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
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
A 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