signet

Signet is a software tool that addresses the challenge of detecting selection acting on multiple genes affecting complex traits' evolution. Advances in high-throughput sequencing technologies have led to a gap between data production and functional data analysis. Traditional methods treat loci independently, missing important knowledge from network-level emerging properties. However, phenotypes result from interactions between numerous genes. In this context, gene network analysis provides a powerful framework to study the evolution of adaptive traits and facilitates the interpretation of genome-wide data.

Signet uses a simulated annealing approach to search for subnetworks of genes that directly interact with each other and present unusual evolutionary features. This subnetwork search is a typical combinatorial optimization problem, which Signet solves effectively. The software tool has been applied to find signals of adaptation to high altitudes in human populations. The results reveal a clear polygenic basis influenced by many genetic components. In other words, many genes contribute to the adaptation to high-altitude, not just a few.

Signet improves on gene-level classical tests for selection by identifying new candidate genes and biological processes involved in adaptation to altitude. By analyzing gene networks, Signet identifies new candidate genes that classical tests may have missed. Additionally, it identifies new biological processes involved in adaptation to altitude, enhancing our understanding of the mechanisms underlying adaptation.