CAREER: Leveraging the footprints of archaic ancestry to learn about human history and biology

University Of California-Berkeley, Berkeley CA

Investigators

Abstract

Gene flow from archaic hominins, Neanderthals and Denisovans, into the ancestors of modern humans has shaped genetic and phenotypic variation in modern humans. Regions of archaic ancestry also provide a unique window into past genomic variation and mutation patterns at deep timescales in human evolution. To date, there are only four high coverage archaic genomes sequenced: three of Neanderthals and one Denisovan, thus, our knowledge of the evolutionary history and role of archaic ancestry in humans remains incomplete. This project will develop novel methods to characterize archaic ancestry in modern humans that do not rely on sequenced archaic genomes. We will apply these methods to whole genome sequences from individuals worldwide for whom we also have rich phenotype data, including previously underrepresented groups from South Asia, Africa, and the Americas. This research will be integrated with an education plan including multiple activities intended to expand quantitative biology training to undergraduate and graduate students, including programming, hands-on genomic data analysis and research internships. This project will develop novel statistical methods to characterize archaic introgression and apply it to large-scale cohorts to learn about the legacy of archaic ancestry in modern humans. Specifically, the project will (1) characterize the evolutionary history and legacy of archaic ancestry in non-African populations, (2) build a novel method to characterize the landscape of archaic ancestry in Africans, and (3) leverage the footprints of archaic ancestry to learn about changes in germline mutation rate during human evolution. Our analysis will generate the largest catalog of archaic ancestry segments and help characterize the role of archaic ancestry in shaping complex traits and evolutionary processes in modern humans. Software developed and data analyzed in publications resulting from the research will be made available to the public on the lab's data repository (https://moorjanilab.org/software/) and through github (https://github.com/MoorjaniLab). This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.