2025
A genetic modifier links integrin α5 to the phenotypic variation in fibronectin 1a mutant zebrafish.
Capon S, Maroufidou A, Feltes M, Xu Y, Matharoo D, Jülich D, Holley S, Farber S, Stainier D. A genetic modifier links integrin α5 to the phenotypic variation in fibronectin 1a mutant zebrafish. PLOS Genetics 2025, 21: e1011747. PMID: 40549824, PMCID: PMC12212883, DOI: 10.1371/journal.pgen.1011747.Peer-Reviewed Original ResearchConceptsMutant phenotypePhenotypic variationNonsense mutationGenetic modifiersDouble mutant analysisWhole-genome sequencingITGA5 expressionSevere phenotypeIntegrin alpha 5Mutant analysisMutant larvaeProximal promoterMutantsMutant zebrafishCardia bifidaGene expressionIntegrin A5Genetic backgroundAlpha 5PhenotypeMutationsExpression levelsFibronectinFunctional cardiovascular systemZebrafishGenerating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software
Chen X, Wu Z, Shi X, Cho H, Mukherjee B. Generating synthetic electronic health record data: a methodological scoping review with benchmarking on phenotype data and open-source software. Journal Of The American Medical Informatics Association 2025, 32: 1227-1240. PMID: 40460023, PMCID: PMC12203555, DOI: 10.1093/jamia/ocaf082.Peer-Reviewed Original ResearchConceptsGAN-based methodsElectronic health recordsOpen-source softwareBaseline methodsMIMIC-IIIGenerative adversarial network (GAN)-based methodsAdversarial network (GAN)-based methodsSynthetic electronic health recordsDownstream use casesRule-based methodElectronic health record datasetPrivacy exposurePrivacy protectionEvaluation metricsUse casesCompetitive performanceCondition generation methodSynthetic dataDecision treeBenchmark methodsElectronic health record dataData generationGeneration methodComprehensive benchmarkMIMIC-IVThe Eating Disorders Genetics Initiative 2 (EDGI2): study protocol
Berthold N, MacDermod C, Thornton L, Parker R, Morales S, Hog L, Kennedy H, Guintivano J, Sullivan P, Crowley J, Johnson J, Birgegård A, Fundín B, Frans E, Xu J, “Ngāti Pūkenga” M, Miller A, Aguilar M, Barakat S, Abdulkadir M, White J, Larsen J, Trujillo E, Winterman B, Zhang R, Lawson R, Wonderlich S, Wonderlich J, Schaefer L, Mehler P, Oakes J, Foster M, Gaudiani J, Vacuán E, Compte E, Petersen L, Yilmaz Z, Micali N, Jordan J, Kennedy M, Maguire S, Huckins L, Lu Y, Dinkler L, Martin N, Bulik C. The Eating Disorders Genetics Initiative 2 (EDGI2): study protocol. BMC Psychiatry 2025, 25: 532. PMID: 40419993, PMCID: PMC12105188, DOI: 10.1186/s12888-025-06777-5.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAvoidant/restrictive food intake disorderCalculate polygenic risk scoresNational Patient RegisterPsychiatric Genomics ConsortiumEating disordersLongstanding illnessEating Disorders Working GroupUnited StatesPatient RegisterCase-controlNew ZealandCase identificationGenetic architectureEvaluate clinical outcomesFood intake disorderRisk scoreClinically relevant phenotypesStudy protocolQuestionnaire batteryFunctional biologySymptom levelsAncestral backgroundMetabolic traitsGenomics ConsortiumClinical phenotypes among patients with familial forms of Chiari malformation type 1.
Mekbib K, Muñoz W, Allington G, Zhao S, Mehta N, Fortes C, Shohfi J, Fan B, Nelson-Williams C, DeSpenza T, Butler W, Alper S, Jackson E, Kahle K. Clinical phenotypes among patients with familial forms of Chiari malformation type 1. Journal Of Neurosurgery Pediatrics 2025, 36: 109-118. PMID: 40315599, DOI: 10.3171/2025.1.peds24187.Peer-Reviewed Original ResearchConceptsChiari malformation type 1Connective tissue disordersClinical phenotypeNeurological comorbiditiesType 1Family membersCerebellar tonsillar herniationPatient-parent triosEhlers-Danlos syndromeWhole-exome sequencingNeck painTonsillar herniationCraniocervical junctionNeural compressionCSF obstructionTissue disordersUnivariate analysisCohort studyFamily historyClinical symptomsNeurosurgical managementNeurodevelopmental conditionsVariable symptomsForamen magnumPatientsLeveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies
Jian X, Zhang D, Yu Z, Xu H, Bian J, Wu Y, Tong J, Chen Y. Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies. Journal Of Biomedical Informatics 2025, 166: 104839. PMID: 40316004, DOI: 10.1016/j.jbi.2025.104839.Peer-Reviewed Original ResearchConceptsClinical Research NetworkAssociation studiesKaiser Permanente WashingtonBreast cancer eventsManual chart reviewRisk factor identificationHealth recordsCancer eventsEHR dataPhenotyping algorithmsCohort dataPatient clinical outcomesResearch NetworkRandom sampleADRDChart reviewFactor identificationCohortMean square errorMethod improves efficiencyOutcomesSimulation settingsAugmentation methodSBCEAlzheimer's diseaseComputational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference
Thangaraj P, Oikonomou E, Dhingra L, Aminorroaya A, Jayaram R, Suchard M, Khera R. Computational Phenomapping of Randomized Clinical Trial Participants to Enable Assessment of Their Real-World Representativeness and Personalized Inference. Circulation Cardiovascular Quality And Outcomes 2025, 18: e011306. PMID: 40261065, PMCID: PMC12203226, DOI: 10.1161/circoutcomes.124.011306.Peer-Reviewed Original ResearchConceptsElectronic health record patientElectronic health recordsDistance metricRandomized clinical trialsElectronic health record dataMachine learning methodsYale New Haven Health SystemElectronic health record cohortRandomized clinical trial participantsLearning methodsHeart failureClinical trial participationTOPCAT participantsReal worldMultidimensional metricRCT participantsHealth recordsTreatment effectsHealth systemCharacteristics of patientsRandomized clinical trial cohortsTrial participantsMetricsUnited StatesNovel statisticMyeloperoxidase impacts vascular function by altering perivascular adipocytes’ secretome and phenotype in obesity
Hof A, Landerer M, Peitsmeyer P, Herzog R, Alber J, Ahdab M, Nettersheim F, Mehrkens D, Geißen S, Braumann S, Guthoff H, von Stein P, Nemade H, Picard F, Braun R, Hoyer F, Brüning J, Pfeifer A, Hildebrand S, Winkels H, Baldus S, Adam M, Schäkel J, Mollenhauer M. Myeloperoxidase impacts vascular function by altering perivascular adipocytes’ secretome and phenotype in obesity. Cell Reports Medicine 2025, 6: 102087. PMID: 40252642, PMCID: PMC12147848, DOI: 10.1016/j.xcrm.2025.102087.Peer-Reviewed Original ResearchConceptsPerivascular adipose tissueVascular functionEndothelial functionObesity-related cardiovascular diseaseImmune cell frequenciesAdipose tissuePerivascular adipose tissue inflammationReduced arterial stiffnessInflammatory cytokine releaseHuman white adipocytesInfluence vascular functionObese patientsAdipocyte marker expressionMyeloid cellsCardiovascular morbidityImmune cellsCytokine releaseEndothelial dysfunctionIncreased inflammationAdipocyte secretomeCell frequencyMarker expressionMPO levelsConsumption in vivoNitrotyrosine formationPhenotypic complexities of rare heterozygous neurexin-1 deletions
Fernando M, Fan Y, Zhang Y, Tokolyi A, Murphy A, Kammourh S, Deans P, Ghorbani S, Onatzevitch R, Pero A, Padilla C, Williams S, Flaherty E, Prytkova I, Cao L, Knowles D, Fang G, Slesinger P, Brennand K. Phenotypic complexities of rare heterozygous neurexin-1 deletions. Nature 2025, 642: 710-720. PMID: 40205044, DOI: 10.1038/s41586-025-08864-9.Peer-Reviewed Original ResearchMeSH KeywordsAlternative SplicingAnimalsCalcium-Binding ProteinsCell Adhesion Molecules, NeuronalDNA Copy Number VariationsFemaleGABAergic NeuronsGene DeletionHeterozygoteHumansInduced Pluripotent Stem CellsLoss of Function MutationMaleMiceNeural Cell Adhesion MoleculesPhenotypeSequence DeletionSynapsesConceptsLoss-of-functionGain-of-functionGain-of-function mechanismCopy number variantsSynaptic activityCell type-specific effectsCell adhesion proteinsPrecision medicineIncreased wild-typeSplicing resultsAlternative splicingIsoform repertoireNRXN1 deletionsAberrant splicingHuman induced pluripotent stem cellsPatient-specific mutationsIncreased synaptic activityDecreased synaptic activityMutant isoformsNRXN1Associated with riskPluripotent stem cellsHeterozygous deletionWild-typeDeletionAlcohol use disorder and body mass index show genetic pleiotropy and shared neural associations
Malone S, Davis C, Piserchia Z, Setzer M, Toikumo S, Zhou H, Winterlind E, Gelernter J, Justice A, Leggio L, Rentsch C, Kranzler H, Gray J. Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations. Nature Human Behaviour 2025, 9: 1056-1066. PMID: 40164914, DOI: 10.1038/s41562-025-02148-y.Peer-Reviewed Original ResearchConceptsAlcohol use disorderUse disorderBrain regionsGenotype-Tissue ExpressionSingle-nucleotide polymorphismsPolygenic overlapAssociated with alcohol use disorderCaudate nucleus volumeBody mass indexMultiple brain regionsConjunctional false discovery rateNeurobiological overlapExecutive functionNeurobiological mechanismsNeural associationsBrain phenotypesNucleus volumeFalse discovery rate methodFalse discovery rateGenetic architectureVariant effectsMass indexGenetic pleiotropyDiscovery rateTissue enrichmentBeyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records
Yan C, Grabowska M, Thakkar R, Dickson A, Embí P, Feng Q, Denny J, Kerchberger V, Malin B, Wei W. Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records. Journal Of The American Medical Informatics Association 2025, 32: 1007-1014. PMID: 40156924, PMCID: PMC12089765, DOI: 10.1093/jamia/ocaf055.Peer-Reviewed Original ResearchConceptsElectronic health recordsSensorineural hearing lossHearing lossDiagnosis codesHealth recordsPhenotype risk scorePatient recordsClinical terminologyProstate cancerType 2 diabetes mellitusRisk scoreDementiaExpert reviewT2DMPHEMAPPhenotype patientsProstatePatientsCancerPositive casesPhecodesResearch reliabilityDiagnosisRecordsPhenotypeOptimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants
Yuan M, Goovaerts S, Lee M, Devine J, Richmond S, Walsh S, Shriver M, Shaffer J, Marazita M, Peeters H, Weinberg S, Claes P. Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants. Briefings In Bioinformatics 2025, 26: bbaf090. PMID: 40062617, PMCID: PMC11891655, DOI: 10.1093/bib/bbaf090.Peer-Reviewed Original ResearchConceptsRare variant association studiesGenome-wide association studiesComplex morphological traitsGenomic lociSNP heritabilityAssociation studiesRare variantsPhenotypic variationMorphological traitsAxes of phenotypic variationContext of genome-wide association studiesVariant association studiesIndividuals of European ancestryGene-based testsLinkage disequilibrium score regressionRare genetic variantsGenomic relatednessOptimal phenotypeUnrelated individualsGenetic variantsRelevant traitsEuropean ancestryScore regressionPhenotype distributionFamily dataModeling SMAD2 Mutations in Induced Pluripotent Stem Cells Provides Insights Into Cardiovascular Disease Pathogenesis
Ward T, Morton S, Venturini G, Tai W, Jang M, Gorham J, Delaughter D, Wasson L, Khazal Z, Homsy J, Gelb B, Chung W, Bruneau B, Brueckner M, Tristani-Firouzi M, DePalma S, Seidman C, Seidman J. Modeling SMAD2 Mutations in Induced Pluripotent Stem Cells Provides Insights Into Cardiovascular Disease Pathogenesis. Journal Of The American Heart Association 2025, 14: e036860. PMID: 40028843, PMCID: PMC12184555, DOI: 10.1161/jaha.124.036860.Peer-Reviewed Original ResearchConceptsLoss-of-functionCongenital heart diseaseChromatin accessibilityMissense variantsCHD probandsPluripotent stem cellsHomozygous loss-of-functionCHD-associated genesHeterozygous loss-of-functionTranscription factor bindingMutant induced pluripotent stem cellsChromatin immunoprecipitation dataChromatin peaksStem cellsChromatin interactionsInduced pluripotent stem cellsFactor bindingTranscription factor NanogExome sequencingImmunoprecipitation dataTranscription factorsRNA sequencingChromatinMissenseMolecular consequencesRecessive genetic contribution to congenital heart disease in 5,424 probands
Dong W, Jin S, Sierant M, Lu Z, Li B, Lu Q, Morton S, Zhang J, López-Giráldez F, Nelson-Williams C, Knight J, Zhao H, Cao J, Mane S, Gruber P, Lek M, Goldmuntz E, Deanfield J, Giardini A, Mital S, Russell M, Gaynor J, Cnota J, Wagner M, Srivastava D, Bernstein D, Porter G, Newburger J, Roberts A, Yandell M, Yost H, Tristani-Firouzi M, Kim R, Seidman J, Chung W, Gelb B, Seidman C, Lifton R, Brueckner M. Recessive genetic contribution to congenital heart disease in 5,424 probands. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2419992122. PMID: 40030011, PMCID: PMC11912448, DOI: 10.1073/pnas.2419992122.Peer-Reviewed Original ResearchConceptsRecessive genotypeCHD probandsCongenital heart diseaseAssociated with laterality defectsGene-based analysisAnalyzed whole-exome sequencingLeft-sided congenital heart diseaseWhole-exome sequencingCongenital heart disease phenotypeAshkenazi Jewish probandsOffspring of consanguineous unionsSingle-cell transcriptomicsCHD geneExome sequencingMouse notochordSecreted proteinsConsanguineous familyFounder variantGenesSignificant enrichmentLaterality phenotypesHeart diseaseProbandsAbnormal contractile functionConsanguineous unionsBrain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors
Deiana G, He J, Cabrera-Mendoza B, Ciccocioppo R, Napolioni V, Polimanti R. Brain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors. Translational Psychiatry 2025, 15: 61. PMID: 39979292, PMCID: PMC11842717, DOI: 10.1038/s41398-025-03288-5.Peer-Reviewed Original ResearchConceptsImaging-derived phenotypesBrain imaging-derived phenotypesAlcohol drinkingBrain structuresProcessing of social cuesCorrelates of smoking behaviorRelationship of brain structureSequencing Consortium of AlcoholGlobal genetic correlationsSmoking behaviorSuperior longitudinal fasciculusAssociated with smoking initiationTobacco smokeTobacco smoking behaviorLatent causal variable approachesNicotine useBrain regionsPremotor cortexSocial cuesWhite matter hyperintensitiesLongitudinal fasciculusChemosensory processingCortical thicknessMendelian randomizationPleiotropic mechanismsNongenetic adaptation by collective migration
Vo L, Avgidis F, Mattingly H, Edmonds K, Burger I, Balasubramanian R, Shimizu T, Kazmierczak B, Emonet T. Nongenetic adaptation by collective migration. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2423774122. PMID: 39970001, PMCID: PMC11874451, DOI: 10.1073/pnas.2423774122.Peer-Reviewed Original ResearchConceptsGene regulationCollective migrationPhenotype distributionPhenotypic compositionStress response pathwaysSwimming phenotypeCell populationsBacterial populationsStress responseAbundance distributionMultidimensional phenotypesGenetic mutationsPhenotypeDiverse environmentsEnvironmental conditionsGenesMutationsSwimming behaviorChanging environmentDoubling timeMigrating populationCellsRegulationMigrationAdaptationEvaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies
Jing N, Lu Y, Tong J, Weaver J, Ryan P, Xu H, Chen Y. Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies. Journal Of Biomedical Informatics 2025, 163: 104787. PMID: 39904407, PMCID: PMC12180398, DOI: 10.1016/j.jbi.2025.104787.Peer-Reviewed Original ResearchConceptsType I errorIntegrated likelihood estimatorsElectronic health recordsUse-case analysisLikelihood estimationLow prevalence outcomesUse-casesBias reductionNaive methodEffect sizeSynthetic dataPhenotyping algorithmsEstimation biasReal-world scenariosStatistical inferenceSimulation studyAssociation effect sizesAccurate prior informationBinary outcomesPoint estimatesAssociation estimatesStatistical powerHealth recordsKnowledge-guidedOutcome prevalenceBridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia
Guo H, Xiao Y, Dong S, Yang J, Zhao P, Zhao T, Cai A, Tang L, Liu J, Wang H, Hua R, Liu R, Wei Y, Sun D, Liu Z, Xia M, He Y, Wu Y, Si T, Womer F, Xu F, Tang Y, Wang J, Zhang W, Zhang X, Wang F. Bridging animal models and humans: neuroimaging as intermediate phenotypes linking genetic or stress factors to anhedonia. BMC Medicine 2025, 23: 38. PMID: 39849528, PMCID: PMC11755933, DOI: 10.1186/s12916-025-03850-4.Peer-Reviewed Original ResearchConceptsIntermediate phenotypesCore symptoms of depressionAmplitude of low-frequency fluctuationNeuroimaging patternsSubtypes of depressionCore depressive symptomsExpression of risk genesDiverse clinical populationsSymptoms of depressionRodent modelsAssociated with depressionLow-frequency fluctuationsStress-related changesDepression subtypesCore symptomsCross-species validationPsychiatric disordersNeuropsychiatric disordersDepressive symptomsBehavioral manifestationsStress modelDepression cohortClinical populationsSensorimotor regionsAnhedoniaBayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data
Im Y, Li R, Ma S. Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data. Statistics In Medicine 2025, 44: e10350. PMID: 39840672, PMCID: PMC11774474, DOI: 10.1002/sim.10350.Peer-Reviewed Original ResearchMultiomics dissection of human RAG deficiency reveals distinctive patterns of immune dysregulation but a common inflammatory signature
Bosticardo M, Dobbs K, Delmonte O, Martins A, Pala F, Kawai T, Kenney H, Magro G, Rosen L, Yamazaki Y, Yu H, Calzoni E, Lee Y, Liu C, Stoddard J, Niemela J, Fink D, Castagnoli R, Ramba M, Cheng A, Riley D, Oikonomou V, Shaw E, Belaid B, Keles S, Al-Herz W, Cancrini C, Cifaldi C, Baris S, Sharapova S, Schuetz C, Gennery A, Freeman A, Somech R, Choo S, Giliani S, Güngör T, Drozdov D, Meyts I, Moshous D, Neven B, Abraham R, El-Marsafy A, Kanariou M, King A, Licciardi F, Cruz-Muñoz M, Palma P, Poli C, Adeli M, Algeri M, Alroqi F, Bastard P, Bergerson J, Booth C, Brett A, Burns S, Butte M, Padem N, de la Morena M, Dbaibo G, de Ravin S, Dimitrova D, Djidjik R, Dorna M, Dutmer C, Elfeky R, Facchetti F, Fuleihan R, Geha R, Gonzalez-Granado L, Haljasmägi L, Ale H, Hayward A, Hifanova A, Ip W, Kaplan B, Kapoor N, Karakoc-Aydiner E, Kärner J, Keller M, Dávila Saldaña B, Kiykim A, Kuijpers T, Kuznetsova E, Latysheva E, Leiding J, Locatelli F, Alva-Lozada G, McCusker C, Celmeli F, Morsheimer M, Ozen A, Parvaneh N, Pasic S, Plebani A, Preece K, Prockop S, Sakovich I, Starkova E, Torgerson T, Verbsky J, Walter J, Ward B, Wisner E, Draper D, Myint-Hpu K, Truong P, Lionakis M, Similuk M, Walkiewicz M, Klion A, Holland S, Oguz C, Bogunovic D, Kisand K, Su H, Tsang J, Kuhns D, Villa A, Rosenzweig S, Pittaluga S, Notarangelo L, Ghosh R, Siefert B, Tokita M, Yan J, Jodarski C, Kamen M, Gore R, Reynolds-Lallement N, Lewis K, Bannon S, Borges A, Gentile N. Multiomics dissection of human RAG deficiency reveals distinctive patterns of immune dysregulation but a common inflammatory signature. Science Immunology 2025, 10: eadq1697. PMID: 39792639, PMCID: PMC12087669, DOI: 10.1126/sciimmunol.adq1697.Peer-Reviewed Original ResearchConceptsRAG deficiencyRecombination-activating geneImmune dysregulationInflammatory signaturePattern of immune dysregulationT helper 2B cell developmentType I interferonOmenn syndromeImmunological phenotypeImmune profileSelf-antigensB cellsClinical managementDefective TI interferonCellular indicesImmunopathologyPatientsMultiomics approachPhenotypeHypomorphic formDysregulationLineage-specific contributionsDeficiencyAcid sphingomyelinase deficiency and Gaucher disease: Underdiagnosed and often treatable causes of hepatomegaly, splenomegaly, and low HDL cholesterol in lean individuals
Mistry P, Cassiman D, Jones S, Lachmann R, Lukina E, Prada C, Wasserstein M, Thurberg B, Foster M, Patel R, Underhill L, Peterschmitt M. Acid sphingomyelinase deficiency and Gaucher disease: Underdiagnosed and often treatable causes of hepatomegaly, splenomegaly, and low HDL cholesterol in lean individuals. Hepatology Communications 2025, 9: e0621. PMID: 39774103, PMCID: PMC11717527, DOI: 10.1097/hc9.0000000000000621.Peer-Reviewed Original ResearchConceptsGaucher disease type 1Acid sphingomyelinase deficiencyHDL cholesterolSphingomyelinase deficiencyDifferential diagnosis of patientsBiomarkers of disease activityLipoprotein phenotypeLower body mass indexMean HDL cholesterolLow HDL cholesterolLiver function testsDiagnosis of patientsBody mass indexModerate hepatosplenomegalySpleen volumeLipid abnormalitiesLow HDLDiagnostic delayIrreversible complicationsLiver volumeLyso-sphingomyelinMultisystemic manifestationsDisease activityLDL cholesterolDifferential diagnosis
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