Dataset: Using genome-wide RAD sequences to resolve rapid radiations: A case study from the Cactaceae

The reconstruction of relationships within recently radiated groups is challenging even when massive amounts of sequencing data are available. The use of restriction site-associated DNA sequencing (RAD-Seq) to this end is promising. Here, we assessed the performance of RAD-Seq to infer the species-level phylogeny of the rapidly radiating genus Cereus (Cactaceae). To examine how the amount of genomic data affects resolution in this group, we used distinct datasets and implemented different analyses. We sampled 52 individuals of Cereus, representing 18 of the 25 species currently recognized, plus members of the closely allied genera Cipocereus and Praecereus, and other 11 Cactaceae genera as outgroups. Three scenarios of permissiveness to missing data were carried out in iPyRAD, assembling datasets with 4330% (333 loci), 45% (1440 loci), and 70% (6141 loci) of missing data. For each dataset, Maximum Likelihood (ML) trees were generated using two supermatrices, i.e., only SNPs and SNPs plus invariant sites. Accuracy and resolution were improved when the dataset with the highest number of loci was used (6141 loci), despite the high percentage of missing data included (70%). Coalescent trees estimated using SVDQuartets and ASTRAL are similar to those obtained by the ML reconstructions. Overall, we reconstruct a well-supported phylogeny of Cereus, which is resolved as monophyletic and composed of four main clades with high support in their internal relationships. Our findings also provide insights into the impact of missing data for phylogeny reconstruction using RAD loci. SamplingOur dataset includes 63 samples spanning 52 ingroups of <em>Cereus </em>and 11 outgroups (Table 1). ddRAD library preparation and sequencing 157Genomic DNA was extracted from root tissues using the DNeasy Plant Mini Kit (Qiagen). ddRAD libraries were prepared using high fidelity EcoRI and HPAII restriction enzymes following Campos et al. (2017) and Khan et al. (2019). Details of library preparation and sequencing are shown in Supplementary materialBioinformatics analyses Raw data were trimmed for adapters and quality filtered before SNPs calling. The quality of sequencing data was checked with FastQC 0.11.2 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc), visualized in MultiQC 1.0 (https://github.com/ewels/MultiQC), and filtered with SeqyClean 1.9.12 (Zhbannikov et al., 2017) using the following settings: minimum quality (Phred Score 20), minimum size (&gt;65 bp), and Illumina contaminants (UniVec.fas). We used the iPyRAD pipeline (available at http://github.com/dereneaton/ipyrad) to identify homology among reads, make SNP calls, and format output files. The following parameter settings were implemented: mindepth_majrule = 6 (minimum depth for majority-rule base calling), clust_threshold = 0.85 (clustering threshold for de novo assembly), filter_adapters = 2 (strict filter), max_Hs_consens = 6 (maximum heterozygotes in consensus), min_samples_locus (minimum percentage of samples per locus 184for output). For the latter, values varied in three distinct scenarios concerning the permissiveness to missing data. These scenarios considered that the final set of loci should have at least 39 samples (scenario 1, approximately 30% of missing data), 26 samples (scenario 2, approximately 45% of missing data), or 13 samples (scenario 3, approximately 70% of missing data). After SNP calling, CD-HIT (Li and Godzik, 2006; Fu et al., 2012) was used to identify reverse-complement duplicates in the loci recovered by iPyRAD.