Reconstructing evolutionary history using ancient and modern human genomes
Priya Moorjani, Professor
Center for Computational Biology, Computer Science
Applications for Spring 2024 are closed for this project.
Evolutionary history shapes the diversity around us. The historical signatures of our past such as population mixtures, bottlenecks and expansions, as well as human diseases and natural selection, have left traces in our genomes. In our lab, we develop computational methods and analyze large-scale genomic datasets from present-day and ancient DNA samples to learn about human population history. In this project, we would apply population genetics simulations and analyze genomic data from worldwide individuals to characterize the history of non-African populations, including the timing and strength of the bottleneck associated with the out of Africa migration. Our results will provide valuable insights into this key historical event of human evolution and will allow us to quantify the impact of demographic events including population size changes and Neanderthal introgression to the founding and spread of modern human populations.
Role: The project will involve data analysis using large-scale human genomics datasets and conducting population genetics simulations to explore the various demographic models and parameters related to the history of modern humans. The student will also have the opportunity to work with other lab members to develop technical skills in computational and statistical genetics.
Qualifications: Proficiency in a programming language (e.g., C, C++, Python or equivalent) is required. Candidates with experience with machine learning and AI methods are encouraged to apply. A prospective undergraduate researcher should expect to commit a minimum of 12 hours per week to research during the semester and full-time during the summer for a minimum of two consecutive semesters including summer. Students who are looking for research experience, ideally with the goal of doing an honors thesis, will be strongly favored.
Day-to-day supervisor for this project: Yulin Zhang, Ph.D. candidate
Hours: 12 or more hours
Related website: http://moorjanilab.org/
Biological & Health Sciences Digital Humanities and Data Science