2025
PTPN2 and Leukopenia in Individuals With Normal TPMT and NUDT15 Metabolizer Status Taking Azathioprine
Daniel L, Nepal P, Zanussi J, Dickson A, Straub P, Miller‐Fleming T, Wei W, Hung A, Cox N, Kawai V, Mosley J, Stein C, Feng Q, Liu G, Tao R, Chung C. PTPN2 and Leukopenia in Individuals With Normal TPMT and NUDT15 Metabolizer Status Taking Azathioprine. Clinical And Translational Science 2025, 18: e70220. PMID: 40442974, PMCID: PMC12122386, DOI: 10.1111/cts.70220.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significancePrincipal components of ancestryImmune cell developmentGenetic risk factorsDose-dependent side effectsAssociation studiesGenetic dataSide effects of azathioprineIntronic variantsElectronic health recordsVanderbilt's electronic health recordEffect of azathioprineCell developmentPTPN2Replication cohortTPMTHealth recordsNUDT15NIH-funded projectDrug discontinuationThiopurine useBioVUXanthine oxidase inhibitorLeukopeniaPenetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology
Silva A, Sønderby I, Kirov G, Abdellaoui A, Agartz I, Ames D, Armstrong N, Artiges E, Banaschewski T, Bassett A, Bearden C, Blangero J, Boen R, Boomsma D, Bülow R, Butcher N, Calhoun V, Campbell L, Chow E, Ciufolini S, Craig M, Crespo-Farroco B, Cunningham A, Dalvie S, Daly E, Dazzan P, de Geus E, de Zubicaray G, Doherty J, Donohoe G, Drakesmith M, Espeseth T, Frouin V, Garavan H, Glahn D, Goodrich-Hunsaker N, Gowland P, Grabe H, Grigis A, Gudbrandsen M, Gutman B, Haavik J, Håberg A, Hall J, Heinz A, Hohmann S, Hottenga J, Jacquemont S, Jahanshad N, Jonas R, Jones D, Jönsson E, Koops S, Kumar K, Le Hellard S, Lemaitre H, Liu J, Lundervold A, Martinot J, Mather K, McDonald-McGinn D, McMahon K, McRae A, Medland S, Moreau C, Murphy K, Murphy D, Murray R, Nees F, Owen M, Martinot M, Orfanos D, Paus T, Poustka L, Marques T, Roalf D, Sachdev P, Scheffler F, Schmitt J, Schumann G, Steen V, Stein D, Strike L, Teumer A, Thalamuthu A, Thomopoulos S, Tordesillas-Gutiérrez D, Trollor J, Uhlmann A, Vajdi A, van ’t Ent D, van Amelsvoort T, van den Bree M, van der Meer D, Vázquez-Bourgon J, Villalón-Reina J, Völker U, Völzke H, Vorstman J, Westlye L, Williams N, Wittfeld K, Wright M, Thompson P, Andreassen O, Linden D, group E. Penetrance of neurodevelopmental copy number variants is associated with variations in cortical morphology. Biological Psychiatry Cognitive Neuroscience And Neuroimaging 2025 PMID: 40414598, DOI: 10.1016/j.bpsc.2025.05.010.Peer-Reviewed Original ResearchCopy number variantsDevelopmental disordersNeurobiological mechanismsPenetration scoresMechanisms of genetic riskAssociated with variationBrain magnetic resonance imagingCohort of patientsCortical surface areaT1-weighted brain magnetic resonance imagingMagnetic resonance imagingCortical morphometric featuresGenetic dataLingual gyrusClinical phenotypeSubcortical morphologyIncreased riskNeuroimaging dataSchizophreniaBrain abnormalitiesNeurodevelopmental conditionsIntracranial volumeCerebral cortexResonance imagingCortical morphologyPsychiatric Genetics in Clinical Practice: Essential Knowledge for Mental Health Professionals.
Besterman A, Alnor M, Castaño M, DeLisi L, Grice D, Lohoff F, Middeldorp C, Müller D, Quattrone D, Nurnberger J, Nurmi E, Ross D, Soda T, Schulze T, Trost B, Vilella E, Yap C, Zai G, Moreno-De-Luca D. Psychiatric Genetics in Clinical Practice: Essential Knowledge for Mental Health Professionals. American Journal Of Psychiatry 2025, appiajp20240295. PMID: 40134266, DOI: 10.1176/appi.ajp.20240295.Peer-Reviewed Original ResearchMental health cliniciansHealth cliniciansPsychiatric geneticsHolistic model of careGenetic architecture of psychiatric disordersModels of careMental health outcomesApplication of genetic informationMental health professionalsGene-environment interactionsClinical practiceRare genetic variantsGenetics professionalsPower of geneticsHealth professionalsHealth outcomesEffective communication strategiesOngoing educationPatient educationFamilial aggregationGenetic architectureSubspecialty expertsGenetic dataNon-genetic factorsGenetic informationThe human and non-human primate developmental GTEx projects
Bell T, Blanchard T, Hernandez R, Linn R, Taylor D, VonDran M, Ahooyi T, Beitra D, Bernieh A, Delaney M, Faith M, Fattahi E, Footer D, Gilbert M, Guambaña S, Gulino S, Hanson J, Hattrell E, Heinemann C, Kreeb J, Leino D, Mcdevitt L, Palmieri A, Pfeiffer M, Pryhuber G, Rossi C, Rasool I, Roberts R, Salehi A, Savannah E, Stachowicz K, Stokes D, Suplee L, Van Hoose P, Wilkins B, Williams-Taylor S, Zhang S, Ardlie K, Getz G, Lappalainen T, Montgomery S, Aguet F, Anderson L, Bernstein B, Choudhary A, Domenech L, Gaskell E, Johnson M, Liu Q, Marderstein A, Nedzel J, Okonda J, Padhi E, Rosano M, Russell A, Walker B, Sestan N, Gerstein M, Milosavljevic A, Borsari B, Cho H, Clarke D, Deveau A, Galeev T, Gobeske K, Hameed I, Huttner A, Jensen M, Jiang Y, Li J, Liu J, Liu Y, Ma J, Mane S, Meng R, Nadkarni A, Ni P, Park S, Petrosyan V, Pochareddy S, Salamon I, Xia Y, Yates C, Zhang M, Zhao H, Conrad D, Feng G, Brady F, Boucher M, Carbone L, Castro J, del Rosario R, Held M, Hennebold J, Lacey A, Lewis A, Lima A, Mahyari E, Moore S, Okhovat M, Roberts V, de Castro S, Wessel B, Zaniewski H, Zhang Q, Arguello A, Baroch J, Dayal J, Felsenfeld A, Ilekis J, Jose S, Lockhart N, Miller D, Minear M, Parisi M, Price A, Ramos E, Zou S. The human and non-human primate developmental GTEx projects. Nature 2025, 637: 557-564. PMID: 39815096, PMCID: PMC12013525, DOI: 10.1038/s41586-024-08244-9.Peer-Reviewed Original ResearchConceptsChromatin accessibility dataFunctional genomic studiesWhole-genome sequencingEffects of genetic variationSpatial gene expression profilesNon-human primatesGenotype-Tissue ExpressionGene expression profilesGenomic studiesGene regulationGenetic dataGenetic variationGenomic researchDonor diversityCommunity engagementHuman evolutionEarly developmental defectsGene expressionCell statesDevelopmental programmeHuman diseasesExpression profilesAdult tissuesDevelopmental defectsSingle-cellBrain morphology mediating the effects of common genetic risk variants on Alzheimer's disease.
Breddels E, Snihirova Y, Pishva E, Gülöksüz S, Blokland G, Luykx J, Andreassen O, Linden D, van der Meer D. Brain morphology mediating the effects of common genetic risk variants on Alzheimer's disease. Journal Of Alzheimer's Disease Reports 2025, 9: 25424823251328300. PMID: 40144144, PMCID: PMC11938454, DOI: 10.1177/25424823251328300.Peer-Reviewed Original ResearchLate-onset Alzheimer's diseaseGenetic risk variantsMendelian randomizationRisk of late-onset Alzheimer's diseaseInferior lateral ventricleUK BiobankAlzheimer's Disease Neuroimaging InitiativeCausal pathwaysAlzheimer's diseaseRisk variantsClinical dataLateral ventricleNeurobiological pathwaysBiobankAlzheimerGenetic dataDiseaseAssociated with alterationsGenetic variationBrain morphologyEntorhinal cortexRiskMeasures of brain morphology
2024
Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations
Liu J, Borsari B, Li Y, Liu S, Gao Y, Xin X, Lou S, Jensen M, Garrido-Martín D, Verplaetse T, Ash G, Zhang J, Girgenti M, Roberts W, Gerstein M. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell 2024, 188: 515-529.e15. PMID: 39706190, DOI: 10.1016/j.cell.2024.11.012.Peer-Reviewed Original ResearchGenome-wide association studiesCase-control genome-wide association studyMultivariate genome-wide association studyGenetic lociAssociation studiesGenetic dataGenetic associationPhenotypeGeneticsEnvironmental factorsDetection powerELFN1Adolescent Brain Cognitive DevelopmentLociGenesPsychiatric disordersADORA3Digital phenotypingIntegrative factor-adjusted sparse generalized linear models
Xu F, Ma S, Zhang Q. Integrative factor-adjusted sparse generalized linear models. Journal Of Statistical Computation And Simulation 2024, 95: 764-780. DOI: 10.1080/00949655.2024.2439450.Peer-Reviewed Original ResearchVariable selection consistencyHigh-dimensional dataIncreased accessibility of dataSelection consistencyConsistency propertiesCorrelated covariatesGeneralized linear modelVariable selectionAnalysis of genetic dataAccessibility of dataIdiosyncratic componentsCompetitive performanceCovariatesGenetic dataLinear modelSample sizeImprove model performanceEstimationIntegrated analysisModel estimatesLatent factorsModel performancePractical useConsistencyCollaborative Survival Analysis on Predicting Alzheimer’s Disease Progression
Xu W, Wang S, Shen L, Zhao Y. Collaborative Survival Analysis on Predicting Alzheimer’s Disease Progression. Statistics In Biosciences 2024, 1-24. DOI: 10.1007/s12561-024-09459-0.Peer-Reviewed Original ResearchAlzheimer's diseaseGenes associated with neuronal developmentAlzheimer's Disease Neuroimaging InitiativeGenetic dataAlzheimer's disease progressionGenetic variationPhenotypic varianceAD progressionGenetic featuresNeuronal developmentGenetic biomarkersAlzheimerADNI databaseMild cognitive impairmentSNPsCanonical correlation analysisGenesStructural MRI scansCombination of brain imagingBrain imagingGeneticsProgression of mild cognitive impairmentAD literatureTime-to-event outcomesTime-to-event predictionsRESOLVING THE CHALLENGES OF BIG-DATA IMAGING GENETICS ANALYSIS TO UNDERSTAND GENETIC AND ENVIRONMENTAL RISK FACTORS IN PSYCHIATRIC DISORDERS
Kochunov P, Nichols T, Blangero J, Medland S, Glahn D, Hong E. RESOLVING THE CHALLENGES OF BIG-DATA IMAGING GENETICS ANALYSIS TO UNDERSTAND GENETIC AND ENVIRONMENTAL RISK FACTORS IN PSYCHIATRIC DISORDERS. European Neuropsychopharmacology 2024, 87: 21-22. DOI: 10.1016/j.euroneuro.2024.08.056.Peer-Reviewed Original ResearchGenetic analysisGenome-wide associationBrain patternsImaging genetic analysisImaging genetic datasetGenetic resolutionGenetic risk factorsGenetic datasetsGenetic dataDepressive disorderPsychiatric disordersRisk of development of dementiaVariance componentsHigh-resolution neuroimagingDevelopment of dementiaNurturing interactionsEnvironmental risk factorsAlzheimer's diseaseGenetic correlationsRisk factorsTreatment effectsDisordersLongitudinal datasetPsychosisSNPsInbreeding avoidance, competition and natal dispersal in a pair-living, genetically monogamous mammal, Azara’s owl monkey (Aotus azarae)
Corley M, de la Chica A, van der Heide G, Rotundo M, Caccone A, Fernandez-Duque E. Inbreeding avoidance, competition and natal dispersal in a pair-living, genetically monogamous mammal, Azara’s owl monkey (Aotus azarae). Royal Society Open Science 2024, 11: 240379. PMID: 39113772, PMCID: PMC11305132, DOI: 10.1098/rsos.240379.Peer-Reviewed Original ResearchAzara's owl monkeysNatal dispersalInbreeding avoidanceMating systemPair-livingPotential matesNatal groupMaintenance of social organizationTiming of natal dispersalLife history stagesPopulation structureGenetic dataMonogamous mammalsWild populationsParental careIndividual fitnessEcological factorsRegulating dispersalAotus azaraeDispersal patternsInbreedingStep-parentsAgonistic conflictsMatingMammalsLatrophilin-2 mediates fluid shear stress mechanotransduction at endothelial junctions
Tanaka K, Chen M, Prendergast A, Zhuang Z, Nasiri A, Joshi D, Hintzen J, Chung M, Kumar A, Mani A, Koleske A, Crawford J, Nicoli S, Schwartz M. Latrophilin-2 mediates fluid shear stress mechanotransduction at endothelial junctions. The EMBO Journal 2024, 43: 3175-3191. PMID: 38886581, PMCID: PMC11294477, DOI: 10.1038/s44318-024-00142-0.Peer-Reviewed Original ResearchLatrophilin-2Affinity purification methodCell-cell junctionsHuman genetic dataPECAM-1SiRNA screenGenetic dataEndothelial cell response to fluid shear stressGA proteinsDownstream eventsEndothelial-specific knockoutG-proteinActivity assayShear stress mechanotransductionPlexin-D1Endothelial signalingJunctional complexesPurification methodVE-cadherinResponse to fluid shear stressVascular developmentGA residuesEndothelial junctionsGPCRsVEGF receptorsUnveiling Gene Interactions in Alzheimer’s Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach
Sanders K, Manuel A, Liu A, Leng B, Chen X, Zhao Z. Unveiling Gene Interactions in Alzheimer’s Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach. Epigenomes 2024, 8: 14. PMID: 38651367, PMCID: PMC11036294, DOI: 10.3390/epigenomes8020014.Peer-Reviewed Original ResearchEnrichment analysisGenome-wide association studiesAlzheimer's diseaseDense module searchDNA methylation dataGene network modulesFunctional enrichment analysisGWAS signalsGWAS datasetsTarget enrichmentAssociation studiesGenetic dataEpigenetic signalsNetwork-based toolCause of dementiaGene interactionsAD pathogenesisMethylation dataDNA methylationBIN1 geneAD pathologyComplex diseasesGenesSignificant cell typeDrug repurposing effortsStatistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods
Cheek C, Lindner P, Grigorenko E. Statistical and Machine Learning Analysis in Brain-Imaging Genetics: A Review of Methods. Behavior Genetics 2024, 54: 233-251. PMID: 38336922, DOI: 10.1007/s10519-024-10177-y.Peer-Reviewed Original Research
2023
Digital biobanks are underutilized in dermatology and create opportunities to reduce the burden of skin disease
Jumonville G, Hong D, Khan A, DeWan A, Leal S, Weng C, Petukhova L. Digital biobanks are underutilized in dermatology and create opportunities to reduce the burden of skin disease. British Journal Of Dermatology 2023, 190: 566-568. PMID: 37936310, PMCID: PMC10941321, DOI: 10.1093/bjd/ljad439.Peer-Reviewed Original ResearchConceptsBurden of skin diseaseGenetic architectureDiscover genesGenetic dataGene-environment interactionsClinical areasBiobank dataHealth dataMedical careDisease mechanismsGlobal burdenDisease relationshipsMedical interventionsDrug repurposingPharmacogenetic relationshipBiobankSkin diseasesGlobal burden of skin diseaseGenesKnowledge promisesAdverse eventsCareDermatologyHealthDisease79. A LARGE-SCALE GENOME-WIDE ASSOCIATION STUDY OF COCAINE USE DISORDER
Deak J, Zhou H, Levey D, Galimberti M, Farajzadeh L, Hougaard D, Jennings M, Davis L, Sanchez-Roige S, Demontis D, Børglum A, Kranzler H, Gaziano M, Stein M, Gelernter J. 79. A LARGE-SCALE GENOME-WIDE ASSOCIATION STUDY OF COCAINE USE DISORDER. European Neuropsychopharmacology 2023, 75: s99. DOI: 10.1016/j.euroneuro.2023.08.184.Peer-Reviewed Original ResearchGenome-wide association studiesLarge genome-wide association studiesGWS lociBackground Genome-wide association studiesAssociation studiesLarge-scale genome-wide association studiesTissue expression analysisGenetic architectureGWAS analysisIndividuals of AfricanGWAS discoverySingle locusExpression analysisGenetic dataChromosome 11Silico analysisGenetic discoveriesMillion Veteran ProgramChromosome 13Biological understandingLociEuropean ancestryBiological componentsVeteran ProgramNovel findingsA statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases
Liu W, Deng W, Chen M, Dong Z, Zhu B, Yu Z, Tang D, Sauler M, Lin C, Wain L, Cho M, Kaminski N, Zhao H. A statistical framework to identify cell types whose genetically regulated proportions are associated with complex diseases. PLOS Genetics 2023, 19: e1010825. PMID: 37523391, PMCID: PMC10414598, DOI: 10.1371/journal.pgen.1010825.Peer-Reviewed Original ResearchConceptsCell typesDisease-associated tissuesWide association studyComplex diseasesCell type proportionsDisease-relevant tissuesReal GWAS dataFunctional genesTranscriptomic dataGWAS dataGenetic dataAssociation studiesNovel statistical frameworkChronic obstructive pulmonary diseaseStatistical frameworkObstructive pulmonary diseaseIdiopathic pulmonary fibrosisBreast cancer riskType proportionsBlood CD8Pulmonary diseasePulmonary fibrosisPredictive biomarkersLung tissueBreast cancerDecentralized Parallel Independent Component Analysis for Multimodal, Multisite Data
Panichvatana C, Chen J, Baker B, Thapaliya B, Calhoun V, Liu J. Decentralized Parallel Independent Component Analysis for Multimodal, Multisite Data. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2023, 00: 1-4. PMID: 38083130, DOI: 10.1109/embc40787.2023.10340070.Peer-Reviewed Original ResearchDeep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data
Dolci G, Rahaman M, Galazzo I, Cruciani F, Abrol A, Chen J, Fu Z, Duan K, Menegaz G, Calhoun V. Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data. 2023, 00: 1-5. DOI: 10.1109/icasspw59220.2023.10193683.Peer-Reviewed Original Research
2022
Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks
Dey R, Zhou W, Kiiskinen T, Havulinna A, Elliott A, Karjalainen J, Kurki M, Qin A, Lee S, Palotie A, Neale B, Daly M, Lin X. Efficient and accurate frailty model approach for genome-wide survival association analysis in large-scale biobanks. Nature Communications 2022, 13: 5437. PMID: 36114182, PMCID: PMC9481565, DOI: 10.1038/s41467-022-32885-x.Peer-Reviewed Original ResearchConceptsAssociation analysisGenetic variants associated with ageLow frequency variantsState-of-the-art optimization strategiesNatural history of complex diseasesPhenome-wide associationFrequency variantsPopulation structureGenetic dataWhite British ancestryElectronic health recordsUK Biobank participantsCensoring ratesSaddlepoint approximationComplex diseasesSimulation studyPhenotypeBiobank participantsHealth recordsUK BiobankBritish ancestryOptimal strategyAncestrySaddlepointRelatednessNetwork Tau spreading is vulnerable to the expression gradients of APOE and glutamatergic-related genes
Montal V, Diez I, Kim C, Orwig W, Bueichekú E, Gutiérrez-Zúñiga R, Bejanin A, Pegueroles J, Dols-Icardo O, Vannini P, El-Fakhri G, Johnson K, Sperling R, Fortea J, Sepulcre J. Network Tau spreading is vulnerable to the expression gradients of APOE and glutamatergic-related genes. Science Translational Medicine 2022, 14: eabn7273. PMID: 35895837, PMCID: PMC9942690, DOI: 10.1126/scitranslmed.abn7273.Peer-Reviewed Original ResearchConceptsTau spreadingIntracellular accumulation of tau proteinAccumulation of tau proteinAlzheimer's diseasePathological tau accumulationGenetic gradientGenetic dataTau proteinNeurofibrillary tanglesSynaptic genesTau accumulationGenetic traitsGenesDrug development strategiesExpression gradientIntracellular accumulationTauExpressionCognitively normal older participantsHuman brain cortexTanglesProteinApoTraitsAlzheimer
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