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 dyspraxiaAutosomesGenome
2013
Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data
Yaari G, Heiden J, Uduman M, Gadala-Maria D, Gupta N, Stern JN, O’Connor K, Hafler DA, Laserson U, Vigneault F, Kleinstein SH. Models of Somatic Hypermutation Targeting and Substitution Based on Synonymous Mutations from High-Throughput Immunoglobulin Sequencing Data. Frontiers In Immunology 2013, 4: 358. PMID: 24298272, PMCID: PMC3828525, DOI: 10.3389/fimmu.2013.00358.Peer-Reviewed Original ResearchAccurate background modelSynonymous mutationsNon-coding regionsParticular codon usageNon-functional sequencesComputational analysis methodsObserved mutation patternExisting modelsBackground modelInfluence of selectionCodon usageSHM targetingBase compositionImproved modelSequencing dataNucleotide substitutionsAnalysis methodStatistical analysisFunctional sequencesMutation targetingB-cell cancersModelSomatic hypermutation patternsMutationsHypermutation patterns