Advances in high throughput sequencing technologies have created a gap between data production and functional data analysis. Indeed, phenotypes result from interactions between numerous genes, but traditional methods treat loci independently, missing important knowledge brought by network-level emerging properties. Therefore, detecting selection acting on multiple genes affecting the evolution of complex traits remains challenging. 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. We developed a method to analyse gene networks that is suitable to evidence polygenic selection. The general idea is to search biological pathways for subnetworks of genes that directly interact with each other and that present unusual evolutionary features. Subnetwork search is a typical combinatorial optimization problem that we solve using a simulated annealing approach. We have applied our methodology to find signals of adaptation to high-altitude in human populations. We show that this adaptation has a clear polygenic basis and is influenced by many genetic components. Our approach, implemented in the R package signet, improves on gene-level classical tests for selection by identifying both new candidate genes and new biological processes involved in adaptation to altitude.

中文翻译：

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在生物途径选择中检测基因子网

高通量测序技术的进步已经在数据生产和功能数据分析之间造成了差距。实际上，表型是由众多基因之间的相互作用产生的，但是传统方法却独立地处理基因座，缺少网络级新兴特性带来的重要知识。因此，检测作用于影响复杂性状进化的多个基因的选择仍然具有挑战性。在这种情况下，基因网络分析提供了一个强大的框架来研究适应性状的演变，并促进了全基因组数据的解释。我们开发了一种分析基因网络的方法，适用于证明多基因选择。总体思路是在生物途径中寻找彼此直接相互作用并呈现出异常进化特征的基因子网络。子网搜索是我们使用模拟退火方法解决的典型组合优化问题。我们已应用我们的方法来寻找适应人类高海拔地区的信号。我们表明，这种适应具有明确的多基因基础，并受许多遗传因素的影响。我们的方法，在R包中实现signet通过识别新的候选基因和适应海拔高度的新生物学过程，改进了基因水平的经典检测方法。