Zuoheng Anita Wang, PhD
Associate Professor of BiostatisticsCards
About
Titles
Associate Professor of Biostatistics
Associate Professor, Biomedical Informatics & Data Science
Biography
Dr. Wang is Associate Professor of Biostatistics and of Biomedical Informatics and Data Science at Yale University. Her research focuses on combining genetics, genomics, immunology, and statistical modeling to answer biologically important questions in genetic epidemiological studies. Dr. Wang's statistical expertise lies in longitudinal data analysis, varying coefficient models, mixed effects models, kernel machine methods, mediation analysis, machine learning methods, and network analysis. She develops statistically innovative methods and computationally efficient tools in large-scale genetic and genomic studies to identify genetic susceptibility variants and advance the understanding of the etiology of complex diseases including breast cancer, alcohol and drug abuse, asthma, autism, obesity, lung and cardiovascular diseases. Current studies include using next-generation sequencing data to detect rare genetic variants in longitudinal genetic studies, combining knowledge in genomics and immunology to understand the risk of breast cancer survival, addressing statistical challenges in single-cell RNA sequencing data and spatial transcriptomics, and machine learning for risk prediction in electronic health records data.
Appointments
Biostatistics
Associate Professor TenurePrimaryBiomedical Informatics & Data Science
Associate Professor TenureSecondary
Other Departments & Organizations
- Biomedical Informatics & Data Science
- Biostatistics
- Center for Biomedical Data Science
- Center for Brain & Mind Health
- Computational Biology and Biomedical Informatics
- Genomics, Genetics, and Epigenetics
- Safdar Lab
- Yale Cancer Center
- Yale Combined Program in the Biological and Biomedical Sciences (BBS)
- Yale School of Public Health
- Yale Ventures
Education & Training
- PhD
- University of Chicago (2009)
- MS
- University of Florida (2004)
- BS
- University of Science and Technology of China (2001)
Research
Publications
2024
Detecting 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