title: Integrative data mining highlights candidate genes for monogenic myopathies.
abstract: Inherited myopathies are a heterogeneous group of disabling disorders with still barely understood pathological mechanisms. Around 40% of afflicted patients remain without a molecular diagnosis after exclusion of known genes. The advent of high-throughput sequencing has opened avenues to the discovery of new implicated genes, but a working list of prioritized candidate genes is necessary to deal with the complexity of analyzing large-scale sequencing data. Here we used an integrative data mining strategy to analyze the genetic network linked to myopathies, derive specific signatures for inherited myopathy and related disorders, and identify and rank candidate genes for these groups. Training sets of genes were selected after literature review and used in Manteia, a public web-based data mining system, to extract disease group signatures in the form of enriched descriptor terms, which include functional annotation, human and mouse phenotypes, as well as biological pathways and protein interactions. These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.
authors.affiliation: Dept. of Translational Medicine and Neurogenetics, IGBMC, INSERM U964, CNRS UMR7104, University of Strasbourg, Collège de France, Illkirch, Strasbourg, France; Departamento de Neurologia, Faculdade de Medicina de São Paulo (FMUSP), São Paulo, Brazil.
authors.forename: Osorio
authors.initials: O
authors.lastname: Abath Neto
authors.affiliation: Dept. of Development & Stem Cells, IGBMC, INSERM U964, CNRS UMR7104, University of Strasbourg, Collège de France, Illkirch, Strasbourg, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Tassy
authors.affiliation: Dept. of Translational Medicine and Neurogenetics, IGBMC, INSERM U964, CNRS UMR7104, University of Strasbourg, Collège de France, Illkirch, Strasbourg, France; Faculté de Médecine, Laboratoire de Diagnostic Génétique, Nouvel Hopital Civil, Strasbourg, France.
authors.forename: Valérie
authors.initials: V
authors.lastname: Biancalana
authors.affiliation: Departamento de Neurologia, Faculdade de Medicina de São Paulo (FMUSP), São Paulo, Brazil.
authors.forename: Edmar
authors.initials: E
authors.lastname: Zanoteli
authors.affiliation: Dept. of Development & Stem Cells, IGBMC, INSERM U964, CNRS UMR7104, University of Strasbourg, Collège de France, Illkirch, Strasbourg, France.
authors.forename: Olivier
authors.initials: O
authors.lastname: Pourquié
authors.affiliation: Dept. of Translational Medicine and Neurogenetics, IGBMC, INSERM U964, CNRS UMR7104, University of Strasbourg, Collège de France, Illkirch, Strasbourg, France.
authors.forename: Jocelyn
authors.initials: J
authors.lastname: Laporte
mesh_terms: D057225:Data Mining / Q000379:methods*; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies / Q000379:methods*; D020022:Genetic Predisposition to Disease; D006801:Humans; D009135:Muscular Diseases / Q000235:genetics*; D010641:Phenotype