2023
Common genetic factors among autoimmune diseases
Harroud A, Hafler D. Common genetic factors among autoimmune diseases. Science 2023, 380: 485-490. PMID: 37141355, DOI: 10.1126/science.adg2992.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMultimodal genomic dataEvolutionary originDisease geneticsPolygenic basisPrecise geneSelection pressureGenomic dataMolecular consequencesAssociation studiesGenetic studiesFunctional experimentsGenetic effectsRisk variantsCommon genetic factorsAncient populationsCurrent understandingPotential therapeutic implicationsGenetic factorsKey immune cellsGenesGeneticsWidespread sharingImmune cellsValuable insights
2022
Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure
Qu R, Kluger Y, Yang J, Zhao J, Hafler D, Krause D, Bersenev A, Bosenberg M, Hurwitz M, Lucca L, Kluger H. Longitudinal single-cell analysis of a patient receiving adoptive cell therapy reveals potential mechanisms of treatment failure. Molecular Cancer 2022, 21: 219. PMID: 36514045, PMCID: PMC9749221, DOI: 10.1186/s12943-022-01688-5.Peer-Reviewed Original ResearchConceptsAdoptive cell therapySingle-cell analysisDepth single-cell analysisSingle-cell RNAACT productsDisease progressionT-cell receptor sequencingCell therapyFamily genesFeatures of exhaustionMultiple tumor typesCell expansionGenesNew clonotypesTIL preparationsClonal cell expansionCytokine therapyTreatment failureSerial bloodClonesEffector functionsSerial samplesTumor typesCellular therapyTherapy
2018
Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk
Consortium I, Mitrovič M, Patsopoulos N, Beecham A, Dankowski T, Goris A, Dubois B, D’hooghe M, Lemmens R, Van Damme P, Søndergaard H, Sellebjerg F, Sorensen P, Ullum H, Thørner L, Werge T, Saarela J, Cournu-Rebeix I, Damotte V, Fontaine B, Guillot-Noel L, Lathrop M, Vukusik S, Gourraud P, Andlauer T, Pongratz V, Buck D, Gasperi C, Bayas A, Heesen C, Kümpfel T, Linker R, Paul F, Stangel M, Tackenberg B, Bergh F, Warnke C, Wiendl H, Wildemann B, Zettl U, Ziemann U, Tumani H, Gold R, Grummel V, Hemmer B, Knier B, Lill C, Luessi F, Dardiotis E, Agliardi C, Barizzone N, Mascia E, Bernardinelli L, Comi G, Cusi D, Esposito F, Ferrè L, Comi C, Galimberti D, Leone M, Sorosina M, Mescheriakova J, Hintzen R, van Duijn C, Theunissen C, Bos S, Myhr K, Celius E, Lie B, Spurkland A, Comabella M, Montalban X, Alfredsson L, Stridh P, Hillert J, Jagodic M, Piehl F, Jelčić I, Martin R, Sospedra M, Ban M, Hawkins C, Hysi P, Kalra S, Karpe F, Khadake J, Lachance G, Neville M, Santaniello A, Caillier S, Calabresi P, Cree B, Cross A, Davis M, Haines J, de Bakker P, Delgado S, Dembele M, Edwards K, Fitzgerald K, Hakonarson H, Konidari I, Lathi E, Manrique C, Pericak-Vance M, Piccio L, Schaefer C, McCabe C, Weiner H, Goldstein J, Olsson T, Hadjigeorgiou G, Taylor B, Tajouri L, Charlesworth J, Booth D, Harbo H, Ivinson A, Hauser S, Compston A, Stewart G, Zipp F, Barcellos L, Baranzini S, Martinelli-Boneschi F, D’Alfonso S, Ziegler A, Oturai A, McCauley J, Sawcer S, Oksenberg J, De Jager P, Kockum I, Hafler D, Cotsapas C. Low-Frequency and Rare-Coding Variation Contributes to Multiple Sclerosis Risk. Cell 2018, 175: 1679-1687.e7. PMID: 30343897, PMCID: PMC6269166, DOI: 10.1016/j.cell.2018.09.049.Peer-Reviewed Original ResearchConceptsRare coding variationsGenome-wide association studiesNon-coding variationCommon variant signalsSubstantial linkage disequilibriumLow-frequency variantsNovel genesCell homeostasisAssociation studiesComplex neurological diseasesLinkage disequilibriumGenetic variantsCommon variantsHeritabilityRich resourceGenesVariantsKey pathogenic roleIndividual familiesEpistasisAdditive effectBiologyHomeostasisMutationsNeurological diseases
2014
Monoallelic expression of the human FOXP2 speech gene
Adegbola AA, Cox GF, Bradshaw EM, Hafler DA, Gimelbrant A, Chess A. Monoallelic expression of the human FOXP2 speech gene. Proceedings Of The National Academy Of Sciences Of The United States Of America 2014, 112: 6848-6854. PMID: 25422445, PMCID: PMC4460484, DOI: 10.1073/pnas.1411270111.Peer-Reviewed Original ResearchMeSH KeywordsApraxiasComparative Genomic HybridizationFemaleForkhead Transcription FactorsGene Expression ProfilingGene Expression Regulation, DevelopmentalGenes, X-LinkedHumansPolymorphism, Single NucleotideReverse Transcriptase Polymerase Chain ReactionSequence Analysis, DNASequence DeletionSpeechX Chromosome InactivationConceptsRandom monoallelic expressionMonoallelic expressionAllele-specific expressionNumber of genesHuman Mendelian disordersForkhead box P2 (FOXP2) geneP2 geneAutosomal genesMore genesAutosomal genomeX chromosomeGene expressionHaploinsufficiency phenotypeMendelian disordersGenesDevelopmental verbal dyspraxiaFOXP2 mutationsIntriguing possibilityFOXP2 geneExpressionRecent descriptionMutationsVerbal dyspraxiaAutosomesGenomeSmall-Molecule RORγt Antagonists Inhibit T Helper 17 Cell Transcriptional Network by Divergent Mechanisms
Xiao S, Yosef N, Yang J, Wang Y, Zhou L, Zhu C, Wu C, Baloglu E, Schmidt D, Ramesh R, Lobera M, Sundrud MS, Tsai PY, Xiang Z, Wang J, Xu Y, Lin X, Kretschmer K, Rahl PB, Young RA, Zhong Z, Hafler DA, Regev A, Ghosh S, Marson A, Kuchroo VK. Small-Molecule RORγt Antagonists Inhibit T Helper 17 Cell Transcriptional Network by Divergent Mechanisms. Immunity 2014, 40: 477-489. PMID: 24745332, PMCID: PMC4066874, DOI: 10.1016/j.immuni.2014.04.004.Peer-Reviewed Original ResearchMeSH KeywordsAndrostenolsAnimalsBenzeneacetamidesBenzhydryl CompoundsCell DifferentiationCell Line, TumorCell LineageCytokinesDigoxinEncephalomyelitis, Autoimmune, ExperimentalGene Regulatory NetworksHeterocyclic Compounds, 4 or More RingsHumansMiceMice, Inbred C57BLMice, KnockoutMultiple SclerosisMyelin-Oligodendrocyte GlycoproteinNuclear Receptor Subfamily 1, Group F, Member 3Peptide FragmentsProtein BindingStructure-Activity RelationshipSystems BiologyTh17 CellsT-Lymphocyte SubsetsTranscription, GeneticTranscriptional ActivationConceptsTranscriptional networksSignature genesCis-regulatory sitesStrong transcriptional effectsInterconnected regulatory networkCell signature genesSystem-scale analysisTranscriptional regulationDirect repressorTarget lociTranscriptome sequencingRegulatory networksDNA bindingTranscriptional effectsCell lineagesCell differentiationT-cell lineageDirect activatorDivergent mechanismsT cell differentiationSpecific inhibitorDistinct mechanismsPotential therapeutic compoundsGenesRetinoid-related orphan receptor gamma tCommon Genetic Variants Modulate Pathogen-Sensing Responses in Human Dendritic Cells
Lee MN, Ye C, Villani AC, Raj T, Li W, Eisenhaure TM, Imboywa SH, Chipendo PI, Ran FA, Slowikowski K, Ward LD, Raddassi K, McCabe C, Lee MH, Frohlich IY, Hafler DA, Kellis M, Raychaudhuri S, Zhang F, Stranger BE, Benoist CO, De Jager PL, Regev A, Hacohen N. Common Genetic Variants Modulate Pathogen-Sensing Responses in Human Dendritic Cells. Science 2014, 343: 1246980. PMID: 24604203, PMCID: PMC4124741, DOI: 10.1126/science.1246980.Peer-Reviewed Original ResearchMeSH KeywordsAdultAutoimmune DiseasesCommunicable DiseasesDendritic CellsEscherichia coliFemaleGene-Environment InteractionGenetic LociGenome-Wide Association StudyHEK293 CellsHost-Pathogen InteractionsHumansInfluenza A virusInterferon Regulatory Factor-7Interferon-betaLipopolysaccharidesMaleMiddle AgedPolymorphism, Single NucleotideQuantitative Trait LociSTAT Transcription FactorsTranscriptomeYoung AdultConceptsGenetic variationPathogen-responsive genesHuman genetic variationGenetic variantsIRF transcription factorsCommon genetic variantsType I IFN inductionFunctional annotationExpression responsesTranscription factorsI IFN inductionCausal variantsPathogen sensingEnvironmental stimuliComplex diseasesCommon variantsCommon allelesIFN inductionComputational approachVariantsCellsInductionGenesInterindividual variationSTAT
2013
Clinical relevance and functional consequences of the TNFRSF1A multiple sclerosis locus
Ottoboni L, Frohlich IY, Lee M, Healy BC, Keenan BT, Xia Z, Chitnis T, Guttmann CR, Khoury SJ, Weiner HL, Hafler DA, De Jager PL. Clinical relevance and functional consequences of the TNFRSF1A multiple sclerosis locus. Neurology 2013, 81: 1891-1899. PMID: 24174586, PMCID: PMC3843384, DOI: 10.1212/01.wnl.0000436612.66328.8a.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsArginineChemokine CXCL10FemaleGene Expression RegulationGenetic Predisposition to DiseaseGenotypeGlutamineHEK293 CellsHumansImmunologic FactorsLongitudinal StudiesMaleMonocytesMultiple SclerosisMutationPhorbol EstersReceptors, Tumor Necrosis Factor, Type IRNA IsoformsSignal TransductionTumor Necrosis Factor-alphaConceptsTNFRSF1A locusSusceptibility allelesFunctional consequencesRobust transcriptional responseTranscriptional responseCytoplasmic domainRNA isoformsTNF-α stimulationRho GTPaseMS susceptibility genesMS geneG proteinsSusceptibility genesMolecular levelTNF pathwayGenesAltered expressionLociTNF-α pathwayAllelesRisk allelesPathwayGTPaseImmune functionTransmembraneNetwork-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls
Consortium I, Baranzini S, Khankhanian P, Patsopoulos N, Li M, Stankovich J, Cotsapas C, Søndergaard H, Ban M, Barizzone N, Bergamaschi L, Booth D, Buck D, Cavalla P, Celius E, Comabella M, Comi G, Compston A, Cournu-Rebeix I, D’alfonso S, Damotte V, Din L, Dubois B, Elovaara I, Esposito F, Fontaine B, Franke A, Goris A, Gourraud P, Graetz C, Guerini F, Guillot-Noel L, Hafler D, Hakonarson H, Hall P, Hamsten A, Harbo H, Hemmer B, Hillert J, Kemppinen A, Kockum I, Koivisto K, Larsson M, Lathrop M, Leone M, Lill C, Macciardi F, Martin R, Martinelli V, Martinelli-Boneschi F, McCauley J, Myhr K, Naldi P, Olsson T, Oturai A, Pericak-Vance M, Perla F, Reunanen M, Saarela J, Saker-Delye S, Salvetti M, Sellebjerg F, Sørensen P, Spurkland A, Stewart G, Taylor B, Tienari P, Winkelmann J, Consortium W, Zipp F, Ivinson A, Haines J, Sawcer S, DeJager P, Hauser S, Oksenberg J. Network-Based Multiple Sclerosis Pathway Analysis with GWAS Data from 15,000 Cases and 30,000 Controls. American Journal Of Human Genetics 2013, 92: 854-865. PMID: 23731539, PMCID: PMC3958952, DOI: 10.1016/j.ajhg.2013.04.019.Peer-Reviewed Original ResearchConceptsPathway analysisNetwork-based pathway analysisGenome-wide association studiesSubnetworks of genesExtended linkage disequilibriumNon-HLA susceptibility lociHigh-confidence candidatesSubsequent genetic studiesComplex traitsSubstantial genetic componentSignificant lociGWAS dataAssociation studiesGene levelGenetic studiesNominal statistical evidenceSusceptibility lociGenesLinkage disequilibriumSusceptibility variantsGenetic componentRelated pathwaysLociHuman leukocyte antigen (HLA) regionPowerful approach
2011
Interrogating the complex role of chromosome 16p13.13 in multiple sclerosis susceptibility: independent genetic signals in the CIITA–CLEC16A–SOCS1 gene complex
Zuvich RL, Bush WS, McCauley JL, Beecham AH, De Jager PL, Consortium T, Ivinson A, Compston A, Hafler D, Hauser S, Sawcer S, Pericak-Vance M, Barcellos L, Mortlock D, Haines J. Interrogating the complex role of chromosome 16p13.13 in multiple sclerosis susceptibility: independent genetic signals in the CIITA–CLEC16A–SOCS1 gene complex. Human Molecular Genetics 2011, 20: 3517-3524. PMID: 21653641, PMCID: PMC3153306, DOI: 10.1093/hmg/ddr250.Peer-Reviewed Original ResearchMeSH KeywordsCCCTC-Binding FactorChromosomes, Human, Pair 16FemaleGenetic Predisposition to DiseaseGenome-Wide Association StudyGenotypeHumansLectins, C-TypeLinkage DisequilibriumLogistic ModelsMaleMonosaccharide Transport ProteinsMultiple SclerosisQuantitative Trait LociRepressor ProteinsSuppressor of Cytokine Signaling 1 ProteinSuppressor of Cytokine Signaling ProteinsConceptsIndependent genetic signalsGenetic signalsLymphoblastoid cell linesChromosome 16p13Cis expression QTLsOpen chromatin configurationCell linesLinkage disequilibrium patternsExpression array dataH3K27 methylationHistone modificationsGenomic regionsKb stretchStrong genetic componentSingle nucleotide polymorphismsChromatin configurationExpression correlationGene complexDisequilibrium patternsDisease locusGenesCorrelated expressionGenetic componentFunctional mechanismsLociA knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility
Bush WS, McCauley JL, DeJager PL, Dudek SM, Hafler DA, Gibson RA, Matthews PM, Kappos L, Naegelin Y, Polman CH, Hauser SL, Oksenberg J, Haines JL, Ritchie MD. A knowledge-driven interaction analysis reveals potential neurodegenerative mechanism of multiple sclerosis susceptibility. Genes & Immunity 2011, 12: 335-340. PMID: 21346779, PMCID: PMC3136581, DOI: 10.1038/gene.2011.3.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesGene-gene interactionsCytoskeleton regulatory proteinsCytoskeletal regulationGenetic architectureGene clusterInteraction analysisSingle-locus analysisGWAS dataRegulatory proteinsBiological contextRelated genesAssociation studiesSusceptibility lociWeak main effectsPhospholipase CGenetic effectsΒ isoformsComplex diseasesBiological mechanismsNeurodegenerative mechanismsNew genetic effectsEpistasisACTN1Genes
2010
Variation Within DNA Repair Pathway Genes and Risk of Multiple Sclerosis
Briggs FB, Goldstein BA, McCauley JL, Zuvich RL, De Jager PL, Rioux JD, Ivinson AJ, Compston A, Hafler DA, Hauser SL, Oksenberg JR, Sawcer SJ, Pericak-Vance MA, Haines JL, Barcellos LF, Consortium F. Variation Within DNA Repair Pathway Genes and Risk of Multiple Sclerosis. American Journal Of Epidemiology 2010, 172: 217-224. PMID: 20522537, PMCID: PMC3658128, DOI: 10.1093/aje/kwq086.Peer-Reviewed Original ResearchConceptsDNA repair pathway genesPathway genesMultiple sclerosisExcision repairGeneral transcription factor IIHDouble-strand break repairTranscription factor IIHDNA repair pathwaysNucleotide excision repairRisk of MSBase excision repairPrimary genetic risk factorProminent genetic componentHuman leukocyte antigenComplex autoimmune diseaseSingle nucleotide polymorphism (SNP) variantsCentral nervous systemLogistic regression modelingGenetic risk factorsSingle nucleotide polymorphismsBreak repairRepair pathwaysCandidate genesAutoimmune diseasesGenesGenome-wide Association Study in a High-Risk Isolate for Multiple Sclerosis Reveals Associated Variants in STAT3 Gene
Jakkula E, Leppä V, Sulonen AM, Varilo T, Kallio S, Kemppinen A, Purcell S, Koivisto K, Tienari P, Sumelahti ML, Elovaara I, Pirttilä T, Reunanen M, Aromaa A, Oturai AB, Søndergaard HB, Harbo HF, Mero IL, Gabriel SB, Mirel DB, Hauser SL, Kappos L, Polman C, De Jager PL, Hafler DA, Daly MJ, Palotie A, Saarela J, Peltonen L. Genome-wide Association Study in a High-Risk Isolate for Multiple Sclerosis Reveals Associated Variants in STAT3 Gene. American Journal Of Human Genetics 2010, 86: 285-291. PMID: 20159113, PMCID: PMC2820168, DOI: 10.1016/j.ajhg.2010.01.017.Peer-Reviewed Original ResearchConceptsSTAT3 geneGenome-wide association studiesRare risk allelesComplex traitsRisk lociRisk allelesAssociated variantsAssociation studiesRecent GWASInternal isolateLociCommon variantsGenetic riskGenesAllelesCritical roleSTAT3Small odds ratiosHeterogeneous populationVariantsGWASIsolatesProtective haplotypeTraitsSNPsGenetic variation in the IL7RA/IL7 pathway increases multiple sclerosis susceptibility
Zuvich RL, McCauley JL, Oksenberg JR, Sawcer SJ, De Jager PL, International Multiple Sclerosis Genetics Consortium, Aubin C, Cross AH, Piccio L, Aggarwal NT, Evans D, Hafler DA, Compston A, Hauser SL, Pericak-Vance MA, Haines JL. Genetic variation in the IL7RA/IL7 pathway increases multiple sclerosis susceptibility. Human Genetics 2010, 127: 525-535. PMID: 20112030, PMCID: PMC2854871, DOI: 10.1007/s00439-010-0789-4.Peer-Reviewed Original ResearchConceptsSingle nucleotide polymorphismsGene regionCase-control data setsPutative functional relationshipsNovel gene regionsIndependent case-control data setDense SNP mapReceptor alpha-chain geneIllumina Infinium BeadChipExperiment-wise significanceNovel associationsAlpha chain geneGenetic architectureComplex traitsStrong genetic componentGenetic variationSNP mapInfinium BeadChipAffordable genotypingBiological pathwaysGenesGenetic componentChain geneTYK2 geneNumerous family studies
2009
Autoimmunity risk alleles in costimulation pathways
Maier LM, Hafler DA. Autoimmunity risk alleles in costimulation pathways. Immunological Reviews 2009, 229: 322-336. PMID: 19426231, DOI: 10.1111/j.1600-065x.2009.00777.x.Peer-Reviewed Original ResearchConceptsTumor necrosis factorGenome-wide association scansHuman autoimmune diseasesAutoimmune diseasesCommon human autoimmune diseasesInducible T-cell costimulator ligandGenetic study designsAssociation scanImmune related genesRelated genesCytotoxic T-lymphocyte antigen-4T-lymphocyte antigen-4Common autoimmune diseaseCell death 1Allelic variantsMember 15Environmental risk factorsDrug designCostimulatory mechanismsMember 4PathwayGenesCostimulation pathwayDeath-1Common pathwayReplication analysis identifies TYK2 as a multiple sclerosis susceptibility factor
Ban M, Goris A, Lorentzen Å, Baker A, Mihalova T, Ingram G, Booth DR, Heard RN, Stewart GJ, Bogaert E, Dubois B, Harbo HF, Celius EG, Spurkland A, Strange R, Hawkins C, Robertson NP, Dudbridge F, Wason J, De Jager PL, Hafler D, Rioux JD, Ivinson AJ, McCauley JL, Pericak-Vance M, Oksenberg JR, Hauser SL, Sexton D, Haines J, Sawcer S. Replication analysis identifies TYK2 as a multiple sclerosis susceptibility factor. European Journal Of Human Genetics 2009, 17: 1309-1313. PMID: 19293837, PMCID: PMC2782567, DOI: 10.1038/ejhg.2009.41.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesNon-synonymous single nucleotide polymorphismsRecent genome-wide association studiesLevel of phosphorylationAmino acid substitutionsTyrosine kinase 2 geneKinase 2 geneSingle-nucleotide polymorphism resultsSingle nucleotide polymorphismsKinase domainMultiple sclerosis susceptibility genesAssociation studiesAcid substitutionsFunctional roleSusceptibility genesNucleotide polymorphismsPolymorphism resultsTrio familiesReplication analysisGenesLociTYK2Susceptibility factorsPhosphorylationMultiple sclerosis
2008
Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines
Choy E, Yelensky R, Bonakdar S, Plenge RM, Saxena R, De Jager PL, Shaw SY, Wolfish CS, Slavik JM, Cotsapas C, Rivas M, Dermitzakis ET, Cahir-McFarland E, Kieff E, Hafler D, Daly MJ, Altshuler D. Genetic Analysis of Human Traits In Vitro: Drug Response and Gene Expression in Lymphoblastoid Cell Lines. PLOS Genetics 2008, 4: e1000287. PMID: 19043577, PMCID: PMC2583954, DOI: 10.1371/journal.pgen.1000287.Peer-Reviewed Original ResearchConceptsLymphoblastoid cell linesBiological noiseGenome-wide significanceInternational HapMap ProjectDrug responseCell linesGenotype-phenotype relationshipsIndividual mRNAsEQTL SNPsGenetic analysisGene expressionHapMap projectHuman cellsHuman traitsNon-genetic factorsQTLMetabolic stateModel systemGenesMRNA levelsBaseline growth ratesSpurious associationsGrowth ratePharmacogenetic experimentsEQTLs
2007
Allelic variant in CTLA4 alters T cell phosphorylation patterns
Maier LM, Anderson DE, De Jager PL, Wicker LS, Hafler DA. Allelic variant in CTLA4 alters T cell phosphorylation patterns. Proceedings Of The National Academy Of Sciences Of The United States Of America 2007, 104: 18607-18612. PMID: 18000051, PMCID: PMC2141824, DOI: 10.1073/pnas.0706409104.Peer-Reviewed Original ResearchConceptsT cell antigen receptorAllelic variationMemory T cellsAutoimmune diseasesCell antigen receptorT cell signalingT cellsFunctional effectsDisease susceptibility allelesCell signalingPhosphorylation patternPhosphorylation levelsSusceptibility variantsTCR stimulationAllelic variantsHuman immune cellsAntigen receptorGenesImmune cellsHealthy individualsCTLA4 geneCellsSpecific mAbsCTLA4Disease