Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F1 hybrids

Abstract

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.

Genomic Prediction Using Low Density Marker Panels in Aquaculture: Performance Across Species, Traits, and Genotyping Platforms

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Advantage of clonal deployment in Norway spruce (Picea abies (L.) H. Karst)

“…To consider the genetic diversity, a deep simulation based on the current data could be done in the future as other studies (Rosvall et al 1998;Weng et al 2010). The study of genomic selection (GS) has been conducted currently in many commercial tree species, such as Norway spruce (Chen et al 2018), white spruce, loblolly pine, radiate pine, Maritime pine (Bartholomé et al 2016;Isik et al 2016), and Eucalyptus (Resende et al 2012;Denis and Bouvet 2013;Tan et al 2017), but focusing on the seedling progeny trials. If a prospective deployment strategy would entail the establishment of a conventional Norway spruce clonal field test in combination with SE technology (Egertsdotter 2019), the whole process would require ca.…”

Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados

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