Data from: Linkage disequilibrium network analysis (LDna) gives a global view of chromosomal inversions, local adaptation and geographic structure

Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyze such data lag behind the ability to generate it, particularly in non-model species. Linkage disequilibrium (LD, the non-random association of alleles from different loci) is a highly sensitive indicator of many evolutionary phenomena including chromosomal inversions, local adaptation and geographical structure. Here we present linkage disequilibrium network analysis (LDna), which accesses information on LD shared between multiple loci genome-wide. In LD networks, vertices represent loci and connections between vertices represent the LD between them. We analyzed such networks in two test cases: a new Restriction-site Associated DNA sequence (RAD-seq) dataset for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterised single nucleotide polymorphism (SNP) dataset from 21 three-spined stickleback individuals. In each case we readily identified five distinct LD network clusters (single outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, further population-genetic analyses supported the inference that each SOC corresponds to a large inversion, consistent with previous cytological studies. For sticklebacks, we inferred that each SOC was associated with a distinct evolutionary phenomenon: two chromosomal inversions, local adaptation, population-demographic history and geographic structure. LDna is thus a useful exploratory tool, able to give a global overview of LD associated with diverse evolutionary phenomena and identify loci potentially involved. LDna does not require a linkage map or reference genome, so is applicable to any population-genomic dataset, making it especially valuable for non-model species.近年来，测序技术的进步使得几乎所有物种均可获取群体基因组数据。然而，此类数据的分析方法却滞后于其生成能力，这一差距在非模式物种中尤为显著。连锁不平衡（Linkage disequilibrium, LD），即不同位点等位基因间的非随机关联，是诸多进化现象的高灵敏度指示指标，包括染色体倒位、局部适应与种群地理结构等。本文提出连锁不平衡网络分析（linkage disequilibrium network analysis, LDna）方法，该方法可获取全基因组范围内多个位点间共享的连锁不平衡信息。在LD网络中，顶点代表遗传位点，顶点间的连接则代表二者之间的连锁不平衡程度。我们通过两个测试案例对该方法展开验证分析：其一为针对东南亚疟疾媒介白氏按蚊（Anopheles baimaii）的全新限制性酶切位点相关DNA测序（Restriction-site Associated DNA sequence, RAD-seq）数据集；其二为来自21尾三棘刺鱼个体的已被充分表征的单核苷酸多态性（single nucleotide polymorphism, SNP）数据集。在两个案例中，我们均快速识别出五个独立的单一异常簇（single outlier clusters, SOCs），每个簇均包含大量以高强度LD相互连接的位点。针对白氏按蚊的进一步群体遗传学分析证实，每个SOC均对应一段大型染色体倒位，这与此前的细胞学研究结果相符。对于三棘刺鱼，我们推断每个SOC分别与不同的进化现象相关：两段染色体倒位、局部适应、种群人口统计学历史以及种群地理结构。综上，LDna是一款实用的探索性分析工具，能够全局概览与各类进化现象相关的连锁不平衡情况，并识别出潜在参与相关过程的遗传位点。LDna无需依赖连锁图谱或参考基因组，因此可应用于任意群体基因组数据集，对于非模式物种而言尤其具有应用价值。