2025
The Psychiatric Genomics Consortium: discoveries and directions
Agrawal A, Bulik C, Abebe D, Andreassen O, Atkinson E, Choi K, Corvin A, Davies H, Davis L, Docherty A, Edenberg H, Franke B, Gelernter J, Giusti-Rodríguez P, Hettema J, Hjerling-Leffler J, Huang H, Johnson E, Lewis C, Lu Y, Lynall M, Martin J, McIntosh A, Montalvo-Ortiz J, Mullins N, Nievergelt C, O'Connell K, O'Donovan M, Okewole A, Peterson R, Posthuma D, Sebat J, Smoller J, Sud R, Viswanath B, Walters J, Won H, Wray N, Sullivan P, Consortium T. The Psychiatric Genomics Consortium: discoveries and directions. The Lancet Psychiatry 2025 PMID: 40582370, DOI: 10.1016/s2215-0366(25)00124-5.Peer-Reviewed Original ResearchPsychiatric Genomics ConsortiumGenome-wide association studiesFunctional genomics dataRare genetic variationPsychiatric disordersGenomic dataAssociation studiesGenetic variationGenomic discoveriesNeurodevelopmental conditionsPolygenic riskPolygenic scoresGenomics ConsortiumGenetic causeRare variantsRare variationImplementation processGlobal morbidityFunctional attributesPriority areasIntegrated findingsMultiple psychiatric disordersNext phaseVariantsDiscoveryProtocol for finding genetic variation associated with unmeasured traits through GenomicSEM common-factor GWAS
Johnston K, Signer R, Huckins L. Protocol for finding genetic variation associated with unmeasured traits through GenomicSEM common-factor GWAS. STAR Protocols 2025, 6: 103905. PMID: 40531628, PMCID: PMC12213288, DOI: 10.1016/j.xpro.2025.103905.Peer-Reviewed Original ResearchGenetics of Response to ECT, TMS, Ketamine and Esketamine
Franklin C, Altinay M, Bailey K, Bhati M, Carr B, Conroy S, Khurshid K, McDonald W, Mickey B, Murrough J, Nestor S, Nickl‐Jockschat T, Reti I, Sanacora G, Trapp N, Viswanath B, Wright J, Zandi P, Potash J. Genetics of Response to ECT, TMS, Ketamine and Esketamine. American Journal Of Medical Genetics Part B Neuropsychiatric Genetics 2025, e33038. PMID: 40525674, DOI: 10.1002/ajmg.b.33038.Peer-Reviewed Original ResearchGenome-wide association studiesSingle nucleotide polymorphismsTreatment responseEffects of variantsElectroconvulsive therapyGenetic predictors of treatment responseTreatment modalitiesAssociation studiesCandidate genesFactors affecting treatment responseAssociated with treatment responseResponse to available treatmentsGene studiesNucleotide polymorphismsTreatment-resistant mood disordersGenetic predictors of responseTreatment-resistant depressionPredictors of treatment responsePredictors of responseGroup of patientsGenesTranscranial magnetic stimulationGeneticsGenetic predictorsClinical responseGenome-wide meta-analysis identifies nine loci to be associated with higher risk of hepatocellular carcinoma development.
Ghouse J, Gellert-Kristensen H, O’Rourke C, Seidelin A, Thorleifsson G, Sveinbjörnsson G, Tragante V, Konkwo C, Brancale J, Vilarinho S, Eyrich T, Ahlberg G, Bundgaard J, Rand S, Lundegaard P, Sørensen E, Mikkelsen C, Træholt J, Erikstrup C, Dinh K, Bruun M, Jensen B, Bay J, Brunak S, Banasik K, Ullum H, Consortium E, Laisk T, Mägi R, Nadauld L, Knowlton K, Knight S, Gluud L, Vistisen K, Björnsson E, Ulfarsson M, Sulem P, Holm H, Pedersen O, Ostrowski S, Gudbjartsson D, Rafnar T, Stefansson K, Lassen U, Pommergaard H, Hillingsø J, Andersen J, Bundgaard H, Stender S. Genome-wide meta-analysis identifies nine loci to be associated with higher risk of hepatocellular carcinoma development. JHEP Reports 2025, 101485. DOI: 10.1016/j.jhepr.2025.101485.Peer-Reviewed Original ResearchGenome-wide association studiesAssociated with higher riskGenome-wide statistical significanceIncident hepatocellular carcinomaMendelian randomization analysisGenome-wide meta-analysisIdentified variantsPer-allele effectsMeta-analysisMendelian randomizationGenetic risk lociPrevalent obesityRandomization analysisAlcohol intakeMeta-analysesRisk lociAssociation studiesRisk factorsGenetic variantsGenetic underpinningsRisk of hepatocellular carcinomaLociGenetic effectsCohortConcordant effectsPTPN2 and Leukopenia in Individuals With Normal TPMT and NUDT15 Metabolizer Status Taking Azathioprine
Daniel L, Nepal P, Zanussi J, Dickson A, Straub P, Miller‐Fleming T, Wei W, Hung A, Cox N, Kawai V, Mosley J, Stein C, Feng Q, Liu G, Tao R, Chung C. PTPN2 and Leukopenia in Individuals With Normal TPMT and NUDT15 Metabolizer Status Taking Azathioprine. Clinical And Translational Science 2025, 18: e70220. PMID: 40442974, PMCID: PMC12122386, DOI: 10.1111/cts.70220.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide significancePrincipal components of ancestryImmune cell developmentGenetic risk factorsDose-dependent side effectsAssociation studiesGenetic dataSide effects of azathioprineIntronic variantsElectronic health recordsVanderbilt's electronic health recordEffect of azathioprineCell developmentPTPN2Replication cohortTPMTHealth recordsNUDT15NIH-funded projectDrug discontinuationThiopurine useBioVUXanthine oxidase inhibitorLeukopeniaTargeted analysis of dyslexia-associated regions on chromosomes 6, 12 and 15 in large multigenerational cohorts
Chapman N, Navas P, Dorschner M, Mehaffey M, Wigg K, Price K, Naumova O, Kerr E, Guger S, Lovett M, Grigorenko E, Berninger V, Barr C, Wijsman E, Raskind W. Targeted analysis of dyslexia-associated regions on chromosomes 6, 12 and 15 in large multigenerational cohorts. PLOS ONE 2025, 20: e0324006. PMID: 40424442, PMCID: PMC12112411, DOI: 10.1371/journal.pone.0324006.Peer-Reviewed Original ResearchConceptsEvidence of associationLarge-scale sequencing studiesCis-acting regulatory regionsGenome-wide association studiesAggregating rare variantsRare exonic variantsDetected significant evidenceSingle nucleotide polymorphismsGenomic variationDeleterious variantsAssociated with reduced performanceAssociation studiesLarge-effectRegulatory elementsTranscriptional regulationRegulatory regionsQuantitative phenotypesCandidate genesExonic variantsChromosome 6Sequencing studiesSingle variantsCoding exonsMultiple traitsGenetic basisLeveraging Alzheimer’s Disease Omics to Identify Pleiotropic Genes Contributing to Neurodegeneration in Primary Open-Angle Glaucoma
Tolosa-Tort P, DeWan A. Leveraging Alzheimer’s Disease Omics to Identify Pleiotropic Genes Contributing to Neurodegeneration in Primary Open-Angle Glaucoma. Molecular Neurobiology 2025, 1-10. PMID: 40411683, DOI: 10.1007/s12035-025-05074-2.Peer-Reviewed Original ResearchPrimary open-angle glaucomaOpen-angle glaucomaOpen-angle glaucoma riskPrimary open-angle glaucoma riskIntraocular pressure controlGene expressionGenome-wide association studiesAlzheimer's diseasePOAG riskGlaucoma riskIrreversible blindnessCurrent therapiesGlaucomaTreatment strategiesMultivariate analysisPathogenic mechanismsEvidence of pleiotropyPressure controlNeuronal repairPrioritized genesBrain cortexAssociation studiesEarly detectionNeuroprotective pathwaysDiseaseExamining socioeconomic differences in sepsis risk and mediation by modifiable factors: a Mendelian randomization study
Stensrud V, Rogne T, Flatby H, Mohus R, Gustad L, Nilsen T. Examining socioeconomic differences in sepsis risk and mediation by modifiable factors: a Mendelian randomization study. BMC Infectious Diseases 2025, 25: 739. PMID: 40410669, PMCID: PMC12103053, DOI: 10.1186/s12879-025-11130-y.Peer-Reviewed Original ResearchConceptsSummary-level dataSocioeconomic differencesGenome-wide association studiesGenetic instrumentsEducational attainmentMendelian randomizationMR analysisStandard deviation increaseBias due to population stratificationOdds ratioPreventive factorsMR-Egger regressionExamined socioeconomic differencesMultivariable MR analysisEffect of smoking initiationAssociation studiesMendelian randomization studiesEffects of educational attainmentDeviation increaseYears of educationBody mass indexBackgroundEducational attainmentDynastic effectsMedian OREgger regressionCharacterizing the Social Epigenome in Mexican Patients with Early-Onset Psychosis
Ruiz-Ramos D, Martínez-Magaña J, Juárez-Rojop I, Nolasco-Rosales G, Sosa-Hernández F, Cruz-Castillo J, Cavazos J, Callejas A, Zavaleta-Ramírez P, Zorrilla-Dosal J, Lanzagorta N, Nicolini H, Montalvo-Ortiz J, Glahn D, Genis-Mendoza A. Characterizing the Social Epigenome in Mexican Patients with Early-Onset Psychosis. Genes 2025, 16: 591. PMID: 40428414, PMCID: PMC12111507, DOI: 10.3390/genes16050591.Peer-Reviewed Original ResearchConceptsEarly-onset psychosisRisk scoreEpigenetic ageEpigenome-wide association studiesAssociation studiesYears of educationShort life expectancyMexican patientsPsychiatric admissionsAssociation of DNA methylationSocial epigenomicsYears of schoolingAccelerated epigenetic agingLife expectancyAssociated with panic disorderEnvironmental exposuresEarly-onsetGlobal functioningClinical characteristicsClinical manifestationsDNA methylationAgeEpigenetic mechanismsPsychosisManifestation of psychosisGenome-wide analyses identify 30 loci associated with obsessive–compulsive disorder
Strom N, Gerring Z, Galimberti M, Yu D, Halvorsen M, Abdellaoui A, Rodriguez-Fontenla C, Sealock J, Bigdeli T, Coleman J, Mahjani B, Thorp J, Bey K, Burton C, Luykx J, Zai G, Alemany S, Andre C, Askland K, Bäckman J, Banaj N, Barlassina C, Nissen J, Bienvenu O, Black D, Bloch M, Børte S, Bosch R, Breen M, Brennan B, Brentani H, Buxbaum J, Bybjerg-Grauholm J, Byrne E, Cabana-Dominguez J, Camarena B, Camarena A, Cappi C, Carracedo A, Casas M, Cavallini M, Ciullo V, Cook E, Crosby J, Cullen B, De Schipper E, Delorme R, Djurovic S, Elias J, Estivill X, Falkenstein M, Fundin B, Garner L, Gironda C, Goes F, Grados M, Grove J, Guo W, Haavik J, Hagen K, Harrington K, Havdahl A, Höffler K, Hounie A, Hucks D, Hultman C, Janecka M, Jenike E, Karlsson E, Kelley K, Klawohn J, Krasnow J, Krebs K, Lange C, Lanzagorta N, Levey D, Lindblad-Toh K, Macciardi F, Maher B, Mathes B, McArthur E, McGregor N, McLaughlin N, Meier S, Miguel E, Mulhern M, Nestadt P, Nurmi E, O’Connell K, Osiecki L, Ousdal O, Palviainen T, Pedersen N, Piras F, Piras F, Potluri S, Rabionet R, Ramirez A, Rauch S, Reichenberg A, Riddle M, Ripke S, Rosário M, Sampaio A, Schiele M, Skogholt A, Sloofman L, Smit J, Artigas M, Thomas L, Tifft E, Vallada H, van Kirk N, Veenstra-VanderWeele J, Vulink N, Walker C, Wang Y, Wendland J, Winsvold B, Yao Y, Zhou H, Agrawal A, Alonso P, Berberich G, Bucholz K, Bulik C, Cath D, Denys D, Eapen V, Edenberg H, Falkai P, Fernandez T, Fyer A, Gaziano J, Geller D, Grabe H, Greenberg B, Hanna G, Hickie I, Hougaard D, Kathmann N, Kennedy J, Lai D, Landén M, Hellard S, Leboyer M, Lochner C, McCracken J, Medland S, Mortensen P, Neale B, Nicolini H, Nordentoft M, Pato M, Pato C, Pauls D, Piacentini J, Pittenger C, Posthuma D, Ramos-Quiroga J, Rasmussen S, Richter M, Rosenberg D, Ruhrmann S, Samuels J, Sandin S, Sandor P, Spalletta G, Stein D, Stewart S, Storch E, Stranger B, Turiel M, Werge T, Andreassen O, Børglum A, Walitza S, Hveem K, Hansen B, Rück C, Martin N, Milani L, Mors O, Reichborn-Kjennerud T, Ribasés M, Kvale G, Mataix-Cols D, Domschke K, Grünblatt E, Wagner M, Zwart J, Breen G, Nestadt G, Kaprio J, Arnold P, Grice D, Knowles J, Ask H, Verweij K, Davis L, Smit D, Crowley J, Scharf J, Stein M, Gelernter J, Mathews C, Derks E, Mattheisen M. Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder. Nature Genetics 2025, 57: 1389-1401. PMID: 40360802, PMCID: PMC12165847, DOI: 10.1038/s41588-025-02189-z.Peer-Reviewed Original ResearchConceptsObsessive-compulsive disorderGenome-wide association studiesGenetic riskObsessive-compulsive disorder casesGenome-wide significant lociMedium spiny neuronsGenome-wide analysisMajor histocompatibility complexGene-based approachPsychiatric disordersSpiny neuronsTourette syndromeAnorexia nervosaSignificant lociEffector genesAssociation studiesAssociated with excitatory neuronsMultiple genesGenetic variantsAssociated with inflammatory bowel diseaseBody mass indexGenetic heritabilityDisordersExcitatory neuronsInflammatory bowel diseaseIntegrating HiTOP and RDoC frameworks Part I: Genetic architecture of externalizing and internalizing psychopathology
Davis C, Khan Y, Toikumo S, Jinwala Z, Boomsma D, Levey D, Gelernter J, Kember R, Kranzler H. Integrating HiTOP and RDoC frameworks Part I: Genetic architecture of externalizing and internalizing psychopathology. Psychological Medicine 2025, 55: e138. PMID: 40336358, PMCID: PMC12094639, DOI: 10.1017/s0033291725000856.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMultivariate genome-wide association studyGenetic architectureComorbid forms of psychopathologyHierarchical Taxonomy of PsychopathologyBivariate causal mixture modelResearch Domain CriteriaTaxonomy of PsychopathologyPsychiatric classification systemsConfirmatory factor modelsInternalizing psychopathologyComorbid formsGenetic liabilityPsychopathologyTwo-factorHierarchical taxonomyModerate genetic correlationLatent factorsAssociation studiesBiological basisHiTOPReference panelGenetic underpinningsFactor modelGenetic variantsMulti-ancestry genome-wide association meta-analysis of buprenorphine treatment response
Davis C, Khan Y, Crist R, Vickers-Smith R, Hartwell E, Gelernter J, Kampman K, Kember R, Le Moigne A, Laffont C, Kranzler H. Multi-ancestry genome-wide association meta-analysis of buprenorphine treatment response. Neuropsychopharmacology 2025, 1-8. PMID: 40328918, DOI: 10.1038/s41386-025-02117-z.Peer-Reviewed Original ResearchGenome-wide association studiesTreatment responseOpioid use disorderGenome-wide significant lociGWAS meta-analysesCross-ancestry meta-analysisClinical characteristicsGenome-wide association meta-analysisGenetic predictors of treatment responseMeta-analysisPresence of chronic painAssociation meta-analysisUse disorderPartial agonist buprenorphinePhenome-wide association analysisTreat opioid use disorderPredictors of treatment responseExtended-release buprenorphineMillion Veteran ProgramSignificant lociLead variantsCross-ancestryAssociation studiesOdds of treatment responseAssociation analysisMulti‐ancestry genome‐wide association study of topiramate's effects on heavy alcohol use
Davis C, Jinwala Z, Justice A, Rentsch C, Kranzler H. Multi‐ancestry genome‐wide association study of topiramate's effects on heavy alcohol use. Alcohol Clinical And Experimental Research 2025, 49: 1197-1205. PMID: 40322892, PMCID: PMC12174493, DOI: 10.1111/acer.70069.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMillion Veteran ProgramGenome-wide significancePolygenic scoresAlcohol consumptionAssociation studiesAlcohol Use Disorders Identification Test-Consumption (AUDIT-C) scoresGenome-wide association study samplesPhenome-wide association studyAlcohol useElectronic health recordsMeta-analysisGWAS meta-analysisMulti-ancestry genome-wide association studyTopiramate's effectsAlcohol use disorder diagnosisCross-ancestry meta-analysisFrequency of alcohol useCandidate gene studiesHeavy alcohol useHealth recordsVeteran ProgramAlcohol-related liver diseaseCross-ancestryTreatment responseAdvancing translational exposomics: bridging genome, exposome and personalized medicine
Sarigiannis D, Karakitsios S, Anesti O, Stem A, Valvi D, Sumner S, Chatzi L, Snyder M, Thompson D, Vasiliou V. Advancing translational exposomics: bridging genome, exposome and personalized medicine. Human Genomics 2025, 19: 48. PMID: 40307849, PMCID: PMC12044731, DOI: 10.1186/s40246-025-00761-6.Peer-Reviewed Original ResearchConceptsExposome-wide association studyBridge genomicsLifestyle exposuresEnhancing causal inferencePublic health decision-makingEnvironmental health researchHealth decision-makingMulti-omics technologiesGenomic variationGenomic dataAssociation studiesHealth outcomesBioinformatics approachHealth researchPrecision preventionGenetic variabilityExposome dataExposure-response relationshipMulti-omicsGenomeInternal exposomeVulnerable populationsComplex diseasesDisease phenotypePublic healthLeveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies
Jian X, Zhang D, Yu Z, Xu H, Bian J, Wu Y, Tong J, Chen Y. Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies. Journal Of Biomedical Informatics 2025, 166: 104839. PMID: 40316004, DOI: 10.1016/j.jbi.2025.104839.Peer-Reviewed Original ResearchConceptsClinical Research NetworkAssociation studiesKaiser Permanente WashingtonBreast cancer eventsManual chart reviewRisk factor identificationHealth recordsCancer eventsEHR dataPhenotyping algorithmsCohort dataPatient clinical outcomesResearch NetworkRandom sampleADRDChart reviewFactor identificationCohortMean square errorMethod improves efficiencyOutcomesSimulation settingsAugmentation methodSBCEAlzheimer's diseaseBayesian Longitudinal Network Regression With Application to Brain Connectome Genetics
Li C, Tian X, Gao S, Wang S, Wang G, Zhao Y, Zhao Y. Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics. Statistics In Medicine 2025, 44: e70069. PMID: 40277222, DOI: 10.1002/sim.70069.Peer-Reviewed Original ResearchConceptsSample relatednessLongitudinal genome-wide association studiesGenome-wide association studiesBrain imaging genetic studiesMultivariate phenotypesGenetic signalsImaging genetics studiesAssociation studiesGenetic studiesGenetic variantsGenetic underpinningsGenetic contributionGenetic effectsRelatednessAdolescent Brain Cognitive DevelopmentBrain functional connectivityFunctional organizationBiological architectureFunctional connectivityRobust inferenceGeneticsPhenotypeAnalytical challengesPosterior inferenceBrain network configurationThe retrotransposon-derived capsid genes PNMA1 and PNMA4 maintain reproductive capacity
Wood T, Henriques W, Cullen H, Romero M, Blengini C, Sarathy S, Sorkin J, Bekele H, Jin C, Kim S, Wang X, Laureau R, Chemiakine A, Khondker R, Isola J, Stout M, Gennarino V, Mogessie B, Jain D, Schindler K, Suh Y, Wiedenheft B, Berchowitz L. The retrotransposon-derived capsid genes PNMA1 and PNMA4 maintain reproductive capacity. Nature Aging 2025, 5: 765-779. PMID: 40263616, PMCID: PMC12180178, DOI: 10.1038/s43587-025-00852-y.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesTranscription factor MYBL1Gonadal tissueMale gonadal tissueRNA intermediateEvolutionary innovationHuman genomeAssociation studiesHost genomeProtein self-assemblyDevelopmental regulationCapsid geneCapsid-like structuresHuman cellsCapsid formationYears ago4,5RetrotransposonsGenomeSequenceGenesRNAReproductive capacityPNMA1Reproductive functionMouse modelIdentification of genetic architecture shared between schizophrenia and Alzheimer’s disease
Liu H, Xie Y, Ji Y, Zhou Y, Xu J, Tang J, Liu N, Ding H, Qin W, Liu F, Yu C. Identification of genetic architecture shared between schizophrenia and Alzheimer’s disease. Translational Psychiatry 2025, 15: 150. PMID: 40240757, PMCID: PMC12003746, DOI: 10.1038/s41398-025-03348-w.Peer-Reviewed Original ResearchConceptsPolygenic overlapFalse discovery rateGenetic architectureAlzheimer's diseaseConditional/conjunctional false discovery rateConditional false discovery rateGenome-wide association studiesIndividuals of European ancestryConcordant effect directionsGenetic risk architectureMolecular genetic mechanismsHeritable brain disorderAssociation studiesGenetic mechanismsGenetic variantsEuropean ancestryGenetic associationObserved comorbiditySchizophreniaSNPsDiscovery rateCognitive declineRisk architectureBrain disordersGenetic correlationsPsychiatric genetics in the diverse landscape of Latin American populations
Bruxel E, Rovaris D, Belangero S, Chavarría-Soley G, Cuellar-Barboza A, Martínez-Magaña J, Nagamatsu S, Nievergelt C, Núñez-Ríos D, Ota V, Peterson R, Sloofman L, Adams A, Albino E, Alvarado A, Andrade-Brito D, Arguello-Pascualli P, Bandeira C, Bau C, Bulik C, Buxbaum J, Cappi C, Corral-Frias N, Corrales A, Corsi-Zuelli F, Crowley J, Cupertino R, da Silva B, De Almeida S, De la Hoz J, Forero D, Fries G, Gelernter J, González-Giraldo Y, Grevet E, Grice D, Hernández-Garayua A, Hettema J, Ibáñez A, Ionita-Laza I, Lattig M, Lima Y, Lin Y, López-León S, Loureiro C, Martínez-Cerdeño V, Martínez-Levy G, Melin K, Moreno-De-Luca D, Muniz Carvalho C, Olivares A, Oliveira V, Ormond R, Palmer A, Panzenhagen A, Passos-Bueno M, Peng Q, Pérez-Palma E, Prieto M, Roussos P, Sanchez-Roige S, Santamaría-García H, Shansis F, Sharp R, Storch E, Tavares M, Tietz G, Torres-Hernández B, Tovo-Rodrigues L, Trelles P, Trujillo-ChiVacuan E, Velásquez M, Vera-Urbina F, Voloudakis G, Wegman-Ostrosky T, Zhen-Duan J, Zhou H, Santoro M, Nicolini H, Atkinson E, Giusti-Rodríguez P, Montalvo-Ortiz J. Psychiatric genetics in the diverse landscape of Latin American populations. Nature Genetics 2025, 57: 1074-1088. PMID: 40175716, PMCID: PMC12133068, DOI: 10.1038/s41588-025-02127-z.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesPsychiatric genomicsPsychiatric genome-wide association studiesLarge-scale genome-wide association studiesGenetic risk lociNon-European populationsGenetic diversityRisk lociGenetic admixtureBurden of psychiatric disordersAssociation studiesPsychiatric disordersEuropean ancestryPsychiatric geneticsGenomeHealthcare disparitiesConsortium effortLatin American populationsPromote equityEnvironmental factorsDiversityAmerican populationDiverse landscapeLociAncestryOptimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants
Yuan M, Goovaerts S, Lee M, Devine J, Richmond S, Walsh S, Shriver M, Shaffer J, Marazita M, Peeters H, Weinberg S, Claes P. Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants. Briefings In Bioinformatics 2025, 26: bbaf090. PMID: 40062617, PMCID: PMC11891655, DOI: 10.1093/bib/bbaf090.Peer-Reviewed Original ResearchConceptsRare variant association studiesGenome-wide association studiesComplex morphological traitsGenomic lociSNP heritabilityAssociation studiesRare variantsPhenotypic variationMorphological traitsAxes of phenotypic variationContext of genome-wide association studiesVariant association studiesIndividuals of European ancestryGene-based testsLinkage disequilibrium score regressionRare genetic variantsGenomic relatednessOptimal phenotypeUnrelated individualsGenetic variantsRelevant traitsEuropean ancestryScore regressionPhenotype distributionFamily data
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