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
2020
Epigenetic fine-mapping: identification of causal mechanisms for autoimmunity
Lincoln MR, Axisa PP, Hafler DA. Epigenetic fine-mapping: identification of causal mechanisms for autoimmunity. Current Opinion In Immunology 2020, 67: 50-56. PMID: 32977183, DOI: 10.1016/j.coi.2020.09.002.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesMolecular mechanismsSusceptibility lociIndividual susceptibility lociFundamental genetic basisCausal molecular mechanismsPathogenic cell typesSpecific molecular mechanismsGenetic susceptibility lociEpigenetic techniquesGenetic basisGenetic lociAssociation studiesCell typesLociRecent advancesMechanismGeneticsAutoimmune diseasesSpectrum of autoimmunityCausal mechanismsEtiological mechanismsInflammatory diseasesTranslationAutoimmunity
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
2016
Multiple sclerosis
Axisa PP, Hafler DA. Multiple sclerosis. Current Opinion In Neurology 2016, 29: 345-353. PMID: 27058221, PMCID: PMC7882195, DOI: 10.1097/wco.0000000000000319.Peer-Reviewed Original ResearchConceptsMultiple sclerosisGenome-wide association studiesAssociation studiesMultiple sclerosis (MS) etiologyMultiple sclerosis progressionMultiple sclerosis patientsHigh-throughput genetic analysisImmune cell functionNumerous candidate biomarkersWide association studyMechanisms of neurodegenerationImmunomodulatory treatmentSclerosis patientsClinical outcomesTreatment arsenalDisease progressionImmune regulationSclerosisNew biomarkersCandidate biomarkersPatient careGenetic variationGenetic analysisCell functionProgression
2014
Genetic and epigenetic fine mapping of causal autoimmune disease variants
Farh KK, Marson A, Zhu J, Kleinewietfeld M, Housley WJ, Beik S, Shoresh N, Whitton H, Ryan RJ, Shishkin AA, Hatan M, Carrasco-Alfonso MJ, Mayer D, Luckey CJ, Patsopoulos NA, De Jager PL, Kuchroo VK, Epstein CB, Daly MJ, Hafler DA, Bernstein BE. Genetic and epigenetic fine mapping of causal autoimmune disease variants. Nature 2014, 518: 337-343. PMID: 25363779, PMCID: PMC4336207, DOI: 10.1038/nature13835.Peer-Reviewed Original ResearchConceptsCausal variantsAutoimmune diseasesT cellsRegulatory T cellsNon-coding risk variantsT cell subsetsEnhancer-associated RNAsGenome-wide association studiesPrimary immune cellsCandidate causal variantsGene regulatory modelsImmune cellsImmune stimulationB cellsGene activationFine mappingTranscription factorsMaster regulatorHistone acetylationImmune differentiationSequence determinantsGene expressionAssociation studiesDiseaseHuman diseasesSP0104 The Molecular Basis of Autoimmune Disease
Hafler D. SP0104 The Molecular Basis of Autoimmune Disease. Annals Of The Rheumatic Diseases 2014, 73: 27. DOI: 10.1136/annrheumdis-2014-eular.6254.Peer-Reviewed Original ResearchGenome-wide association studiesNon-coding regionsConsensus transcription factorNumerous genetic associationsDistinct cell typesDifferent autoimmune diseasesAutoimmune diseasesChromatin mapsTh17 cellsGWAS hitsHigh NaCl levelsTranscription factorsDNA sequencesMolecular basisGenetic dataCausal mutationsDisease riskAssociation studiesMechanistic basisCommon SNPsNucleotide variantsAP-1Risk SNPsCell typesSpecific disruption
2013
Network-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
Genome‐wide meta‐analysis identifies novel multiple sclerosis susceptibility loci
Patsopoulos NA, Esposito F, Reischl J, Lehr S, Bauer D, Heubach J, Sandbrink R, Pohl C, Edan G, Kappos L, Miller D, Montalbán J, Polman C, Freedman M, Hartung H, Arnason B, Comi G, Cook S, Filippi M, Goodin D, Jeffery D, O'Connor P, Ebers G, Langdon D, Reder A, Traboulsee A, Zipp F, Schimrigk S, Hillert J, Bahlo M, Booth D, Broadley S, Brown M, Browning B, Browning S, Butzkueven H, Carroll W, Chapman C, Foote S, Griffiths L, Kermode A, Kilpatrick T, Lechner-Scott J, Marriott M, Mason D, Moscato P, Heard R, Pender M, Perreau V, Perera D, Rubio J, Scott R, Slee M, Stankovich J, Stewart G, Taylor B, Tubridy N, Willoughby E, Wiley J, Matthews P, Boneschi F, Compston A, Haines J, Hauser S, McCauley J, Ivinson A, Oksenberg J, Pericak-Vance M, Sawcer S, De Jager P, Hafler D, de Bakker P. Genome‐wide meta‐analysis identifies novel multiple sclerosis susceptibility loci. Annals Of Neurology 2011, 70: 897-912. PMID: 22190364, PMCID: PMC3247076, DOI: 10.1002/ana.22609.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSingle nucleotide polymorphismsSusceptibility lociHapMap Phase IIUnique single nucleotide polymorphismsGene discovery effortsNew susceptibility lociStrongest cis effectsMS genome-wide association studiesQuantitative trait analysisFlanking genesGenetic architectureRNA expression dataMultiple sclerosis susceptibility lociIntergenic regionSecond intronNew lociNovel susceptibility allelesAdditional lociTrait analysisAssociation studiesExpression dataChromosome 2p21LociFunctional consequencesThe CD6 Multiple Sclerosis Susceptibility Allele Is Associated with Alterations in CD4+ T Cell Proliferation
Kofler DM, Severson CA, Mousissian N, De Jager PL, Hafler DA. The CD6 Multiple Sclerosis Susceptibility Allele Is Associated with Alterations in CD4+ T Cell Proliferation. The Journal Of Immunology 2011, 187: 3286-3291. PMID: 21849685, DOI: 10.4049/jimmunol.1100626.Peer-Reviewed Original ResearchMeSH KeywordsAllelesAntigens, CDAntigens, Differentiation, T-LymphocyteCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCell ProliferationCell SeparationCells, CulturedFemaleFlow CytometryGenetic Predisposition to DiseaseGenotypeHumansMaleMultiple SclerosisPhenotypeReverse Transcriptase Polymerase Chain ReactionRisk FactorsRNA, Small InterferingConceptsGenome-wide association studiesAssociation studiesAllelic variantsNew susceptibility lociSusceptibility allelesRisk allelesProliferation defectExon 5Risk-associated allelesSingle nucleotide polymorphismsExtracellular binding sitesCD6 geneSusceptibility lociLinkage disequilibriumMS risk alleleSelective knockdownT cell activationNucleotide polymorphismsAltered proliferationCell proliferationGenetic associationAllelesLong-term activationBinding sitesMS susceptibility allelesA 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 effectsEpistasisACTN1GenesModeling the cumulative genetic risk for multiple sclerosis from genome-wide association data
Wang JH, Pappas D, De Jager PL, Pelletier D, de Bakker PI, Kappos L, Polman CH, Australian and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene), Chibnik LB, Hafler DA, Matthews PM, Hauser SL, Baranzini SE, Oksenberg JR. Modeling the cumulative genetic risk for multiple sclerosis from genome-wide association data. Genome Medicine 2011, 3: 3. PMID: 21244703, PMCID: PMC3092088, DOI: 10.1186/gm217.Peer-Reviewed Original ResearchGenome-wide association studiesGenome-wide association dataDAVID functional annotation toolMS geneticsAssociation dataFunctional annotation toolAvailable genome-wide association dataRecent genome-wide association studiesPathway enrichment analysisNovel genetic associationsNervous system developmentPolygenic modelCumulative genetic riskGene OntologyGWAS datasetsEnrichment analysisGenetic riskAssociation studiesDisease locusCell adhesionSignificant enrichmentNeuronal signalingAnalysis of covarianceIonotropic glutamate receptorsGenetic association
2010
Genome-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 haplotypeTraitsSNPs
2009
Comprehensive follow-up of the first genome-wide association study of multiple sclerosis identifies KIF21B and TMEM39A as susceptibility loci
, , McCauley J, Zuvich R, Beecham A, De Jager P, Konidari I, Whitehead P, Aubin C, Ban M, Pobywajlo S, Briskin R, Romano S, Aggarwal N, Piccio L, McArdle W, Strachan D, Evans D, Cross A, Cree B, Rioux J, Barcellos L, Ivinson A, Compston A, Hafler D, Hauser S, Oksenberg J, Sawcer S, Pericak-Vance M, Haines J. Comprehensive follow-up of the first genome-wide association study of multiple sclerosis identifies KIF21B and TMEM39A as susceptibility loci. Human Molecular Genetics 2009, 19: 953-962. PMID: 20007504, PMCID: PMC2816610, DOI: 10.1093/hmg/ddp542.Peer-Reviewed Original ResearchConceptsGenome-wide association studiesSingle nucleotide polymorphismsAssociation studiesFirst genome-wide association studyGenome-wide association screenNumerous complex diseasesSusceptibility lociInitial genome-wide association studyGenome-wide significanceNovel susceptibility lociComplex genetic diseasesHundreds of associationsSNP hitsGWAS studiesGenetic diseasesLociComplex diseasesOriginal screenTMEM39AInitial associationIndependent data setsReplicationKIF21BInitial replicationScreenReplication 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