Summary
Background
Lethal epidemics like the Black Death have killed millions of people and must pose an extreme selective pressure on any genetic variant that confers disease protection. Therefore, such epidemics have been hypothesized to play a key role in the evolution of humans. To what extent this is true, is a fundamental question of wide interest. Yet, it remains unanswered, in part due to limitations of the current methods to detect signatures of selection.
Objectives
I wish to accomplish three linked goals with the proposed project. The first goal is to develop new statistical methods for detecting signatures of adaptive natural selection driven by lethal epidemics. The second goal is to use these new methods to investigate the role of epidemics in recent human evolution by applying them to genetic data from several recent epidemics. The third goal is to gain insights into mechanisms that protect against infectious disease via the identification of genetic variants that have been under selection because they confer disease protection.
Methods
To reach these goals, extensive simulations will be performed to carefully characterize the genetic signatures of adaptive selection acting on a protective genetic variant during an epidemic. Then new statistical methods that can detect these signatures will be developed. Next, the new methods will be applied to several real datasets, including one from a recent Ebola epidemic. Finally, all signatures of selection detected in these real datasets will be further investigated.
Expected outcome and importance
This project will deliver new statistical methods that will move the field of selection studies a substantial step beyond the state-of-the-art by filling an important methodological gap. It will also yield key insights into the role of epidemics in recent evolutionary history. Finally, it has the potential to provide new knowledge on the genetics of disease resistance that could help prevent future lethal epidemics.
Lethal epidemics like the Black Death have killed millions of people and must pose an extreme selective pressure on any genetic variant that confers disease protection. Therefore, such epidemics have been hypothesized to play a key role in the evolution of humans. To what extent this is true, is a fundamental question of wide interest. Yet, it remains unanswered, in part due to limitations of the current methods to detect signatures of selection.
Objectives
I wish to accomplish three linked goals with the proposed project. The first goal is to develop new statistical methods for detecting signatures of adaptive natural selection driven by lethal epidemics. The second goal is to use these new methods to investigate the role of epidemics in recent human evolution by applying them to genetic data from several recent epidemics. The third goal is to gain insights into mechanisms that protect against infectious disease via the identification of genetic variants that have been under selection because they confer disease protection.
Methods
To reach these goals, extensive simulations will be performed to carefully characterize the genetic signatures of adaptive selection acting on a protective genetic variant during an epidemic. Then new statistical methods that can detect these signatures will be developed. Next, the new methods will be applied to several real datasets, including one from a recent Ebola epidemic. Finally, all signatures of selection detected in these real datasets will be further investigated.
Expected outcome and importance
This project will deliver new statistical methods that will move the field of selection studies a substantial step beyond the state-of-the-art by filling an important methodological gap. It will also yield key insights into the role of epidemics in recent evolutionary history. Finally, it has the potential to provide new knowledge on the genetics of disease resistance that could help prevent future lethal epidemics.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/804679 |
| Start date: | 01-01-2019 |
| End date: | 31-12-2024 |
| Total budget - Public funding: | 1 499 600,00 Euro - 1 499 600,00 Euro |
Cordis data
Original description
BackgroundLethal epidemics like the Black Death have killed millions of people and must pose an extreme selective pressure on any genetic variant that confers disease protection. Therefore, such epidemics have been hypothesized to play a key role in the evolution of humans. To what extent this is true, is a fundamental question of wide interest. Yet, it remains unanswered, in part due to limitations of the current methods to detect signatures of selection.
Objectives
I wish to accomplish three linked goals with the proposed project. The first goal is to develop new statistical methods for detecting signatures of adaptive natural selection driven by lethal epidemics. The second goal is to use these new methods to investigate the role of epidemics in recent human evolution by applying them to genetic data from several recent epidemics. The third goal is to gain insights into mechanisms that protect against infectious disease via the identification of genetic variants that have been under selection because they confer disease protection.
Methods
To reach these goals, extensive simulations will be performed to carefully characterize the genetic signatures of adaptive selection acting on a protective genetic variant during an epidemic. Then new statistical methods that can detect these signatures will be developed. Next, the new methods will be applied to several real datasets, including one from a recent Ebola epidemic. Finally, all signatures of selection detected in these real datasets will be further investigated.
Expected outcome and importance
This project will deliver new statistical methods that will move the field of selection studies a substantial step beyond the state-of-the-art by filling an important methodological gap. It will also yield key insights into the role of epidemics in recent evolutionary history. Finally, it has the potential to provide new knowledge on the genetics of disease resistance that could help prevent future lethal epidemics.
Status
SIGNEDCall topic
ERC-2018-STGUpdate Date
27-04-2024
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