2025
GWAS meta-meta-analysis and related analyses revealed a shared genetic background between ADHD and narcolepsy
Modestino E, Sharafshah A, Lewandrowski K, Carey E, Mohankumar K, Thanos P, Pinhasov A, Bowirrat A, Baron D, Gold M, Elman I, Gardner E, Fuehrlein B, Zeine F, Jafari N, Dennen C, Lewandrowski A, Badgaiyan R, Blum K. GWAS meta-meta-analysis and related analyses revealed a shared genetic background between ADHD and narcolepsy. Academia Molecular Biology And Genomics 2025, 2 DOI: 10.20935/acadmolbiogen7751.Peer-Reviewed Original ResearchProtein-protein interactionsGene listsAttention-deficit hyperactivity disorderReward Deficiency SyndromeGWAS meta-analysesGWAS Catalog databasePGx analysisPGx dataFamily genesSystems biologyGenetic basisGWASShared genesSusceptibility to narcolepsyGenesGenetic backgroundRBFOX1PGxMeta-meta-analysisComprehensive data miningDopaminergic reward systemTherapeutic targetFOXP2Potential endophenotypesAddictive behaviorsEnhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS
Goovaerts S, Naqvi S, Hoskens H, Herrick N, Yuan M, Shriver M, Shaffer J, Walsh S, Weinberg S, Wysocka J, Claes P. Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS. Communications Biology 2025, 8: 439. PMID: 40087503, PMCID: PMC11909261, DOI: 10.1038/s42003-025-07875-6.Peer-Reviewed Original ResearchConceptsCranial vault shapeVault shapeGenomic lociGenetic discovery effortsSNP discoveryCraniofacial developmentGenetic architectureGWAS dataGWAS studiesTranscription factorsGenetic studiesCranial vaultGenetic understandingShape variationSignaling pathwayBrain shapeExperimental biologyBrain shape variationCraniofacial complexFDR methodLociDiscovery effortsFacial shapeWealth of knowledgeGWASImplications of gene × environment interactions in post-traumatic stress disorder risk and treatment
Seah C, Sidamon-Eristoff A, Huckins L, Brennand K. Implications of gene × environment interactions in post-traumatic stress disorder risk and treatment. Journal Of Clinical Investigation 2025, 135: e185102. PMID: 40026250, PMCID: PMC11870735, DOI: 10.1172/jci185102.Peer-Reviewed Original ResearchConceptsPost-traumatic stress disorderGene x environment interactionsGenetic component of riskLimitations of genetic studiesTreating post-traumatic stress disorderExposure to traumatic stressPost-traumatic stress disorder riskInteraction of traumaGenetic screeningGenetic studiesGenetic componentEnvironment interactionMolecular mechanismsStress disorderPTSD riskTraumatic exposureTraumatic stressTraumatic experiencesDisorder riskGenetic factorsNovel therapeuticsBiological mechanismsGWASGeneral populationGenes
2024
Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population
Vanneste M, Hoskens H, Goovaerts S, Matthews H, Devine J, Aponte J, Cole J, Shriver M, Marazita M, Weinberg S, Walsh S, Richmond S, Klein O, Spritz R, Peeters H, Hallgrímsson B, Claes P. Syndrome-informed phenotyping identifies a polygenic background for achondroplasia-like facial variation in the general population. Nature Communications 2024, 15: 10458. PMID: 39622794, PMCID: PMC11612227, DOI: 10.1038/s41467-024-54839-1.Peer-Reviewed Original ResearchConceptsMultivariate GWASMendelian phenotypesComplex traitsPolygenic backgroundMendelian disordersPolygenic basisGenetic variationGenetic variantsGenetic intersectionAchondroplasia phenotypePhenotypic spectrumPhenotypeGenesSkeletal developmentShape axesCraniofacial shapeGWASFacial variationsThree-dimensional facial scansGeneral populationTraitsControl scoresVariationControl samplesVariantsSupervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression
Hodgson L, Li Y, Iturria-Medina Y, Stratton J, Wolf G, Krishnaswamy S, Bennett D, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression. Communications Biology 2024, 7: 591. PMID: 38760483, PMCID: PMC11101463, DOI: 10.1038/s42003-024-06273-8.Peer-Reviewed Original ResearchConceptsGene programAlzheimer's diseaseLate-onset Alzheimer's diseaseAD risk lociCell type-specificSingle-nucleus RNA sequencingRisk lociAD brainAlzheimer's disease progressionSnRNA-seqRNA sequencingAD pathophysiologySignaling cascadesTranscriptome modulationProgressive neurodegenerative diseaseCell-typeGWASNeurodegenerative diseasesNeuronal lossGlial cellsTranscriptomeLociGenesPseudo-trajectoriesDisease progressionDiversity in genetic risk of recurrent stroke: a genome-wide association study meta-analysis
Aldridge C, Armstrong N, Sunmonu N, Becker C, Palakshappa D, Lindgren A, Pedersen A, Stanne T, Jern C, Maguire J, Hsu F, Keene K, Sale M, Irvin M, Worrall B. Diversity in genetic risk of recurrent stroke: a genome-wide association study meta-analysis. Frontiers In Stroke 2024, 3: 1338636. DOI: 10.3389/fstro.2024.1338636.Peer-Reviewed Original ResearchPolygenic risk scoresAncestral groupsGenome-wide significant variantsGenome-wide association study meta-analysisGenetic riskRecurrent stroke riskGWAS modelsEffective allelesRisk of incident strokeGenetic lociAssociated with incident ischemic strokeRecurrent strokeStudy meta-analysisSignificant variantsStroke riskIncident ischemic strokeGenetic studiesBiological insightsEuropean ancestryGenetic associationGenesMYH11 geneBiological relevanceLong-term disabilityGWAS
2023
Genetic associations with longevity are on average stronger in females than in males
Zeng Y, Chen H, Liu X, Song Z, Yao Y, Lei X, Lv X, Cheng L, Chen Z, Bai C, Yin Z, Lv Y, Lu J, Li J, Land K, Yashin A, O'Rand A, Sun L, Yang Z, Tao W, Gu J, Gottschalk W, Tan Q, Christensen K, Hesketh T, Tian X, Yang H, Egidi V, Caselli G, Robine J, Wang H, Shi X, Vaupel J, Lutz M, Nie C, Min J. Genetic associations with longevity are on average stronger in females than in males. Heliyon 2023, 10: e23691. PMID: 38192771, PMCID: PMC10772631, DOI: 10.1016/j.heliyon.2023.e23691.Peer-Reviewed Original ResearchGenome-wide complex trait analysisGenome-wide association study datasetGenetic associationSex-specific genetic variantsSex-specific genesGenetic variantsComplex trait analysisReplication analysisPolygenic risk score analysisIndividual genesGenetic datasetsCandidate genesBiological functionsTrait analysisRisk score analysisGenesMiddle-age controlsLongevityIndependent datasetsLongevity StudyUnderlying mechanismGWASFuture investigationsVariantsSNPsGWAs Identify DNA Variants Influencing Eyebrow Thickness Variation in Europeans and Across Continental Populations
Peng F, Xiong Z, Zhu G, Hysi P, Eller R, Wu S, Adhikari K, Chen Y, Li Y, Gonzalez-José R, Schüler-Faccini L, Bortolini M, Acuña-Alonzo V, Canizales-Quinteros S, Gallo C, Poletti G, Bedoya G, Rothhammer F, Uitterlinden A, Ikram M, Nijsten T, Ruiz-Linares A, Wang S, Walsh S, Spector T, Martin N, Kayser M, Liu F, Consortium I. GWAs Identify DNA Variants Influencing Eyebrow Thickness Variation in Europeans and Across Continental Populations. Journal Of Investigative Dermatology 2023, 143: 1317-1322.e11. PMID: 37085041, DOI: 10.1016/j.jid.2022.11.026.Peer-Reviewed Original ResearchLeveraging GWAS data derived from a large cooperative group trial to assess the risk of taxane-induced peripheral neuropathy (TIPN) in patients being treated for breast cancer: Part 2—functional implications of a SNP cluster associated with TIPN risk in patients being treated for breast cancer
Lustberg M, Wu X, Fernández-Martínez J, de Andrés-Galiana E, Philips S, Leibowitz J, Schneider B, Sonis S. Leveraging GWAS data derived from a large cooperative group trial to assess the risk of taxane-induced peripheral neuropathy (TIPN) in patients being treated for breast cancer: Part 2—functional implications of a SNP cluster associated with TIPN risk in patients being treated for breast cancer. Supportive Care In Cancer 2023, 31: 178. PMID: 36809570, PMCID: PMC11344472, DOI: 10.1007/s00520-023-07617-6.Peer-Reviewed Original ResearchConceptsGWAS dataSNP clustersFunctional analysisGO termsNon-protein coding genesGene Ontology termsGene Set Enrichment AnalysisCluster of SNPsNervous system developmentCoding genesRetinoic acid bindingOntology termsProtein kinase C bindingEnrichment analysisMetabolic processesGenesAcid bindingGlycosyltransferase activitySNPsPathological implicationsGWASC bindingGene signaturePhenotypeTransferase activityMulti-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing
Chen F, Wang X, Jang S, Quach B, Weissenkampen J, Khunsriraksakul C, Yang L, Sauteraud R, Albert C, Allred N, Arnett D, Ashley-Koch A, Barnes K, Barr R, Becker D, Bielak L, Bis J, Blangero J, Boorgula M, Chasman D, Chavan S, Chen Y, Chuang L, Correa A, Curran J, David S, Fuentes L, Deka R, Duggirala R, Faul J, Garrett M, Gharib S, Guo X, Hall M, Hawley N, He J, Hobbs B, Hokanson J, Hsiung C, Hwang S, Hyde T, Irvin M, Jaffe A, Johnson E, Kaplan R, Kardia S, Kaufman J, Kelly T, Kleinman J, Kooperberg C, Lee I, Levy D, Lutz S, Manichaikul A, Martin L, Marx O, McGarvey S, Minster R, Moll M, Moussa K, Naseri T, North K, Oelsner E, Peralta J, Peyser P, Psaty B, Rafaels N, Raffield L, Reupena M, Rich S, Rotter J, Schwartz D, Shadyab A, Sheu W, Sims M, Smith J, Sun X, Taylor K, Telen M, Watson H, Weeks D, Weir D, Yanek L, Young K, Young K, Zhao W, Hancock D, Jiang B, Vrieze S, Liu D. Multi-ancestry transcriptome-wide association analyses yield insights into tobacco use biology and drug repurposing. Nature Genetics 2023, 55: 291-300. PMID: 36702996, PMCID: PMC9925385, DOI: 10.1038/s41588-022-01282-x.Peer-Reviewed Original ResearchConceptsTWAS methodsExpression quantitative trait loci (eQTL) dataQuantitative trait loci dataTranscriptome-wide associationWide association studyGenome-wide association study summary statisticsWhole genome sequencesSubsequent fine mappingEQTL datasetNew genesLoci dataFine mappingPhenotypic effectsTobacco use phenotypesDiverse ancestryAssociation studiesBiological relevanceEuropean ancestryGenesAncestryGWASSummary statisticsBiologyDrug repurposingDiversity
2022
HiChIPdb: a comprehensive database of HiChIP regulatory interactions
Zeng W, Liu Q, Yin Q, Jiang R, Wong H. HiChIPdb: a comprehensive database of HiChIP regulatory interactions. Nucleic Acids Research 2022, 51: d159-d166. PMID: 36215037, PMCID: PMC9825415, DOI: 10.1093/nar/gkac859.Peer-Reviewed Original ResearchConceptsRegulatory interactionsChromatin conformation capture methodsCell typesArchitecture of DNADiverse cell typesComprehensive annotationGene regulationRegulatory genesHiChIPInteraction databasesRegulatory mechanismsTissue homeostasisCell differentiationFunctional interactionsComprehensive databaseCell linesDisease developmentGenesCellsCapture methodCohesinGWASH3K27acChromatinSNPsMulti-trait genome-wide association study of opioid addiction: OPRM1 and beyond
Gaddis N, Mathur R, Marks J, Zhou L, Quach B, Waldrop A, Levran O, Agrawal A, Randesi M, Adelson M, Jeffries PW, Martin NG, Degenhardt L, Montgomery GW, Wetherill L, Lai D, Bucholz K, Foroud T, Porjesz B, Runarsdottir V, Tyrfingsson T, Einarsson G, Gudbjartsson DF, Webb BT, Crist RC, Kranzler HR, Sherva R, Zhou H, Hulse G, Wildenauer D, Kelty E, Attia J, Holliday EG, McEvoy M, Scott RJ, Schwab SG, Maher BS, Gruza R, Kreek MJ, Nelson EC, Thorgeirsson T, Stefansson K, Berrettini WH, Gelernter J, Edenberg HJ, Bierut L, Hancock DB, Johnson EO. Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond. Scientific Reports 2022, 12: 16873. PMID: 36207451, PMCID: PMC9546890, DOI: 10.1038/s41598-022-21003-y.Peer-Reviewed Original ResearchConceptsGenome-wide significant associationMulti-trait genome-wide association studyNovel genome-wide significant associationsGenome-wide association studiesGenomic structural equationGene-based analysisRelated traitsAssociation studiesGenetic correlationsEuropean ancestryA118G variantConsortium dataNew geneticsG variantGWASPPP6CLociPleiotropicGeneticsVariantsTraitsPhenotypeOA phenotypeFurinAncestryRegulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG
Duren Z, Chang F, Naqing F, Xin J, Liu Q, Wong W. Regulatory analysis of single cell multiome gene expression and chromatin accessibility data with scREG. Genome Biology 2022, 23: 114. PMID: 35578363, PMCID: PMC9109353, DOI: 10.1186/s13059-022-02682-2.Peer-Reviewed Original ResearchConceptsGene expressionChromatin accessibility dataCis-regulatory potentialCis-regulatory elementsCis-regulatory networksProfile of gene expressionNetwork inference accuracyGWAS variantsChromatin accessibilityRegulatory networksMultiomics dataChromatinRegulatory analysisExpressionGWASInflammatory bowel diseaseBowel diseaseVariantsAccess dataGenome-wide association meta-analysis identifies 29 new acne susceptibility loci
Mitchell B, Saklatvala J, Dand N, Hagenbeek F, Li X, Min J, Thomas L, Bartels M, Jan Hottenga J, Lupton M, Boomsma D, Dong X, Hveem K, Løset M, Martin N, Barker J, Han J, Smith C, Rentería M, Simpson M. Genome-wide association meta-analysis identifies 29 new acne susceptibility loci. Nature Communications 2022, 13: 702. PMID: 35132056, PMCID: PMC8821634, DOI: 10.1038/s41467-022-28252-5.Peer-Reviewed Original ResearchConceptsRisk lociSusceptibility lociGenome-wide significant lociGenome-wide association meta-analysisIdentified risk lociEuropean ancestry cohortsGWAS Meta-AnalysisAssociation meta-analysisHeritable skin disordersAncestry cohortsSignificant lociFine-mappingPolygenic risk scoresSeverely inflamed lesionsLociGenetic aetiologyPsychiatric traitsLong-term psychosocial consequencesMeta-analysisSkin disordersEQTLIndependent cohortGWASPsychosocial consequencesHormone-sensitive cancers
2021
Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia
Kachuri L, Jeon S, DeWan AT, Metayer C, Ma X, Witte JS, Chiang CWK, Wiemels JL, de Smith AJ. Genetic determinants of blood-cell traits influence susceptibility to childhood acute lymphoblastic leukemia. American Journal Of Human Genetics 2021, 108: 1823-1835. PMID: 34469753, PMCID: PMC8546033, DOI: 10.1016/j.ajhg.2021.08.004.Peer-Reviewed Original ResearchMeSH KeywordsAdultAgedBiomarkers, TumorBlood PlateletsCase-Control StudiesChildFemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLymphocytesMaleMendelian Randomization AnalysisMiddle AgedMonocytesNeutrophilsPrecursor Cell Lymphoblastic Leukemia-LymphomaPrognosisProspective StudiesQuantitative Trait LociUnited KingdomConceptsTrait-associated variantsMulti-trait GWASBlood cell homeostasisWide association studyGenetic risk lociTrait variationHematologic traitsRisk lociAssociation studiesCell typesGenetic determinantsLociInfluence susceptibilityUK BiobankMendelian randomization analysisGWASEtiological relevanceRandomization analysisTraitsHomeostasisSusceptibilityAcute lymphoblastic leukemiaBi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions
Levey DF, Stein MB, Wendt FR, Pathak GA, Zhou H, Aslan M, Quaden R, Harrington KM, Nuñez YZ, Overstreet C, Radhakrishnan K, Sanacora G, McIntosh AM, Shi J, Shringarpure SS, Concato J, Polimanti R, Gelernter J. Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions. Nature Neuroscience 2021, 24: 954-963. PMID: 34045744, PMCID: PMC8404304, DOI: 10.1038/s41593-021-00860-2.Peer-Reviewed Original ResearchConceptsTranscriptome-wide association studyMillion Veteran ProgramTranscriptome-wide association study (TWAS) analysisGenomic risk lociComplex psychiatric traitsGenetic architectureRisk lociGene expressionAssociation studiesLikely pathogenicityPsychiatric traitsVeteran ProgramNew therapeutic directionEuropean ancestryNew insightsAncestryUK BiobankAfrican ancestrySubstantial replicationExpressionLarge independent cohortsGWASTherapeutic directionsGenesLociA multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer
López de Maturana E, Rodríguez JA, Alonso L, Lao O, Molina-Montes E, Martín-Antoniano IA, Gómez-Rubio P, Lawlor R, Carrato A, Hidalgo M, Iglesias M, Molero X, Löhr M, Michalski C, Perea J, O’Rorke M, Barberà VM, Tardón A, Farré A, Muñoz-Bellvís L, Crnogorac-Jurcevic T, Domínguez-Muñoz E, Gress T, Greenhalf W, Sharp L, Arnes L, Cecchini L, Balsells J, Costello E, Ilzarbe L, Kleeff J, Kong B, Márquez M, Mora J, O’Driscoll D, Scarpa A, Ye W, Yu J, García-Closas M, Kogevinas M, Rothman N, Silverman D, Albanes D, Arslan A, Beane-Freeman L, Bracci P, Brennan P, Bueno-de-Mesquita B, Buring J, Canzian F, Du M, Gallinger S, Gaziano J, Goodman P, Gunter M, LeMarchand L, Li D, Neale R, Peters U, Petersen G, Risch H, Sánchez M, Shu X, Thornquist M, Visvanathan K, Zheng W, Chanock S, Easton D, Wolpin B, Stolzenberg-Solomon R, Klein A, Amundadottir L, Marti-Renom M, Real F, Malats N. A multilayered post-GWAS assessment on genetic susceptibility to pancreatic cancer. Genome Medicine 2021, 13: 15. PMID: 33517887, PMCID: PMC7849104, DOI: 10.1186/s13073-020-00816-4.Peer-Reviewed Original ResearchConceptsSilico functional analysisFunctional analysisPublic genomic informationUnfolded protein responseMeta-analysis p-valueLow-frequency variantsPc locusGWAS hitsGenomic informationPhenotypic varianceProtein responseSpatial autocorrelation analysisER stressMajor regulatorFrequency variantsPancreatic acinar cellsGenetic susceptibilityCandidate variantsFactor interplayComplex diseasesIndependent variantsGWASInherited basisLow p-valuesAcinar cellsRevisiting the genome-wide significance threshold for common variant GWAS
Chen Z, Boehnke M, Wen X, Mukherjee B. Revisiting the genome-wide significance threshold for common variant GWAS. G3: Genes, Genomes, Genetics 2021, 11: jkaa056. PMID: 33585870, PMCID: PMC8022962, DOI: 10.1093/g3journal/jkaa056.Peer-Reviewed Original ResearchConceptsGenome-wide significance thresholdP-value thresholdGWAS meta-analysesMeta-analysis consortiumExcessive false positive ratesSignificance thresholdGene set enrichmentBenjamini-Yekutieli procedureModest-sized studiesFDR-controlling proceduresGlobal lipidsMeta-analysesPathway analysisGWASReplication studyP-valueIncreased discoveryMultiple testing strategiesSample sizePositive discoveriesBenjamini-HochbergLipid levelsTesting strategiesDownstream workFDR
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 ResearchConceptsExpression 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 diseasesGWASiDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching
Kuo T, Jiang X, Tang H, Wang X, Bath T, Bu D, Wang L, Harmanci A, Zhang S, Zhi D, Sofia H, Ohno-Machado L. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Medical Genomics 2020, 13: 98. PMID: 32693816, PMCID: PMC7372776, DOI: 10.1186/s12920-020-0715-0.Peer-Reviewed Original Research
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