Investigate demographic changes in human evolutionary history through genetic analysis
Priya Moorjani, Professor
Center for Computational Biology, Molecular and Cell Biology
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Unraveling the first migrations of humans out of Africa has invoked great interest among researchers from a wide range of disciplines. With the advent of genome-wide DNA sequencing techniques and an increase in the availability of ancient samples, genetics offers important tools for testing different hypothesis related to human evolutionary history. In Europe, Asia and North Africa, interbreeding between modern humans and archaic hominins including Neanderthals and Denisovans took place several times. Neanderthal-derived DNA has been found in the genomes of most or possibly all contemporary populations, varying noticeably by region. It accounts for 1–4% of modern genomes for people outside Sub-Saharan Africa.
Our lab studies human evolutionary genetics using genomic data from present-day and ancient DNA samples. We aim to understand how different populations relate to each other and what are some of the genes related to human adaptation and diseases. To this end, we develop computational and statistical methods and perform population genetic simulations, as well as analyze large-scale genomic datasets. In this project, we would analyze genomic data from worldwide individuals to characterize the history of admixture between archaic hominins, Neanderthals and Denisovans, and non-African populations. Our results will provide valuable insight into the key historical event of human evolution and will allow us to quantify the impact of demographic events including population size changes and archaic introgression to the founding and spread of modern human populations.
Role: The student will be responsible for generating large genomic datasets through simulations, analyzing these datasets, comparing with real data analysis and exploring the various demographic models and parameters related to the history of non-Africans. The student will also have the opportunity to work with other lab members to explore and improve methods for demographic history inference in humans and other species.
Qualifications: Proficiency in Python is required. Data 100 or equivalent data science course is required. Students must demonstrate a strong interest in biology and questions about human origins. A prospective undergraduate researcher should expect to commit a minimum of 12 hours per week to research during the semester. 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
Biological & Health Sciences Digital Humanities and Data Science Engineering, Design & Technologies Mathematical and Physical Sciences