2024
Genetic mapping across autoimmune diseases reveals shared associations and mechanisms
Lincoln M, Connally N, Axisa P, Gasperi C, Mitrovic M, van Heel D, Wijmenga C, Withoff S, Jonkers I, Padyukov L, Rich S, Graham R, Gaffney P, Langefeld C, Vyse T, Hafler D, Chun S, Sunyaev S, Cotsapas C. Genetic mapping across autoimmune diseases reveals shared associations and mechanisms. Nature Genetics 2024, 56: 838-845. PMID: 38741015, DOI: 10.1038/s41588-024-01732-8.Peer-Reviewed Original ResearchConceptsGenetic mapResolution of genetic mappingExpression quantitative trait lociFine-mapping resolutionQuantitative trait lociGenomic lociTrait lociPolygenic disorderAllelesRisk allelesLociPathogenic mechanismsImmune systemAutoimmune mechanismsAutoimmune diseasesInflammatory diseasesTraitsMechanismDiseaseSample collectionExpression
2019
Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility
Patsopoulos N, Baranzini S, Santaniello A, Shoostari P, Cotsapas C, Wong G, Beecham A, James T, Replogle J, Vlachos I, McCabe C, Pers T, Brandes A, White C, Keenan B, Cimpean M, Winn P, Panteliadis I, Robbins A, Andlauer T, Zarzycki O, Dubois B, Goris A, Søndergaard H, Sellebjerg F, Sorensen P, Ullum H, Thørner L, Saarela J, Cournu-Rebeix I, Damotte V, Fontaine B, Guillot-Noel L, Lathrop M, Vukusic S, Berthele A, Pongratz V, Buck D, Gasperi C, Graetz C, Grummel V, Hemmer B, Hoshi M, Knier B, Korn T, Lill C, Luessi F, Mühlau M, Zipp F, Dardiotis E, Agliardi C, Amoroso A, Barizzone N, Benedetti M, Bernardinelli L, Cavalla P, Clarelli F, Comi G, Cusi D, Esposito F, Ferrè L, Galimberti D, Guaschino C, Leone M, Martinelli V, Moiola L, Salvetti M, Sorosina M, Vecchio D, Zauli A, Santoro S, Mancini N, Zuccalà M, Mescheriakova J, van Duijn C, Bos S, Celius E, Spurkland A, Comabella M, Montalban X, Alfredsson L, Bomfim I, Gomez-Cabrero D, Hillert J, Jagodic M, Lindén M, Piehl F, Jelčić I, Martin R, Sospedra M, Baker A, Ban M, Hawkins C, Hysi P, Kalra S, Karpe F, Khadake J, Lachance G, Molyneux P, Neville M, Thorpe J, Bradshaw E, Caillier S, Calabresi P, Cree B, Cross A, Davis M, de Bakker P, Delgado S, Dembele M, Edwards K, Fitzgerald K, Frohlich I, Gourraud P, Haines J, Hakonarson H, Kimbrough D, Isobe N, Konidari I, Lathi E, Lee M, Li T, An D, Zimmer A, Madireddy L, Manrique C, Mitrovic M, Olah M, Patrick E, Pericak-Vance M, Piccio L, Schaefer C, Weiner H, Lage K, Compston A, Hafler D, Harbo H, Hauser S, Stewart G, D’Alfonso S, Hadjigeorgiou G, Taylor B, Barcellos L, Booth D, Hintzen R, Kockum I, Martinelli-Boneschi F, McCauley J, Oksenberg J, Oturai A, Sawcer S, Ivinson A, Olsson T, De Jager P. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 2019, 365 PMID: 31604244, PMCID: PMC7241648, DOI: 10.1126/science.aav7188.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCell Cycle ProteinsChromosome MappingChromosomes, Human, XGene FrequencyGenetic LociGenome-Wide Association StudyGenomicsGTPase-Activating ProteinsHumansInheritance PatternsMajor Histocompatibility ComplexMicrogliaMultiple SclerosisPolymorphism, Single NucleotideQuantitative Trait LociRNA-SeqTranscriptomeConceptsMajor histocompatibility complexMultiple sclerosisImmune cellsBrain-resident immune cellsPeripheral immune cellsPeripheral immune responseCentral nervous systemExtended major histocompatibility complexAutoimmune processControl subjectsHuman microgliaImmune responseNervous systemImmune systemHistocompatibility complexPutative susceptibility genesMicrogliaX variantGenetic architectureSusceptibility genesGenomic mapGenetic dataExpression profilesM geneSusceptibility variants
2014
Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes
Raj T, Rothamel K, Mostafavi S, Ye C, Lee MN, Replogle JM, Feng T, Lee M, Asinovski N, Frohlich I, Imboywa S, Von Korff A, Okada Y, Patsopoulos NA, Davis S, McCabe C, Paik HI, Srivastava GP, Raychaudhuri S, Hafler DA, Koller D, Regev A, Hacohen N, Mathis D, Benoist C, Stranger BE, De Jager PL. Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes. Science 2014, 344: 519-523. PMID: 24786080, PMCID: PMC4910825, DOI: 10.1126/science.1249547.Peer-Reviewed Original ResearchMeSH KeywordsAdaptive ImmunityAllelesAlzheimer DiseaseAutoimmune DiseasesAutoimmunityCD4-Positive T-LymphocytesEthnicityGenetic Predisposition to DiseaseGenome-Wide Association StudyHumansImmunity, InnateMonocytesMultiple SclerosisNeurodegenerative DiseasesParkinson DiseasePolymorphism, Single NucleotideQuantitative Trait LociRheumatic FeverTranscriptomeConceptsSpecific immune cell typesHuman immune functionImmune cell typesMulti-ethnic cohortCell-autonomous effectsAutoimmune diseasesT cellsImmune functionParkinson's diseaseHealthy individualsInnate immunityRisk allelesDiseaseExpression quantitative trait loci (eQTL) studiesQuantitative trait loci studiesSusceptibility allelesPutative functional assignmentsCausal regulatory variantsDisease-associated lociDisease susceptibility variantsCell typesSusceptibility variantsTrans-eQTLsFunctional assignmentRegulatory variantsCommon 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
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 mechanismsLoci
2009
Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score
De Jager PL, Chibnik LB, Cui J, Reischl J, Lehr S, Simon KC, Aubin C, Bauer D, Heubach JF, Sandbrink R, Tyblova M, Lelkova P, the steering committees of the BENEFIT B, Havrdova E, Pohl C, Horakova D, Ascherio A, Hafler D, Karlson E. Integration of genetic risk factors into a clinical algorithm for multiple sclerosis susceptibility: a weighted genetic risk score. The Lancet Neurology 2009, 8: 1111-1119. PMID: 19879194, PMCID: PMC3099419, DOI: 10.1016/s1474-4422(09)70275-3.Peer-Reviewed Original ResearchConceptsWeighted genetic risk scoreEpstein-Barr virusHealth Study IMultiple sclerosisC-statisticRisk factorsGenetic risk scoreImmune responseRisk scoreNurses' Health Study IDiagnosis of MSNon-genetic risk factorsHigh-risk individualsMultiple sclerosis susceptibilityEnvironmental risk factorsGenetic risk factorsNHS cohortDerivation cohortTherapeutic trialsMS riskProspective studyClinical algorithmImportant clinical applicationsHigher oddsSusceptibility loci
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 ResearchMeSH KeywordsCell LineDNA, MitochondrialDrug ResistanceGene Expression ProfilingGenetic VariationHerpesvirus 4, HumanHumansLymphocytesPhenotypeQuantitative Trait LociRNA, MessengerConceptsLymphoblastoid 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