Applying Two Different Bioinformatic Approaches to Discover Novel Genes Associated with Hereditary Hearing Loss via Whole-Exome Sequencing: ENDEAVOUR and HomozygosityMapper

Topology Driven Analysis of Protein - Protein Interactome for Prioritizing Key Comorbid Genes via Sub Graph Based Average Path Length Centrality

In gene-based therapies, local perturbations associated with one disease can lead to comorbidity as it influences the pathways involved with the other diseases. The key genes orchestrating the common biological mechanisms are need to be prioritized for addressing the challenges introduced by the cross talks between disease modules. Here, a local centrality measure named Sub graph based Average Path length Double Specific Betweenness centrality (SAPDSB) for prioritizing the comorbid genes via Protein-Protein Interaction Network (PPIN) analysis is presented. This approach can be used to identify putative biomarkers which can be repurposed for the management of comorbidity. Proposed network based topological measure is designed specifically to prioritize the comorbid genes that are most likely to be present in the overlap of disease modules. In order to attain this, the estimated average path length of the seed network which holds Protein-Protein Interactions (PPIs) of the disease genes is exploited. Prioritized comorbid genes are further pruned using centrality-based cut-off values and specificity scores. The biological significance of the resultant genes is corroborated with connectivity analysis using leave-one-out method, pathway enrichment analysis and a comparative analysis using single disease-based gene prioritization tools. For performance analysis, proposed approach is tested using case studies involving common diseases and rare neurodegenerative diseases. For case study1, diseases such as Diabetes, Carcinoma and Alzheimer's are considered in a pairwise manner while for case study2, Amyotrophic Lateral Sclerosis (ALS) and Spinal Muscular Atrophy (SMA) are considered. As outcome, prioritized candidate genes and biological pathways associated with respective disease pairs have been found. The associations from top 10 candidate genes in different disease pair combinations of Diabetes-Carcinoma-Alzheimer's revealed common genes like CREBBP, TP53, HSP90AA1 and the common pathway namely p53 pathway feedback loops 2. Out of the pathways retrieved from the top 10 genes associated with ALS-SMA disease pair, 60% of unique pathways are found to be leading to both diseases and its comorbidities. Comparative analysis of the proposed method with recent similar approach also reported a clear degree of benefits in performance.

Novel MYO15A variants are associated with hearing loss in the two Iranian pedigrees

BACKGROUND

Clinical genetic diagnosis of non-syndromic hearing loss (NSHL) is quite challenging. With regard to its high heterogeneity as well as large size of some genes, it is also really difficult to detect causative mutations using traditional approaches. One of the recent technologies called whole-exome sequencing (WES) has been thus developed in this domain to remove the limitations of conventional methods.

METHODS

This study was a report on a research study of two unrelated pedigrees with multiple affected cases of hearing loss (HL). Accordingly, clinical evaluations and genetic analysis were performed in both families.

RESULTS

The results of WES data analysis to uncover autosomal recessive non-syndromic hearing loss (ARNSHL) disease-causing variants was reported in the present study. Initial analysis identified two novel variants of MYO15A i.e. c.T6442A:p.W2148R and c.10504dupT:p.C3502Lfs*15 correspondingly which were later confirmed by Sanger validations and segregation analyses. According to online prediction tools, both identified variants seemed to have damaging effects.

CONCLUSION

In this study, whole exome sequencing were used as a first approach strategy to identify the two novel variants in MYO15A in two Iranian families with ARNSHL.

Phenotypic and genotypic characterization of families with complex intellectual disability identified pathogenic genetic variations in known and novel disease genes

Intellectual disability (ID), which presents itself during childhood, belongs to a group of neurodevelopmental disorders (NDDs) that are clinically widely heterogeneous and highly heritable, often being caused by single gene defects. Indeed, NDDs can be attributed to mutations at over 1000 loci, and all type of mutations, ranging from single nucleotide variations (SNVs) to large, complex copy number variations (CNVs), have been reported in patients with ID and other related NDDs. In this study, we recruited seven different recessive NDD families with comorbidities to perform a detailed clinical characterization and a complete genomic analysis that consisted of a combination of high throughput SNP-based genotyping and whole-genome sequencing (WGS). Different disease-associated loci and pathogenic gene mutations were identified in each family, including known (n = 4) and novel (n = 2) mutations in known genes (NAGLU, SLC5A2, POLR3B, VPS13A, SYN1, SPG11), and the identification of a novel disease gene (n = 1; NSL1). Functional analyses were additionally performed in a gene associated with autism-like symptoms and epileptic seizures for further proof of pathogenicity. Lastly, detailed genotype-phenotype correlations were carried out to assist with the diagnosis of prospective families and to determine genomic variation with clinical relevance. We concluded that the combination of linkage analyses and WGS to search for disease genes still remains a fruitful strategy for complex diseases with a variety of mutated genes and heterogeneous phenotypic manifestations, allowing for the identification of novel mutations, genes, and phenotypes, and leading to improvements in both diagnostic strategies and functional characterization of disease mechanisms.