Page 74 - Annual report 2021-22
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Annual Report 2021-22 |






               Vinod Scaria

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               Vinod  Scaria  works  on  genome  informatics  that  includes  mendelian  genetic  diseases,  genetic
               epidemiology  of  human  diseases,  pathogen  genomics  (SARS-CoV2),  pharmacogenomics  and  non-
               coding RNA biology.

               In collaboration with Sridhar Sivasubbu, he developed the IndiGen app as part of the GenomeApp
               project. The IndiGenome Card (GenomeApp) is a unique ID and barcode on a card which can be used
               to access the genomic data interpretation on a cloud service over a mobile/web application. The
               system enables relevance of interpretation of genetic variants and an easy method to access specific
               genetic findings on clinical request. These could be for the primary indications (indications for the
               diagnosis of the disease or genetic variants) or secondary indications (secondary indications could be
               for modifier loci, pharmacogenomic variants, incidental findings etc.) which can provide insights into
               the prognosis of the disease. The GenomeApp was developed, and pilot tested in Indian samples.

               Over 1200 samples were collected through the clinical network. Following the genomic sequencing
               and analysis, genetic screening assays for several genetic disorders were developed. These included
               NGS and capillary sequencing based genetic screening assays. The goal was to couple the NGS genetic
               screening assays and the GenomeCard for communicating the genomic evidence. The IndiGen App is
               available on Google play store and has more than 1000+ downloads. The data on pharmacogenetic
               variants have been widely accessed by the users of the app.

               Under the INGEN-COV2 (Integrative Genomics of COVID-19), a collaborative project between IGIB and
               CCMB,  Vinod Scaria’s lab was involved in analyzing of the sequencing data of the SARS-CoV2 genomes
               from samples collected at CSIR-IGIB. Of the 510 genomes sequenced, 319 were deposited in GISAID.
               Longitudinal sampling from 79 COVID-19 positive mothers and infants born were enrolled in the study.
               During  this  period,  India  also  witnessed  the  challenging  2nd  wave  which  was  primarily  over-
               represented  by  the  Delta  variant.  With  increased  ability  of  transmission,  Delta  variant  also  was
               dominating the vaccination breakthroughs. Thus, during this period, SARS-CoV-2 genome sequencing
               was performed to understand these aspects.

               Another important focus area of his lab involves understanding idiopathic intellectual disability for
               which next generation sequencing (NGS) is used. Intellectual Disability (ID) encompasses a broad class
               of  neurodevelopmental  disorders  that  affects  up  to  2-3%  of  the  population.  Patients  with  ID  are
               screened  in  clinical  settings  utilizing  karyotyping  for  known  chromosomal  syndromes  like  Down's
               syndrome, chromosomal microarray (CMA) to detect submicroscopic aberrations across the genome,
               or focused molecular testing for a suspected ID-causing single gene abnormality. With the growing
               number  of  genetic  causes  of  ID  being  discovered,  targeted  sequencing  without  specific  clinical
               indications is becoming  more difficult. As a result, next-generation sequencing is becoming more
               popular  since  it  enables  the  identification  of  single-nucleotide  alterations  throughout  the  whole
               genome or exome (WGS/WES). NGS based approaches offer the possibility to identify the genetic
               cause for patients in which all previously available genetic tests, including karyotyping, specific gene
               analysis, or microarray analysis do not reveal any causative abnormalities.
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