2024
Genome-Wide Meta-Analysis Identifies Multiple Germline Genetic Variants Associated with Increased Risk of Follicular Lymphoma
Joseph V, Clay-Gilmour A, Park H, Breeze C, Sucheston-Campbell L, Arias J, Waller R, Güler M, Nieters A, Ekstroem Smedby K, Davídsson Ó, Wang S, Lan Q, Hjalgrim H, Slager S, McKay J, Cerhan J, Berndt S, Rothman N. Genome-Wide Meta-Analysis Identifies Multiple Germline Genetic Variants Associated with Increased Risk of Follicular Lymphoma. Blood 2024, 144: 4339-4339. DOI: 10.1182/blood-2024-211262.Peer-Reviewed Original ResearchGenome-wide association studiesTranscriptome wide association studyBlood cell traitsLeukocyte telomere lengthMendelian randomizationGermline genetic variantsB cell developmentClass II genesFollicular lymphomaAssociation studiesHLA class IRisk variantsGenetic variantsFL casesGenome-wide significant lociGenome-wide significant variantsWhole blood gene expression dataHLA regionII genesRegulation of B cell developmentBlood gene expression dataControls of European ancestryGene Ontology termsGene set analysisGenetically high-risk populations94. THE GENETIC ARCHITECTURE OF MOOD AND ANXIETY DISORDER SYMPTOMS
Schultz L, Mollon J, Jacquemont S, Almasy L, Glahn D. 94. THE GENETIC ARCHITECTURE OF MOOD AND ANXIETY DISORDER SYMPTOMS. European Neuropsychopharmacology 2024, 87: 100. DOI: 10.1016/j.euroneuro.2024.08.208.Peer-Reviewed Original ResearchEuropean ancestryPrefrontal cortexPsychiatric diagnosisGene set analysisAssociated with genesInverse-variance weighted meta-analysisICD-10 psychiatric diagnosisMeta-analysisAnxiety disorder symptomsUK Biobank (UKBMulti-ancestry meta-analysisGenome-wide significant variantsGenetic architectureRisk lociMSigDB geneChromosome 8Confirmatory factor modelsGenomic risk lociEuropean ancestry individualsAnxiety disordersDisorder symptomsMood/anxiety symptomsIdentified risk lociDopamine receptorsFrontal cortexDiversity 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
Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population
DeWan A, Cahill M, Cornejo-Sanchez D, Li Y, Dong Z, Fabiha T, Sun H, Wang G, Leal S. Variants in JAZF1 are associated with asthma, type 2 diabetes, and height in the United Kingdom biobank population. Frontiers In Genetics 2023, 14: 1129389. PMID: 37377600, PMCID: PMC10291233, DOI: 10.3389/fgene.2023.1129389.Peer-Reviewed Original ResearchComplex traitsGenome-wide significant variantsFine-mapping analysisGenomic regionsMajor genetic componentAssociation analysisSusceptibility variantsGenetic componentSignificant variantsGenetic variantsSuggestive associationTraitsPhenotypeVariantsBiobank dataGenesNon-overlapping regionsRegionJAZF1Univariate association analysisType 2 diabetes
2022
Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring
Campos A, Ingold N, Huang Y, Mitchell B, Kho P, Han X, García-Marín L, Ong J, Agee M, Aslibekyan S, Auton A, Babalola E, Bell R, Bielenberg J, Bryc K, Bullis E, Cameron B, Coker D, Dhamija D, Das S, Elson S, Filshtein T, Fletez-Brant K, Fontanillas P, Freyman W, Gandhi P, Heilbron K, Hicks B, Hinds D, Huber K, Jewett E, Jiang Y, Kleinman A, Kukar K, Lin K, Lowe M, Luff M, McCreight J, McIntyre M, McManus K, Micheletti S, Moreno M, Mountain J, Mozaffari S, Nandakumar P, Noblin E, O’Connell J, Petrakovitz A, Poznik G, Shastri A, Shelton J, Shi J, Shringarpure S, Tian C, Tran V, Tung J, Wang X, Wang W, Weldon C, Wilton P, Law M, Yokoyama J, Martin N, Dong X, Cuellar-Partida G, MacGregor S, Aslibekyan S, Rentería M. Discovery of genomic loci associated with sleep apnea risk through multi-trait GWAS analysis with snoring. Sleep 2022, 46: zsac308. PMID: 36525587, PMCID: PMC9995783, DOI: 10.1093/sleep/zsac308.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesBody mass indexComplex traitsLatent causal variable methodSA riskAnalysis of genome-wide association studiesMulti-trait analysis of genome-wide association studyMultisite chronic painPhenome-wide screenGenome-wide significant variantsEffect of body mass indexGenetic correlationsMeta-analysisGenome-wide significanceEvidence of associationCohort of participantsSevere health conditionsChronic obstructive pulmonary diseaseHigh blood pressureObstructive pulmonary diseaseMulti-trait analysisHealth conditionsGWAS analysisAssociation studiesSignificant variants
2021
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example
Hatoum AS, Wendt FR, Galimberti M, Polimanti R, Neale B, Kranzler HR, Gelernter J, Edenberg HJ, Agrawal A. Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example. Drug And Alcohol Dependence 2021, 229: 109115. PMID: 34710714, PMCID: PMC9358969, DOI: 10.1016/j.drugalcdep.2021.109115.Peer-Reviewed Original ResearchConceptsGenome-wide significant variantsCandidate gene predictionGenetic predictionRandom SNPsPolygenic traitRandom phenotypeCandidate SNPsSimulated phenotypesPsychiatric geneticsGenetic machineSignificant variantsBinary phenotypesCandidate variantsSNPsAncestryPhenotypeAllele frequenciesVariantsMachine learning modelsGenetic testsLearning modelMulti-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study
Hu Y, Bien S, Nishimura K, Haessler J, Hodonsky C, Baldassari A, Highland H, Wang Z, Preuss M, Sitlani C, Wojcik G, Tao R, Graff M, Huckins L, Sun Q, Chen M, Mousas A, Auer P, Lettre G, Tang W, Qi L, Thyagarajan B, Buyske S, Fornage M, Hindorff L, Li Y, Lin D, Reiner A, North K, Loos R, Raffield L, Peters U, Avery C, Kooperberg C. Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study. BMC Genomics 2021, 22: 432. PMID: 34107879, PMCID: PMC8191001, DOI: 10.1186/s12864-021-07745-5.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesPlatelet traitsAfrican AmericansPopulation ArchitectureAssociation studiesAssociation analysisAncestry-specific genome-wide association studiesEuropean ancestryGenome-wide association analysisAttenuation of effect estimatesGenome-wide significant variantsVariant association analysisGenome-wide significanceRacially/ethnically diverse populationsPopulations of European ancestryGenetic association studiesAncestry-specificComplex traitsSignificant variantsHispanics/LatinosMultiple genesAncestry groupsEffect estimatesEA populationsEA participantsEfficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes
Bi W, Zhou W, Dey R, Mukherjee B, Sampson J, Lee S. Efficient mixed model approach for large-scale genome-wide association studies of ordinal categorical phenotypes. American Journal Of Human Genetics 2021, 108: 825-839. PMID: 33836139, PMCID: PMC8206161, DOI: 10.1016/j.ajhg.2021.03.019.Peer-Reviewed Original ResearchConceptsOrdinal categorical phenotypesGenome-wide association studiesCategorical phenotypesGenome-wide significant variantsRare variantsPhenotype distributionControlled type I error ratesType I error rateMixed model approachArray genotypingAssociation studiesCommon variantsQuantitative traitsSignificant variantsLogistic mixed modelsLack of analysis toolsUK BiobankLinear mixed model approachPhenotypeAssociation TestVariantsMixed modelsSignificance levelMAFTraits
2018
GWAS and network analysis of co‐occurring nicotine and alcohol dependence identifies significantly associated alleles and network
Xiang B, Yang B, Zhou H, Kranzler H, Gelernter J. GWAS and network analysis of co‐occurring nicotine and alcohol dependence identifies significantly associated alleles and network. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2018, 180: 3-11. PMID: 30488612, PMCID: PMC6918694, DOI: 10.1002/ajmg.b.32692.Peer-Reviewed Original ResearchMeSH KeywordsAdultAlcoholismAllelesBlack or African AmericanComorbidityEthanolFemaleG(M2) Activator ProteinGene FrequencyGene Regulatory NetworksGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMaleMiddle AgedNicotinePolymorphism, Single NucleotideProtein Interaction MapsRisk FactorsTobacco Use DisorderWhite PeopleConceptsGene subnetworksProtein-protein interaction (PPI) network analysisGenome-wide significant variantsInteraction network analysisGene-set analysisFunctional enrichment analysisSignificant SNPsQuantitative lociNerve growth factor pathwayGene enrichmentEnrichment analysisNetwork analysisGenetic traitsGrowth factor pathwaysRisk genesSignificant variantsGenesStudy of AddictionSNPsFactor pathwayGM2AAmphetamine addictionGenetic riskGWASSubnetworks
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