Gouy Alexandre, Daub Joséphine T, Excoffier Laurent
Institute of Ecology and Evolution, University of Berne, Baltzerstrasse 6, 3012 Berne, Switzerland.
Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
Nucleic Acids Res. 2017 Sep 19;45(16):e149. doi: 10.1093/nar/gkx626.
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.
高通量测序技术的进步在数据生成与功能数据分析之间造成了差距。的确，表型是众多基因之间相互作用的结果，但传统方法独立处理基因座，遗漏了网络层面新出现的特性所带来的重要知识。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。在这种背景下，基因网络分析为研究适应性性状的进化提供了一个强大的框架，并有助于对全基因组数据的解读。我们开发了一种分析基因网络的方法，该方法适用于证明多基因选择。总体思路是在生物途径中搜索彼此直接相互作用且呈现异常进化特征的基因子网。子网搜索是一个典型的组合优化问题，我们使用模拟退火方法来解决。我们已应用我们的方法来寻找人类群体适应高海拔的信号。我们表明这种适应具有明确的多基因基础，并受到许多遗传成分的影响。我们在R包signet中实现的方法，通过识别参与海拔适应的新候选基因和新生物过程，改进了基因水平上的经典选择测试。
