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 factorsVariantsMitochondrial Heteroplasmy Is a Novel Predictor of Chronic Lymphocytic Leukemia Risk
Pasca S, Hong Y, Shi W, Puiu D, Lake N, Lek M, Guallar E, Arking D, Gondek L. Mitochondrial Heteroplasmy Is a Novel Predictor of Chronic Lymphocytic Leukemia Risk. Blood 2024, 144: 4054-4054. DOI: 10.1182/blood-2024-210250.Peer-Reviewed Original ResearchMitochondrial heteroplasmyClonal hematopoiesis of indeterminate potentialMtDNA heteroplasmyWhole-exome sequencing dataSomatic mutationsPresence of somatic mutationsExome sequencing dataCancer-associated genesClonal hematopoiesisClonal expansionVariant allele frequencyAssociated with myeloid malignanciesMtDNA variantsMitochondrial DNAPresence of mutationsSequence dataUK Biobank (UKBBiologically significant roleDeleterious mutationsHeteroplasmyChronic lymphocytic leukemia riskAllele frequenciesOncogenic transformationMitochondrial functionMyeloid genesQuantifying constraint in the human mitochondrial genome
Lake N, Ma K, Liu W, Battle S, Laricchia K, Tiao G, Puiu D, Ng K, Cohen J, Compton A, Cowie S, Christodoulou J, Thorburn D, Zhao H, Arking D, Sunyaev S, Lek M. Quantifying constraint in the human mitochondrial genome. Nature 2024, 635: 390-397. PMID: 39415008, PMCID: PMC11646341, DOI: 10.1038/s41586-024-08048-x.Peer-Reviewed Original ResearchMitochondrial genomeDeleterious variationMtDNA mutator modelHuman mitochondrial genomeGenome Aggregation DatabaseMtDNA variationMtDNA variantsMitochondrial DNANoncoding regionsMitochondrial proteinsRRNA geneGenetic variationMtDNAThree-dimensional structureMutation modelPathogenic variationDisease relevanceAggregation DatabaseGenomeLarge-scale population datasetRRNAConstrained sitesGenesTRNAPopulation datasetsA 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 impactSaturation mutagenesis-reinforced functional assays for disease-related genes
Ma K, Huang S, Ng K, Lake N, Joseph S, Xu J, Lek A, Ge L, Woodman K, Koczwara K, Cohen J, Ho V, O'Connor C, Brindley M, Campbell K, Lek M. Saturation mutagenesis-reinforced functional assays for disease-related genes. Cell 2024, 187: 6707-6724.e22. PMID: 39326416, PMCID: PMC11568926, DOI: 10.1016/j.cell.2024.08.047.Peer-Reviewed Original ResearchDisease-related genesDisease-causing genetic variantsGenome-wide resolutionMutation scanning methodsSingle-nucleotide variantsDeep mutational scanning methodFunctional assaysDisease genesComputational predictorsSaturation mutagenesisHuman geneticsGenetic variantsGenesVariantsSmurfAssayMutagenesisLARGE1GeneticsDisease severityExpanding the genetics and phenotypes of ocular congenital cranial dysinnervation disorders
Jurgens J, Barry B, Chan W, MacKinnon S, Whitman M, Ruiz P, Pratt B, England E, Pais L, Lemire G, Groopman E, Glaze C, Russell K, Singer-Berk M, Di Gioia S, Lee A, Andrews C, Shaaban S, Wirth M, Bekele S, Toffoloni M, Bradford V, Foster E, Berube L, Rivera-Quiles C, Mensching F, Sanchis-Juan A, Fu J, Wong I, Zhao X, Wilson M, Weisburd B, Lek M, Consortium O, Abarca-Barriga H, Al-Haddad C, Berman J, Bothun E, Capasso J, Chacon-Camacho O, Chang L, Christiansen S, Ciccarelli M, Cordonnier M, Cox G, Curry C, Dagi L, Dahm T, David K, Davitt B, De Berardinis T, Demer J, Désir J, D’Esposito F, Drack A, Eggenberger E, Elder J, Elliott A, Epley K, Feldman H, Ferreira C, Flaherty M, Fulton A, Gerth-Kahlert C, Gottlob I, Grill S, Halliday D, Hanisch F, Hay E, Heidary G, Holder C, Horton J, Iannaccone A, Isenberg S, Johnston S, Kahana A, Katowitz J, Kazlas M, Kerr N, Kimonis V, Ko M, Koc F, Larsen D, Lay-Son G, Ledoux D, Levin A, Levy R, Lyons C, Mackey D, Magli A, Mantagos I, Marti C, Maystadt I, McKenzie F, Menezes M, Mikail C, Miller D, Miller K, Mills M, Miyana K, Moller H, Mullineaux L, Nishimura J, Noble A, Pandey P, Pavone P, Penzien J, Petersen R, Phalen J, Poduri A, Polo C, Prasov L, Ramos F, Ramos-Caceres M, Robb R, Rossillion B, Sahin M, Singer H, Smith L, Sorkin J, Soul J, Staffieri S, Stalker H, Stasheff S, Strassberg S, Strominger M, Taranath D, Thomas I, Traboulsi E, Ugrin M, VanderVeen D, Vincent A, G M, Wabbels B, Wong A, Woods C, Wu C, Yang E, Yeung A, Young T, Zenteno J, Zubcov-Iwantscheff A, Zwaan J, Brand H, Talkowski M, MacArthur D, O’Donnell-Luria A, Robson C, Hunter D, Engle E. Expanding the genetics and phenotypes of ocular congenital cranial dysinnervation disorders. Genetics In Medicine 2024, 27: 101216. PMID: 39033378, PMCID: PMC11739428, DOI: 10.1016/j.gim.2024.101216.Peer-Reviewed Original ResearchCongenital cranial dysinnervation disordersPrioritized variantsProtein-coding regionsSingle-nucleotide variantsDe novo variantsAnimal model phenotypesGenetically heterogeneous disorderAnalysis of pedigreesGenes associated with syndromesGenome sequenceStructural variantsMendelian conditionsModel phenotypesGenotype/phenotype correlationGenetic etiologyGenotype/phenotype associationsPathogenic variant(sGenesFunctional studiesSyndrome phenotypeSyndrome componentsPhenotypeGeneticsProbandsVariantsHigh-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 rateTranslating 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 ResearchGlis2 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 muscleTranscriptsP299 Over-expression of FKRP in heart induces myocarditis and dilated cardiomyopathy in LGMD2I/R9 mice
Huang S, Ma K, Cohen J, Ho V, Xu J, Gauthier L, O'Connor C, Ge L, Woodman K, Lek M. P299 Over-expression of FKRP in heart induces myocarditis and dilated cardiomyopathy in LGMD2I/R9 mice. Neuromuscular Disorders 2023, 33: s118. DOI: 10.1016/j.nmd.2023.07.209.Peer-Reviewed Original ResearchGene replacement therapyReplacement therapySkeletal muscleFKRP geneLeft ventricular cavity sizeEvidence of myocarditisHigh expressionLow ejection fractionVentricular cavity sizeAutosomal recessive disorderCardiac involvementEjection fractionInflammatory infiltrationCardiac statusCardiac outputFatal cardiotoxicityFatal myocarditisDosed miceInclusion criteriaHeart sectionsMouse modelDystrophic miceDystrophic pathologyFKRP mutationsPatientsDeleterious 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 cancersVariantsDNAMutationsCopiesCellsNoncoding variants alter GATA2 expression in rhombomere 4 motor neurons and cause dominant hereditary congenital facial paresis
Tenney A, Di Gioia S, Webb B, Chan W, de Boer E, Garnai S, Barry B, Ray T, Kosicki M, Robson C, Zhang Z, Collins T, Gelber A, Pratt B, Fujiwara Y, Varshney A, Lek M, Warburton P, Van Ryzin C, Lehky T, Zalewski C, King K, Brewer C, Thurm A, Snow J, Facio F, Narisu N, Bonnycastle L, Swift A, Chines P, Bell J, Mohan S, Whitman M, Staffieri S, Elder J, Demer J, Torres A, Rachid E, Al-Haddad C, Boustany R, Mackey D, Brady A, Fenollar-Cortés M, Fradin M, Kleefstra T, Padberg G, Raskin S, Sato M, Orkin S, Parker S, Hadlock T, Vissers L, van Bokhoven H, Jabs E, Collins F, Pennacchio L, Manoli I, Engle E. Noncoding variants alter GATA2 expression in rhombomere 4 motor neurons and cause dominant hereditary congenital facial paresis. Nature Genetics 2023, 55: 1149-1163. PMID: 37386251, PMCID: PMC10335940, DOI: 10.1038/s41588-023-01424-9.Peer-Reviewed Original ResearchConceptsSingle-nucleotide variantsGATA2 expressionHereditary congenital facial paresisBranchial motor neuronsLoss of GATA3Temporal gene regulationRare Mendelian diseasesChromosome 3q21-q22Autosomal dominant disorderSilencing in vitroNoncoding variationNoncoding variantsFacial paresisMendelian diseasesGene regulationRegulatory regionsHeterozygous duplicationDominant disorderMouse modelReporter expressionType 1Efferent neuronsMotor neuronsGATA2In vivo
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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