2025
Hierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling
Li J, Zhang Q, Ma S, Fang K, Xu Y. Hierarchical Multi‐Label Classification With Gene‐Environment Interactions in Disease Modeling. Statistics In Medicine 2025, 44: e10330. PMID: 39865593, PMCID: PMC12201914, DOI: 10.1002/sim.10330.Peer-Reviewed Original ResearchConceptsHierarchical multi-label classificationMulti-label classificationGene-environment interaction analysisGene-environmentEfficient expectation-maximizationGene-environment interactionsSemi-supervised scenariosCancer Genome AtlasUnlabeled dataInteraction analysisExpectation-maximizationGenome AtlasSuperior performanceHierarchical responseDisease outcomeClassificationPenalized estimatorsPractice settingsDisease modelsBiomedical studiesAnalysis literatureE effectsLupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders
Zhou H, Balint D, Shi Q, Vartanian T, Kriegel M, Brito I. Lupus and inflammatory bowel disease share a common set of microbiome features distinct from other autoimmune disorders. Annals Of The Rheumatic Diseases 2025, 84: 93-105. PMID: 39874239, PMCID: PMC11868722, DOI: 10.1136/ard-2024-225829.Peer-Reviewed Original ResearchProtein-protein interaction analysisMicrobial signaturesMicrobial profilesEffector-like proteinsSignaling pathwayInterleukin-12 signaling pathwayDisease mechanismsBacteria-derived proteinsMetagenomic datasetsMicrobiome featuresMicrobial underpinningsFunctional genesMicrobial biomarkersInteraction analysisMicrobial influenceInflammatory bowel diseaseMicrobial mechanismsGlucocorticoid signalingProteinGlucocorticoid receptorCritical roleAutoimmune diseasesPathwayBowel diseasePotential importance
2024
High-Dimensional Gene–Environment Interaction Analysis
Wu M, Li Y, Ma S. High-Dimensional Gene–Environment Interaction Analysis. Annual Review Of Statistics And Its Application 2024 DOI: 10.1146/annurev-statistics-112723-034315.Peer-Reviewed Original ResearchFixed- and random-effects analysisG-E interaction analysisG-E interactionsVariable selectionFrequentist analysisGene-environmentRandom effects analysisGeneral frameworkStatistical propertiesProgression of complex diseasesDimension reductionHypothesis testingG-EComplex diseasesGenetic factorsInteraction analysisNonlinear effect analysisStatistical perspectiveDisease outcomeEnvironmental factorsPrediction-basedEstimation-based
2023
Profiling neuronal methylome and hydroxymethylome of opioid use disorder in the human orbitofrontal cortex
Rompala G, Nagamatsu S, Martínez-Magaña J, Nuñez-Ríos D, Wang J, Girgenti M, Krystal J, Gelernter J, Hurd Y, Montalvo-Ortiz J. Profiling neuronal methylome and hydroxymethylome of opioid use disorder in the human orbitofrontal cortex. Nature Communications 2023, 14: 4544. PMID: 37507366, PMCID: PMC10382503, DOI: 10.1038/s41467-023-40285-y.Peer-Reviewed Original ResearchConceptsOpioid use disorderMulti-omics findingsGene expression patternsCo-methylation analysisGene expression profilesMulti-omics profilingGene networksDNA methylationNeuronal methylomesDNA hydroxymethylationMethylomic analysisExpression patternsExpression profilesEpigenetic disturbancesUse disordersPsychiatric traitsOrbitofrontal cortexOpioid-related drugsPostmortem orbitofrontal cortexEnvironmental factorsDrug interaction analysisOUD treatmentHuman orbitofrontal cortexOpioid signalingInteraction analysisA Brain-Behavior Interaction Analysis for Dyadic Brain Responses during Eye Contact between Parents and Children
Lee R, Friedman J, O'Brien M, Ren Z, Hong L, Sajda P, Tottenham N. A Brain-Behavior Interaction Analysis for Dyadic Brain Responses during Eye Contact between Parents and Children. Proceedings Of The International Society For Magnetic Resonance In Medicine ... Scientific Meeting And Exhibition. 2023 DOI: 10.58530/2023/1027.Peer-Reviewed Original Research
2022
An exploratory study of problematic shopping and problematic video gaming in adolescents
Greenberg NR, Zhai ZW, Hoff RA, Krishnan-Sarin S, Potenza MN. An exploratory study of problematic shopping and problematic video gaming in adolescents. PLOS ONE 2022, 17: e0272228. PMID: 35947621, PMCID: PMC9365157, DOI: 10.1371/journal.pone.0272228.Peer-Reviewed Original ResearchConceptsProblematic video gamingNegative reinforcementAddictive behaviorsVideo gamingAggressive behaviorMaladaptive copingHealth/High school studentsPsychosocial impairmentStress managementConnecticut high school studentsAdolescentsPhysical injuryGamingPossible interventionsPrevention effortsExploratory studySerious fightingInteraction analysisImpairmentMeasuresCopingFurther researchAnxietyRelationshipMyasthenia gravis-specific aberrant neuromuscular gene expression by medullary thymic epithelial cells in thymoma
Yasumizu Y, Ohkura N, Murata H, Kinoshita M, Funaki S, Nojima S, Kido K, Kohara M, Motooka D, Okuzaki D, Suganami S, Takeuchi E, Nakamura Y, Takeshima Y, Arai M, Tada S, Okumura M, Morii E, Shintani Y, Sakaguchi S, Okuno T, Mochizuki H. Myasthenia gravis-specific aberrant neuromuscular gene expression by medullary thymic epithelial cells in thymoma. Nature Communications 2022, 13: 4230. PMID: 35869073, PMCID: PMC9305039, DOI: 10.1038/s41467-022-31951-8.Peer-Reviewed Original ResearchConceptsMedullary thymic epithelial cellsEctopic expressionCellular composition estimationSingle-cell RNA sequencingThymic epithelial cellsSubpopulation of medullary thymic epithelial cellsEpithelial cellsMG-thymomaRNA sequencingGene expressionCell-cell interaction analysisCell migrationComprehensive atlasEctopic germinal center formationInteraction analysisDendritic cell migrationGerminal center formationMyasthenia gravisCellsTranscriptomeCXCL12-CXCR4Cell accumulationT/B cells
2021
S63 Genome-wide sex-by-SNP interaction analysis of susceptibility to idiopathic pulmonary fibrosis
Leavy O, Allen R, Oldham J, Guillen-Guio B, Stockwell A, Braybrooke R, Hubbard R, Ma S, Fingerlin T, Kaminski N, Zhang Y, Schwartz D, Yaspan B, Maher T, Molyneaux P, Flores C, Noth I, Jenkins R, Wain L. S63 Genome-wide sex-by-SNP interaction analysis of susceptibility to idiopathic pulmonary fibrosis. Thorax 2021, 76: a42-a42. DOI: 10.1136/thorax-2021-btsabstracts.69.Peer-Reviewed Original ResearchGenome-wide association studiesSNP interaction analysisIPF susceptibilityGenetic determinantsIndependent variantsIPF riskDifferent biological pathwaysGenetic lociSuggestive statistical significancePromoter regionBiological pathwaysAssociation studiesCommon genetic risk factorCase-control data setsGenetic componentIndependent datasetsInteraction analysisSignificant variantsSignificance thresholdBiological mechanismsSNP interactionsGenetic risk factorsEuropean descentFurther insightVariants
2020
In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease
Cahill ME, Loeb M, Dewan AT, Montgomery RR. In-Depth Analysis of Genetic Variation Associated with Severe West Nile Viral Disease. Vaccines 2020, 8: 744. PMID: 33302579, PMCID: PMC7768385, DOI: 10.3390/vaccines8040744.Peer-Reviewed Original ResearchAdditional novel variantsWest Nile virusNovel genetic variantsComprehensive genetic studiesGenetic Variation AssociatedGenetic architectureGene-gene interaction analysisNovel lociGene targetsLocus analysisBiological roleGenetic studiesGenetic variantsVirus datasetCell linesVariation AssociatedSevere West Nile neuroinvasive diseaseNovel variantsMosquito-borne virusViable targetViral diseasesNile virusInteraction analysisGenesLociGenome-Wide Gene–Diabetes and Gene–Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia
Tang H, Jiang L, Stolzenberg-Solomon RZ, Arslan AA, Beane Freeman LE, Bracci PM, Brennan P, Canzian F, Du M, Gallinger S, Giles GG, Goodman PJ, Kooperberg C, Le Marchand L, Neale RE, Shu XO, Visvanathan K, White E, Zheng W, Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Blackford A, Bueno-de-Mesquita B, Buring JE, Campa D, Chanock SJ, Childs E, Duell EJ, Fuchs C, Gaziano JM, Goggins M, Hartge P, Hassam MH, Holly EA, Hoover RN, Hung RJ, Kurtz RC, Lee IM, Malats N, Milne RL, Ng K, Oberg AL, Orlow I, Peters U, Porta M, Rabe KG, Rothman N, Scelo G, Sesso HD, Silverman DT, Thompson IM, Tjønneland A, Trichopoulou A, Wactawski-Wende J, Wentzensen N, Wilkens LR, Yu H, Zeleniuch-Jacquotte A, Amundadottir LT, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Chatterjee N, Klein AP, Li D, Kraft P, Wei P. Genome-Wide Gene–Diabetes and Gene–Obesity Interaction Scan in 8,255 Cases and 11,900 Controls from PanScan and PanC4 Consortia. Cancer Epidemiology Biomarkers & Prevention 2020, 29: 1784-1791. PMID: 32546605, PMCID: PMC7483330, DOI: 10.1158/1055-9965.epi-20-0275.Peer-Reviewed Original ResearchConceptsSNP levelGenome-wide association study datasetGenome-wide levelGene-based analysisGWAS summary statisticsJoint effect testsGxE analysisGWAS top hitsPopulation substructureSignificant GxE interactionGene levelGene-environment interaction analysisAdditional genetic factorsTop hitsEnvironmental variablesGenetic variantsDiabetes/obesityGxE interactionsPancreatic cancerStudy sitesGenetic factorsMajor modifiable risk factorHit regionsModifiable risk factorsInteraction analysis
2019
Omega-3 Fatty Acids and Genome-Wide Interaction Analyses Reveal DPP10–Pulmonary Function Association
Xu J, Gaddis N, Bartz T, Hou R, Manichaikul A, Pankratz N, Smith A, Sun F, Terzikhan N, Markunas C, Patchen B, Schu M, Beydoun M, Brusselle G, Eiriksdottir G, Zhou X, Wood A, Graff M, Harris T, Ikram M, Jacobs D, Launer L, Lemaitre R, O’Connor G, Oelsner E, Psaty B, Vasan R, Rohde R, Rich S, Rotter J, Seshadri S, Smith L, Tiemeier H, Tsai M, Uitterlinden A, Voruganti V, Xu H, Zilhão N, Fornage M, Zillikens M, London S, Barr R, Dupuis J, Gharib S, Gudnason V, Lahousse L, North K, Steffen L, Cassano P, Hancock D. Omega-3 Fatty Acids and Genome-Wide Interaction Analyses Reveal DPP10–Pulmonary Function Association. American Journal Of Respiratory And Critical Care Medicine 2019, 199: 631-642. PMID: 30199657, PMCID: PMC6396866, DOI: 10.1164/rccm.201802-0304oc.Peer-Reviewed Original ResearchMeSH KeywordsAgedalpha-Linolenic AcidBiomarkersDipeptidyl-Peptidases and Tripeptidyl-PeptidasesDocosahexaenoic AcidsEicosapentaenoic AcidFatty Acids, Omega-3Fatty Acids, UnsaturatedFemaleForced Expiratory VolumeGenome-Wide Association StudyHumansMaleMiddle AgedPolymorphism, Single NucleotideRespiratory Physiological PhenomenaSex FactorsSmokingVital CapacityConceptsSNP associationsGenome-wide interaction analysisHeart and Aging ResearchMeta-analysesCohort-specific resultsAssociated with lower FVCGenome-wide analysisGenomic Epidemiology ConsortiumAssociated with FEV<sub>1</sub> anPulmonary function testsEpidemiology ConsortiumFatty acidsEffect modificationFunctional associationN-3 PUFAPulmonary healthLower FVCGenetic susceptibilitySpirometric measurementsCohortPolyunsaturated fatty acidsAssociationMeasures of pulmonary function testsInteraction analysisFVC
2018
Gene-Gene and Gene-Environment Interactions
DeWan AT. Gene-Gene and Gene-Environment Interactions. Methods In Molecular Biology 2018, 1793: 89-110. PMID: 29876893, DOI: 10.1007/978-1-4939-7868-7_7.BooksConceptsComplex traitsGene-environment interactionsGenome-wide interaction analysisGenetic variantsGenetic architectureDense panelMultiple test correctionGene-geneTest correctionExplicit testsTraitsHigher-order interactionsRare frequencyInteraction analysisVariantsInteractionSmall effect sizesReplicationInteraction resultsOrder interactions
2013
The genomic landscape of cohesin-associated chromatin interactions
DeMare LE, Leng J, Cotney J, Reilly SK, Yin J, Sarro R, Noonan JP. The genomic landscape of cohesin-associated chromatin interactions. Genome Research 2013, 23: 1224-1234. PMID: 23704192, PMCID: PMC3730097, DOI: 10.1101/gr.156570.113.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBinding SitesCCCTC-Binding FactorCell Cycle ProteinsChromatinChromatin ImmunoprecipitationChromosomal Proteins, Non-HistoneEnhancer Elements, GeneticGene Expression Regulation, DevelopmentalGenomeHistonesLimb BudsMiceMice, Inbred C57BLOrgan SpecificityPromoter Regions, GeneticProtein SubunitsRepressor ProteinsConceptsPaired-end tag sequencingGenome-wide scaleInsulator protein CTCFChromatin interaction analysisEnhancer-promoter interactionsEnhancer-promoter communicationEmbryonic stem cellsChromatin stateProtein CTCFChromatin interactionsTag sequencingDNA loopsRegulatory architectureMouse limbRegulatory outputMouse embryosGenomic landscapeMultiple tissuesCohesinStem cellsCTCFPromoterDemarcate regionsInteraction analysisGenome
2012
Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions
Mukherjee B, Ko Y, VanderWeele T, Roy A, Park S, Chen J. Principal interactions analysis for repeated measures data: application to gene–gene and gene–environment interactions. Statistics In Medicine 2012, 31: 2531-2551. PMID: 22415818, PMCID: PMC4046647, DOI: 10.1002/sim.5315.Peer-Reviewed Original ResearchConceptsGene-environment interactionsGene-geneLongitudinal cohort studyNormative Aging StudyHealth outcomesMain effect termsMeasured outcomesAging StudyOccupational historyEpistasis modelsEnvironmental exposuresMain effectLongitudinal natureLongitudinal dataResampling-based methodsCell meansClassification arrayQuantitative traitsInteraction analysisRobust classLeading eigenvaluesSimulation studyTime-varying effectsSubject-specificOutcomes
2011
Distinct patterns of somatic alterations in a lymphoblastoid and a tumor genome derived from the same individual
Galante P, Parmigiani R, Zhao Q, Caballero O, de Souza J, Navarro F, Gerber A, Nicolás M, Salim A, Silva A, Edsall L, Devalle S, Almeida L, Ye Z, Kuan S, Pinheiro D, Tojal I, Pedigoni R, de Sousa R, Oliveira T, de Paula M, Ohno-Machado L, Kirkness E, Levy S, da Silva W, Vasconcelos A, Ren B, Zago M, Strausberg R, Simpson A, de Souza S, Camargo A. Distinct patterns of somatic alterations in a lymphoblastoid and a tumor genome derived from the same individual. Nucleic Acids Research 2011, 39: 6056-6068. PMID: 21493686, PMCID: PMC3152357, DOI: 10.1093/nar/gkr221.Peer-Reviewed Original ResearchConceptsTumor genomesSomatic alterationsProtein-protein interaction analysisSynonymous substitutionsKEGG analysisEndogenous mutagensGenomeTumorigenic transformationNucleotide substitutionsBreast tumor cell linesReplication errorsTumor cell linesGenetic alterationsFrequency of mutationsCell linesTumorigenesisMutationsSynergistic functionDistinct patternsInteraction analysisLymphoblastoidAlterationsGenesSame individualPathwayA knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility
Bush WS, McCauley JL, DeJager PL, Dudek SM, Hafler DA, Gibson RA, Matthews PM, Kappos L, Naegelin Y, Polman CH, Hauser SL, Oksenberg J, Haines JL, Ritchie MD. A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility. Genes & Immunity 2011, 12: 335-340. PMID: 21346779, PMCID: PMC3136581, DOI: 10.1038/gene.2011.3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGene-gene interactionsCytoskeleton regulatory proteinsCytoskeletal regulationGenetic architectureGene clusterInteraction analysisSingle-locus analysisGWAS dataRegulatory proteinsBiological contextRelated genesAssociation studiesSusceptibility lociWeak main effectsPhospholipase CGenetic effectsΒ isoformsComplex diseasesBiological mechanismsNeurodegenerative mechanismsNew genetic effectsEpistasisACTN1Genes
1999
Regulation of Neurabin I Interaction with Protein Phosphatase 1 by Phosphorylation †
McAvoy T, Allen P, Obaishi H, Nakanishi H, Takai Y, Greengard P, Nairn A, Hemmings H. Regulation of Neurabin I Interaction with Protein Phosphatase 1 by Phosphorylation †. Biochemistry 1999, 38: 12943-12949. PMID: 10504266, DOI: 10.1021/bi991227d.Peer-Reviewed Original ResearchConceptsProtein phosphatase 1Neurabin IPP1 activityPhosphatase 1Two-hybrid interaction analysisActin-binding proteinsCo-immunoprecipitation experimentsMimic phosphorylationSerine 461Phosphorylated residuesGlutathione S-transferaseOverlay assaysFusion proteinSignaling mechanismGamma isoformsCAMP pathwayPhosphorylationS-transferaseProteinTryptic digestPKARegulationHPLC-MS analysisInteraction analysisS461
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply