2023
Transcriptional signatures of heroin intake and relapse throughout the brain reward circuitry in male mice
Browne C, Futamura R, Minier-Toribio A, Hicks E, Ramakrishnan A, Martínez-Rivera F, Estill M, Godino A, Parise E, Torres-Berrío A, Cunningham A, Hamilton P, Walker D, Huckins L, Hurd Y, Shen L, Nestler E. Transcriptional signatures of heroin intake and relapse throughout the brain reward circuitry in male mice. Science Advances 2023, 9: eadg8558. PMID: 37294757, PMCID: PMC10256172, DOI: 10.1126/sciadv.adg8558.Peer-Reviewed Original ResearchConceptsOpioid use disorderHeroin intakeContext-induced drug-seekingBrain reward circuitryHeroin self-administrationRNA-seqDrug seekingReward circuitryGenome-wide association study dataSelf-administrationHeroin exposureDrug-takingIntegration of RNA-seq dataUse disorderPatterns of transcriptional regulationRNA-seq dataBehavioral outcomesMale miceMolecular changesTranscriptional regulationRegion-specificGene candidatesRNA sequencingHeroinBioinformatics analysis
2022
What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding
Huckins LM, Signer R, Johnson J, Wu YK, Mitchell KS, Bulik CM. What next for eating disorder genetics? Replacing myths with facts to sharpen our understanding. Molecular Psychiatry 2022, 27: 3929-3938. PMID: 35595976, PMCID: PMC9718676, DOI: 10.1038/s41380-022-01601-y.Peer-Reviewed Original ResearchUsing phenotype risk scores to enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder
Wendt FR, Pathak GA, Deak JD, De Angelis F, Koller D, Cabrera-Mendoza B, Lebovitch DS, Levey DF, Stein MB, Kranzler HR, Koenen KC, Gelernter J, Huckins LM, Polimanti R. Using phenotype risk scores to enhance gene discovery for generalized anxiety disorder and posttraumatic stress disorder. Molecular Psychiatry 2022, 27: 2206-2215. PMID: 35181757, PMCID: PMC9133008, DOI: 10.1038/s41380-022-01469-y.Peer-Reviewed Original Research
2021
Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits
Martin J, Khramtsova E, Goleva S, Blokland G, Traglia M, Walters R, Hübel C, Coleman J, Breen G, Børglum A, Demontis D, Grove J, Werge T, Bralten J, Bulik C, Lee P, Mathews C, Peterson R, Winham S, Wray N, Edenberg H, Guo W, Yao Y, Neale B, Faraone S, Petryshen T, Weiss L, Duncan L, Goldstein J, Smoller J, Stranger B, Davis L, Consortium S, Alda M, Bortolato M, Burton C, Byrne E, Carey C, Erdman L, Huckins L, Mattheisen M, Robinson E, Stahl E. Examining Sex-Differentiated Genetic Effects Across Neuropsychiatric and Behavioral Traits. Biological Psychiatry 2021, 89: 1127-1137. PMID: 33648717, PMCID: PMC8163257, DOI: 10.1016/j.biopsych.2020.12.024.Peer-Reviewed Original ResearchConceptsSex-differential effectsSex differencesGenetic architectureBehavioral traitsSingle nucleotide polymorphism (SNP)-based heritabilityGenetic correlationsGenome-wide association summary statisticsSNP-based heritabilityMultiple gene setsOrigins of sex differencesAssociation summary statisticsIdentified 4 genesGene-based approachRisk-taking behaviorIdentified genesGene setsWell-powered studiesBehavioral phenotypesBiological functionsGenetic contributionBetween-sexGenetic effectsTrait pairsGenetic correlation estimatesNeuron-related
2020
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City
Paranjpe I, Russak A, De Freitas J, Lala A, Miotto R, Vaid A, Johnson K, Danieletto M, Golden E, Meyer D, Singh M, Somani S, Kapoor A, O'Hagan R, Manna S, Nangia U, Jaladanki S, O’Reilly P, Huckins L, Glowe P, Kia A, Timsina P, Freeman R, Levin M, Jhang J, Firpo A, Kovatch P, Finkelstein J, Aberg J, Bagiella E, Horowitz C, Murphy B, Fayad Z, Narula J, Nestler E, Fuster V, Cordon-Cardo C, Charney D, Reich D, Just A, Bottinger E, Charney A, Glicksberg B, Nadkarni G, Center M. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City. BMJ Open 2020, 10: e040736. PMID: 33247020, PMCID: PMC7702220, DOI: 10.1136/bmjopen-2020-040736.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAgedAged, 80 and overComorbidityCOVID-19C-Reactive ProteinCritical CareFemaleFibrin Fibrinogen Degradation ProductsHospital MortalityHospitalizationHospitalsHumansLymphocytesMaleMiddle AgedNew York CityPandemicsProcalcitoninRetrospective StudiesRisk FactorsSARS-CoV-2Young AdultConceptsIn-hospital mortalityHospitalised patientsPre-existing conditionsInstitutional electronic health recordsElectronic health recordsHealth System hospitalsMount Sinai Health SystemUrban hospital systemMount Sinai Health System hospitalsSinai Health SystemStudy periodIntensive careHealth recordsInvestigate in-hospital mortalityCohort of hospitalised patientsPublic health crisisHealth systemRetrospective cohort studyHospital systemSystem hospitalsGlobal public health crisisClinical characteristicsCohort studyCOVID-19New York CityMachine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation
Vaid A, Somani S, Russak A, De Freitas J, Chaudhry F, Paranjpe I, Johnson K, Lee S, Miotto R, Richter F, Zhao S, Beckmann N, Naik N, Kia A, Timsina P, Lala A, Paranjpe M, Golden E, Danieletto M, Singh M, Meyer D, O'Reilly P, Huckins L, Kovatch P, Finkelstein J, Freeman R, Argulian E, Kasarskis A, Percha B, Aberg J, Bagiella E, Horowitz C, Murphy B, Nestler E, Schadt E, Cho J, Cordon-Cardo C, Fuster V, Charney D, Reich D, Bottinger E, Levin M, Narula J, Fayad Z, Just A, Charney A, Nadkarni G, Glicksberg B. Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation. Journal Of Medical Internet Research 2020, 22: e24018. PMID: 33027032, PMCID: PMC7652593, DOI: 10.2196/24018.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAdolescentAdultAgedAged, 80 and overBetacoronavirusCohort StudiesCoronavirus InfectionsCOVID-19Electronic Health RecordsFemaleHospital MortalityHospitalizationHospitalsHumansMachine LearningMaleMiddle AgedNew York CityPandemicsPneumonia, ViralPrognosisRisk AssessmentROC CurveSARS-CoV-2Young AdultConceptsElectronic health recordsNew York CityYork CityMount Sinai Health SystemSinai Health SystemMortality predictionAdmitted to hospitalAt-risk patientsHealth recordsHealth systemEHR dataIn-hospital mortalityEarly identification of high-risk patientsCOVID-19Identification of high-risk patientsMultiple hospitalsStudy populationPatient characteristicsSingle hospitalHospitalArea under the receiver operating characteristic curveEarly identificationPredicting mortalityCohort of patientsCOVID-19 pandemicAnalysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts
Huckins L, Chatzinakos C, Breen M, Hartmann J, Klengel T, da Silva Almeida A, Dobbyn A, Girdhar K, Hoffman G, Klengel C, Logue M, Lori A, Maihofer A, Morrison F, Nguyen H, Park Y, Ruderfer D, Sloofman L, van Rooij S, Consortium P, Baker D, Chen C, Cox N, Duncan L, Geyer M, Glatt S, Im H, Risbrough V, Smoller J, Stein D, Yehuda R, Liberzon I, Koenen K, Jovanovic T, Kellis M, Miller M, Bacanu S, Nievergelt C, Buxbaum J, Sklar P, Ressler K, Stahl E, Daskalakis N. Analysis of Genetically Regulated Gene Expression Identifies a Prefrontal PTSD Gene, SNRNP35, Specific to Military Cohorts. Cell Reports 2020, 31: 107716. PMID: 32492425, PMCID: PMC7359754, DOI: 10.1016/j.celrep.2020.107716.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsCase-Control StudiesCohort StudiesDexamethasoneDown-RegulationGene Expression RegulationGene Regulatory NetworksGenetic Predisposition to DiseaseHumansLeukocytesMaleMiceMice, Inbred C57BLMilitary PersonnelPrefrontal CortexRepressor ProteinsRibonucleoproteins, Small NuclearRNA InterferenceRNA, Small InterferingStress Disorders, Post-TraumaticConceptsPost-traumatic stress disorderGenetically regulated gene expressionPost-traumatic stress disorder casesDorsolateral prefrontal cortexGene expressionU12 intron splicingPrefrontal cortexStress disorderDeployment stressTranscriptome imputationTissue-specific gene expressionDifferential gene expressionMilitary cohortZNF140U12 intronsGenetic heterogeneityExpression changesFunctional roleExogenous glucocorticoidsPeripheral leukocytesEuropean cohortCortexCohortDisordersExpression
2019
Synergistic effects of common schizophrenia risk variants
Schrode N, Ho SM, Yamamuro K, Dobbyn A, Huckins L, Matos MR, Cheng E, Deans PJM, Flaherty E, Barretto N, Topol A, Alganem K, Abadali S, Gregory J, Hoelzli E, Phatnani H, Singh V, Girish D, Aronow B, Mccullumsmith R, Hoffman GE, Stahl EA, Morishita H, Sklar P, Brennand KJ. Synergistic effects of common schizophrenia risk variants. Nature Genetics 2019, 51: 1475-1485. PMID: 31548722, PMCID: PMC6778520, DOI: 10.1038/s41588-019-0497-5.Peer-Reviewed Original ResearchMeSH KeywordsChloride ChannelsCRISPR-Cas SystemsFemaleFurinGene EditingGene Expression RegulationGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansInduced Pluripotent Stem CellsMaleMonomeric Clathrin Assembly ProteinsPolymorphism, Single NucleotideQuantitative Trait LociSchizophreniaSNARE ProteinsConceptsExpression quantitative trait lociComplex genetic disorderEQTL genesCommon variantsQuantitative trait lociRisk variantsGene expression differencesPsychiatric disease riskCommon risk variantsPluripotent stem cellsSchizophrenia risk variantsGenetic disordersTrait lociGene perturbationsGenetic approachesExpression differencesGene editingStem cellsGeneralizable phenomenonSynaptic functionGenesVariantsCRISPRLociSpecific effectsPenetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems
Zheutlin A, Dennis J, Karlsson Linnér R, Moscati A, Restrepo N, Straub P, Ruderfer D, Castro V, Chen C, Ge T, Huckins L, Charney A, Kirchner H, Stahl E, Chabris C, Davis L, Smoller J. Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients Across Four Health Care Systems. American Journal Of Psychiatry 2019, 176: 846-855. PMID: 31416338, PMCID: PMC6961974, DOI: 10.1176/appi.ajp.2019.18091085.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresHealth care systemCare systemGenetic riskAssociated with schizophreniaPolygenic risk score distributionPhenome-wide association studyMeasures of genetic riskRisk scoreHighest risk decileHealth care settingsElectronic health recordsOdds of schizophreniaAssociated with other phenotypesCare settingsRisk decileHealth recordsHigher oddsHealth consequencesResearch cohortAssociation studiesHealthEarly interventionMeta-analysisPersonality disorderGenome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa
Watson H, Yilmaz Z, Thornton L, Hübel C, Coleman J, Gaspar H, Bryois J, Hinney A, Leppä V, Mattheisen M, Medland S, Ripke S, Yao S, Giusti-Rodríguez P, Hanscombe K, Purves K, Adan R, Alfredsson L, Ando T, Andreassen O, Baker J, Berrettini W, Boehm I, Boni C, Perica V, Buehren K, Burghardt R, Cassina M, Cichon S, Clementi M, Cone R, Courtet P, Crow S, Crowley J, Danner U, Davis O, de Zwaan M, Dedoussis G, Degortes D, DeSocio J, Dick D, Dikeos D, Dina C, Dmitrzak-Weglarz M, Docampo E, Duncan L, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Farmer A, Favaro A, Fernández-Aranda F, Fichter M, Fischer K, Föcker M, Foretova L, Forstner A, Forzan M, Franklin C, Gallinger S, Giegling I, Giuranna J, Gonidakis F, Gorwood P, Mayora M, Guillaume S, Guo Y, Hakonarson H, Hatzikotoulas K, Hauser J, Hebebrand J, Helder S, Herms S, Herpertz-Dahlmann B, Herzog W, Huckins L, Hudson J, Imgart H, Inoko H, Janout V, Jiménez-Murcia S, Julià A, Kalsi G, Kaminská D, Kaprio J, Karhunen L, Karwautz A, Kas M, Kennedy J, Keski-Rahkonen A, Kiezebrink K, Kim Y, Klareskog L, Klump K, Knudsen G, La Via M, Le Hellard S, Levitan R, Li D, Lilenfeld L, Lin B, Lissowska J, Luykx J, Magistretti P, Maj M, Mannik K, Marsal S, Marshall C, Mattingsdal M, McDevitt S, McGuffin P, Metspalu A, Meulenbelt I, Micali N, Mitchell K, Monteleone A, Monteleone P, Munn-Chernoff M, Nacmias B, Navratilova M, Ntalla I, O’Toole J, Ophoff R, Padyukov L, Palotie A, Pantel J, Papezova H, Pinto D, Rabionet R, Raevuori A, Ramoz N, Reichborn-Kjennerud T, Ricca V, Ripatti S, Ritschel F, Roberts M, Rotondo A, Rujescu D, Rybakowski F, Santonastaso P, Scherag A, Scherer S, Schmidt U, Schork N, Schosser A, Seitz J, Slachtova L, Slagboom P, Slof-Op ‘t Landt M, Slopien A, Sorbi S, Świątkowska B, Szatkiewicz J, Tachmazidou I, Tenconi E, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tyszkiewicz-Nwafor M, Tziouvas K, van Elburg A, van Furth E, Wagner G, Walton E, Widen E, Zeggini E, Zerwas S, Zipfel S, Bergen A, Boden J, Brandt H, Crawford S, Halmi K, Horwood L, Johnson C, Kaplan A, Kaye W, Mitchell J, Olsen C, Pearson J, Pedersen N, Strober M, Werge T, Whiteman D, Woodside D, Stuber G, Gordon S, Grove J, Henders A, Juréus A, Kirk K, Larsen J, Parker R, Petersen L, Jordan J, Kennedy M, Montgomery G, Wade T, Birgegård A, Lichtenstein P, Norring C, Landén M, Martin N, Mortensen P, Sullivan P, Breen G, Bulik C. Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa. Nature Genetics 2019, 51: 1207-1214. PMID: 31308545, PMCID: PMC6779477, DOI: 10.1038/s41588-019-0439-2.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesTwin-based heritability estimatesEating Disorders Working GroupPsychiatric Genomics ConsortiumAnorexia nervosaBody-mass indexSignificant lociGenetic architectureRisk lociGenetics InitiativeGenomics ConsortiumLow body-mass indexMetabo-psychiatric disorderGenetic correlationsMetabolic componentsLociCases of anorexia nervosaPhysical activityAnthropometric traitsPsychiatric disordersHeritability estimatesAnorexia Nervosa Genetics InitiativeNervosaImprove outcomesGenetic analyses of diverse populations improves discovery for complex traits
Wojcik G, Graff M, Nishimura K, Tao R, Haessler J, Gignoux C, Highland H, Patel Y, Sorokin E, Avery C, Belbin G, Bien S, Cheng I, Cullina S, Hodonsky C, Hu Y, Huckins L, Jeff J, Justice A, Kocarnik J, Lim U, Lin B, Lu Y, Nelson S, Park S, Poisner H, Preuss M, Richard M, Schurmann C, Setiawan V, Sockell A, Vahi K, Verbanck M, Vishnu A, Walker R, Young K, Zubair N, Acuña-Alonso V, Ambite J, Barnes K, Boerwinkle E, Bottinger E, Bustamante C, Caberto C, Canizales-Quinteros S, Conomos M, Deelman E, Do R, Doheny K, Fernández-Rhodes L, Fornage M, Hailu B, Heiss G, Henn B, Hindorff L, Jackson R, Laurie C, Laurie C, Li Y, Lin D, Moreno-Estrada A, Nadkarni G, Norman P, Pooler L, Reiner A, Romm J, Sabatti C, Sandoval K, Sheng X, Stahl E, Stram D, Thornton T, Wassel C, Wilkens L, Winkler C, Yoneyama S, Buyske S, Haiman C, Kooperberg C, Le Marchand L, Loos R, Matise T, North K, Peters U, Kenny E, Carlson C. Genetic analyses of diverse populations improves discovery for complex traits. Nature 2019, 570: 514-518. PMID: 31217584, PMCID: PMC6785182, DOI: 10.1038/s41586-019-1310-4.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesComplex traitsBiology of complex traitsDiverse populationsEvidence of effect-size heterogeneityGenome-wide effortsLarge-scale genomic studiesReduce health disparitiesNon-European individualsHighest burden of diseaseMulti-ethnic participantsEffect-size heterogeneityBurden of diseaseRepresentation of diverse populationsGWAS associationsNovel lociRisk prediction scoreAdmixed populationsFine-mappingGenetic architectureAssociation studiesGenomic studiesHealth disparitiesHealthcare disparitiesPopulation ArchitectureAssociations Between Attention-Deficit/Hyperactivity Disorder and Various Eating Disorders: A Swedish Nationwide Population Study Using Multiple Genetically Informative Approaches
Yao S, Kuja-Halkola R, Martin J, Lu Y, Lichtenstein P, Norring C, Birgegård A, Yilmaz Z, Hübel C, Watson H, Baker J, Almqvist C, Consortium E, Adan R, Ando T, Baker J, Bergen A, Berrettini W, Birgegård A, Boni C, Perica V, Brandt H, Burghardt R, Cassina M, Cesta C, Clementi M, Coleman J, Cone R, Courtet P, Crawford S, Crow S, Crowley J, Danner U, Davis O, de Zwaan M, Dedoussis G, Degortes D, DeSocio J, Dick D, Dikeos D, Dmitrzak-Weglarz M, Docampo E, Egberts K, Ehrlich S, Escaramís G, Esko T, Estivill X, Favaro A, Fernández-Aranda F, Fichter M, Finan C, Fischer K, Föcker M, Foretova L, Forzan M, Franklin C, Gaspar H, Gonidakis F, Gorwood P, Gratacos M, Guillaume S, Guo Y, Hakonarson H, Halmi K, Hatzikotoulas K, Hauser J, Hebebrand J, Helder S, Hendriks J, Herpertz-Dahlmann B, Herzog W, Hilliard C, Hinney A, Huckins L, Hudson J, Huemer J, Imgart H, Inoko H, Jiménez-Murcia S, Johnson C, Jordan J, Juréus A, Kalsi G, Kaminska D, Kaplan A, Kaprio J, Karhunen L, Karwautz A, Kas M, Kaye W, Kennedy J, Kennedy M, Keski-Rahkonen A, Kiezebrink K, Kim Y, Klump K, Knudsen G, Koeleman B, Koubek D, La Via M, Landén M, Levitan R, Li D, Lichtenstein P, Lilenfeld L, Lissowska J, Magistretti P, Maj M, Mannik K, Martin N, McDevitt S, McGuffin P, Merl E, Metspalu A, Meulenbelt I, Micali N, Mitchell J, Mitchell K, Monteleone P, Monteleone A, Mortensen P, Munn-Chernoff M, Nacmias B, Nilsson I, Norring C, Ntalla I, O’Toole J, Pantel J, Papezova H, Parker R, Rabionet R, Raevuori A, Rajewski A, Ramoz N, Rayner N, Reichborn-Kjennerud T, Ricca V, Ripke S, Ritschel F, Roberts M, Rotondo A, Rybakowski F, Santonastaso P, Scherag A, Schmidt U, Schork N, Schosser A, Seitz J, Slachtova L, Slagboom P, Landt M, Slopien A, Smith T, Sorbi S, Strengman E, Strober M, Sullivan P, Szatkiewicz J, Szeszenia-Dabrowska N, Tachmazidou I, Tenconi E, Thornton L, Tortorella A, Tozzi F, Treasure J, Tsitsika A, Tziouvas K, van Elburg A, van Furth E, Wade T, Wagner G, Walton E, Watson H, Woodside D, Yao S, Yilmaz Z, Zeggini E, Zerwas S, Zipfel S, Alfredsson L, Andreassen O, Aschauer H, Barrett J, Bencko V, Carlberg L, Cichon S, Cohen-Woods S, Dina C, Ding B, Espeseth T, Floyd J, Gallinger S, Gambaro G, Giegling I, Herms S, Janout V, Julià A, Klareskog L, Le Hellard S, Leboyer M, Lundervold A, Marsal S, Mattingsdal M, Navratilova M, Ophoff R, Palotie A, Pinto D, Ripatti S, Rujescu D, Scherer S, Scott L, Sladek R, Soranzo N, Southam L, Steen V, Wichmann H, Widen E, Breen G, Bulik C, Thornton L, Magnusson P, Bulik C, Larsson H. Associations Between Attention-Deficit/Hyperactivity Disorder and Various Eating Disorders: A Swedish Nationwide Population Study Using Multiple Genetically Informative Approaches. Biological Psychiatry 2019, 86: 577-586. PMID: 31301758, PMCID: PMC6776821, DOI: 10.1016/j.biopsych.2019.04.036.Peer-Reviewed Original ResearchConceptsPolygenic risk scoresAttention-deficit/hyperactivity disorder polygenic risk scoresAttention-deficit/hyperactivity disorderQuantitative genetic modelRisk scoreGenetic associationGenetic correlationsEating disordersRegister-based informationAnorexia nervosaPopulation-based sampleGenetic modelsDegree of relatednessGenetically informed approachesAttention-deficit/hyperactivityNationwide population studyMaternal half-sistersCorrelates of attention-deficit/hyperactivity disorderFull-sistersAttention-deficit/hyperactivity disorder symptomsFamilial coaggregationNationwide populationGenetically informative designsShared etiologySubscales Drive
2017
Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa
Huckins L, Hatzikotoulas K, Southam L, Thornton L, Steinberg J, Aguilera-McKay F, Treasure J, Schmidt U, Gunasinghe C, Romero A, Curtis C, Rhodes D, Moens J, Kalsi G, Dempster D, Leung R, Keohane A, Burghardt R, Ehrlich S, Hebebrand J, Hinney A, Ludolph A, Walton E, Deloukas P, Hofman A, Palotie A, Palta P, van Rooij F, Stirrups K, Adan R, Boni C, Cone R, Dedoussis G, van Furth E, Gonidakis F, Gorwood P, Hudson J, Kaprio J, Kas M, Keski-Rahonen A, Kiezebrink K, Knudsen G, Slof-Op 't Landt M, Maj M, Monteleone A, Monteleone P, Raevuori A, Reichborn-Kjennerud T, Tozzi F, Tsitsika A, van Elburg A, Collier D, Sullivan P, Breen G, Bulik C, Zeggini E. Investigation of common, low-frequency and rare genome-wide variation in anorexia nervosa. Molecular Psychiatry 2017, 23: 1169-1180. PMID: 29155802, PMCID: PMC5828108, DOI: 10.1038/mp.2017.88.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significanceGenomic search spaceGenome-wide variationLow-frequency variantsDetect low-frequency variantsPopulations of European originDe novo replicationIntergenic variantVariant associationsExome-chipAssociation studiesIntronic variantsRare variantsIn silicoEuropean originVariantsEffect sizeLociOPCMLWell-poweredSilicoReplicationNeuropsychiatric disorders
2014
A genome-wide association study of anorexia nervosa
Boraska V, Franklin C, Floyd J, Thornton L, Huckins L, Southam L, Rayner N, Tachmazidou I, Klump K, Treasure J, Lewis C, Schmidt U, Tozzi F, Kiezebrink K, Hebebrand J, Gorwood P, Adan R, Kas M, Favaro A, Santonastaso P, Fernández-Aranda F, Gratacos M, Rybakowski F, Dmitrzak-Weglarz M, Kaprio J, Keski-Rahkonen A, Raevuori A, Van Furth E, Slof-Op 't Landt M, Hudson J, Reichborn-Kjennerud T, Knudsen G, Monteleone P, Kaplan A, Karwautz A, Hakonarson H, Berrettini W, Guo Y, Li D, Schork N, Komaki G, Ando T, Inoko H, Esko T, Fischer K, Männik K, Metspalu A, Baker J, Cone R, Dackor J, DeSocio J, Hilliard C, O'Toole J, Pantel J, Szatkiewicz J, Taico C, Zerwas S, Trace S, Davis O, Helder S, Bühren K, Burghardt R, de Zwaan M, Egberts K, Ehrlich S, Herpertz-Dahlmann B, Herzog W, Imgart H, Scherag A, Scherag S, Zipfel S, Boni C, Ramoz N, Versini A, Brandys M, Danner U, de Kovel C, Hendriks J, Koeleman B, Ophoff R, Strengman E, van Elburg A, Bruson A, Clementi M, Degortes D, Forzan M, Tenconi E, Docampo E, Escaramís G, Jiménez-Murcia S, Lissowska J, Rajewski A, Szeszenia-Dabrowska N, Slopien A, Hauser J, Karhunen L, Meulenbelt I, Slagboom P, Tortorella A, Maj M, Dedoussis G, Dikeos D, Gonidakis F, Tziouvas K, Tsitsika A, Papezova H, Slachtova L, Martaskova D, Kennedy J, Levitan R, Yilmaz Z, Huemer J, Koubek D, Merl E, Wagner G, Lichtenstein P, Breen G, Cohen-Woods S, Farmer A, McGuffin P, Cichon S, Giegling I, Herms S, Rujescu D, Schreiber S, Wichmann H, Dina C, Sladek R, Gambaro G, Soranzo N, Julia A, Marsal S, Rabionet R, Gaborieau V, Dick D, Palotie A, Ripatti S, Widén E, Andreassen O, Espeseth T, Lundervold A, Reinvang I, Steen V, Le Hellard S, Mattingsdal M, Ntalla I, Bencko V, Foretova L, Janout V, Navratilova M, Gallinger S, Pinto D, Scherer S, Aschauer H, Carlberg L, Schosser A, Alfredsson L, Ding B, Klareskog L, Padyukov L, Courtet P, Guillaume S, Jaussent I, Finan C, Kalsi G, Roberts M, Logan D, Peltonen L, Ritchie G, Barrett J, Estivill X, Hinney A, Sullivan P, Collier D, Zeggini E, Bulik C. A genome-wide association study of anorexia nervosa. Molecular Psychiatry 2014, 19: 1085-1094. PMID: 24514567, PMCID: PMC4325090, DOI: 10.1038/mp.2013.187.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesAssociation studiesGenome-wide significanceReplicate data setsCase-control sampleReplicated genotypesGlobal meta-analysisIntronic variantsGenetics ConsortiumGene studiesEuropean ancestryDe novoIn silicoAN casesAnorexia nervosaReplicationMeta-analyzedMeta-analysisSPATA13Discovery dataFAM124BReplicate resultsLower body weightCul3Discovery