Abstract:

Advances in DNA sequencing are generating large quantities of genomic data. These data make it possible to answer fundamental biological questions such as how humans evolved and what are the genes underlying disease. The translation of this data into biological insights, however, poses computational and statistical challenges.

In this talk, I will begin with an overview of relevant concepts in genetics and evolution. I will then describe my efforts to develop computational methods to understand population admixtures -- an important process in human evolution in which populations interbreed and exchange genes. We applied these methods to a long-standing problem in human evolution -- the relationship between present-day human populations and extinct relatives like Neanderthals. Using our methods, we find evidence for admixture between the ancestors of Neanderthals and present-day non-Africans within the last 100,000 years. Given this admixture event, we infer the first genome-scale map of Neanderthal ancestry in present-day humans. By analyzing this map, we are obtaining new insights into processes that have shaped modern humans as well as into how genetic variants modulate phenotype.

Bio:

Sriram Sankararaman is a post-doctoral fellow in the Department of Genetics at Harvard Medical School. His research interests lie at the interface of computer science, statistics and biology. He is interested in developing statistical machine learning algorithms to understand evolutionary processes, the genetics of complex phenotypes and genomic privacy. He received a Bachelor's degree in Computer Science and Engineering from Indian Institute of Technology, Madras (2004) and a Ph.D. in Computer Science from U C Berkeley (2010). He is the recipient of a NIH K99/R00 Pathway to Independence Award (2014), a Simons Research fellowship (2014), a Harvard Science of the Human Past fellowship (2012) and a Berkeley fellowship (2004).