Specific phenotype semantics facilitate gene prioritization in clinical exome sequencing.


Journal

European journal of human genetics : EJHG
ISSN: 1476-5438
Titre abrégé: Eur J Hum Genet
Pays: England
ID NLM: 9302235

Informations de publication

Date de publication:
09 2019
Historique:
received: 16 05 2018
accepted: 15 04 2019
revised: 21 02 2019
pubmed: 6 5 2019
medline: 13 6 2020
entrez: 5 5 2019
Statut: ppublish

Résumé

Selection and prioritization of phenotype-centric variants remains a challenging part of variant analysis and interpretation in clinical exome sequencing. Phenotype-driven shortlisting of patient-specific gene lists can avoid missed diagnosis. Here, we analyzed the relevance of using primary Human Phenotype Ontology identifiers (HPO IDs) in prioritizing Mendelian disease genes across 30 in-house, 10 previously reported, and 10 recently published cases using three popular web-based gene prioritization tools (OMIMExplorer, VarElect & Phenolyzer). We assessed partial HPO-based gene prioritization using randomly chosen and top 10%, 30%, and 50% HPO IDs based on information content and found high variance within rank ratios across the former vs the latter. This signified that randomly selected less-specific HPO IDs for a given disease phenotype performed poorly by ranking probe gene farther away from the top rank. In contrast, the use of top 10%, 30%, and 50% HPO IDs individually could rank the probe gene among the top 1% in the ranked list of genes that was equivalent to the results when the full list of HPO IDs were used. Hence, we conclude that use of just the top 10% of HPO IDs chosen based on information content is sufficient for ranking the probe gene at top position. Our findings provide practical guidance for utilizing structured phenotype semantics and web-based gene-ranking tools to aid in identifying known as well unknown candidate gene associations in Mendelian disorders.

Identifiants

pubmed: 31053788
doi: 10.1038/s41431-019-0412-7
pii: 10.1038/s41431-019-0412-7
pmc: PMC6777628
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1389-1397

Références

Tetreault M, Bareke E, Nadaf J, Alirezaie N, Majewski J. Whole-exome sequencing as a diagnostic tool: current challenges and future opportunities. Expert Rev Mol Diagn. 2015;15:1–12.
doi: 10.1586/14737159.2015.1039516
Valencia CA, Husami A, Holle J, Johnson JA, Qian Y, Mathur A et al. Clinical impact and cost-effectiveness of whole exome sequencing as a diagnostic tool: a pediatric center’s experience. Front Pediatr 2015;3:67.
Soden SE, Saunders CJ, Willig LK, Farrow EG, Smith LD, Petrikin JE, et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci Transl Med. 2014;6:265ra168.
doi: 10.1126/scitranslmed.3010076
Srivastava S, Cohen JS, Vernon H, Barañano K, McClellan R, Jamal L, et al. Clinical whole-exome sequencing in child neurology practice. Ann Neurol. 2014;76:473–83.
doi: 10.1002/ana.24251
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. 2017;100:695–705.
Eilbeck K, Quinlan A, Yandell M. Settling the score: variant prioritization and Mendelian disease. Nat Rev Genet. 2017;18:599–12.
Seco CZ, Wesdorp M, Feenstra I, Pfundt R, Hehir-Kwa JY, Lelieveld SH et al. The diagnostic yield of whole-exome sequencing targeting a gene panel for hearing impairment in The Netherlands. 2017;25:308–14.
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. 2017;171:855–62.
doi: 10.1001/jamapediatrics.2017.1755
Lee H, Deignan JL, Dorrani N, Strom SP, Kantarci S, Quintero-Rivera F, et al. Clinical exome sequencing for genetic identification of rare mendelian disorders. JAMA. 2014;312:1880.
doi: 10.1001/jama.2014.14604
O’Donnell-Luria AH, Miller DT. A Clinician’s perspective on clinical exome sequencing. Hum Genet. 2016;135:643–54.
doi: 10.1007/s00439-016-1662-x
Tomar S, Sethi R, Sundar G, Quah TC, Quah BL, Lai PS. Mutation spectrum of RB1 mutations in retinoblastoma cases from Singapore with implications for genetic management and counselling. PLoS ONE. 2017;12:e0178776.
doi: 10.1371/journal.pone.0178776
Travaglini L, Aiello C, Stregapede F, D’Amico A, Alesi V, Ciolfi A et al. The impact of next-generation sequencing on the diagnosis of pediatric-onset hereditary spastic paraplegias: new genotype-phenotype correlations for rare HSP-related genes. Neurogenetics 2018;19:111–21.
Tranebjærg L, Strenzke N, Lindholm S, Rendtorff ND, Poulsen H, Khandelia H, et al. The CAPOS mutation in ATP1A3 alters Na/K-ATPase function and results in auditory neuropathy which has implications for management. Hum Genet. 2018;137:111–27.
doi: 10.1007/s00439-017-1862-z
Seaby EG, Pengelly RJ, Ennis S. Exome sequencing explained: a practical guide to its clinical application. Brief Funct Genomics. 2016;15:374–84.
doi: 10.1093/bfgp/elv054
Stelzer G, Plaschkes I, Oz-Levi D, Alkelai A, Olender T, Zimmerman S, et al. VarElect: the phenotype-based variation prioritizer of the GeneCards Suite. BMC Genomics. 2016;17):444.
doi: 10.1186/s12864-016-2722-2
Robinson PN. Deep phenotyping for precision medicine. Hum Mutat. 2012;33:777–80.
doi: 10.1002/humu.22080
Bone WP, Washington NL, Buske OJ, Adams DR, Davis J, Draper D, et al. Computational evaluation of exome sequence data using human and model organism phenotypes improves diagnostic efficiency. Genet Med. 2016;18:608–17.
doi: 10.1038/gim.2015.137
Zemojtel T, Kohler S, Mackenroth L, Jäger M, Hecht J, Krawitz P, et al. Effective diagnosis of genetic disease by computational phenotype analysis of the disease-associated genome. Sci Transl Med. 2014;6:252ra123–252ra123.
doi: 10.1126/scitranslmed.3009262
Masino AJ, Dechene ET, Dulik MC, Wilkens A, Spinner NB, Krantz ID, et al. Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology. BMC Bioinformatics. 2014;15:248.
doi: 10.1186/1471-2105-15-248
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. 2008;83:610–5.
doi: 10.1016/j.ajhg.2008.09.017
Cheung WA, Ouellette BFF, Wasserman WW. Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity. BMC Med Genomics. 2013;6:S3.
doi: 10.1186/1755-8794-6-S2-S3
Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32:267D–270.
doi: 10.1093/nar/gkh061
Rothwell DJ, Cote RA, Cordeau JP, Boisvert MA. Developing a standard data structure for medical language--the SNOMED proposal. Proc Symp Comput Appl Med Care 1993;695–9.
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. 2019;47:D1018–D1027.
doi: 10.1093/nar/gky1105
Li J, Lin X, Teng Y, Qi S, Xiao D, Zhang J, et al. A comprehensive evaluation of disease phenotype networks for gene prioritization. PLoS ONE. 2016;11:1–18.
Börnigen D, Tranchevent LC, Bonachela-Capdevila F, Devriendt K, De Moor B, De Causmaecker P, et al. An unbiased evaluation of gene prioritization tools. Bioinformatics. 2012;28:3081–8.
doi: 10.1093/bioinformatics/bts581
Tranchevent LC, Capdevila FB, Nitsch D, de Moor B, de Causmaecker P, Moreau Y. A guide to web tools to prioritize candidate genes. Brief Bioinformatics. 2011;12:22–32.
doi: 10.1093/bib/bbq007
Masoudi-Nejad A, Meshkin A. Gene prioritization: rationale, methodologies and algorithms. 66 (Publisher Springer International Publishing AG, Cham, Switzerland, 2014).
Yang H, Robinson PN, Wang K. Phenolyzer: phenotype-based prioritization of candidate genes for human diseases. Nat Methods. 2015;12:841–3.
doi: 10.1038/nmeth.3484
James RA, Campbell IM, Chen ES, Boone PM, Rao MA, Bainbridge MN, et al. A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics. Genome Med. 2016;8:13.
doi: 10.1186/s13073-016-0261-8
Koparir A, Karatas OF, Yuceturk B, Yuksel B, Bayrak AO, Gerdan OF, et al. Novel POC1A mutation in primordial dwarfism reveals new insights for centriole biogenesis. Hum Mol Genet. 2015;24:5378–87.
doi: 10.1093/hmg/ddv261
Izumi R, Niihori T, Takahashi T, Suzuki N, Tateyama M, Watanabe C, et al. Genetic profile for suspected dysferlinopathy identified by targeted next-generation sequencing. Neurol Genet. 2015;1:e36.
doi: 10.1212/NXG.0000000000000036
Knierim E, Gill E, Seifert F, von Moers A, Schuelke M. A recessive mutation in beta-IV-spectrin (SPTBN4) associates with congenital myopathy, neuropathy, and central deafness. Hum Genet. 2017;136:903–10.
doi: 10.1007/s00439-017-1814-7
Quintana AM, Yu H-C, Brebner A, Pupavac M, Geiger EA, Watson A, et al. Mutations in THAP11 cause an inborn error of cobalamin metabolism and developmental abnormalities. Hum Mol Genet. 2017;26:2838–49.
doi: 10.1093/hmg/ddx157
Monies D, Abouelhoda M, AlSayed M, Alhassnan Z, Alotaibi M, Kayyali H, et al. The landscape of genetic diseases in Saudi Arabia based on the first 1000 diagnostic panels and exomes. Hum Genet. 2017;136:921–39.
doi: 10.1007/s00439-017-1821-8
Rodriguez-Zabala M, Aza-Carmona M, Rivera-Pedroza CI, Belinchón A, Guerrero-Zapata I, Barraza-García J. et al. FGF9 mutation causes craniosynostosis along with multiple synostoses. Hum Mutat 2017;38:1471–76.
Wambach JA, Stettner GM, Haack TB, Writzl K, Škofljanec A, Maver A et al. Survival among children with “Lethal” congenital contracture syndrome 11 caused by novel mutations in the gliomedin gene (GLDN). Hum Mutat 2017;38:1477–84.
Le SV, Le PHT, Van LeTK, Kieu Huynh TT, Hang Do TT. A mutation in GABRB3 associated with Dravet syndrome. Am J Med Genet Part A. 2017;173:2126–31.
doi: 10.1002/ajmg.a.38282
Guala D, Sonnhammer ELL. A large-scale benchmark of gene prioritization methods. Sci Rep. 2017;7:46598.
doi: 10.1038/srep46598
Posey JE, Harel T, Liu P, Rosenfeld JA, James RA, Coban Akdemir ZH, et al. Resolution of disease phenotypes resulting from multilocus genomic variation. N Engl J Med. 2017;376:21–31.
doi: 10.1056/NEJMoa1516767
Baynam G, Walters M, Claes P, Kung S, LeSouef P, Dawkins H, et al. Phenotyping: targeting genotype’s rich cousin for diagnosis. J Paediatr Child Health. 2015;51:381–6.
doi: 10.1111/jpc.12705
Requena T, Gallego-Martinez A, Lopez-Escamez JA. A pipeline combining multiple strategies for prioritizing heterozygous variants for the identification of candidate genes in exome datasets. Hum Genomics. 2017;11:11.
doi: 10.1186/s40246-017-0107-5
Johannes Birgmeier A, Haeussler M, Deisseroth CA, Jagadeesh KA, Ratner AJ, Guturu H et al. AMELIE accelerates Mendelian patient diagnosis directly from the primary literature. bioRxiv 2017;1–23.
Köhler S, Schulz MH, Krawitz P, Bauer S, Dölken S, Ott CE, et al. Clinical diagnostics in human genetics with semantic similarity searches in ontologies. Am J Hum Genet. 2009;85:457–64.
doi: 10.1016/j.ajhg.2009.09.003

Auteurs

Swati Tomar (S)

Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore.

Raman Sethi (R)

Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore.

Poh San Lai (PS)

Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System (NUHS), 1E Kent Ridge Road, 119228, Singapore. paelaips@nus.edu.sg.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH