Efficiency of different PCR-based marker systems for assessment of Iris pumila genetic diversity

We investigated informativeness and effectiveness of different marker types (ISSR, IRAP, REMAP, RGAP and LP-PCR that employ primers based on the conservative sequences of abiotic stress response genes) to study genetic diversity of Iris pumila L. By the number of amplicons per primer, number of polymorphic amplicons per primer and resolving power index (Rp), ISSR-markers were the most efficient followed by LP-PCR-markers. In order of decreasing value of indicators of genetic diversity "the percentage of polymorphic bands", and "the average JaccardA > genetic distance between plants", marker systems may be arranged as follows: ISSR > RAPD > LP-PC > RGAP a parts per thousand IRAP. For ISSR-markers, the percentage of polymorphic bands was 1.3-1.7 times higher than for the others, and the average genetic distance was 1.2-1.3 times higher. Different marker systems were ranked by the value of NeiA > gene diversity and the ShannonA > index as follows: ISSR > RAPD a parts per thousand LP-PCR > RGAP a parts per thousand IRAP, with the highest and the lowest values differing 1.4 times. Genetic population structure was investigated with program Structure 2.3. The data of all marker systems suggest that all genomes under study belonged to one population. The PCoA and cluster analyses based on genetic distances showed distinctions in clustering generated from different markers data and summarized data, as well as the lack of strong clusters. Mantel test revealed significant positive correlation between the matrices of genetic distances generated by the data of almost all marker systems. The strongest correlation was found between RGAP- and IRAP-markers (r = 0.452, p = 0.01) and between RGAP and ISSR (r = 0.430, p = 0.01). ISSR, RAPD and LP-PCR proved to be more effective for the study of I. pumila genetic diversity, nevertheless, joint use of different marker systems will provide a more comprehensive assessment of variation in different genomic regions.