M. T. Ebbert, Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight, Genome Biol, vol.20, p.97, 2019.

J. Desvignes, VarAFT: a variant annotation and filtration system for human next generation sequencing data, Nucleic Acids Res, vol.46, pp.545-553, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01852493

A. R. Quinlan and I. M. Hall, BEDTools: a flexible suite of utilities for comparing genomic features, Bioinformatics, vol.26, pp.841-842, 2010.

A. R. Quinlan, BEDTools: The Swiss-Army Tool for Genome Feature Analysis, Current Protocols in Bioinformatics, vol.47, 2014.

M. Krahn, A National French consensus on gene lists for the diagnosis of myopathies using nextgeneration sequencing, Eur. J. Hum. Genet, vol.27, pp.349-352, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02434896

S. Gorokhova, Comparing targeted exome and whole exome approaches for genetic diagnosis of neuromuscular disorders, Appl Transl Genom, vol.7, pp.26-31, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01610017

A. Tarasov, A. J. Vilella, E. Cuppen, I. J. Nijman, and P. Prins, Sambamba: fast processing of NGS alignment formats, Bioinformatics, vol.31, pp.2032-2034, 2015.

B. S. Pedersen and A. R. Quinlan, Mosdepth: quick coverage calculation for genomes and exomes, Bioinformatics, vol.34, pp.867-868, 2018.

K. D. Doig, PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories, Genome Med, vol.9, p.38, 2017.

M. Münz, CoverView: a sequence quality evaluation tool for next generation sequencing data, Wellcome Open Res, vol.3, p.36, 2018.

;. C. Availability, J. M. , F. R. , M. B. , and M. , Competing interests: The authors declare that they have no competing financial or non-financial interests, Authors contributions: M.G. wrote the software, participated in the designing of the tool and in writing of the manuscript. M

L. V. Anderson, K. Davison, J. A. Moss, I. Richard, M. Fardeau et al., Characterization of Monoclonal Antibodies to Calpain 3 and Protein Expression in Muscle from Patients with Limb-Girdle Muscular Dystrophy Type 2A, The American Journal of Pathology, vol.153, issue.4, pp.1169-79, 1998.

E. Bahrami-samani and Y. Xing, Discovery of Allele-Specific Protein-RNA Interactions in Human Transcriptomes, American Journal of Human Genetics, vol.104, issue.3, pp.492-502, 2019.

A. Barp, P. Laforet, L. Bello, G. Tasca, J. Vissing et al., European Muscle MRI Study in Limb Girdle Muscular Dystrophy Type R1/2A (LGMDR1/LGMD2A), Journal of Neurology, vol.267, issue.1, pp.45-56, 2020.

C. Bonnet, S. Krieger, M. Vezain, A. Rousselin, I. Tournier et al., Screening BRCA1 and BRCA2 Unclassified Variants for Splicing Mutations Using Reverse Transcription PCR on Patient RNA and an Ex Vivo Assay Based on a Splicing Reporter Minigene, Journal of Medical Genetics, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02336393

J. Cheng, T. Y. Duong-nguyen, K. J. Cygan, W. G. Muhammed-hasan-Çelik, ?. Fairbrother et al., MMSplice: Modular Modeling Improves the Predictions of Genetic Variant Effects on Splicing, Genome Biology, vol.20, issue.1, p.48, 2019.

D. N. Cooper, M. Krawczak, C. Polychronakos, C. Tyler-smith, and H. Kehrer-sawatzki, Where Genotype Is Not Predictive of Phenotype: Towards an Understanding of the Molecular Basis of Reduced Penetrance in Human Inherited Disease, Human Genetics, vol.132, issue.10, pp.1077-1130, 2013.

F. Desmet, D. Hamroun, M. Lalande, G. Collod-béroud, M. Claustres et al., Human Splicing Finder: An Online Bioinformatics Tool to Predict Splicing Signals, Nucleic Acids Research, vol.37, issue.9, p.67, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00396239

S. Duguez, M. Bartoli, and I. Richard, Calpain 3: A Key Regulator of the Sarcomere?, FEBS Journal, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01610048

M. Duno, M. Sveen, M. Schwartz, and J. Vissing, cDNA Analyses of CAPN3 Enhance Mutation Detection and Reveal a Low Prevalence of LGMD2A Patients in Denmark, European Journal of Human Genetics: EJHG, vol.16, issue.8, pp.935-975, 2008.

W. G. Fairbrother, D. Holste, C. B. Burge, and P. A. Sharp, Single Nucleotide Polymorphism-Based Validation of Exonic Splicing Enhancers, PLoS Biology, 2004.

M. Fanin, L. Fulizio, A. C. Nascimbeni, M. Spinazzi, G. Piluso et al., Molecular Diagnosis in LGMD2A: Mutation Analysis or Protein Testing?, Human Mutation, vol.24, issue.1, pp.52-62, 2004.

M. Fardeau, B. Eymard, C. Mignard, F. M. Tomé, I. Richard et al., Chromosome 15-Linked Limb-Girdle Muscular Dystrophy: Clinical Phenotypes in Reunion Island and French Metropolitan Communities, Neuromuscular Disorders, 1996.

I. F. Fokkema, E. M. Peter, . Taschner, C. P. Gerard, J. Schaafsma et al., LOVD v.2.0: The next Generation in Gene Variant Databases, Human Mutation, vol.32, issue.5, pp.557-63, 2011.

C. M. Gault, F. Martin, W. Mei, F. Bai, J. B. Black et al., Aberrant Splicing in Maize Reveals a Conserved Role for U12 Splicing in Eukaryotic Multicellular Development, Proceedings of the National Academy of Sciences of the United States of America, vol.114, issue.11, pp.2195-2204, 2017.

B. D. Gelb, . For-the-clingen-rasopathy-working, H. Group, M. W. Cavé, K. W. Dillon et al., ClinGen's RASopathy Expert Panel Consensus Methods for Variant Interpretation, Genetics in Medicine, 2018.

J. M. Havrilla, S. Brent, R. M. Pedersen, A. R. Layer, and . Quinlan, A Map of Constrained Coding Regions in the Human Genome, Nature Genetics, vol.51, issue.1, pp.88-95, 2019.

Z. Hu, C. Yu, M. Furutsuki, G. Andreoletti, M. Ly et al., VIPdb, a Genetic Variant Impact Predictor Database, Human Mutation, vol.40, issue.9, pp.1202-1216, 2019.

K. A. Jagadeesh, M. Joseph, J. S. Paggi, P. D. Ye, D. N. Stenson et al., S-CAP Extends Pathogenicity Prediction to Genetic Variants That Affect RNA Splicing, Nature Genetics, vol.51, issue.4, pp.755-63, 2019.

K. Jaganathan, S. K. Panagiotopoulou, J. F. Mcrae, S. Fazel-darbandi, D. Knowles et al., Predicting Splicing from Primary Sequence with Deep Learning, Cell, vol.176, issue.3, pp.535-583, 2019.

D. Karolchik, A. S. Hinrichs, T. S. Furey, K. M. Roskin, C. W. Sugnet et al., The UCSC Table Browser Data Retrieval Tool, Nucleic Acids Research, vol.32, pp.493-96, 2004.

M. A. Kelly, C. Caleshu, A. Morales, J. Buchan, Z. Wolf et al., Adaptation and Validation of the ACMG/AMP Variant Classification Framework for MYH7-Associated Inherited Cardiomyopathies: Recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel, Genetics in Medicine: Official Journal of the American College of Medical Genetics, vol.20, issue.3, pp.351-59, 2018.

V. Kergourlay, G. Raï, G. Blandin, D. Salgado, C. Béroud et al., Identification of Splicing Defects Caused by Mutations in the Dysferlin Gene, Human Mutation, vol.35, issue.12, pp.1532-1573, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01610021

M. Krahn, V. Biancalana, M. Cerino, A. Perrin, L. Michel-calemard et al., A National French Consensus on Gene Lists for the Diagnosis of Myopathies Using next-Generation Sequencing, European Journal of Human Genetics: EJHG, vol.27, issue.3, pp.349-52, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02434896

K. Lee, K. Krempely, M. E. Roberts, M. J. Anderson, F. Carneiro et al., Specifications of the ACMG/AMP Variant Curation Guidelines for the Analysis of Germline CDH1 Sequence Variants, Human Mutation, vol.39, issue.11, pp.1553-68, 2018.

Z. Lu, P. Jiang, and Y. Xing, Genetic Variation of Pre-mRNA Alternative Splicing in Human Populations, Wiley Interdisciplinary Reviews. RNA, vol.3, issue.4, pp.581-92, 2012.

M. Mort, T. Sterne-weiler, B. Li, E. V. Ball, D. N. Cooper et al., MutPred Splice: Machine Learning-Based Prediction of Exonic Variants That Disrupt Splicing, Genome Biology, vol.15, issue.1, p.19, 2014.

M. Nissim-rafinia and B. Kerem, Splicing Regulation as a Potential Genetic Modifier, Trends in Genetics: TIG, vol.18, issue.3, pp.123-150, 2002.

A. M. Oza, T. Marina, S. E. Distefano, B. J. Hemphill, A. R. Cushman et al., Expert Specification of the ACMG/AMP Variant Interpretation Guidelines for Genetic Hearing Loss, Human Mutation, vol.39, issue.11, pp.1593-1613, 2018.

E. Park, Z. Pan, Z. Zhang, L. Lin, and Y. Xing, The Expanding Landscape of Alternative Splicing Variation in Human Populations, American Journal of Human Genetics, vol.102, issue.1, pp.11-26, 2018.

F. Puppo, E. Dionnet, M. Gaillard, P. Gaildrat, C. Castro et al., Identification of Variants in the 4q35 Gene FAT1 in Patients with a Facioscapulohumeral Dystrophy-like Phenotype, Human Mutation, vol.36, issue.4, pp.443-53, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01662841

I. Richard, C. Roudaut, A. Saenz, R. Pogue, J. E. Grimbergen et al., Calpainopathy-a Survey of Mutations and Polymorphisms, American Journal of Human Genetics, vol.64, issue.6, pp.1524-1564, 1999.

S. Richards, N. Aziz, S. Bale, D. Bick, S. Das 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, Genetics in Medicine: Official Journal of the American College of Medical Genetics, vol.17, issue.5, pp.405-429, 2015.

C. F. Rowlands, D. Baralle, and J. M. Ellingford, Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing, Cells, vol.8, issue.12, 2019.

,

R. Savisaar and L. D. Hurst, Estimating the Prevalence of Functional Exonic Splice Regulatory Information, Human Genetics, vol.136, issue.9, pp.1059-78, 2017.

J. D. Stephenson, A. Roman, A. Laskowski, M. E. Nightingale, J. M. Hurles et al., VarMap: A Web Tool for Mapping Genomic Coordinates to Protein Sequence and Structure and Retrieving Protein Structural Annotations, Bioinformatics, vol.35, issue.22, pp.4854-56, 2019.

J. Théry, S. Christophe, P. Krieger, F. Gaildrat, M. Révillion et al., Contribution of Bioinformatics Predictions and Functional Splicing Assays to the Interpretation of Unclassified Variants of the BRCA Genes, European Journal of Human Genetics: EJHG, vol.19, issue.10, pp.1052-58, 2011.

I. Tournier, M. Vezain, A. Martins, F. Charbonnier, S. Baert-desurmont et al., A Large Fraction of Unclassified Variants of the Mismatch Repair Genes MLH1 and MSH2 Is Associated with Splicing Defects, Human Mutation, vol.29, issue.12, pp.1412-1436, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02336389

J. J. Turunen, H. Elina, B. Niemelä, M. J. Verma, and . Frilander, The Significant Other: Splicing by the Minor Spliceosome, Wiley Interdisciplinary Reviews. RNA, vol.4, issue.1, pp.61-76, 2013.

A. Woolfe, J. C. Mullikin, and L. E. , Genomic Features Defining Exonic Variants That Modulate Splicing, Genome Biology, vol.11, issue.2, p.20, 2010.

G. Yeo and C. B. Burge, Maximum Entropy Modeling of Short Sequence Motifs with Applications to RNA Splicing Signals, Journal of Computational Biology: A Journal of Computational Molecular Cell Biology, vol.11, issue.2-3, pp.377-94, 2004.

. Zhou, M. N. Xin, M. R. Edmonson, A. Wilkinson, G. Patel et al., Exploring Genomic Alteration in Pediatric Cancer Using ProteinPaint, Bibliographie 1000 Genomes Project Consortium, vol.48, pp.68-74, 2015.

A. N. Abou-tayoun, T. Pesaran, M. T. Distefano, A. Oza, H. L. Rehm et al., Recommendations for interpreting the loss of function PVS1, ClinGen SVI), 2018.

, ACMG/AMP variant criterion, Human Mutation, vol.39, pp.1517-1524

R. Acuna-hidalgo, H. Sengul, M. Steehouwer, M. Van-de-vorst, S. H. Vermeulen et al., Ultra-sensitive Sequencing Identifies High Prevalence of Clonal Hematopoiesis-Associated Mutations throughout Adult Life, The American Journal of Human Genetics, vol.101, pp.50-64, 2017.

A. Allot, Y. Peng, C. Wei, K. Lee, L. Phan et al., LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC, Nucleic Acids Res, vol.46, pp.530-536, 2018.

L. M. Amendola, G. P. Jarvik, M. C. Leo, H. M. Mclaughlin, Y. Akkari et al., Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium, American Journal of Human Genetics, vol.98, pp.1067-1076, 2016.

L. V. Anderson, K. Davison, J. A. Moss, I. Richard, M. Fardeau et al., Characterization of monoclonal antibodies to calpain 3 and protein expression in muscle from patients with limb-girdle muscular dystrophy type 2A, Am. J. Pathol, vol.153, pp.1169-1179, 1998.

. Ba1_exception_list, BA1 Exception List, 2018.

, BA1_Exception_List_Nomination_Form BA1 Exception List Nomination Form

A. Barp, P. Laforet, L. Bello, G. Tasca, J. Vissing et al., European muscle MRI study in limb girdle muscular dystrophy type R1/2A (LGMDR1/LGMD2A), J. Neurol, vol.267, pp.45-56, 2020.

L. J. Bean, B. Funke, C. M. Carlston, J. L. Gannon, S. Kantarci et al., Diagnostic gene sequencing panels: from design to report-a technical standard of the American College of, Medical Genetics and Genomics (ACMG). Genet. Med, 2019.

L. G. Biesecker, S. M. Harrison, and C. , The ACMG/AMP reputable source criteria for the interpretation of sequence variants, Genet. Med, vol.20, pp.1687-1688, 2018.

J. Birgmeier, C. A. Deisseroth, L. E. Hayward, L. M. Galhardo, A. P. Tierno et al., AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature, Genet. Med, vol.22, pp.362-370, 2020.

C. Bonnet, S. Krieger, M. Vezain, A. Rousselin, I. Tournier et al.,

, Screening BRCA1 and BRCA2 unclassified variants for splicing mutations using reverse transcription PCR on patient RNA and an ex vivo assay based on a splicing reporter minigene, Journal of Medical Genetics, vol.45, pp.438-446

S. E. Brnich, A. N. Abou-tayoun, F. J. Couch, G. R. Cutting, M. S. Greenblatt et al., Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework, Genome Medicine, vol.12, p.3, 2019.

H. P. Buermans and J. T. Den-dunnen, Next generation sequencing technology: Advances and applications, Biochim. Biophys. Acta, vol.1842, pp.1932-1941, 2014.

J. Cheng, T. Y. Nguyen, K. J. Cygan, M. H. Çelik, W. G. Fairbrother et al., MMSplice: modular modeling improves the predictions of genetic variant effects on splicing, Genome Biol, vol.20, p.48, 2019.

. Clingen_lysosomal_storage_disorders_expert_panel, ClinGen Lysosomal Storage Disorders Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1, 2019.

. Clingen_tp53_expert_panel, ClinGen TP53 Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1, 2019.

D. N. Cooper, M. Krawczak, C. Polychronakos, C. Tyler-smith, and H. Kehrer-sawatzki, Where genotype is not predictive of phenotype: towards an understanding of the molecular basis of reduced penetrance in human inherited disease, Human Genetics, vol.132, pp.1077-1130, 2013.

F. Desmet, D. Hamroun, M. Lalande, G. Collod-béroud, M. Claustres et al., Human Splicing Finder: an online bioinformatics tool to predict splicing signals, Nucleic Acids Res, vol.37, p.67, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00396239

J. Desvignes, M. Bartoli, V. Delague, M. Krahn, M. Miltgen et al., VarAFT: a variant annotation and filtration system for human next generation sequencing data, Nucleic Acids Res, vol.46, pp.545-553, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01852493

M. T. Distefano, S. E. Hemphill, A. M. Oza, R. K. Siegert, A. R. Grant et al., ClinGen expert clinical validity curation of 164 hearing loss gene-disease pairs, Genetics in Medicine, vol.21, pp.2239-2247, 2019.

K. D. Doig, A. Fellowes, A. H. Bell, A. Seleznev, D. Ma et al., PathOS: a decision support system for reporting high throughput sequencing of cancers in clinical diagnostic laboratories, 2017.

, Genome Med, vol.9, p.38

M. T. Ebbert, T. D. Jensen, K. Jansen-west, J. P. Sens, J. S. Reddy et al., Systematic analysis of dark and camouflaged genes reveals disease-relevant genes hiding in plain sight, Genome Biol, vol.20, p.97, 2019.

G. A. Erikson, D. L. Bodian, M. Rueda, B. Molparia, E. R. Scott et al., Whole-Genome Sequencing of a Healthy Aging Cohort, Cell, vol.165, pp.1002-1011, 2016.

M. Fanin, L. Fulizio, A. C. Nascimbeni, M. Spinazzi, G. Piluso et al., Molecular diagnosis in LGMD2A: mutation analysis or protein testing?, Hum. Mutat, vol.24, pp.52-62, 2004.

P. Gaildrat, A. Killian, A. Martins, I. Tournier, T. Frébourg et al., Use of Splicing Reporter Minigene Assay to Evaluate the Effect on Splicing of Unclassified Genetic Variants, pp.249-257, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02336373

B. D. Gelb, H. Cavé, M. W. Dillon, K. W. Gripp, J. A. Lee et al., ClinGen's RASopathy Expert Panel consensus methods for variant interpretation, Genetics in Medicine: Official Journal of the American College of Medical Genetics, vol.20, pp.1334-1345, 2018.

R. Ghosh, N. Oak, and S. E. Plon, Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines, Genome Biol, vol.18, p.225, 2017.

R. Ghosh, S. M. Harrison, H. L. Rehm, S. E. Plon, L. G. Biesecker et al., Updated recommendation for the benign stand-alone ACMG/AMP criterion, Sequence Variant Interpretation Working Group, vol.39, pp.1525-1530, 2018.

S. Gorokhova, V. Biancalana, N. Lévy, J. Laporte, M. Bartoli et al., Clinical massively parallel sequencing for the diagnosis of myopathies, Rev. Neurol, vol.171, pp.558-571, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01610014

S. Gorokhova, M. Cerino, Y. Mathieu, S. Courrier, J. Desvignes et al., Comparing targeted exome and whole exome approaches for genetic diagnosis of neuromuscular disorders, Appl Transl Genom, vol.7, pp.26-31, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01610017

M. Haeussler, A. S. Zweig, C. Tyner, M. L. Speir, K. R. Rosenbloom et al., The UCSC Genome Browser database: 2019 update, Nucleic Acids Res, vol.47, pp.853-858, 2019.

S. M. Hosseini, R. Kim, S. Udupa, G. Costain, R. Jobling et al., Reappraisal of Reported Genes for Sudden Arrhythmic Death, Circulation, vol.138, pp.1195-1205, 2018.

C. Houdayer, V. Caux-moncoutier, S. Krieger, M. Barrois, F. Bonnet et al., Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants, Human Mutation, vol.33, pp.1228-1238, 2012.

X. Luo, S. Feurstein, S. Mohan, C. C. Porter, S. A. Jackson et al., ClinGen Myeloid Malignancy Variant Curation Expert Panel recommendations for germline RUNX1 variants, Blood Advances, vol.3, pp.2962-2979, 2019.

G. Matthijs, E. Souche, M. Alders, A. Corveleyn, S. Eck et al., Guidelines for diagnostic next-generation sequencing, Eur J Hum Genet, vol.24, pp.2-5, 2016.

J. L. Mester, R. Ghosh, T. Pesaran, R. Huether, R. Karam et al., Gene-specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel, Human Mutation, vol.39, pp.1581-1592, 2018.

M. Mort, T. Sterne-weiler, B. Li, E. V. Ball, D. N. Cooper et al., MutPred Splice: machine learning-based prediction of exonic variants that disrupt splicing, Genome Biol, vol.15, p.19, 2014.

M. Münz, S. Mahamdallie, S. Yost, A. Rimmer, E. Poyastro-pearson et al., CoverView: a sequence quality evaluation tool for next generation sequencing data, Wellcome Open Res, vol.3, p.36, 2018.

A. C. Nascimbeni, M. Fanin, E. Tasca, A. , and C. , Transcriptional and translational effects of intronic CAPN3 gene mutations, Hum. Mutat, vol.31, pp.1658-1669, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00613752

S. B. Ng, E. H. Turner, P. D. Robertson, S. D. Flygare, A. W. Bigham et al., Targeted capture and massively parallel sequencing of 12 human exomes, Nature, vol.461, pp.272-276, 2009.

A. M. Oza, M. T. Distefano, S. E. Hemphill, B. J. Cushman, A. R. Grant et al., Expert specification of the ACMG/AMP variant interpretation guidelines for genetic hearing loss, Human Mutation, vol.39, pp.1593-1613, 2018.

E. Park, Z. Pan, Z. Zhang, L. Lin, and Y. Xing, The Expanding Landscape of Alternative Splicing Variation in Human Populations, The American Journal of Human Genetics, vol.102, pp.11-26, 2018.

B. S. Pedersen and A. R. Quinlan, Mosdepth: quick coverage calculation for genomes and exomes, Bioinformatics, vol.34, pp.867-868, 2018.

A. R. Quinlan, BEDTools: The Swiss-Army Tool for Genome Feature Analysis, Curr Protoc Bioinformatics, vol.47, pp.11-12, 2014.

A. R. Quinlan and I. M. Hall, BEDTools: a flexible suite of utilities for comparing genomic features, Bioinformatics, vol.26, pp.841-842, 2010.

H. L. Rehm, J. S. Berg, L. D. Brooks, C. D. Bustamante, J. P. Evans et al., ClinGen--the Clinical Genome Resource, N. Engl. J. Med, vol.372, pp.2235-2242, 2015.

I. Richard, C. Roudaut, A. Saenz, R. Pogue, J. E. Grimbergen et al., Calpainopathy-a survey of mutations and polymorphisms, Am. J. Hum. Genet, vol.64, pp.1524-1540, 1999.

S. Richards, N. Aziz, S. Bale, D. Bick, S. Das 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, Genetics in Medicine: Official Journal of the American College of Medical Genetics, vol.17, pp.405-424, 2015.

P. Romanet, M. Odou, M. North, A. Saveanu, L. Coppin et al., Proposition of adjustments to the ACMG-AMP framework for the interpretation of MEN1 missense variants, Human Mutation, vol.40, pp.661-674, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02461447

C. F. Rowlands, D. Baralle, and J. M. Ellingford, Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing, Cells, vol.8, p.1513, 2019.

R. Savisaar and L. D. Hurst, Estimating the prevalence of functional exonic splice regulatory information, Hum. Genet, vol.136, pp.1059-1078, 2017.

E. M. Scott, A. Halees, Y. Itan, E. G. Spencer, Y. He et al., Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery, Nat. Genet, vol.48, pp.1071-1076, 2016.

B. A. Seifert, J. L. Mcglaughon, S. A. Jackson, D. I. Ritter, M. E. Roberts et al., Determining the clinical validity of hereditary colorectal cancer and polyposis susceptibility genes using the Clinical Genome Resource Clinical Validity Framework, Genetics in Medicine, vol.21, pp.1507-1516, 2019.

N. T. Strande, E. R. Riggs, A. H. Buchanan, O. Ceyhan-birsoy, M. Distefano et al., Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource, Am. J. Hum. Genet, vol.100, pp.895-906, 2017.

N. T. Strande, E. R. Riggs, A. H. Buchanan, O. Ceyhan-birsoy, M. Distefano et al., Evaluating the Clinical Validity of Gene-Disease Associations: An Evidence-Based Framework Developed by the Clinical Genome Resource, Am. J. Hum. Genet, vol.100, pp.895-906, 2017.

, SVI Recommendation for in trans Criterion (PM3) -Version, p.3, 2019.

. Svi_recommendation_for_ps2_and_pm6_criteria, PS2/PM6: Recommendation for de novo PS2, p.6, 2018.

, ACMG/AMP criteria (Version 1.0)

D. Taliun, D. N. Harris, M. D. Kessler, J. Carlson, Z. A. Szpiech et al., , 2019.

A. Tarasov, A. J. Vilella, E. Cuppen, I. J. Nijman, and P. Prins, Sambamba: fast processing of NGS alignment formats, Bioinformatics, vol.31, pp.2032-2034, 2015.

S. Srivastava, J. A. Love-nichols, K. A. Dies, D. H. Ledbetter, C. L. Martin et al., Meta-analysis and multidisciplinary consensus statement: exome sequencing is a first-tier clinical diagnostic test for individuals with neurodevelopmental disorders, Genetics in Medicine, vol.21, pp.2413-2421, 2019.

J. C. Théry, S. Krieger, P. Gaildrat, F. Révillion, M. Buisine et al., Contribution of bioinformatics predictions and functional splicing assays to the interpretation of unclassified variants of the BRCA genes, Eur. J. Hum. Genet, vol.19, pp.1052-1058, 2011.

. Topmed, The NHLBI Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Program. BRAVO variant browser: University of Michigan and NHLBI, 2018.

I. Tournier, M. Vezain, A. Martins, F. Charbonnier, S. Baert-desurmont et al., A large fraction of unclassified variants of the mismatch repair genes MLH1 and MSH2 is associated with splicing defects, Hum. Mutat, vol.29, pp.1412-1424, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02336389

N. Whiffin, E. Minikel, R. Walsh, A. H. O'donnell-luria, K. Karczewski et al., Using high-resolution variant frequencies to empower clinical genome interpretation, Genet. Med, vol.19, pp.1151-1158, 2017.

G. Yeo and C. B. Burge, Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals, J. Comput. Biol, vol.11, pp.377-394, 2004.

D. B. Zastrow, H. Baudet, W. Shen, A. Thomas, Y. Si et al., Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene, Human Mutation, vol.39, pp.1569-1580, 2018.

A. Ziegler, E. Colin, D. Goudenège, and D. Bonneau, A snapshot of some pLI score pitfalls, Human Mutation, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02616943

A. Dans-dystrophies-musculaires-de-duchenne, S. Becker, ;. Et-comprendre, and . Amendola, Sur le plan international, les recommandations émises par l'ACMG-AMP (American College of, Medical Genetics & the Association for Molecular Pathology, 2016.

, Puisque l'interprétation des variants constitue une étape essentielle et critique pour l'homogénéisation inter-laboratoire des rendus de résultats

, AG a souhaité initier un Groupe de Travail spécifique dans l'objectif d'aboutir à un consensus national, dans le contexte actuel de la mise en place prochaine des premières plateformes PFMG2025

, DefiScience (Filière de Santé Maladies Rares du Développement Cérébral et de la Déficience Intellectuelle) et FILNEMUS (Filière de Santé des Maladies Rares Neuromusculaires), où des travaux d'homogénéisation de l'interprétation des variants de séquence ont été débutés, et seront disponibles comme base de travail. Les recommandations ACMG-AMP étant déjà largement appliquées par de nombreux laboratoires de diagnostic génétique en France, l'objectif qui nous semble à la fois important et réalisable à court terme serait une adoption officielle, Cette problématique a été abordée en particulier dans le cadre des commissions de diagnostic génétique des FNMR (Filières de soin Nationales de Maladies Rares ) AnDDi-Rares (Filière de Santé Anomalies du Développement et Déficience intellectuelle de Causes Rares)

, Pour cette raison, un groupe de travail représentatif des différentes parties constituantes du Réseau NGS-Diag a été mis en place pour procéder selon la démarche suivante

, Elaboration du présent document de recommandations en français, issu d'un document très complet déjà réalisé par l'équipe de Rouen

, et contact/lien à établir avec l'ACMG (notamment pour suivre les évolutions des recommandations de l'ACMG, et d'être associé à des versants d'évolution de ces recommandations). A ce stade aussi, des interactions avec le CREFIX (Centre de référence, d'innovation, d'expertise et de transfert)

, Relai des guidelines au sein des FNMR/réseaux d'oncogénétique, et travaux spécifiques éventuels au niveau des FNMR/réseaux d'oncogénétique (échange de fichiers vcf, spécifications si nécessaire de certains items de l'interprétation

, Il peut s'agir d'ajouter des arguments d'interprétation pour les métaboliciens (poids des dosages enzymatiques), les oncogénéticiens (poids des analyses somatiques), les cardiogénéticiens, etc? Ces recommandations

A. Tayoun, La 2 ème version de ce document prend en compte les modifications des recommandations ACMG publiées depuis la sortie de la première version de ce document (NGSDIAG_001), 2018.

R. Myh7, . Pah, . Surdités, G. Runx1, and . Gelb, et les recommandations gène-spécifiques élaborées par des Groupes d'experts du réseau ClinGen ou autres experts dans le domaine, vol.2, 2018.

. Bean, Un autre point important qui n'était pas abordé dans la première version de ce document est le choix des gènes sur les panels diagnostiques. L'objectif d'un panel de gènes à visée diagnostique est de maximiser la sensibilité clinique et de minimiser les explorations inutiles ainsi que le stress secondaires à la découverte des variants dont la signification clinique est incertaine, Les recommandations récentes de American College of Medical Genetics and Genomics (ACMG) visent à clarifier quels gènes sont adaptés pour être inclus dans les panels diagnostiques, 2019.

, Si l'indication est le diagnostic des cas cliniquement complexes, les GUS (Gene of uncertain significance) peuvent être inclus. Dans ce cas, le consentement type exome/génome est requis. De plus, dans le compte rendu, il est nécessaire de clairement séparer les variants identifiés dans les GUS des variants identifiés dans les GAD. La classification maximale attribué à un variant identifié dans un gène sans lien gène-pathologie établie (GUS) ne

, Plus le nombre de sujets testés attestant de la co-ségrégation du génotype avec le phénotype est important ou plus les sujets testés sont éloignés dans l'arbre généalogique, plus l'argument peut avoir un poids élevé. Afin de préciser ce critère, il a été proposé (Jarvik and Browning, Les nouveaux recommandations de groupe de travail ClinGen SVI propose le système de calcul des scores PS2/PM6(SVI_Recommendation_for_PS2_and_PM6_criteria, 2016.

, En fonction de la probabilité N calculée, une pondération associée est proposée de la manière suivante : Données issues d'une seule famille : N ? 1/32 : argument fort

, N ? 1/16 : argument moyen (PM)

, N ? 1/8 : argument faible (PP)

, Données issues de plusieurs familles : N ? 1/16 : argument fort

, N ? 1/8 : argument moyen (PM)

, N ? 1/4 : argument faible (PP)

, Des exemples de calcul pour la détermination de la probabilité « N », selon les différents modes de transmission, sont donnés dans l'article de Jarvik et Browning (Jarvik and Browning, Am J Hum Genet, 2016.

, Une façon plus simple d'évaluer la ségrégation est d'utiliser les tableaux avec des LOD scores précalculés, Cette approche est adoptée par les groupes ClinGen (les instructions détaillées disponibles à

R. Acuna-hidalgo, H. Sengul, M. Steehouwer, M. Van-de-vorst, S. H. Vermeulen et al., Ultra-sensitive Sequencing Identifies High Prevalence of Clonal Hematopoiesis-Associated Mutations throughout Adult Life, Am. J. Hum. Genet, vol.101, pp.50-64, 2017.

A. Allot, Y. Peng, C. Wei, K. Lee, L. Phan et al., LitVar: a semantic search engine for linking genomic variant data in PubMed and PMC, Nucleic Acids Res, vol.46, pp.530-536, 2018.

L. M. Amendola, G. P. Jarvik, M. C. Leo, H. M. Mclaughlin, Y. Akkari et al., Performance of ACMG-AMP Variant-Interpretation Guidelines among Nine Laboratories in the Clinical Sequencing Exploratory Research Consortium, Am. J. Hum. Genet, vol.98, pp.1067-1076, 2016.

. Ba1_exception_list, BA1 Exception List, 2018.

, BA1_Exception_List_Nomination_Form BA1 Exception List Nomination Form

L. J. Bean, B. Funke, C. M. Carlston, J. L. Gannon, S. Kantarci et al., Diagnostic gene sequencing panels: from design to report-a technical standard of the American College of Medical Genetics and Genomics (ACMG), Genet. Med. Off. J. Am. Coll. Med. Genet, 2019.

L. G. Biesecker, S. M. Harrison, and C. , The ACMG/AMP reputable source criteria for the interpretation of sequence variants, Genet. Med. Off. J. Am. Coll. Med. Genet, vol.20, pp.1687-1688, 2018.

J. Birgmeier, C. A. Deisseroth, L. E. Hayward, L. M. Galhardo, A. P. Tierno et al., AVADA: toward automated pathogenic variant evidence retrieval directly from the full-text literature, Genet. Med. Off. J. Am. Coll. Med. Genet, vol.22, pp.362-370, 2020.

S. E. Brnich, A. N. Abou-tayoun, F. J. Couch, G. R. Cutting, M. S. Greenblatt et al., Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework, Genome Med, vol.12, p.3, 2019.

J. Cheng, T. Y. Nguyen, K. J. Cygan, M. H. Çelik, W. G. Fairbrother et al., MMSplice: modular modeling improves the predictions of genetic variant effects on splicing, Genome Biol, vol.20, p.48, 2019.

. Clingen_lysosomal_storage_disorders_expert_panel, ClinGen Lysosomal Storage Disorders Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1, 2019.

. Clingen_tp53_expert_panel, ClinGen TP53 Expert Panel Specifications to the ACMG/AMP Variant Interpretation Guidelines Version 1, 2019.

F. Desmet, D. Hamroun, M. Lalande, G. Collod-béroud, M. Claustres et al., Human Splicing Finder: an online bioinformatics tool to predict splicing signals, Nucleic Acids Res, vol.37, p.67, 2009.
URL : https://hal.archives-ouvertes.fr/inserm-00396239

G. A. Erikson, D. L. Bodian, M. Rueda, B. Molparia, E. R. Scott et al., Whole-Genome Sequencing of a Healthy Aging Cohort, Cell, vol.165, pp.1002-1011, 2016.

B. D. Gelb, H. Cavé, M. W. Dillon, K. W. Gripp, J. A. Lee et al., ClinGen's RASopathy Expert Panel consensus methods for variant interpretation, Genet. Med. Off. J. Am. Coll. Med. Genet, vol.20, pp.1334-1345, 2018.

R. Ghosh, N. Oak, and S. E. Plon, Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines, Genome Biol, vol.18, p.225, 2017.

R. Ghosh, S. M. Harrison, H. L. Rehm, S. E. Plon, L. G. Biesecker et al., Updated recommendation for the benign stand-alone ACMG/AMP criterion, Sequence Variant Interpretation Working Group, vol.39, pp.1525-1530, 2018.

M. Haeussler, A. S. Zweig, C. Tyner, M. L. Speir, K. R. Rosenbloom et al., The UCSC Genome Browser database: 2019 update, Nucleic Acids Res, vol.47, pp.853-858, 2019.

C. Houdayer, V. Caux-moncoutier, S. Krieger, M. Barrois, F. Bonnet et al., Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants, Hum. Mutat, vol.33, pp.1228-1238, 2012.

Z. Hu, C. Yu, M. Furutsuki, G. Andreoletti, M. Ly et al., VIPdb, a genetic Variant Impact Predictor Database, Hum. Mutat, vol.40, pp.1202-1214, 2019.

N. Huang, I. Lee, E. M. Marcotte, and M. E. Hurles, Characterising and Predicting Haploinsufficiency in the Human Genome, PLoS Genet, vol.6, 2010.

N. M. Ioannidis, J. H. Rothstein, V. Pejaver, S. Middha, S. K. Mcdonnell et al., REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants, Am. J. Hum. Genet, vol.99, pp.877-885, 2016.

K. A. Jagadeesh, J. M. Paggi, J. S. Ye, P. D. Stenson, D. N. Cooper et al., S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing, Nat. Genet, vol.51, pp.755-763, 2019.

K. Jaganathan, S. Kyriazopoulou-panagiotopoulou, J. F. Mcrae, S. F. Darbandi, D. Knowles et al., Predicting Splicing from Primary Sequence with Deep Learning, Cell, vol.176, pp.535-548, 2019.

K. J. Karczewski, L. C. Francioli, G. Tiao, B. B. Cummings, J. Alföldi et al., Variation across 141,456 human exomes and genomes reveals the spectrum of loss-offunction, 2019.

M. A. Kelly, C. Caleshu, A. Morales, J. Buchan, Z. Wolf et al., Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel, Genet. Med. Off. J. Am. Coll. Med. Genet, vol.20, pp.351-359, 2018.

W. J. Kent, C. W. Sugnet, T. S. Furey, K. M. Roskin, T. H. Pringle et al., The human genome browser at UCSC, Genome Res, vol.12, pp.996-1006, 2002.

K. Lee, K. Krempely, M. E. Roberts, M. J. Anderson, F. Carneiro et al., Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants, Hum. Mutat, vol.39, pp.1553-1568, 2018.

K. Lee, K. Krempely, M. E. Roberts, M. J. Anderson, F. Carneiro et al., Specifications of the ACMG/AMP variant curation guidelines for the analysis of germline CDH1 sequence variants, Hum. Mutat, vol.39, pp.1553-1568, 2018.

X. Luo, S. Feurstein, S. Mohan, C. C. Porter, S. A. Jackson et al., ClinGen Myeloid Malignancy Variant Curation Expert Panel recommendations for germline RUNX1 variants, Blood Adv, vol.3, pp.2962-2979, 2019.

J. L. Mester, R. Ghosh, T. Pesaran, R. Huether, R. Karam et al., Gene-specific criteria for PTEN variant curation: Recommendations from the ClinGen PTEN Expert Panel, Hum. Mutat, vol.39, pp.1581-1592, 2018.

A. M. Oza, M. T. Distefano, S. E. Hemphill, B. J. Cushman, A. R. Grant et al., Expert specification of the ACMG/AMP variant interpretation guidelines for genetic hearing loss, Hum. Mutat, vol.39, pp.1593-1613, 2018.

S. Richards, N. Aziz, S. Bale, D. Bick, S. Das 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. Off. J. Am. Coll. Med. Genet, vol.17, pp.405-424, 2015.

P. Romanet, M. Odou, M. North, A. Saveanu, L. Coppin et al., Proposition of adjustments to the ACMG-AMP framework for the interpretation of MEN1 missense variants, Hum. Mutat, vol.40, pp.661-674, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02461447

E. M. Scott, A. Halees, Y. Itan, E. G. Spencer, Y. He et al., Characterization of Greater Middle Eastern genetic variation for enhanced disease gene discovery, Nat. Genet, vol.48, pp.1071-1076, 2016.

, SVI Recommendation for in trans Criterion (PM3) -Version, p.3, 2019.

. Svi_recommendation_for_ps2_and_pm6_criteria, PS2/PM6: Recommendation for de novo PS2, p.6, 2018.

, ACMG/AMP criteria (Version 1.0)

D. Taliun, D. N. Harris, M. D. Kessler, J. Carlson, Z. A. Szpiech et al., , 2019.

. Topmed, The NHLBI Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Program. BRAVO variant browser: University of Michigan and NHLBI, 2018.

N. Whiffin, E. Minikel, R. Walsh, A. H. O'donnell-luria, K. Karczewski et al., Using high-resolution variant frequencies to empower clinical genome interpretation, Genet. Med. Off. J. Am. Coll. Med. Genet, vol.19, pp.1151-1158, 2017.

G. Yeo and C. B. Burge, Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals, J. Comput. Biol. J. Comput. Mol. Cell Biol, vol.11, pp.377-394, 2004.

D. B. Zastrow, H. Baudet, W. Shen, A. Thomas, Y. Si et al., Unique aspects of sequence variant interpretation for inborn errors of metabolism (IEM): The ClinGen IEM Working Group and the Phenylalanine Hydroxylase Gene, Hum. Mutat, vol.39, pp.1569-1580, 2018.

A. Ziegler, E. Colin, D. Goudenège, and D. Bonneau, A snapshot of some pLI score pitfalls, Hum. Mutat, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02616943