Revista de Medicina de Laboratorio 00077 / http://dx.doi.org/10.20960/revmedlab.00077

Resumen| PDF

Originales

Priorización de variantes de exoma mediante un sistema automático que emplea términos HPO

José Miguel Lezana Rosales, Diego Tuñón Le Poultel, Juan Francisco Quesada Espinosa, Emma Soengas Gonda, Ana Arteche-López, Carmen Palma Milla, Irene Gómez Manjón, María Isabel Álvarez-Mora, Rubén Pérez de la Fuente, María Teresa Sánchez Calvín, María José Gómez Rodríguez, Marta Moreno García

Prepublicado: 2021-08-06

Publicado: 2021-08-06

VOLUMEN 2, NÚM. 2, mayo-agosto (2021), PAG. 59-69

Logo Descargas Número de descargas: 6700 Logo Visitas Número de visitas: 3872 Citas Citas: 0

Compártelo:

Introducción: la secuenciación del exoma completo (WES) representa en la actualidad el estudio de primera elección para diagnosticar molecularmente enfermedades genéticas probablemente monogénicas con elevada heterogeneidad genética, o que solapan fenotípicamente con otras enfermedades y por ello requieren analizar multitud de genes para llegar al diagnóstico. Mediante WES se identifican miles de variantes, cuyo número final depende del tamaño de captura empleado y pipeline bioinformático aplicado. Esto requiere implementar técnicas de filtrado y priorización: una posibilidad es emplear paneles virtuales de genes (WES subpanelado), pero en paneles amplios aparecen multitud de variantes, lo que requiere una evaluación pormenorizada por el analista. El uso de términos de ontología de fenotipo (HPO) permite filtrar las variantes a través de asociaciones HPO-gen y establecer un sistema de priorización. El objetivo de este trabajo es el desarrollo de un sistema de priorización automática de variantes empleando términos HPO. Material y métodos: reanálisis de 33 pacientes diagnosticados previamente por WES subpanelado empleando un sistema de priorización basado en términos HPO y en las características inherentes a las variantes detectadas. Resultados: tras el reanálisis se determinó que las variantes que explicaban el fenotipo clínico se encontraban en las primeras posiciones de la lista de variantes priorizadas (media: 1,43; SD: 0,87). Conclusión: el sistema de priorización desarrollado permite la detección de variantes asociadas a las patologías estudiadas de forma más eficiente que por WES subpanelado, al encontrarse las variantes relacionadas con fenotipo ordenadas según su potencial patogenicidad. Este sistema representaría, por tanto, el primer abordaje en el análisis de variantes genéticas de WES.

Palabras Clave: Genética. Secuenciación masiva. Exoma. Fenotipo. Ontología de gen.

Descargar PDF

Bibliografía
Artículos Relacionados

Voelkerding K V., Dames SA, Durtschi JD. Next-generation sequencing:from basic research to diagnostics [Internet]. Vol. 55, Clinical Chemistry. Clin Chem; 2009 [cited 2020 Oct 15]. p. 641–58. Available from: https://pubmed.ncbi.nlm.nih.gov/19246620/
DOI: 10.1373/clinchem.2008.112789

Stark Z, Schofield D, Alam K, Wilson W, Mupfeki N, Macciocca I, et al. Prospective comparison of the cost-effectiveness of clinical whole-exome sequencing with that of usual care overwhelmingly supports early use and reimbursement. Genet Med [Internet]. 2017 Aug 26;19(8):867–74. Available from: http://www.nature.com/articles/gim2016221
DOI: 10.1038/gim.2016.221

Tan TY, Dillon OJ, Stark Z, Schofield D, Alam K, Shrestha R, et al. Diagnostic impact and cost-effectiveness of whole-exome sequencing for ambulant children with suspected monogenic conditions. JAMA Pediatr [Internet]. 2017 Sep 1 [cited 2020 Oct 15];171(9):855–62. Available from: https://pubmed.ncbi.nlm.nih.gov/28759686/
DOI: 10.1001/jamapediatrics.2017.1755

Payne K, Gavan SP, Wright SJ, Thompson AJ. Cost-effectiveness analyses of genetic and genomic diagnostic tests. Nat Rev Genet [Internet]. 2018;19(4):235–46. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29353875
DOI: 10.1038/nrg.2017.108

Masseroli M, Galati O, Manzotti M, Gibert K, Pinciroli F. Inherited disorder phenotypes: controlled annotation and statistical analysis for knowledge mining from gene lists. BMC Bioinformatics [Internet]. 2005;6(Suppl 4):S18. Available from: http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-6-S4-S18
DOI: 10.1186/1471-2105-6-S4-S18

Bajdik CD, Kuo B, Rusaw S, Jones S, Brooks-Wilson A. CGMIM: automated text-mining of Online Mendelian Inheritance in Man (OMIM) to identify genetically-associated cancers and candidate genes. BMC Bioinformatics [Internet]. 2005 Mar 29;6:78. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15796777
DOI: 10.1186/1471-2105-6-78

van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JAM. A text-mining analysis of the human phenome. Eur J Hum Genet [Internet]. 2006 May 22;14(5):535–42. Available from: http://www.nature.com/articles/5201585
DOI: 10.1038/sj.ejhg.5201585

Robinson PN, Köhler S, Bauer S, Seelow D, Horn D, Mundlos S. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am J Hum Genet [Internet]. 2008 Nov;83(5):610–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18950739
DOI: 10.1016/j.ajhg.2008.09.017

Köhler S, Carmody L, Vasilevsky N, Jacobsen JOB, Danis D, Gourdine JP, et al. Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Res [Internet]. 2019 Jan 8 [cited 2020 Oct 15];47(D1):D1018–27. Available from: https://pubmed.ncbi.nlm.nih.gov/30476213/

Köhler S. Improved ontology-based similarity calculations using a study-wise annotation model. Database [Internet]. 2018 Jan 1;2018. Available from: https://academic.oup.com/database/article/doi/10.1093/database/bay026/4953405
DOI: 10.1093/database/bay026

Mungall CJ, Washington NL, Nguyen-Xuan J, Condit C, Smedley D, Köhler S, et al. Use of model organism and disease databases to support matchmaking for human disease gene discovery. Hum Mutat [Internet]. 2015 Oct;36(10):979–84. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26269093
DOI: 10.1002/humu.22857

Mungall CJ, McMurry JA, Köhler S, Balhoff JP, Borromeo C, Brush M, et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res [Internet]. 2017;45(D1):D712–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27899636
DOI: 10.1093/nar/gkw1128

Ramoni RB, Mulvihill JJ, Adams DR, Allard P, Ashley EA, Bernstein JA, et al. The Undiagnosed Diseases Network: Accelerating Discovery about Health and Disease. Am J Hum Genet [Internet]. 2017;100(2):185–92. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28157539

Taruscio D, Groft SC, Cederroth H, Melegh B, Lasko P, Kosaki K, et al. Undiagnosed Diseases Network International (UDNI): White paper for global actions to meet patient needs. Mol Genet Metab [Internet]. 2015 Dec;116(4):223–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26596705
DOI: 10.1016/j.ymgme.2015.11.003

Lochmüller H, Torrent I Farnell J, Le Cam Y, Jonker AH, Lau LP, Baynam G, et al. The International Rare Diseases Research Consortium: Policies and Guidelines to maximize impact. Eur J Hum Genet [Internet]. 2017;25(12):1293–302. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29158551
DOI: 10.1038/s41431-017-0008-z

Boycott KM, Rath A, Chong JX, Hartley T, Alkuraya FS, Baynam G, et al. International Cooperation to Enable the Diagnosis of All Rare Genetic Diseases. Am J Hum Genet [Internet]. 2017 May 4;100(5):695–705. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28475856
DOI: 10.1016/j.ajhg.2017.04.003

Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet Med. 2015 May;17(5):405–24.
DOI: 10.1038/gim.2015.30

Roy S, Coldren C, Karunamurthy A, Kip NS, Klee EW, Lincoln SE, et al. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists [Internet]. Vol. 20, Journal of Molecular Diagnostics. Elsevier B.V.; 2018 [cited 2020 Oct 15]. p. 4–27. Available from: https://pubmed.ncbi.nlm.nih.gov/29154853/
DOI: 10.1016/j.jmoldx.2017.11.003

Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. 2013 Mar 16 [cited 2016 Mar 7];3. Available from: http://arxiv.org/abs/1303.3997

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods [Internet]. 2012 Apr 4 [cited 2020 Oct 15];9(4):357–9. Available from: https://www.nature.com/articles/nmeth.1923
DOI: 10.1038/nmeth.1923

Van der Auwera GA, Carneiro MO, Hartl C, Poplin R, del Angel G, Levy-Moonshine A, et al. From fastQ data to high-confidence variant calls: The genome analysis toolkit best practices pipeline. Curr Protoc Bioinforma. 2013;(SUPL.43).
DOI: 10.1002/0471250953.bi1110s43

Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, Mcewen R, et al. VarDict: A novel and versatile variant caller for next-generation sequencing in cancer research. Nucleic Acids Res [Internet]. 2016 Jun 20 [cited 2020 Oct 15];44(11):108. Available from: https://cghub.
DOI: 10.1093/nar/gkw227

Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res [Internet]. 2010 Sep;38(16):e164. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20601685
DOI: 10.1093/nar/gkq603

McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GRS, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol [Internet]. 2016 Dec 6;17(1):122. Available from: http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0974-4
DOI: 10.1186/s13059-016-0974-4

Auton A, Abecasis GR, Altshuler DM, Durbin RM, Bentley DR, Chakravarti A, et al. A global reference for human genetic variation. Nature [Internet]. 2015 Sep 30 [cited 2015 Sep 30];526(7571):68–74. Available from: http://dx.doi.org/10.1038/nature15393
DOI: 10.1038/nature15393

Karczewski KJ, Weisburd B, Thomas B, Solomonson M, Ruderfer DM, Kavanagh D, et al. The ExAC browser: displaying reference data information from over 60 000 exomes. Nucleic Acids Res [Internet]. 2017;45(D1):D840–5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27899611
DOI: 10.1093/nar/gkw971

Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature [Internet]. 2020 May 28 [cited 2020 Oct 15];581(7809):434–43. Available from: https://doi.org/10.1038/s41586-020-2308-7
DOI: 10.1038/s41586-020-2308-7

Jezela‐Stanek A, Ciara E, Jurkiewicz D, Kucharczyk M, Jędrzejowska M, Chrzanowska KH, et al. The phenotype‐driven computational analysis yields clinical diagnosis for patients with atypical manifestations of known intellectual disability syndromes. Mol Genet Genomic Med [Internet]. 2020 Sep 26;8(9). Available from: https://onlinelibrary.wiley.com/doi/10.1002/mgg3.1263
DOI: 10.1002/mgg3.1263

Robinson PN, Kohler S, Oellrich A, Wang K, Mungall CJ, Lewis SE, et al. Improved exome prioritization of disease genes through cross-species phenotype comparison. Genome Res [Internet]. 2014 Feb 1;24(2):340–8. Available from: http://genome.cshlp.org/cgi/doi/10.1101/gr.160325.113
DOI: 10.1101/gr.160325.113

Cipriani V, Pontikos N, Arno G, Sergouniotis PI, Lenassi E, Thawong P, et al. An Improved Phenotype-Driven Tool for Rare Mendelian Variant Prioritization: Benchmarking Exomiser on Real Patient Whole-Exome Data. Genes (Basel) [Internet]. 2020 Apr 23;11(4):460. Available from: https://www.mdpi.com/2073-4425/11/4/460
DOI: 10.3390/genes11040460