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Abstract
Here, we perform cross-generational GS analysis on coastal Douglas-fir (Pseudotsuga menziesii), reflecting trans-generational selective breeding application. A total of 1321 trees, representing 37 full-sib F1 families from 3 environments in British Columbia, Canada, were used as the training population for (1) EBVs (estimated breeding values) of juvenile height (HTJ) in the F1 generation predicting genomic EBVs of HTJ of 136 individuals in the F2 generation, (2) deregressed EBVs of F1 HTJ predicting deregressed genomic EBVs of F2 HTJ, (3) F1 mature height (HT35) predicting HTJ EBVs in F2, and (4) deregressed F1 HT35 predicting genomic deregressed HTJ EBVs in F2. Ridge regression best linear unbiased predictor (RR-BLUP), generalized ridge regression (GRR), and Bayes-B GS methods were used and compared to pedigree-based (ABLUP) predictions. GS accuracies for scenarios 1 (0.92, 0.91, and 0.91) and 3 (0.57, 0.56, and 0.58) were similar to their ABLUP counterparts (0.92 and 0.60, respectively) (using RR-BLUP, GRR, and Bayes-B). Results using deregressed values fell dramatically for both scenarios 2 and 4 which approached zero in many cases. Cross-generational GS validation of juvenile height in Douglas-fir produced predictive accuracies almost as high as that of ABLUP. Without capturing LD, GS cannot surpass the prediction of ABLUP. Here we tracked pedigree relatedness between training and validation sets. More markers or improved distribution of markers are required to capture LD in Douglas-fir. This is essential for accurate forward selection among siblings as markers that track pedigree are of little use for forward selection of individuals within controlled pollinated families.
Highlights
There is a strong drive to incorporate genomic selection (GS) methodologies, as first proposed by Meuwissen et al 2001, into forest tree selective breeding
Whilst this relationship is exploited for selection in current breeding programs, the ultimate aim of using GS should be to capture true linkage disequilibrium (LD) across populations and traits to uncover presently unknown variation, and possibly unknown traits (Beaulieu et al 2014a; Grattapaglia 2017)
We degregressed EBVs to tease apart LD from familial relationships, which may subsequently be preferential only in advanced breeding programs (Grattapaglia 2017)
Summary
There is a strong drive to incorporate genomic selection (GS) methodologies, as first proposed by Meuwissen et al 2001, into forest tree selective breeding. Complex traits (such as height, growth, and wood quality) are amenable to selection with the use of dense marker data Given this statistical advantage, it is anticipated that GS may be implemented into tree selective breeding, as it has been done in livestock breeding (Van Eenennaam et al 2014), resulting in higher genetic gain per unit time for traits of interest. It is anticipated that GS may be implemented into tree selective breeding, as it has been done in livestock breeding (Van Eenennaam et al 2014), resulting in higher genetic gain per unit time for traits of interest This will largely be achieved through the reduction of trait evaluation time for such late expressing traits, leading to a faster turnover in breeding generations, a significant time-sink in current breeding programs (Hayes et al 2009; Heffner et al 2010). Breeding programs will become more dynamic as they will be able to ensure adaptation to capricious influences such as climate change and biotic disturbance in less time (Grattapaglia 2014)
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