PMERGE: Computational filtering of paralogous sequences from RAD-seq data

Restriction-site associated DNA sequencing (RAD-seq) can identify and score thousands of genetic markers from a group of samples for population-genetics studies. One challenge of de novo RAD-seq analysis is to distinguish paralogous sequence variants (PSVs) from true single-nucleotide polymorphisms (SNPs) associated with orthologous loci. In the absence of a reference genome, it is difficult to differentiate true SNPs from PSVs, and their impact on downstream analysis remains unclear. Here, we introduce a network-based approach, PMERGE that connects fragments based on their DNA sequence similarity to identify probable PSVs. Applying our method to de novo RAD-seq data from 150 Atlantic salmon (Salmo salar) samples collected from 15 locations across the Southern Newfoundland coast allowed the identification of 87% of total PSVs identified through alignment to the Atlantic salmon genome. Removal of these paralogs altered the inferred population structure, highlighting the potential impact of filtering in RAD-seq analysis. PMERGE is also applied to a green crab (Carcinus maenas) data set consisting of 242 samples from 11 different locations and was successfully able to identify and remove the majority of paralogous loci (62%). The PMERGE software can be run as part of the widely used Stacks analysis package.