Missense and truncating variants in <i>CHD5</i> in a dominant neurodevelopmental disorder with intellectual disability, behavioral disturbances, and epilepsy

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Abstract

Located in the critical 1p36 microdeletion region, the chromodomain helicase DNA-binding protein 5 ( CHD5) gene encodes a subunit of the nucleosome remodeling and deacetylation (NuRD) complex required for neuronal development. Pathogenic variants in six of nine chromodomain (CHD) genes cause autosomal dominant neurodevelopmental disorders, while CHD5-related disorders are still unknown. Thanks to GeneMatcher and international collaborations, we assembled a cohort of 16 unrelated individuals harboring heterozygous CHD5 variants, all identified by exome sequencing. Twelve patients had de novo CHD5 variants, including ten missense and two splice site variants. Three familial cases had nonsense or missense variants segregating with speech delay, learning disabilities, and/or craniosynostosis. One patient carried a frameshift variant of unknown inheritance due to unavailability of the father. The most common clinical features included language deficits (81%), behavioral symptoms (69%), intellectual disability (64%), epilepsy (62%), and motor delay (56%). Epilepsy types were variable, with West syndrome observed in three patients, generalized tonic–clonic seizures in two, and other subtypes observed in one individual each. Our findings suggest that, in line with other CHD-related disorders, heterozygous CHD5 variants are associated with a variable neurodevelopmental syndrome that includes intellectual disability with speech delay, epilepsy, and behavioral problems as main features.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00439-021-02283-2.

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Most cited references 41

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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

Sue Richards, Nazneen Aziz, Sherri Bale … (2015)

The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.

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The mutational constraint spectrum quantified from variation in 141,456 humans

Konrad J. Karczewski, Laurent C. Francioli, Grace Tiao … (2021)

Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.

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A method and server for predicting damaging missense mutations

Ivan Adzhubei, Steffen Schmidt, Leonid Peshkin … (2010)

To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1

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Author and article information

Contributors

Christel Depienne:

ORCID: http://orcid.org/0000-0002-7212-9554

christel.depienne@uni-due.de

Cyril Mignot:

ORCID: http://orcid.org/0000-0002-3462-3463

cyril.mignot@aphp.fr

Journal

Journal ID (nlm-ta): Hum Genet

Journal ID (iso-abbrev): Hum Genet

Title: Human Genetics

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 0340-6717

ISSN (Electronic): 1432-1203

Publication date (Electronic): 4 May 2021

Publication date PMC-release: 4 May 2021

Publication date (Print): 2021

Volume: 140

Issue: 7

Pages: 1109-1120

Affiliations

[1 ]GRID grid.5718.b, ISNI 0000 0001 2187 5445, Institute of Human Genetics, , University Hospital Essen, University Duisburg-Essen, ; Essen, Germany

[2 ]Département de Génétique, Centre de Référence Déficiences Intellectuelles de Causes Rares, Groupe Hospitalier Pitié-Salpêtrière and Hôpital Trousseau, APHP, Sorbonne Université, Paris, France

[3 ]GRID grid.462844.8, ISNI 0000 0001 2308 1657, Institut du Cerveau (ICM), UMR S 1127, Inserm U1127, CNRS UMR 7225, , Sorbonne Université, ; 75013 Paris, France

[4 ]GRID grid.428467.b, GeneDx, ; Gaithersburg, MD USA

[5 ]GRID grid.268441.d, ISNI 0000 0001 1033 6139, Department of Human Genetics, , Yokohama City University Graduate School of Medicine, ; Yokohama, 236-0004 Japan

[6 ]GRID grid.410714.7, ISNI 0000 0000 8864 3422, Department of Pediatrics, , Showa University School of Medicine, ; Tokyo, 142-8666 Japan

[7 ]GRID grid.268394.2, ISNI 0000 0001 0674 7277, Department of Pediatrics, , Yamagata University Faculty of Medicine, ; Yamagata, 990-9585 Japan

[8 ]GRID grid.413711.1, Department of Pediatrics, , Amphia Hospital, ; Breda, The Netherlands

[9 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Division of Medical Genetics, Department of Pediatrics, , Duke University School of Medicine, ; Durham, NC 27710 USA

[10 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Departments of Neuroscience and Pediatrics, Division of Neurology, Rady Children’s Hospital, , UCSD, San Diego and Rady Children’s Institute for Genomic Medicine, ; San Diego, CA USA

[11 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Institute of Medical Genetics, , University of Zurich, ; Schlieren, 8952 Zurich, Switzerland

[12 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Rare Disease Initiative Zurich, , Clinical Research Priority Program for Rare Diseases University of Zurich, ; 8032 Zurich, Switzerland

[13 ]GRID grid.7692.a, ISNI 0000000090126352, Department of Metabolic Diseases, , University Medical Centre Utrecht, ; Utrecht, The Netherlands

[14 ]GRID grid.410463.4, ISNI 0000 0004 0471 8845, Clinique de Génétique, , CHU Lille, ; 59000 Lille, France

[15 ]GRID grid.410463.4, ISNI 0000 0004 0471 8845, Institut de Génétique Médicale, , CHRU Lille, Université de Lille, ; Lille, France

[16 ]GRID grid.411162.1, ISNI 0000 0000 9336 4276, Service de Génétique Médicale, , CHU de Poitiers, ; Poitiers, France

[17 ]GRID grid.11166.31, ISNI 0000 0001 2160 6368, EA3808 NEUVACOD, , University of Poitiers, ; Poitiers, France

[18 ]GRID grid.31151.37, Unité Fonctionnelle d’Innovation Diagnostique des Maladies Rares, FHU-TRANSLAD, France Hospitalo-Universitaire Médecine Translationnelle et Anomalies du Développement (TRANSLAD), Centre Hospitalier Universitaire Dijon Bourgogne, , CHU Dijon Bourgogne, ; Dijon, France

[19 ]GRID grid.5613.1, ISNI 0000 0001 2298 9313, INSERM-Université de Bourgogne UMR1231 GAD « Génétique Des Anomalies du Développement », FHU-TRANSLAD, , UFR Des Sciences de Santé, ; Dijon, France

[20 ]GRID grid.31151.37, Centre de Référence Maladies Rares «Anomalies du Développement et Syndromes Malformatifs », Centre de Génétique, FHU‐TRANSLAD, , CHU Dijon Bourgogne, ; Dijon, France

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

[22 ]GRID grid.411418.9, ISNI 0000 0001 2173 6322, CHU Sainte-Justine Research Center, ; Montreal, QC H3T 1C5 Canada

[23 ]GRID grid.411418.9, ISNI 0000 0001 2173 6322, Sainte-Justine Hospital, University of Montreal, ; Montreal, QC H3T 1C5 Canada

[24 ]GRID grid.22072.35, ISNI 0000 0004 1936 7697, Department of Medical Genetics and Alberta Children’s Hospital Research Institute, Cumming School of Medicine, , University of Calgary, ; Calgary, AB T2N 4N1 Canada

[25 ]GRID grid.505613.4, Department of Biochemistry, , Hamamatsu University School of Medicine, ; Hamamatsu, 431-3192 Japan

[26 ]GRID grid.257016.7, ISNI 0000 0001 0673 6172, Department of Pediatrics, , Hirosaki University Graduate School of Medicine and School of Medicine, ; Hirosaki, 036-8562 Japan

[27 ]GRID grid.414152.7, ISNI 0000 0004 0604 6974, Department of Pediatrics, , Hirosaki National Hospital, ; Hirosaki, 036-8545 Japan

[28 ]Aomori City Health Center, Aomori, 030-0962 Japan

[29 ]GRID grid.418307.9, ISNI 0000 0000 8571 0933, Greenwood Genetic Center, ; Greenwood, SC 29646 USA

[30 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Clinical Genetics, Amsterdam UMC, , University of Amsterdam, ; Amsterdam, The Netherlands

[31 ]GRID grid.7177.6, ISNI 0000000084992262, Laboratory of Genome Diagnostics, Department of Clinical Genetics, Amsterdam UMC, , University of Amsterdam, ; Amsterdam, The Netherlands

[32 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Hansjörg Wyss Department of Plastic Surgery, , NYU Langone Health, ; New York, NY USA

Author information

Ilaria Parenti http://orcid.org/0000-0002-1825-6237

Daphné Lehalle http://orcid.org/0000-0002-2193-8685

Caroline Nava http://orcid.org/0000-0003-1272-0518

Erin Torti http://orcid.org/0000-0001-5790-1051

Elsa Leitão http://orcid.org/0000-0001-5051-9714

Takeshi Mizuguchi http://orcid.org/0000-0003-4998-1244

Naomichi Matsumoto http://orcid.org/0000-0001-9846-6500

Mitsuhiro Kato http://orcid.org/0000-0003-1485-8553

Kazuyuki Nakamura http://orcid.org/0000-0003-0266-9341

Stella A. de Man http://orcid.org/0000-0002-3895-4649

Heidi Cope http://orcid.org/0000-0003-0586-9277

Anita Rauch http://orcid.org/0000-0003-2930-3163

Peter M. van Hasselt http://orcid.org/0000-0001-9821-8398

Florence Petit http://orcid.org/0000-0002-1368-1023

Thomas Smol http://orcid.org/0000-0002-0119-5896

Frédéric Bilan http://orcid.org/0000-0001-8589-6018

Arthur Sorlin http://orcid.org/0000-0001-8008-9145

Antonio Vitobello http://orcid.org/0000-0003-3717-8374

Christophe Philippe http://orcid.org/0000-0001-7098-6520

Ingrid M. B. H. van de Laar http://orcid.org/0000-0002-2523-1230

Philippe M. Campeau http://orcid.org/0000-0001-9713-7107

Ping Yee Billie Au http://orcid.org/0000-0002-9533-7217

Mitsuko Nakashima http://orcid.org/0000-0001-7941-1774

Hirotomo Saitsu http://orcid.org/0000-0003-0723-0960

Tatsuya Yamamoto http://orcid.org/0000-0002-1277-5298

Raymond J. Louie http://orcid.org/0000-0002-6788-1151

Michael J. Lyons http://orcid.org/0000-0002-8644-5507

Andrew T. Timberlake http://orcid.org/0000-0002-8926-9692

Sabine A. Fuchs http://orcid.org/0000-0001-9147-2406

Christel Depienne http://orcid.org/0000-0002-7212-9554

Cyril Mignot http://orcid.org/0000-0002-3462-3463

Article

Publisher ID: 2283

DOI: 10.1007/s00439-021-02283-2

PMC ID: 8197709

PubMed ID: 33944996

SO-VID: 0ae78613-8201-4af7-8d37-d81144b17631

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 29 January 2021

Date accepted : 15 April 2021

Funding

Funded by: Fondation Maladies Rares

Funded by: Bio-Psy Labex

Funded by: Investissements d’avenir” program ANR-10-IAIHU-06

Funded by: Japan Agency for Medical Research and Development (AMED)

Award ID: JP20ek0109486

Award ID: JP20dm0107090

Award ID: JP20ek0109301

Award ID: JP20ek0109348

Award ID: JP20kk0205012

Award Recipient : Naomichi Matsumoto

Funded by: JSPS KAKENHI

Award ID: JP17H01539

Award ID: JP20K08164

Award Recipient : Takeshi Mizuguchi

Funded by: Intramural research grants for Neurological and Psychiatric Disorders of NCNP from the Ministry of Health, Labour and Welfare

Award ID: 30-6 and 30-7

Award Recipient : Naomichi Matsumoto

Funded by: FundRef http://dx.doi.org/10.13039/100007449, Takeda Science Foundation;

Funded by: NIH Common Fund, through the Office of Strategic Coordination/Office of the NIH Director

Award ID: U01HG007672

Award Recipient : Vandana Shashi

Funded by: Universität Duisburg-Essen (3149)

Open Access :

Open Access funding enabled and organized by Projekt DEAL.

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Missense and truncating variants in CHD5 in a dominant neurodevelopmental disorder with intellectual disability, behavioral disturbances, and epilepsy

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Genome Integrity

Most cited references 41

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

The mutational constraint spectrum quantified from variation in 141,456 humans

A method and server for predicting damaging missense mutations

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