There are now hundreds of ancient genomes available from a wide range of species, including humans. Such data provide a direct window into the history of demography and natural selection in the recent past, and have greatly contributed to the understanding of how humans colonized the world following the out-of-Africa dispersal. Nonetheless, the statistical methods for analyzing ancient DNA have lagged behind the developments in sequencing technology. The proposed research will develop novel methods based on population genomics theory to advance the field of ancient DNA research. These methods will be applied to data from ancient humans to provide new insights into the last 50 thousand years of human evolution. Moreover, via collaboration with international ancient DNA laboratories, applications of these methods will contribute to the understanding of animal domestication, a key innovation that allowed humans to form complex societies. We will develop statistical methods to assess the relationship of ancient samples to present-day individuals in a way that explicitly accounts for the fact that alleles arose as new mutations and are thus at lower frequency in ancient samples. By working with collaborators studying pig domestication, we will test hypotheses about how movement of humans coincided with the advent of agriculture and the transportation of domesticated animals. We will also develop methodology to elucidate details of how modern humans and archaic humans, such as Neandertals interbred. By leveraging patterns of variation in Neandertal DNA sharing within and among Eurasian populations, we will determine the number of times that humans and Neandertals interbred, and determine demographic structure in the pan-continental Neandertal population. Finally, we will use ancient DNA to refine our understanding of natural selection during horse domestication and human dispersal. Working with international collaborators, we will use ancient DNA to detect genes that were critical to horse domestication, and determine whether the mutations existed before domestication or if they arose de novo following domestication. We will also examine how the distribution of fitness effects has changed over time and space in humans by comparing the distribution of fitness effects inferred for ancient samples of varying ages, in order to gain an understanding of how human adaptation has been shaped by demographic history and climate change over the last hundred thousand years.

Public Health Relevance

Understanding the forces that create and maintain human genetic variation on a global scale is critical for implementing precision medicine, in which medical treatment is tailored to specific individuals. Ancient DNA enables a direct window into both the demographic history of humans as well as the way that natural selections shaped the human genome as humans dispersed throughout the world and colonized novel environments. Similarly, because the domestication of animals, including pigs and horses, was essential in allowing humans to inhabit new habitats, obtaining a detailed understanding of how and when humans utilized different animals will elucidate paths toward dealing with public health issues such as hunger and malnutrition. !

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Unknown (R35)
Project #
5R35GM124745-02
Application #
9551002
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Janes, Daniel E
Project Start
2017-09-01
Project End
2022-07-31
Budget Start
2018-08-01
Budget End
2019-07-31
Support Year
2
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Temple University
Department
Biology
Type
Schools of Arts and Sciences
DUNS #
057123192
City
Philadelphia
State
PA
Country
United States
Zip Code
19122
Schraiber, Joshua G (2018) Assessing the Relationship of Ancient and Modern Populations. Genetics 208:383-398
Schroeder, Hannes; Sikora, Martin; Gopalakrishnan, Shyam et al. (2018) Origins and genetic legacies of the Caribbean Taino. Proc Natl Acad Sci U S A 115:2341-2346
Mendes, Fábio K; Fuentes-González, Jesualdo A; Schraiber, Joshua G et al. (2018) A multispecies coalescent model for quantitative traits. Elife 7: