Detecting Polygenic Adaptation Targeting Gene Expression Regulation In Humans Using eQTL Networks.

ended

Polygenic adaptation, in which small changes in allele frequencies co-occur at multiple variants, has been proposed to be a major adaptive mechanism for complex phenotypes. Most approaches to detect polygenic adaptation consist in combining signatures of positive selection across functionally homogenous sets of genes or variants. However, few studies have looked at regulatory variants and none have accounted for the tissue-specificity of gene expression. Here, we propose to combine network biology and population genetics methods in order to detect polygenic adaptation acting on complex phenotypes through gene expression regulation. First, we will identify communities of regulatory variants that coregulate groups of genes, by representing both cis- and trans-expression quantitative trait loci as bipartite graphs. We will then search for communities enriched for signatures of weak positive selection to identify regulatory variants under polygenic adaptation. After evaluating the power of our approach using simulations, we will apply it to data from several tissues from the GTEx project. This will allow us to identify and characterise biological functions evolving under polygenic adaptation, taking into account the tissue-specificity of their expression. We thus hope to better understand the extent to which polygenic adaptation shaped the human genetic diversity and susceptibility to complex diseases. The PATTERNS project will be led by the experienced researcher (ER), who has worked on network biology during her postdoc in the USA. She will collaborate with the supervisor who is an expert in theoretical population genetics, and receive training in teaching, grant writing and management and communication. This will help the ER in her path to independence by strengthening her unique profile at the intersection of system biology and population genetics. The host institution will in turn benefit from her experience and network in the USA.