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 inhibitorLeukopeniaIncidence and Risk Factors for Steroid-associated Osteonecrosis in Children and Adolescents: A Systematic Review of the Literature
Johnson T, Naz H, Taylor V, Farook S, Hofmann G, Harbacheck K, Pham N, Smith S, Chao K, Lee T, Goodman S, Shea K. Incidence and Risk Factors for Steroid-associated Osteonecrosis in Children and Adolescents: A Systematic Review of the Literature. Journal Of Pediatric Orthopaedics 2025, 45: 337-347. PMID: 40078093, DOI: 10.1097/bpo.0000000000002919.Peer-Reviewed Original ResearchConceptsSystematic reviewClinical decision-makingCohort studyRisk factorsSteroid-associated osteonecrosisControlled TrialsNon-Hispanic raceCochrane Central Registry of Controlled TrialsMeta-analyses guidelinesPediatric orthopaedic surgeonsCentral Registry of Controlled TrialsRegistry of Controlled TrialsProspective cohort studyStandard clinical pathwayBody mass indexComprehensive systematic reviewRandomized Controlled TrialsCochrane Central RegistryRetrospective cohort studyScreened titlesFunctional disabilityGenetic risk factorsSystemic corticosteroid exposureClinical pathwayYears of ageA computable electronic health record ARDS classifier recapitulates an association between the MUC5B promoter polymorphism and ARDS in critically ill adults.
Kerchberger V, McNeil J, Zheng N, Chang D, Rosenberger C, Rogers A, Bastarache J, Feng Q, Wei W, Ware L. A computable electronic health record ARDS classifier recapitulates an association between the MUC5B promoter polymorphism and ARDS in critically ill adults. CHEST Critical Care 2025, 100150. DOI: 10.1016/j.chstcc.2025.100150.Peer-Reviewed Original ResearchElectronic health recordsCritically Ill AdultsElectronic health record dataMUC5B Promoter PolymorphismIll adultsAt-risk adultsNegative predictive valuePositive predictive valueDiagnostic billing codesHealth recordsHospital participationGenetic risk factorsDNA biobanksBilling codesBioVUStudy designPromoter polymorphismCohort of critically ill adultsAt-riskCohen's kappaModerate agreementRisk factorsGenotyped cohortPredictive valueBiobankSex‐Specific Association Between Polymorphisms in Estrogen Receptor Alpha Gene (ESR1) and Depression: A Genome‐Wide Association Study of All of Us and UK Biobank Data
Hu Y, Che M, Zhang H. Sex‐Specific Association Between Polymorphisms in Estrogen Receptor Alpha Gene (ESR1) and Depression: A Genome‐Wide Association Study of All of Us and UK Biobank Data. Genetic Epidemiology 2025, 49: e70004. PMID: 40007508, PMCID: PMC11924109, DOI: 10.1002/gepi.70004.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSingle-nucleotide polymorphismsAssociation studiesAlpha geneEstrogen receptor alpha geneGenetic risk factorsRisk lociGenomic associationsMajor depressive disorderMDD phenotypesGenetic studiesGenetic associationRisk factors of MDDGenesESR1 geneUK BiobankESR1Participant genotypesPolymorphismSex-SpecificSex-specific associationsDepressive disorderRacial/ethnic disparitiesFindings lack consistencyLength of lifeThe effect of polygenic risk score on PD risk and phenotype in LRRK2 G2019S and GBA1 carriers
Goldstein O, Shani S, Gana-Weisz M, Elkoshi N, Casey F, Sun Y, Chandratre K, Cedarbaum J, Blauwendraat C, Bar-Shira A, Thaler A, Gurevich T, Mirelman A, Giladi N, Orr-Urtreger A, Alcalay R. The effect of polygenic risk score on PD risk and phenotype in LRRK2 G2019S and GBA1 carriers. Journal Of Parkinson’s Disease 2025, 15: 291-299. PMID: 39973498, DOI: 10.1177/1877718x241310722.Peer-Reviewed Original ResearchPolygenic risk scoresPolygenic risk score modelPolygenic risk score associationsLRRK2 G2019S carriersGenetic risk factorsPD riskNon-carriersGenetic groupsDisease riskRisk scoreLRRK2 G2019SRisk factorsEffect of polygenic risk scoresGenetically complex conditionsCalculate polygenic risk scoresG2019S carriersElevated PRSAssessed PD riskAssociated with increased PD riskAshkenazi Jewish ancestryAssociated with PD riskModify disease riskGenotype callsGBA1 variantsLRRK2-G2019S-PDApplication of Stem Cells to Understanding Psychiatric Disorders
Birtele M, Quadrato G, Brennand K. Application of Stem Cells to Understanding Psychiatric Disorders. 2025, 53-64. DOI: 10.1093/med/9780197640654.003.0005.Peer-Reviewed Original ResearchPsychiatric disordersHuman induced pluripotent stem cellsAnimal models of autism spectrum disorderModel of autism spectrum disorderNeuroimaging studies of patientsModel psychiatric disordersAutism spectrum disorderStem cellsBipolar disorderDerivation of human induced pluripotent stem cellsNeuroimaging studiesApplication of stem cellsNeural processesSpectrum disorderStudy of patientsMental illnessPluripotent stem cellsDisordersComplex genetic risk factorsGenetic risk factorsTherapeutic approachesAnimal modelsNeural pathologyRisk factorsDisease predisposition
2024
Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of ADX‐chromosome-wide association study for Alzheimer’s disease
Le Borgne J, Gomez L, Heikkinen S, Amin N, Ahmad S, Choi S, Bis J, Grenier-Boley B, Rodriguez O, Kleineidam L, Young J, Tripathi K, Wang L, Varma A, Campos-Martin R, van der Lee S, Damotte V, de Rojas I, Palmal S, Lipton R, Reiman E, McKee A, De Jager P, Bush W, Small S, Levey A, Saykin A, Foroud T, Albert M, Hyman B, Petersen R, Younkin S, Sano M, Wisniewski T, Vassar R, Schneider J, Henderson V, Roberson E, DeCarli C, LaFerla F, Brewer J, Swerdlow R, Van Eldik L, Hamilton-Nelson K, Paulson H, Naj A, Lopez O, Chui H, Crane P, Grabowski T, Kukull W, Asthana S, Craft S, Strittmatter S, Cruchaga C, Leverenz J, Goate A, Kamboh M, George-Hyslop P, Valladares O, Kuzma A, Cantwell L, Riemenschneider M, Morris J, Slifer S, Dalmasso C, Castillo A, Küçükali F, Peters O, Schneider A, Dichgans M, Rujescu D, Scherbaum N, Deckert J, Riedel-Heller S, Hausner L, Molina-Porcel L, Düzel E, Grimmer T, Wiltfang J, Heilmann-Heimbach S, Moebus S, Tegos T, Scarmeas N, Dols-Icardo O, Moreno F, Pérez-Tur J, Bullido M, Pastor P, Sánchez-Valle R, Álvarez V, Boada M, García-González P, Puerta R, Mir P, Real L, Piñol-Ripoll G, García-Alberca J, Royo J, Rodriguez-Rodriguez E, Soininen H, de Mendonça A, Mehrabian S, Traykov L, Hort J, Vyhnalek M, Thomassen J, Pijnenburg Y, Holstege H, van Swieten J, Ramakers I, Verhey F, Scheltens P, Graff C, Papenberg G, Giedraitis V, Boland A, Deleuze J, Nicolas G, Dufouil C, Pasquier F, Hanon O, Debette S, Grünblatt E, Popp J, Ghidoni R, Galimberti D, Arosio B, Mecocci P, Solfrizzi V, Parnetti L, Squassina A, Tremolizzo L, Borroni B, Nacmias B, Spallazzi M, Seripa D, Rainero I, Daniele A, Bossù P, Masullo C, Rossi G, Jessen F, Fernandez V, Kehoe P, Frikke-Schmidt R, Tsolaki M, Sánchez-Juan P, Sleegers K, Ingelsson M, Haines J, Farrer L, Mayeux R, Wang L, Sims R, DeStefano A, Schellenberg G, Seshadri S, Amouyel P, Williams J, van der Flier W, Ramirez A, Pericak-Vance M, Andreassen O, Van Duijn C, Hiltunen M, Ruiz A, Dupuis J, Martin E, Lambert J, Kunkle B, Bellenguez C. X‐chromosome-wide association study for Alzheimer’s disease. Molecular Psychiatry 2024, 30: 2335-2346. PMID: 39633006, PMCID: PMC12092188, DOI: 10.1038/s41380-024-02838-5.Peer-Reviewed Original ResearchX-chromosome inactivationX chromosomeAssociation studiesAlzheimer's diseaseGenome-wide significant signalsX chromosome-wide association studyGenome-wide association studiesAD casesEscape X chromosome inactivationNon-pseudoautosomal regionRandom X-chromosome inactivationSignificant lociGenetic risk factorsGenetic landscapeLociIndex variantsSignificant signalsAlzheimerRisk factorsXq25FRMPD4Dach2PJA1XWASSignalA multimodal Neuroimaging-Based risk score for mild cognitive impairment
Zendehrouh E, Sendi M, Abrol A, Batta I, Hassanzadeh R, Calhoun V. A multimodal Neuroimaging-Based risk score for mild cognitive impairment. NeuroImage Clinical 2024, 45: 103719. PMID: 39637673, PMCID: PMC11664180, DOI: 10.1016/j.nicl.2024.103719.Peer-Reviewed Original ResearchMild cognitive impairment riskMild cognitive impairmentMild cognitive impairment groupRisk of mild cognitive impairmentRisk scoreUK Biobank participantsFunctional network connectivityCognitive impairmentPrecursor to ADSignificant cognitive declineBiobank participantsUK BiobankMild cognitive impairment individualsGenetic risk factorsAlzheimer's diseaseFunctional MRIHigh-risk groupStructural MRIAD riskRisk factorsCognitive declineFeatures of CNGray matterDifferentiate CNParticipantsRESOLVING 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 datasetPsychosisSNPsPhenome-Wide Association Study of Latent Autoimmune Diabetes from a Southern Mexican Population Implicates rs7305229 with Plasmatic Anti-Glutamic Acid Decarboxylase Autoantibody (GADA) Levels
Nolasco-Rosales G, Martínez-Magaña J, Juárez-Rojop I, Rodríguez-Sánchez E, Ruiz-Ramos D, Villatoro-Velázquez J, Bustos-Gamiño M, Medina-Mora M, Tovilla-Zárate C, Cruz-Castillo J, Nicolini H, Genis-Mendoza A. Phenome-Wide Association Study of Latent Autoimmune Diabetes from a Southern Mexican Population Implicates rs7305229 with Plasmatic Anti-Glutamic Acid Decarboxylase Autoantibody (GADA) Levels. International Journal Of Molecular Sciences 2024, 25: 10154. PMID: 39337639, PMCID: PMC11432505, DOI: 10.3390/ijms251810154.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesPhenome-wide association studyAssociation studiesGenetics of LADANon-European populationsGenome-wideGenetic risk factorsGENESIS packageSoutheastern MexicoPopulation implicationsPheWASFAIM2Risk genotypesAnti-glutamic acid decarboxylase autoantibodiesGlutamate decarboxylase autoantibodiesAutoimmune diabetesBody adiposity measuresMexican populationLatent autoimmune diabetesPLINKBody mass indexGenesAdiposity measuresChildhood obesityType 2 diabetesNeoself-antigens are the primary target for autoreactive T cells in human lupus
Mori S, Kohyama M, Yasumizu Y, Tada A, Tanzawa K, Shishido T, Kishida K, Jin H, Nishide M, Kawada S, Motooka D, Okuzaki D, Naito R, Nakai W, Kanda T, Murata T, Terao C, Ohmura K, Arase N, Kurosaki T, Fujimoto M, Suenaga T, Kumanogoh A, Sakaguchi S, Ogawa Y, Arase H. Neoself-antigens are the primary target for autoreactive T cells in human lupus. Cell 2024, 187: 6071-6087.e20. PMID: 39276775, DOI: 10.1016/j.cell.2024.08.025.Peer-Reviewed Original ResearchSystemic lupus erythematosusAutoreactive T cellsT cellsMHC-IISelf-antigensDevelopment of lupus-like diseaseCD4<sup>+</sup> T cellsEpstein-Barr virus reactivationPathogenesis of systemic lupus erythematosusRisk factorsSystemic lupus erythematosus patientsMajor histocompatibility complex class IIHistocompatibility complex class IILupus-like diseaseLupus T cellsHuman lupusGenetic risk factorsVirus reactivationLupus erythematosusAdult micePrimary targetTrigger autoimmunityClass IIPeptide presentationInvariant chainAdvancing Genetic Testing in Kidney Diseases: Report From a National Kidney Foundation Working Group
Franceschini N, Feldman D, Berg J, Besse W, Chang A, Dahl N, Gbadegesin R, Pollak M, Rasouly H, Smith R, Winkler C, Gharavi A, Group N, Ars E, Bekheirnia M, Bier L, Bleyer A, Fuller L, Halbritter J, Harris P, Kiryluk K, Knoers N, Kopp J, Kramer H, Lagas S, Lieske J, Lu W, Mannon R, Markowitz G, Moe O, Nadkarni G, Nast C, Parekh R, Pei Y, Reed K, Rehm H, Richards D, Roberts M, Sabatello M, Salant D, Sampson M, Sanna-Cherchi S, Santoriello D, Sedor J, Sneddon T, Watnick T, Wilfond B, Williams W, Wong C. Advancing Genetic Testing in Kidney Diseases: Report From a National Kidney Foundation Working Group. American Journal Of Kidney Diseases 2024, 84: 751-766. PMID: 39033956, PMCID: PMC11585423, DOI: 10.1053/j.ajkd.2024.05.010.Peer-Reviewed Original ResearchGenetic testingAllied health professionalsImplementation of genetic testingModified Delphi processChronic kidney diseaseScreening of kidney diseasesHealth professionalsWorking GroupKidney diseaseGenetic risk factorsDelphi processWorking group of expertsNational Kidney FoundationPolygenic causeDisease of multiple causesClinical decisionsRisk factorsGroup of expertsCause of kidney diseaseKidney FoundationGenetic basisMultiple causesGroup consensusGenetic causeMonogenic disordersmTOR inhibition enhances synaptic and mitochondrial function in Alzheimer’s disease in an APOE genotype-dependent manner
Sanganahalli B, Mihailovic J, Vekaria H, Coman D, Yackzan A, Flemister A, Aware C, Wenger K, Hubbard W, Sullivan P, Hyder F, Lin A. mTOR inhibition enhances synaptic and mitochondrial function in Alzheimer’s disease in an APOE genotype-dependent manner. Cerebrovascular And Brain Metabolism Reviews 2024, 44: 1745-1758. PMID: 38879800, PMCID: PMC11494852, DOI: 10.1177/0271678x241261942.Peer-Reviewed Original ResearchResponse to rapamycinE3FAD miceMitochondrial functionAlzheimer's diseaseMammalian target of rapamycinAD genetic risk factorsApolipoprotein E4Neuronal mitochondrial functionMitochondrial oxidative metabolismE4FAD miceHuman APOE4 geneTCA cycle rateGenetic risk factorsGenotype-dependent mannerE3FADTarget of rapamycinAPOE4 geneAPOE3 alleleGlutamate-glutamine cycleAPOE4 carriersBioenergetic measurementsE4FADMetabolic functionsAPOE genotypeMammalian target of rapamycin inhibitionThe genetics of trichotillomania and excoriation disorder: A systematic review
Reid M, Lin A, Farhat L, Fernandez T, Olfson E. The genetics of trichotillomania and excoriation disorder: A systematic review. Comprehensive Psychiatry 2024, 133: 152506. PMID: 38833896, PMCID: PMC11513794, DOI: 10.1016/j.comppsych.2024.152506.Peer-Reviewed Original ResearchSystematic reviewGenome-wide researchGenome-wide associationDNA sequencing studiesDiscovery of risk genesWeb of ScienceGenetic factorsObsessive-compulsive disorderGenetic epidemiologyGenetic risk factorsSequencing studiesRisk genesGeneral populationMolecular geneticsExcoriation disorderRisk factorsGeneticsFirst-line medicationPsychiatric disordersObsessive-compulsive related disordersObsessive-compulsive disorder spectrumBody-focused repetitive behaviorsDevelopment of trichotillomaniaPsycINFOGenomeSingle-cell multi-cohort dissection of the schizophrenia transcriptome
Ruzicka W, Mohammadi S, Fullard J, Davila-Velderrain J, Subburaju S, Tso D, Hourihan M, Jiang S, Lee H, Bendl J, Voloudakis G, Haroutunian V, Hoffman G, Roussos P, Kellis M, Akbarian S, Abyzov A, Ahituv N, Arasappan D, Almagro Armenteros J, Beliveau B, Berretta S, Bharadwaj R, Bhattacharya A, Bicks L, Brennand K, Capauto D, Champagne F, Chatterjee T, Chatzinakos C, Chen Y, Chen H, Cheng Y, Cheng L, Chess A, Chien J, Chu Z, Clarke D, Clement A, Collado-Torres L, Cooper G, Crawford G, Dai R, Daskalakis N, Deep-Soboslay A, Deng C, DiPietro C, Dracheva S, Drusinsky S, Duan Z, Duong D, Dursun C, Eagles N, Edelstein J, Emani P, Galani K, Galeev T, Gandal M, Gaynor S, Gerstein M, Geschwind D, Girdhar K, Goes F, Greenleaf W, Grundman J, Guo H, Guo Q, Gupta C, Hadas Y, Hallmayer J, Han X, Hawken N, He C, Henry E, Hicks S, Ho M, Ho L, Huang Y, Huuki-Myers L, Hwang A, Hyde T, Iatrou A, Inoue F, Jajoo A, Jensen M, Jiang L, Jin P, Jin T, Jops C, Jourdon A, Kawaguchi R, Kleinman J, Kleopoulos S, Kozlenkov A, Kriegstein A, Kundaje A, Kundu S, Lee C, Lee D, Li J, Li M, Lin X, Liu S, Liu J, Liu J, Liu C, Liu S, Lou S, Loupe J, Lu D, Ma S, Ma L, Margolis M, Mariani J, Martinowich K, Maynard K, Mazariegos S, Meng R, Myers R, Micallef C, Mikhailova T, Ming G, Monte E, Montgomery K, Moore J, Moran J, Mukamel E, Nairn A, Nemeroff C, Ni P, Norton S, Nowakowski T, Omberg L, Page S, Park S, Patowary A, Pattni R, Pertea G, Peters M, Phalke N, Pinto D, Pjanic M, Pochareddy S, Pollard K, Pollen A, Pratt H, Przytycki P, Purmann C, Qin Z, Qu P, Quintero D, Raj T, Rajagopalan A, Reach S, Reimonn T, Ressler K, Ross D, Rozowsky J, Ruth M, Sanders S, Schneider J, Scuderi S, Sebra R, Sestan N, Seyfried N, Shao Z, Shedd N, Shieh A, Shin J, Skarica M, Snijders C, Song H, State M, Stein J, Steyert M, Sudhof T, Snyder M, Tao R, Therrien K, Tsai L, Urban A, Vaccarino F, van Bakel H, Vo D, Wamsley B, Wang T, Wang S, Wang D, Wang Y, Warrell J, Wei Y, Weimer A, Weinberger D, Wen C, Weng Z, Whalen S, White K, Willsey A, Won H, Wong W, Wu H, Wu F, Wuchty S, Wylie D, Xu S, Yap C, Zeng B, Zhang P, Zhang C, Zhang B, Zhang J, Zhang Y, Zhou X, Ziffra R, Zeier Z, Zintel T. Single-cell multi-cohort dissection of the schizophrenia transcriptome. Science 2024, 384: eadg5136. PMID: 38781388, DOI: 10.1126/science.adg5136.Peer-Reviewed Original ResearchConceptsGenetic risk factorsRisk factorsTranscriptional changesHeterogeneity of schizophreniaNeuronal cell statesSchizophrenia pathophysiologySingle-cell dissectionExcitatory neuronsEffective therapySchizophrenia transcriptomicsCortical cytoarchitectureSingle-cell atlasGenomic variantsCell groupsHuman prefrontal cortexMolecular pathwaysSchizophreniaTranscriptional alterationsTranscriptomic changesPrefrontal cortexCell statesAlterationsTherapyPathophysiologyDissectionCharacterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry
Jiang L, Gangireddy S, Dickson A, Xin Y, Yan C, Kawai V, Cox N, Linton M, Wei W, Stein C, Feng Q. Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry. Journal Of Lipid Research 2024, 65: 100569. PMID: 38795861, PMCID: PMC11231545, DOI: 10.1016/j.jlr.2024.100569.Peer-Reviewed Original ResearchIndividuals of AAElectronic health recordsMild-to-moderate HTGGenetic risk factorsAfrican ancestryEuropean ancestryGenetic profileIndividuals of European ancestryRisk factorsNormal TGLongitudinal electronic health recordsSevere hypertriglyceridemiaPolygenic risk scoresCohort of AA patientsPatients of African ancestryAPOA5 p.Metabolic genesFunctional variantsCardiovascular risk factorsHigher triglyceride levelsHealth recordsVariant allelesAncestryRisk scoreAA patients1030 APOE Genotype and Sleep Architecture
Cho G, Buxton O, Pan Y, Miner B. 1030 APOE Genotype and Sleep Architecture. Sleep 2024, 47: a442-a443. DOI: 10.1093/sleep/zsae067.01030.Peer-Reviewed Original ResearchInverse linear trendAPOE4 carrier statusCarrier statusNon-carriersAPOE4 genotypeBurden of Alzheimer's diseaseSlow wave sleepSleep Heart Health StudyAllele carrier statusAPOE4 carriersAPOE4 alleleHealth StudyAssociated with timeReduced sleep qualityGenetic risk factorsLinear regression modelsLinear trendMarital statusIncreasing burdenSleep architectureNon-significant resultsSleep qualityIn-homeEducational attainmentSleep durationLow-frequency inherited complement receptor variants are associated with purpura fulminans
Bendapudi P, Nazeen S, Ryu J, Söylemez O, Robbins A, Rouaisnel B, O'Neil J, Pokhriyal R, Yang M, Colling M, Pasko B, Bouzinier M, Tomczak L, Collier L, Barrios D, Ram S, Toth-Petroczy A, Krier J, Fieg E, Dzik W, Hudspeth J, Pozdnyakova O, Nardi V, Knight J, Maas R, Sunyaev S, Losman J. Low-frequency inherited complement receptor variants are associated with purpura fulminans. Blood 2024, 143: 1032-1044. PMID: 38096369, PMCID: PMC10950473, DOI: 10.1182/blood.2023021231.Peer-Reviewed Original ResearchConceptsPurpura fulminansGermline whole-exome sequencingFunction-altering variantsInvestigate genetic risk factorsAssociated with purpura fulminansMultivariate logistic regression analysisWhole-exome sequencingInfectious purpura fulminansAssociated with PFCases of PFPatient sample collectionLogistic regression analysisBurden testsGenetic risk factorsFunctional characterizationSevere sepsisComplement receptor CR3Sepsis-inducedDisease phenotypeReceptor variantsDisease acuityRisk factorsMedical recordsLimited statistical powerInherited defectsUsing 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 risk
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