Gaining insights into human evolution and disease prevention from adaptive natural selection driven by lethal epidemics

Project Information

SELECTIONDRIVEN

Grant agreement ID: 804679

DOI

10.3030/804679

Project closed

EC signature date 7 November 2018

Start date 1 January 2019

End date 30 June 2025

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 499 600,00

EU contribution

€ 1 499 600,00

Coordinated by

KOBENHAVNS UNIVERSITET
Denmark

Periodic Reporting for period 4 - SELECTIONDRIVEN (Gaining insights into human evolution and disease prevention from adaptive natural selection driven by lethal epidemics)

Reporting period: 2023-07-01 to 2025-06-30

Summary of the context and overall objectives of the project

The goal of this project was to investigate to what extent severe deadly epidemics, like the Black Death, has genetically affected different species, including humans. In particular, the main focus was on the question of whether such epidemics have driven positive natural selection on genetic variants that protect carriers against disease. The motivation was 1) to gain a better understanding of the potential long-term effects that a severe epidemic can have on a population, 2) to gain a better understanding of what role epidemics have played in evolution of different species and 3) to hopefully identify genetic variants that protect against the studied epidemic diseases, and thus gain an understanding of how to protect against them in the future.

To answer the question, we had four aims. First we aimed to use simulations to investigate whether the currently used methods for detecting selection are able to detect this type of selection and if so which method that is best suited for it (aim 1). Second, if necessary, we aimed to improve the currently used methods or provide new and better ones (aim 2). Third, we aimed to apply the best method(s) to real genetic data from different populations that have undergone an epidemic to see if there are signatures of epidemic driven selection (aim 3). And finally, if we found any such signatures, our aim was to investigate these further in appropriate follow-up studies (aim 4).

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

My team and I ended up primarily focusing on the first of the above-mentioned aims. Specifically we put a lot of effort into developing a computational simulation framework that allows its users to estimate power for a given study design (sample size, sampling time(s) and selection detection method) and a given epidemic. Using this framework, we then showed that most studies of selection driven by epidemics so far have been underpowered, including a study that was published in Nature in 2022. The latter was a result that ended up leading to us contributing to a rebuttal of that study (Barton*, Santander* et al, 2025). We also estimated that we had far too few samples to study several of the epidemics we originally planned to work on and how many samples we needed to look for at study the Black Death for instance. Finally, we made the tool freely available in the hope that future studies of selection driven by epidemics will be appropriately powered (Santander & Moltke, 2025).

We also took several first steps towards fulfilling the second aim, i.e. to develop a new and better method for detecting natural selection driven by epidemics. Specifically, we developed two new computational methods for detecting identity-by-descent (IBD), a feature in genetic data that we believe has great potential to be used for a new and better method for detecting selection. Both have resulted in an article and a freely available computational method (Nøhr et al, 2021 and Severson, Korneliussen & Moltke, 2022).

Finally, we also took several steps towards fulfilling the third aim. Specifically, we investigated real data relevant for three different epidemics: a severe Rinderpest epidemic in Cape Buffalo, a severe epidemic of kuru (a prion disease) in the 1900s among some of the communities in the Eastern Highlands of Papua New Guinea and the Black Death in European humans around 1349. The initial studies of the first two datasets led to papers about Cape Buffalo and a number of human communities in Eastern Highlands of Papua New Guinea, respectively (Quinn et al. 2023 and Quinn et al 2024). However, they also led to the conclusion that we did not have enough samples available to be able to detect selection with the data at hand for the rinderpest and the kuru epidemics. For the third epidemic, we initially reached the same conclusion. But near the end of the project, we finally managed to start a collaboration which led to the generation of a large enough dataset. We have started analysing this data and this has led to really interesting (but not yet published) results.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

While performing power calculations is standard within e.g. the field of medical genetics, doing so is (perhaps surprisingly) beyond the state of the art within population genetics in general and selection studies in particular. So much so that underpowered studies have as recent as 2022 been published in prestigious journals like Nature (e.g. Klunk et al. 2022). The framework for estimating power that we have developed as part of this project has 1) made it clear that the field of population genetics needs to consider study design and power when performing selection studies and 2) made performing such power calculations a lot easier. Hence, I believe that the work performed as part of this ERC project has the potential to markedly improve how such studies are performed in the future.

An illustration of the main output of the key software tool developed