Rare Genetic Disorders (RGDs) are clinical conditions with underlying genetic origins. Though RGDs are of low prevalence and individually rare, collectively they affect a considerable number of people in a highly populous country like India. The diagnosis of RGDs is challenging due to the lack of awareness and the genetic heterogeneity and variety of overlapping symptoms they present with, as well as the unavailability of accurate genetic tests. Where available, the cost of associated diagnostic and medical tests is beyond the reach of most people in our country.
Palliative treatment (where available) relies on obtaining a correct diagnosis of the disorder as early as possible. We are working towards developing diagnostic kits for RGDs that are cost effective, suitable for carrier and newborn screening, and specific for disease associated genetic mutations common among the Indian population.
Vertical Lead:
Investigators: Shivranjani C Moharir, Harvinder Kour Khera, Iliyas Rashid
Activities:
The actual proportion of human genetic diseases caused due to copy number variations is unknown. With the advent of molecular techniques and whole genome sequencing-based approaches, the underlying cause of several genetic disorders can be unfolded. We are working towards identifying Indian population specific mutations and developing indigenous diagnostic tools and kits for population level screening. Initially, the target disorder is spinal muscular atrophy (SMA), with goals to later expand to other RGDs. The survival motor neuron genes (SMN1 and SMN2) are the causative genes for SMA with copy number variations and gene conversion events eventually leading to a degeneration of motor neurons.
Multiplex ligation-dependent probe amplification (MLPA) is a multiplex, semi-quantitative method for diagnostic testing of genetic disorders. The method is suitable for the identification of deletions or duplications over a broad range, from SNPs to chromosomal aneuploidies, given a suitable set of probes covering the entire target region. By coupling MLPA amplified probes with sequencing, one can include many hundreds of probes in a single reaction. The incorporation of an Next Generation Sequencing (NGS)-based detection approach would make the diagnostic strategy suitable for population level and carrier screening as multiplexing large number of samples for many disorders in a single assay would cut down the cost.
We designed an MLPA-NGS assay to detect the SNPs responsible for SMN1 to SMN2 gene conversion as well as to identify the presence or absence of all other exons in SMN1/2 giving a complete diagnosis of gene conversion and copy number of the SMN genes. The development of the MLPA-NGS technique as a diagnostic kit for SMA is currently under progress. Probes identifying the exon 7/ 8 deletions in SMN1 gene (exon 7/8 deletion is the causative factor in more than 95% of SMA cases) and other exon deletions are included, along with reference probes. We are validating the assay with other approaches, and developing a digital PCR-based approach for identifying exon 7/8 deletion in parallel.
Investigators: Shivranjani C Moharir, Harvinder Kour Khera
Collaborators:
Centre for Cellular and Molecular Biology, Hyderabad
We are developing a repository for clinical data on RGDs in the form of a database that can collate and store information of such diseases in the Indian context and focus on the genomic causes and mutations specific to the Indian population. The database would include a wide variety of information related to RGDs from sources such as OMIM and Orphanet and will include local prevalence, affected genes, pathogenic variants, gene regulatory factors, the role of non-coding RNAs, along with patient information. Clinical data (primary source) will be sourced from our partner hospitals and research organizations. Gene annotations and sequences, reports and information on local trends will be obtained from online databases such as Ensembl and NCBI-gene databases (secondary sources) to perform a comparative analysis. The datasets will be managed and stored using a relational database management system (RDBMS).
A web-based platform will be developed by incorporating analytical and statistical tools for clinical data analysis and interpretive output. Interactive search and query features will be built in. Apart from providing information on patient care services, the analytical platform will facilitate pedigree analysis from patient to family and population level using clinical data, a novelty of this database. Custom programs are in place for automated data extraction and presentation via the user-friendly front-end of the GenTIGS web interface. The browser enables viewing and interactive searching of all the collected information on RGDs and their relevant gene(s) (PubMed and GeneID links), along with structural and functional gene information (from NCBI-GeneDB and OMIM). We are currently working on incorporating a pipeline at the back end to analyse genome and exome level population data to identify pathogenic variants.
Investigators: Iliyas Rashid, Shivranjani C Moharir