2024
Neuronal-specific methylome and hydroxymethylome analysis reveal significant loci associated with alcohol use disorder
Andrade-Brito D, Núñez-Ríos D, Martínez-Magaña J, Nagamatsu S, Rompala G, Zillich L, Witt S, Clark S, Lattig M, Montalvo-Ortiz J, Alvarez V, Benedek D, Che A, Cruz D, Davis D, Girgenti M, Hoffman E, Holtzheimer P, Huber B, Kaye A, Keane T, Krystal J, Labadorf A, Logue M, Marx B, Mash D, McKee A, Miller M, Montalvo-Ortiz J, Noller C, Schnurr P, Scott W, Stein T, Ursano R, Williamson D, Wolf E, Young K. Neuronal-specific methylome and hydroxymethylome analysis reveal significant loci associated with alcohol use disorder. Frontiers In Genetics 2024, 15: 1345410. PMID: 38633406, PMCID: PMC11021708, DOI: 10.3389/fgene.2024.1345410.Peer-Reviewed Original ResearchAssociated with alcohol use disorderAlcohol use disorderOrbitofrontal cortexEpigenome-wide association studiesUse disorderStudy of alcohol use disorderHuman orbitofrontal cortexAlcohol-related traitsHuman brainPostmortem brain samplesHuman postmortem brain samplesEnrichment analysisDifferential CpG sitesPostmortem brain tissueGenome-wide levelOxidative bisulfite sequencingAssessed 5Functional enrichment analysisBrain tissueFalse discovery rateBisulfite sequencingAssociation studiesDifferential methylationIdentified genesDNA methylation
2023
The genomics of visuospatial neurocognition in obsessive-compulsive disorder: A preliminary GWAS
Alemany-Navarro M, Tubío-Fungueiriño M, Almeida S, Cruz R, Lombroso A, Real E, Soria V, Bertolín S, Fernández-Prieto M, Alonso P, Menchón J, Carracedo A, Segalàs C. The genomics of visuospatial neurocognition in obsessive-compulsive disorder: A preliminary GWAS. Journal Of Affective Disorders 2023, 333: 365-376. PMID: 37094658, DOI: 10.1016/j.jad.2023.04.060.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSingle nucleotide polymorphismsSuggestive signalsCase/control genome-wide association studyPreliminary genome-wide association studyGenome-wide levelGenome-wide significanceGenomic basisComplex traitsGenomic regionsWhole genomeGenetic basisAssociation studiesGenetic characterizationAssociation of SNPsNucleotide polymorphismsGenomicsTraitsGenomeGenesNeuropsychological traitsPolymorphismPromising avenueSignalsOCD cases
2020
Genome-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
2017
Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis
Zhao D, Lin M, Pedrosa E, Lachman HM, Zheng D. Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis. BMC Genomics 2017, 18: 860. PMID: 29126398, PMCID: PMC5681780, DOI: 10.1186/s12864-017-4261-x.Peer-Reviewed Original ResearchConceptsMonoallelic expressionHuman brain cellsGene expressionMonoallelic gene expressionAllelic gene expressionGenome-wide levelSingle-cell RNA-seq datasetsRNA-seq data analysisAllelic expression studiesSingle-cell RNA-seq data analysisRNA-seq datasetsSingle nucleotide variantsBrain cellsCellular identityAutosomal genesNeuronal diversityExpression studiesNucleotide variantsCorrelated expressionGenesIndividual cellsHuman psychiatric disordersNeuronal cellsSingle cellsCell function
2016
Piwi maintains germline stem cells and oogenesis in Drosophila through negative regulation of Polycomb group proteins
Peng JC, Valouev A, Liu N, Lin H. Piwi maintains germline stem cells and oogenesis in Drosophila through negative regulation of Polycomb group proteins. Nature Genetics 2016, 48: 283-291. PMID: 26780607, PMCID: PMC4767590, DOI: 10.1038/ng.3486.Peer-Reviewed Original ResearchTests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification
Boonstra P, Mukherjee B, Gruber S, Ahn J, Schmit S, Chatterjee N. Tests for Gene-Environment Interactions and Joint Effects With Exposure Misclassification. American Journal Of Epidemiology 2016, 183: 237-247. PMID: 26755675, PMCID: PMC4724093, DOI: 10.1093/aje/kwv198.Peer-Reviewed Original ResearchConceptsG-E interactionsPresence of exposure misclassificationExposure misclassificationImpact of exposure misclassificationGene-environment (G-EGene-environment interactionsGenome-wide levelGenome-wide searchGenome-wide testingGenetic susceptibility lociJoint testDisease-gene relationshipsGene-environmentGenetic risk factorsType I error rateFamily-wise type I error rateSusceptibility lociG-EGenetic associationRisk factorsStatistical powerJoint effectsSimulation studyMisclassificationPublished simulation studies
2015
Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke
Traylor M, Zhang CR, Adib-Samii P, Devan WJ, Parsons OE, Lanfranconi S, Gregory S, Cloonan L, Falcone GJ, Radmanesh F, Fitzpatrick K, Kanakis A, Barrick TR, Moynihan B, Lewis CM, Boncoraglio GB, Lemmens R, Thijs V, Sudlow C, Wardlaw J, Rothwell PM, Meschia JF, Worrall BB, Levi C, Bevan S, Furie KL, Dichgans M, Rosand J, Markus HS, Rost N, Smoller S, Sorkin J, Wang X, Selim M, Pikula A, Wolf P, Debette S, Seshadri S, de Bakker P, Chasman D, Rexrode K, Chen I, Rotter J, Luke M, Sale M, Lee T, Chang K, Elkind M, Goldstein L, James M, Breteler M, O'Donnell C, Leys D, Carty C, Kidwell C, Olesen J, Sharma P, Rich S, Tatlisumak T, Happola O, Bijlenga P, Soriano C, Giralt E, Roquer J, Jimenez-Conde J, Cotlarcius I, Hardy J, Korostynski M, Boncoraglio G, Ballabio E, Parati E, Mateusz A, Urbanik A, Dziedzic T, Jagiella J, Gasowski J, Wnuk M, Olszanecki R, Pera J, Slowik A, Juchniewicz K, Levi C, Nyquist P, Cendes I, Cabral N, Franca P, Goncalves A, Keller L, Crisby M, Kostulas K, Lemmens R, Ahmadi K, Opherk C, Duering M, Dichgans M, Malik R, Gonik M, Staals J, Melander O, Burri P, Sadr-Nabavi A, Romero J, Biffi A, Anderson C, Falcone G, Brouwers B, Rosand J, Rost N, Du R, Kourkoulis C, Battey T, Lubitz S, Mueller-Myhsok B, Meschia J, Brott T, Pare G, Pichler A, Enzinger C, Schmidt H, Schmidt R, Seiler S, Blanton S, Yamada Y, Bersano A, Rundek T, Sacco R, Yvonne Chan Y, Gschwendtner A, Deng Z, Barr T, Gwinn K, Corriveau R, Singleton A, Waddy S, Launer L, Chen C, Le K, Lee W, Tan E, Olugbodi A, Rothwell P, Schilling S, Mok V, Lebedeva E, Jern C, Jood K, Olsson S, Kim H, Lee C, Kilarski L, Markus H, Peycke J, Bevan S, Sheu W, Chiou H, Chern J, Giraldo E, Taqi M, Jain V, Lam O, Howard G, Woo D, Kittner S, Mitchell B, Cole J, O'Connell J, Milewicz D, Illoh K, Worrall B, Stine C, Karaszewski B, Werring D, Sofat R, Smalley J, Lindgren A, Hansen B, Norrving B, Smith G, Martín J, Thijs V, Klijn K, van't Hof F, Algra A, Macleod M, Perry R, Arnett D, Pezzini A, Padovani A, Cramer S, Fisher M, Saleheen D, Broderick J, Kissela B, Doney A, Sudlow C, Rannikmae K, Silliman S, McDonough C, Walters M, Pedersen A, Nakagawa K, Chang C, Dobbins M, McArdle P, Chang Y, Brown R, Brown D, Holliday E, Kalaria R, Maguire J, Attia J, Farrall M, Giese A, Fornage M, Majersik J, Cushman M, Keene K, Bennett S, Tirschwell D, Psaty B, Reiner A, Longstreth W, Spence D, Montaner J, Fernandez-Cadenas I, Langefeld C, Bushnell C, Heitsch L, Lee J, Sheth K. Genome-wide meta-analysis of cerebral white matter hyperintensities in patients with stroke. Neurology 2015, 86: 146-153. PMID: 26674333, PMCID: PMC4731688, DOI: 10.1212/wnl.0000000000002263.Peer-Reviewed Original ResearchConceptsGenome-wide significant associationGenetic associationGenome-wide levelGenome-wide significanceMeta-analysis testingIndependent lociSuggestive lociGenetic lociLociDisease mechanismsNovel associationsGenetic susceptibilityPopulationCommon genetic susceptibilityDirection of effectReference dataset
2014
Genome‐wide association discoveries of alcohol dependence
Zuo L, Lu L, Tan Y, Pan X, Cai Y, Wang X, Hong J, Zhong C, Wang F, Zhang X, Vanderlinden LA, Tabakoff B, Luo X. Genome‐wide association discoveries of alcohol dependence. American Journal On Addictions 2014, 23: 526-539. PMID: 25278008, PMCID: PMC4187224, DOI: 10.1111/j.1521-0391.2014.12147.x.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide levelPotential biological functionsGWAS samplesADH clusterGenome-wide association discoveryRisk variantsBiological functionsAlcohol dehydrogenase clusterWide significant associationsRobust risk locusCis-eQTLsRisk lociNrd1Association studiesKIAA0040PKNOX2RNA expressionImportant roleHTR7Replicable associationsIndividual samplesSERINC2Mouse brainVariantsAxonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data
Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen G, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D. Axonal guidance signaling pathway interacting with smoking in modifying the risk of pancreatic cancer: a gene- and pathway-based interaction analysis of GWAS data. Carcinogenesis 2014, 35: 1039-1045. PMID: 24419231, PMCID: PMC4004205, DOI: 10.1093/carcin/bgu010.Peer-Reviewed Original ResearchConceptsIngenuity Pathway AnalysisKEGG pathwaysSingle nucleotide polymorphism (SNP) levelAxonal guidanceGenome-wide levelGene ontology pathwaysSalivary secretion pathwaysSlit/RoboGenes/SNPsOntology pathwaysPolymorphism levelSecretion pathwayGWAS dataGene setsPancreatic Cancer Case-Control ConsortiumCanonical pathwaysPathway analysisAxon guidanceGenesSNPsRegion SNPsPathwayIPA analysisPancreatic cancerDiscovery studiesGenes–Environment Interactions in Obesity- and Diabetes-Associated Pancreatic Cancer: A GWAS Data Analysis
Tang H, Wei P, Duell EJ, Risch HA, Olson SH, Bueno-de-Mesquita HB, Gallinger S, Holly EA, Petersen GM, Bracci PM, McWilliams RR, Jenab M, Riboli E, Tjønneland A, Boutron-Ruault MC, Kaaks R, Trichopoulos D, Panico S, Sund M, Peeters PH, Khaw KT, Amos CI, Li D. Genes–Environment Interactions in Obesity- and Diabetes-Associated Pancreatic Cancer: A GWAS Data Analysis. Cancer Epidemiology Biomarkers & Prevention 2014, 23: 98-106. PMID: 24136929, PMCID: PMC3947145, DOI: 10.1158/1055-9965.epi-13-0437-t.Peer-Reviewed Original ResearchConceptsPancreatic cancerIngenuity Pathway AnalysisSingle nucleotide polymorphismsAssociation of obesityPancreatic Cancer Case-Control ConsortiumRisk of obesityAlterable risk factorsPancreatic cancer riskRisk factor dataLogistic regression modelsGenome-wide levelAdditional large datasetsMajor contributing genesInsulin resistanceRisk factorsInflammatory responseCancer riskGene-environment interactionsObesityGene-environment interaction analysisSignificant interactionDiabetesGWAS data analysisCancerGenetic susceptibility
2013
Elucidating the Landscape of Aberrant DNA Methylation in Hepatocellular Carcinoma
Song M, Tiirikainen M, Kwee S, Okimoto G, Yu H, Wong L. Elucidating the Landscape of Aberrant DNA Methylation in Hepatocellular Carcinoma. PLOS ONE 2013, 8: e55761. PMID: 23437062, PMCID: PMC3577824, DOI: 10.1371/journal.pone.0055761.Peer-Reviewed Original ResearchConceptsDifferentially Methylated RegionsPromoter CpG islandsCpG islandsDNA methylationDifferential methylationMethylation changesGenome-wide methylation profilingDM locusGenome-wide levelDifferential methylation patternsAberrant DNA methylationPotential biological functionsSignificant differential methylationGene bodiesIllumina HumanMethylation450 BeadChipGenomic regionsIntergenic regionMethylated regionsMethylation patternsCellular developmentEpigenetic changesGene promoterGene listsMethylation profilingBiological functions
2010
Genome-wide Association Study of Prostate Cancer Mortality
Penney K, Pyne S, Schumacher F, Sinnott J, Mucci L, Kraft P, Ma J, Oh W, Kurth T, Kantoff P, Giovannucci E, Stampfer M, Hunter D, Freedman M. Genome-wide Association Study of Prostate Cancer Mortality. Cancer Epidemiology Biomarkers & Prevention 2010, 19: 2869-2876. PMID: 20978177, PMCID: PMC3197738, DOI: 10.1158/1055-9965.epi-10-0601.Peer-Reviewed Original ResearchConceptsProstate cancer mortalityGenome-wide significanceCancer mortalityHealth Professionals Follow-up StudyLethal prostate cancerFollow-up studyPhysicians' Health StudyGenome-wide association studiesProstate cancerLethal prostate cancer casesOdds ratio <Genome-wide levelTop-ranking SNPsIndependent follow-up studyClinical decision makingHealth StudyProstate cancer casesCancer casesNo SNPAggressive course of diseaseIndependent SNPsAssociation studiesGenetic markersLogistic regressionBiological insights
2007
Evidence for association between multiple complement pathway genes and AMD
Dinu V, Miller PL, Zhao H. Evidence for association between multiple complement pathway genes and AMD. Genetic Epidemiology 2007, 31: 224-237. PMID: 17266113, DOI: 10.1002/gepi.20204.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsPathway levelGenome-wide association studiesGenome-wide levelWide association studyGenome levelAdditional genesPathway genesAssociation studiesBiological knowledgeSNP signalsComplement factor HGenotype dataGenesNucleotide polymorphismsForms of AMDComplement pathway genesRisk allelesFactor HMBL2 single-nucleotide polymorphismsIntronsHaplotypesComplement pathwayAllelesPathway
2004
Genomic regions controlling corticosterone levels in rats
Potenza MN, Brodkin ES, Joe B, Luo X, Remmers EF, Wilder RL, Nestler EJ, Gelernter J. Genomic regions controlling corticosterone levels in rats. Biological Psychiatry 2004, 55: 634-641. PMID: 15013833, DOI: 10.1016/j.biopsych.2003.11.005.Peer-Reviewed Original ResearchConceptsGenomic regionsQuantitative trait locus (QTL) analysisGenome-wide levelSpecific genomic regionsUnderstanding of susceptibilitySignificant QTLGenomic backgroundChromosome 4Locus analysisF2 progenyGenetic differencesSuggestive significanceDisease susceptibilityQTLFirst identificationCongenic animalsDeoxyribonucleic acidGenetic factorsProgenyIdentificationRegionSusceptibilityLevels
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