Clinical and technical assessment of MedExome vs. NGS panels in patients with suspected genetic disorders in Southwestern Ontario.
Alleles
Base Composition
Consanguinity
DNA Copy Number Variations
Exome
Gene Library
Genetic Diseases, Inborn
/ genetics
Genetic Variation
High-Throughput Nucleotide Sequencing
/ methods
Homozygote
Humans
INDEL Mutation
Molecular Diagnostic Techniques
/ methods
Ontario
Point Mutation
Sequence Alignment
Exome Sequencing
/ methods
Workflow
Journal
Journal of human genetics
ISSN: 1435-232X
Titre abrégé: J Hum Genet
Pays: England
ID NLM: 9808008
Informations de publication
Date de publication:
May 2021
May 2021
Historique:
received:
31
07
2020
accepted:
05
10
2020
revised:
24
09
2020
pubmed:
24
10
2020
medline:
17
8
2021
entrez:
23
10
2020
Statut:
ppublish
Résumé
The adaptation of a broad genomic sequencing approach in the clinical setting has been accompanied by considerations regarding the clinical utility, technical performance, and diagnostic yield compared to targeted genetic approaches. We have developed MedExome, an integrated framework for sequencing, variant calling (SNVs, Indels, and CNVs), and clinical assessment of ~4600 medically relevant genes. We compared the technical performance of MedExome with the whole-exome and targeted gene-panel sequencing, assessed the reasons for discordance, and evaluated the added clinical yield of MedExome in a cohort of unresolved subjects suspected of genetic disease. Our analysis showed that despite a higher average read depth in panels (3058 vs. 855), MedExome yielded full coverage of the enriched regions (>20X) and 99% variant concordance rate with panels. The discordance rate was associated with low-complexity regions, high-GC content, and low allele fractions, observed in both platforms. MedExome yielded full sensitivity in detecting clinically actionable variants, and the assessment of 138 patients with suspected genetic conditions resulted in 76 clinical reports (31 full [22.1%], 3 partial, and 42 uncertain/possible molecular diagnoses). MedExome sequencing has comparable performance in variant detection to gene panels. Added diagnostic yield justifies expanded implementation of broad genomic approaches in unresolved patients; however, cost-benefit and health systems impact warrants assessment.
Identifiants
pubmed: 33093641
doi: 10.1038/s10038-020-00860-3
pii: 10.1038/s10038-020-00860-3
doi:
Types de publication
Comparative Study
Evaluation Study
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
451-464Références
Rehm HL. Disease-targeted sequencing: a cornerstone in the clinic. Nat Rev Genet. 2013;14:295.
doi: 10.1038/nrg3463
Shashi V, McConkie-Rosell A, Rosell B, Schoch K, Vellore K, McDonald M, et al. The utility of the traditional medical genetics diagnostic evaluation in the context of next-generation sequencing for undiagnosed genetic disorders. Genet Med. 2014;16:176–82.
doi: 10.1038/gim.2013.99
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;312:1880–7.
doi: 10.1001/jama.2014.14604
Yavarna T, Al-Dewik N, Al-Mureikhi M, Ali R, Al-Mesaifri F, Mahmoud L, et al. High diagnostic yield of clinical exome sequencing in Middle Eastern patients with Mendelian disorders. Hum Genet. 2015;134:967–80.
doi: 10.1007/s00439-015-1575-0
Makrythanasis P, Nelis M, Santoni FA, Guipponi M, Vannier A, Béna F, et al. Diagnostic exome sequencing to elucidate the genetic basis of likely recessive disorders in consanguineous families. Hum Mutat. 2014;35:1203–10.
doi: 10.1002/humu.22617
Anagnostopoulou K, Pons R, Dinopoulos A, Vartzelis G, Gika A, Skouteli E, et al. Whole exome sequencing in the diagnosis of neuropaediatric diseases. Eur J Paediatr Neuro. 2017;21:e56.
doi: 10.1016/j.ejpn.2017.04.908
Stivers T, Timmermans S. The actionability of exome sequencing testing results. Socio Health Illn. 2017;39:1542–56.
doi: 10.1111/1467-9566.12614
Levy MA, Santos S, Kerkhof J, Stuart A, Aref‐Eshghi E, Guo F, et al. Implementation of an NGS‐based sequencing and gene fusion panel for clinical screening of patients with suspected hematologic malignancies. Eur J Hematol. 2019;103:178–89.
doi: 10.1111/ejh.13272
Aref-Eshghi E, Kerkhof J, Pedro VP, France GroupeDI, Barat-Houari M, Ruiz-Pallares N, et al. Evaluation of DNA methylation episignatures for diagnosis and phenotype correlations in 42 Mendelian neurodevelopmental disorders. Am J Hum Genet. 2020;106:356–70.
doi: 10.1016/j.ajhg.2020.01.019
Lanktree MB, Sadikovic B, Waye JS, Levstik A, Lanktree BB, Yudin J, et al. Clinical evaluation of a hemochromatosis next‐generation sequencing gene panel. Eur J Hematol. 2017;98:228–34.
doi: 10.1111/ejh.12820
Schenkel LC, Kerkhof J, Stuart A, Reilly J, Eng B, Woodside C, et al. Clinical next-generation sequencing pipeline outperforms a combined approach using sanger sequencing and multiplex ligationdependent probe amplification in targeted gene panel analysis. J Mol Diagn. 2016;18:657–67.
doi: 10.1016/j.jmoldx.2016.04.002
Kerkhof J, Schenkel LC, Reilly J, McRobbie S, Aref-Eshghi E, Stuart A, et al. Clinical validation of copy number variant detection from targeted next-generation sequencing panels. J Mol Diagn. 2017;19:905–20.
doi: 10.1016/j.jmoldx.2017.07.004
Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv. 2013;1303:3997.
Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. 1000 Genome Project Data Processing Subgroup. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25:2078–9.
doi: 10.1093/bioinformatics/btp352
Lai Z, Markovets A, Ahdesmaki M, Chapman B, Hofmann O, McEwen R, et al. VarDict: a novel and versatile variant caller for nextgeneration sequencing in cancer research. Nucleic Acids Res. 2016;44:e108.
doi: 10.1093/nar/gkw227
Sandmann S, De Graaf AO, Karimi M, Van Der Reijden BA, Hellström-Lindberg E, Jansen JH, et al. Evaluating variant calling tools for non-matched next-generation sequencing data. Sci Rep. 2017;7:43169.
doi: 10.1038/srep43169
Mandelker D, Schmidt RJ, Ankala A, Gibson KM, Bowser M, Sharma H, et al. Navigating highly homologous genes in a molecular diagnostic setting: a resource for clinical next-generation sequencing. Genet Med. 2016;18:1282.
doi: 10.1038/gim.2016.58
Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25:2865–71.
doi: 10.1093/bioinformatics/btp394
Pajusalu S, Pfundt R, Vissers LE, Kwint MP, Reimand T, Õunap K, et al. Identifying long indels in exome sequencing data of patients with intellectual disability. bioRxiv. 2018;244756.
Plagnol V, Curtis J, Epstein M, Mok KY, Stebbings E, Grigoriadou S, et al. A robust model for read count data in exome sequencing experiments and implications for copy number variant calling. Bioinformatics. 2012;28:2747–54.
doi: 10.1093/bioinformatics/bts526
Ellingford JM, Campbell C, Barton S, Bhaskar S, Gupta S, Taylor RL, et al. Validation of copy number variation analysis for next-generation sequencing diagnostics. Eur J Hum Genet. 2017;25:719–24.
doi: 10.1038/ejhg.2017.42
Sund KL, Rehder CW. Detection and reporting of homozygosity associated with consanguinity in the clinical laboratory. Hum Herd. 2014;77:217–24.
doi: 10.1159/000362448
Sund KL, Zimmerman SL, Thomas C, Mitchell AL, Prada CE, Grote L, et al. Regions of homozygosity identified by SNP microarray analysis aid in the diagnosis of autosomal recessive disease and incidentally detect parental blood relationships. Genet Med. 2013;15:70.
doi: 10.1038/gim.2012.94
Morgan M, Pagès H, Obenchain V, Hayden N. Rsamtools: binary alignment (BAM), FASTA, variant call (BCF), and tabix file import. R package version 2.4.0. http://bioconductor.org/packages/Rsamtools . Accessed 19 Sep 2020.
Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y, et al. Origins and functional impact of copy number variation in the human genome. Nature. 2010;464:704.
doi: 10.1038/nature08516
Girdea M, Dumitriu S, Fiume M, Bowdin S, Boycott KM, Chénier S, et al. PhenoTips: patient phenotyping software for clinical and research use. Hum Mutat. 2013;34:1057–65.
doi: 10.1002/humu.22347
Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J, et al. ACMG Laboratory Quality Assurance Committee. 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;17:405.
doi: 10.1038/gim.2015.30
Wright CF, Prigmore E, Rajan D, Handsaker J, McRae J, Kaplanis J, 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
Yang R, Nelson AC, Henzler C, Thyagarajan B, Silverstein KA. ScanIndel: a hybrid framework for indel detection via gapped alignment, split reads and de novo assembly. Genome Med. 2015;7:127.
doi: 10.1186/s13073-015-0251-2
Monroe GR, Frederix GW, Savelberg SM, De Vries TI, Duran KJ, Van Der Smagt JJ, et al. Effectiveness of whole-exome sequencing and costs of the traditional diagnostic trajectory in children with intellectual disability. Genet Med. 2016;18:949.
doi: 10.1038/gim.2015.200
Timmermans S, Tietbohl C, Skaperdas E. Narrating uncertainty: variants of uncertain significance (VUS) in clinical exome sequencing. BioSocieties. 2017;12:439–58.
doi: 10.1057/s41292-016-0020-5
Cummings BB, Marshall JL, Tukiainen T, Lek M, Donkervoort S, Foley AR, et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci Transl Med. 2017;9:eaal5209.
doi: 10.1126/scitranslmed.aal5209
Aref-Eshghi E, Bend EG, Colaiacovo S, Caudle M, Chakrabarti R, Napier M, et al. Diagnostic utility of genome-wide DNA methylation testing in genetically unsolved individuals with suspected hereditary conditions. Am J Hum Genet. 2019;104:685–700.
doi: 10.1016/j.ajhg.2019.03.008
Aref‐Eshghi E, Bourque DK, Kerkhof J, Carere DA, Ainsworth P, Sadikovic B, et al. Genome‐wide DNA methylation and RNA analyses enable reclassification of two variants of uncertain significance in a patient with clinical Kabuki syndrome. Hum Mutat. 2019;40:1684–9.
doi: 10.1002/humu.23833
Aref-Eshghi E, Schenkel LC, Lin H, Skinner C, Ainsworth P, Paré G, et al. Clinical validation of a genome-wide DNA methylation assay for molecular diagnosis of imprinting disorders. J Mol Diagn. 2017;19:848–56.
doi: 10.1016/j.jmoldx.2017.07.002
Aref-Eshghi E, Rodenhiser DI, Schenkel LC, Lin H, Skinner C, Ainsworth P, et al. Genomic DNA methylation signatures enable concurrent diagnosis and clinical genetic variant classification in neurodevelopmental syndromes. Am J Hum Genet. 2018;102:156–74.
doi: 10.1016/j.ajhg.2017.12.008
Aref-Eshghi E, Schenkel LC, Lin H, Skinner C, Ainsworth P, Paré G, et al. The defining DNA methylation signature of Kabuki syndrome enables functional assessment of genetic variants of unknown clinical significance. Epigenetics. 2017;12:923–33.
doi: 10.1080/15592294.2017.1381807
Bend EG, Aref-Eshghi E, Everman DB, Rogers RC, Cathey SS, Prijoles EJ, et al. Gene domain-specific DNA methylation episignatures highlight distinct molecular entities of ADNP syndrome. Clin Epigenet. 2019;11:64.
doi: 10.1186/s13148-019-0658-5
Aref-Eshghi E, McGee JD, Pedro VP, Kerkhof J, Stuart A, Ainsworth PJ, et al. Genetic and epigenetic profiling of BRCA1/2 in ovarian tumors reveals additive diagnostic yield and evidence of a genomic BRCA1/2 DNA methylation signature. J Hum Genet. 2020. https://doi.org/10.1038/s10038-020-0780-4 .
Schenkel LC, Aref-Eshghi E, Skinner C, Ainsworth P, Lin H, Paré G, et al. Peripheral blood epi-signature of Claes-Jensen syndrome enables sensitive and specific identification of patients and healthy carriers with pathogenic mutations in KDM5C. Clin Epigenet. 2018;10:21.
doi: 10.1186/s13148-018-0453-8
Krzyzewska IM, Maas SM, Henneman P, vd Lip K, Venema A, Baranano K, et al. A genome-wide DNA methylation signature for SETD1B-related syndrome. Clin Epigenet. 2019;11:156.
doi: 10.1186/s13148-019-0749-3
Ciolfi A, Aref-Eshghi E, Pizzi S, Pedace L, Miele E, Kerkhof J, et al. Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature. Clin Epigenet. 2020;12:7.
doi: 10.1186/s13148-019-0804-0
Aref-Eshghi E, Schenkel LC, Ainsworth P, Lin H, Rodenhiser DI, Cutz JC, et al. Genomic DNA methylation-derived algorithm enables accurate detection of malignant prostate tissues. Front Oncol. 2018;8:100.
doi: 10.3389/fonc.2018.00100
Aref-Eshghi E, Bend EG, Hood RL, Schenkel LC, Carere DA, Chakrabarti R, et al. BAFopathies’ DNA methylation epi-signatures demonstrate diagnostic utility and functional continuum of Coffin– Siris and Nicolaides–Baraitser syndromes. Nat Commun. 2018;9:4885.
doi: 10.1038/s41467-018-07193-y