SNP and Haplotype-Based Genomic Selection of Quantitative Traits in Eucalyptus globulus

Abstract

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

Keywords: Bayesian models; genomic prediction; haplotype blocks; predictive ability.

Figure 1

Predictive ability (PA) of (a) tree height (HT), (b) diameter at breast height (DBH), (c) stem straightness (ST), (d) branch quality (BQ), and (e) wood density (WD). Models based on SNP markers (SNP), haplotypes (HAP), and haplotypes with SNPs that were not assigned to a haplotype (HAP-SNP) are represented by black, dark gray and light gray bars, respectively. BA, BB, BC, BL, and BRR correspond to Bayes A, Bayes B, Bayes C, Bayesian Least Absolute Shrinkage and Selection Operator, and Bayesian Ridge Regression, respectively. Each box-plot represents the distribution of PA values for 100 cycles of cross-validation.

Figure 2

Linear regression plots relating estimated breeding values (pedigree-based Estimated Breeding Values; EBVs) and genomic-based EBVs (GEBVs). (a) EBVs and GEBVs for tree height (HT); (b) EBVs and GEBVs for diameter at breast height (DBH); (c) EBVs and GEBVs for stem straightness (ST); (d) EBVs and GEBVs for branch quality (BQ); and (e) EBVs and GEBVs for wood density (WD).