2020
Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits
Quach BC, Bray MJ, Gaddis NC, Liu M, Palviainen T, Minica CC, Zellers S, Sherva R, Aliev F, Nothnagel M, Young KA, Marks JA, Young H, Carnes MU, Guo Y, Waldrop A, Sey NYA, Landi MT, McNeil DW, Drichel D, Farrer LA, Markunas CA, Vink JM, Hottenga JJ, Iacono WG, Kranzler HR, Saccone NL, Neale MC, Madden P, Rietschel M, Marazita ML, McGue M, Won H, Winterer G, Grucza R, Dick DM, Gelernter J, Caporaso NE, Baker TB, Boomsma DI, Kaprio J, Hokanson JE, Vrieze S, Bierut LJ, Johnson EO, Hancock DB. Expanding the genetic architecture of nicotine dependence and its shared genetics with multiple traits. Nature Communications 2020, 11: 5562. PMID: 33144568, PMCID: PMC7642344, DOI: 10.1038/s41467-020-19265-z.Peer-Reviewed Original ResearchConceptsGenome-wide significant lociGenome-wide association studiesNearby gene expressionExpression of genesSmoking traitsGenetic architectureSignificant lociGenetic variationMultiple traitsGene expressionAssociation studiesLociTraitsGenetic knowledgeComposite phenotypeUK BiobankExpressionTENM2GNAI1GenesGeneticsVariantsPhenotype
2015
Dissecting ancestry genomic background in substance dependence genome-wide association studies
Polimanti R, Yang C, Zhao H, Gelernter J. Dissecting ancestry genomic background in substance dependence genome-wide association studies. Pharmacogenomics 2015, 16: 1487-1498. PMID: 26267224, PMCID: PMC4632979, DOI: 10.2217/pgs.15.91.Peer-Reviewed Original ResearchMeSH KeywordsAlcoholismAlgorithmsAllelesBlack or African AmericanGene FrequencyGene-Environment InteractionGenetic Predisposition to DiseaseGenetic VariationGenome-Wide Association StudyHaplotypesHumansMolecular Sequence AnnotationOpioid-Related DisordersSubstance-Related DisordersTobacco Use DisorderWhite PeopleConceptsGenome-wide association studiesGenomic backgroundFunctional allelesAssociation studiesCommon functional allelesWide association studyLocal haplotype structureGenetic lociSD traitHaplotype structureRelevant genesGenesLociInteractive partnersPopulation diversityHigh frequency differencesAllelesFrequency differenceGenomeTraitsDiversityRoleVariants
2014
GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation
Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: A Statistical Approach to Prioritizing GWAS Results by Integrating Pleiotropy and Annotation. PLOS Genetics 2014, 10: e1004787. PMID: 25393678, PMCID: PMC4230845, DOI: 10.1371/journal.pgen.1004787.Peer-Reviewed Original ResearchMeSH KeywordsDatabases, GeneticGenetic Diseases, InbornGenetic PleiotropyGenome-Wide Association StudyGenotypeHumansModels, StatisticalMolecular Sequence AnnotationPhenotypePolymorphism, Single NucleotideConceptsGenome-wide association studiesFunctional annotationGWAS datasetsAnnotation informationStatistical approachMultiple GWAS datasetsGenome-wide markersPowerful statistical methodsSingle-phenotype analysisCentral nervous system genesRisk variantsNervous system genesGenotype-Tissue Expression (GTEx) databaseComplex diseasesGWAS data setsSignificant pleiotropic effectsCommon risk basisDifferent complex diseasesDNase-seq dataCell linesStatistical inferenceGenetic architectureGWAS hitsGWAS resultsNovel statistical approach