2022
Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci
Deak JD, Zhou H, Galimberti M, Levey DF, Wendt FR, Sanchez-Roige S, Hatoum AS, Johnson EC, Nunez YZ, Demontis D, Børglum AD, Rajagopal VM, Jennings MV, Kember RL, Justice AC, Edenberg HJ, Agrawal A, Polimanti R, Kranzler HR, Gelernter J. Genome-wide association study in individuals of European and African ancestry and multi-trait analysis of opioid use disorder identifies 19 independent genome-wide significant risk loci. Molecular Psychiatry 2022, 27: 3970-3979. PMID: 35879402, PMCID: PMC9718667, DOI: 10.1038/s41380-022-01709-1.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significant risk lociAssociation studiesVariant associationsLarge-scale genome-wide association studiesGenetic correlationsSignificant risk lociPsychiatric Genomics ConsortiumMulti-trait analysisPolygenic risk score analysisSingle-variant associationsGWS lociGenetic architectureIndividuals of EuropeanGWS associationsRisk lociGene regionGenomics ConsortiumMillion Veteran ProgramSusceptibility lociAfrican ancestryLociRisk score analysisGenetic informativenessSNPs one
2021
Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example
Hatoum AS, Wendt FR, Galimberti M, Polimanti R, Neale B, Kranzler HR, Gelernter J, Edenberg HJ, Agrawal A. Ancestry may confound genetic machine learning: Candidate-gene prediction of opioid use disorder as an example. Drug And Alcohol Dependence 2021, 229: 109115. PMID: 34710714, PMCID: PMC9358969, DOI: 10.1016/j.drugalcdep.2021.109115.Peer-Reviewed Original ResearchConceptsGenome-wide significant variantsCandidate gene predictionGenetic predictionRandom SNPsPolygenic traitRandom phenotypeCandidate SNPsSimulated phenotypesPsychiatric geneticsGenetic machineSignificant variantsBinary phenotypesCandidate variantsSNPsAncestryPhenotypeAllele frequenciesVariantsMachine learning modelsGenetic testsLearning model
2019
International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci
Nievergelt CM, Maihofer AX, Klengel T, Atkinson EG, Chen CY, Choi KW, Coleman JRI, Dalvie S, Duncan LE, Gelernter J, Levey DF, Logue MW, Polimanti R, Provost AC, Ratanatharathorn A, Stein MB, Torres K, Aiello AE, Almli LM, Amstadter AB, Andersen SB, Andreassen OA, Arbisi PA, Ashley-Koch AE, Austin SB, Avdibegovic E, Babić D, Bækvad-Hansen M, Baker DG, Beckham JC, Bierut LJ, Bisson JI, Boks MP, Bolger EA, Børglum AD, Bradley B, Brashear M, Breen G, Bryant RA, Bustamante AC, Bybjerg-Grauholm J, Calabrese JR, Caldas- de- Almeida J, Dale AM, Daly MJ, Daskalakis NP, Deckert J, Delahanty DL, Dennis MF, Disner SG, Domschke K, Dzubur-Kulenovic A, Erbes CR, Evans A, Farrer LA, Feeny NC, Flory JD, Forbes D, Franz CE, Galea S, Garrett ME, Gelaye B, Geuze E, Gillespie C, Uka AG, Gordon SD, Guffanti G, Hammamieh R, Harnal S, Hauser MA, Heath AC, Hemmings SMJ, Hougaard DM, Jakovljevic M, Jett M, Johnson EO, Jones I, Jovanovic T, Qin XJ, Junglen AG, Karstoft KI, Kaufman ML, Kessler RC, Khan A, Kimbrel NA, King AP, Koen N, Kranzler HR, Kremen WS, Lawford BR, Lebois LAM, Lewis CE, Linnstaedt SD, Lori A, Lugonja B, Luykx JJ, Lyons MJ, Maples-Keller J, Marmar C, Martin AR, Martin NG, Maurer D, Mavissakalian MR, McFarlane A, McGlinchey RE, McLaughlin KA, McLean SA, McLeay S, Mehta D, Milberg WP, Miller MW, Morey RA, Morris CP, Mors O, Mortensen PB, Neale BM, Nelson EC, Nordentoft M, Norman SB, O’Donnell M, Orcutt HK, Panizzon MS, Peters ES, Peterson AL, Peverill M, Pietrzak RH, Polusny MA, Rice JP, Ripke S, Risbrough VB, Roberts AL, Rothbaum AO, Rothbaum BO, Roy-Byrne P, Ruggiero K, Rung A, Rutten BPF, Saccone NL, Sanchez SE, Schijven D, Seedat S, Seligowski AV, Seng JS, Sheerin CM, Silove D, Smith AK, Smoller JW, Sponheim SR, Stein DJ, Stevens JS, Sumner JA, Teicher MH, Thompson WK, Trapido E, Uddin M, Ursano RJ, van den Heuvel LL, Van Hooff M, Vermetten E, Vinkers CH, Voisey J, Wang Y, Wang Z, Werge T, Williams MA, Williamson DE, Winternitz S, Wolf C, Wolf EJ, Wolff JD, Yehuda R, Young RM, Young KA, Zhao H, Zoellner LA, Liberzon I, Ressler KJ, Haas M, Koenen KC. International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci. Nature Communications 2019, 10: 4558. PMID: 31594949, PMCID: PMC6783435, DOI: 10.1038/s41467-019-12576-w.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesDisease genesAssociation studiesGenome-wide significant lociAfrican-ancestry analysesNon-coding RNAsGenetic risk lociParkinson's disease genesEuropean ancestry populationsNovel genesSignificant lociGenetic variationSpecific lociRisk lociAdditional lociLociAncestry populationsCommon variantsHeritability estimatesGenesGWASRNABiologySNPsPARK2
2013
Functional variation of the transthyretin gene among human populations and its correlation with amyloidosis phenotypes
Polimanti R, Di Girolamo M, Manfellotto D, Fuciarelli M. Functional variation of the transthyretin gene among human populations and its correlation with amyloidosis phenotypes. Amyloid 2013, 20: 256-262. PMID: 24111657, DOI: 10.3109/13506129.2013.844689.Peer-Reviewed Original ResearchConceptsHuman populationTTR-related amyloidosisCis-regulatory variantsGenetic variantsNon-coding variantsGenomes Project databaseAdditional genetic variantsDisease-causing mutationsGene functionTranscription factorsKb regionCardiac developmentSilico analysisFunctional variationTTR geneRegulatory functionsGenotype-phenotype correlationGenesFunctional impactDisease phenotypeNon-African individualsSignificant diversityMutationsPhenotypeTransthyretin gene
2000
Human GST Loci as Markers of Evolutionary Forces: GSTO1*E155del and GSTO1*E208K Polymorphisms May Be Under Natural Selection Induced by Environmental Arsenic
Polimanti R, Piacentini S, De Angelis F, De Stefano GF, Fuciarelli M. Human GST Loci as Markers of Evolutionary Forces: GSTO1*E155del and GSTO1*E208K Polymorphisms May Be Under Natural Selection Induced by Environmental Arsenic. Disease Markers 2000, 31: 231-239. PMID: 22045430, PMCID: PMC3826775, DOI: 10.3233/dma-2011-0821.Peer-Reviewed Original ResearchMeSH KeywordsArsenicBiotransformationBlack PeopleEcuadorEnvironmental ExposureEnvironmental PollutantsEvolution, MolecularGene FrequencyGene-Environment InteractionGenetic MarkersGenotypeGlutathione TransferaseHumansIndians, South AmericanLinkage DisequilibriumMalePolymorphism, Single NucleotideSelection, GeneticConceptsGST genesPopulation demographic historyGlutatione S-transferasesAllele frequenciesEvolutionary forcesDemographic historyNatural selectionSelective pressureNull phenotypeNeutral polymorphismsPopulation relationshipsHapMap dataS-transferaseGST SNPsGST lociEnvironmental arsenicGenesWorldwide populationPCR-RFLP methodAfrican populationsPolymorphism