Evaluating variants classified as pathogenic in ClinVar in the DDD Study.
developmental disorders
exome sequencing
genomic medicine
reanalysis
variant interpretation
Journal
Genetics in medicine : official journal of the American College of Medical Genetics
ISSN: 1530-0366
Titre abrégé: Genet Med
Pays: United States
ID NLM: 9815831
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
30
07
2020
accepted:
14
10
2020
revised:
14
10
2020
pubmed:
6
11
2020
medline:
4
6
2021
entrez:
5
11
2020
Statut:
ppublish
Résumé
Automated variant filtering is an essential part of diagnostic genome-wide sequencing but may generate false negative results. We sought to investigate whether some previously identified pathogenic variants may be being routinely excluded by standard variant filtering pipelines. We evaluated variants that were previously classified as pathogenic or likely pathogenic in ClinVar in known developmental disorder genes using exome sequence data from the Deciphering Developmental Disorders (DDD) study. Of these ClinVar pathogenic variants, 3.6% were identified among 13,462 DDD probands, and 1134/1352 (83.9%) had already been independently communicated to clinicians using DDD variant filtering pipelines as plausibly pathogenic. The remaining 218 variants failed consequence, inheritance, or other automated variant filters. Following clinical review of these additional variants, we were able to identify 112 variants in 107 (0.8%) DDD probands as potential diagnoses. Lower minor allele frequency (<0.0005%) and higher gold star review status in ClinVar (>1 star) are good predictors of a previously identified variant being plausibly diagnostic for developmental disorders. However, around half of previously identified pathogenic variants excluded by automated variant filtering did not appear to be disease-causing, underlining the continued need for clinical evaluation of candidate variants as part of the diagnostic process.
Identifiants
pubmed: 33149276
doi: 10.1038/s41436-020-01021-9
pii: S1098-3600(21)04959-5
pmc: PMC7935711
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
571-575Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Medical Research Council
ID : MC_UU_00007/3
Pays : United Kingdom
Organisme : Department of Health
ID : HICF-1009-003
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 098051
Pays : United Kingdom
Références
Boycott KM, Vanstone MR, Bulman DE, MacKenzie AE. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat Rev Genet. 2013;14:681–691.
doi: 10.1038/nrg3555
Boycott KM, Rath A, Chong JX, et al. International cooperation to enable the diagnosis of all rare genetic diseases. Am J Hum Genet. 2017;100:695–705.
doi: 10.1016/j.ajhg.2017.04.003
Clark MM, Stark Z, Farnaes L, et al. Meta-analysis of the diagnostic and clinical utility of genome and exome sequencing and chromosomal microarray in children with suspected genetic diseases. NPJ Genom Med. 2018;3:16.
doi: 10.1038/s41525-018-0053-8
Karczewski KJ, Francioli LC, Tiao G, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581:434–443.
Jalali Sefid Dashti M, Gamieldien J. A practical guide to filtering and prioritizing genetic variants. BioTechniques. 2017;62:18–30.
doi: 10.2144/000114492
Liu P, Meng L, Normand EA, et al. Reanalysis of clinical exome sequencing data. N Engl J Med. 2019;380:2478–2480.
doi: 10.1056/NEJMc1812033
Wright CF, McRae JF, Clayton S, et al. Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders. Genet Med. 2018;20:1216–1223.
doi: 10.1038/gim.2017.246
Deciphering Developmental Disorders Study. Large-scale discovery of novel genetic causes of developmental disorders. Nature. 2015;519:223–228.
doi: 10.1038/nature14135
Wright CF, Fitzgerald TW, Jones WD, et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet. 2015;385:1305–1314.
doi: 10.1016/S0140-6736(14)61705-0
Firth HV, Richards SM, Bevan AP, et al. DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am J Hum Genet. 2009;84:524–533.
doi: 10.1016/j.ajhg.2009.03.010
Köhler S, Doelken SC, Mungall CJ, et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 2014;42:D966–D974.
doi: 10.1093/nar/gkt1026
Thormann A, Halachev M, McLaren W, et al. Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP. Nat Commun. 2019;10:2373.
doi: 10.1038/s41467-019-10016-3
Harrison SM, Riggs ER, Maglott DR, et al. Using clinvar as a resource to support variant interpretation. Curr Protoc Hum Genet. 2016;89:8.16.1–8.16.23.
Lord J, Gallone G, Short PJ, et al. Pathogenicity and selective constraint on variation near splice sites. Genome Res. 2019;29:159–170.
doi: 10.1101/gr.238444.118
Wright CF, Prigmore E, Rajan D, et al. Clinically-relevant postzygotic mosaicism in parents and children with developmental disorders in trio exome sequencing data. Nat Commun. 2019;10:2985.
doi: 10.1038/s41467-019-11059-2
Doherty ES, Lacbawan F, Hadley DW, et al. Muenke syndrome (FGFR3-related craniosynostosis): expansion of the phenotype and review of the literature. Am J Med Genet A. 2007;143A:3204–3215.
doi: 10.1002/ajmg.a.32078
Cassa CA, Tong MY, Jordan DM. Large numbers of genetic variants considered to be pathogenic are common in asymptomatic individuals. Hum Mutat. 2013;34:1216–1220.
doi: 10.1002/humu.22375
Shah N, Hou Y-CC, Yu H-C, et al. Identification of misclassified clinvar variants via disease population prevalence. Am J Hum Genet. 2018;102:609–619.
doi: 10.1016/j.ajhg.2018.02.019
Dibbens LM, Tarpey PS, Hynes K, et al. X-linked protocadherin 19 mutations cause female-limited epilepsy and cognitive impairment. Nat Genet. 2008;40:776–781.
doi: 10.1038/ng.149
Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248–249.
doi: 10.1038/nmeth0410-248