Abstract
Aim This study has three broad aims: a) to develop genus-specific primers for High Resolution Melt analysis (HRM) of members of Cyclopia Vent., b) test the haplotype discrimination of HRM compared to Sanger sequencing, and c) provide a case study using HRM to detect novel haplotype variation in wild C. subternata Vogel. populations.
Location The Cape Floristic Region (CFR), located along the southern Cape of South Africa.
Methods Polymorphic loci were detected through a screening process of sequencing 12 non-coding chloroplast DNA regions across 14 Cyclopia species. Twelve genus-specific primer combinations were designed around variable cpDNA loci, four of which failed to amplify under PCR, the eight remaining were applied to test the specificity, sensitivity and accuracy of HRM. The three top performing HRM regions were then applied to detect novel haplotypes in wild C. subternata populations, and phylogeographic patterns of C. subternata were explored.
Results We present a framework for applying HRM to non-model systems. HRM accuracy varied across the regions screened using the genus-specific primers developed, ranging between 56 and 100 %. The nucleotide variation failing to produce distinct melt curves is discussed. The top three performing regions, having 100 % specificity (i.e. different haplotypes were never grouped into the same cluster, no false negatives), were able to detect novel haplotypes in wild C. subternata populations with high accuracy (96%). Sensitivity below 100 % (i.e. a single haplotype being clustered into multiple unique groups during HRM curve analysis, false positives) was resolved through sequence confirmation of each cluster resulting in a final accuracy of 100 %. Phylogeographic analyses revealed that wild C. subternata populations tend to exhibit phylogeographic structuring across mountain ranges (accounting for 73.8 % of genetic variation base on an AMOVA), and genetic differentiation between populations increases with distance (p < 0.05 for IBD analyses).
Conclusions After screening for regions with high HRM clustering specificity — akin to the screening process associated with most PCR based markers — the technology was found to be a high throughput tool for detecting genetic variation in non-model plants.
