2024
Leveraging 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 framework
2023
Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression
Lin C, Liu W, Jiang W, Zhao H. Robustness of quantifying mediating effects of genetically regulated expression on complex traits with mediated expression score regression. Biology Methods And Protocols 2023, 8: bpad024. PMID: 37901453, PMCID: PMC10599978, DOI: 10.1093/biomethods/bpad024.Peer-Reviewed Original ResearchExpression quantitative trait lociGenome-wide association studiesComplex traitsGene expression regulationGenetic association signalsQuantitative trait lociScore regressionDisease-associated variantsSNP annotationGene annotationExpression regulationGWAS resultsTrait lociTrait heritabilityEQTL effectsAssociation signalsGene expressionAssociation studiesGene effectsSNP effectsHuman diseasesHeritabilityTraitsBiological realityAnnotation
2022
Benchmarking automated cell type annotation tools for single-cell ATAC-seq data
Wang Y, Sun X, Zhao H. Benchmarking automated cell type annotation tools for single-cell ATAC-seq data. Frontiers In Genetics 2022, 13: 1063233. PMID: 36583014, PMCID: PMC9792779, DOI: 10.3389/fgene.2022.1063233.Peer-Reviewed Original ResearchCell type annotationScATAC-seq dataScRNA-seq dataScATAC-seqCell typesSingle-cell ATAC-seq dataAvailable single-cell datasetsRegulatory genomic regionsScRNA-seq data setsSingle-cell datasetsATAC-seq dataNovel cell typesSimilar cell typesSeurat v3Genomic regionsSequencing depthComplex tissuesDeep annotationAnnotationCellular compositionHuman tissuesType annotationsAnnotation toolAnnotation methodLabel transfer
2020
Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortium
2019
Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome
Lu Q, Zhao H. Improving Genetic Association Analysis through Integration of Functional Annotations of the Human Genome. 2019, 679-30. DOI: 10.1002/9781119487845.ch24.Peer-Reviewed Original ResearchGenome-wide association studiesFunctional annotationHuman genomeAssociation analysisAnnotation dataFunctional annotation dataPost-GWAS analysisSummary association statisticsGenetic association analysisGWAS findingsGWAS dataIntegrative analysisAssociation studiesComplex diseasesAssociation statisticsGenetic associationGenomeComputational methodsAnnotationTraitsDirect applicationStatistical powerMost diseasesInterpretable metricsTens of thousands
2017
Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease
Lu Q, Powles RL, Abdallah S, Ou D, Wang Q, Hu Y, Lu Y, Liu W, Li B, Mukherjee S, Crane PK, Zhao H. Systematic tissue-specific functional annotation of the human genome highlights immune-related DNA elements for late-onset Alzheimer’s disease. PLOS Genetics 2017, 13: e1006933. PMID: 28742084, PMCID: PMC5546707, DOI: 10.1371/journal.pgen.1006933.Peer-Reviewed Original ResearchConceptsTissue typesNon-coding elementsNon-coding genomeComplex human diseasesLate-onset Alzheimer's diseaseIndividual cell typesRelevant tissue typesGWAS traitsTranscriptomic annotationGenome annotationFunctional annotationDNA elementsHeritability enrichmentHuman genomeLarge international consortiaVariety of cellsGenomeHuman diseasesAnnotation dataCell typesGenetic variantsOrgan system categoriesComplex diseasesSimilar localizationAnnotationLeveraging functional annotations in genetic risk prediction for human complex diseases
Hu Y, Lu Q, Powles R, Yao X, Yang C, Fang F, Xu X, Zhao H. Leveraging functional annotations in genetic risk prediction for human complex diseases. PLOS Computational Biology 2017, 13: e1005589. PMID: 28594818, PMCID: PMC5481142, DOI: 10.1371/journal.pcbi.1005589.Peer-Reviewed Original ResearchMeSH KeywordsChromosome MappingData Interpretation, StatisticalData MiningDatabases, GeneticEpigenomicsGenetic Association StudiesGenetic Predisposition to DiseaseGenetic VariationGenome, HumanHumansLinkage DisequilibriumPolymorphism, Single NucleotideProportional Hazards ModelsQuantitative Trait LociRisk AssessmentConceptsGenome-wide association studiesFunctional annotationGenetic risk predictionDisease-associated genetic variantsLinkage disequilibriumIdentification of thousandsWide association studyHuman complex diseasesComplex diseasesGWAS summary statisticsHuman genetics researchAssociation studiesAnnoPredGenotype dataGenetic researchGenetic variantsRelevant variantsAnnotationDisequilibriumMost diseasesDiverse typesSummary statisticsVariantsBayesian frameworkPrecision medicine
2015
A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data
Lu Q, Hu Y, Sun J, Cheng Y, Cheung KH, Zhao H. A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data. Scientific Reports 2015, 5: 10576. PMID: 26015273, PMCID: PMC4444969, DOI: 10.1038/srep10576.Peer-Reviewed Original ResearchConceptsHuman genomeFunctional regionsStatistical frameworkAnnotation dataFunctional annotation dataWhole-genome annotationNon-coding regionsGenomic conservationHigh-throughput experimentsENCODE projectExperimental annotationsGenomeUnsupervised statistical learningFunctional potentialHuman geneticsStatistical learningComputational predictionsIntegrated analysisAnnotationAnnotation methodDiverse typesPowerful toolGeneticsMajor goalWeb server