Saswati Saha (TAGC)

Systems genetics: Developing new approaches integrating network analyses and modeling to elucidate the genetic mechanisms underlying cardiac aging in Drosophila

Team: Christine Brun (TAGC), Laurent Perrin (TAGC)

Her background

April 2019 - present | CENTURI postdoctoral fellow

2016 - 2019 | PhD in Medical Statistics, University of Bremen (Germany)

2014 - 2015 | Business analyst, Genpact (Bengaluru, India)

2013 - 2014 | Consultant, Ernst & Young (Mumbai, India)

2011 - 2013 | MSc degree in Statistics - Indian Statistical Institute (Kolkata, India)

About her postdoctoral project

Senescence is a major determinant of life expectancy in an aging population and heart disease and disorders increase with age. However, the genetic basis for interindividual variation in cardiac senescence remains largely unknown. Our objective is to identify the genetic architecture of cardiac aging in a natural population of drosophila, the Drosophila Genetic Reference Panel (DGRP). Our Genome Wide Association (GWA) analyses and epistatic interactions have identified a large number of genes involved, hindering intuitive and knowledge-based analyses. However, in order to reveal the gene/protein network(s) of interactions, dependencies and regulations underlying the cardiac aging phenotypes, we need to identify (i) the biological processes involved and (ii) the more relevant candidates for further experimental investigations. For this, we propose to couple machine learning and network analyses linked in an innovative iterative framework allowing unblocking bottlenecks encountered by previous GWA published analyses.

By considering the effect of the genetic variations at all scales, in a systems genetics framework, this research program is highly interdisciplinary, at the interface of biology and computer science. It relies on the alliance between a team specialized in the genetics of cardiac aging and a team specialized in network biology at TAGC. We will capitalize on our joined expertise in Drosophila genetics, biostatistics and bioinformatics to decipher the natural variations associated with cardiac senescence.