2025
High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties
Zhang J, Rajendran B, Desai S, Gibson J, DiPalermo J, LoRusso P, Kong Y, Zhao H, Cecchini M, Schalper K. High MGMT expression identifies aggressive colorectal cancer with distinct genomic features and immune evasion properties. Journal For ImmunoTherapy Of Cancer 2025, 13: e011653. PMID: 40935566, DOI: 10.1136/jitc-2025-011653.Peer-Reviewed Original ResearchConceptsTumor-infiltrating lymphocytesCD8+ tumor-infiltrating lymphocytesPromoter methylation statusMGMT expressionMGMT overexpressionClinical significanceAllogeneic peripheral blood mononuclear cellsCD8+ T cellsMGMT promoter methylation statusKilling of malignant cellsMGMT-expressing cellsMultiplexed quantitative immunofluorescencePeripheral blood mononuclear cellsAggressive clinical courseMGMT protein expressionAggressive colorectal cancerMethylation statusExpression of MGMTLevels of MGMT proteinMGMT protein levelsImmune evasion propertiesBlood mononuclear cellsMutational featuresAdaptive immune evasionSomatic mutation burdenSuperGLUE facilitates an explainable training framework for multi-modal data analysis
Liu T, Zhao J, Zhao H. SuperGLUE facilitates an explainable training framework for multi-modal data analysis. Cell Reports Methods 2025, 5: 101167. PMID: 40914154, DOI: 10.1016/j.crmeth.2025.101167.Peer-Reviewed Original ResearchConceptsData integrationProbabilistic deep learningMulti-modal data analysisInference of gene regulatory networksMulti-modal data integrationDeep learningGene regulatory networksTraining frameworkBaseline modelRegulatory networksComplex biological systemsRegulatory relationshipsSensing dataCell statesGlobal structureArea of active researchActive researchOmicsBiological featuresScalable methodFrameworkBiological systemsStatistical modelNetworkBiological linkagesspVelo: 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 inferenceLeveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations
Zhou G, Yolou I, Xie Y, Zhao H. Leveraging local ancestry and cross-ancestry genetic architecture to improve genetic prediction of complex traits in admixed populations. American Journal Of Human Genetics 2025, 112: 1923-1935. PMID: 40633541, PMCID: PMC12252582, DOI: 10.1016/j.ajhg.2025.06.010.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAdmixed individualsNon-European populationsLocal ancestryTransferability of PRSPerformance of polygenic risk scoresAdmixed populationsCross-ancestryPolygenic risk score calculatorGenetic prediction of complex traitsGenetic predictionEffect sizePrediction of complex traitsPopulation ArchitectureUK BiobankPolygenic predictionAdmixed AmericansAncestry clustersGenetic architectureComplex traitsPRS modelRisk scoreGenetic variantsAncestryIndividualsIncorporating local ancestry information to predict genetically associated DNA methylation in admixed populations
Cheng Y, Zhou G, Li H, Zhang X, Justice A, Martinez C, Aouizerat B, Xu K, Zhao H. Incorporating local ancestry information to predict genetically associated DNA methylation in admixed populations. Briefings In Bioinformatics 2025, 26: bbaf325. PMID: 40622482, PMCID: PMC12232425, DOI: 10.1093/bib/bbaf325.Peer-Reviewed Original ResearchConceptsMethylome-wide association studiesAdmixed populationsComplex traitsLocal ancestryAssociation studiesDNA methylationAssociated with complex traitsLocal ancestry informationPopulations of European ancestryCpG methylation levelsNon-European populationsMeasurement of methylationAncestry informationCpG sitesMethylation levelsEuropean ancestryEpigenetic underpinningsCpGAncestryTraitsMethylationAmerican populationAfrican American populationDNAPopulationRobust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk
Hu J, Ye Y, Zhang C, Ruan Y, Natarajan P, Zhao H. Robust pleiotropy-decomposed polygenic scores identify distinct contributions to elevated coronary artery disease polygenic risk. PLOS Computational Biology 2025, 21: e1013191. PMID: 40570042, PMCID: PMC12212871, DOI: 10.1371/journal.pcbi.1013191.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresCAD-PRSUK BiobankCoronary artery disease polygenic risk scoreSummary-level dataCAD-related traitsSamples of European ancestryCoronary artery diseaseHigh-risk individualsPotential genetic heterogeneityCurrent smokingPolygenic scoresPolygenic riskTargeted interventionsEuropean ancestryRisk scorePleiotropic regionsRisk predictionGenetic heterogeneityBiological functionsPleiotropySignificant interactionPhenotypic heterogeneityBlood pressureDisease interpretationscMODAL: 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 inferenceBuilding a unified model for drug synergy analysis powered by large language models
Liu T, Chu T, Luo X, Zhao H. Building a unified model for drug synergy analysis powered by large language models. Nature Communications 2025, 16: 4537. PMID: 40374634, PMCID: PMC12081637, DOI: 10.1038/s41467-025-59822-y.Peer-Reviewed Original ResearchA novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics
Cheng Z, Ren Y, Wang X, Zhang Y, Hua Y, Zhao H, Lu H. A novel prognostic framework for HBV-infected hepatocellular carcinoma: insights from ferroptosis and iron metabolism proteomics. Briefings In Bioinformatics 2025, 26: bbaf216. PMID: 40381315, PMCID: PMC12085197, DOI: 10.1093/bib/bbaf216.Peer-Reviewed Original ResearchConceptsHepatocellular carcinoma patientsHepatocellular carcinomaHCC patientsPrognostic modelAdverse prognosisClinically relevant risk groupsRisk groupsTreatment of hepatocellular carcinomaTumor immune microenvironmentAlpha-fetoprotein levelsHigh-risk HCC patientsRelevant risk groupsPrognosis of HCC patientsHBV-infected hepatocellular carcinomaIron metabolismOverall survivalDifferential expression patternsDistant metastasisImmune microenvironmentTumor sizeLiver cancer progressionTumor differentiationMicrovascular invasionPredictive nomogramValidation cohortA 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 profilingJointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation
Xu L, Zhou G, Jiang W, Zhang H, Dong Y, Guan L, Zhao H. JointPRS: A data-adaptive framework for multi-population genetic risk prediction incorporating genetic correlation. Nature Communications 2025, 16: 3841. PMID: 40268942, PMCID: PMC12019179, DOI: 10.1038/s41467-025-59243-x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenetic risk predictionUK BiobankGenome-wide association study summary statisticsAdmixed American populationsRisk predictionGenetic correlationsNon-European populationsContinental populationsAssociation studiesReal-data applicationBinary traitsTrait predictionSummary statisticsMultiple populationsAmerican populationData-adaptive approachSample sizeData applicationsAOUPopulationBiobankData scenarioTraitsProbabilistic exponential family inverse regression and its applications
Pang D, Zhu R, Zhao H, Wang T. Probabilistic exponential family inverse regression and its applications. Biometrics 2025, 81: ujaf065. PMID: 40407023, DOI: 10.1093/biomtc/ujaf065.Peer-Reviewed Original ResearchConceptsExponential familyDouble exponential familyHigh-dimensional regressionLow-dimensional reductionHierarchical likelihoodData exampleInverse regressionDiscrete predictorsSimulation studyDiscrete dataHigh-dimensional dataParallelizable algorithmContinuous predictorsPresence–absence recordsDimension reductionResponse variablesAccumulation of high dimensional dataHigh-throughput sequencing technologyFactor model frameworkLatent factorsRecords of speciesSequence readsSingle-cell studiesSequencing technologiesCommunity ecologyA semicompeting risks model with an application to UK Biobank data to identify risk factors for diabetes onset and progression
Sheikh T, Zhao H. A semicompeting risks model with an application to UK Biobank data to identify risk factors for diabetes onset and progression. Biometrics 2025, 81: ujaf003. PMID: 40417914, PMCID: PMC12104815, DOI: 10.1093/biomtc/ujaf003.Peer-Reviewed Original ResearchConceptsUK Biobank dataRisk factorsBiobank dataType 2 diabetesUKB dataHealth concernVolunteer participantsDisease stageComplex diseasesT2D developmentNongenetic factorsDisease etiologyDiabetes onsetT2DModel fitRisk modelDiabetesRiskPower prior approachDeathUKBMultiple disease stagesTerminal eventNonterminal eventHealthGenomic 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 unionsA Bayesian approach to correcting the attenuation bias of regression using polygenic risk score
Zhou G, Qie X, Zhao H. A Bayesian approach to correcting the attenuation bias of regression using polygenic risk score. Genetics 2025, 229: iyaf018. PMID: 39891671, PMCID: PMC12168083, DOI: 10.1093/genetics/iyaf018.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresRisk scoreEstimation of regression coefficientsBayesian approachMeasurement error modelEstimation of coefficientsCoverage probabilityBayesian measurement error modelsAttenuation biasCredible intervalsCoefficient estimatesUK BiobankLogistic regressionMeasurement errorRegression coefficientsRegression modelsComplex traitsRegression analysisScoresEstimationError modelRegressionBiobankErrorCovariatesBidirectional relationship between epigenetic age and stroke, dementia, and late-life depression
Rivier C, Szejko N, Renedo D, Clocchiatti-Tuozzo S, Huo S, de Havenon A, Zhao H, Gill T, Sheth K, Falcone G. Bidirectional relationship between epigenetic age and stroke, dementia, and late-life depression. Nature Communications 2025, 16: 1261. PMID: 39893209, PMCID: PMC11787333, DOI: 10.1038/s41467-024-54721-0.Peer-Reviewed Original ResearchThis study shows a bidirectional link between accelerated epigenetic aging and brain health events like stroke, dementia, and depression, supporting new prevention strategies for aging-related conditions.Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors
Demarais Z, Conlon C, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Torres-Lopez V, Sheth K, Meeker D, Zhao H, Ohno-Machado L, Acosta J, Huo S, Falcone G. Polygenic Susceptibility to Diabetes and Poor Glycemic Control in Stroke Survivors. Neurology 2025, 104: e210276. PMID: 39889253, DOI: 10.1212/wnl.0000000000210276.Peer-Reviewed Original ResearchConceptsStroke survivorsWorse glycemic controlPoor glycemic controlStroke patientsAssociated with worse glycemic controlGlycemic controlPolygenic risk scoresClinical management of stroke patientsAssociated with poor glycemic controlManagement of stroke patientsCross-sectional designGenetic association studiesUncontrolled diabetesSusceptibility to T2DMUK BiobankType 2 diabetes mellitusAdverse vascular outcomesRisk scoreAssociation studiesHemoglobin A1cSurvivorsVascular outcomesSusceptibility to diabetesStrokeDiabetesThe 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
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