Connectedness among test series in mixed linear models of genetic evaluation for forest trees

Abstract

In forest tree species with large natural ranges, there are usually several to many separate breeding populations, each designed to capture elite material suited to a particular geographic region. Separate test series are often dedicated to each population. Because the aim is to optimise gain in the meta-population, it is important to ensure that test series are linked so that individuals can be compared across test series as well as within. Computer simulation was used to determine the most efficient strategy for obtaining linkage. The average accuracy of a genetic value contrast between individuals in the same and in different test series was used as the criterion for assessing the optimal level of linkage. Accuracy is a function of the elements of the inverse coefficient matrix for a mixed linear model within a best linear unbiased prediction framework (BLUP). Material used to link test series was either common test families, common check-lots such as seed orchard bulks, or families generated by inter-crossing parents from different test series. Use of common test families was the most efficient strategy for the scenarios tested, which included having 50 parents crossed to produce 50 test families in each of three populations. For a low-heritability scenario, the amount of linkage material, relative to test material, needed to be 8 and 12 %, for progeny and parents, respectively, in order for a contrast between individuals in different test series to have equivalent accuracy as a contrast between individuals in the same test series. Other strategies were less efficient in terms of the amount of linkage material needed to obtain this equivalency.

Development of an Advanced-Generation Multi-Objective Breeding Population for the 4th Cycle of Chinese Fir (Cunninghamia lanceolata (Lamb.) Hook.)

“…The experimental materials were obtained from the 4th cycle selection/candidate population of Chinese fir for breeding and deployment, comprising 233 individuals. These individuals were selected based on the volume, disease resistance, survival, and wood density from a range of experiments, including the 2nd and 3rd cycle breeding populations [21,22], long-term range-wide provenance tests, and the regional deployment test of a selected national elite family line and family lines for infertile sites [23][24][25]. According to the pedigree analysis (Supplementary Figure S1), the test material could be divided into four generations: 1st (n = 2), 2nd (n = 56), 3rd (n = 146), and 4th (n = 29) (Supplementary Table S1).…”

Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance–covariance matrix

“…In the work of Foulley et al [ 3 , 4 ] and Laloë et al [ 5 , 6 ], genetic connectedness is regarded as a measure of predictability, where predictability is the random effect extension of estimability [ 7 ]. More recently, this was the approach to connectedness taken by Kerr et al [ 8 ]. An estimable function [ 9 , 10 ] is defined in the context of a fixed effect model.…”