Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases
2022; Elsevier BV; Volume: 24; Issue: 5 Linguagem: Inglês
10.1016/j.jmoldx.2022.02.003
ISSN1943-7811
AutoresGemma Bullich, Leslie Matalonga, Montserrat Pujadas, Anastasios Papakonstantinou, Davide Piscia, Raúl Tonda, Rafael Artuch, P. Gallano, Glòria Garrabou, Juan R. González, Daniel Grinberg, Míriam Guitart, Steven Laurie, Conxi Lázaro, Cristina Luengo, Ramón Martí, Montserrat Milà, David Ovelleiro, Genı́s Parra, Aurora Pujol, Eduardo F. Tizzano, Alfons Macaya, Francesc Palau, Antònia Ribes, Luis A. Pérez‐Jurado, Sergi Beltrán, Agatha Schlüter, Agustí Rodríguez‐Palmero, Alejandro Cáceres, A. Nascimento, Àngels García‐Cazorla, Anna M. Cueto‐González, Anna Marcé‐Grau, A. Lô, Antonio Federico Martínez‐Monseny, Aurora Sánchez, Belén de la Fuente García, Belén Pérez‐Dueñas, Bernat Gel, Berta Fusté, Carles Hernández-Ferrer, Carlos Casasnovas, C. Ortez, César Arjona, Cristina Hernando‐Davalillo, Daniel Natera‐de Benito, Daniel Picó Amador, David Gómez‐Andrés, Dèlia Yubero, Dolors Pelegrí-Sisó, Edgard Verdura, Elena García‐Arumí, Elisabeth Castellanos, Elisabeth Gabau, Ester Tobías, Fermina López‐Grondona, Francesc Cardellach, Francesc Josep García‐García, Francina Munell, Frederic Tort, Gemma Aznar, Gemma Olivé-Cirera, Gemma Tell‐Martí, Gerard Muñoz-Pujol, Ida Paramonov, Ignacio Blanco, Irene Madrigal, Irene Valenzuela, Marta Gut, Ivon Cuscó, Jean-Rémi Trotta, Jordi Cruz, Jordi Díaz‐Manera, José C. Milisenda, Josep Ma Grau, Judit García‐Villoria, Judith Armstrong, Judith Cantó, Júlia Sala‐Coromina, Laia Rodríguez‐Revenga, Laura Alías, Laura Gort, Lidia González‐Quereda, Mar Costa, Marcos Fernández-Callejo, Marcos López‐Sánchez, María Isabel Álvarez‐Mora, Marta Gut, Mercedes Serrano, Miquel Raspall‐Chaure, Mireia del Toro, Mónica Bayés, Neus Baena Díez, Nino Spataro, Núria Capdevila, Olatz Ugarteburu, Patricia Muñoz‐Cabello, P. Romero Duque, Raquel Rabionet, Ricard Rojas‐García, Rosa Calvo, Roser Urreizti, Sara Bernal, Susana Boronat, Susana Balcells, Teresa Vendrell,
Tópico(s)Genetic Syndromes and Imprinting
ResumoMany patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%). Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%). Rare diseases collectively affect 3.5% to 5.9% of the worldwide population, and around 72% of them are of genetic origin.1Nguengang Wakap S. Lambert D.M. Olry A. Rodwell C. Gueydan C. Lanneau V. Murphy D. Le Cam Y. Rath A. Estimating cumulative point prevalence of rare diseases: analysis of the Orphanet database.Eur J Hum Genet. 2020; 28: 165-173Crossref PubMed Scopus (294) Google Scholar Patients with rare diseases often undergo a years-long diagnostic odyssey characterized by multiple tests with little or no success. Health system costs ascribed to rare disease patients are an important public health issue, highlighting the need for improved access to early diagnosis and care coordination.2Walker C.E. Mahede T. Davis G. Miller L.J. Girschik J. Brameld K. Sun W. Rath A. Aymé S. Zubrick S.R. Baynam G.S. Molster C. Dawkins H.J.S. Weeramanthri T.S. The collective impact of rare diseases in Western Australia: an estimate using a population-based cohort.Genet Med. 2017; 19: 546-552Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar,3Neu M.B. Bowling K.M. Cooper G.M. Clinical utility of genomic sequencing.Curr Opin Pediatr. 2019; 31: 732-738Crossref Scopus (9) Google Scholar Reaching a molecular diagnosis in a timely manner shortens the diagnostic odyssey, and can guide therapeutic strategies, improve clinical management, and provide genetic counseling for patients and their families with respect to recurrence risk and prenatal options.3Neu M.B. Bowling K.M. Cooper G.M. Clinical utility of genomic sequencing.Curr Opin Pediatr. 2019; 31: 732-738Crossref Scopus (9) Google Scholar Reanalysis of existing genomic data has emerged as an effective approach to increasing the diagnostic yield of previously undiagnosed patients, not only because of the rapid path of discovery of novel gene-disease associations, but also due to improvements in analytical workflows, reclassification of previously unrecognized variants, and/or availability of new phenotypic data.4Wright C.F. McRae J.F. Clayton S. Gallone G. Aitken S. FitzGerald T.W. Jones P. Prigmore E. Rajan D. Lord J. Sifrim A. Kelsell R. Parker M.J. Barrett J.C. Hurles M.E. FitzPatrick D.R. Firth H.V. DDD StudyMaking new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders.Genet Med. 2018; 20: 1216-1223Abstract Full Text Full Text PDF PubMed Scopus (169) Google Scholar However, systematic reanalysis coupled with reinterpretation of the results requires iterative communication between researchers, clinicians, and families as diagnoses can be made years after the initial sequencing and analysis were performed.3Neu M.B. Bowling K.M. Cooper G.M. Clinical utility of genomic sequencing.Curr Opin Pediatr. 2019; 31: 732-738Crossref Scopus (9) Google Scholar Systems like the RD-Connect Genome-Phenome Analysis Platform (GPAP; https://platform.rd-connect.eu, last accessed December 6, 2021) facilitate such communication and collaboration between clinicians and researchers within a trustworthy environment. The RD-Connect GPAP is an International Rare Diseases Research Consortium (IRDiRC)–recognized resource that brings together pseudonymized clinical/phenotypic and genomic data with tools and services to enable data sharing, analysis and interpretation for rare disease diagnosis, and gene discovery.5Thompson R. Johnston L. Taruscio D. Monaco L. Béroud C. Gut I.G. Hansson M.G. 't Hoen P.-B.A. Patrinos G.P. Dawkins H. Ensini M. Zatloukal K. Koubi D. Heslop E. Paschall J.E. Posada M. Robinson P.N. Bushby K. Lochmüller H. RD-Connect: an integrated platform connecting databases, registries, biobanks and clinical bioinformatics for rare disease research.J Gen Intern Med. 2014; 29: S780-S787Crossref PubMed Scopus (129) Google Scholar,6Lochmüller H. Badowska D.M. Thompson R. Knoers N.V. Aartsma-Rus A. Gut I. Wood L. Harmuth T. Durudas A. Graessner H. Schaefer F. Riess O. RD-Connect consortium, NeurOmics consortium, EURenOmics consortiumRD-Connect, NeurOmics and EURenOmics: collaborative European initiative for rare diseases.Eur J Hum Genet. 2018; 26: 778-785Crossref PubMed Scopus (35) Google Scholar The Undiagnosed Rare Disease Program of Catalonia (URD-Cat) aims to provide the Catalan Health System with personalized genomic medicine as a fully integrated service for patients with rare diseases, initially as a pilot project for rare diseases with neurologic involvement. This project involves the main groups that work in rare diseases in Catalonia: 15 consolidated research groups belonging to 7 Health Research Institutes in Barcelona Hospital del Mar Research Institute (IMIM); Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS); Institut d'Investigació Biomèdica de Bellvitge (IDIBELL); Hospital Sant Joan de Déu (HSJD); Vall d'Hebron Institut de Recerca (VHIR); Hospital de la Santa Creu i Sant Pau (HSP); Institut Germans Trias i Pujol (IGTP); the National Center for Genomic Analysis–Center for Genomic Regulation (CNAG-CRG); the Barcelona Institute for Global Health (ISGlobal); the National Supercomputing Center of Barcelona (BSC); and the Spanish Rare Disease Patient Federation (FEDER). A multidisciplinary team of >140 professionals, including clinicians, geneticists, bioinformaticians, biochemists, technicians, and software engineers, participate in the project. A customized version of the GPAP has been deployed for the URD-Cat project to meet the specific requirements needed to be integrated with a National Health System regarding data privacy, interoperability, availability, sustainability, and scalability, among others. To date, the project has systematically collated clinical and phenotypic information from 928 undiagnosed index cases and their relatives (total of 1569 individuals) for which most of the available diagnostic tests had been performed without yielding a positive result. For 323 of those index cases (543 of those individuals, including relatives), previously existing good quality genomic data (panel, exome, or genome) were reanalyzed. Furthermore, additional sequencing data (exome, genome, or transcriptome) were generated in nine index cases with a single heterozygous pathogenic or likely pathogenic variant in an autosomal recessive gene identified through the reanalysis. This study describes the workflow applied and the diagnostic yield after reanalysis. The effect of the phenotype annotation quality and the sequencing approach as well as the reasons why diagnosed patients were not diagnosed in the original analysis are also explored. Pseudonymized clinical information from undiagnosed patients and their family members was collected from medical records by clinicians or geneticists from participating hospitals using PhenoTips.7Girdea M. Dumitriu S. Fiume M. Bowdin S. Boycott K.M. Chénier S. Chitayat D. Faghfoury H. Meyn M.S. Ray P.N. So J. Stavropoulos D.J. Brudno M. PhenoTips: patient phenotyping software for clinical and research use.Hum Mutat. 2013; 34: 1057-1065Crossref PubMed Scopus (161) Google Scholar A specific form was generated in collaboration with them to collect all relevant data: demographic data, disease category, family history and pedigree, personal history, clinical symptoms, biochemical analysis, and previous genomic and nongenomic tests performed. Clinical symptoms were collected using standardized Human Phenotype Ontology (HPO) terms.8Robinson P.N. Köhler S. Bauer S. Seelow D. Horn D. Mundlos S. The human phenotype ontology: a tool for annotating and analyzing human hereditary disease.Am J Hum Genet. 2008; 83: 610-615Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar Orphanet Rare Disease Ontology (https://www.orpha.net/consor/cgi-bin/index.php, last accessed December 6, 2021) and Online Mendelian Inheritance in Man (https://www.omim.org, last accessed December 6, 2021) codes were used to enter a clinical and molecular diagnosis, respectively.9Rath A. Olry A. Dhombres F. Brandt M.M. Urbero B. Ayme S. Representation of rare diseases in health information systems: the Orphanet approach to serve a wide range of end users.Hum Mutat. 2012; 33: 803-808Crossref PubMed Scopus (222) Google Scholar Quality of the phenotypic data was assessed using the Monarch star rating system integrated within PhenoTips. The Monarch star rating system is an annotation sufficiency meter developed by the Monarch initiative that assesses the breadth and depth of the phenotype annotation profile for a given patient in the context of all curated human and model organisms using a five-star rating system: bad (0 to 1.4), fair (1.5 to 2.4), good (2.5 to 3.4), very good (3.5 to 4.4), and excellent (4.5 to 5).10Mungall C.J. McMurry J.A. Köhler S. Balhoff J.P. Borromeo C. Brush M. Carbon S. Conlin T. Dunn N. Engelstad M. Foster E. Gourdine J.P. Jacobsen J.O.B. Keith D. Laraway B. Lewis S.E. NguyenXuan J. Shefchek K. Vasilevsky N. Yuan Z. Washington N. Hochheiser H. Groza T. Smedley D. Robinson P.N. Haendel M.A. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.Nucleic Acids Res. 2017; 45: D712-D722Crossref PubMed Scopus (155) Google Scholar All phenotypic entries were reviewed and approved by two clinical experts from another participating center. The reviewers were allowed to ask for clarifications or additional information to be included in the records during the review process. Among all of the undiagnosed patients and relatives whose information was submitted to PhenoTips, 331 index cases (560 individuals including relatives) from seven hospitals were considered for reanalysis. The inclusion criteria were as follows: i) available and previously analyzed genomic data (genome, exome, or panel), ii) clinical suspicion of rare disease with neurologic involvement, iii) clinical suspicion of a genetic etiology, iv) availability of clinical information and disease progression (from the patients and the family members), and v) written informed consent of the patient or parents/guardians, enabling the use of the data in the URD-Cat project. This study was approved by the local ethics committees from each URD-Cat partnering institution. The URD-Cat GPAP, a customized version of the RD-Connect GPAP, was implemented for the URD-Cat project. Existing FASTQ files, all of them obtained with Illumina (San Diego, CA) sequencing platforms, and corresponding metadata (sequencing approach, capture kit, singleton or extended family analysis, and DNA source) were submitted for processing to the URD-Cat GPAP. All of the samples were bioinformatically processed at the CNAG-CRG using the RD-Connect pipeline,11Laurie S. Fernandez-Callejo M. Marco-Sola S. Trotta J.-R. Camps J. Chacón A. Espinosa A. Gut M. Gut I. Heath S. Beltran S. From wet-lab to variations: concordance and speed of bioinformatics pipelines for whole genome and whole exome sequencing.Hum Mutat. 2016; 37: 1263-1271Crossref PubMed Scopus (29) Google Scholar which is based on GATK best practices.12DePristo M.A. Banks E. Poplin R. Garimella K.V. Maguire J.R. Hartl C. Philippakis A.A. del Angel G. Rivas M.A. Hanna M. McKenna A. Fennell T.J. Kernytsky A.M. Sivachenko A.Y. Cibulskis K. Gabriel S.B. Altshuler D. Daly M.J. A framework for variation discovery and genotyping using next-generation DNA sequencing data.Nat Genet. 2011; 43: 491-498Crossref PubMed Scopus (6753) Google Scholar Briefly, sequencing reads were mapped to human genome build GRCh37d5 using BWA-MEM version 0.7.15.13Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM.arXiv. 2013; ([Preprint] doi:10.48550/arXiv.1303.3997)Google Scholar The resulting BAM files were sorted, and duplicate reads were removed using Picard version 1.110 (http://broadinstitute.github.io/picard, last accessed September 6, 2021). Insertion and deletion realignment and base quality score recalibration were performed using GATK version 3.6.14McKenna A. Hanna M. Banks E. Sivachenko A. Cibulskis K. Kernytsky A. Garimella K. Altshuler D. Gabriel S. Daly M. DePristo M.A. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.Genome Res. 2010; 20: 1297-1303Crossref PubMed Scopus (13737) Google Scholar Single-nucleotide variants and short insertions and deletions were called using GATK version 3.6 HaplotypeCaller tool.12DePristo M.A. Banks E. Poplin R. Garimella K.V. Maguire J.R. Hartl C. Philippakis A.A. del Angel G. Rivas M.A. Hanna M. McKenna A. Fennell T.J. Kernytsky A.M. Sivachenko A.Y. Cibulskis K. Gabriel S.B. Altshuler D. Daly M.J. A framework for variation discovery and genotyping using next-generation DNA sequencing data.Nat Genet. 2011; 43: 491-498Crossref PubMed Scopus (6753) Google Scholar Single-nucleotide variants and insertions and deletions with a minimum depth of coverage of 8 and a minimum genotype quality of 30 were released to the URD-Cat GPAP. Copy number variants (CNVs) were detected with ExomeDepth for exomes and gene panels,15Plagnol V. Curtis J. Epstein M. Mok K.Y. Stebbings E. Grigoriadou S. Wood N.W. Hambleton S. Burns S.O. Thrasher A.J. Kumararatne D. Doffinger R. Nejentsev S. A robust model for read count data in exome sequencing experiments and implications for copy number variant calling.Bioinformatics. 2012; 28: 2747-2754Crossref PubMed Scopus (351) Google Scholar analyzing together all of the samples captured with the same kit. CNVs on the sex chromosomes were evaluated by comparison against samples from the same sex only. CNVs were called only for groups of samples in which there were at least 10 samples captured with the same kit (Supplemental Table S1). The CNV results were crossed with sets of common CNVs from Conrad et al16Conrad D.F. Pinto D. Redon R. Feuk L. Gokcumen O. Zhang Y. Aerts J. Andrews T.D. Barnes C. Campbell P. Fitzgerald T. Hu M. Ihm C.H. Kristiansson K. Macarthur D.G. Macdonald J.R. Onyiah I. Pang A.W.C. Robson S. Stirrups K. Valsesia A. Walter K. Wei J. Tyler-Smith C. Carter N.P. Lee C. Scherer S.W. Hurles M.E. Wellcome Trust Case Control ConsortiumOrigins and functional impact of copy number variation in the human genome.Nature. 2010; 464: 704-712Crossref PubMed Scopus (1374) Google Scholar and Database of Genomic Variants Gold Standard data set (http://dgv.tcag.ca, last accessed May 21, 2021).17MacDonald J.R. Ziman R. Yuen R.K.C. Feuk L. Scherer S.W. The Database of Genomic Variants: a curated collection of structural variation in the human genome.Nucleic Acids Res. 2014; 42: D986-D992Crossref PubMed Scopus (721) Google Scholar All of the CNVs obtained were released to the URD-Cat platform. Additional sequencing data were obtained for nine index cases with a single heterozygous pathogenic or likely pathogenic variant in an autosomal recessive gene identified through the reanalysis. Genome (n = 5) or transcriptome (n = 2) sequencing was performed if the existing data were an exome, whereas exome sequencing (n = 2) was performed if the existing data were a panel. Kinship between all individuals with a genome or an exome was computed on alignment files (BAM) with Somalier version 2.618Pedersen B.S. Bhetariya P.J. Brown J. Marth G. Jensen R.L. Bronner M.P. Underhill H.R. Quinlan A.R. Somalier: rapid relatedness estimation for cancer and germline studies using efficient genome sketches.Genome Med. 2020; 12: 62Crossref Scopus (11) Google Scholar to identify putative duplicates and inconsistent family relationships. Runs of homozygosity (RoHs) were computed on the genetic variants from all genomic experiments using the PLINK software version 1.9019Purcell S. Neale B. Todd-Brown K. Thomas L. Ferreira M.A.R. Bender D. Maller J. Sklar P. de Bakker P.I.W. Daly M.J. Sham P.C. PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet. 2007; 81: 559-575Abstract Full Text Full Text PDF PubMed Scopus (18606) Google Scholar following previously suggested parameters.20Kancheva D. Atkinson D. De Rijk P. Zimon M. Chamova T. Mitev V. Yaramis A. Maria Fabrizi G. Topaloglu H. Tournev I. Parman Y. Parma Y. Battaloglu E. Estrada-Cuzcano A. Jordanova A. Novel mutations in genes causing hereditary spastic paraplegia and Charcot-Marie-Tooth neuropathy identified by an optimized protocol for homozygosity mapping based on whole-exome sequencing.Genet Med. 2016; 18: 600-607Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar The total length of the RoH from each individual was used to estimate if it was an offspring from a consanguineous couple, according to previously described thresholds.21Matalonga L. Laurie S. Papakonstantinou A. Piscia D. Mereu E. Bullich G. Thompson R. Horvath R. Pérez-Jurado L. Riess O. Gut I. van Ommen G.-J. Lochmüller H. Beltran S. RD–Connect Genome-Phenome Analysis Platform and URD-Cat Data ContributorsImproved diagnosis of rare disease patients through systematic detection of runs of homozygosity.J Mol Diagn. 2020; 22: 1205-1215Abstract Full Text Full Text PDF PubMed Scopus (10) Google Scholar Genomic data were analyzed by geneticists from participating centers using the URD-Cat GPAP, which has many functionalities, including standard filters and annotations (population databases and variant pathogenicity prediction tools), filters by clinical data (genes of interest, genes associated with patient's HPO terms entered in PhenoTips, Online Mendelian Inheritance in Man codes, and in silico panels), links to multiple external resources, and data sharing between authorized users (internal matchmaking by querying all of the data). Variant filtering and prioritization followed the guidelines established by geneticists in the URD-Cat project. Briefly, users selected the singleton, pair, trio, or quartet to analyze and applied all possible inheritance patterns to filter out the variants accordingly. Cutoffs for the population databases filters (GnomAD, 1000 Genomes, and internal database) were set on the basis of the inheritance: minor allele frequency <0.02 for autosomal recessive or X-linked in men; and minor allele frequency <0.01 for autosomal dominant or X-linked in women. Then, different filtering criteria were applied to look for the following: i) previously known variants: previously tagged by another user or reported in ClinVar database (https://www.ncbi.nlm.nih.gov/clinvar/, last accessed December 2, 2021); ii) variants in genes associated with patient's phenotype: filter by genes associated with at least one patient's HPO term, predefined or custom gene lists, or Online Mendelian Inheritance in Man clinical features; iii) variants predicted to be highly likely pathogenic based on SnpEff prediction22Cingolani P. Platts A. Wang L.L. Coon M. Nguyen T. Wang L. Land S.J. Lu X. Ruden D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3.Fly. 2012; 6: 80-92Crossref PubMed Scopus (5112) Google Scholar; and iv) candidate pathogenic CNVs based on Online Mendelian Inheritance in Man database (https://www.omim.org, last accessed November 22, 2021) and Database of Genomic Variants (http://dgv.tcag.ca, last accessed May 21, 2021).17MacDonald J.R. Ziman R. Yuen R.K.C. Feuk L. Scherer S.W. The Database of Genomic Variants: a curated collection of structural variation in the human genome.Nucleic Acids Res. 2014; 42: D986-D992Crossref PubMed Scopus (721) Google Scholar The filtering settings can be saved and applied recurrently to further analyses to speed up the filtering process. Variants were prioritized by clinical researchers leveraging their expertise and the functionalities in the platform, including scored prioritization of variants according to patient's HPO terms with Exomiser.23Smedley D. Jacobsen J.O.B. Jäger M. Köhler S. Holtgrewe M. Schubach M. Siragusa E. Zemojtel T. Buske O.J. Washington N.L. Bone W.P. Haendel M.A. Robinson P.N. Next-generation diagnostics and disease-gene discovery with the Exomiser.Nat Protoc. 2015; 10: 2004-2015Crossref PubMed Scopus (150) Google Scholar In cases with suspected consanguinity, the RoH filter was used to narrow down the list of candidate variants to only those within RoH of at least 500 Kb. Specific chromosomal positions (such as known pathogenic variants) could be selected individually or through the upload of a BED file. Variant interpretation was done by geneticists and clinical experts based on variant classification following the American College of Medical Genetics and Genomics guidelines,24Richards S. Aziz N. Bale S. Bick D. Das S. Gastier-Foster J. Grody W.W. Hegde M. Lyon E. Spector E. Voelkerding K. Rehm H.L. ACMG Laboratory Quality Assurance CommitteeStandards 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: 405-424Abstract Full Text Full Text PDF PubMed Scopus (12735) Google Scholar,25Riggs E.R. Andersen E.F. Cherry A.M. Kantarci S. Kearney H. Patel A. Raca G. Ritter D.I. South S.T. Thorland E.C. Pineda-Alvarez D. Aradhya S. Martin C.L. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen).Genet Med. 2020; 22: 245-257Abstract Full Text Full Text PDF PubMed Scopus (346) Google Scholar the clinical fit, and the familial segregation. Validation and segregation of the candidate pathogenic variants were performed by Sanger sequencing or comparative genomic hybridization arrays. Segregation with the disease was assessed for all patients unless otherwise indicated. Functional studies were performed when the variant and/or the gene had not been previously associated with the disease. Reporting of findings to diagnosed patients, or their families, was done according to the procedures of the corresponding managing hospital, which typically includes a genetic report and counseling. The Fisher exact test was used to evaluate whether phenotype annotation quality and sequencing approaches were significantly different between diagnosed and undiagnosed groups of patients. Nonparametric Kruskal-Wallis test was done to compare the median number of HPO terms between diagnosed and undiagnosed patients. To remove annotation redundancy, only the most specific HPO terms were considered by counting only terms from leaf nodes or nodes without selected parent or child nodes. P < 0.05 was considered statistically significant for all analyses. Available genomics data from a total of 331 patients with neurologic diseases (560 individuals including relatives) were uploaded to the URD-Cat GPAP. Data were processed as indicated in Materials and Methods. The mean depth of coverage was 78.9× for panels, 74.1× for exomes, and 31.2× for genomes (Supplemental Table S2). Genomic relatedness was computed between 491 individuals for which there was a genome or an exome with average coverage >10×. Six exomes with average coverage ≤10× and 63 panels were not included in the relatedness analysis. Predicted kinship was compared with the reported family relationships. Inconsistencies were found in seven families, including duplicated individuals (n = 3), true siblings reported as individuals belonging to different families (n = 2), and true siblings reported as the same individual (n = 2). These inconsistencies were corrected in the URD-Cat platform. After complete evaluation of the cohort, a total of 17 individuals were excluded because they were duplicates (n = 3), no proband was available (n = 4), the proband was affected by a nonneurologic disease (Munchausen syndrome; n = 3), or they were solved by another ongoing project (n = 7). Therefore, reanalysis of genomic data and variant interpretation was finally performed in a total of 323 probands (543 individuals including relatives) between 1 and 8 years (median, 4 years) after the data were originally generated. The 323 probands included 186 males (57.6%) and 137 females (42.4%) (sex ratio, 1.4). They were classified into eight disease categories, according to the main clinical features: progressive neurodegenerative diseases (33.4%), neuromuscular diseases (17.3%), epilepsy/nonepileptic paroxysmal disorders (16.1%), inherited metabolic disorders (12.1%), intellectual disabilities/autism spectrum disorders (11.2%), movement disorders (7.1%), central nervous system malformations (1.6%), and other diseases (1.2%) (Table 1).Table 1Characteristics of the 323 Probands Classified by the Main Disease CategoryDisease categoryProbands, NSexConsanguinity (PhenoTips)Sequencing strategyFamily analysisMaleFemaleYesNoUnknownPanelExomeGenomeSingletonsPairsTriosQuadsProgressive neurodegenerative diseases10870 (64.8)38 (35.2)12 (11.1)74 (68.5)22 (20.4)2 (1.9)103 (95.4)3 (2.8)73 (67.6)18 (16.7)16 (14.8)1 (0.9)Neuromuscular diseases5631 (55.4)25 (44.6)6 (10.7)41 (73.2)9 (16.1)28 (50.0)28 (50.0)0 (0.0)49 (87.5)4 (7.1)2 (3.6)1 (1.8)Epilepsy/nonepileptic paroxysmal disorders5227 (51.9)25 (48.1)5 (9.6)28 (53.8)19 (36.5)2 (3.8)50 (96.2)0 (0.0)17 (32.7)3 (5.8)30 (57.7)2 (3.8)Inherited metabolic disorders3914 (35.9)25 (64.1)6 (15.4)27 (69.2)6 (15.4)9 (23.1)30 (76.9)0 (0.0)21 (53.8)3 (7.7)13 (33.3)2 (5.1)Intellectual disabilities/autism spectrum disorders3625 (69.4)11 (30.6)0 (0.0)26 (72.2)10 (27.8)6 (16.7)30 (83.3)0 (0.0)17 (47.2)2 (5.6)12 (33.3)5 (13.9)Movement disorders2312 (52.2)11 (47.8)2 (8.7)13 (56.5)8 (34.8)13 (56.5)10 (43.5)0 (0.0)18 (78.3)1 (4.3)4 (17.4)0 (0.0)Central nervous system malformations54 (80.0)1 (20.0)1 (20.0)4 (80.0)0 (0.0)2 (40.0)3 (60.0)0 (0.0)5 (100.0)0 (0.0)0 (0.0)0 (0.0)Other diseases43 (75.0)1 (25.0)1 (25.0)3 (75.0)0 (0.0)1 (25.0)3 (75.0)0 (0.0)3 (75.0)0 (0.0)1 (25.0)0 (0.0)Total323186 (57.6)137 (42.4)33 (10.2)216 (66.9)74 (22.9)63 (19.5)257 (79.6)3 (0.9)203 (62.8)31 (9.6)78 (24.1)11 (3.4)Data are given as number (percentage). Probands were classified into each disease category by the clinicians or researchers from the referring hospital. Other diseases included syndromic diseases that did not fit in any other group. Open table in a new tab Data are given as number (percentage). Probands were classified into each disease category by the clinicians or researchers from the referring hospital. Other diseases included syndromic diseases that did not fit in any other group. Genetic variant-derived RoH analysis to predict the absence or presence of consanguinity (consanguinity status) was performed in the 257 probands with exome data available. A total of 24 probands were predicted to be part of a consanguineous (n = 18) or likely consanguineous (n = 6) family. In the medical records,
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