Periodic Reporting for period 1 - TimeAdapt (Tracking Genetic Adaptation of Populations Using Time-Series Genomic Data)
Période du rapport: 2019-08-01 au 2021-07-31
Individuals constituting natural populations are genetically different. This genetic variation determines the ability of populations to adapt to environmental changes and, ultimately, their persistence through time. Thus, the study of genetic diversity of populations has applications to human health, exploited natural or artificial populations (e.g. fisheries and crops respectively), harmful organisms (e.g. infectious diseases, crop pests) and endangered species. The relative importance of mutation, genetic drift, gene flow and natural selection along the history of a population shape the structure of its genetic pool. Understanding the strength and interactions of these forces is one of the main goals of population genetics, which uses mathematical models to make inferences on these processes from population genetic data.
The archetype of the population genetic study uses current genetic diversity of populations to make inferences on their past history and to predict their evolution (and, eventually, make management plans). However, such an approach attempts to obtain a detailed description of a complex dynamics from a single snapshot. Time-series population genetic data offer a powerful way to track genetic changes through time, and provide a better picture of the processes acting on the population.
The integration of ancient DNA analysis to archaeological research in the last decade has allowed to directly characterize the ancestral genetic pool of current human populations. These studies have known a considerable success in characterising the history of the peopling of Europe. These previous studies have revealed that the genetic pool of modern-day Europeans results from the admixture of three groups: (1) hunter-gatherers (2) farmers from Anatolia, and (3) herders from the Pontic-Caspian Steppe. Several genes associated to diet, pigmentation, immunity and height present changes in genetic diversity between these three groups (characterized with aDNA) and modern day populations that suggest the action of selection. These results offer an overall picture of the genetic history of Europe, but many important details are still missing: time, strength and dynamics of adaptation remain unknown. Model-based statistical inference will be paramount for the understanding of historical factors affecting the genetic adaptation of these populations (e.g. was time of selection associated to climatic events?).
The first objective of this project is to develop a statistical framework for the joint inference of the demographic and adaptive history of populations from temporal population genetic data. This method is based on the use of computer simulations to generate a large panel of data from different hypothetical models that is compared to the real data, allowing to identify the model(s) that best explain the observations.
The second objective of this project is to apply the method to data from ancient and modern European populations to better characterize their demographic and adaptive history, building on previous results and data.
At the present stage of the project the first objective has been fully achieved and a method (with its corresponding software) is available to analyse temporal population genetic data. This method can be applied to a other datasets addressing a diverse rage of evolutionary questions and applications, as presented above (i.e. conservation of endangered species, management of exploited populations and harmful organisms). Its application to better understand human prehistory is an ongoing work.
The archetype of the population genetic study uses current genetic diversity of populations to make inferences on their past history and to predict their evolution (and, eventually, make management plans). However, such an approach attempts to obtain a detailed description of a complex dynamics from a single snapshot. Time-series population genetic data offer a powerful way to track genetic changes through time, and provide a better picture of the processes acting on the population.
The integration of ancient DNA analysis to archaeological research in the last decade has allowed to directly characterize the ancestral genetic pool of current human populations. These studies have known a considerable success in characterising the history of the peopling of Europe. These previous studies have revealed that the genetic pool of modern-day Europeans results from the admixture of three groups: (1) hunter-gatherers (2) farmers from Anatolia, and (3) herders from the Pontic-Caspian Steppe. Several genes associated to diet, pigmentation, immunity and height present changes in genetic diversity between these three groups (characterized with aDNA) and modern day populations that suggest the action of selection. These results offer an overall picture of the genetic history of Europe, but many important details are still missing: time, strength and dynamics of adaptation remain unknown. Model-based statistical inference will be paramount for the understanding of historical factors affecting the genetic adaptation of these populations (e.g. was time of selection associated to climatic events?).
The first objective of this project is to develop a statistical framework for the joint inference of the demographic and adaptive history of populations from temporal population genetic data. This method is based on the use of computer simulations to generate a large panel of data from different hypothetical models that is compared to the real data, allowing to identify the model(s) that best explain the observations.
The second objective of this project is to apply the method to data from ancient and modern European populations to better characterize their demographic and adaptive history, building on previous results and data.
At the present stage of the project the first objective has been fully achieved and a method (with its corresponding software) is available to analyse temporal population genetic data. This method can be applied to a other datasets addressing a diverse rage of evolutionary questions and applications, as presented above (i.e. conservation of endangered species, management of exploited populations and harmful organisms). Its application to better understand human prehistory is an ongoing work.
The work performed in the project mainly consist in the development of software capable to simulate and analyse data. Building on existent software and libraries, a pipeline has been developed to perform the necessary simulations and calculations. The performance of the methods has then been thoroughly evaluated by analysing simulated data for which we know the the true value of parameters and the true generative model.
Results from this project have been presented in four international conferences and one preprint. The software implementing the statistical methods developed is distributed under free license. As part of the project dissemination activities a postgraduate short course was imparted at Uppsala University and there were contributions to three science festivals targeting the general public, including a video available online.
Results from this project have been presented in four international conferences and one preprint. The software implementing the statistical methods developed is distributed under free license. As part of the project dissemination activities a postgraduate short course was imparted at Uppsala University and there were contributions to three science festivals targeting the general public, including a video available online.
The method developed in this projects is one of the first methods that allows the joint inference of demography and selection from population genetic data. Traditionally studied separately, previous methods do not account the interactions of both processes which can bias the inferences obtained. We show, through the use of simulations, that our method can correct some of these biases. This development opens a new way to address the analysis of population genetic data that can have an impact in all population genetics applications, including on many species of economic and societal interest, such as the study of endangered species or populations, species of agronomic interest, etc.