Wei Jiang, PhD
Associate Research Scientist in BiostatisticsCards
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Associate Research Scientist in Biostatistics
Biography
Dr Wei Jiang received his PhD in Electronic and Computer Engineering from the Hong Kong University of Science and Technology. His research interests lie in the fields of Bioinformatics and Biostatistics. He currently focus on developing computational and statistical methods for analyzing data from genome-wide association studies (GWAS) to explore the genetic mechanisms of human diseases. The research works were published in Nature Communications, American Journal of Human Genetics, PLoS Genetics, Briefings in Bioinformatics, Bioinformatics etc, and the paper for designing replication studies of GWAS received the Best Paper Award in Asia Pacific Bioinformatics Conference (APBC) 2016 held in San Francisco, US. Detailed biography can be found in the personal website: http://wjiang.eu.org.
Education & Training
- PhD
- The Hong Kong University of Science and Technology, Electronic and Computer Engineering (2016)
- BEngSci
- Tsinghua University, Automation (2011)
Research
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Overview
The human body is a complex system, which makes it extremely challenged to model how molecular components regulate the traits. With the development of sequencing technologies, data of molecular signals inside the human body are released and accumulating. The ultimate goal of his research is to develop a multi-omics data analysis framework to explore molecular regulatory mechanisms of complex traits, so that we can precisely control our health outcomes from different molecular signals.
His current research focus on developing statistical or computational methods for analyzing data from genome-wide association studies (GWAS). Genome contains the most fundamental information distinguishing each person, and GWAS directly investigate the relationship of our genomes to complex traits. He developed a series of methods for exploring replicable genetic factors, quantifying genetic contribution, and predicting genetic risks for complex traits based on GWAS data.
Medical Research Interests
Public Health Interests
ORCID
0000-0001-6120-5278
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Hongyu Zhao, PhD
Andrew DeWan, PhD, MPH
Francesc Lopez-Giraldez, PhD
Junhui Zhang, MD
Kaya Bilguvar, MD, PhD
Leying Guan
Genome-Wide Association Study
Genomics
Publications
2025
Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE
Dong Z, Jiang W, Shen J, Li H, Xie Y, DeWan A, Zhao H. Incorporating additive genetic effects and linkage disequilibrium information to discover gene-environment interactions using BV-LDER-GE. Genome Biology 2025, 26: 332. PMID: 41044620, PMCID: PMC12492645, DOI: 10.1186/s13059-025-03815-z.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsGene-environment interactionsComplex traitsLinkage disequilibriumLinkage disequilibrium informationGenetic effectsLD informationGenetic epidemiologyGene-environmentE interactionG-XPartial informationStatistical powerDisease etiologyGenetic factorsEnvironmental factorsTraitsAdditive effectGenomeBivariateDisequilibriumEpidemiologyFactorsRegressionStatistical methodsLinkageJointPRS: 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenome-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 scenarioTraitsGenomic 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsCongenital 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 abnormalities
2024
LDER-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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsHuman 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 studyIntegration 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsExpression 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 variantsTuning 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 ResearchCitationsAltmetricHigh-Dimensional Asymptotic Behavior of Inference Based on Gwas Summary Statistics
Jiang J, Jiang W, Paul D, Zhang Y, Zhao H. High-Dimensional Asymptotic Behavior of Inference Based on Gwas Summary Statistics. Statistica Sinica 2024 DOI: 10.5705/ss.202021.0060.Peer-Reviewed Original ResearchCitationsConcepts
2022
Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation
Song S, Jiang W, Zhang Y, Hou L, Zhao H. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. American Journal Of Human Genetics 2022, 109: 802-811. PMID: 35421325, PMCID: PMC9118121, DOI: 10.1016/j.ajhg.2022.03.013.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsLinkage disequilibrium score regressionComplex traitsSingle nucleotide polymorphismsSNP heritabilityGenome-wide association studiesDisequilibrium score regressionHigh-throughput technologiesHeritable phenotypesAssociation studiesGenetic studiesCryptic relatednessLD informationScore regressionHeritabilityGenetic contributionHeritability estimationPopulation stratificationDisease mechanismsTraitsLD matrixOnly summary statisticsUK BiobankPolygenicitySummary statisticsRelatedness
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Details are provided in Song, S., Jiang, W., Zhang, Y., Hou, L. and Zhao, H., 2022. Leveraging LD eigenvalue regression to improve the estimation of SNP heritability and confounding inflation. The American Journal of Human Genetics, 109(5), pp.802-811.
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