Final Report Summary - EVOLGA (Modeling the Evolutionary Properties of Complex Genetic Architectures)
The genetic architecture of morphological, physiological and behavioral characters (or phenotype) conditions and constrains the biodiversity of species and their ability to adapt to environmental changes. Gathering and processing qualitative and quantitative information about the genetic mechanisms that underly traits of interest is thus of tremendous importance in plant and animal breeding, medicine, and evolutionary genetics. However, the complexity of genetic architectures is overwhelming. Recent advances in molecular biology, evo-devo and quantitative genetics have indeed highlighted how intricated were genetic, metabolic and developmental regulation mechanisms in the expression of the gene-to-phenotype relationship.
Evolutionary quantitative genetics aims at predicting the evolutionary properties of a population or a species without detailing explicitly the complexity of the genotype-phenotype relationship. To do so, the models that are frequently used do not pretend to provide an exhaustive description of the genetic architecture, but rather to summarize it through some simple parameters, expected to catch key properties of the genotype-phenotype map in a population. The sharp contrast between the complexity of real architectures and the simple picture provided by most quantitative genetics models has often lead to some debate on the relevance of qualitative and quantitative predictions derived from the mathematical simplification of the evolutionary theory.
The philosophy of this 3-year project, named "EVOLGA", was to challenge the capacity for some widely used models to describe and predict the evolutionary potential of populations and species, by contrasting their predictions with empirical data and/or more realistic models. The research objectives detailed thus focused on the validation and the improvement of the part of the theory of evolution dealing with the complexity of the link between genomes and observable characters.
The original project detailed two major objectives. Objective (i) was dealing with the importance of genetic interactions (epistasis) on the evolutionary properties of quantitative traits, while objective (ii) was focusing on the impact of considering multiple characters when studying evolution. In the course of the project, a third objective was defined as (iii) studying the evolutionary dynamics of the genome due to non-adaptive processes, focusing in particular on the impact of transposable elements, which are very common repeated sequences that are suspected to invade genomes in a parasitic way.
The EVOLGA project went along the lines proposed in the original proposal. Objectives (i) and (ii) have been convincingly fulfilled, with several scientific papers published (2 for objective (i), and 3 for objective (ii)) and several others in preparation. In addition, a new objective (labelled "iii" in the mid-period report) has been defined rapidly after the beginning of the project, and was the focus of 3 publications.
By constructing and exploring theoretical models, we could show how genetic interactions could play a major role in evolution, by allowing complex and deep changes in the genetic architectures. Based on experimental data, we have also demonstrated that the lask of independence between characters could slow down, or even prevent, their respective evolution. Finally, our work on the genome dynamics have provided interesting insights on the impact of "selfish" DNA sequences, which can persist in genomes without being useful for the host species. These results, highlighting the complexity of some evolutionary processes at different levels, from the genome to observable characters, will contribute to build a more precise understanding of how, why, and when species adapt to their environment.
Each of the research lines explored during the EVOLGA project has opened new perspectives and new questions. Based on the network of collaborators involved in the project, we will try to explore these perspectives, with two different approaches: (i) building new theoretical models to address the novel issues, and (ii) using the statistical tools developped during the project to analyse real data from the literature (e.g. published genome sequences), but also from experiments designed to explore specific questions. These perspectives are concrete and rely on two PhD projects (one hosted at the institute and the other through a collaboration) starting in October 2013.
Evolutionary quantitative genetics aims at predicting the evolutionary properties of a population or a species without detailing explicitly the complexity of the genotype-phenotype relationship. To do so, the models that are frequently used do not pretend to provide an exhaustive description of the genetic architecture, but rather to summarize it through some simple parameters, expected to catch key properties of the genotype-phenotype map in a population. The sharp contrast between the complexity of real architectures and the simple picture provided by most quantitative genetics models has often lead to some debate on the relevance of qualitative and quantitative predictions derived from the mathematical simplification of the evolutionary theory.
The philosophy of this 3-year project, named "EVOLGA", was to challenge the capacity for some widely used models to describe and predict the evolutionary potential of populations and species, by contrasting their predictions with empirical data and/or more realistic models. The research objectives detailed thus focused on the validation and the improvement of the part of the theory of evolution dealing with the complexity of the link between genomes and observable characters.
The original project detailed two major objectives. Objective (i) was dealing with the importance of genetic interactions (epistasis) on the evolutionary properties of quantitative traits, while objective (ii) was focusing on the impact of considering multiple characters when studying evolution. In the course of the project, a third objective was defined as (iii) studying the evolutionary dynamics of the genome due to non-adaptive processes, focusing in particular on the impact of transposable elements, which are very common repeated sequences that are suspected to invade genomes in a parasitic way.
The EVOLGA project went along the lines proposed in the original proposal. Objectives (i) and (ii) have been convincingly fulfilled, with several scientific papers published (2 for objective (i), and 3 for objective (ii)) and several others in preparation. In addition, a new objective (labelled "iii" in the mid-period report) has been defined rapidly after the beginning of the project, and was the focus of 3 publications.
By constructing and exploring theoretical models, we could show how genetic interactions could play a major role in evolution, by allowing complex and deep changes in the genetic architectures. Based on experimental data, we have also demonstrated that the lask of independence between characters could slow down, or even prevent, their respective evolution. Finally, our work on the genome dynamics have provided interesting insights on the impact of "selfish" DNA sequences, which can persist in genomes without being useful for the host species. These results, highlighting the complexity of some evolutionary processes at different levels, from the genome to observable characters, will contribute to build a more precise understanding of how, why, and when species adapt to their environment.
Each of the research lines explored during the EVOLGA project has opened new perspectives and new questions. Based on the network of collaborators involved in the project, we will try to explore these perspectives, with two different approaches: (i) building new theoretical models to address the novel issues, and (ii) using the statistical tools developped during the project to analyse real data from the literature (e.g. published genome sequences), but also from experiments designed to explore specific questions. These perspectives are concrete and rely on two PhD projects (one hosted at the institute and the other through a collaboration) starting in October 2013.