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

Periodic Reporting for period 2 - SELECTIONDRIVEN (Gaining insights into human evolution and disease prevention from adaptive natural selection driven by lethal epidemics)

Reporting period: 2020-07-01 to 2021-12-31

The goal of this project is to investigate to what extent severe epidemics, like the Black Death, has affected different species, including humans, genetically throughout history. In particular, the main focus is whether epidemics have driven positive natural selection on genetic variants that protect carriers against disease. The answer to this question will give us a better understanding of the potential long-term effects that a severe epidemic can have on a population, which with the current COVID-19 pandemic seems extremely timely. Moreover, it will give us a better understanding of what role epidemics have played in evolution of different species. Finally, the hope is that the project will lead to the identification of genetic variants that protects against the studied epidemic diseases, and thus might help us understand how to protect against them in the future.

To answer the question, we will first aim to use simulations to investigate if the current methods for detecting selection are able to detect this type of selection and if so which method
that is best suited (aim 1). We will then, if necessary, aim to improve the current methods or provide new and better ones (aim 2). Subsequently, we will aim 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 selection driven by this epidemic (aim 3). And finally, if we find any such signatures, we will investigate these further in appropriate follow-up studies (aim 4).
My team and I have mainly been focusing on the first of the above-mentioned aims, namely the simulation setup that will allow us to assess both currently existing and new methods for detecting selection driven by epidemics. We have managed to get a versatile simulation framework up and running and have started simulating data that mimics different scenarios with epidemics. So far it looks like most current methods for detecting selection have limited power to detect selection driven by an epidemic unless a large number of samples are available. However, this part is still work in progress along with a paper that describes the framework and results.

We have also taken the first steps towards the second aim, i.e. to develop a new and better method for detecting natural selection driven by epidemics. Specifically, we have 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 articles (by the end of the reporting period one of these was published and one was in review) and two freely available computational methods.

Finally, we have started working on real data from a species that have undergone severe epidemic, namely the Cape Buffalo that as a result of Rinderpest had population size reduction of up to 90% in a single population. This is still work in progress.
We have so far developed two new computational methods that allow the inference of IBD under circumstances that previous methods could not handle. In addition to this, we expect to provide a freely available simulation framework that will allow people to assess under what circumstances different selection methods will work for their species and epidemic of interest. Moreover, we will apply this framework to several real datasets, which will hopefully lead to both new knowledge about the evolutionary role of epidemics and the discovery of one or more genetic variants that confer protection against future epidemics.