2020
Leveraging functional annotation to identify genes associated with complex diseases
Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, Lu Q, Zhao H. Leveraging functional annotation to identify genes associated with complex diseases. PLOS Computational Biology 2020, 16: e1008315. PMID: 33137096, PMCID: PMC7660930, DOI: 10.1371/journal.pcbi.1008315.Peer-Reviewed Original ResearchMeSH KeywordsEpigenesis, GeneticGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMolecular Sequence AnnotationPolymorphism, Single NucleotideQuantitative Trait LociConceptsExpression quantitative trait lociComplex traitsNovel lociIdentification of eQTLGene expressionTranscriptome-wide association study methodLinkage disequilibriumQuantitative trait lociAssociation study methodsCombined Annotation Dependent Depletion (CADD) scoresAnnotation-dependent depletion scoreExpression levelsDisease-associated genesEpigenetic annotationsEpigenetic informationFunctional annotationTrait lociGenetic variationGenesPrevious GWASLociGenetic effectsTraitsComplex diseasesGWAS
2017
A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics
Lu Q, Li B, Ou D, Erlendsdottir M, Powles RL, Jiang T, Hu Y, Chang D, Jin C, Dai W, He Q, Liu Z, Mukherjee S, Crane PK, Zhao H. A Powerful Approach to Estimating Annotation-Stratified Genetic Covariance via GWAS Summary Statistics. American Journal Of Human Genetics 2017, 101: 939-964. PMID: 29220677, PMCID: PMC5812911, DOI: 10.1016/j.ajhg.2017.11.001.Peer-Reviewed Original ResearchConceptsGWAS summary statisticsGenome-wide association studiesComplex traitsSingle nucleotide polymorphismsGenetic covarianceGenetic architectureLarge-scale genome-wide association studiesStrong genetic covarianceDistinct genetic architecturesSignificant genetic covarianceLate-onset Alzheimer's diseaseHigh minor allele frequencyGenetic profileFunctional genomeAmyotrophic lateral sclerosisMajor neurodegenerative diseasesMinor allele frequencyGenetic basisAssociation studiesTraitsLarge-scale inferenceSummary statisticsBiological interpretabilityAllele frequenciesNeurodegenerative diseasesSystematic 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 ResearchMeSH KeywordsAlzheimer DiseaseDatabases, GeneticEpigenomicsGenome, HumanGenome-Wide Association StudyHumansMolecular Sequence AnnotationOrgan SpecificityPolymorphism, Single NucleotideTranscriptomeConceptsTissue 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 localizationAnnotation
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
2014
GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics 2014, 10: e1004787. PMID: 25393678, PMCID: PMC4230845, DOI: 10.1371/journal.pgen.1004787.Peer-Reviewed Original ResearchMeSH KeywordsDatabases, GeneticGenetic Diseases, InbornGenetic PleiotropyGenome-Wide Association StudyGenotypeHumansModels, StatisticalMolecular Sequence AnnotationPhenotypePolymorphism, Single NucleotideConceptsGenome-wide association studiesFunctional annotationGWAS datasetsAnnotation informationStatistical approachMultiple GWAS datasetsGenome-wide markersPowerful statistical methodsSingle-phenotype analysisCentral nervous system genesRisk variantsNervous system genesGenotype-Tissue Expression (GTEx) databaseComplex diseasesGWAS data setsSignificant pleiotropic effectsCommon risk basisDifferent complex diseasesDNase-seq dataCell linesStatistical inferenceGenetic architectureGWAS hitsGWAS resultsNovel statistical approach