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.
Highlights
A large number of disorders affecting skeletal muscle have a genetic basis, with multiple modes of inheritance
To identify novel candidate genes for myopathies, we established an integrated data mining approach aiming first to extract specific signatures for disease groups encompassing previously implicated genes, and to use these signatures to search for additional matching genes in the human genome
Different myopathy groups appear using only Gene Ontology (GO) terms (Figure 2C), while IA terms, even considering a lower threshold of 5 terms shared between genes, create smaller clusters of genes that interact closely by sharing the same pathways, interactions complexes or motifs, such as constituents of collagen VI, genes responsible for the assembly of nicotinic cholinergic receptors, or conglomerated proteins involved with the sarcomere (Figure 2D)
Summary
A large number of disorders affecting skeletal muscle have a genetic basis, with multiple modes of inheritance They are classified based on phenotype and histopathological features into several groups, which include muscular dystrophies, congenital myopathies and myotonic syndromes, among others (Table 1) [1]. It is estimated that around 40% of patients afflicted with myopathies remain without a molecular diagnosis, supporting the implication of additional genes [6,7]. Further identification of these genes is the focus of a tremendous research effort at present, and will help understand pathological mechanisms and defining novel drug targets
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