Genomics-based breeding in forest trees

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Forest tree genetic improvement programs are long-term and resource demanding thus their genetic gain trajectory is shallow as compared to their annual crop counterparts. Shortening the breeding and/or testing phases would secure speedy generation turnover yielding substantial gains per unit time and cost. Marker-assisted selection has been proposed as a means to speeding genetic improvement programs through unravelling genotype-phenotype relationships. Unfortunately, this approach is only effective for major genes with large effects, whereas many genes of small effect underlie most complex polygenic traits. Population genetics methods such as DNA fingerprinting and pedigree reconstruction (e.g., Breeding without Breeding (BwB)) have been proven effective in reducing the cost and time associated with breeding and testing. Recent advances in DNA sequencing technology coupled with the development of a revolutionary approach to selection known as genomic selection (GS) have created a breeding paradigm shift where selection is no longer based on phenotypic values but rather is based on predicted phenotypic values from genome-wide sequencing genotypes. This approach allowed faster selection and breeding as phenotypes can be predicted irrespective of age and developmental phase. Here, I propose joining BwB with GS to capitalize on the synergetic effect of both approaches which is expected to speed generation turnover thus creating a steeper genetic gain trajectory. Since GS predicts phenotypic values based on developed models, these models' accuracy and transferability over time and space are of vital importance. This proposal seeks to evaluate GS models accuracies and to shed light on their "universal" use across sites and age classes. If these models show universality across sites and ages then additional savings will be attained. The proposed research is interdisciplinary covering bioinformatics, genomics, advanced statistics and quantitative genetics.