Comparison of Microsatellites and SNP Markers in Genetic Diversity Level of Two Scots Pine Stands

Abstract

Scots pine (Pinus silvestris), is one of the dominant species in Poland and one of the main forest tree species in northern and central Europe. This species is of great economic importance. The Scots pine is highly adaptable to changing environmental conditions. A number of ecotypes have been characterized and the formation of these ecotypes are related with development of different phenotypic characteristics; morphological, physiological, and ecological. Molecular studies, based on DNA polymorphism, have been used for more than 20 years to analyze the genetic diversity of the Scots pine population. The most popular are microsatellite markers due to the fact of wide availability and high polymorphism. However, the use of these markers is also associated with certain limitations, due to complex mutation models or high incidence of homoplasia. These features are prompting scientists to look for alternative types of markers such as, for example, SNP. In our study we conducted a comparison of the basic parameters of genetic variability of two Scots pine stands (25 and 24 trees in each) for 20 SNP markers and 4 microsatellite markers. For the 20 SNP loci the observed heterozygosity (Ho) was equal to 0.34 for both stands and the expected heterozygosity (He) was equal to 0.34 for the first stand and 0.37 for the second. No statistically significant genetic distance was observed between them. For the microsatellite markers observed heterozygosity (Ho) was 0.81 and 0.74, and the expected heterozygosity (He) was equal to 0.85 and 0.85 respectively for the stands, and similarly no statistical significant genetic distance was observed. Literature data of different genetic markers showed the higher informativeness of randomly chosen microsatellites than single nucleotide polymorphism (SNP) markers for study population differentiation. But some analyzes confirm that the appropriate number of SNP markers can be more informative for population structure inference.