Exome sequencing in routine diagnostics: a generic test for 254 patients with primary immunodeficiencies

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Abstract

Background

Diagnosis of primary immunodeficiencies (PIDs) is complex and cumbersome yet important for the clinical management of the disease. Exome sequencing may provide a genetic diagnosis in a significant number of patients in a single genetic test.

Methods

In May 2013, we implemented exome sequencing in routine diagnostics for patients suffering from PIDs. This study reports the clinical utility and diagnostic yield for a heterogeneous group of 254 consecutively referred PID patients from 249 families. For the majority of patients, the clinical diagnosis was based on clinical criteria including rare and/or unusual severe bacterial, viral, or fungal infections, sometimes accompanied by autoimmune manifestations. Functional immune defects were interpreted in the context of aberrant immune cell populations, aberrant antibody levels, or combinations of these factors.

Results

For 62 patients (24%), exome sequencing identified pathogenic variants in well-established PID genes. An exome-wide analysis diagnosed 10 additional patients (4%), providing diagnoses for 72 patients (28%) from 68 families altogether. The genetic diagnosis directly indicated novel treatment options for 25 patients that received a diagnosis (34%).

Conclusion

Exome sequencing as a first-tier test for PIDs granted a diagnosis for 28% of patients. Importantly, molecularly defined diagnoses indicated altered therapeutic options in 34% of cases. In addition, exome sequencing harbors advantages over gene panels as a truly generic test for all genetic diseases, including in silico extension of existing gene lists and re-analysis of existing data.

Electronic supplementary material

The online version of this article (10.1186/s13073-019-0649-3) contains supplementary material, which is available to authorized users.

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Most cited references 34

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The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Aaron McKenna, Matthew Hanna, Eric R. Banks … (2010)

Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.

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Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.

Jennifer E. Posey, Tamar Harel, Pengfei Liu … (2017)

Background Whole-exome sequencing can provide insight into the relationship between observed clinical phenotypes and underlying genotypes. Methods We conducted a retrospective analysis of data from a series of 7374 consecutive unrelated patients who had been referred to a clinical diagnostic laboratory for whole-exome sequencing; our goal was to determine the frequency and clinical characteristics of patients for whom more than one molecular diagnosis was reported. The phenotypic similarity between molecularly diagnosed pairs of diseases was calculated with the use of terms from the Human Phenotype Ontology. Results A molecular diagnosis was rendered for 2076 of 7374 patients (28.2%); among these patients, 101 (4.9%) had diagnoses that involved two or more disease loci. We also analyzed parental samples, when available, and found that de novo variants accounted for 67.8% (61 of 90) of pathogenic variants in autosomal dominant disease genes and 51.7% (15 of 29) of pathogenic variants in X-linked disease genes; both variants were de novo in 44.7% (17 of 38) of patients with two monoallelic variants. Causal copy-number variants were found in 12 patients (11.9%) with multiple diagnoses. Phenotypic similarity scores were significantly lower among patients in whom the phenotype resulted from two distinct mendelian disorders that affected different organ systems (50 patients) than among patients with disorders that had overlapping phenotypic features (30 patients) (median score, 0.21 vs. 0.36; P=1.77×10(-7)). Conclusions In our study, we found multiple molecular diagnoses in 4.9% of cases in which whole-exome sequencing was informative. Our results show that structured clinical ontologies can be used to determine the degree of overlap between two mendelian diseases in the same patient; the diseases can be distinct or overlapping. Distinct disease phenotypes affect different organ systems, whereas overlapping disease phenotypes are more likely to be caused by two genes encoding proteins that interact within the same pathway. (Funded by the National Institutes of Health and the Ting Tsung and Wei Fong Chao Foundation.).

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Copy number variation detection and genotyping from exome sequence data

Niklas Krumm, Peter Sudmant, Arthur Ko … (2012)

While exome sequencing is readily amenable to single-nucleotide variant discovery, the sparse and nonuniform nature of the exome capture reaction has hindered exome-based detection and characterization of genic copy number variation. We developed a novel method using singular value decomposition (SVD) normalization to discover rare genic copy number variants (CNVs) as well as genotype copy number polymorphic (CNP) loci with high sensitivity and specificity from exome sequencing data. We estimate the precision of our algorithm using 122 trios (366 exomes) and show that this method can be used to reliably predict (94% overall precision) both de novo and inherited rare CNVs involving three or more consecutive exons. We demonstrate that exome-based genotyping of CNPs strongly correlates with whole-genome data (median r 2 = 0.91), especially for loci with fewer than eight copies, and can estimate the absolute copy number of multi-allelic genes with high accuracy (78% call level). The resulting user-friendly computational pipeline, CoNIFER ( co py n umber i nference f rom e xome r eads), can reliably be used to discover disruptive genic CNVs missed by standard approaches and should have broad application in human genetic studies of disease.

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Author and article information

Contributors

Alexander Hoischen: +31243619639 , alexander.hoischen@radboudumc.nl

Journal

Journal ID (nlm-ta): Genome Med

Journal ID (iso-abbrev): Genome Med

Title: Genome Medicine

Publisher: BioMed Central (London )

ISSN (Electronic): 1756-994X

Publication date (Electronic): 17 June 2019

Publication date PMC-release: 17 June 2019

Publication date Collection: 2019

Volume: 11

Electronic Location Identifier: 38

Affiliations

[1 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Department of Human Genetics, , Radboud University Medical Center, ; Nijmegen, The Netherlands

[2 ]ISNI 0000 0000 8994 5086, GRID grid.1026.5, Department of Genetics and Molecular Pathology, Centre for Cancer Biology, , SA Pathology and the University of South Australia, ; Adelaide, South Australia Australia

[3 ]ISNI 0000 0004 0593 1832, GRID grid.415277.2, Department of Pediatrics, Children’s specialist Hospital, , King Fahad Medical City, ; Riyadh, Saudi Arabia

[4 ]ISNI 0000 0001 0428 6825, GRID grid.29906.34, Department of Medical Biology, Faculty of Medicine, , Akdeniz University, ; Antalya, Turkey

[5 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Department of Pediatric immunology, Pediatrics, , Radboud University Medical Center, ; Nijmegen, The Netherlands

[6 ]ISNI 0000 0004 0593 1832, GRID grid.415277.2, Department of Pediatric Hematology and Oncology, Comprehensive Cancer center, , King Fahad Medical City, ; Riyadh, Saudi Arabia

[7 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Radboud Expertise Center for Immunodeficiency and Autoinflammation, Department of Internal Medicine, , Radboud University Medical Center, ; Nijmegen, The Netherlands

[8 ]ISNI 0000000090126352, GRID grid.7692.a, Department of Pediatric Infectious Diseases and Immunology, Wilhelmina Children’s Hospital, , University Medical Center Utrecht, ; Utrecht, The Netherlands

[9 ]ISNI 0000 0000 9558 4598, GRID grid.4494.d, Department of Genetics, , University of Groningen, University Medical Center Groningen, ; Groningen, the Netherlands

[10 ]GRID grid.487647.e, Princess Máxima Center for Pediatric Oncology, ; Utrecht, the Netherlands

[11 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Department of Pediatric Rheumatology, Pediatrics, , Radboud University Medical Center, ; Nijmegen, The Netherlands

[12 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Clinical Genetics, , Maastricht University Medical Center+, ; Maastricht, The Netherlands

[13 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, PEDEGO Research Unit and Medical Research Center Oulu, , University of Oulu, ; Oulu, Finland

[14 ]ISNI 0000 0004 4685 4917, GRID grid.412326.0, Department of Clinical Genetics, Oulu University Hospital, ; Oulu, Finland

[15 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Department of Hematology, , Radboud University Medical Center, ; Nijmegen, The Netherlands

[16 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Clinical Immunology, , Maastricht University Medical Center, ; Maastricht, The Netherlands

[17 ]ISNI 0000 0004 1936 7443, GRID grid.7914.b, Department of Clinical Science, Department of Informatics, Computational Biology Unit, , University of Bergen, ; 5020 Bergen, Norway

[18 ]ISNI 000000040459992X, GRID grid.5645.2, Department of Clinical Genetics, Erasmus MC, , University Medical Center, ; Rotterdam, The Netherlands

[19 ]Department of Pediatrics, School for Nutrition and Translational Research in Metabolism (NUTRIM), Maastricht University Medical Center+, Maastricht University, Maastricht, The Netherlands

[20 ]ISNI 0000 0001 0462 7212, GRID grid.1006.7, Institute of Genetic Medicine, , Newcastle University, ; Newcastle-upon-Tyne, UK

[21 ]ISNI 0000 0004 0444 9382, GRID grid.10417.33, Department of Human Genetics and Department of Internal Medicine, , Radboud University Medical Center, ; P.O. Box 9101, 6500 HB Nijmegen, The Netherlands

Article

Publisher ID: 649

DOI: 10.1186/s13073-019-0649-3

PMC ID: 6572765

PubMed ID: 31203817

SO-VID: 711d0356-43b6-48a5-a2d2-a838ea92101a

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

History

Date received : 10 January 2019

Date accepted : 17 May 2019

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003246, Nederlandse Organisatie voor Wetenschappelijk Onderzoek;

Award ID: Spinoza grant

Award ID: 918-15-667

Award Recipient : Joris A. Veltman Mihai G. Netea

Funded by: FundRef http://dx.doi.org/10.13039/501100000781, European Research Council;

Award ID: 310372

Award Recipient : Mihai G. Netea

Funded by: European Research Council

Award ID: 779257

Award Recipient : Alexander Hoischen

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ScienceOpen disciplines: Molecular medicine

Keywords: routine diagnostics,genetic diagnosis,exome sequencing,primary immunodeficiencies

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ScienceOpen disciplines: Molecular medicine

Keywords: routine diagnostics, genetic diagnosis, exome sequencing, primary immunodeficiencies

Exome sequencing in routine diagnostics: a generic test for 254 patients with primary immunodeficiencies

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Abstract

Background

Methods

Results

Conclusion

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cGMP: Generators, Effectors and Therapeutic Implications Conference

Most cited references 34

The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

Resolution of Disease Phenotypes Resulting from Multilocus Genomic Variation.

Copy number variation detection and genotyping from exome sequence data

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