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Exploring genomic selection in conifers

University of British Columbia

Breeding conifer species for phenotypic improvement is challenging due to delayed expression of important phenotypes related to productivity and their late sexual maturity, causing long recurrent selection cycles. Genomic selection (GS) can address such shortcomings through early prediction of phenotypes based on large numbers of jointly considered genomic markers, typically, single nucleotide polymorphisms (SNPs). Additionally, current conifer breeding genetic evaluations are based on pedigree-based predictions. However, the maximization of genetic gain in breeding programs is contingent on the accuracy of the predicted breeding values and precision of the estimated genetic parameters, which can also be improved using GS. While GS has become a new paradigm in animal breeding, it is still in its infancy for tree improvement. Thus, GS requires validation before it can be operationally implemented. Collectively, this dissertation explores some of the challenges associated with the application of GS in forest tree improvement programs. Namely, the efficiency of GS compared to traditional phenotypic selection, methods to implement GS in a cost-efficient manner, and the prediction accuracy (PA) of phenotypes across generations, life-stages, and environments. To address these challenges I structured this dissertation into three analyses which use several GS methodologies, three genotyping platforms, and three conifer species. The first study explores the temporal decay and relative efficiency of GS PA for interior spruce (Picea engelmannii × glauca). The second study investigates the use of single-step GS (ssGBLUP) to improve the precision and accuracy of genetic parameter estimates for white spruce (Picea glauca). The third study focuses on the combined use of ssGBLUP and climate data to improve intra- and inter-generation PA in unobserved environments for Douglas-fir (Pseudotsuga menziesii). The results from these three studies demonstrated that: i) updating GS models requires iv phenotypic data at least mid-rotation age to accurately reflect mature growth traits, ii) the relative efficiency of GS is greater than traditional selection assuming a 25% reduction in breeding cycle length, iii) ssGBLUP is an effective tool for improvement in the genetic evaluation of openpollinated mating designs, and iv) inclusion of climate variables as environmental covariates in the GS models yields improvement in PA for unobserved environments.

Text

2019-04-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/69425

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/