Gene coexpression networks reveal key drivers of phenotypic divergence in lake whitefish

BACKGROUND: A functional understanding of processes involved in adaptive divergence is one of the awaiting opportunities afforded by high throughput transcriptomic technologies. Functional analysis of co-expressed genes has succeeded in the biomedical field in identifying key drivers of disease pathways. However, in ecology and evolutionary biology, functional interpretation of transcriptomic data is still limited. RESULTS: Here we used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify modules of co-expressed genes in muscle and brain tissue of a lake whitefish backcross progeny. Modules were connected to gradients of known adaptive traits involved in the ecological speciation process between benthic and limnetic ecotypes. Key drivers, i.e. hub genes of functional modules related to reproduction, growth, and behavior were identified, and module preservation was assessed in natural populations. Using this approach, we identified modules of co-expressed genes involved in ph...背景：对适应性分化相关过程的功能解析，是高通量转录组学技术（high throughput transcriptomic technologies）所提供的待挖掘研究机遇之一。共表达基因（co-expressed genes）功能分析已在生物医学领域成功鉴定出疾病通路的关键驱动因子，然而在生态学与进化生物学领域，转录组数据的功能解读仍较为有限。 结果：本研究采用加权基因共表达网络分析（Weighted Gene Co-Expression Network Analysis，WGCNA），在湖白鱼回交后代的肌肉与脑组织中鉴定共表达基因模块。将模块与底栖（benthic）与浮游（limnetic）生态型间生态物种形成过程中涉及的已知适应性性状梯度建立关联。本研究鉴定了与繁殖、生长及行为相关的功能模块的关键驱动因子，即枢纽基因（hub genes），并在自然种群中评估了模块保守性（module preservation）。通过该方法，我们鉴定了参与……的共表达基因模块。