Markery RAPD sprzężone z genami cech ilościowych sosny zwyczajnej (Pinus sylvestris L.)

Quantitative variation is a feature of many economically important traits in forest trees, such as yield, quality or disease resistant, which are assumed to be influenced by many interacting loci, and are called quantitative traits loci (QTL). The method for finding and locating QTLs is called QTL mapping and requires multi-locus genotypes (molecular markers) and phenotypes (quantitative traits), which are measured on all individuals of segregating population. Next phenotypic values are statistically associated with genotypic values, usually using multiple regression or maximum likelihood methods to identify markers that have a strong association with the quantitative trait. This article will describe a modification of QTL analysis, bulk segregant analysis (BSA), that has been shown to work well with genes having major effects and that obviates the need for constructing detailed genetic maps. This approach has been used successfully with composite population of maize to locate QTLs associated with yield under severe drought. In order to find molecular markers linked to quantitative traits, economically important in Scots pines breeding program we decided to combine two different techniques RAPD and BSA. Half-sib progeny of plus tree have been grouped according to the expression of six economically important traits. To assess the diversity and the separation of progeny groups we used two multivariate statistical methods: hierarchical cluster analysis and principal component analysis (PCA). The largest positive values of correlation coefficients were found for the first principal component and the surface of the needle ( r = 0.96), its length ( r = 0.85) and width ( r = 0.65). The second principal component was strongly correlated with height ( r = 0.77) and DBH ( r = 0.68), while the third was positively correlated with the amount of needles ( r = 0.64). Features strongly correlated with each of the first three principal components identify common genetic factors. We can conclude that the most important genetic factor, which makes the largest part of the variability of phenotypic traits, is factor responsible for the characteristics of the leaf. The second main component can be regarded as a genetic factor responsible for the development of the main stem (trunk), and a third factor is responsible for the foliage. We analyzed 10 primer combinations and identified locus OPA02-1500bp, probably linked to height and DBH. In the future this approach can be also extended to the study another quantitative traits.