Genomics-Enabled Management of Genetic Resources in Radiata Pine

Abstract

Traditional tree improvement is cumbersome and costly. Our main objective was to assess the extent to which genomic data can currently accelerate and improve decision making in this field. We used diameter at breast height (DBH) and wood density (WD) data for 4430 tree genotypes and single-nucleotide polymorphism (SNP) data for 2446 tree genotypes. Pedigree reconstruction was performed using a combination of maximum likelihood parentage assignment and matching based on identity-by-state (IBS) similarity. In addition, we used best linear unbiased prediction (BLUP) methods to predict phenotypes using SNP markers (GBLUP), recorded pedigree information (ABLUP), and single-step “blended” BLUP (HBLUP) combining SNP and pedigree information. We substantially improved the accuracy of pedigree records, resolving the inconsistent parental information of 506 tree genotypes. This led to substantially increased predictive ability (i.e., by up to 87%) in HBLUP analyses compared to a baseline from ABLUP. Genomic prediction was possible across populations and within previously untested families with moderately large training populations (N = 800–1200 tree genotypes) and using as few as 2000–5000 SNP markers. HBLUP was generally more effective than traditional ABLUP approaches, particularly after dealing appropriately with pedigree uncertainties. Our study provides evidence that genome-wide marker data can significantly enhance tree improvement. The operational implementation of genomic selection has started in radiata pine breeding in New Zealand, but further reductions in DNA extraction and genotyping costs may be required to realise the full potential of this approach.

High density linkage maps, genetic architecture, and genomic prediction of growth and wood properties in Pinus radiata

“…Our findings on within-family genomic prediction complement results from a previous study, in which we used a much wider training population and the resulting within-family predictive abilities were moderate for WD, but very low or absent for DBH [ 40 ]. This was also the case when adding up to 40 individuals from the respective mapping family to the wider training population [ 40 ].…”

“…Our findings on within-family genomic prediction complement results from a previous study, in which we used a much wider training population and the resulting within-family predictive abilities were moderate for WD, but very low or absent for DBH [ 40 ]. This was also the case when adding up to 40 individuals from the respective mapping family to the wider training population [ 40 ]. In contrast, the alternative approach we assessed here (i.e., training GBLUP models specifically within each family), resulted in predictive abilities that increased stably with training population size and reached a plateau with ca.…”

“…4 , Additional file 18 : Fig. S3) and across much wider breeding populations [ 40 ]. Thus, low-density genotyping platforms can considerably improve the cost-benefit ratio of ongoing genomic selection experiments in radiata pine.…”

“…We used standard ridge regression/GBLUP genomic prediction methodology [ 36 , 37 ] as implemented in the rrBLUP R package [ 38 ] and random cross-validations [ 39 ] to calculate the predictive ability (the correlation of predicted and “observed” phenotypes for the validation population) of GBLUP as applied to unknown phenotypes in the QTL and FWK populations. In contrast to a previous study Klápště et al [ 40 ], in which we used a much wider training population, both training and test sets were randomly drawn (i.e., according to the desired training population size) only from the respective mapping population. This simulates a breeding scenario where a small subset of a full-sib family is field-tested to obtain phenotypic data, and then a family-specific GBLUP model is used to predict phenotypes for a larger subset of genotypes that were either stored (e.g., cryopreserved) or obtained by repeating the original controlled cross.…”

High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers

“…2020 ; Isik 2022 ). However, the high cost associated with the acquisition of large-scale genotypic and phenotypic data still represents a barrier for the routine use of genomic prediction in tree breeding programs ( Klápště et al . 2022 ).…”