Whole Genome Sequencing Expands Diagnostic Utility and Improves Clinical Management in Pediatric Medicine

Dimitri J Stavropoulos, Daniele Merico, Rebekah Jobling, Sarah Bowdin, Nasim Monfared, Bhooma Thiruvahindrapuram, Thomas Nalpathamkalam, Giovanna Pellecchia, Ryan K C Yuen, Michael J Szego, Robin Z Hayeems, Randi Zlotnik Shaul, Michael Brudno, Marta Girdea, Brendan Frey, Babak Alipanahi, Sohnee Ahmed, Riyana Babul-Hirji, Ramses Badilla Porras, Melissa T Carter, Lauren Chad, Ayeshah Chaudhry, David Chitayat, Soghra Jougheh Doust, Cheryl Cytrynbaum, Lucie Dupuis, Resham Ejaz, Leona Fishman, Andrea Guerin, Bita Hashemi, Mayada Helal, Stacy Hewson, Michal Inbar-Feigenberg, Peter Kannu, Natalya Karp, Raymond Kim, Jonathan Kronick, Eriskay Liston, Heather MacDonald, Saadet Mercimek-Mahmutoglu, Roberto Mendoza-Londono, Enas Nasr, Graeme Nimmo, Nicole Parkinson, Nada Quercia, Julian Raiman, Maian Roifman, Andreas Schulze, Andrea Shugar, Cheryl Shuman, Pierre Sinajon, Komudi Siriwardena, Rosanna Weksberg, Grace Yoon, Chris Carew, Raith Erickson, Richard A Leach, Robert Klein, Peter N Ray, M Stephen Meyn, Stephen W Scherer, Ronald D Cohn, Christian R Marshall, Dimitri J Stavropoulos, Daniele Merico, Rebekah Jobling, Sarah Bowdin, Nasim Monfared, Bhooma Thiruvahindrapuram, Thomas Nalpathamkalam, Giovanna Pellecchia, Ryan K C Yuen, Michael J Szego, Robin Z Hayeems, Randi Zlotnik Shaul, Michael Brudno, Marta Girdea, Brendan Frey, Babak Alipanahi, Sohnee Ahmed, Riyana Babul-Hirji, Ramses Badilla Porras, Melissa T Carter, Lauren Chad, Ayeshah Chaudhry, David Chitayat, Soghra Jougheh Doust, Cheryl Cytrynbaum, Lucie Dupuis, Resham Ejaz, Leona Fishman, Andrea Guerin, Bita Hashemi, Mayada Helal, Stacy Hewson, Michal Inbar-Feigenberg, Peter Kannu, Natalya Karp, Raymond Kim, Jonathan Kronick, Eriskay Liston, Heather MacDonald, Saadet Mercimek-Mahmutoglu, Roberto Mendoza-Londono, Enas Nasr, Graeme Nimmo, Nicole Parkinson, Nada Quercia, Julian Raiman, Maian Roifman, Andreas Schulze, Andrea Shugar, Cheryl Shuman, Pierre Sinajon, Komudi Siriwardena, Rosanna Weksberg, Grace Yoon, Chris Carew, Raith Erickson, Richard A Leach, Robert Klein, Peter N Ray, M Stephen Meyn, Stephen W Scherer, Ronald D Cohn, Christian R Marshall

Abstract

The standard of care for first-tier clinical investigation of the etiology of congenital malformations and neurodevelopmental disorders is chromosome microarray analysis (CMA) for copy number variations (CNVs), often followed by gene(s)-specific sequencing searching for smaller insertion-deletions (indels) and single nucleotide variant (SNV) mutations. Whole genome sequencing (WGS) has the potential to capture all classes of genetic variation in one experiment; however, the diagnostic yield for mutation detection of WGS compared to CMA, and other tests, needs to be established. In a prospective study we utilized WGS and comprehensive medical annotation to assess 100 patients referred to a paediatric genetics service and compared the diagnostic yield versus standard genetic testing. WGS identified genetic variants meeting clinical diagnostic criteria in 34% of cases, representing a 4-fold increase in diagnostic rate over CMA (8%) (p-value = 1.42e-05) alone and >2-fold increase in CMA plus targeted gene sequencing (13%) (p-value = 0.0009). WGS identified all rare clinically significant CNVs that were detected by CMA. In 26 patients, WGS revealed indel and missense mutations presenting in a dominant (63%) or a recessive (37%) manner. We found four subjects with mutations in at least two genes associated with distinct genetic disorders, including two cases harboring a pathogenic CNV and SNV. When considering medically actionable secondary findings in addition to primary WGS findings, 38% of patients would benefit from genetic counseling. Clinical implementation of WGS as a primary test will provide a higher diagnostic yield than conventional genetic testing and potentially reduce the time required to reach a genetic diagnosis.

Keywords: Chromosomal Microarray Analysis; Diagnostic Yield; Whole Genome Sequencing.

Conflict of interest statement

Competing Interests: DM RJ, NM, BT, TN, GP, RKCY, MS, RH, RZS, MB, MG, BF, BA, SA, MTC, LC, AC, CC, LD, RE, LF, AG, BH, MH, SH, MIF, PK, NK, RK, JK, EL, HM, SMM, RML, EN, GN, NP, NQ, JR, MR, AS, AS, CS, PS, KS, RW, GY, CC, SWS, RDC, and CRM declare no conflicts of interest. SB, DJS, PNR and MSM are scientific advisors for Gene42 Inc., which provides support services for the free (open source) PhenoTips software. RE and RK are employees of Complete Genomics. RAL was an employee of Complete Genomics at the time of the study and is currently employed by WuXi NextCODE Genomics.

Figures

Figure 1
Figure 1
Overview of study design comparing the diagnostic yield of whole-genome sequencing compared with standard of care genetic testing. CMA, chromosomal microarray analysis; SNV, single-nucleotide variant; CNV, copy-number variant; HPO, Human Phenotype Ontology; Dx, diagnostic.

References

    1. Liu, L. et al. Global, regional, and national causes of child mortality in 2000-13, with projections to inform post-2015 priorities: an updated systematic analysis. Lancet 385, 430–440 (2015).
    1. Shevell, M. et al. Practice parameter: evaluation of the child with global developmental delay: report of the Quality Standards Subcommittee of the American Academy of Neurology and The Practice Committee of the Child Neurology Society. Neurology 60, 367–380 (2003).
    1. McCandless, S. E. , Brunger, J. W. & Cassidy, S. B. The burden of genetic disease on inpatient care in a children's hospital. Am. J. Hum. Genet. 74, 121–127 (2004).
    1. Hall, J. G. The impact of birth defects and genetic diseases. Arch. Pediatr. Adolesc. Med. 151, 1082–1083 (1997).
    1. Coulter, M. E. et al. Chromosomal microarray testing influences medical management. Genet. Med. 13, 770–776 (2011).
    1. Lee, C. & Scherer, S. W. The clinical context of copy number variation in the human genome. Expert Rev. Mol. Med. 12, e8 (2010).
    1. Miller, D. T. et al. Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. Am. J. Hum. Genet. 86, 749–764 (2010).
    1. Yang, Y. et al. Molecular findings among patients referred for clinical whole-exome sequencing. JAMA 312, 1870–1879 (2014).
    1. Lee, H. et al. Clinical exome sequencing for genetic identification of rare Mendelian disorders. JAMA 312, 1880–1887 (2014).
    1. de Ligt, J. et al. Detection of clinically relevant copy number variants with whole-exome sequencing. Hum. Mutat. 34, 1439–1448 (2013).
    1. Pang, A. W. , Macdonald, J. R. , Yuen, R. K. , Hayes, V. M. & Scherer, S. W. Performance of high-throughput sequencing for the discovery of genetic variation across the complete size spectrum. G3 (Bethesda) 4, 63–65 (2014).
    1. Lupski, J. R. et al. Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. N. Engl. J. Med. 362, 1181–1191 (2010).
    1. Herdewyn, S. et al. Whole-genome sequencing reveals a coding non-pathogenic variant tagging a non-coding pathogenic hexanucleotide repeat expansion in C9orf72 as cause of amyotrophic lateral sclerosis. Hum. Mol. Genet. 21, 2412–2419 (2012).
    1. Bae, B. I. et al. Evolutionarily dynamic alternative splicing of GPR56 regulates regional cerebral cortical patterning. Science 343, 764–768 (2014).
    1. Weedon, M. N. et al. Recessive mutations in a distal PTF1A enhancer cause isolated pancreatic agenesis. Nat. Genet. 46, 61–64 (2014).
    1. Jiang, Y. H. et al. Detection of clinically relevant genetic variants in autism spectrum disorder by whole-genome sequencing. Am. J. Hum. Genet. 93, 249–263 (2013).
    1. Gilissen, C. et al. Genome sequencing identifies major causes of severe intellectual disability. Nature 511, 344–347 (2014).
    1. Taylor, J. C. et al. Factors influencing success of clinical genome sequencing across a broad spectrum of disorders. Nat. Genet. 47, 717–726 (2015).
    1. Ali-Khan, S. E. , Daar, A. S. , Shuman, C. , Ray, P. N. & Scherer, S. W. Whole genome scanning: resolving clinical diagnosis and management amidst complex data. Pediatr. Res. 66, 357–363 (2009).
    1. Green, R. C. et al. ACMG recommendations for reporting of incidental findings in clinical exome and genome sequencing. Genet. Med. 15, 565–574 (2013).
    1. Hehir-Kwa, J. Y. et al. Towards a European consensus for reporting incidental findings during clinical NGS testing. Eur. J. Hum. Genet. 23, 1601–1606 (2015).
    1. Kearney, H. M. , Thorland, E. C. , Brown, K. K. , Quintero-Rivera, F. & South, S. T. Working Group of the American College of Medical Genetics Laboratory Quality Assurance C et al. American College of Medical Genetics standards and guidelines for interpretation and reporting of postnatal constitutional copy number variants. Genet. Med. 13, 680–685 (2011).
    1. Richards, S. 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. 17, 405–424 (2015).
    1. Yuen, R. K. et al. Whole-genome sequencing of quartet families with autism spectrum disorder. Nat. Med. 21, 185–191 (2015).
    1. Wright, C. F. et al. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data. Lancet 385, 1305–1314 (2015).
    1. Soden, S. E. et al. Effectiveness of exome and genome sequencing guided by acuity of illness for diagnosis of neurodevelopmental disorders. Sci. Transl. Med. 6, 265ra168 (2014).
    1. Willig, L. K. et al. Whole-genome sequencing for identification of Mendelian disorders in critically ill infants: a retrospective analysis of diagnostic and clinical findings. Lancet Respir. Med. 3, 377–387 (2015).
    1. Girdea, M. et al. PhenoTips: patient phenotyping software for clinical and research use. Hum. Mutat. 34, 1057–1065 (2013).
    1. Robinson, P. N. et al. The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. Am. J. Hum. Genet. 83, 610–615 (2008).
    1. Drmanac, R. et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 327, 78–81 (2010).
    1. Carnevali, P. et al. Computational techniques for human genome resequencing using mated gapped reads. J. Comput. Biol. 19, 279–292 (2012).
    1. Wang, K. , Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).
    1. Genomes Project C. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).
    1. Tennessen, J. A. et al. Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science 337, 64–69 (2012).
    1. Pollard, K. S. , Hubisz, M. J. , Rosenbloom, K. R. & Siepel, A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 20, 110–121 (2010).
    1. Ng, P. C. & Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 11, 863–874 (2001).
    1. Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
    1. Reva, B. , Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).
    1. Kircher, M. et al. A general framework for estimating the relative pathogenicity of human genetic variants. Nat. Genet. 46, 310–315 (2014).
    1. Xiong, H. Y. et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806 (2015).
    1. Landrum, M. J. et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42, D980–D985 (2014).
    1. Stenson, P. D. et al. The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum. Genet. 133, 1–9 (2014).
    1. Solomon, B. D. , Nguyen, A. D. , Bear, K. A. & Wolfsberg, T. G. Clinical genomic database. Proc. Natl Acad. Sci. USA 110, 9851–9855 (2013).
    1. Smith, C. L. , Goldsmith, C. A. & Eppig, J. T. The Mammalian Phenotype Ontology as a tool for annotating, analyzing and comparing phenotypic information. Genome Biol. 6, R7 (2005).
    1. MacDonald, J. R. , Ziman, R. , Yuen, R. K. , Feuk, L. & Scherer, S. W. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 42, D986–D992 (2014).
    1. Pinto, D. et al. Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants. Nat. Biotechnol. 29, 512–520 (2011).

Source: PubMed

Sponsors and Collaborators

Medical Conditions

Drug Interventions

3
Subscribe