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Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine

Citation

Nantongo, JS and Potts, BM and Klapste, J and Graham, NJ and Dungey, HS and Fitzgerald, H and O'Reilly-Wapstra, JM, Genomic selection for resistance to mammalian bark stripping and associated chemical compounds in radiata pine, G3: Genes, Genomes, Genetics Article jkac245. ISSN 2160-1836 (In Press) [Refereed Article]


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Copyright Statement

Copyright (2022) The Author(s). Published by Oxford University Press on behalf of Genetics Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

DOI: doi:10.1093/g3journal/jkac245

Abstract

The integration of genomic data into genetic evaluations can facilitate the rapid selection of superior genotypes and accelerate the breeding cycle in trees. In this study, 390 trees from 74 control-pollinated families were genotyped using a 36K Axiom SNP array. A total of 15,624 high-quality SNPs were used to develop genomic prediction models for mammalian bark stripping, tree height and selected primary and secondary chemical compounds in the bark. Genetic parameters from different genomic prediction methods-single-trait best linear unbiased prediction based on a marker-based relationship matrix (GBLUP), multi-trait single-step GBLUP which integrated the marker-based and pedigree-based relationship matrices (ssGBLUP) and the single-trait generalised ridge regression (GRR) - were compared to equivalent single- or multi-trait pedigree-based approaches (ABLUP). The influence of the statistical distribution of data on the genetic parameters was assessed. Results indicated that the heritability estimates were increased nearly 2-fold with genomic models compared to the equivalent pedigree-based models. Predictive accuracy of the ssGBLUP was higher than the ABLUP for most traits. Allowing for heterogeneity in marker effects through the use of GRR did not markedly improve predictive ability over GBLUP, arguing that most of the chemical traits are modulated by many genes with small effects. Overall, the traits with low pedigree-based heritability benefited more from genomic models compared to the traits with high pedigree-based heritability. There was no evidence that data skewness or presence of outliers affected the genomic or pedigree-based genetic estimates.

Item Details

Item Type:Refereed Article
Keywords:genomics, chemistry, defense, bark stripping, Pinus radiata, genomic prediction, GenPred, shared data resource
Research Division:Agricultural, Veterinary and Food Sciences
Research Group:Forestry sciences
Research Field:Tree improvement (incl. selection and breeding)
Objective Division:Manufacturing
Objective Group:Wood, wood products and paper
Objective Field:Wood products
UTAS Author:Nantongo, JS (Mrs Judith Nantongo)
UTAS Author:Potts, BM (Professor Brad Potts)
UTAS Author:Fitzgerald, H (Mr Hugh Fitzgerald)
UTAS Author:O'Reilly-Wapstra, JM (Professor Julianne O'Reilly-Wapstra)
ID Code:153954
Year Published:In Press
Funding Support:Australian Research Council (LP140100602)
Deposited By:Plant Science
Deposited On:2022-10-18
Last Modified:2022-11-03
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