2024
Sex differences in proteomics of cardiovascular disease: results from the Yale-CMD registry
Liu Y, Wang Z, Collins S, Testani J, Kleinstein S, Safdar B. Sex differences in proteomics of cardiovascular disease: results from the Yale-CMD registry. European Heart Journal 2024, 45: ehae666.3091. DOI: 10.1093/eurheartj/ehae666.3091.Peer-Reviewed Original ResearchCoronary microvascular dysfunctionCoronary artery diseaseUpregulation of lipidCardiovascular diseaseAcute heart failureCoronary artery disease cohortHistory of diabetesPathophysiology of CVDProximity extension assayBody mass indexSignificant sex differencesRegulation of blood flowSerum proteomic profilesSex differencesProteomic profilingAngiogenesis-related proteinsCardiac PET/CTHemodynamic instabilityIschemic symptomsMicrovascular dysfunctionFalse discovery rateHeart failureFebrile illnessFunctional pathway analysisMass indexSDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data
Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Wei H, Justet A, Adams T, Homer R, Amei A, Rosas I, Kaminski N, Wang Z, Yan X. SDePER: a hybrid machine learning and regression method for cell-type deconvolution of spatial barcoding-based transcriptomic data. Genome Biology 2024, 25: 271. PMID: 39402626, PMCID: PMC11475911, DOI: 10.1186/s13059-024-03416-2.Peer-Reviewed Original ResearchDetecting time‐varying genetic effects in Alzheimer's disease using a longitudinal genome‐wide association studies model
Zhuang X, Xu G, Amei A, Cordes D, Wang Z, Oh E, Initiative F. Detecting time‐varying genetic effects in Alzheimer's disease using a longitudinal genome‐wide association studies model. Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring 2024, 16: e12597. PMID: 38855650, PMCID: PMC11157162, DOI: 10.1002/dad2.12597.Peer-Reviewed Original ResearchGenome-wide association studiesSingle nucleotide polymorphismsLongitudinal genome-wide association studiesGWAS modelsAssociation studiesGenetic effectsAlzheimer's diseaseSingle nucleotide polymorphism effectsNational Alzheimer's Coordinating Center dataAge-dependent genetic effectsImpairment statusProgression of Alzheimer's diseaseEffects of apoEAge-stratified analysesGenetic signalsGenetic lociNucleotide polymorphismsLongitudinal phenotypesPathway analysisInitiative participantsAmyloid accumulationAmyloidStandardized uptake value ratioCenter dataAmyloid positron emission tomographyComputationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts
Zhang X, Hu Y, Vandenhoudt R, Yan C, Marconi V, Cohen M, Wang Z, Justice A, Aouizerat B, Xu K. Computationally inferred cell-type specific epigenome-wide DNA methylation analysis unveils distinct methylation patterns among immune cells for HIV infection in three cohorts. PLOS Pathogens 2024, 20: e1012063. PMID: 38466776, PMCID: PMC10957090, DOI: 10.1371/journal.ppat.1012063.Peer-Reviewed Original ResearchCD4+ T cellsEpigenome-wide association studiesPeripheral blood mononuclear cellsHIV infectionHIV pathogenesisT cellsCpG sitesNatural killer (NK) cellsCell typesAssociated with HIV infectionCD8+ T cellsMethylation patternsCpG methylationDNA methylationEpigenome-wide DNA methylation analysisBlood mononuclear cellsImmune cell typesDifferentially methylated CpG sitesUnique CpG sitesDifferential CpG methylationDNA methylation analysisSignificant CpG sitesArray-based methodsGene set enrichment analysisComputational deconvolution methodsRETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.
Xu G, Amei A, Wu W, Liu Y, Shen L, Oh E, Wang Z. RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION. The Annals Of Applied Statistics 2024, 18: 487-505. PMID: 38577266, PMCID: PMC10994004, DOI: 10.1214/23-aoas1798.Peer-Reviewed Original Research
2023
Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu Y, Zhao J, Adams T, Wang N, Schupp J, Wu W, McDonough J, Chupp G, Kaminski N, Wang Z, Yan X. Correction: iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. BMC Bioinformatics 2023, 24: 394. PMID: 37858060, PMCID: PMC10588114, DOI: 10.1186/s12859-023-05523-6.Peer-Reviewed Original ResearchtRFtarget 2.0: expanding the targetome landscape of transfer RNA-derived fragments
Li N, Yao S, Yu G, Lu L, Wang Z. tRFtarget 2.0: expanding the targetome landscape of transfer RNA-derived fragments. Nucleic Acids Research 2023, 52: d345-d350. PMID: 37811890, PMCID: PMC10767876, DOI: 10.1093/nar/gkad815.Peer-Reviewed Original ResearchCis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits
Cheng Y, Justice A, Wang Z, Li B, Hancock D, Johnson E, Xu K. Cis-meQTL for cocaine use-associated DNA methylation in an HIV-positive cohort show pleiotropic effects on multiple traits. BMC Genomics 2023, 24: 556. PMID: 37730558, PMCID: PMC10510240, DOI: 10.1186/s12864-023-09661-2.Peer-Reviewed Original ResearchConceptsDNA methylationMultiple traitsPleiotropic effectsGenetic variantsAberrant DNA methylationPhenome-wide association studyCis-meQTLsComplex traitsRelevant traitsDNAm sitesEnrichment analysisMeQTLsAssociation studiesSignificant traitsTraitsImmune pathwaysMethylationNew insightsMendelian randomizationImmunological functionsGenesVariantsCausal rolePathwayCpGiDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects
Liu Y, Zhao J, Adams T, Wang N, Schupp J, Wu W, McDonough J, Chupp G, Kaminski N, Wang Z, Yan X. iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects. BMC Bioinformatics 2023, 24: 318. PMID: 37608264, PMCID: PMC10463720, DOI: 10.1186/s12859-023-05432-8.Peer-Reviewed Original ResearchDifferences in Mortality Among Patients With Asthma and COPD Hospitalized With COVID-19
Liu Y, Rajeevan H, Simonov M, Lee S, Wilson F, Desir G, Vinetz J, Yan X, Wang Z, Clark B, Possick J, Price C, Lutchmansingh D, Ortega H, Zaeh S, Gomez J, Cohn L, Gautam S, Chupp G. Differences in Mortality Among Patients With Asthma and COPD Hospitalized With COVID-19. The Journal Of Allergy And Clinical Immunology In Practice 2023, 11: 3383-3390.e3. PMID: 37454926, PMCID: PMC10787810, DOI: 10.1016/j.jaip.2023.07.006.Peer-Reviewed Original ResearchConceptsChronic obstructive pulmonary diseaseType 2 inflammationCOVID-19 severitySOFA scoreAirway diseaseNoneosinophilic asthmaSequential Organ Failure Assessment scoreOrgan Failure Assessment scoreSevere coronavirus disease 2019Higher SOFA scoreMedian SOFA scoreRetrospective cohort studyObstructive pulmonary diseaseOdds of mortalityLower SOFA scoresCells/μLCOVID-19 outcomesCoronavirus disease 2019Logistic regression analysisCOVID-19Clinical confoundersAsthma patientsCohort studyImmunological factorsClinical features
2022
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang Z, Yan X. Computational and Statistical Methods for Single-Cell RNA Sequencing Data. Springer Handbooks Of Computational Statistics 2022, 3-35. DOI: 10.1007/978-3-662-65902-1_1.ChaptersSingle-cell RNA sequencing technologySingle-cell RNA sequencing dataRNA sequencing technologyPhenotype of interestRNA sequencing dataDifferential expression analysisScRNA-seq dataStatistical methodsSequencing technologiesExpression analysisDropout imputationSequencing dataSeq dataDroplet-based technologiesDropout eventsDisease pathogenesisPopulation composition changesData normalizationHigh noise levelsPhenotypeNoise levelTherapeuticsComposition changes
2020
tRFtarget: a database for transfer RNA-derived fragment targets
Li N, Shan N, Lu L, Wang Z. tRFtarget: a database for transfer RNA-derived fragment targets. Nucleic Acids Research 2020, 49: d254-d260. PMID: 33035346, PMCID: PMC7779015, DOI: 10.1093/nar/gkaa831.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBase PairingBase SequenceCaenorhabditis elegansDatabases, Nucleic AcidDrosophila melanogasterGene OntologyHumansMiceMolecular Sequence AnnotationNucleic Acid ConformationNucleic Acid HybridizationRhodobacter sphaeroidesRNA, MessengerRNA, Small UntranslatedRNA, TransferSchizosaccharomycesThermodynamicsXenopusZebrafishConceptsTarget genesTransfer RNASmall non-coding RNAsGene Ontology annotationsNon-coding RNAsFunctional pathway analysisAccessible web-based databaseMolecular functionsOntology annotationsBiological functionsPathway analysisMolecular mechanismsPhysiological processesTarget predictionHuman diseasesGenesMRNA transcriptsRNAWeb-based databaseConvenient linkTRFImportant roleRNAhybridTargetIntaRNA
2019
Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming
Fu L, Wang M, Wang Z, Song X, Tang S. Maximum likelihood estimation of nonlinear mixed-effects models with crossed random effects by combining first-order conditional linearization and sequential quadratic programming. International Journal Of Biomathematics 2019, 12: 1950040. DOI: 10.1142/s1793524519500402.Peer-Reviewed Original ResearchSequential quadratic programmingNLME modelsMaximum likelihood estimationNonlinear mixed effects modelsParameter estimationQuadratic programmingGeneral formulationLikelihood estimationRandom effectsStandard statistical packagesVariance-covariance matrixModel linearizationMethod convergesConditional expansionComputational algorithmComputational optimizationNormal assumptionNLME modelingError termSimulation studyLinearizationMixed effects modelsEstimationHigh accuracyAlgorithm
2015
Isolation and characterization of a poplar d-type cyclin gene associated with the SHORT-ROOT/SCARECROW network
Xu M, Liu S, Xuan L, Huang M, Wang Z. Isolation and characterization of a poplar d-type cyclin gene associated with the SHORT-ROOT/SCARECROW network. Trees 2015, 30: 255-263. DOI: 10.1007/s00468-015-1296-y.Peer-Reviewed Original ResearchAdventitious root developmentRegulatory networksCyclin genesProtoplast transient expression assaysRoot developmentD-type cyclin genesRoot radial patterningGFP fusion proteinBiomolecular fluorescence complementationTransient expression assaysCortex cell layersD-type cyclinsRoot patterningArabidopsis rootsFluorescence complementationRadial patterningCell specificationModel organismsCycle genesDaughter cellsExpression assaysKey regulatorFusion proteinGenesPericlinal divisions
2014
A Genome-Wide Association Study on Obesity and Obesity-Related Traits
Wang K, Li W, Zhang C, Wang Z, Glessner J, Grant S, Zhao H, Hakonarson H, Price R. A Genome-Wide Association Study on Obesity and Obesity-Related Traits. 2014, 57-69. DOI: 10.1201/b16443-4.Peer-Reviewed Original Research
2012
Prognostic significance of the AJCC staging in patients with squamous cell carcinoma of the oropharynx.
Acevedo-Gadea C, Baumgart M, Wang Z, Deshpande H, Davies M, Decker R, Sasaki C, Judson B, Herbst R, Morgensztern D. Prognostic significance of the AJCC staging in patients with squamous cell carcinoma of the oropharynx. Journal Of Clinical Oncology 2012, 30: 5529-5529. DOI: 10.1200/jco.2012.30.15_suppl.5529.Peer-Reviewed Original ResearchSquamous cell carcinomaOP cancerOverall survivalHuman papillomavirusOral cavityAJCC stagePrognostic significanceCell carcinomaStage IFrequency of HPVNeck squamous cell carcinomaCurrent AJCC systemEnd Results registryKaplan-Meier methodEpidemiology of headL. PatientsPrognostic impactAJCC stagingOropharyngeal cancerPrognostic factorsStage IVBSurveillance EpidemiologyAJCC systemPatient populationClinical behaviorInfluence of extracapsular extension on lymph node staging for patients with squamous cell carcinoma of the head and neck.
Baumgart M, Acevedo-Gadea C, Wang Z, Buta E, Davies M, Deshpande H, Decker R, Sasaki C, Judson B, Herbst R, Morgensztern D. Influence of extracapsular extension on lymph node staging for patients with squamous cell carcinoma of the head and neck. Journal Of Clinical Oncology 2012, 30: 5532-5532. DOI: 10.1200/jco.2012.30.15_suppl.5532.Peer-Reviewed Original ResearchPoor prognostic factorSquamous cell carcinomaExtracapsular extensionOral cavityOverall survivalPrognostic factorsCell carcinomaIndependent poor prognostic factorNeck squamous cell carcinomaCox proportional hazards modelSignificant OS differenceSquamous cell cancerProportional hazards modelPrognostic impactSurveillance EpidemiologyCell cancerPoor outcomeKaplan-MeierECE statusOS differenceAJCC manualEligibility criteriaHazards modelPrimary siteSurvival curvesCorrection: A Genome-Wide Association Study on Obesity and Obesity-Related Traits
Wang K, Li W, Zhang C, Wang Z, Glessner J, Grant S, Zhao H, Hakonarson H, Price R. Correction: A Genome-Wide Association Study on Obesity and Obesity-Related Traits. PLOS ONE 2012, 7: 10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580. PMCID: PMC3293772, DOI: 10.1371/annotation/a34ee94e-3e6a-48bd-a19e-398a4bb88580.Peer-Reviewed Original Research
2009
Joint Functional Mapping of Quantitative Trait Loci for HIV-1 and CD4+ Dynamics
Wang Z, Li Y, Li Q, Wu R. Joint Functional Mapping of Quantitative Trait Loci for HIV-1 and CD4+ Dynamics. The International Journal Of Biostatistics 2009, 5 DOI: 10.2202/1557-4679.1136.Peer-Reviewed Original ResearchUnifying statistical modelStatistical propertiesMathematical equationsMathematical functionsStatistical modelCovariance structureGeneral frameworkSimulation studyHybrid algorithmReal dataHypothesis testDynamicsCurve parametersDifferent dynamic processesNew modelTime-dependent dynamic changesEquationsStatistical analysisModelParametersDynamic genetic effectsAlgorithmFormulationSpecific quantitative trait lociDynamic process
2004
A statistical model for functional mapping of quantitative trait loci regulating drug response
Gong Y, Wang Z, Liu T, Zhao W, Zhu Y, Johnson J, Wu R. A statistical model for functional mapping of quantitative trait loci regulating drug response. The Pharmacogenomics Journal 2004, 4: 315-321. PMID: 15263889, DOI: 10.1038/sj.tpj.6500262.Peer-Reviewed Original ResearchConceptsQuantitative trait lociComplex traitsGenetic mappingTrait lociDifferent QTL genotypesPopulation genetic parametersFunction-valued traitsDrug responseMultiple genesSpecific genesHigh-resolution mappingRecombination eventsQTL genotypesDisequilibrium mappingPolymorphic markersGenetic parametersFunctional mappingTraitsGenesLociPharmacogenomics researchEnvironmental influencesPowerful toolHistoric times