Zaid Ahmad

Detecting natural selection with ancient and modern DNA data

I’m interested in developing computational methods to quantitatively describe the impact of natural selection on various traits and genes expressed in modern human populations. Detecting and inferring natural selection is central to understanding how populations of individuals have evolved over time. Advances in next-generation genomic sequencing technologies have made it possible to extract high-quality DNA data from ancient relics such as fossils. Specifically, I want to understand how this ancient DNA data can help us detect selection, and I’ll also be testing methods that I develop with real ancient DNA data from Europe. Combining both modern and ancient DNA samples into an analysis will allow me to paint a clear picture of how our genome has evolved in response to various environmental factors over the past ten thousand years. I’ll be developing scalable methods that can use both ancient and modern DNA data to accurately detect selection across hundreds of generations of people. Ultimately, this project will further my fundamental understanding of human origin and history.

Message to Sponsor

Thank you so much for funding this research experience. The SURF fellowship this summer is important to my exploration of different research interests. The opportunity to working at the intersection of statistics, computation, and biology is exciting and further confirms my interest in interdisciplinary subjects. I am now a much more able researcher, and I am convinced that pursuing research in computational biology is something I'd like to continue in the future.
  • Major: Electrical Engineering and Computer Science, Mathematics
  • Sponsor: McKinley Fund
  • Mentor: Rasmus Nielsen