Priya Moorjani, Professor

Closed (1) Leveraging present-day and ancient genomes to learn about human evolution

Applications for fall 2021 are now closed for this project.

Recent breakthroughs in sequencing have greatly improved our ability to discern previously obscured details of human history. The sequencing of Neanderthals and Denisovans, revealed that modern human have ancestry from these archaic hominins and allowed us to identify locations in the human genome that are likely Neanderthal or Denisovan in origin. Time series information from ancient DNA genome sequences data may allow us to locate genomic regions of archaic hominin origin that have been beneficial for modern human populations. To identify potential adaptive regions, we will model the effects of adaptation on genomes using various statistical learning methods including multinomial regression. Identification of adaptive genes will provide valuable insight into the environmental pressures faced by early humans migrating to new regions and will allow us to quantify the impacts of genomic segments inherited from archaic humans.


The student will be responsible for conducting population genetics simulations matching available ancient genome sequence data to train the model and test the performance of the method. This will include simulating various human demographic histories and selection scenarios. The student will also have the opportunity to work with other lab members to explore and improve methods for detecting regions of adaptation in humans and other species.

Qualifications: Proficiency in Python and R is required. The student will meet with the mentor every week and attend group meetings. The student will maintain a e-notebooks to track research progress and register for academic credits. Candidates who have completed core statistics and computer science classes are preferred. Experience with NGS data analysis is preferred.

Weekly Hours: 12 or more hours

Off-Campus Research Site: Project can be done remotely.

Related website: https://moorjanilab.org

Closed (2) Evolution of mutation rate across primates

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

Germline mutations are the ultimate source of genetic differences among individuals and across species; they provide the raw material for selection to act on, as well as play a role in many diseases. As mutations occur steadily over time, they provide a record of the time elapsed and hence a “molecular clock” for dating evolutionary events. However, despite strong constraints on the replication machinery, recent studies have shown that the mutation rate as well as the mutation spectra evolves rapidly across closely related species and also varies among humans. Thus, to investigate the causes of interspecies variation in mutation rate and to build robust models of evolution, we are interested in estimating direct pedigree-based mutation rates in humans and other primates. This will allow us to learn about the determinants of mutation rate and the mechanisms impacting its evolution across species.

Undergraduate will take responsibility for: 1) Applying standard pipelines for sequencing alignment and mapping to identify de novo mutations in pedigrees, 2) Compare variation in mutation rates across species. The student will learn about cutting edge methods for mapping and alignment of human sequence data, and will contribute to research publications associated with this work., Post-Doc

Qualifications: Proficiency in Python or C++ (required), Prior experience in genomic data analysis (desirable), knowledge of statistics and population genetics theory (desirable), Machine Learning (desirable). We prefer to recruit Sophomores or Juniors, with the expectation that they will work towards an honors thesis in their senior year.

Weekly Hours: 12 or more hours

Related website: https://moorjanilab.org/

Closed (3) Study of genomic data to learn about human history and evolution

Closed. This professor is continuing with Spring 2021 apprentices on this project; no new apprentices needed for Fall 2021.

The study of genetic data combined with appropriate statistical tools can provide fine-grained resolution of evolutionary history that can provide insights about human origin and adaptation. In this regard, ancient DNA analyses— the study of genetic material from individuals that died hundreds or thousands of years ago— have revolutionized the research in human evolution by making it possible to directly observe patterns of genetic variation that existed in the past. Despite the growing realization of the importance of ancient DNA, we still lack adequate computational tools to fully leverage this unique resource to deepen our understanding of human evolution and adaptation. This project will involve performing simulations and empirical data analysis to investigate the performance of common genomic tools with ancient DNA data. An extension of the project will be to implement new methods to analyze ancient DNA samples.

The student will be responsible for conducting population genetics simulations matching available ancient genome sequence data to train the model and test the performance of the method. This will include simulating various human demographic histories and selection scenarios.

Qualifications: Proficiency in Python and R is required. The student will meet with the mentor every week and attend group meetings. The student will maintain a e-notebook to track research progress and register for academic credits. Candidates who have completed core statistics and computer science classes are preferred. We prefer to recruit Sophomores or Juniors, with the expectation that they will work towards an honors thesis in their senior year.

Weekly Hours: 12 or more hours

Off-Campus Research Site: Project can be done remotely.

Related website: https://moorjanilab.org/

Closed (4) Computational analysis of genomic data to learn about human evolution and adaptation

Applications for fall 2021 are now closed for this project.

The study of genetic data combined with appropriate statistical tools can provide fine-grained resolution of evolutionary history that can provide insights about human origin and adaptation. In the Moorjani lab (https://moorjanilab.org/), we use genetic data from ancient specimens and present-day individuals to reconstruct evolutionary events (selection, founder events and admixtures) and identify key genetic variants related to adaptation and disease. The lab develops statistical and computational methods to gain insight into evolutionary processes and outcomes from population genomic data. A variety of projects are available and can be tailored to the student’s interest. A few of them include:
1. Inferring local ancestry using machine learning approaches in individuals of mixed ancestry (referred to as admixed individuals).
2. Identifying selection in admixed populations.
3. Studying the variation in mutation rates across human populations.

Projects will include a combination of data analysis, simulations, and literature search.


The student will be responsible for implementing a computational method and testing its performance by running simulations.

Qualifications: Proficiency in Python and R is required. The student will meet with the mentor every week and attend group meetings. The student will maintain a e-notebook to track research progress and register for academic credits. Candidates who have completed core statistics and computer science classes are preferred. Students who are looking for research experience, ideally with the goal of doing an honors thesis are preferred.

Weekly Hours: 12 or more hours

Off-Campus Research Site: Project can be done remotely.

Related website: https://moorjanilab.org/