Featured Publications
Integration 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 ResearchMeSH KeywordsChromosome MappingGenome-Wide Association StudyLinkage DisequilibriumPhenotypePolymorphism, Single NucleotideQuantitative Trait LociConceptsExpression 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 variantsLeveraging 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 ResearchMeSH KeywordsGenome-Wide Association StudyHumansLinkage DisequilibriumModels, GeneticMultifactorial InheritancePhenotypePolymorphism, Single NucleotideConceptsLinkage 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 statisticsRelatednessA fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics
Zhou G, Zhao H. A fast and robust Bayesian nonparametric method for prediction of complex traits using summary statistics. PLOS Genetics 2021, 17: e1009697. PMID: 34310601, PMCID: PMC8341714, DOI: 10.1371/journal.pgen.1009697.Peer-Reviewed Original ResearchConceptsBayesian nonparametric methodParameter tuningNonparametric methodsExternal reference panelSummary statisticsComputational resourcesParallel algorithmBlock structureExplicit assumptionsExisting methodsStatisticsSeparate validation dataAccurate risk prediction modelsAssumptionPrediction modelPredictionAlgorithm
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 ResearchConceptsHuman 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 study
2023
Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies
Zhang C, Zhang Y, Zhang Y, Zhao H. Benchmarking of local genetic correlation estimation methods using summary statistics from genome-wide association studies. Briefings In Bioinformatics 2023, 24: bbad407. PMID: 37974509, PMCID: PMC10654488, DOI: 10.1093/bib/bbad407.Peer-Reviewed Original Research
2021
Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program
Stein MB, Levey DF, Cheng Z, Wendt FR, Harrington K, Pathak GA, Cho K, Quaden R, Radhakrishnan K, Girgenti MJ, Ho YA, Posner D, Aslan M, Duman RS, Zhao H, Polimanti R, Concato J, Gelernter J. Genome-wide association analyses of post-traumatic stress disorder and its symptom subdomains in the Million Veteran Program. Nature Genetics 2021, 53: 174-184. PMID: 33510476, PMCID: PMC7972521, DOI: 10.1038/s41588-020-00767-x.Peer-Reviewed Original ResearchConceptsGenome-wide association analysisAssociation analysisMillion Veteran ProgramGenomic structural equation modelingSignificant lociGenetic varianceGene expressionDrug repositioning candidatesBiological coherenceVeteran ProgramMultiple testing correctionSymptom phenotypeLociRepositioning candidatesAfrican ancestryHeritabilityPhenotypeAncestryExpressionPTSD symptom factorsRegionSubdomainsEnrichment
2020
Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies
Munn‐Chernoff M, Johnson EC, Chou Y, Coleman JRI, Thornton LM, Walters RK, Yilmaz Z, Baker JH, Hübel C, Gordon S, Medland SE, Watson HJ, Gaspar HA, Bryois J, Hinney A, Leppä VM, Mattheisen M, Ripke S, Yao S, Giusti‐Rodríguez P, Hanscombe KB, Adan RAH, Alfredsson L, Ando T, Andreassen OA, Berrettini WH, Boehm I, Boni C, Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone RD, Courtet P, Crow S, Crowley JJ, Danner UN, Davis OSP, de Zwaan M, Dedoussis G, Degortes D, DeSocio JE, Dick DM, Dikeos D, Dina C, Dmitrzak‐Weglarz M, Docampo E, Duncan LE, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández‐Aranda F, Fichter MM, Fischer K, Föcker M, Foretova L, Forstner AJ, Forzan M, Franklin CS, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder SG, Herms S, Herpertz‐Dahlmann B, Herzog W, Huckins LM, Hudson JI, Imgart H, Inoko H, Janout V, Jiménez‐Murcia S, Julià A, Kalsi G, Kaminská D, Karhunen L, Karwautz A, Kas MJH, Kennedy JL, Keski‐Rahkonen A, Kiezebrink K, Kim Y, Klump KL, Knudsen GPS, La Via MC, Le Hellard S, Levitan RD, Li D, Lilenfeld L, Lin BD, Lissowska J, Luykx J, Magistretti PJ, Maj M, Mannik K, Marsal S, Marshall CR, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone AM, Monteleone P, Nacmias B, Navratilova M, Ntalla I, O'Toole JK, Ophoff RA, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn‐Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer SW, Schmidt U, Schork NJ, Schosser A, Seitz J, Slachtova L, Slagboom PE, Landt M, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz JP, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz‐Nwafor M, Tziouvas K, van Elburg A, van Furth E, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen AW, Boden JM, Brandt H, Crawford S, Halmi KA, Horwood LJ, Johnson C, Kaplan AS, Kaye WH, Mitchell J, Olsen CM, Pearson JF, Pedersen NL, Strober M, Werge T, Whiteman DC, Woodside DB, Grove J, Henders AK, Larsen JT, Parker R, Petersen LV, Jordan J, Kennedy MA, Birgegård A, Lichtenstein P, Norring C, Landén M, Mortensen PB, Polimanti R, McClintick JN, Adkins AE, Aliev F, Bacanu S, Batzler A, Bertelsen S, Biernacka JM, Bigdeli TB, Chen L, Clarke T, Degenhardt F, Docherty AR, Edwards AC, Foo JC, Fox L, Frank J, Hack LM, Hartmann AM, Hartz SM, Heilmann‐Heimbach S, Hodgkinson C, Hoffmann P, Hottenga J, Konte B, Lahti J, Lahti‐Pulkkinen M, Lai D, Ligthart L, Loukola A, Maher BS, Mbarek H, McIntosh AM, McQueen MB, Meyers JL, Milaneschi Y, Palviainen T, Peterson RE, Ryu E, Saccone NL, Salvatore JE, Sanchez‐Roige S, Schwandt M, Sherva R, Streit F, Strohmaier J, Thomas N, Wang J, Webb BT, Wedow R, Wetherill L, Wills AG, Zhou H, Boardman JD, Chen D, Choi D, Copeland WE, Culverhouse RC, Dahmen N, Degenhardt L, Domingue BW, Frye MA, Gäebel W, Hayward C, Ising M, Keyes M, Kiefer F, Koller G, Kramer J, Kuperman S, Lucae S, Lynskey MT, Maier W, Mann K, Männistö S, Müller‐Myhsok B, Murray AD, Nurnberger JI, Preuss U, Räikkönen K, Reynolds MD, Ridinger M, Scherbaum N, Schuckit MA, Soyka M, Treutlein J, Witt SH, Wodarz N, Zill P, Adkins DE, Boomsma DI, Bierut LJ, Brown SA, Bucholz KK, Costello EJ, de Wit H, Diazgranados N, Eriksson JG, Farrer LA, Foroud TM, Gillespie NA, Goate AM, Goldman D, Grucza RA, Hancock DB, Harris KM, Hesselbrock V, Hewitt JK, Hopfer CJ, Iacono WG, Johnson EO, Karpyak VM, Kendler KS, Kranzler HR, Krauter K, Lind PA, McGue M, MacKillop J, Madden PAF, Maes HH, Magnusson PKE, Nelson EC, Nöthen MM, Palmer AA, Penninx BWJH, Porjesz B, Rice JP, Rietschel M, Riley BP, Rose RJ, Shen P, Silberg J, Stallings MC, Tarter RE, Vanyukov MM, Vrieze S, Wall TL, Whitfield JB, Zhao H, Neale BM, Wade TD, Heath AC, Montgomery GW, Martin NG, Sullivan PF, Kaprio J, Breen G, Gelernter J, Edenberg HJ, Bulik CM, Agrawal A. Shared genetic risk between eating disorder‐ and substance‐use‐related phenotypes: Evidence from genome‐wide association studies. Addiction Biology 2020, 26: e12880. PMID: 32064741, PMCID: PMC7429266, DOI: 10.1111/adb.12880.Peer-Reviewed Original ResearchLeveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies
Song S, Jiang W, Hou L, Zhao H. Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies. PLOS Computational Biology 2020, 16: e1007565. PMID: 32045423, PMCID: PMC7039528, DOI: 10.1371/journal.pcbi.1007565.Peer-Reviewed Original ResearchConceptsEffect size distributionClass of methodsReal data applicationOnly summary statisticsTheoretical resultsSummary statisticsExtensive simulation resultsLD informationSimulation resultsData applicationsFirst methodImportant problemOptimal propertiesGenetic risk predictionAccurate predictionPrediction accuracyStandard PRSStatisticsPrediction method
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 diseasesJoint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction
Hu Y, Lu Q, Liu W, Zhang Y, Li M, Zhao H. Joint modeling of genetically correlated diseases and functional annotations increases accuracy of polygenic risk prediction. PLOS Genetics 2017, 13: e1006836. PMID: 28598966, PMCID: PMC5482506, DOI: 10.1371/journal.pgen.1006836.Peer-Reviewed Original ResearchLeveraging 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
2001
Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test
Zhang S, Zhang K, Li J, Sun F, Zhao H. Test of Association for Quantitative Traits in General Pedigrees: The Quantitative Pedigree Disequilibrium Test. Genetic Epidemiology 2001, 21: s370-s375. PMID: 11793701, DOI: 10.1002/gepi.2001.21.s1.s370.Peer-Reviewed Original ResearchConceptsQuantitative pedigree disequilibrium testPedigree disequilibrium testQuantitative traitsTraits of interestGenetic Analysis Workshop 12Disequilibrium testGeneral pedigreesSequence dataCandidate genesGenetic markersGenetic linkageQualitative traitsLinkage disequilibriumTraitsLarge pedigreePresence of linkagePedigreeStatistical methodsFamilyNuclear familiesTests of associationGenesUnrelated nuclear familiesLinkageDisequilibriumThe Power of Transmission Disequilibrium Tests for Quantitative Traits
Li J, Wang D, Dong J, Jiang R, Zhang K, Zhang S, Zhao H, Sun F. The Power of Transmission Disequilibrium Tests for Quantitative Traits. Genetic Epidemiology 2001, 21: s632-s637. PMID: 11793752, DOI: 10.1002/gepi.2001.21.s1.s632.Peer-Reviewed Original Research
2000
Transmission/Disequilibrium Tests Using Multiple Tightly Linked Markers
Zhao H, Zhang S, Merikangas K, Trixler M, Wildenauer D, Sun F, Kidd K. Transmission/Disequilibrium Tests Using Multiple Tightly Linked Markers. American Journal Of Human Genetics 2000, 67: 936-946. PMID: 10968775, PMCID: PMC1287895, DOI: 10.1086/303073.Peer-Reviewed Original ResearchHaplotypes and Linkage Disequilibrium at the Phenylalanine Hydroxylase Locus, PAH, in a Global Representation of Populations
Kidd J, Pakstis A, Zhao H, Lu R, Okonofua F, Odunsi A, Grigorenko E, Tamir B, Friedlaender J, Schulz L, Parnas J, Kidd K. Haplotypes and Linkage Disequilibrium at the Phenylalanine Hydroxylase Locus, PAH, in a Global Representation of Populations. American Journal Of Human Genetics 2000, 66: 1882-1899. PMID: 10788337, PMCID: PMC1378054, DOI: 10.1086/302952.Peer-Reviewed Original ResearchLinkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation model
Zhang S, Zhao H. Linkage disequilibrium mapping in populations of variable size using the decay of haplotype sharing and a stepwise‐mutation model. Genetic Epidemiology 2000, 19: s99-s105. PMID: 11055377, DOI: 10.1002/1098-2272(2000)19:1+<::aid-gepi15>3.0.co;2-1.Peer-Reviewed Original Research
1999
The Interpretation of the Parameters in the Transmission/Disequilibrium Test
Zhao H. The Interpretation of the Parameters in the Transmission/Disequilibrium Test. American Journal Of Human Genetics 1999, 64: 326-328. PMID: 9915979, PMCID: PMC1377738, DOI: 10.1086/302208.Peer-Reviewed Original Research
1998
A global survey of haplotype frequencies and linkage disequilibrium at the DRD2 locus
Kidd K, Morar B, Castiglione C, Zhao H, Pakstis A, Speed W, Bonne-Tamir B, Lu R, Goldman D, Lee C, Nam Y, Grandy D, Jenkins T, Kidd J. A global survey of haplotype frequencies and linkage disequilibrium at the DRD2 locus. Human Genetics 1998, 103: 211-227. PMID: 9760208, DOI: 10.1007/s004390050809.Peer-Reviewed Original Research