BackgroundGenomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F1 hybrids to develop genomic prediction models for eight phenotypic traits - basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA).ResultsHeritabilities estimated using the realized genomic relationship matrix (GRM) were considerably higher than estimates based on the expected pedigree, mainly due to inconsistencies in the expected pedigree that were readily corrected by the GRM. Moreover, the GRM more precisely capture Mendelian sampling among related individuals, such that the genetic covariance was based on the true proportion of the genome shared between individuals. PA improved considerably when increasing the size of the training set and by enhancing relatedness to the validation set. Prediction models trained on pure species parents could not predict well in F1 hybrids, indicating that model training has to be carried out in hybrid populations if one is to predict in hybrid selection candidates. The different genomic prediction methods provided similar results for all traits, therefore either GBLUP or rrBLUP represents better compromises between computational time and prediction efficiency. Only slight improvement was observed in PA when more than 5000 SNPs were used for all traits. Using SNPs in intergenic regions provided slightly better PA than using SNPs sampled exclusively in genic regions.ConclusionsThe size and composition of the training set and number of SNPs used are the two most important factors for model prediction, compared to the statistical methods and the genomic location of SNPs. Furthermore, training the prediction model based on pure parental species only provide limited ability to predict traits in interspecific hybrids. Our results provide additional promising perspectives for the implementation of genomic prediction in Eucalyptus breeding programs by the selection of interspecific hybrids.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-017-1059-6) contains supplementary material, which is available to authorized users.

Background: Genomic prediction is a genomics assisted breeding methodology that can increase genetic gains by accelerating the breeding cycle and potentially improving the accuracy of breeding values. In this study, we use 41,304 informative SNPs genotyped in a Eucalyptus breeding population involving 90 E.grandis and 78 E.urophylla parents and their 949 F 1 hybrids to develop genomic prediction models for eight phenotypic traits -basic density and pulp yield, circumference at breast height and height and tree volume scored at age three and six years. We assessed the impact of different genomic prediction methods, the composition and size of the training and validation set and the number and genomic location of SNPs on the predictive ability (PA).

This study was performed to estimate the abilities of eucalyptus clones to exercise as well as to tolerate competition and to compare their behaviors under auto- or allocompetition. Six commercial clones, belonging to PLANTAR S/A enterprise were evaluated for breast height circumference (BHC), total height (TH) and volume (VOL). At three locations of Minas Gerais, Brazil (two in Curvelo and one in Felixlândia) the clones were planted in two spaces. At 36 months of age each clone was evaluated for exercising and toleration competition amongst each other. The design for each experiment was similar to that of the nine-hole system; the center clone being under competition and the eight surrounding the center clone exercising competition. Each clone under competition was repeated eight times; therefore, for each spacing and location, six contiguous experiments were conducted. From the mean values; the parameters of ability to exercise competition (ci), ability to tolerate competition (tj), the specific competitive ability (sij) and the performance per se of the clones (aj) were estimated using a model similar to that of diallel crosses. The clones differed as to their ci, tj and aj. No one clone exhibited high and positive ci and tj. Regardless of location, spacing, or clone, the performance of autocompetition is similar to that of allocompetition. This indicates that a mixture of clones, if advantageous from the management or industrial point of view, may be performed without harm to the volume of wood produced.

Research Highlights: This study provides a comprehensive set of wood and pulping properties of Acacia crassicarpa A.Cunn. ex Benth. to assess variation and efficient sampling strategies for whole-tree level phenotyping. Background and Objectives: A. crassicarpa is an important tree species in Southeast Asia, with limited knowledge about its wood properties. The objective of this study was to characterize important wood properties and pulping performance of improved germplasm of the species. Furthermore, we investigated within-tree patterns of variation and evaluated the efficiency of phenotyping strategies. Materials and Methods: Second-generation progeny trials were studied, where forty 50-month-old trees were selected for destructive sampling and assessed for wood density, kraft pulp yield, α-cellulose, carbohydrate composition, and lignin content and composition (S/G ratio). We estimated the phenotypic correlations among traits determined within-tree longitudinal variation and its importance for whole-tree level phenotyping. Results: The mean whole-tree disc basic density was 481 kg/m3, and the screened kraft pulp yield was 53.8%. The reliabilities of each sampling position to predict whole-tree properties varied with different traits. For basic density, pulp yield, and glucose content, the ground-level sampling could reliably predict the whole-tree property. With near infrared reflectance spectroscopy predictions as an indirect measurement method for disc basic density, we verified reduced reliability values for breast height sampling but sufficiently correlated to allow accurate ranking and efficient selection of genotypes in a breeding program context. Conclusions: We demonstrated the quality of A. crassicarpa as a wood source for the pulping industry. The wood and pulping traits have high levels of phenotypic variation, and standing tree sampling strategies can be performed for both ranking and high-accuracy phenotyping purposes.

The past three decades have seen considerable research into the molecular genetics and genomics of forest trees, and a variety of new tools and methods have emerged that could have practical applications in applied breeding programs. Applied breeders may lack specialized knowledge required to evaluate claims made about the advantages of new methods over existing practices and are faced with the challenge of deciding whether to invest in new approaches or continue with current practices. Researchers, on the other hand, often lack experience with constraints faced by applied breeding programs and may not be well-equipped to evaluate the suitability of the method they have developed to a particular program. Our goal here is to outline social, biological, and economic constraints relevant to applied breeding programs to inform researchers, and to summarize some new methods and how they may address those constraints to inform breeders. The constraints faced by programs breeding tropical species grown over large areas in relatively uniform climates with rotations shorter than 10 years differ greatly from those facing programs breeding boreal species deployed in many different environments, each with relatively small areas, with rotations of many decades, so different genomic tools are likely to be appropriate.