2025
The case-only design is a powerful approach to detect interactions but should be used with caution
Dong R, Wang G, DeWan A, Leal S. The case-only design is a powerful approach to detect interactions but should be used with caution. BMC Genomics 2025, 26: 222. PMID: 40050722, PMCID: PMC11884093, DOI: 10.1186/s12864-025-11318-1.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesComputer SimulationGene-Environment InteractionHumansModels, GeneticPrevalenceResearch DesignSample SizeConceptsCase-only designRare disease assumptionType I error rateIncreased type I error ratesDisease prevalenceInvestigated type I errorComplex traitsInteraction termsInteraction effect sizesDetect interactionsCase-control designControlled type I error ratesSample sizeHigher disease prevalenceEffect sizeLow disease prevalenceType I errorPrevalenceExposure frequencyGenesType I andDesign studyEnvironmental factorsTraitsEnvironment interaction
2024
LDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information
Dong Z, Jiang W, Li H, DeWan A, Zhao H. LDER-GE estimates phenotypic variance component of gene–environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. Briefings In Bioinformatics 2024, 25: bbae335. PMID: 38980374, PMCID: PMC11232466, DOI: 10.1093/bib/bbae335.Peer-Reviewed Original ResearchConceptsHuman complex traitsComplex traitsGene-environment interactionsGene-environmentLinkage disequilibriumPhenotypic variance componentsPhenotypic varianceProportion of phenotypic varianceSummary statisticsEuropean ancestry subjectsUK Biobank dataAssociation summary statisticsComplete linkage disequilibriumControlled type I error ratesLD informationLD matrixVariance componentsBiobank dataType I error rateEuropean ancestrySample size increaseGenetic effectsTraitsE-I pairsSimulation study
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
2000
A Genome Scan for Renal Function among Hypertensives: the HyperGEN Study
DeWan A, Arnett D, Atwood L, Province M, Lewis C, Hunt S, Eckfeldt J. A Genome Scan for Renal Function among Hypertensives: the HyperGEN Study. American Journal Of Human Genetics 2000, 68: 136-144. PMID: 11115379, PMCID: PMC1234906, DOI: 10.1086/316927.Peer-Reviewed Original ResearchMeSH KeywordsBlack or African AmericanBlack PeopleBody Mass IndexChromosomes, Human, Pair 1Chromosomes, Human, Pair 3Chromosomes, Human, Pair 6CreatineFemaleGenotypeHumansHypertensionKidneyKidney Function TestsLod ScoreMaleMiddle AgedModels, GeneticNuclear FamilyPhenotypeQuantitative Trait, HeritableSoftwareWhite PeopleConceptsCreatinine clearanceRenal functionAfrican American subjectsWhite subjectsHypertension Genetic Epidemiology Network (HyperGEN) studyLarge biracial sampleResidual creatinine clearanceComplications of hypertensionCreatinine clearance measurementsGood evidenceHypertensive individualsKidney functionMean ageAmerican subjectsBiracial sampleHypertensive siblingsApolipoprotein DHyperGEN studyClearanceClearance measurementsHypertensionCandidate genesSpecific genetic regionsUnderlying genetic componentSubjects
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