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Evaluating the accuracy of genomic prediction of growth and wood traits in two Eucalyptus species and their F-1 hybrids
Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap. Biomaterials Division, Stora Enso AB, Nacka SE-13104, Sweden.
Vise andre og tillknytning
2017 (engelsk)Inngår i: BMC Plant Biology, ISSN 1471-2229, E-ISSN 1471-2229, Vol. 17, artikkel-id 110Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

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). Results: Heritabilities 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 F-1 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. Conclusions: The 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.

sted, utgiver, år, opplag, sider
BioMed Central, 2017. Vol. 17, artikkel-id 110
Emneord [en]
Genomic relationship, Genomic heritability, Two-generation, Genome annotation, High-density SNP-chip, Bayesian LASSO, GBLUP, rrBLUP
HSV kategori
Identifikatorer
URN: urn:nbn:se:umu:diva-138559DOI: 10.1186/s12870-017-1059-6ISI: 000404909700001PubMedID: 28662679OAI: oai:DiVA.org:umu-138559DiVA, id: diva2:1140897
Tilgjengelig fra: 2017-09-13 Laget: 2017-09-13 Sist oppdatert: 2018-06-09bibliografisk kontrollert
Inngår i avhandling
1. Genomic selection and genome-wide association studies to dissect quantitative traits in forest trees
Åpne denne publikasjonen i ny fane eller vindu >>Genomic selection and genome-wide association studies to dissect quantitative traits in forest trees
2018 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The convergence of quantitative genetics of complex traits with genomic technologies is quickly becoming an innovative approach to explore fundamental genetic questions and also have practical consequences for implementations in tree breeding. In this thesis, I used genomic selection and genome-wide association studies (GWAS) to dissect the genetic basis of quantitative traits, i.e. growth, phenology and wood property traits. I also assessed the importance of dominance and epistatic effects in hybrid Eucalyptus. Both dominance and epistasis are important in hybrids, as they are the likely contributing to the genetic basis of heterosis. To successfully implement genomic selection models, several important factors have to be considered. I found that for a good model establishment, both the size and composition of the training population, as well as the number of SNPs to be important considered. Based on the optimal models, additive, dominance and epistasis genetic effects of growth and wood traits have been estimated to evaluate genetic parameters and how these influence the prediction accuracy, which can be used in selecting elite breeding individuals or clones. I also addressed the advantage of genotyping-based analyses by showing that we could accurately correct pedigree information errors. More importantly, genotyping-based analyses capture both Mendelian segregation variation within full-sib families and cryptic genetic links through unknown common ancestors, which are not available from traditional pedigree data. GWAS were used to analyse growth and phenology related traits. Using a single-trait GWAS method, we identified a region strongly associated with the timing of bud set in Populus tremula, a trait with high heritability. For the growth related traits, we found that a multi-traits GWAS approach was more powerful than single-trait analyses as it identified more associated SNPs in hybrid Eucalyptus. Moreover, many more novel associated SNPs were identified from considering over-dominance effects in the GWAS analyses. After annotating the associated SNPs I show that these functional candidate genes were related to growth and responding to abiotic and biotic stress. In summary, the results of genomic selection and GWAS provided a deeper understanding of the genetic backgrounds of quantitative traits in forest trees.

sted, utgiver, år, opplag, sider
Umeå: Umeå University, 2018. s. 38
Emneord
Genomic prediction, genome-wide association study, additive effects, dominance effects, epistasis effects, realized relationship matrix, Eucalyptus grandis, Eucalyptus urophylla
HSV kategori
Identifikatorer
urn:nbn:se:umu:diva-145497 (URN)978-91-7601-849-1 (ISBN)
Disputas
2018-04-06, Lilla hörsalen (KB.E3.01), KBC-byggnaden, Umeå, 10:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2018-03-16 Laget: 2018-03-07 Sist oppdatert: 2018-07-19bibliografisk kontrollert

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