2020
Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals
Xu K, Li B, McGinnis KA, Vickers-Smith R, Dao C, Sun N, Kember RL, Zhou H, Becker WC, Gelernter J, Kranzler HR, Zhao H, Justice AC. Genome-wide association study of smoking trajectory and meta-analysis of smoking status in 842,000 individuals. Nature Communications 2020, 11: 5302. PMID: 33082346, PMCID: PMC7598939, DOI: 10.1038/s41467-020-18489-3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesLarge genome-wide association studiesMillion Veteran ProgramAssociation studiesExpression quantitative trait lociQuantitative trait lociChromatin interactionsComplex traitsFunctional annotationTrait lociSequencing ConsortiumDozen genesSignificant lociSmoking phenotypesLociMultiple populationsNew insightsPhenotypeVeteran ProgramGenetic vulnerabilityGenesTraitsAnnotationEuropean AmericansConsortium
2018
Using DNA methylation to validate an electronic medical record phenotype for smoking
McGinnis KA, Justice AC, Tate JP, Kranzler HR, Tindle HA, Becker WC, Concato J, Gelernter J, Li B, Zhang X, Zhao H, Crothers K, Xu K, Group F. Using DNA methylation to validate an electronic medical record phenotype for smoking. Addiction Biology 2018, 24: 1056-1065. PMID: 30284751, PMCID: PMC6541538, DOI: 10.1111/adb.12670.Peer-Reviewed Original ResearchConceptsVeterans Aging Cohort StudyAging Cohort StudyStrong associationDNA methylation sitesSmoking metricsCohort studyCurrent smokingSmoking statusSpearman correlation coefficientBiomarker cohortBlood samplesSmoking behaviorCriterion standardLogistic regressionSmokingSmoking phenotypesCurve analysisGroup assignmentText notesAssociationDescriptive statisticsPhenotypeCorrelation coefficientGenetic discoveriesPercent