Genome‐wide RAD sequence data provide unprecedented resolution of species boundaries and relationships in the Lake Victoria cichlid adaptive radiation

Abstract

Although population genomic studies using next generation sequencing (NGS) data are becoming increasingly common, studies focusing on phylogenetic inference using these data are in their infancy. Here, we use NGS data generated from reduced representation genomic libraries of restriction-site-associated DNA (RAD) markers to infer phylogenetic relationships among 16 species of cichlid fishes from a single rocky island community within Lake Victoria's cichlid adaptive radiation. Previous attempts at sequence-based phylogenetic analyses in Victoria cichlids have shown extensive sharing of genetic variation among species and no resolution of species or higher-level relationships. These patterns have generally been attributed to the very recent origin (<15,000 years) of the radiation, and ongoing hybridization between species. We show that as we increase the amount of sequence data used in phylogenetic analyses, we produce phylogenetic trees with unprecedented resolution for this group. In trees derived from our largest data supermatrices (3 to >5.8 million base pairs in width), species are reciprocally monophyletic with high bootstrap support, and the majority of internal branches on the tree have high support. Given the difficulty of the phylogenetic problem that the Lake Victoria cichlid adaptive radiation represents, these results are striking. The strict interpretation of the topologies we present here warrants caution because many questions remain about phylogenetic inference with very large genomic data set and because we can with the current analysis not distinguish between effects of shared ancestry and post-speciation gene flow. However, these results provide the first conclusive evidence for the monophyly of species in the Lake Victoria cichlid radiation and demonstrate the power that NGS data sets hold to resolve even the most difficult of phylogenetic challenges.

Exploring the phylogeography of a hexaploid freshwater fish by RAD sequencing

“…As the level of missing data is allowed to increase, so does the signal of phylogeographic structure. This phenomenon has been briefly described in the literature (Campagna et al., 2015; Huang & Knowles, 2014; Takahashi, Nagata, & Sota, 2014; Wagner et al., 2013) and should be viewed as a motivation to include more missing data to reduce potential biases from only examining highly conserved regions. However, our final datasets retained relatively low levels of missing data—19.7% within our 723 SNP dataset and 0.63% within the DIYABC reduced SNP dataset of 661 markers.…”

“…This method is popular and has been used in many studies since its inception (Figure S1). RAD sequencing has been used, particularly in fish, to identify population divergence (Boehm, Waldman, Robinson, & Hickerson, 2015; Ferchaud & Hansen, 2016; Larson et al., 2014), for SNP identification in polyploid fish (Hohenlohe, Amish, Catchen, Allendorf, & Luikart, 2011; Ogden et al., 2013; Palti et al., 2014), in phylogeographic studies (Macher et al., 2015; Reitzel, Herrera, Layden, Martindale, & Shank, 2013), for QTL analysis (Gagnaire, Normandeau, Pavey, & Bernatchez, 2013; Houston et al., 2012; Yoshizawa et al., 2015), for linkage mapping (Brieuc, Waters, Seeb, & Naish, 2014; Henning, Lee, Franchini, & Meyer, 2014), in hybridization studies (Hand et al., 2015; Lamer et al., 2014; Pujolar et al., 2014), for exploration of genome architecture and evolution (Brawand et al., 2014; Kai et al., 2014; Waples, Seeb, & Seeb, 2016), and in phylogenetic analyses (Gonen, Bishop, & Houston, 2015; Wagner et al., 2013). This methodology should be particularly suited to phylogeographic studies as the inference power from large numbers of markers may identify patterns that are not easily visible in traditional analyses based on relatively few loci (Davey et al., 2011).…”

Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds

“…These studies, using both simulated and empirical data, have quantified the number of retained homologous loci among species with varying divergence dates. Simulations suggest that hundreds of orthologous, phylogenetically informative loci can be identified in species with divergence times of up to 60 million years (Cariou, Duret, & Charlat, 2016a,2016b; Rubin, Ree, & Moreau, 2012), but empirical studies have successfully applied RADseq to phylogenetic studies of species only from more recent radiations (<100,000 years; Keller et al., 2013; Wagner et al., 2013) and find that the number of shared SNPs drops, sometimes quickly, as divergence dates increase (Lexer et al., 2013; Pante et al., 2015). Among our two studied seal species, harbor and gray, that diverged approximately 5 mya (Arnason et al., 2006), we found over 100,000 homologous RAD loci.…”

Outlier analyses to test for local adaptation to breeding grounds in a migratory arctic seabird

“…For example, clinal variations in color in the European barn owl ( Tyto alba ) and tawny owl ( Strix aluco ) seem to be maintained by environmental selection (Antoniazza, Burri, Fumagalli, Goudet, & Roulin, 2010; Karell, Ahola, Karstinen, Valkama, & Brommer, 2011), and clinal variation in allele frequencies in functional genes has been associated with various environmental factors, such as altitude (Bonin, Taberlet, Miaud, & Pompanon, 2006; McCracken et al., 2009), latitude (de Jong, Collins, Beldade, Brakefield, & Zwaan, 2013), and photoperiod (Bradshaw & Holzapfel, 2008). In recent years, several studies have highlighted the utility of genome‐wide scans to investigate mechanisms of population divergence and local adaptation (e.g., resolution of species boundaries in Lake Victoria cichlids, Wagner et al., 2013; parallel adaptation in salmonid fishes, Miller et al., 2012; burrowing behavior in oldfield mice Peromyscus polionotus , Weber, Peterson, & Hoekstra, 2013). An exponential increase in the number of genotyped loci and coverage of the genome over previous methods has also increased power to detect loci that deviate from a neutral model of evolution (i.e., outlier loci) and potentially underlie adaptation.…”