Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids - Simiqueli, Guilherme Ferreira; Resende, Rafael Tassinari; Takahashi, Elizabete Keiko; de Sousa, João Edesio; Grattapaglia, Dario (Frontiers in Plant Science, 2023)
Introduction: Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions no effects of recombination, selection, drift and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by genomic selection across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus.Methods: Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of them genotyped with 16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1400 selection candidates, 197 of them genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (six years). Seedlings were then grown to harvest, measured, and their pedigree-based breeding and genotypic values compared to their originally predicted genomic counterparts.Results: Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training.GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections were highest for GBLUP_G (45% to 60%) that GBLUP_G+D. To advance the pure species RRS populations GS models were best when trained on pure species than hybrid data, and HBLUP yielded 20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees.Discussion: We demonstrate that genomic data effectively enables accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs and accelerating RRS breeding.