Evaluating the Performance of a Clinical Genome Sequencing Program for Diagnosis of Rare Genetic Disease, Seen Through the Lens of Craniosynostosis

Overview

Journal Genet Med

Publisher Elsevier

Specialty Genetics

Date 2021 Aug 25

PMID 34429528

Citations 15

Authors

Zerin Hyder

Eduardo Calpena

Yang Pei

Rebecca S Tooze

Helen Brittain

Stephen R F Twigg

Deirdre Cilliers

Jenny E V Morton

Emma McCann

Astrid Weber

Louise C Wilson

Andrew G L Douglas

Ruth McGowan

Anna Need

Andrew Bond

Ana Lisa Taylor Tavares

Ellen R A Thomas

Susan L Hill

Zandra C Deans

Freya Boardman-Pretty

Mark Caulfield

Richard H Scott

Andrew O M Wilkie

Affiliations

Soon will be listed here.

Abstract

Purpose: Genome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease.

Methods: GS data from 114 probands with craniosynostosis and their relatives (314 samples), negative on routine genetic testing, were scrutinized by a specialized research team, and diagnoses compared with those made by 100kGP.

Results: Sixteen likely pathogenic/pathogenic variants were identified by 100kGP. Eighteen additional likely pathogenic/pathogenic variants were identified by the research team, indicating that for craniosynostosis, 100kGP panels had a diagnostic sensitivity of only 47%. Measures that could have augmented diagnoses were improved calling of existing panel genes (+18% sensitivity), review of updated panels (+12%), comprehensive analysis of de novo small variants (+29%), and copy-number/structural variants (+9%). Recent NHS England recommendations that partially incorporate these measures should achieve 85% overall sensitivity (+38%).

Conclusion: GS identified likely pathogenic/pathogenic variants in 29.8% of previously undiagnosed patients with craniosynostosis. This demonstrates the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical GS.

Citing Articles

Vedovato-Dos-Santos J, Tooze R, Sithambaram S, McCann E, Alanay Y, Dogan O Eur J Hum Genet. 2025; .

PMID: 40033098 DOI: 10.1038/s41431-025-01824-x.

Cardiomyopathies in 100,000 genomes project: interval evaluation improves diagnostic yield and informs strategies for ongoing gene discovery.

Josephs K, Seaby E, May P, Theotokis P, Yu J, Andreou A Genome Med. 2024; 16(1):125.

PMID: 39472908 PMC: 11520845. DOI: 10.1186/s13073-024-01390-9.

A Comparison of Structural Variant Calling from Short-Read and Nanopore-Based Whole-Genome Sequencing Using Optical Genome Mapping as a Benchmark.

Pei Y, Tanguy M, Giess A, Dixit A, Wilson L, Gibbons R Genes (Basel). 2024; 15(7).

PMID: 39062704 PMC: 11276380. DOI: 10.3390/genes15070925.

The impact of inversions across 33,924 families with rare disease from a national genome sequencing project.

Pagnamenta A, Yu J, Walker S, Noble A, Lord J, Dutta P Am J Hum Genet. 2024; 111(6):1140-1164.

PMID: 38776926 PMC: 11179413. DOI: 10.1016/j.ajhg.2024.04.018.

The phenotype of MEGF8-related Carpenter syndrome (CRPT2) is refined through the identification of eight new patients.

Watts L, Bertoli M, Attie-Bitach T, Roux N, Rausell A, Paschal C Eur J Hum Genet. 2024; 32(7):864-870.

PMID: 38760421 PMC: 11220001. DOI: 10.1038/s41431-024-01624-9.

References

Tonne E, Due-Tonnessen B, Mero I, Straume Wiig U, Kulseth M, Vigeland M . Benefits of clinical criteria and high-throughput sequencing for diagnosing children with syndromic craniosynostosis. Eur J Hum Genet. 2020; 29(6):920-929. PMC: 8187391. DOI: 10.1038/s41431-020-00788-4. View

Smedley D, Smith K, Martin A, Thomas E, McDonagh E, Cipriani V . 100,000 Genomes Pilot on Rare-Disease Diagnosis in Health Care - Preliminary Report. N Engl J Med. 2021; 385(20):1868-1880. PMC: 7613219. DOI: 10.1056/NEJMoa2035790. View

Tolchin D, Yeager J, Prasad P, Dorrani N, Russi A, Martinez-Agosto J . De Novo SOX6 Variants Cause a Neurodevelopmental Syndrome Associated with ADHD, Craniosynostosis, and Osteochondromas. Am J Hum Genet. 2020; 106(6):830-845. PMC: 7273536. DOI: 10.1016/j.ajhg.2020.04.015. View

Karczewski K, Francioli L, Tiao G, Cummings B, Alfoldi J, Wang Q . The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020; 581(7809):434-443. PMC: 7334197. DOI: 10.1038/s41586-020-2308-7. View

Thormann A, Halachev M, McLaren W, Moore D, Svinti V, Campbell A . Flexible and scalable diagnostic filtering of genomic variants using G2P with Ensembl VEP. Nat Commun. 2019; 10(1):2373. PMC: 6542828. DOI: 10.1038/s41467-019-10016-3. View

Justice C, Yagnik G, Kim Y, Peter I, Jabs E, Erazo M . A genome-wide association study identifies susceptibility loci for nonsyndromic sagittal craniosynostosis near BMP2 and within BBS9. Nat Genet. 2012; 44(12):1360-4. PMC: 3736322. DOI: 10.1038/ng.2463. View

Gilissen C, Hehir-Kwa J, Tjwan Thung D, van de Vorst M, van Bon B, Willemsen M . Genome sequencing identifies major causes of severe intellectual disability. Nature. 2014; 511(7509):344-7. DOI: 10.1038/nature13394. View

Justice C, Cuellar A, Bala K, Sabourin J, Cunningham M, Crawford K . A genome-wide association study implicates the BMP7 locus as a risk factor for nonsyndromic metopic craniosynostosis. Hum Genet. 2020; 139(8):1077-1090. PMC: 7415527. DOI: 10.1007/s00439-020-02157-z. View

Turro E, Astle W, Megy K, Graf S, Greene D, Shamardina O . Whole-genome sequencing of patients with rare diseases in a national health system. Nature. 2020; 583(7814):96-102. PMC: 7610553. DOI: 10.1038/s41586-020-2434-2. View

10.

Stavropoulos D, Merico D, Jobling R, Bowdin S, Monfared N, Thiruvahindrapuram B . Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine. NPJ Genom Med. 2017; 1. PMC: 5447450. DOI: 10.1038/npjgenmed.2015.12. View

11.

Johnson D, Wilkie A . Craniosynostosis. Eur J Hum Genet. 2011; 19(4):369-76. PMC: 3060331. DOI: 10.1038/ejhg.2010.235. View

12.

Martin A, Williams E, Foulger R, Leigh S, Daugherty L, Niblock O . PanelApp crowdsources expert knowledge to establish consensus diagnostic gene panels. Nat Genet. 2019; 51(11):1560-1565. DOI: 10.1038/s41588-019-0528-2. View

13.

Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Kallberg M . Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2015; 32(8):1220-2. DOI: 10.1093/bioinformatics/btv710. View

14.

Twigg S, Wilkie A . A Genetic-Pathophysiological Framework for Craniosynostosis. Am J Hum Genet. 2015; 97(3):359-77. PMC: 4564941. DOI: 10.1016/j.ajhg.2015.07.006. View

15.

Collins R, Brand H, Karczewski K, Zhao X, Alfoldi J, Francioli L . A structural variation reference for medical and population genetics. Nature. 2020; 581(7809):444-451. PMC: 7334194. DOI: 10.1038/s41586-020-2287-8. View

16.

. Prevalence and architecture of de novo mutations in developmental disorders. Nature. 2017; 542(7642):433-438. PMC: 6016744. DOI: 10.1038/nature21062. View

17.

Richards S, Aziz N, Bale S, Bick D, Das S, Gastier-Foster J . 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(5):405-24. PMC: 4544753. DOI: 10.1038/gim.2015.30. View

18.

Calpena E, Cuellar A, Bala K, Swagemakers S, Koelling N, McGowan S . SMAD6 variants in craniosynostosis: genotype and phenotype evaluation. Genet Med. 2020; 22(9):1498-1506. PMC: 7462747. DOI: 10.1038/s41436-020-0817-2. View

19.

Mentzer S, Sundberg J, Awgulewitsch A, Chao H, Carpenter D, Zhang W . The mouse hairy ears mutation exhibits an extended growth (anagen) phase in hair follicles and altered Hoxc gene expression in the ears. Vet Dermatol. 2008; 19(6):358-67. PMC: 3061202. DOI: 10.1111/j.1365-3164.2008.00709.x. View

20.

Roller E, Ivakhno S, Lee S, Royce T, Tanner S . Canvas: versatile and scalable detection of copy number variants. Bioinformatics. 2016; 32(15):2375-7. DOI: 10.1093/bioinformatics/btw163. View