2024
Mitochondrial heteroplasmy improves risk prediction for myeloid neoplasms
Hong Y, Pasca S, Shi W, Puiu D, Lake N, Lek M, Ru M, Grove M, Prizment A, Joshu C, Platz E, Guallar E, Arking D, Gondek L. Mitochondrial heteroplasmy improves risk prediction for myeloid neoplasms. Nature Communications 2024, 15: 10133. PMID: 39578475, PMCID: PMC11584845, DOI: 10.1038/s41467-024-54443-3.Peer-Reviewed Original ResearchConceptsClonal hematopoiesis of indeterminate potentialClonal hematopoiesisVariant allele fractionHeteroplasmic variantsIndeterminate potentialMyeloid neoplasmsHeteroplasmyMultiple mutationsAllele fractionMutationsHigh-risk groupPathogenic risk factorsMarkersRisk score modelDeleteriousnessSpliceosomeHematologic malignanciesRisk stratificationNeoplasm developmentNeoplasmsNeoplasm incidenceRisk factorsVariantsA cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders
Lee A, Ayers L, Kosicki M, Chan W, Fozo L, Pratt B, Collins T, Zhao B, Rose M, Sanchis-Juan A, Fu J, Wong I, Zhao X, Tenney A, Lee C, Laricchia K, Barry B, Bradford V, Jurgens J, England E, Lek M, MacArthur D, Lee E, Talkowski M, Brand H, Pennacchio L, Engle E. A cell type-aware framework for nominating non-coding variants in Mendelian regulatory disorders. Nature Communications 2024, 15: 8268. PMID: 39333082, PMCID: PMC11436875, DOI: 10.1038/s41467-024-52463-7.Peer-Reviewed Original ResearchConceptsNon-coding variantsCranial motor neuronsMendelian disordersIn vivo transgenic assayPredictor of enhancer activityCis-regulatory elementsMulti-omic frameworkWhole-genome sequencingEnhanced activityVariant discoveryGenome sequenceChromatin accessibilityPutative enhancersHistone modificationsRegulatory elementsGene expression assaysGene predictionTransgenic assaysEpigenomic profilingMendelian casesExpression assaysMutational enhancementCongenital cranial dysinnervation disordersCell typesFunctional impactTranslating multiscale research in rare disease
Hooper K, Justice M, Lek M, Liu K, Rauen K. Translating multiscale research in rare disease. Disease Models & Mechanisms 2024, 17: dmm052009. PMID: 38982973, PMCID: PMC11261626, DOI: 10.1242/dmm.052009.Peer-Reviewed Original ResearchHigh-throughput assays to assess variant effects on disease
Ma K, Gauthier L, Cheung F, Huang S, Lek M. High-throughput assays to assess variant effects on disease. Disease Models & Mechanisms 2024, 17: dmm050573. PMID: 38940340, PMCID: PMC11225591, DOI: 10.1242/dmm.050573.Peer-Reviewed Original ResearchConceptsDeep mutational scanningGenetic variantsRare disease diagnosticsRare genetic variantsDisease mechanismsHigh-throughput assaySequencing effortsInvestigation of variantsMutational scanningModel cell lineVariant effectsMolecular toolsCell linesCell survival rateFunctional assaysDrug resistanceDisease diagnosticsDisease-relevant assaysVariantsClinical case reportBiological mechanismsAssayCase reportClinical reportsSurvival rateGlis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease
Zhang C, Rehman M, Tian X, Pei S, Gu J, Bell T, Dong K, Tham M, Cai Y, Wei Z, Behrens F, Jetten A, Zhao H, Lek M, Somlo S. Glis2 is an early effector of polycystin signaling and a target for therapy in polycystic kidney disease. Nature Communications 2024, 15: 3698. PMID: 38693102, PMCID: PMC11063051, DOI: 10.1038/s41467-024-48025-6.Peer-Reviewed Original ResearchConceptsMouse models of autosomal dominant polycystic kidney diseaseModel of autosomal dominant polycystic kidney diseasePolycystin signalingAutosomal dominant polycystic kidney diseasePolycystin-1Polycystic kidney diseaseTreat autosomal dominant polycystic kidney diseaseGlis2Primary ciliaKidney tubule cellsSignaling pathwayMouse modelDominant polycystic kidney diseasePotential therapeutic targetTranslatomeAntisense oligonucleotidesKidney diseasePolycystinMouse kidneyFunctional effectorsCyst formationTherapeutic targetInactivationFunctional targetPharmacological targets
2023
Flavones provide resistance to DUX4-induced toxicity via an mTor-independent mechanism
Cohen J, Huang S, Koczwara K, Woods K, Ho V, Woodman K, Arbiser J, Daman K, Lek M, Emerson C, DeSimone A. Flavones provide resistance to DUX4-induced toxicity via an mTor-independent mechanism. Cell Death & Disease 2023, 14: 749. PMID: 37973788, PMCID: PMC10654915, DOI: 10.1038/s41419-023-06257-2.Peer-Reviewed Original ResearchConceptsMTOR-independent mechanismsFacioscapulohumeral muscular dystrophyDUX4 transcriptsDUX4 activityMultiple signal transduction pathwaysSignal transduction pathwaysTherapeutic developmentDUX4 proteinDUX4 expressionTransduction pathwaysPolyadenylation sitesChromosome 4DUX4 geneMechanisms of toxicityAutophagy pathwayExpression of ULK1DUX4Cellular autophagyCell deathRelevant pathwaysMuscular dystrophyMolecular methodsPathwaySkeletal muscleTranscriptsDeleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality
Hong Y, Battle S, Shi W, Puiu D, Pillalamarri V, Xie J, Pankratz N, Lake N, Lek M, Rotter J, Rich S, Kooperberg C, Reiner A, Auer P, Heard-Costa N, Liu C, Lai M, Murabito J, Levy D, Grove M, Alonso A, Gibbs R, Dugan-Perez S, Gondek L, Guallar E, Arking D. Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality. Nature Communications 2023, 14: 6113. PMID: 37777527, PMCID: PMC10542802, DOI: 10.1038/s41467-023-41785-7.Peer-Reviewed Original ResearchConceptsSingle nucleotide variantsOwn circular genomeState of heteroplasmyAging-related diseasesNuclear genomeMitochondrial genomeCircular genomeMtDNA single nucleotide variantsMitochondrial DNASomatic cellsMitochondrial mutationsMtDNA heteroplasmyGenomeNucleotide variantsHeteroplasmyDNA moleculesFunctional roleMitochondriaUK BiobankCertain cancersVariantsDNAMutationsCopiesCells
2017
Improving genetic diagnosis in Mendelian disease with transcriptome sequencing
Cummings BB, Marshall JL, Tukiainen T, Lek M, Donkervoort S, Foley AR, Bolduc V, Waddell LB, Sandaradura SA, O’Grady G, Estrella E, Reddy HM, Zhao F, Weisburd B, Karczewski KJ, O’Donnell-Luria A, Birnbaum D, Sarkozy A, Hu Y, Gonorazky H, Claeys K, Joshi H, Bournazos A, Oates EC, Ghaoui R, Davis MR, Laing NG, Topf A, Consortium G, Kang PB, Beggs AH, North KN, Straub V, Dowling JJ, Muntoni F, Clarke NF, Cooper ST, Bönnemann CG, MacArthur DG. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Science Translational Medicine 2017, 9 PMID: 28424332, PMCID: PMC5548421, DOI: 10.1126/scitranslmed.aal5209.Peer-Reviewed Original ResearchConceptsTranscriptome sequencingRNA-seqCurrent diagnostic ratePrior genetic analysisTranscript level changesTriple-helical domainDeep intronic regionsWhole-genome sequencingSplice-altering variantsInterpretation of variantsRepeat motifsGenomic analysisHelical domainMendelian disease diagnosisGenetic analysisMendelian diseasesIntronic regionsSkeletal muscle samplesSequencingRare disease diagnosisIntronic mutationOverall diagnosis rateStandard diagnostic approachRare muscle disorderComplementary diagnostic tool
2016
Analysis of protein-coding genetic variation in 60,706 humans
Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, O’Donnell-Luria A, Ware JS, Hill AJ, Cummings BB, Tukiainen T, Birnbaum DP, Kosmicki JA, Duncan LE, Estrada K, Zhao F, Zou J, Pierce-Hoffman E, Berghout J, Cooper DN, Deflaux N, DePristo M, Do R, Flannick J, Fromer M, Gauthier L, Goldstein J, Gupta N, Howrigan D, Kiezun A, Kurki MI, Moonshine AL, Natarajan P, Orozco L, Peloso GM, Poplin R, Rivas MA, Ruano-Rubio V, Rose SA, Ruderfer DM, Shakir K, Stenson PD, Stevens C, Thomas BP, Tiao G, Tusie-Luna MT, Weisburd B, Won HH, Yu D, Altshuler DM, Ardissino D, Boehnke M, Danesh J, Donnelly S, Elosua R, Florez JC, Gabriel SB, Getz G, Glatt SJ, Hultman CM, Kathiresan S, Laakso M, McCarroll S, McCarthy MI, McGovern D, McPherson R, Neale BM, Palotie A, Purcell SM, Saleheen D, Scharf JM, Sklar P, Sullivan PF, Tuomilehto J, Tsuang MT, Watkins HC, Wilson JG, Daly MJ, MacArthur DG. Analysis of protein-coding genetic variation in 60,706 humans. Nature 2016, 536: 285-291. PMID: 27535533, PMCID: PMC5018207, DOI: 10.1038/nature19057.Peer-Reviewed Original ResearchConceptsGenetic variationProtein-coding genetic variationProtein-coding genesDNA sequence dataHuman genetic diversityHuman genetic variationDNA sequence changesHuman disease phenotypesCandidate disease-causing variantsClasses of mutationsExome Aggregation ConsortiumProtein-truncating variantsMutational recurrenceStrong selectionGenetic diversitySequence dataDiverse ancestryDisease-causing variantsSequence changesSequence variantsGenesDisease phenotypeFunctional interpretationVariantsDirect evidencePatterns 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 ResearchConceptsGenic 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 ResearchConceptsPrion 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
2015
Use of Whole-Exome Sequencing for Diagnosis of Limb-Girdle Muscular Dystrophy: Outcomes and Lessons Learned
Ghaoui R, Cooper ST, Lek M, Jones K, Corbett A, Reddel SW, Needham M, Liang C, Waddell LB, Nicholson G, O’Grady G, Kaur S, Ong R, Davis M, Sue CM, Laing NG, North KN, MacArthur DG, Clarke NF. Use of Whole-Exome Sequencing for Diagnosis of Limb-Girdle Muscular Dystrophy: Outcomes and Lessons Learned. JAMA Neurology 2015, 72: 1424-1432. PMID: 26436962, DOI: 10.1001/jamaneurol.2015.2274.Peer-Reviewed Original ResearchConceptsLGMD-related genesLimb-girdle muscular dystrophyWhole-exome sequencingMyopathy genesBiopsy specimensDiagnostic rateMutations of CHD7Follow-up screeningMuscular dystrophyAccurate clinical examinationLikely pathogenic mutationsMuscle biopsy specimensTubular aggregate myopathyCongenital myasthenic syndromeGenetic diagnosisDiagnostic success rateNeuromuscular clinicMuscle weaknessMyopathic changesClinical examinationHistopathological resultsAncillary investigationsMyasthenic syndromeCommon causeDiagnostic yieldEffect of predicted protein-truncating genetic variants on the human transcriptome
Rivas MA, Pirinen M, Conrad DF, Lek M, Tsang EK, Karczewski KJ, Maller JB, Kukurba KR, DeLuca DS, Fromer M, Ferreira PG, Smith KS, Zhang R, Zhao F, Banks E, Poplin R, Ruderfer DM, Purcell SM, Tukiainen T, Minikel EV, Stenson PD, Cooper DN, Huang KH, Sullivan TJ, Nedzel J, Consortium T, Consortium T, Bustamante CD, Li JB, Daly MJ, Guigo R, Donnelly P, Ardlie K, Sammeth M, Dermitzakis ET, McCarthy MI, Montgomery SB, Lappalainen T, MacArthur DG, Segre A, Young T, Gelfand E, Trowbridge C, Ward L, Kheradpour P, Iriarte B, Meng Y, Palmer C, Esko T, Winckler W, Hirschhorn J, Kellis M, Getz G, Shablin A, Li, Zhou Y, Nobel A, Rusyn I, Wright F, Battle A, Mostafavi S, Mele M, Reverter F, Goldmann J, Koller D, Gamazon E, Im H, Konkashbaev A, Nicolae D, Cox N, Flutre T, Wen X, Stephens M, Pritchard J, Tu Z, Zhang B, Huang T, Long Q, Lin L, Yang J, Zhu J, Liu J, Brown A, Mestichelli B, Tidwell D, Lo E, Salvatore M, Shad S, Thomas J, Lonsdale J, Choi R, Karasik E, Ramsey K, Moser M, Foster B, Gillard B, Syron J, Fleming J, Magazine H, Hasz R, Walters G, Bridge J, Miklos M, Sullivan S, Barker L, Traino H, Mosavel M, Siminoff L, Valley D, Rohrer D, Jewel S, Branton P, Sobin L, Barcus M, Qi L, Hariharan P, Wu S, Tabor D, Shive C, Smith A, Buia S, Undale A, Robinson K, Roche N, Valentino K, Britton A, Burges R, Bradbury D, Hambright K, Seleski J, Korzeniewski G, Erickson K, Marcus Y, Tejada J, Taherian M, Lu C, Robles B, Basile M, Mash D, Volpi S, Struewing J, Temple G, Boyer J, Colantuoni D, Little R, Koester S, Carithers L, Moore H, Guan P, Compton C, Sawyer S, Demchok J, Vaught J, Rabiner C, Lockhart N, Friedlander M, Hoen P, Monlong J, GonzĂ lez-Porta M, Kurbatova N, Griebel T, Barann M, Wieland T, Greger L, van Iterson M, Almlof J, Ribeca P, Pulyakhina I, Esser D, Giger T, Tikhonov A, Sultan M, Bertier G, Lizano E, Buermans H, Padioleau I, Schwarzmayr T, Karlberg O, Ongen H, Kilpinen H, Beltran S, Gut M, Kahlem K, Amstislavskiy V, Stegle O, Flicek P, Strom T, Lehrach H, Schreiber S, Sudbrak R, Carracedo A, Antonarakis S, Hasler R, Syvanen A, van Ommen G, Brazma A, Meitinger T, Rosenstiel P, Gut I, Estivill X. Effect of predicted protein-truncating genetic variants on the human transcriptome. Science 2015, 348: 666-669. PMID: 25954003, PMCID: PMC4537935, DOI: 10.1126/science.1261877.Peer-Reviewed Original ResearchConceptsGenotype-Tissue ExpressionGenetic variantsProtein-truncating variantsEffects of variantsDosage compensationClass of variantsTranscript decayGene functionTranscriptome dataHuman transcriptomeGenetic variationGEUVADIS projectGene inactivationSplice junctionsGenome interpretationTranscriptome effectsFunctional interpretationClinical genome interpretationFunctional effectsPositional effectsImproved predictive modelVariantsTranscriptomeProfound effectInactivation
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