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.

The Genomics of Arthrogryposis, a Complex Trait: Candidate Genes and Further Evidence for Oligogenic Inheritance

“…41,42 Further evidence that the pathogenicity of the variants in RYR3 probably contribute to the arthrogryposis phenotype comes from bioinformatics studies; Abath Neto et al performed a computational data-mining study based on gene ontology (GO) terms, human phenotype ontology (HPO) terms, pathways, complexes, and protein motifs, and they found that RYR3 is highly ranked as one of the congenital myopathy genes. 43 Mutations in RYR1, a paralog of RYR3, were shown to cause MYOM2 is specifically expressed in the heart and skeletal muscle, the cardiac and arthrogryposis findings can be potentially explained by loss of function (LoF) mutation of MYOM2. Further evidence for probable pathogenicity comes from functional studies that showed that MYOM2 interacts directly with dysferlin, which plays an essential role in sarcolemma repair; abnormalities of dysferlin cause a wide variety of myopathies called dysferlinopathies.…”

Ryanodine receptor type 3 (RYR3) as a novel gene associated with a myopathy with nemaline bodies

“…In addition, an integrated data-mining strategy ranking genes with known or potential importance for skeletal muscle has previously highlighted RYR3 as a candidate gene for CMs, myotonic syndromes and ion channel muscle diseases [18]. Some of the other candidates from Neto et al [18] are now known disease genes, i.e. MYO18B and CASQ1.…”

Resequencing array for gene variant detection in malignant hyperthermia and butyrylcholinestherase deficiency

“…Genetic and phenotypic heterogeneity is a common source of uncertainty in the diagnosis of several inherited musculoskeletal disorders, as well as in investigating their etiology. High-throughput sequencing can help outline the underlying genomic features in these disorders through variant discovery in many genes simultaneously [38,39], but the ultra-high output poses limits to its practicability as a diagnostic tool [24][25][26][27][28][29]. For diagnostic purposes, full sequencing of a panel of firmly established gene candidates can be adequately handled with resequencing arrays, which have already been successfully applied in clinical trials [33].…”