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

Abstract

Recent advances in sequencing allow population-genomic data to be generated for virtually any species. However, approaches to analyse such data lag behind the ability to generate it, particularly in nonmodel species. Linkage disequilibrium (LD, the nonrandom 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 genomewide. In LD networks, vertices represent loci, and connections between vertices represent the LD between them. We analysed such networks in two test cases: a new restriction-site-associated DNA sequence (RAD-seq) data set for Anopheles baimaii, a Southeast Asian malaria vector; and a well-characterized single nucleotide polymorphism (SNP) data set 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 it is applicable to any population-genomic data set, making it especially valuable for nonmodel species.

Coupling, Reinforcement, and Speciation

“…Now that genome-wide data can be gathered and new tools are becoming available that help to access complex patterns of LD (e.g., Baird 2015; Kemppainen et al 2015), there are important opportunities to make progress in this direction. However, it will be critical to link genetic patterns to an understanding of phenotypes and their role in reproductive isolation (as emphasized by ) in order to distinguish coupling processes.…”

In the presence of population structure: From genomics to candidate genes underlying local adaptation

“…SNPs that show extreme AFD and LD are likely to contain many false positives, especially if no other information is considered. These genomic signatures, however, show a promising future in identifying recent local adaptation within a statistical framework (Kemppainen et al, ). Such an approach can avoid several complications of GEA methods, including (a) errors in measures of environmental variables; (b) an increase in false positives if more than one environmental variable is needed to capture the genetic basis of local adaptation; and (c) difficulties in choosing the correct minimum allele frequency to avoid spurious associations.…”

Conservation genomics of natural and managed populations: building a conceptual and practical framework

“…Kemppainen et al . () present a useful exploratory tool (named LDna) able to give a global overview of LD associated with diverse evolutionary phenomena and identify potentially related loci. Based on simulations, (R.K. Waples, W.A.…”