Monkol Lek, PhD
Assistant Professor of GeneticsDownloadHi-Res Photo
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Assistant Professor of Genetics
Biography
Monkol received an undergraduate degree in Engineering (Computer Engineering) in 2000 at the University of New South Wales (UNSW) and then worked for IBM for 3.5 years. He returned to UNSW and completed undergraduate degrees in Science (Physiology) and Engineering (Bioinformatics) and received the University Medal in 2007. He completed his PhD (Medicine) at the University of Sydney in 2012 with the thesis topic: Functional differences between alpha-actinin-2 and alpha-actinin-3. Monkol did his post-doctoral training in Daniel MacArthur’s lab based at Massachusetts General Hospital, Harvard Medical School and the Broad Institute.
He was the lead author/analyst for the Exome Aggregation Consortium (ExAC) project that was published in Nature 2016. He went on to lead the NIH funded Broad Center for Mendelian Genomics (CMG) analysis team. As lead analyst, he oversaw the analysis strategy for all major CMG collaborations and organized monthly meetings to foster sharing of new methods and analysis amongst the rare disease community. He also coordinated the data processing and preliminary analysis of NIH Gabriella Miller Kids First (GMKF) cohorts sequenced or reprocessed at the Broad Institute.
Monkol has a strong passion for rare muscle disease research as a patient with Limb Girdle Muscular dystrophy (LGMD2G). During his time in the Broad Institute, he lead the exome/genome analysis of MYOSEQ (European cohort of >1000 LGMD patients) and SeqNMD (an international consortium of undiagnosed rare muscle diseases) which has resulted in novel disease gene discovery.
Appointments
Genetics
Assistant ProfessorPrimary
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Education & Training
- Senior Research Fellow
- Broad Institute of MIT and Harvard (2017)
- Post-doctoral Fellow
- Harvard Medical School (2016)
- PhD
- University of Sydney, Medicine (2012)
- BSc
- University of New South Wales, Physiology (2007)
- BE
- University of New South Wales, Bioinformatics (2007)
- BE
- University of New South Wales, Computer Engineering (2001)
Research
Overview
Medical Subject Headings (MeSH)
Genetics; Genetics, Medical; Genomics; Neuromuscular Diseases
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Lek Lab
Research at a Glance
Yale Co-Authors
Frequent collaborators of Monkol Lek's published research.
Publications Timeline
A big-picture view of Monkol Lek's research output by year.
Research Interests
Research topics Monkol Lek is interested in exploring.
Shushu Huang, MD, PhD
Nicole J. Lake, PhD
Justin Cohen, PhD
14Publications
10,767Citations
Publications
2024
Quantifying 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, 1-8. PMID: 39415008, DOI: 10.1038/s41586-024-08048-x.Peer-Reviewed Original ResearchAltmetricConceptsMitochondrial 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 datasetsSaturation 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 PMID: 39326416, DOI: 10.1016/j.cell.2024.08.047.Peer-Reviewed Original ResearchCitationsAltmetricConceptsDisease-related genesDisease-causing genetic variantsGenome-wide resolutionMutation scanning methodsSingle-nucleotide variantsDeep mutational scanning methodFunctional assaysDisease genesComputational predictorsSaturation mutagenesisHuman geneticsGenetic variantsGenesVariantsSmurfAssayMutagenesisLARGE1GeneticsDisease severityHigh-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 ResearchCitationsMeSH Keywords and ConceptsConceptsDeep 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 rate
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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMTOR-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 ResearchConceptsGene 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 mutationsPatients
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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsTranscriptome 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenetic 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsGenic 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsMuscle 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsPrion 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
Clinical Trials
Current Trials
Pediatric Genomics Discovery Program (PGDP)
HIC ID1411014977RoleSub InvestigatorPrimary Completion Date12/31/2023Recruiting ParticipantsGenderBoth
Academic Achievements & Community Involvement
honor Development Grant
National AwardMuscular Dystrophy AssociationDetails01/01/2016United Stateshonor CJ Martin Fellowship
International AwardAustralian National Health and Medical Research Council (NHMRC)Details06/01/2013Australiahonor Sir Keith Murdoch Fellowship
International AwardAustralian American AssociationDetails08/01/2012Australiahonor Australian Post-graduate Award
International AwardUniversity of SydneyDetails03/01/2009Australiahonor University Medal
International AwardUniversity of New South WalesDetails03/01/2008Australia
News
News
- September 10, 2024
NIH Recognizes Yale’s Expertise in the Genetics of Rare Diseases
- June 07, 2024
Polycystic Kidney Disease Treatment: Gene Target Identified at Yale
- May 20, 2022
Single Cell Analysis Technologies Help Generate Unprecedented Maps of Disease
- August 31, 2021
Yale Geneticist With Rare Disease to Build Pediatric Cell Atlas of Skeletal Muscle
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The Anlyan Center
300 Cedar Street
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