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 factorsVariantsSaturation 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 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 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 rate
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 databaseExomeVariationVariantsConsortium
2015
Effect 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, Gonzàlez-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