary A, Domenech L, Gaskell E, Johnson M, Liu Q, Marderstein A, Nedzel J, Okonda J, Padhi E, Rosano M, Russell A, Walker B, Sestan N, Gerstein M, Milosavljevic A, Borsari B, Cho H, Clarke D, Deveau A, Galeev T, Gobeske K, Hameed I, Huttner A, Jensen M, Jiang Y, Li J, Liu J, Liu Y, Ma J, Mane S, Meng R, Nadkarni A, Ni P, Park S, Petrosyan V, Pochareddy S, Salamon I, Xia Y, Yates C, Zhang M, Zhao H, Conrad D, Feng G, Brady F, Boucher M, Carbone L, Castro J, del Rosario R, Held M, Hennebold J, Lacey A, Lewis A, Lima A, Mahyari E, Moore S, Okhovat M, Roberts V, de Castro S, Wessel B, Zaniewski H, Zhang Q, Arguello A, Baroch J, Dayal J, Felsenfeld A, Ilekis J, Jose S, Lockhart N, Miller D, Minear M, Parisi M, Price A, Ramos E, Zou S. The human and non-human primate developmental GTEx projects. Nature 2025, 637: 557-564. PMID: 39815096, PMCID: PMC12013525, DOI: 10.1038/s41586-024-08244-9.Peer-Reviewed Original ResearchConceptsChromatin accessibility dataFunctional genomic studiesWhole-genome sequencingEffects of genetic variationSpatial gene expression profilesNon-human primatesGenotype-Tissue ExpressionGene expression profilesGenomic studiesGene regulationGenetic dataGenetic variationGenomic researchDonor diversityCommunity engagementHuman evolutionEarly developmental defectsGene expressionCell statesDevelopmental programmeHuman diseasesExpression profilesAdult tissuesDevelopmental defectsSingle-cell
2024
Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains
Zhu B, Park J, Coffey S, Russo A, Hsu I, Wang J, Su C, Chang R, Lam T, Gopal P, Ginsberg S, Zhao H, Hafler D, Chandra S, Zhang L. Single-cell transcriptomic and proteomic analysis of Parkinson’s disease brains. Science Translational Medicine 2024, 16: eabo1997. PMID: 39475571, PMCID: PMC12372474, DOI: 10.1126/scitranslmed.abo1997.Peer-Reviewed Original ResearchConceptsProteomic analysisAlzheimer's diseasePrefrontal cortexBrain cell typesGenetics of PDParkinson's diseaseCell-cell interactionsChaperone expressionSingle-nucleus transcriptomesExpressed genesTranscriptional changesPostmortem human brainPostmortem brain tissueDiseased brainSynaptic proteinsSingle-cellDown-regulationBrain cell populationsBrain regionsCell typesNeurodegenerative disordersLate-stage PDParkinson's disease brainsDisease etiologyNeuronal vulnerabilitySANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchDecoding 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 ResearchMeSH KeywordsAnimalsHSP40 Heat-Shock ProteinsMembrane ProteinsMiceMice, KnockoutNeurogliaNeuronsSynapsesTranscriptomeConceptsSynapse 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 responseSynapseFine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes
Chen Z, Guo X, Tao R, Huyghe J, Law P, Fernandez-Rozadilla C, Ping J, Jia G, Long J, Li C, Shen Q, Xie Y, Timofeeva M, Thomas M, Schmit S, Díez-Obrero V, Devall M, Moratalla-Navarro F, Fernandez-Tajes J, Palles C, Sherwood K, Briggs S, Svinti V, Donnelly K, Farrington S, Blackmur J, Vaughan-Shaw P, Shu X, Lu Y, Broderick P, Studd J, Harrison T, Conti D, Schumacher F, Melas M, Rennert G, Obón-Santacana M, Martín-Sánchez V, Oh J, Kim J, Jee S, Jung K, Kweon S, Shin M, Shin A, Ahn Y, Kim D, Oze I, Wen W, Matsuo K, Matsuda K, Tanikawa C, Ren Z, Gao Y, Jia W, Hopper J, Jenkins M, Win A, Pai R, Figueiredo J, Haile R, Gallinger S, Woods M, Newcomb P, Duggan D, Cheadle J, Kaplan R, Kerr R, Kerr D, Kirac I, Böhm J, Mecklin J, Jousilahti P, Knekt P, Aaltonen L, Rissanen H, Pukkala E, Eriksson J, Cajuso T, Hänninen U, Kondelin J, Palin K, Tanskanen T, Renkonen-Sinisalo L, Männistö S, Albanes D, Weinstein S, Ruiz-Narvaez E, Palmer J, Buchanan D, Platz E, Visvanathan K, Ulrich C, Siegel E, Brezina S, Gsur A, Campbell P, Chang-Claude J, Hoffmeister M, Brenner H, Slattery M, Potter J, Tsilidis K, Schulze M, Gunter M, Murphy N, Castells A, Castellví-Bel S, Moreira L, Arndt V, Shcherbina A, Bishop D, Giles G, Southey M, Idos G, McDonnell K, Abu-Ful Z, Greenson J, Shulman K, Lejbkowicz F, Offit K, Su Y, Steinfelder R, Keku T, van Guelpen B, Hudson T, Hampel H, Pearlman R, Berndt S, Hayes R, Martinez M, Thomas S, Pharoah P, Larsson S, Yen Y, Lenz H, White E, Li L, Doheny K, Pugh E, Shelford T, Chan A, Cruz-Correa M, Lindblom A, Hunter D, Joshi A, Schafmayer C, Scacheri P, Kundaje A, Schoen R, Hampe J, Stadler Z, Vodicka P, Vodickova L, Vymetalkova V, Edlund C, Gauderman W, Shibata D, Toland A, Markowitz S, Kim A, Chanock S, van Duijnhoven F, Feskens E, Sakoda L, Gago-Dominguez M, Wolk A, Pardini B, FitzGerald L, Lee S, Ogino S, Bien S, Kooperberg C, Li C, Lin Y, Prentice R, Qu C, Bézieau S, Yamaji T, Sawada N, Iwasaki M, Le Marchand L, Wu A, Qu C, McNeil C, Coetzee G, Hayward C, Deary I, Harris S, Theodoratou E, Reid S, Walker M, Ooi L, Lau K, Zhao H, Hsu L, Cai Q, Dunlop M, Gruber S, Houlston R, Moreno V, Casey G, Peters U, Tomlinson I, Zheng W. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes. Nature Communications 2024, 15: 3557. PMID: 38670944, PMCID: PMC11053150, DOI: 10.1038/s41467-024-47399-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCredible causal variantsColorectal cancer susceptibility genesSusceptibility genesAssociation signalsAnalysis of single-cell RNA-seq dataAnalysis of whole-exome sequencing dataGenome-wide association study dataColorectal cancer risk lociSingle-cell RNA-seq dataTarget genesWhole-exome sequencing dataFunctional genomic investigationsFine-mapping analysisRNA-seq dataExome sequencing dataTissue-specific transcriptomesColorectal cancerCancer susceptibility genesCausal variantsFine-mappingRisk lociMethylome dataSequence dataGenomic investigations
2023
OTTERS: a powerful TWAS framework leveraging summary-level reference data
Dai Q, Zhou G, Zhao H, Võsa U, Franke L, Battle A, Teumer A, Lehtimäki T, Raitakari O, Esko T, Epstein M, Yang J. OTTERS: a powerful TWAS framework leveraging summary-level reference data. Nature Communications 2023, 14: 1271. PMID: 36882394, PMCID: PMC9992663, DOI: 10.1038/s41467-023-36862-w.Peer-Reviewed Original ResearchA novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy
Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Briefings In Bioinformatics 2023, 24: bbac616. PMID: 36631398, PMCID: PMC9851324, DOI: 10.1093/bib/bbac616.Peer-Reviewed Original Research
2020
Transcriptomic organization of the human brain in post-traumatic stress disorder
Girgenti MJ, Wang J, Ji D, Cruz DA, Stein M, Gelernter J, Young K, Huber B, Williamson D, Friedman M, Krystal J, Zhao H, Duman R. Transcriptomic organization of the human brain in post-traumatic stress disorder. Nature Neuroscience 2020, 24: 24-33. PMID: 33349712, DOI: 10.1038/s41593-020-00748-7.Peer-Reviewed Original ResearchMeSH KeywordsAdultAutopsyBrain ChemistryCohort StudiesDepressive Disorder, MajorFemaleGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInterneuronsMaleMiddle AgedNerve Tissue ProteinsSex CharacteristicsStress Disorders, Post-TraumaticTranscriptomeYoung AdultConceptsGenome-wide association studiesSignificant gene networksDifferential gene expressionSystems-level evidenceSignificant genetic liabilityMajor depressive disorder cohortGene networksTranscriptomic organizationTranscriptomic landscapeDownregulated setsGenomic networksGene expressionAssociation studiesMolecular determinantsExtensive remodelingGenotype dataSexual dimorphismSignificant divergenceMolecular profileNetwork analysisELFN1TranscriptsDimorphismPostmortem tissueDivergence
2019
NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution
Tang D, Park S, Zhao H. NITUMID: Nonnegative matrix factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics 2019, 36: 1344-1350. PMID: 31593244, PMCID: PMC8215918, DOI: 10.1093/bioinformatics/btz748.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 disordersSpatiotemporal transcriptomic divergence across human and macaque brain development
Zhu Y, Sousa AMM, Gao T, Skarica M, Li M, Santpere G, Esteller-Cucala P, Juan D, Ferrández-Peral L, Gulden FO, Yang M, Miller DJ, Marques-Bonet T, Imamura Kawasawa Y, Zhao H, Sestan N. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 2018, 362 PMID: 30545855, PMCID: PMC6900982, DOI: 10.1126/science.aat8077.Peer-Reviewed Original ResearchConceptsBrain developmentHuman nervous system developmentHuman brain developmentNervous system developmentPostnatal patternSingle-cell transcriptomic dataSpatiotemporal transcriptional regulationBrain regionsNeuropsychiatric disordersLate fetalPrefrontal cortexTranscriptomic programsHuman dataTranscriptomic divergenceTranscriptional regulationTranscriptomic differencesAutism spectrum disorderTranscriptomic dataDisordersTranscriptomic patternsSpectrum disorderIntegrative analysisPathogenesis
2017
Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease
Lu Q, Powles RL, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane PK, Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLOS Genetics 2017, 13: e1006933. PMID: 28742084, PMCID: PMC5546707, DOI: 10.1371/journal.pgen.1006933.Peer-Reviewed Original ResearchConceptsTissue typesNon-coding elementsNon-coding genomeComplex human diseasesLate-onset Alzheimer's diseaseIndividual cell typesRelevant tissue typesGWAS traitsTranscriptomic annotationGenome annotationFunctional annotationDNA elementsHeritability enrichmentHuman genomeLarge international consortiaVariety of cellsGenomeHuman diseasesAnnotation dataCell typesGenetic variantsOrgan system categoriesComplex diseasesSimilar localizationAnnotation
2013
Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies
Hou L, Chen M, Zhang CK, Cho J, Zhao H. Guilt by rewiring: gene prioritization through network rewiring in Genome Wide Association Studies. Human Molecular Genetics 2013, 23: 2780-2790. PMID: 24381306, PMCID: PMC3990172, DOI: 10.1093/hmg/ddt668.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesWide association studyDisease-associated genesGWAS signalsNetwork rewiringAssociation studiesFunctional genomic informationGene expression networksCo-expression networkDisease-associated pathwaysExpression networksGene networksGenomic informationAssociation signalsGene prioritizationDisease genesDisease locusSusceptibility lociGenesAssociation principleRewiringDisease associationsLociMillions of candidatesDisease conditions
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