The human evolutionary past: ancient DNA and computational genomics

Lead Research Organisation: The Francis Crick Institute


Human biology and disease is ultimately a product of evolution. The Ancient Genomics Laboratory uses prehistoric DNA to reconstruct in which ways our genetic makeup has changed in response to environmental and lifestyle factors of the past tens of thousands of years of our evolution, during which modern human populations spread and populated the world.

We obtain DNA from ancient skeletal material, and study genetic diversity through time using modern computer methods. Studying ancient DNA is the only way by which we can directly study human evolutionary change and understand the history that shaped our genetic variation and ancestry today. We are interested in questions that range from modern human origins, to recent genetic adaptations to diet, lifestyle, and infectious disease.

The evolutionary perspective is important because it can teach us how human populations responded and adapted to health challenges in prehistory, and find genetic underpinnings that protect against disease, which can contribute to finding new cures and response programs.

Technical Summary

This work was supported by the Francis Crick Institute which receives its core funding from the UK Medical Research Council (FC001000), the Wellcome Trust (FC001000),and Cancer Research UK (FC001000)

Human biology and disease is the outcome of historical evolutionary processes, and the large-scale retrieval of ancient DNA now provides the opportunity to directly study the dynamics and signatures of these processes in the human genome through time and space. The Crick Ancient Genomics Lab will develop new molecular and computational approaches with the overarching goal to study how ecological and cultural factors have shaped human population structure and biology during momentous transitions in prehistory.

Previous work of ours has studied how agriculture spread over the world from independent origins in the Near East, east Asia and west Africa to Europe, the south Pacific and southern Africa. These independent origins and subsequent migrations provide experimental replicates with which we can learn which adaptations–e.g. genes, regulatory pathways, and phenotypes–are the unifying mechanisms with which human populations have adapted to the radical change in lifestyle that agriculture and sedentism imposed. The agricultural diet is behind of many present-day health challenges such as diabetes, and the changes in food production and population density also changed selection pressures imposed by infectious disease. By learning how the earliest agriculturalists adapted to the new lifestyle and how these adaptations were conferred by admixture to forager groups as agriculturalists expanded, we can gain insights into the genomic basis of modern-day health challenges which will provide a vital evolutionary perspective to the future genomic era of precision medicine.

To understand these dynamics, we are using cutting-edge ancient DNA techniques coupled with modern computational genomics to reconstruct ancient genomes from populations that lived in the past 20,000 years. This includes a focus on past populations in Africa, extending available ancient DNA and genomics tools for African ancestry populations. We will map the origins of population structure in Africa, guiding GWAS and medical genetics, and document the underpinnings of genetic diversity in the most genetically variable populations on the planet, contributing to their inclusion in precision medicine initiatives of the future. A specific hypothesis, testable only with African ancient DNA, concerns the evolution of genetic resistance to malaria in African populations, where Plasomodium vivax is common in other tropical regions but rare or absent in western and central Africa likely due to a mutation in the Duffy antigen receptor for chemokines in human populations. When and under what circumstances did this resistance evolve and what factors drove its near fixation in many African populations?


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