Data from: Genome scan reveals selection acting on genes linked to stress response in wild pearl millet

Uncovering genomic regions involved in adaption is a major goal in evolutionary biology. High throughput sequencing now makes it possible to tackle this challenge in non-model species. Yet, despite the increasing number of methods targeted to specifically detect genomic footprints of selection, the complex demography of natural populations often causes high rates of false positive in gene discoveries. The aim of this study was to identify climate adaptations in wild pearl millet populations, Pennisetum glaucum (L.) R. Br. ssp. monodii. We focused on two climate gradients, one in Mali and one in Niger. We used a two-step strategy to limit false positive outliers. First, we considered gradients as biological replicates and performed RNA sequencing of 4 populations at the extremities. We combined four methods – 3 based on differentiation among populations and 1 based on diversity patterns within populations – to identify outlier SNPs from a set of 87,218 high quality SNPs. Among 11,155 contigs of pearl millet reference transcriptome, 540 exhibited selection signals as evidenced by at least one of the 4 methods. In a second step, we genotyped 762 samples in 11 additional populations distributed along the gradients using SNPs from the detected contigs and random SNPs as control. We further assessed selection on this large dataset using a differentiation-based method and a correlation with environmental variables based method. Four contigs displayed consistent signatures between the four extreme and additional eleven populations, two of which were linked to abiotic and biotic stress responses.

解析参与适应性演化的基因组区域，是演化生物学领域的核心研究目标之一。如今，高通量测序（High-throughput sequencing）技术已使得在非模式物种中攻克这一研究挑战成为可能。尽管针对特异性检测选择基因组印迹的方法数量日益增多，但自然种群复杂的种群统计学特征往往会在基因发掘过程中造成较高的假阳性率。本研究旨在鉴定野生珍珠粟（Pennisetum glaucum (L.) R. Br. ssp. monodii）种群的气候适应性特征，研究聚焦于两处气候梯度：一处位于马里，另一处位于尼日尔。为限制假阳性异常位点的出现，本研究采用两步法策略：首先，将气候梯度视为生物学重复，对气候梯度两端的4个种群开展RNA测序（RNA sequencing），并整合四种方法——其中三种基于种群间分化水平，一种基于种群内多样性模式——从87218个高质量单核苷酸多态性（Single Nucleotide Polymorphisms, SNPs）位点中筛选异常SNPs。在珍珠粟参考转录组的11155个重叠群（contigs）中，有540个至少被四种方法中的一种检测到选择信号。第二步中，我们利用筛选得到的重叠群对应的SNPs以及作为对照的随机SNPs，对沿气候梯度分布的另外11个种群的762个样本进行基因分型（genotyping）；随后基于该大型数据集，采用基于种群分化的方法与环境变量相关性分析方法，进一步评估选择信号。最终在4个梯度极端种群与新增的11个种群中，共有4个重叠群呈现出一致的选择信号特征，其中2个重叠群与非生物胁迫和生物胁迫应答过程密切相关。