Priya Moorjani, Professor

Closed (1) Leveraging present-day and ancient genomes to learn about natural selection and human adaptation

Applications for Fall 2020 are now closed for this project.

Recent breakthroughs in ancient DNA 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: Qualifications: Proficiency in Python and R is required. We are looking for a student who is interested in questions about human origins and is willing to work with other lab members to develop technical skills in computational and statistical genetics.

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 2020 apprentices on this project; no new apprentices needed for Fall 2020.

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 ancient DNA to learn about human history and evolution

Applications for Fall 2020 are now closed for this project.

Ancient DNA analyses— the study of genetic material from individuals that died hundreds or thousands of years ago— have revolutionized the research in human evolutionary genetics, making it possible to directly observe patterns of genetic variation that existed in the past. Ancient DNA has revealed a number of keys insights into human evolution, including Neanderthal introgression into ancestors of non-Africans, the role of Denisovan ancestry in high altitude adaptation in Tibetans, and major populations shifts and mixtures that occurred during the Neolithic Expansion. 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.

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: Qualifications: Proficiency in Python and R is required. We are looking for a student who is interested in questions about human evolution and is willing to work with other lab members to develop technical skills in computational and statistical genetics.

Weekly Hours: 12 or more hours

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

Related website: https://moorjanilab.org/