Constructing Linkage Disequilibrium Maps For Outcrossing Forest Tree Populations

With the advent of molecular markers, much research on QTL mapping with molecular markers has made great progress in these years. Though molecular genetic research and statistical models to analyze molecular data on trees lay behind those on crops, forest trees are mostly in wild or semi-wild states, so they can provide rich natural variation for QTL mapping, and many statistical models were proposed at this moment, thereinto, linkage disequilibrium-based mapping has become a major focus of recent genome research into mapping complex traits. Based on the rationale of linkage disequilibrium mapping, through an open-pollinated progeny design, we have proposed two statistical models for testing wild population (include forest trees) progeny and will be use for finding QTL(s), mapping and estimating QTL effects in this research. The maximum likelihood approach, EM algorithm and hypothesis testing were implemented to simultaneously estimate the linkage, linkage phase and linkage disequilibrium of markers, to get the maximum likelihood estimate values of genetic parameters such as recombination rate and outcrossing rate, and extend the model to three loci analysis. Monte Carlo simulation studies which include different sample sizes and outcrossing rate have been performed for genetic parameters estimating, in order to investigate the statistical properties of the models. By comparing the results of two models, we can find that multilocus analysis has the higher precision, which was finally applied to study one kind of the gymnosperm called Torreya grandis, corresponding linkage maps were constructed. The models and algorithms we proposed can be effectively used for QTL mapping in forest trees, and the open-pollinated progeny design and a series of analytical and testing strategies provides a standard procedure for testing the statistical significance of QTL involved in the genetic control of complex traits.