2025
The 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, 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, 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 ResearchJoint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation
Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. NeuroImage 2024, 297: 120739. PMID: 39009250, PMCID: PMC11367654, DOI: 10.1016/j.neuroimage.2024.120739.Peer-Reviewed Original ResearchGenetic networksComplex traitsGenetic architecture of complex traitsArchitecture of complex traitsGenome-wide association analysisGenetic correlationsGenetic architectureGenetic variationAssociation analysisGenetic basisPhenotypic similarityGenetic effectsFunctional variationRight hemisphereBrain regionsUK BiobankCortical thicknessTraitsCortical measuresCorrelation networkSignificant pairsHeritabilitySimilarity matrixBrainBrain lobesDNA 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-radiotherapyLeveraging Functional Annotations Improves Cross-Population Genetic Risk Prediction
Ye Y, Xu L, Zhao H. Leveraging Functional Annotations Improves Cross-Population Genetic Risk Prediction. ICSA Book Series In Statistics 2024, 453-471. DOI: 10.1007/978-3-031-50690-1_18.Peer-Reviewed Original ResearchPolygenic risk scoresFunctional annotationGenetic risk predictionStandard PRSPost-GWAS analysisPolygenic risk score modelCross-population predictionNon-European populationsGenetic resultsGenetic studiesRisk predictionCross populationsAnnoPredPRS methodsUK BiobankAnnotationRisk scoreTraits/diseasesLDpredPopulationP+TPoor transferBiobankBayesian frameworkLDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information
Dong Z, Jiang W, Li H, DeWan A, Zhao H. LDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. Briefings In Bioinformatics 2024, 25: bbae335. PMID: 38980374, PMCID: PMC11232466, DOI: 10.1093/bib/bbae335.Peer-Reviewed Original ResearchConceptsHuman complex traitsComplex traitsGene-environment interactionsGene-environmentLinkage disequilibriumPhenotypic variance componentsPhenotypic varianceProportion of phenotypic varianceSummary statisticsEuropean ancestry subjectsUK Biobank dataAssociation summary statisticsComplete linkage disequilibriumControlled type I error ratesLD informationLD matrixVariance componentsBiobank dataType I error rateEuropean ancestrySample size increaseGenetic effectsTraitsE-I pairsSimulation studyStrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records
Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. Npj Digital Medicine 2024, 7: 130. PMID: 38760474, PMCID: PMC11101464, DOI: 10.1038/s41746-024-01120-w.Peer-Reviewed Original ResearchElectronic health recordsWeighted F1MIMIC-IIIClinical decision support systemsMulti-class classificationNatural language processingMIMIC-III datasetHealth recordsMachine learning classifiersDecision support systemArtificial intelligence toolsVascular neurologistsLearning classifiersBinary classificationCross-validation accuracyLanguage processingMeta-modelIntelligence toolsStroke prevention effortsAcute ischemic strokeStroke etiologySupport systemStroke etiology classificationClassification toolClassifierA mediation analysis framework based on variance component to remove genetic confounding effect
Dong Z, Zhao H, DeWan A. A mediation analysis framework based on variance component to remove genetic confounding effect. Journal Of Human Genetics 2024, 69: 301-309. PMID: 38528049, DOI: 10.1038/s10038-024-01232-x.Peer-Reviewed Original ResearchMediation analysis frameworkSingle nucleotide polymorphismsMediation analysisPleiotropic single nucleotide polymorphismsUK Biobank dataConfounding effectsTrait pairsBiobank dataIndividual-levelEpidemiological studiesCausal effectsGenetic signalsEstimated effectsLinear regressionNucleotide polymorphismsStandard errorData analysisGenetic correlationsPhenotypeIndirect effectsPleiotropyVariance componentsOutcomesRegressionStatistical 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 methodsUsing clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank
Hu J, Ye Y, Zhou G, Zhao H. Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank. JNCI Cancer Spectrum 2024, 8: pkae008. PMID: 38366150, PMCID: PMC10919929, DOI: 10.1093/jncics/pkae008.Peer-Reviewed Original ResearchPolygenic risk scoresUK BiobankCancer riskClinical risk factorsRisk of breast cancerRisk factorsPolygenic risk score modelHigh risk of developing cancerRisk of developing cancerLate-onset patientsRisk predictionClinical variablesHigh-risk individualsCox proportional hazards modelsProportional hazards modelGenetic risk factorsBaseline traitsClinical risk modelRisk scoreEarly-onset patientsHazards modelLate-onset groupEarly-onset groupBreast cancerHigh riskEstimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease
Su C, Zhang J, Zhao H. Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease. Journal Of The American Statistical Association 2024, 119: 811-824. PMID: 39280354, PMCID: PMC11394578, DOI: 10.1080/01621459.2023.2297467.Peer-Reviewed Original ResearchIntegration of expression QTLs with fine mapping via SuSiE.
Zhang X, Jiang W, Zhao H. Integration of expression QTLs with fine mapping via SuSiE. PLOS Genetics 2024, 20: e1010929. PMID: 38271473, PMCID: PMC10846745, DOI: 10.1371/journal.pgen.1010929.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGenome-wide association studiesFine-mapping methodsLinkage disequilibriumBody mass indexFine-mappingExpression quantitative trait loci informationGenome-wide association study resultsExpression quantitative trait loci analysisPresence of linkage disequilibriumExternal reference panelGenetic fine-mappingQuantitative trait lociPosterior inclusion probabilitiesInclusion probabilitiesAlzheimer's diseaseExpression QTLsLD patternsComplex traitsCandidate variantsAssociation studiesTrait lociAssociation to causationReference panelFunctional variantsPhenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health
Zekavat S, Jorshery S, Rauscher F, Horn K, Sekimitsu S, Koyama S, Nguyen T, Costanzo M, Jang D, Burtt N, Kühnapfel A, Shweikh Y, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè A, Wiggs J, Del Priore L, Scholz M, Wang J, Natarajan P, Zebardast N. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Science Translational Medicine 2024, 16: eadg4517. PMID: 38266105, DOI: 10.1126/scitranslmed.adg4517.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesRetinal layer thicknessPhotoreceptor segmentsOptical coherence tomographyRetinal layersUK Biobank participantsLIFE-Adult-StudyInherited genetic lociGenome-wide associationGanglion cell complex layerRetinal optical coherence tomography imagesRetinal nerve fiber layerAge-related macular degenerationBiobank participantsEye careNerve fiber layerOptical coherence tomography imagesIncident mortalityMacular OCT imagesLIFE-AdultIndependent associationsAssociation studiesSystemic healthGenetic associationGenome-wide analysisGenomic risk prediction of cardiovascular diseases among type 2 diabetes patients in the UK Biobank
Ye Y, Hu J, Pang F, Cui C, Zhao H. Genomic risk prediction of cardiovascular diseases among type 2 diabetes patients in the UK Biobank. Frontiers In Bioinformatics 2024, 3: 1320748. PMID: 38239805, PMCID: PMC10794561, DOI: 10.3389/fbinf.2023.1320748.Peer-Reviewed Original ResearchTuning parameters for polygenic risk score methods using GWAS summary statistics from training data
Jiang W, Chen L, Girgenti M, Zhao H. Tuning parameters for polygenic risk score methods using GWAS summary statistics from training data. Nature Communications 2024, 15: 24. PMID: 38169469, PMCID: PMC10762162, DOI: 10.1038/s41467-023-44009-0.Peer-Reviewed Original Research
2023
Reply to: Genetic differentiation at probe SNPs leads to spurious results in meQTL discovery
Cheng Y, Li B, Zhang X, Aouizerat B, Zhao H, Xu K. Reply to: Genetic differentiation at probe SNPs leads to spurious results in meQTL discovery. Communications Biology 2023, 6: 1296. PMID: 38129596, PMCID: PMC10739901, DOI: 10.1038/s42003-023-05646-9.Peer-Reviewed Original ResearchscNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchProfilin1 is required to prevent mitotic catastrophe in murine and human glomerular diseases
Tian X, Pedigo C, Li K, Ma X, Bunda P, Pell J, Lek A, Gu J, Zhang Y, Rangel P, Li W, Schwartze E, Nagata S, Lerner G, Perincheri S, Priyadarshini A, Zhao H, Lek M, Menon M, Fu R, Ishibe S. Profilin1 is required to prevent mitotic catastrophe in murine and human glomerular diseases. Journal Of Clinical Investigation 2023, 133: e171237. PMID: 37847555, PMCID: PMC10721156, DOI: 10.1172/jci171237.Peer-Reviewed Original ResearchConceptsProteinuric kidney diseaseKidney diseasePodocyte lossHuman glomerular diseasesMitotic catastrophePodocyte cell cycleSevere proteinuriaCell cycle reentryKidney failureGlomerular diseaseCell cycleKidney tissueG1/S checkpointUnsuccessful repairCyclin D1Glomerular integrityIrregular nucleiTissue-specific lossMouse podocytesPodocytesAltered expressionDiseaseCyclin B1Ribosome affinity purificationMultinucleated cellsBenchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies
Zhang C, Zhang Y, Zhang Y, Zhao H. Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies. Briefings In Bioinformatics 2023, 24: bbad407. PMID: 37974509, PMCID: PMC10654488, DOI: 10.1093/bib/bbad407.Peer-Reviewed Original Research
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