Shiying Wang
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Biography
I am a PhD student in Biostatistics. My research interests are statistical genetics, neuroimaging, and imaging genetics. I am developing novel methods and applying existing statistical methods on genomic data, such as whole-genome sequencing data and biobank scale dataset, to identify genetic variants and genes for diseases and complex traits.
Last Updated on October 20, 2023.
Departments & Organizations
Education & Training
- BS
- Peking University, Biological Science and Psychology
Research
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Research at a Glance
Yale Co-Authors
Frequent collaborators of Shiying Wang's published research.
Publications Timeline
A big-picture view of Shiying Wang's research output by year.
Heping Zhang, PhD
Todd Constable, PhD
Yize Zhao, PhD
9Publications
147Citations
Publications
2024
Variant Selection and Aggregation of Genetic Association Studies in Precision Medicine
Hu J, Wang S, Zhang H. Variant Selection and Aggregation of Genetic Association Studies in Precision Medicine. ICSA Book Series In Statistics 2024, 423-451. DOI: 10.1007/978-3-031-50690-1_17.Peer-Reviewed Original ResearchConceptsAssociation studiesGenetic association studiesPrecision medicineTests of associationHeterogeneous drug responsesDisease risk predictionVariant identificationSignal variantsControl of type I errorDisease riskDrug responseGenomic profilingType I errorRisk predictionGenetic biomarkersVariantsPharmaceutical interventionsMarginal testsAssociation methodAssociationMedicineInterventionIdentificationReplicationDiseaseHeterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition
Wang S, Constable T, Zhang H, Zhao Y. Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition. Journal Of The American Statistical Association 2024, 119: 851-863. PMID: 39371422, PMCID: PMC11451334, DOI: 10.1080/01621459.2024.2311363.Peer-Reviewed Original ResearchCitations
2023
Identification and validation of supervariants reveal novel loci associated with human white matter microstructure
Wang S, Li T, Zhao B, Dai W, Yao Y, Li C, Li T, Zhu H, Zhang H. Identification and validation of supervariants reveal novel loci associated with human white matter microstructure. Genome Research 2023, 34: gr.277905.123. PMID: 38190638, PMCID: PMC10904010, DOI: 10.1101/gr.277905.123.Peer-Reviewed Original ResearchCitationsAltmetricDeep learning identified genetic variants for COVID‐19‐related mortality among 28,097 affected cases in UK Biobank
Liu Z, Dai W, Wang S, Yao Y, Zhang H. Deep learning identified genetic variants for COVID‐19‐related mortality among 28,097 affected cases in UK Biobank. Genetic Epidemiology 2023, 47: 215-230. PMID: 36691909, PMCID: PMC10006374, DOI: 10.1002/gepi.22515.Peer-Reviewed Original ResearchCitationsAltmetric
2021
Sex‐specific associations between traumatic experiences and resting‐state functional connectivity in the Philadelphia Neurodevelopmental Cohort
Wang S, Malins JG, Zhang H, Gruen JR. Sex‐specific associations between traumatic experiences and resting‐state functional connectivity in the Philadelphia Neurodevelopmental Cohort. JCPP Advances 2021, 1: e12049. PMID: 34970657, PMCID: PMC8713563, DOI: 10.1002/jcv2.12049.Peer-Reviewed Original ResearchCitationsAltmetricConceptsResting-state functional connectivityDefault mode networkFunctional magnetic resonance imagingResting-state functional magnetic resonance imagingPhiladelphia Neurodevelopmental CohortTraumatic eventsTraumatic experiencesResting-state networksFunctional connectivityBrain regionsNeurodevelopmental CohortSomatomotor networkSex-specific associationsMultiple cognitive functionsSex-specific clustersMagnetic resonance imagingStructured psychiatric evaluationMultiple psychiatric disordersMode networkCognitive functionAnatomical clusterDistribution parameter valuesResonance imagingAge 14.6 yearsSignificant risk factorsGenetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data
Hu J, Li C, Wang S, Li T, Zhang H. Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data. Human Genomics 2021, 15: 10. PMID: 33536081, PMCID: PMC7856608, DOI: 10.1186/s40246-021-00306-7.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenome-wide association studiesGenetic variantsPower of GWASTraditional genome-wide association studiesTraits of interestMulti-locus interactionsHuman bronchial epithelial cellsSignificant genetic variantsDownregulated genesChromosome 2Genetic basisGenetic factorsAssociation studiesBronchial epithelial cellsCilia dysfunctionSusceptibility lociMitochondrial dysfunctionGenome-wide significant genetic variantsEpithelial cellsGenesMolecular pathogenesisHost genetic factorsUK Biobank dataUK BiobankVariants
2020
Super‐variants identification for brain connectivity
Li T, Hu J, Wang S, Zhang H. Super‐variants identification for brain connectivity. Human Brain Mapping 2020, 42: 1304-1312. PMID: 33236465, PMCID: PMC7927294, DOI: 10.1002/hbm.25294.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsCombination of allelesSingle nucleotide polymorphismsNovel lociBrain connectivityUK Biobank databaseChromosome 1Multiple lociGenetic effectsGenetic variantsNucleotide polymorphismsAssociation detectionLociGenetic associationGenesNeurodegenerative disordersBiobank databaseBrain issuesGenetic biomarkersBrain functionBrain structuresGenomeRSPO2Discovery phaseAssociationTMEM74Supervariants identification for breast cancer
Hu J, Li T, Wang S, Zhang H. Supervariants identification for breast cancer. Genetic Epidemiology 2020, 44: 934-947. PMID: 32808324, PMCID: PMC7924970, DOI: 10.1002/gepi.22350.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenome-wide association studiesCombination of allelesRare variantsNovel lociChromosome 2UK Biobank databaseChromosome 1Multiple lociAssociation studiesLociComplex diseasesGenesBiobank databaseAssociation methodGenomeVariantsTens of thousandsAllelesPolymorphismNovel resultsSignalsClassic conceptIdentification
2019
Common genetic variants have associations with human cortical brain regions and risk of schizophrenia
Bi X, Feng L, Wang S, Lin Z, Li T, Zhao B, Zhu H, Zhang H. Common genetic variants have associations with human cortical brain regions and risk of schizophrenia. Genetic Epidemiology 2019, 43: 548-558. PMID: 30941828, PMCID: PMC6559856, DOI: 10.1002/gepi.22203.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsCortical regionsCortical brain regionsRisk of schizophreniaPrefrontal cortical regionsSymptom durationProdromal symptomsMental disordersSignificant associationBrain regionsCommon genetic variantsPhiladelphia Neurodevelopmental CohortPediatric imagingSchizophreniaNeurodevelopmental CohortCommon variantsHuman brainGenetic variantsHeritable mental disorderMagnetic resonanceAssociationWide association studyAssociation studiesGenetic effectsCohortSymptoms