2024
SANTO: a coarse-to-fine alignment and stitching method for spatial omics
Li H, Lin Y, He W, Han W, Xu X, Xu C, Gao E, Zhao H, Gao X. SANTO: a coarse-to-fine alignment and stitching method for spatial omics. Nature Communications 2024, 15: 6048. PMID: 39025895, PMCID: PMC11258319, DOI: 10.1038/s41467-024-50308-x.Peer-Reviewed Original ResearchJoint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation
Shen J, Zhang Y, Zhu Z, Cheng Y, Cai B, Zhao Y, Zhao H. Joint modeling of human cortical structure: Genetic correlation network and composite-trait genetic correlation. NeuroImage 2024, 297: 120739. PMID: 39009250, PMCID: PMC11367654, DOI: 10.1016/j.neuroimage.2024.120739.Peer-Reviewed Original ResearchGenetic networksComplex traitsGenetic architecture of complex traitsArchitecture of complex traitsGenome-wide association analysisGenetic correlationsGenetic architectureGenetic variationAssociation analysisGenetic basisPhenotypic similarityGenetic effectsFunctional variationRight hemisphereBrain regionsUK BiobankCortical thicknessTraitsCortical measuresCorrelation networkSignificant pairsHeritabilitySimilarity matrixBrainBrain lobesDNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism
Xiao C, Peng G, Conneely K, Zhao H, Felger J, Wommack E, Higgins K, Shin D, Saba N, Bruner D, Miller A. DNA methylation profiles of cancer-related fatigue associated with markers of inflammation and immunometabolism. Molecular Psychiatry 2024, 30: 76-83. PMID: 38977918, DOI: 10.1038/s41380-024-02652-z.Peer-Reviewed Original ResearchGene expressionMethylation lociAssociated with gene expressionHead and neck cancerDNA methylation profilesProtein markersLipid metabolismInvolvement of genesIllumina MethylationEPICDNA methylationRelevant gene expressionEpigenetic modificationsExpression pairsInflammatory markersInflammatory responseLociHead and neck cancer patientsAssociated with inflammatory markersGenesDNAMarkers of inflammationAssociated with fatigueExpressionMethylationPost-radiotherapyLeveraging 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 frameworkDecoding transcriptomic signatures of cysteine string protein alpha–mediated synapse maintenance
Wang N, Zhu B, Allnutt M, Grijalva R, Zhao H, Chandra S. Decoding transcriptomic signatures of cysteine string protein alpha–mediated synapse maintenance. Proceedings Of The National Academy Of Sciences Of The United States Of America 2024, 121: e2320064121. PMID: 38833477, PMCID: PMC11181078, DOI: 10.1073/pnas.2320064121.Peer-Reviewed Original ResearchConceptsSynapse maintenanceTranscriptional changesSynaptogenic adhesion moleculesGene ontology analysisKO miceKO brainMaintenance in vivoCell-cell interactionsGlial cellsSingle-nucleus transcriptomesOntology analysisCspADifferential expressionNeuron-glia interactionsAutophagy-related genesProtein AGenesCell typesNeurodegenerative diseasesInhibitory synapsesLittermate controlsSynaptic pathwaysAdhesion moleculesGlial responseSynapseLDER-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 studyTlr9 deficiency in B cells leads to obesity by promoting inflammation and gut dysbiosis
Wang P, Yang X, Zhang L, Sha S, Huang J, Peng J, Gu J, Pearson J, Hu Y, Zhao H, Wong F, Wang Q, Wen L. Tlr9 deficiency in B cells leads to obesity by promoting inflammation and gut dysbiosis. Nature Communications 2024, 15: 4232. PMID: 38762479, PMCID: PMC11102548, DOI: 10.1038/s41467-024-48611-8.Peer-Reviewed Original ResearchConceptsToll-like receptor 9Gut microbiotaGut microbial communityTransferred to germ-free miceB cellsGerm-free miceTLR9 deficiencyKO miceGene sequencesGerminal center B cellsMicrobial communitiesMarginal zone B cellsGut dysbiosisFollicular helper cellsSelf-DNAMetabolic homeostasisAssociated with increased frequencyPro-inflammatory stateFat tissue inflammationGutHigh-fat dietMicrobiotaHelper cellsT cellsControl miceStrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records
Lee H, Schwamm L, Sansing L, Kamel H, de Havenon A, Turner A, Sheth K, Krishnaswamy S, Brandt C, Zhao H, Krumholz H, Sharma R. StrokeClassifier: ischemic stroke etiology classification by ensemble consensus modeling using electronic health records. Npj Digital Medicine 2024, 7: 130. PMID: 38760474, PMCID: PMC11101464, DOI: 10.1038/s41746-024-01120-w.Peer-Reviewed Original ResearchElectronic health recordsWeighted F1MIMIC-IIIClinical decision support systemsMulti-class classificationNatural language processingMIMIC-III datasetHealth recordsMachine Learning ClassifiersDecision support systemArtificial intelligence toolsVascular neurologistsLearning classifiersBinary classificationCross-validation accuracyLanguage processingMeta-modelIntelligence toolsStroke prevention effortsAcute ischemic strokeStroke etiologySupport systemStroke etiology classificationClassification toolClassifierGlis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease
Zhang C, Rehman M, Tian X, Pei S, Gu J, Bell T, Dong K, Tham M, Cai Y, Wei Z, Behrens F, Jetten A, Zhao H, Lek M, Somlo S. Glis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease. Nature Communications 2024, 15: 3698. PMID: 38693102, PMCID: PMC11063051, DOI: 10.1038/s41467-024-48025-6.Peer-Reviewed Original ResearchConceptsMouse models of autosomal dominant polycystic kidney diseaseModel of autosomal dominant polycystic kidney diseasePolycystin signalingAutosomal dominant polycystic kidney diseasePolycystin-1Polycystic kidney diseaseTreat autosomal dominant polycystic kidney diseaseGlis2Primary ciliaKidney tubule cellsSignaling pathwayMouse modelDominant polycystic kidney diseasePotential therapeutic targetTranslatomeAntisense oligonucleotidesKidney diseasePolycystinMouse kidneyFunctional effectorsCyst formationTherapeutic targetInactivationFunctional targetPharmacological targetsGenome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder
Nievergelt C, Maihofer A, Atkinson E, Chen C, Choi K, Coleman J, Daskalakis N, Duncan L, Polimanti R, Aaronson C, Amstadter A, Andersen S, Andreassen O, Arbisi P, Ashley-Koch A, Austin S, Avdibegoviç E, Babić D, Bacanu S, Baker D, Batzler A, Beckham J, Belangero S, Benjet C, Bergner C, Bierer L, Biernacka J, Bierut L, Bisson J, Boks M, Bolger E, Brandolino A, Breen G, Bressan R, Bryant R, Bustamante A, Bybjerg-Grauholm J, Bækvad-Hansen M, Børglum A, Børte S, Cahn L, Calabrese J, Caldas-de-Almeida J, Chatzinakos C, Cheema S, Clouston S, Colodro-Conde L, Coombes B, Cruz-Fuentes C, Dale A, Dalvie S, Davis L, Deckert J, Delahanty D, Dennis M, Desarnaud F, DiPietro C, Disner S, Docherty A, Domschke K, Dyb G, Kulenović A, Edenberg H, Evans A, Fabbri C, Fani N, Farrer L, Feder A, Feeny N, Flory J, Forbes D, Franz C, Galea S, Garrett M, Gelaye B, Gelernter J, Geuze E, Gillespie C, Goleva S, Gordon S, Goçi A, Grasser L, Guindalini C, Haas M, Hagenaars S, Hauser M, Heath A, Hemmings S, Hesselbrock V, Hickie I, Hogan K, Hougaard D, Huang H, Huckins L, Hveem K, Jakovljević M, Javanbakht A, Jenkins G, Johnson J, Jones I, Jovanovic T, Karstoft K, Kaufman M, Kennedy J, Kessler R, Khan A, Kimbrel N, King A, Koen N, Kotov R, Kranzler H, Krebs K, Kremen W, Kuan P, Lawford B, Lebois L, Lehto K, Levey D, Lewis C, Liberzon I, Linnstaedt S, Logue M, Lori A, Lu Y, Luft B, Lupton M, Luykx J, Makotkine I, Maples-Keller J, Marchese S, Marmar C, Martin N, Martínez-Levy G, McAloney K, McFarlane A, McLaughlin K, McLean S, Medland S, Mehta D, Meyers J, Michopoulos V, Mikita E, Milani L, Milberg W, Miller M, Morey R, Morris C, Mors O, Mortensen P, Mufford M, Nelson E, Nordentoft M, Norman S, Nugent N, O’Donnell M, Orcutt H, Pan P, Panizzon M, Pathak G, Peters E, Peterson A, Peverill M, Pietrzak R, Polusny M, Porjesz B, Powers A, Qin X, Ratanatharathorn A, Risbrough V, Roberts A, Rothbaum A, Rothbaum B, Roy-Byrne P, Ruggiero K, Rung A, Runz H, Rutten B, de Viteri S, Salum G, Sampson L, Sanchez S, Santoro M, Seah C, Seedat S, Seng J, Shabalin A, Sheerin C, Silove D, Smith A, Smoller J, Sponheim S, Stein D, Stensland S, Stevens J, Sumner J, Teicher M, Thompson W, Tiwari A, Trapido E, Uddin M, Ursano R, Valdimarsdóttir U, Van Hooff M, Vermetten E, Vinkers C, Voisey J, Wang Y, Wang Z, Waszczuk M, Weber H, Wendt F, Werge T, Williams M, Williamson D, Winsvold B, Winternitz S, Wolf C, Wolf E, Xia Y, Xiong Y, Yehuda R, Young K, Young R, Zai C, Zai G, Zervas M, Zhao H, Zoellner L, Zwart J, deRoon-Cassini T, van Rooij S, van den Heuvel L, Stein M, Ressler K, Koenen K. Genome-wide association analyses identify 95 risk loci and provide insights into the neurobiology of post-traumatic stress disorder. Nature Genetics 2024, 56: 792-808. PMID: 38637617, PMCID: PMC11396662, DOI: 10.1038/s41588-024-01707-9.Peer-Reviewed Original ResearchConceptsMeta-analysis of genome-wide association studiesGenome-wide significant lociMulti-ancestry meta-analysisGenome-wide association analysisGenome-wide association studiesIndividuals of European ancestryPotential causal genesNative American ancestryMulti-omics approachPost-traumatic stress disorderAdmixed individualsSignificant lociRisk lociCausal genesAssociation studiesAssociation analysisFunctional genesTranscription factorsGenetic studiesAmerican ancestryEuropean ancestryAxon guidanceSynaptic structureLociGenesPolygenic Resistance to Blood Pressure Treatment and Stroke Risk (S6.001)
Huo S, Rivier C, Clocchiatti-Tuozzo S, Renedo D, Zhao H, De Havenon A, Sheth K, Falcone G. Polygenic Resistance to Blood Pressure Treatment and Stroke Risk (S6.001). Neurology 2024, 102 DOI: 10.1212/wnl.0000000000206202.Peer-Reviewed Original ResearchA mediation analysis framework based on variance component to remove genetic confounding effect
Dong Z, Zhao H, DeWan A. A mediation analysis framework based on variance component to remove genetic confounding effect. Journal Of Human Genetics 2024, 69: 301-309. PMID: 38528049, DOI: 10.1038/s10038-024-01232-x.Peer-Reviewed Original ResearchMediation analysis frameworkSingle nucleotide polymorphismsMediation analysisPleiotropic single nucleotide polymorphismsUK Biobank dataConfounding effectsTrait pairsBiobank dataIndividual-levelEpidemiological studiesCausal effectsGenetic signalsEstimated effectsLinear regressionNucleotide polymorphismsStandard errorData analysisGenetic correlationsPhenotypeIndirect effectsPleiotropyVariance componentsOutcomesRegressionCorrelates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder
Bigdeli T, Barr P, Rajeevan N, Graham D, Li Y, Meyers J, Gorman B, Peterson R, Sayward F, Radhakrishnan K, Natarajan S, Nielsen D, Wilkinson A, Malhotra A, Zhao H, Brophy M, Shi Y, O’Leary T, Gleason T, Przygodzki R, Pyarajan S, Muralidhar S, Gaziano J, Huang G, Concato J, Siever L, DeLisi L, Kimbrel N, Beckham J, Swann A, Kosten T, Fanous A, Aslan M, Harvey P. Correlates of suicidal behaviors and genetic risk among United States veterans with schizophrenia or bipolar I disorder. Molecular Psychiatry 2024, 29: 2399-2407. PMID: 38491344, DOI: 10.1038/s41380-024-02472-1.Peer-Reviewed Original ResearchBipolar I disorderSuicidal behaviorElectronic health recordsPolygenic scoresVeterans Health AdministrationSelf-reported SBColumbia-Suicide Severity Rating ScaleBipolar I disorder patientsCorrelates of suicidal behaviorClasses of psychotropic medicationsSelf-injurious behaviorHealth recordsSeverity Rating ScaleDiagnosed mental illnessAssociated with clinical variablesElectronic health record codesEHR domainDepressive disorderC-SSRSLifetime diagnosisSubstance-relatedPsychotropic medicationsSuicidal ideationExternalizing behaviorsSuicide attemptsStatistical methods for assessing the effects of de novo variants on birth defects
Xie Y, Wu R, Li H, Dong W, Zhou G, Zhao H. Statistical methods for assessing the effects of de novo variants on birth defects. Human Genomics 2024, 18: 25. PMID: 38486307, PMCID: PMC10938830, DOI: 10.1186/s40246-024-00590-z.Peer-Reviewed Original ResearchConceptsDe novo variantsAnalyzed de novo variantsDevelopment of next-generation sequencing technologiesNext-generation sequencing technologiesSequencing technologiesImprove statistical powerGenetic heterogeneitySequenced samplesStatistical powerBirth defectsDiseased individualsLow occurrenceCongenital heart diseaseVariantsGenesDeleterious effectsSequenceGeneral workflowStatistical methodsA genotyping array for the globally invasive vector mosquito, Aedes albopictus
Cosme L, Corley M, Johnson T, Severson D, Yan G, Wang X, Beebe N, Maynard A, Bonizzoni M, Khorramnejad A, Martins A, Lima J, Munstermann L, Surendran S, Chen C, Maringer K, Wahid I, Mukherjee S, Xu J, Fontaine M, Estallo E, Stein M, Livdahl T, Scaraffia P, Carter B, Mogi M, Tuno N, Mains J, Medley K, Bowles D, Gill R, Eritja R, González-Obando R, Trang H, Boyer S, Abunyewa A, Hackett K, Wu T, Nguyễn J, Shen J, Zhao H, Crawford J, Armbruster P, Caccone A. A genotyping array for the globally invasive vector mosquito, Aedes albopictus. Parasites & Vectors 2024, 17: 106. PMID: 38439081, PMCID: PMC10910840, DOI: 10.1186/s13071-024-06158-z.Peer-Reviewed Original ResearchConceptsWhole-genome sequencingLow-coverage whole-genome sequencingSNP chipRepetitive elementsGenomic analysisNative rangePatterns of genomic variationWhole-genome sequencing dataSNP chip genotypesPopulation genomic analysesProtein-coding genesLevels of admixtureOrigin of invasionNon-coding regionsPercentage of repetitive elementsGenotyping of samplesChip genotypesGenetic clustersAncestry analysisGenomic variationGenotyping arraysGenotyping platformsMendelian genesGenetic variationGenotyping methodsUsing clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank
Hu J, Ye Y, Zhou G, Zhao H. Using clinical and genetic risk factors for risk prediction of 8 cancers in the UK Biobank. JNCI Cancer Spectrum 2024, 8: pkae008. PMID: 38366150, PMCID: PMC10919929, DOI: 10.1093/jncics/pkae008.Peer-Reviewed Original ResearchPolygenic risk scoresUK BiobankCancer riskClinical risk factorsRisk of breast cancerRisk factorsPolygenic risk score modelHigh risk of developing cancerRisk of developing cancerLate-onset patientsRisk predictionClinical variablesHigh-risk individualsCox proportional hazards modelsProportional hazards modelGenetic risk factorsBaseline traitsClinical risk modelRisk scoreEarly-onset patientsHazards modelLate-onset groupEarly-onset groupBreast cancerHigh riskCorrelates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort
Barr P, Bigdeli T, Meyers J, Peterson R, Sanchez-Roige S, Mallard T, Dick D, Harden K, Wilkinson A, Graham D, Nielsen D, Swann A, Lipsky R, Kosten T, Aslan M, Harvey P, Kimbrel N, Beckham J, Aslan M, Antonelli M, de Asis M, Bauer M, Brophy M, Concato J, Cunningham F, Freedman R, Gaziano M, Gleason T, Harvey P, Huang G, Kelsoe J, Kosten T, Lehner T, Lohr J, Marder S, Miller P, O Leary T, Patterson T, Peduzzi P, Przygodski R, Siever L, Sklar P, Strakowski S, Zhao H, Fanous A, Farwell W, Malhorta A, Mane S, Palacios P, Bigdeli T, Corsey M, Zaluda L, Johnson J, Sueiro M, Cavaliere D, Jeanpaul V, Maffucci A, Mancini L, Deen J, Muldoon G, Whitbourne S, Canive J, Adamson L, Calais L, Fuldauer G, Kushner R, Toney G, Lackey M, Mank A, Mahdavi N, Villarreal G, Muly E, Amin F, Dent M, Wold J, Fischer B, Elliott A, Felix C, Gill G, Parker P, Logan C, McAlpine J, DeLisi L, Reece S, Hammer M, Agbor-Tabie D, Goodson W, Aslam M, Grainger M, Richtand N, Rybalsky A, Al Jurdi R, Boeckman E, Natividad T, Smith D, Stewart M, Torres S, Zhao Z, Mayeda A, Green A, Hofstetter J, Ngombu S, Scott M, Strasburger A, Sumner J, Paschall G, Mucciarelli J, Owen R, Theus S, Tompkins D, Potkin S, Reist C, Novin M, Khalaghizadeh S, Douyon R, Kumar N, Martinez B, Sponheim S, Bender T, Lucas H, Lyon A, Marggraf M, Sorensen L, Surerus C, Sison C, Amato J, Johnson D, Pagan-Howard N, Adler L, Alerpin S, Leon T, Mattocks K, Araeva N, Sullivan J, Suppes T, Bratcher K, Drag L, Fischer E, Fujitani L, Gill S, Grimm D, Hoblyn J, Nguyen T, Nikolaev E, Shere L, Relova R, Vicencio A, Yip M, Hurford I, Acheampong S, Carfagno G, Haas G, Appelt C, Brown E, Chakraborty B, Kelly E, Klima G, Steinhauer S, Hurley R, Belle R, Eknoyan D, Johnson K, Lamotte J, Granholm E, Bradshaw K, Holden J, Jones R, Le T, Molina I, Peyton M, Ruiz I, Sally L, Tapp A, Devroy S, Jain V, Kilzieh N, Maus L, Miller K, Pope H, Wood A, Meyer E, Givens P, Hicks P, Justice S, McNair K, Pena J, Tharp D, Davis L, Ban M, Cheatum L, Darr P, Grayson W, Munford J, Whitfield B, Wilson E, Melnikoff S, Schwartz B, Tureson M, D Souza D, Forselius K, Ranganathan M, Rispoli L, Sather M, Colling C, Haakenson C, Kruegar D, Muralidhar S, Ramoni R, Breeling J, Chang K, O Donnell C, Tsao P, Moser J, Brewer J, Warren S, Argyres D, Stevens B, Humphries D, Do N, Shayan S, Nguyen X, Pyarajan S, Cho K, Hauser E, Sun Y, Wilson P, McArdle R, Dellitalia L, Harley J, Whittle J. Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort. JAMA Psychiatry 2024, 81: 188-197. PMID: 37938835, PMCID: PMC10633411, DOI: 10.1001/jamapsychiatry.2023.4141.Peer-Reviewed Original ResearchSubstance use disordersPolygenic risk scoresMillion Veteran ProgramCorrelates of riskElectronic health recordsPsychiatric problemsComorbid psychiatric problemsViral hepatitis C.Chronic airway obstructionHealth recordsElectronic health record dataUS veteran populationMillion Veteran Program cohortCommon etiologic pathwayHealth care centersHealth record dataInternational Statistical ClassificationSuicide-related behaviorsAirway obstructionCohort studyHepatitis C.Recent genome-wide association studiesLiver diseaseUS veteransCare centerEstimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease
Su C, Zhang J, Zhao H. Estimating Cell-Type-Specific Gene Co-Expression Networks from Bulk Gene Expression Data with an Application to Alzheimer’s Disease. Journal Of The American Statistical Association 2024, 119: 811-824. PMID: 39280354, PMCID: PMC11394578, DOI: 10.1080/01621459.2023.2297467.Peer-Reviewed Original ResearchIntegration 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 ResearchConceptsExpression 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 variantsPhenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health
Zekavat S, Jorshery S, Rauscher F, Horn K, Sekimitsu S, Koyama S, Nguyen T, Costanzo M, Jang D, Burtt N, Kühnapfel A, Shweikh Y, Ye Y, Raghu V, Zhao H, Ghassemi M, Elze T, Segrè A, Wiggs J, Del Priore L, Scholz M, Wang J, Natarajan P, Zebardast N. Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health. Science Translational Medicine 2024, 16: eadg4517. PMID: 38266105, DOI: 10.1126/scitranslmed.adg4517.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesRetinal layer thicknessPhotoreceptor segmentsOptical coherence tomographyRetinal layersUK Biobank participantsLIFE-Adult-StudyInherited genetic lociGenome-wide associationGanglion cell complex layerRetinal optical coherence tomography imagesRetinal nerve fiber layerAge-related macular degenerationBiobank participantsEye careNerve fiber layerOptical coherence tomography imagesIncident mortalityMacular OCT imagesLIFE-AdultIndependent associationsAssociation studiesSystemic healthGenetic associationGenome-wide analysis
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