2024
Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex
Cote A, Young H, Huckins L. Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex. Human Genetics And Genomics Advances 2024, 5: 100311. PMID: 38773772, PMCID: PMC11214266, DOI: 10.1016/j.xhgg.2024.100311.Peer-Reviewed Original ResearchConceptsExpression quantitative trait lociGene co-expressionCo-expressionExpression quantitative trait locus methodGenetic variantsComplex trait heritabilityMultiple testing burdenGene-based testsQuantitative trait lociTrans-eQTLsCis-eQTLsRegulatory variationSequencing datasetsTrait lociGene regulationTrait heritabilityGene functionGene modulesReal-data applicationModule genesGenesTesting burdenDorsolateral prefrontal cortexVariantsComparison to prior studies
2023
Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals
Zhou H, Kember R, Deak J, Xu H, Toikumo S, Yuan K, Lind P, Farajzadeh L, Wang L, Hatoum A, Johnson J, Lee H, Mallard T, Xu J, Johnston K, Johnson E, Nielsen T, Galimberti M, Dao C, Levey D, Overstreet C, Byrne E, Gillespie N, Gordon S, Hickie I, Whitfield J, Xu K, Zhao H, Huckins L, Davis L, Sanchez-Roige S, Madden P, Heath A, Medland S, Martin N, Ge T, Smoller J, Hougaard D, Børglum A, Demontis D, Krystal J, Gaziano J, Edenberg H, Agrawal A, Justice A, Stein M, Kranzler H, Gelernter J. Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals. Nature Medicine 2023, 29: 3184-3192. PMID: 38062264, PMCID: PMC10719093, DOI: 10.1038/s41591-023-02653-5.Peer-Reviewed Original ResearchAlcoholismGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideRacial GroupsGenetically Regulated Gene Expression in the Brain Associated With Chronic Pain: Relationships With Clinical Traits and Potential for Drug Repurposing
Johnston K, Cote A, Hicks E, Johnson J, Huckins L. Genetically Regulated Gene Expression in the Brain Associated With Chronic Pain: Relationships With Clinical Traits and Potential for Drug Repurposing. Biological Psychiatry 2023, 95: 745-761. PMID: 37678542, PMCID: PMC10924073, DOI: 10.1016/j.biopsych.2023.08.023.Peer-Reviewed Original ResearchMeSH KeywordsArrhythmias, CardiacBrainChronic PainDrug RepositioningGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansMetabolic SyndromePhenotypePolymorphism, Single NucleotideTranscriptomeConceptsGenetically regulated gene expressionMultisite chronic painGene-tissue associationsMean pain scoreUnique genesChronic painAssociation studiesS-PrediXcanGene expressionPain scoresTranscriptome-wide association studyPhenome-wide association studyCardiac dysrhythmiasMetabolic syndromeGenome-wide association studiesAssociated with cardiac dysrhythmiasDrug targetsGenotype-phenotype gapChronic pain developmentAssociation study resultsGene expression changesJoint/ligament sprainsDirection of effectTranscriptome imputationPain traitsLeveraging base-pair mammalian constraint to understand genetic variation and human disease
Sullivan P, Meadows J, Gazal S, Phan B, Li X, Genereux D, Dong M, Bianchi M, Andrews G, Sakthikumar S, Nordin J, Roy A, Christmas M, Marinescu V, Wang C, Wallerman O, Xue J, Yao S, Sun Q, Szatkiewicz J, Wen J, Huckins L, Lawler A, Keough K, Zheng Z, Zeng J, Wray N, Li Y, Johnson J, Chen J, Paten B, Reilly S, Hughes G, Weng Z, Pollard K, Pfenning A, Forsberg-Nilsson K, Karlsson E, Lindblad-Toh K, Andrews G, Armstrong J, Bianchi M, Birren B, Bredemeyer K, Breit A, Christmas M, Clawson H, Damas J, Di Palma F, Diekhans M, Dong M, Eizirik E, Fan K, Fanter C, Foley N, Forsberg-Nilsson K, Garcia C, Gatesy J, Gazal S, Genereux D, Goodman L, Grimshaw J, Halsey M, Harris A, Hickey G, Hiller M, Hindle A, Hubley R, Hughes G, Johnson J, Juan D, Kaplow I, Karlsson E, Keough K, Kirilenko B, Koepfli K, Korstian J, Kowalczyk A, Kozyrev S, Lawler A, Lawless C, Lehmann T, Levesque D, Lewin H, Li X, Lind A, Lindblad-Toh K, Mackay-Smith A, Marinescu V, Marques-Bonet T, Mason V, Meadows J, Meyer W, Moore J, Moreira L, Moreno-Santillan D, Morrill K, Muntané G, Murphy W, Navarro A, Nweeia M, Ortmann S, Osmanski A, Paten B, Paulat N, Pfenning A, Phan B, Pollard K, Pratt H, Ray D, Reilly S, Rosen J, Ruf I, Ryan L, Ryder O, Sabeti P, Schäffer D, Serres A, Shapiro B, Smit A, Springer M, Srinivasan C, Steiner C, Storer J, Sullivan K, Sullivan P, Sundström E, Supple M, Swofford R, Talbot J, Teeling E, Turner-Maier J, Valenzuela A, Wagner F, Wallerman O, Wang C, Wang J, Weng Z, Wilder A, Wirthlin M, Xue J, Zhang X. Leveraging base-pair mammalian constraint to understand genetic variation and human disease. Science 2023, 380: eabn2937. PMID: 37104612, PMCID: PMC10259825, DOI: 10.1126/science.abn2937.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBiological EvolutionDiseaseGenetic VariationGenome-Wide Association StudyGenome, HumanGenomicsHumansMolecular Sequence AnnotationPolymorphism, Single NucleotideConceptsHuman genomeHuman diseasesCopy-number variationsHeritable human diseasesGenome annotationVariant annotationGenomic positionsGenomic regionsDisease heritabilityFunctional annotationEvolutionary constraintsAssociation studiesCopy-numberGenetic variationGenetic findingsGenomeCell typesRegulatory landscapeDisease mechanismsAnnotationBiological mechanismsCancer dataMammalsPredictor of functionHeritability
2022
Mapping anorexia nervosa genes to clinical phenotypes
Johnson J, Cote A, Dobbyn A, Sloofman L, Xu J, Cotter L, Charney A, Consortium E, Birgegård A, Jordan J, Kennedy M, Landén M, Maguire S, Martin N, Mortensen P, Thornton L, Bulik C, Huckins L. Mapping anorexia nervosa genes to clinical phenotypes. Psychological Medicine 2022, 53: 2619-2633. PMID: 35379376, PMCID: PMC10123844, DOI: 10.1017/s0033291721004554.Peer-Reviewed Original ResearchMeSH KeywordsAnorexia NervosaGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotideTranscriptomeConceptsBody mass indexGenome-wide association studiesTranscriptome imputationElectronic health record phenotypingAssociation studiesPhenome-wide association studyS-PrediXcan analysisMeasurement of cholesterolTraditional genome-wide association studiesGenes associated with ANAssociation of genetic variantsSubstance useGenetically regulated gene expressionImpact of body mass indexConsequences of aberrant expressionBiobank cohortBiobank populationAssociated with measuresAssociation of ANGWAS findingsSecondary analysisS-PrediXcanDisease riskMass indexSignificant genesPredicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits
Highland H, Wojcik G, Graff M, Nishimura K, Hodonsky C, Baldassari A, Cote A, Cheng I, Gignoux C, Tao R, Li Y, Boerwinkle E, Fornage M, Haessler J, Hindorff L, Hu Y, Justice A, Lin B, Lin D, Stram D, Haiman C, Kooperberg C, Le Marchand L, Matise T, Kenny E, Carlson C, Stahl E, Avery C, North K, Ambite J, Buyske S, Loos R, Peters U, Young K, Bien S, Huckins L. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. American Journal Of Human Genetics 2022, 109: 669-679. PMID: 35263625, PMCID: PMC9069067, DOI: 10.1016/j.ajhg.2022.02.013.Peer-Reviewed Original ResearchMeSH KeywordsCardiovascular DiseasesGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansLife StylePolymorphism, Single NucleotideTranscriptomeConceptsAncestrally diverse populationsBody mass indexCardiometabolic traitsEuropean ancestryGene expressionTranscriptomic imputation modelsNon-EA populationsDiverse populationsWhite British participantsReference panelGenetically regulated gene expressionGene-trait associationsTissue-specific gene expressionPopulation ArchitectureUK BiobankMeasurement of gene expressionPredicting gene expressionMulti-omics datasetsRelevant tissuesSusceptibility lociMass indexComplex traitsIdentified genesNovel associationsDiverse sample
2021
Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study
Hu Y, Bien S, Nishimura K, Haessler J, Hodonsky C, Baldassari A, Highland H, Wang Z, Preuss M, Sitlani C, Wojcik G, Tao R, Graff M, Huckins L, Sun Q, Chen M, Mousas A, Auer P, Lettre G, Tang W, Qi L, Thyagarajan B, Buyske S, Fornage M, Hindorff L, Li Y, Lin D, Reiner A, North K, Loos R, Raffield L, Peters U, Avery C, Kooperberg C. Multi-ethnic genome-wide association analyses of white blood cell and platelet traits in the Population Architecture using Genomics and Epidemiology (PAGE) study. BMC Genomics 2021, 22: 432. PMID: 34107879, PMCID: PMC8191001, DOI: 10.1186/s12864-021-07745-5.Peer-Reviewed Original ResearchMeSH KeywordsGenetic Predisposition to DiseaseGenome-Wide Association StudyGenomicsHumansLeukocytesPhenotypePolymorphism, Single NucleotideConceptsGenome-wide association studiesPlatelet traitsAfrican AmericansPopulation ArchitectureAssociation studiesAssociation analysisAncestry-specific genome-wide association studiesEuropean ancestryGenome-wide association analysisAttenuation of effect estimatesGenome-wide significant variantsVariant association analysisGenome-wide significanceRacially/ethnically diverse populationsPopulations of European ancestryGenetic association studiesAncestry-specificComplex traitsSignificant variantsHispanics/LatinosMultiple genesAncestry groupsEffect estimatesEA populationsEA participantsExamining 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 ResearchMeSH KeywordsFemaleGenome-Wide Association StudyHumansMaleMultifactorial InheritancePhenotypePolymorphism, Single NucleotideSex CharacteristicsConceptsSex-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
Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data
Dueñas H, Seah C, Johnson J, Huckins L. Implicit bias of encoded variables: frameworks for addressing structured bias in EHR–GWAS data. Human Molecular Genetics 2020, 29: r33-r41. PMID: 32879975, PMCID: PMC7530523, DOI: 10.1093/hmg/ddaa192.Peer-Reviewed Original ResearchMeSH KeywordsComputational BiologyDiseaseElectronic Health RecordsGenome-Wide Association StudyHumansPhenotypePolymorphism, Single NucleotidePrejudiceConceptsElectronic health recordsUse of electronic health recordsElectronic health record dataElectronic health record analysisGenome-wide association studiesHealth recordsPhenotype definitionAssociation studiesMedical recordsClinical decisionsPhenotypic characterizationPhenotypic analysisClinically useful insightsPotential biasPresentation of diseaseDegree of biasHomogeneous cohortClinicPhenotypeScalable mannerRecordsCohortBias
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 effectsGene expression imputation across multiple brain regions provides insights into schizophrenia risk
Huckins LM, Dobbyn A, Ruderfer DM, Hoffman G, Wang W, Pardiñas AF, Rajagopal VM, Als TD, T. Nguyen H, Girdhar K, Boocock J, Roussos P, Fromer M, Kramer R, Domenici E, Gamazon ER, Purcell S, Demontis D, Børglum A, Walters J, O’Donovan M, Sullivan P, Owen M, Devlin B, Sieberts S, Cox N, Im H, Sklar P, Stahl E. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nature Genetics 2019, 51: 659-674. PMID: 30911161, PMCID: PMC7034316, DOI: 10.1038/s41588-019-0364-4.Peer-Reviewed Original Research
2018
Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics
Barbeira A, Dickinson S, Bonazzola R, Zheng J, Wheeler H, Torres J, Torstenson E, Shah K, Garcia T, Edwards T, Stahl E, Huckins L, GTEx Consortium, Nicolae D, Cox N, Im H. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nature Communications 2018, 9: 1825. PMID: 29739930, PMCID: PMC5940825, DOI: 10.1038/s41467-018-03621-1.Peer-Reviewed Original ResearchConceptsGene expression variationExpression variationMonogenic disease genesGWAS summary statisticsSpectrum of milder phenotypesGTEx tissuesS-PrediXcanDisease genesTrait etiologyPhenotypic consequencesGenetic variantsHuman phenotypesRegulatory mechanismsMilder phenotypeSummary statisticsSignificant associationPhenotypeGenesTraitsMeta-analysis studySummary dataReference setsPrediXcanIndependent cohortGWASCommon schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection
Pardiñas A, Holmans P, Pocklington A, Escott-Price V, Ripke S, Carrera N, Legge S, Bishop S, Cameron D, Hamshere M, Han J, Hubbard L, Lynham A, Mantripragada K, Rees E, MacCabe J, McCarroll S, Baune B, Breen G, Byrne E, Dannlowski U, Eley T, Hayward C, Martin N, McIntosh A, Plomin R, Porteous D, Wray N, Caballero A, Geschwind D, Huckins L, Ruderfer D, Santiago E, Sklar P, Stahl E, Won H, Agerbo E, Als T, Andreassen O, Bækvad-Hansen M, Mortensen P, Pedersen C, Børglum A, Bybjerg-Grauholm J, Djurovic S, Durmishi N, Pedersen M, Golimbet V, Grove J, Hougaard D, Mattheisen M, Molden E, Mors O, Nordentoft M, Pejovic-Milovancevic M, Sigurdsson E, Silagadze T, Hansen C, Stefansson K, Stefansson H, Steinberg S, Tosato S, Werge T, GERAD1 Consortium, CRESTAR Consortium, Collier D, Rujescu D, Kirov G, Owen M, O’Donovan M, Walters J. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nature Genetics 2018, 50: 381-389. PMID: 29483656, PMCID: PMC5918692, DOI: 10.1038/s41588-018-0059-2.Peer-Reviewed Original ResearchConceptsMutation-intolerant genesGenetic architecture of schizophreniaGenome-wide association studiesChromosome conformation dataGenome fine mappingVariant association signalsAssociation signalsFine-mappingGenetic architectureCausal genesGenomic studiesAssociation studiesRisk variantsSelection pressureGenesLociBrain expressionAssociated with poor qualityBiologyConformational dataDecreased life expectancyChromosomeDebilitating psychiatric conditionAllelesLife expectancy
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
Using ancestry-informative markers to identify fine structure across 15 populations of European origin
Huckins L, Boraska V, Franklin C, Floyd J, Southam L, Sullivan P, Bulik C, Collier D, Tyler-Smith C, Zeggini E, Tachmazidou I. Using ancestry-informative markers to identify fine structure across 15 populations of European origin. European Journal Of Human Genetics 2014, 22: 1190-1200. PMID: 24549058, PMCID: PMC4169539, DOI: 10.1038/ejhg.2014.1.Peer-Reviewed Original ResearchConceptsMinor allele frequencyPopulations of European originGenome-wide association scanAncestry-informative markersEuropean-descent populationsAxes of variationEuropean originGenetic structureAssociation scansPopulation structurePopulation stratificationK chipAllele frequenciesAssociation resultsSNPsInformative axesIlluminaMarkersPopulationVariantsPrincipal component analysisConsortiaComparative methodOriginWellcomeA 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