DOI: 10.5061/dryad.1s04tj3/2
title: 3097SNP_dataset

description: For a subset of 68 of the cats typed at 35SNPs and an additional eight cats collected from across the UK, ddRAD analysis of the samples was conducted using a modification of the protocol by (Peterson et al., 2012), which is described in (Bourgeois et al., 2018). A list of all used samples can be found in Supplementary Material 2. In short, DNA quality was assessed via agarose gel electrophoresis on a 1% gel and only non-degraded DNA (as judged by a tight high molecular weight band against a lambda standard) was selected for the library preparation stage.  It should be noted here that DNA quality requirements for this protocol preclude running the analysis on poor quality or degraded samples (e.g. most historical or non-invasive sample types). DNA was quantified using a Qubit Broad Range dsDNA Assay (Thermofisher Scientific) and normalised to 7 ng µl-1. Each sample was processed in triplicate or quadruplet to enhance evenness of coverage of samples within the library. Individual genomic DNA was restriction-digested using both SbfI and SphI enzymes and Illumina specific sequencing adaptors (P1 & P2) were then ligated to fragment ends. The pooled samples were size selected (320-590bp fragments) by gel electrophoresis, PCR amplified (15 cycles) and the resultant amplicons (ddRAD library) were purified and quantified. Combinatorial inline barcodes (5 or 7 bases long), included in the P1 &P2 adaptors, allowed each sample replicate to be identified post sequencing. The ddRAD library was sequenced on the Illumina MiSeq Platform (a single paired-end run; v2 chemistry, 2 x 160 bases). Positive control samples were run on all libraries.
The sequence data were quality assessed using FastQC (Andrew, 2010) and the reads demultiplexed by barcode and quality filtered using the process_radtags module (default parameters) of the stacks bioinformatics pipeline (Catchen et al. 2013). The retained reads were then trimmed to a standard 135 bases in length. Demultiplexed read files were concatenated into read files for each individual (three or four barcode combinations per individual, see above).  Read 1 and Read 2 files were then joined into a single file per individual.
The individual data files were then processed using the denovo_map.pl module of Stacks (-M 2, -n 1) to assemble and create a catalogue of genetic loci contained in the data. The Stacks scripts export_sql.pl (snps_l=1, -F snps_u=1, -F pare_l=10, -F alle_u=2) was then used to create a whitelist of all loci, which contained exactly one SNP with two alleles, where the minor variant was present in at least 10 samples. This whitelist was then used to filter the data with the populations function (-r 0.75, -m 10) to create a final dataset, where at each locus a minimum of 75% of individuals had been typed with a depth of at least 10 reads. Further filtering was then conducted in PLINK to remove any loci at which there was &gt;33% missing data. This generated a final data matrix, which consisted of a list of 3097 variable SNPs typed in 76 cats: 20 captive, five domestic and 51 wild-living. The overall % missingness in the data matrix was 6.4%. This data set is referred to as the 3097SNP_dataset. We consider this dataset to represent the most unbiased genetic data for the Scottish wildcat so far. The 35 SNP test was derived from European wildcat data, and thus although effort was made to minimise any impact of bias in subsequent re-design of the test for Scotland (Senn & Ogden 2015), there is the potential that hidden issues with reference data or sub-structuring within the wildcat or domestic cat populations could have biasing consequences on this relatively small panel of markers. The purpose of this dataset here is primarily to the verify the performance 35SNP system