After finishing undergraduate and graduate training in India, I moved to Oxford (United Kingdom) for postdoctoral training. I stayed in Oxford for almost a decade, working on the genetics of type 2 diabetes and related traits before moving to the United States to join Human Genetics department at Genentech in January 2020.
Nature Genetics. 2018 Nov;50(11):1505-1513.
For the past decade, my research has primarily focused on the genetic analysis of complex traits, with particular emphasis on the multifactorial form of type 2 diabetes. I have conducted high-throughput analyses of progressively larger meta-analyses (involving diverse ethnicities) to enable the most comprehensive exploration of the genetic architecture of type 2 diabetes, defining the likely universe of disease-causing variation. Integration of these genetic variants influencing type 2 diabetes with diverse sources of genomic (particularly from disease relevant tissues), proteomic, physiological, clinical, and microbiome information, has provided key insights into the molecular, cellular, and physiological basis of type 2 diabetes predisposition. Though my research so far has focused primarily on type 2 diabetes, these findings can be extrapolated to any complex disease and set the scene for focused efforts to translate genetic and genomic knowledge into clinically actionable information across a range of diseases.
At Genentech, in my postdoctoral lab, I will continue to pursue the research on type 2 diabetes using it as a tool to develop approaches that can be implemented across a wide spectrum of diseases. I am particularly excited about following up on my work on partitioned polygenic risk scores (genetic risk variants combined together) that capture information on individual patterns of disease predisposition and have the potential to influence both biomarker discovery and clinical management and also continue developing further approach to prioritize and integrate multiple sources of evidence (including genetic, genomic, proteomic, model perturbation data, and literature), to classify candidate effector genes (and thereby potential drug targets) according to the likelihood that they are involved in development of the disease.