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Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx

1
Institute of Biological Sciences, University of Talca, 2 Norte 685, Talca 3460000, Chile
2
CSIRO–Australian Tree Seed Centre, Acton 2601, Australia
3
Department of Animal Science, Universidade Federal de Viçosa, Viçosa 36570-900, Brazil
*
Author to whom correspondence should be addressed.
Plants 2020, 9(1), 99; https://doi.org/10.3390/plants9010099
Received: 25 November 2019 / Revised: 20 December 2019 / Accepted: 9 January 2020 / Published: 13 January 2020
(This article belongs to the Special Issue Plant Bioinformatics)
High-throughput genotyping techniques have enabled large-scale genomic analysis to precisely predict complex traits in many plant species. However, not all species can be well represented in commercial SNP (single nucleotide polymorphism) arrays. In this study, a high-density SNP array (60 K) developed for commercial Eucalyptus was used to genotype a breeding population of Eucalyptus cladocalyx, yielding only ~3.9 K informative SNPs. Traditional Bayesian genomic models were investigated to predict flowering, stem quality and growth traits by considering the following effects: (i) polygenic background and all informative markers (GS model) and (ii) polygenic background, QTL-genotype effects (determined by GWAS) and SNP markers that were not associated with any trait (GSq model). The estimates of pedigree-based heritability and genomic heritability varied from 0.08 to 0.34 and 0.002 to 0.5, respectively, whereas the predictive ability varied from 0.19 (GS) and 0.45 (GSq). The GSq approach outperformed GS models in terms of predictive ability when the proportion of the variance explained by the significant marker-trait associations was higher than those explained by the polygenic background and non-significant markers. This approach can be particularly useful for plant/tree species poorly represented in the high-density SNP arrays, developed for economically important species, or when high-density marker panels are not available. View Full-Text
Keywords: Bayesian models; deviance information criterion; marker-trait associations; predictive ability Bayesian models; deviance information criterion; marker-trait associations; predictive ability
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Ballesta, P.; Bush, D.; Silva, F.F.; Mora, F. Genomic Predictions Using Low-Density SNP Markers, Pedigree and GWAS Information: A Case Study with the Non-Model Species Eucalyptus cladocalyx. Plants 2020, 9, 99.

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