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Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices

Abstract

Genomic selection based on the single-step genomic best linear unbiased prediction (ssGBLUP) approach is becoming an important tool in forest tree breeding. The quality of the variance components and the predictive ability of the estimated breeding values (GEBV) depends on how well marker-based genomic relationships describe the actual genetic relationships at unobserved causal loci. We investigated the performance of GEBV obtained when fitting models with genomic covariance matrices based on two identity-by-descent (IBD) and two identity-by-state (IBS) relationship measures. Multiple-trait multiple-site ssGBLUP models were fitted to diameter and stem straightness in five open-pollinated progeny trials of Eucalyptus dunnii, genotyped using the EUChip60K. We also fitted the conventional ABLUP model with a pedigree-based covariance matrix. Estimated relationships from the IBD estimators displayed consistently lower standard deviations than those from the IBS approaches. Although ssGBLUP based in IBS estimators resulted in higher trait-site heritabilities, the gain in accuracy of the relationships using IBD estimators has resulted in higher predictive ability and lower bias of GEBV, especially for low-heritability trait-site. ssGBLUP based on IBS and IBD approaches performed considerably better than the traditional ABLUP. In summary, our results advocate the use of the ssGBLUP approach jointly with the IBD relationship matrix in open-pollinated forest tree evaluation.

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Fig. 1: Phenotype frequency distribution.
Fig. 2: Distribution of estimated actual pairwise relatedness coefficients for genotyped trees belonging to the same (half-sibs) or different (unrelated) family, as well as self-relationships (Selfs), obtained by Han & Abney, either considering (GHA) or not (GHA-G) pedigree information, VanRaden (GVR), and Yang (GY) methods.
Fig. 3: Network representation of pedigree-based (A) and combined pedigree-genomic (H) relationship matrices.
Fig. 4: Average predictive ability and prediction bias for diameter at breast height (DBH) and stem straightness normal score (NSTR) of each of the five models fitted: ABLUP and the four single-step GBLUP models (see text for references).

Data availability

Information of the Eucalyptus dunnii trials including phenotypic, pedigree, and genomic data are available in the Zenodo repository, https://doi.org/10.5281/zenodo.4906814.

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Acknowledgements

The authors sincerely acknowledge Dr. Carolina García-Baccino for her help in implementing the IBDLD v3.14 software. Also, thanks go to Dr. “Fito” Cantet who helped us in the interpretation of the results obtained in this study. Particular thanks to Pedro Ernesto Gelid, Victor Oscar Botto, Santiago Becerro, Mauro Rodrigo Surenciski, Santiago Álvarez Cettour, and Nicolás Alanis for help in establishing, maintaining, and assessing the trials and for assistance with field sampling. They would also like to thank the landowner associated with the field trials involved in this study.

Funding

This research was supported by the BIOTECH II platform (grant number 373-780) and the Instituto Nacional de Tecnología Agropecuaria (PNFOR-1104062 and PNFOR-1104064). EJJ’s was supported by doctoral fellowships from Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). EPC’s research was partially supported by the Agencia Nacional de Promoción Científica y Tecnológica of Argentina PICT-2016 1048.

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Correspondence to Esteban J. Jurcic.

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Jurcic, E.J., Villalba, P.V., Pathauer, P.S. et al. Single-step genomic prediction of Eucalyptus dunnii using different identity-by-descent and identity-by-state relationship matrices. Heredity 127, 176–189 (2021). https://doi.org/10.1038/s41437-021-00450-9

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