Chirag J. Patel PhD

Chirag was an software engineer for almost 8 years in the biotechnology industry, developing algorithms for gene sequencing devices. While the work was intellectually stimulating, he wanted an opportunity to deepen skills in computation, bioinformatics, and biomedicine. To fulfill this intellectual desire, he attained a doctorate degree in biomedical informatics at Stanford University. At Stanford, he further honed and developed in the areas of software engineering, genomics, informatics, computer science, and statistics. Wanting ways to translate findings in the clinic, he sought a post-doctoral fellowship in epidemiology, working with John PA Ioannidis.
As a software engineer, graduate student, a post-doctoral associate, and now a junior faculty member at Harvard Medical School, he have wondered about the multifactorial components that influence disease, such as both inherited and environmental factors. It was apparent from my study and work in industry that scientiists had new and robust tools to ascertain how inherited genetic variants influence disease through studies such as the “genome-wide association study”, but he wondered, and continue to wonder, how we can get a more complete picture of human health by consideration of other factors, such as the environment. If we could measure and analyze the envirommental exposure as well as we do with genetics, perhaps we could use this information for better means of prevention and therapy.
The PhRMA foundation grant was very important enabler for me to start this scientific exploration. For example, my PhRMA-sponsored work, has been central to the emerging human exposome project, a new paradigm to quantitatively assess a large array of personal exposures in humans. The PhRMA award has been important to jump-start research in genomics and therapeutics, where we are using big data methods to ascertain new uses for old drugs, or repositioning therapy. Finally, the PhRMA award has helped fund new career starter awards from the NIH/NIEHS (a K99/R00), a small research grant (R21), and gifts from foundations and industry to use public data resources to conduct large-scale gene-by-environment interactions to better characterize disease risk as a function of both inherited and exposure factors.