2025
Genome-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 effectsDynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry
Mohsen H, Blenman K, Emani P, Morris Q, Carrot-Zhang J, Pusztai L. Dynamic clustering of genomics cohorts beyond race, ethnicity—and ancestry. BMC Medical Genomics 2025, 18: 87. PMID: 40375077, PMCID: PMC12082885, DOI: 10.1186/s12920-025-02154-z.Peer-Reviewed Original ResearchConceptsGenomic variationGenomic cohortsStudy of human genomic variationWhole exome sequencing datasetsTrait-specific lociHuman genomic variationCancer-relevant genesGenomic patternsGenomic signalsGenomic studiesSequencing datasetsCancer typesGermline variantsDisease predispositionBiological processesFunctional analysisGeographic scalesPhenotypic continuumClustering patternsPotential driversDiverse data collectionsRace categoriesLociGenesComplete portraitCoarse-grained chromatin dynamics by tracking multiple similarly labeled gene loci
Mader A, Rodriguez A, Yuan T, Surovtsev I, King M, Mochrie S. Coarse-grained chromatin dynamics by tracking multiple similarly labeled gene loci. Biophysical Journal 2025, 124: 2120-2132. PMID: 40369871, DOI: 10.1016/j.bpj.2025.05.008.Peer-Reviewed Original ResearchChromatin polymerChromatin configurationLocus identityGenomic positionsLabeled lociLiving cellsLocus configurationsChromatin dynamicsMultiple lociGene locusChromatin researchChromatinLociSingle-particle trackingFluorescent labelingCorrect assignmentDynamic loopTemporal dynamicsCellsGenesModel polymersLabelingIdentityPsychiatric 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 landscapeLociAncestryEnhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS
Goovaerts S, Naqvi S, Hoskens H, Herrick N, Yuan M, Shriver M, Shaffer J, Walsh S, Weinberg S, Wysocka J, Claes P. Enhanced insights into the genetic architecture of 3D cranial vault shape using pleiotropy-informed GWAS. Communications Biology 2025, 8: 439. PMID: 40087503, PMCID: PMC11909261, DOI: 10.1038/s42003-025-07875-6.Peer-Reviewed Original ResearchConceptsCranial vault shapeVault shapeGenomic lociGenetic discovery effortsSNP discoveryCraniofacial developmentGenetic architectureGWAS dataGWAS studiesTranscription factorsGenetic studiesCranial vaultGenetic understandingShape variationSignaling pathwayBrain shapeExperimental biologyBrain shape variationCraniofacial complexFDR methodLociDiscovery effortsFacial shapeWealth of knowledgeGWASTrans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies
Consortium M, Adams M, Streit F, Meng X, Awasthi S, Adey B, Choi K, Chundru V, Coleman J, Ferwerda B, Foo J, Gerring Z, Giannakopoulou O, Gupta P, Hall A, Harder A, Howard D, Hübel C, Kwong A, Levey D, Mitchell B, Ni G, Ota V, Pain O, Pathak G, Schulte E, Shen X, Thorp J, Walker A, Yao S, Zeng J, Zvrskovec J, Aarsland D, Actkins K, Adli M, Agerbo E, Aichholzer M, Aiello A, Air T, Als T, Andersson E, Andlauer T, Arolt V, Ask H, Bäckman J, Badola S, Ballard C, Banasik K, Bass N, Beekman A, Belangero S, Bigdeli T, Binder E, Bjerkeset O, Bjornsdottir G, Børte S, Bränn E, Braun A, Brodersen T, Brückl T, Brunak S, Bruun M, Burmeister M, Buspavanich P, Bybjerg-Grauholm J, Byrne E, Cai J, Campbell A, Campbell M, Campos A, Castelao E, Cervilla J, Chaumette B, Chen C, Chen H, Chen Z, Cichon S, Colodro-Conde L, Corbett A, Corfield E, Couvy-Duchesne B, Craddock N, Dannlowski U, Davies G, de Geus E, Deary I, Degenhardt F, Dehghan A, DePaulo J, Deuschle M, Didriksen M, Dinh K, Direk N, Djurovic S, Docherty A, Domschke K, Dowsett J, Drange O, Dunn E, Eaton W, Einarsson G, Eley T, Elsheikh S, Engelmann J, Benros M, Erikstrup C, Escott-Price V, Fabbri C, Fang Y, Finer S, Frank J, Free R, Gallo L, Gao H, Gill M, Gilles M, Goes F, Gordon S, Grove J, Gudbjartsson D, Gutierrez B, Hahn T, Hall L, Hansen T, Haraldsson M, Hartman C, Havdahl A, Hayward C, Heilmann-Heimbach S, Herms S, Hickie I, Hjalgrim H, Hjerling-Leffler J, Hoffmann P, Homuth G, Horn C, Hottenga J, Hougaard D, Hovatta I, Huang Q, Hucks D, Huider F, Hunt K, Ialongo N, Ising M, Isometsä E, Jansen R, Jiang Y, Jones I, Jones L, Jonsson L, Kanai M, Karlsson R, Kasper S, Kendler K, Kessler R, Kloiber S, Knowles J, Koen N, Kraft J, Kranzler H, Krebs K, Kallak T, Kutalik Z, Lahtela E, Lake M, Larsen M, Lenze E, Lewins M, Lewis G, Li L, Lin B, Lin K, Lind P, Liu Y, MacIntyre D, MacKinnon D, Maher B, Maier W, Marshe V, Martinez-Levy G, Matsuda K, Mbarek H, McGuffin P, Medland S, Meinert S, Mikkelsen C, Mikkelsen S, Milaneschi Y, Millwood I, Molina E, Mondimore F, Mortensen P, Mulsant B, Naamanka J, Najman J, Nauck M, Nenadić I, Nielsen K, Nolt I, Nordentoft M, Nöthen M, Nyegaard M, O'Donovan M, Oddsson A, Oliveira A, Olsen C, Oskarsson H, Ostrowski S, Owen M, Packer R, Palviainen T, Pan P, Pato C, Pato M, Pedersen N, Pedersen O, Peyrot W, Potash J, Preisig M, Preuss M, Quiroz J, Renteria M, Reynolds C, Rice J, Sakaue S, Santoro M, Schoevers R, Schork A, Schulze T, Send T, Shi J, Sigurdsson E, Singh K, Sinnamon G, Sirignano L, Smeland O, Smith D, Sofer T, Sørensen E, Srinivasan S, Stefansson H, Stefansson K, Straub P, Su M, Tadic A, Teismann H, Teumer A, Thapar A, Thomson P, Thørner L, Topaloudi A, Tsai S, Tzoulaki I, Uhl G, Uitterlinden A, Ullum H, Umbricht D, Ursano R, Van der Auwera S, van Hemert A, Veluchamy A, Viktorin A, Völzke H, Walters G, Wang X, Wani A, Weissman M, Wellmann J, Whiteman D, Wildman D, Willemsen G, Williams A, Winsvold B, Witt S, Xiong Y, Zillich L, Zwart J, Team T, Group C, Team E, Team G, Psychiatry H, Project T, Program V, Andreassen O, Baune B, Berger K, Boomsma D, Børglum A, Breen G, Cai N, Coon H, Copeland W, Creese B, Cruz-Fuentes C, Czamara D, Davis L, Derks E, Domenici E, Elliott P, Forstner A, Gawlik M, Gelernter J, Grabe H, Hamilton S, Hveem K, John C, Kaprio J, Kircher T, Krebs M, Kuo P, Landén M, Lehto K, Levinson D, Li Q, Lieb K, Loos R, Lu Y, Lucae S, Luykx J, Maes H, Magnusson P, Martin H, Martin N, McQuillin A, Middeldorp C, Milani L, Mors O, Müller D, Müller-Myhsok B, Okada Y, Oldehinkel A, Paciga S, Palmer C, Paschou P, Penninx B, Perlis R, Peterson R, Pistis G, Polimanti R, Porteous D, Posthuma D, Rabinowitz J, Reichborn-Kjennerud T, Reif A, Rice F, Ricken R, Rietschel M, Rivera M, Rück C, Salum G, Schaefer C, Sen S, Serretti A, Skalkidou A, Smoller J, Stein D, Stein F, Stein M, Sullivan P, Tesli M, Thorgeirsson T, Tiemeier H, Timpson N, Uddin M, Uher R, van Heel D, Verweij K, Walters R, Wassertheil-Smoller S, Wendland J, Werge T, Zwinderman A, Kuchenbaecker K, Wray N, Ripke S, Lewis C, McIntosh A. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies. Cell 2025, 188: 640-652.e9. PMID: 39814019, PMCID: PMC11829167, DOI: 10.1016/j.cell.2024.12.002.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesCell-type enrichment analysisSingle-cell dataTrans-ancestryAdmixed ancestrySingle-cell analysisFine-mappingPotential repurposing opportunitiesAssociation studiesGene associationsEnrichment analysisReceptor clusteringPolygenic scoresRepurposing opportunitiesPostsynaptic densityCell typesStudies of depressionMedium spiny neuronsAncestryAntidepressant targetSpiny neuronsAmygdala neuronsLociBiological targetsEffective treatment
2024
Increasing the Level of Knock-In of the MT-C34-Encoding Construct into the <i>CXCR4</i> Locus by Modifying Donor DNA with Cas9 Target Sites
Shepelev M, Komkov D, Golubev D, Borovikova S, Mazurov D, Kruglova N. Increasing the Level of Knock-In of the MT-C34-Encoding Construct into the CXCR4 Locus by Modifying Donor DNA with Cas9 Target Sites. Молекулярная Биология 2024, 58 DOI: 10.31857/s0026898424040058.Peer-Reviewed Original ResearchKnock-in efficiencyDonor DNADonor plasmidGenetic constructsKnock-inApplication of genome editing technologiesCleavage in vitroDonor plasmid DNACas9 target sitesDouble-strand breaksInduction of double-strand breaksGenome editing technologyPAM sitesDonor sequenceTruncated targetsCell genomeDNA modificationsInduced cleavageIncreased knock-in efficiencyCRISPR/Cas9 systemCas9LociDNAEditing technologyPlasmid DNADigital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations
Liu J, Borsari B, Li Y, Liu S, Gao Y, Xin X, Lou S, Jensen M, Garrido-Martín D, Verplaetse T, Ash G, Zhang J, Girgenti M, Roberts W, Gerstein M. Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations. Cell 2024, 188: 515-529.e15. PMID: 39706190, DOI: 10.1016/j.cell.2024.11.012.Peer-Reviewed Original ResearchGenome-wide association studiesCase-control genome-wide association studyMultivariate genome-wide association studyGenetic lociAssociation studiesGenetic dataGenetic associationPhenotypeGeneticsEnvironmental factorsDetection powerElfn1Adolescent Brain Cognitive DevelopmentLociGenesPsychiatric disordersADORA3Digital phenotypingThe search to understand the development of the chicken immune system: Differences in expression of MHC class I loci and waves of thymocytes as evolutionary relics?
Halabi S, Rocos N, Kaufman J. The search to understand the development of the chicken immune system: Differences in expression of MHC class I loci and waves of thymocytes as evolutionary relics? Developmental Biology 2024, 519: 38-45. PMID: 39694171, DOI: 10.1016/j.ydbio.2024.12.006.Peer-Reviewed Original ResearchConceptsClass I genesI geneEvolutionary relicMHC class I lociClass I lociPoultry industryResistance to infectious diseasesChicken immune systemClass I expressionCytotoxic T lymphocytesGenetic lociNon-polymorphicI lociGlobal poultry industryChicken MHCClass I moleculesImmune defenceLociNatural killerT cellsDay-old chicksI expressionXenopus frogsAvian immunologyWell-expressedSex-stratified Genomic Structural Equation Models of Posttraumatic Stress Inform PTSD Etiology: L'utilisation de la modélisation génomique par équations structurelles stratifiée par sexe du stress post-traumatique pour expliquer l'étiologie du TSPT
Moo-Choy A, Stein M, Gelernter J, Wendt F. Sex-stratified Genomic Structural Equation Models of Posttraumatic Stress Inform PTSD Etiology: L'utilisation de la modélisation génomique par équations structurelles stratifiée par sexe du stress post-traumatique pour expliquer l'étiologie du TSPT. The Canadian Journal Of Psychiatry 2024, 70: 117-126. PMID: 39654303, PMCID: PMC11629358, DOI: 10.1177/07067437241301016.Peer-Reviewed Original ResearchGenome-wide association studiesGenome-wide significant lociSignificant lociMultivariate genome-wide association studyIndividuals of European ancestryPosttraumatic stress disorderGenomic structural equation modelingPosttraumatic stressUK Biobank (UKBAssociation studiesGenetic basisSymptom combinationsEtiological differencesSex-specific patternsEuropean ancestryAssociation dataPosttraumatic stress disorder diagnosisPosttraumatic stress disorder symptomsSex differencesLociInvestigation of sex differencesGeneticsSymptom etiologyModel of maleTraumatic eventsX-linked deletion of Crossfirre, Firre, and Dxz4 in vivo uncovers diverse phenotypes and combinatorial effects on autosomes
Hasenbein T, Hoelzl S, Smith Z, Gerhardinger C, Gonner M, Aguilar-Pimentel A, Amarie O, Becker L, Calzada-Wack J, Dragano N, da Silva-Buttkus P, Garrett L, Hölter S, Kraiger M, Östereicher M, Rathkolb B, Sanz-Moreno A, Spielmann N, Wurst W, Gailus-Durner V, Fuchs H, Hrabě de Angelis M, Meissner A, Engelhardt S, Rinn J, Andergassen D. X-linked deletion of Crossfirre, Firre, and Dxz4 in vivo uncovers diverse phenotypes and combinatorial effects on autosomes. Nature Communications 2024, 15: 10631. PMID: 39638999, PMCID: PMC11621363, DOI: 10.1038/s41467-024-54673-5.Peer-Reviewed Original ResearchConceptsAutosomal gene regulationRegions genome-wideAllele-specific analysisSex-specific lociLoci in vivoX-linked genesRandom X-chromosome inactivationX-chromosome inactivationSex-specific phenotypesFirre locusGenome-wideIn vivo roleChromatin structureGene regulationX chromosomeEpigenetic featuresDXZ4Epigenetic profilesKnockout studiesLociDiverse phenotypesLncRNA FIRREFunctional roleCombinatorial effectsFIRREX‐chromosome-wide association study for Alzheimer’s disease
Le Borgne J, Gomez L, Heikkinen S, Amin N, Ahmad S, Choi S, Bis J, Grenier-Boley B, Rodriguez O, Kleineidam L, Young J, Tripathi K, Wang L, Varma A, Campos-Martin R, van der Lee S, Damotte V, de Rojas I, Palmal S, Lipton R, Reiman E, McKee A, De Jager P, Bush W, Small S, Levey A, Saykin A, Foroud T, Albert M, Hyman B, Petersen R, Younkin S, Sano M, Wisniewski T, Vassar R, Schneider J, Henderson V, Roberson E, DeCarli C, LaFerla F, Brewer J, Swerdlow R, Van Eldik L, Hamilton-Nelson K, Paulson H, Naj A, Lopez O, Chui H, Crane P, Grabowski T, Kukull W, Asthana S, Craft S, Strittmatter S, Cruchaga C, Leverenz J, Goate A, Kamboh M, George-Hyslop P, Valladares O, Kuzma A, Cantwell L, Riemenschneider M, Morris J, Slifer S, Dalmasso C, Castillo A, Küçükali F, Peters O, Schneider A, Dichgans M, Rujescu D, Scherbaum N, Deckert J, Riedel-Heller S, Hausner L, Molina-Porcel L, Düzel E, Grimmer T, Wiltfang J, Heilmann-Heimbach S, Moebus S, Tegos T, Scarmeas N, Dols-Icardo O, Moreno F, Pérez-Tur J, Bullido M, Pastor P, Sánchez-Valle R, Álvarez V, Boada M, García-González P, Puerta R, Mir P, Real L, Piñol-Ripoll G, García-Alberca J, Royo J, Rodriguez-Rodriguez E, Soininen H, de Mendonça A, Mehrabian S, Traykov L, Hort J, Vyhnalek M, Thomassen J, Pijnenburg Y, Holstege H, van Swieten J, Ramakers I, Verhey F, Scheltens P, Graff C, Papenberg G, Giedraitis V, Boland A, Deleuze J, Nicolas G, Dufouil C, Pasquier F, Hanon O, Debette S, Grünblatt E, Popp J, Ghidoni R, Galimberti D, Arosio B, Mecocci P, Solfrizzi V, Parnetti L, Squassina A, Tremolizzo L, Borroni B, Nacmias B, Spallazzi M, Seripa D, Rainero I, Daniele A, Bossù P, Masullo C, Rossi G, Jessen F, Fernandez V, Kehoe P, Frikke-Schmidt R, Tsolaki M, Sánchez-Juan P, Sleegers K, Ingelsson M, Haines J, Farrer L, Mayeux R, Wang L, Sims R, DeStefano A, Schellenberg G, Seshadri S, Amouyel P, Williams J, van der Flier W, Ramirez A, Pericak-Vance M, Andreassen O, Van Duijn C, Hiltunen M, Ruiz A, Dupuis J, Martin E, Lambert J, Kunkle B, Bellenguez C. X‐chromosome-wide association study for Alzheimer’s disease. Molecular Psychiatry 2024, 30: 2335-2346. PMID: 39633006, PMCID: PMC12092188, DOI: 10.1038/s41380-024-02838-5.Peer-Reviewed Original ResearchX-chromosome inactivationX chromosomeAssociation studiesAlzheimer's diseaseGenome-wide significant signalsX chromosome-wide association studyGenome-wide association studiesAD casesEscape X chromosome inactivationNon-pseudoautosomal regionRandom X-chromosome inactivationSignificant lociGenetic risk factorsGenetic landscapeLociIndex variantsSignificant signalsAlzheimerRisk factorsXq25FRMPD4Dach2PJA1XWASSignalGenome-wide meta-analysis of myasthenia gravis uncovers new loci and provides insights into polygenic prediction
Braun A, Shekhar S, Levey D, Straub P, Kraft J, Panagiotaropoulou G, Heilbron K, Awasthi S, Meleka Hanna R, Hoffmann S, Stein M, Lehnerer S, Mergenthaler P, Elnahas A, Topaloudi A, Koromina M, Palviainen T, Asbjornsdottir B, Stefansson H, Skuladóttir A, Jónsdóttir I, Stefansson K, Reis K, Esko T, Palotie A, Leypoldt F, Stein M, Fontanillas P, Kaprio J, Gelernter J, Davis L, Paschou P, Tannemaat M, Verschuuren J, Kuhlenbäumer G, Gregersen P, Huijbers M, Stascheit F, Meisel A, Ripke S. Genome-wide meta-analysis of myasthenia gravis uncovers new loci and provides insights into polygenic prediction. Nature Communications 2024, 15: 9839. PMID: 39537604, PMCID: PMC11560923, DOI: 10.1038/s41467-024-53595-6.Peer-Reviewed Original ResearchConceptsPerformance of polygenic risk scoresGenome-wide significant hitsGenome-wide association studiesGenome-wide meta-analysisControls of European ancestryGenetic architecturePolygenic risk scoresSignificant hitsAssociation studiesPhenotypic variationPolygenic predictionEuropean ancestryAssociated with early-onsetHuman leukocyte antigen allelesLociEarly-onsetReplication studyNeuromuscular junctionMyasthenia gravisAutoantibody-mediated diseasesAntigen allelesAllelesAncestryDisease manifestationsLate-onset MGThe genetics of severe depression
Franklin C, Achtyes E, Altinay M, Bailey K, Bhati M, Carr B, Conroy S, Husain M, Khurshid K, Lencz T, McDonald W, Mickey B, Murrough J, Nestor S, Nickl-Jockschat T, Nikayin S, Reeves K, Reti I, Selek S, Sanacora G, Trapp N, Viswanath B, Wright J, Sullivan P, Zandi P, Potash J. The genetics of severe depression. Molecular Psychiatry 2024, 30: 1117-1126. PMID: 39406997, DOI: 10.1038/s41380-024-02731-1.Peer-Reviewed Original ResearchGenome-wide association studiesSevere depressionGenome-wide significant lociWhole-exome sequencing studiesAssociated with dysphoriaGenome-wide lociGenome-wide interrogationExome sequencing studiesEarly-onset illnessDegree of impairmentDepressive disorderMDD phenotypesSignificant lociAssociation studiesClinical markers of severitySequencing studiesMDDLociRare variantsClinically actionable findingsEstimates of heritabilityDepressionFamily-basedSevere formGeneticsHBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data
Cheng Y, Cai B, Li H, Zhang X, D’Souza G, Shrestha S, Edmonds A, Meyers J, Fischl M, Kassaye S, Anastos K, Cohen M, Aouizerat B, Xu K, Zhao H. HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data. Genome Biology 2024, 25: 273. PMID: 39407252, PMCID: PMC11476968, DOI: 10.1186/s13059-024-03411-7.Peer-Reviewed Original ResearchConceptsMethylation quantitative trait lociQuantitative trait lociTrait lociMethylation dataFunctional annotation of genetic variantsAnnotation of genetic variantsGenetic variantsBisulfite sequencing dataEffects of genetic variantsBiologically relevant cell typesDNA methylation levelsCell typesFunctional annotationSequence dataComplex traitsMethylation datasetsRelevant cell typesMeQTLsMethylation levelsMethylation regulatorsReal data analysesLociVariantsMethylationDNA23. GENOME-WIDE ASSOCIATION STUDIES OF BINGE-EATING BEHAVIOUR AND ANOREXIA NERVOSA YIELD INSIGHTS INTO THE UNIQUE AND SHARED BIOLOGY OF EATING DISORDER PHENOTYPES
Huckins L, Termorshuizen J, Davies H, Lee S, Johnson J, Munn-Chernoff M, Thornton L, Källberg J, Bulik C, Breen G, Coleman J. 23. GENOME-WIDE ASSOCIATION STUDIES OF BINGE-EATING BEHAVIOUR AND ANOREXIA NERVOSA YIELD INSIGHTS INTO THE UNIQUE AND SHARED BIOLOGY OF EATING DISORDER PHENOTYPES. European Neuropsychopharmacology 2024, 87: 60. DOI: 10.1016/j.euroneuro.2024.08.137.Peer-Reviewed Original ResearchGenome-wide association studiesIdentification of novel lociGenome-wide association study meta-analysisGenetic correlationsEating Disorders Working GroupBinge-eating behaviorNovel lociGenome-wideGenomic lociAssociation studiesLociGenetic componentEating disorder phenotypesPhenotypeEarly onset obesityBE phenotypeSevere early onset obesityDisorder phenotypesTrans-diagnosticAN subtypesAN-BPAN-RAN-R.Eating disordersBinge-eating/purgingF67. CHARACTERIZING THE IMPACT OF AUTOZYGOSITY AND RUNS OF HOMOZYGOSITY ON PSYCHIATRIC DISEASE RISK IN INDIAN POPULATION
Kandasamy K, Ganesh S, Sachdeva P, Mahadevan J, Holla B, Benegal V, Purushottam M, Jain S, Viswanath B. F67. CHARACTERIZING THE IMPACT OF AUTOZYGOSITY AND RUNS OF HOMOZYGOSITY ON PSYCHIATRIC DISEASE RISK IN INDIAN POPULATION. European Neuropsychopharmacology 2024, 87: 242. DOI: 10.1016/j.euroneuro.2024.08.478.Peer-Reviewed Original ResearchGenetic architecture of psychiatric disordersIllumina Global Screening ArrayGlobal Screening ArrayContinental populationsGenetic architectureInbreeding coefficientRecessive locusAutozygosityFROHObsessive compulsive disorderScreening ArrayPLINK v.GenomeInbreedingBipolar disorderDisease-riskPsychiatric diagnosisPsychiatric disordersDisease riskEndogamyIlluminaCase-control differencesLociPLINKROHDetection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
Zhou B, Arthur J, Guo H, Kim T, Huang Y, Pattni R, Wang T, Kundu S, Luo J, Lee H, Nachun D, Purmann C, Monte E, Weimer A, Qu P, Shi M, Jiang L, Yang X, Fullard J, Bendl J, Girdhar K, Kim M, Chen X, Consortium P, Greenleaf W, Duncan L, Ji H, Zhu X, Song G, Montgomery S, Palejev D, Dohna H, Roussos P, Kundaje A, Hallmayer J, Snyder M, Wong H, Urban A. Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders. Cell 2024, 187: 6687-6706.e25. PMID: 39353437, PMCID: PMC11608572, DOI: 10.1016/j.cell.2024.09.014.Peer-Reviewed Original ResearchComplex structural variationsNatural human genetic variationHuman genetic variationCell type-specific expressionHuman-specific evolutionDifferential gene expressionStructural variationsContinental populationsChromatin accessibilityHuman genomeGenetic variationNeural genesGenomeGene expressionRisk allelesMolecular etiologyCell typesGenesPostmortem brainsChromatinLociAllelesMachine-learning-based methodsMultiomicsBrain regionsImproved species assignments across the entire Anopheles genus using targeted sequencing
Boddé M, Makunin A, Teltscher F, Akorli J, Andoh N, Bei A, Chaumeau V, Desamours I, Ekpo U, Govella N, Kayondo J, Kobylinski K, Ngom E, Niang E, Okumu F, Omitola O, Ponlawat A, Rakotomanga M, Rasolonjatovoniaina M, Ayala D, Lawniczak M. Improved species assignments across the entire Anopheles genus using targeted sequencing. Frontiers In Genetics 2024, 15: 1456644. PMID: 39364005, PMCID: PMC11446804, DOI: 10.3389/fgene.2024.1456644.Peer-Reviewed Original ResearchSpecies assignmentSequence dataAmplicon sequencing dataSpecies population structureAccurate species identificationSamples to species levelNuclear lociPopulation structureAmplicon panelSpecies levelSpecies identificationTarget sequenceAnopheles genusGenusSpeciesAmpliconsComparison of samplesSequenceLociMosquitoesIncreased powerMadagascarControl measuresContext-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes
Long E, Yin J, Shin J, Li Y, Li B, Kane A, Patel H, Sun X, Wang C, Luong T, Xia J, Han Y, Byun J, Zhang T, Zhao W, Landi M, Rothman N, Lan Q, Chang Y, Yu F, Amos C, Shi J, Lee J, Kim E, Choi J. Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes. Nature Communications 2024, 15: 7995. PMID: 39266564, PMCID: PMC11392933, DOI: 10.1038/s41467-024-52356-9.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGenome-wide association study lociSusceptibility genesLung cancer susceptibility genesTranscription factor footprintsChromatin accessibility mapsCis-regulatory elementsRisk-associated variantsRare cell typesRegulate gene expressionCell typesCell type-specificCancer susceptibility genesCausal variantsAssociation studiesGene regulationGene functionMultiomics approachTarget genesLociGene expressionGenesType-specificHuman lung cellsCCREs
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