Genetic Structure of a Loblolly Pine Breeding Population at Brazil

Abstract

The genetic structure of a Brazilian loblolly pine (Pinus taedaL.) breeding population, represented by 120 open-pollinated families, was determined using Bayesian inference and genotypes of 15 microsatellite (simple sequence repeat (SSR))lociin 1,130 seedling progeny. The 120 maternal parents had been phenotypically selected about 15 years ago for wood volume in five different forestry plantations (FPs) in the south of Brazil. Additional selection for wood volume, based on a previous progeny test, was applied to the first best (i) and second best (ii) tree per block within each family. We adopted a procedure of “learning samples” to find the most likely number of inferred genetic clusters (K) or ancestral populations. The first hypothesis that was rejected was that the most probable value ofK=5was coincident with the five FPs, since the FPs were,a priori, assumed to be from 5 different backgrounds or origins. It was used the familiar structure of the population to infer the genotypes of maternal ancestors. It was concluded that the maternal generation is the most likely to have been planted by the mixture of three different seed sources or origins, that there are five genetic groups (K=5) in the population of progeny, and that they have been formed from the occurrence of assortative mating and also from a strong pressure in the selection within families. The trees with the best genetic value (i) maintained a higher genetic variability when compared to the trees of second best performance (ii), with higher values of heterozygosity and of numbers of maternal alleles that were kept the same. The migration model that best explains the results is the contact zone model. The population differentiation (FST) was 2-3 times higher in offspring than in relation to the maternal generation. The relevancy of the results and the way they were explored may be of value both for studies of population genetics, as for plant breeding programs, since they help monitoring the population's genetic variability during generations of selection.