Patterns of genic intolerance of rare copy number variation in 59,898 human exomes
Ruderfer DM, Hamamsy T, Lek M, Karczewski KJ, Kavanagh D, Samocha KE, Daly M, MacArthur D, Fromer M, Purcell S. Patterns of genic intolerance of rare copy number variation in 59,898 human exomes. Nature Genetics 2016, 48: 1107-1111. PMID: 27533299, PMCID: PMC5042837, DOI: 10.1038/ng.3638.Peer-Reviewed Original ResearchMeSH KeywordsAdultChildDatabases, GeneticDNA Copy Number VariationsExomeFemaleGene FrequencyGenetic Predisposition to DiseaseGenome, HumanHumansMalePolymorphism, Single NucleotideSchizophreniaConceptsGenic copy number variantsHuman genetic variationExome Aggregation ConsortiumRare copy number variationsCopy number variationsCopy number variantsExome sequencing dataGenetic variationGenic intoleranceHuman exomeSequencing dataPersonal genomesNumber variationsNumber variantsGenomeIntegrated databaseExomeVariationVariantsConsortiumDiagnosis and etiology of congenital muscular dystrophy: We are halfway there
O'Grady GL, Lek M, Lamande SR, Waddell L, Oates EC, Punetha J, Ghaoui R, Sandaradura SA, Best H, Kaur S, Davis M, Laing NG, Muntoni F, Hoffman E, MacArthur DG, Clarke NF, Cooper S, North K. Diagnosis and etiology of congenital muscular dystrophy: We are halfway there. Annals Of Neurology 2016, 80: 101-111. PMID: 27159402, DOI: 10.1002/ana.24687.Peer-Reviewed Original ResearchConceptsMuscle biopsyImmunohistochemical analysisGenetic diagnosisCongenital muscular dystrophy patientsFirst-line toolCandidate gene sequencingCongenital myasthenic syndromeCongenital muscular dystrophyMuscular dystrophy patientsAnn NeurolMyasthenic syndromeUndiagnosed patientsCMD patientsCongenital myopathyLarge cohortProbable diagnosisPatientsGene sequencingClinical phenotypeDystrophy patientsLaminin α2BiopsyDiagnosisChromosomal microarrayCohortQuantifying prion disease penetrance using large population control cohorts
Minikel EV, Vallabh SM, Lek M, Estrada K, Samocha KE, Sathirapongsasuti JF, McLean CY, Tung JY, Yu LP, Gambetti P, Blevins J, Zhang S, Cohen Y, Chen W, Yamada M, Hamaguchi T, Sanjo N, Mizusawa H, Nakamura Y, Kitamoto T, Collins SJ, Boyd A, Will RG, Knight R, Ponto C, Zerr I, Kraus TF, Eigenbrod S, Giese A, Calero M, de Pedro-Cuesta J, Haïk S, Laplanche JL, Bouaziz-Amar E, Brandel JP, Capellari S, Parchi P, Poleggi A, Ladogana A, O’Donnell-Luria A, Karczewski KJ, Marshall JL, Boehnke M, Laakso M, Mohlke KL, Kähler A, Chambert K, McCarroll S, Sullivan PF, Hultman CM, Purcell SM, Sklar P, van der Lee SJ, Rozemuller A, Jansen C, Hofman A, Kraaij R, van Rooij JG, Ikram MA, Uitterlinden AG, van Duijn CM, Consortium E, Daly MJ, MacArthur DG. Quantifying prion disease penetrance using large population control cohorts. Science Translational Medicine 2016, 8: 322ra9. PMID: 26791950, PMCID: PMC4774245, DOI: 10.1126/scitranslmed.aad5169.Peer-Reviewed Original ResearchMeSH KeywordsCase-Control StudiesCohort StudiesGenetic Predisposition to DiseaseHumansMutationPenetrancePrion DiseasesPrionsRisk FactorsConceptsPrion protein genePopulation control cohortPrion disease casesHealthy older individualsPrion protein expressionControl cohortLifetime riskTherapeutic suppressionDisease casesTruncating variantsDisease-causing genotypesOlder individualsBenign variantsDisease prevalenceProtein expressionDisease penetranceDiseaseMissense variantsPrion diseasesControl exomesDisease susceptibilityImpact of variantsGenetic variantsRiskPenetrance