In humans, a lot of phenotypes involved in local adaptation such as immune response to specific pathogens or metabolism of nutrients such as sugar, fats and protein are called "polygenic". This means that they are determined by several genes or genomic regions. Polygenic adaptation has been proposed to be a major adaptive mechanism for complex phenotypes. In this model, the frequency of several slightly advantageous mutations at independent genomic loci increase simultaneously in frequency in the population. Most of these advantageous mutations are believed to be located within non-coding, regulatory regions of the genome. However, detecting polygenic adaptation signatures, in particular outside of coding, genic regions, has proved to be challenging. Most approaches to detect polygenic adaptation consist in combining signatures of positive selection across functionally homogeneous sets of genes or variants. Conversely, few studies have looked at regulatory variants, and none have accounted for the tissue-specificity of gene expression. Here, we proposed to combine network biology and population genetics approaches in order to detect polygenic adaptation acting on complex phenotypes through gene expression regulation, and to identify and characterise biological functions evolving under polygenic adaptation, taking into account the tissue-specificity of their expression.
This project aimed to answer the following questions:
Q1. How can we efficiently detect polygenic selection targeting regulatory variants ?
Q2. Which phenotypes and biological functions have been targeted by polygenic adaptation in humans?
These questions have led to two main results:
1. The development of a statistical approach to detect polygenic selection signals. Its power has been assessed carefully using simulation and its sensitivity to confounding scenarios has been assessed.
2. The identification of groups of genetic variants regulating the expression of groups of functionally-related genes, that can be used as a basis to detect polygenic adaptation targeting gene expression levels.
This project aimed at increasing our general understanding of processes that shaped present-day genetic diversity in human populations, and in particular the impact of polygenic selection on genome-wide diversity. The application of the developed approach will provide a quantitative assessment of the proportion of gene expression variation that can be attributed to groups of genetic variants under polygenic adaptation. In addition, the analysis of polygenic selection in several dataset providing samples of different tissues from hundreds of individuals should provide insights into how evolutionary processes affect phenotypes expressed in various tissues in humans. Finally, by crossing these results with GWAS databases, we should improve our understanding of the role of polygenic adaptation in the evolution of the risks to develop complex diseases, which could help anthropologists and biologists to better understand how complex phenotypes evolve and how side-effects of selection can sometimes lead to an increase in disease risks.
