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, particularly in non-model species. Linkage disequilibrium (LD) 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 RAD sequence data-set 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 clusters in the network (single outlier clusters, SOCs), each comprising many loci connected by high LD. In A. baimaii, each SOC corresponded to a chromosomal inversion on single chromosomal arms, consistent with cytological studies. Among sticklebacks, each SOC was generated by a distinct evolutionary phenomenon: chromosomal inversions, local adaptation, population demographic history and geographic structure. LDna does not require mapping information, so is applicable to any population genomic data-set, including those from non-model species, where the global overview of evolutionary phenomena may be especially valuable. The approach is powerful and rapid in characterizing, and identifying loci associated with, inversions and other evolutionarily important processes, such as local adaptation. Furthermore, LDna can access information about gene interactions across the genome and identifies candidate loci involved in them.