Genomic prediction in contrast to a genome-wide association study in explaining heritable variation of complex growth traits in breeding populations of Eucalyptus

BMC Genomics

Research Article

Patricio R. Muñoz¹  Dario Grattapaglia²  Bárbara S. F. Müller³  Estefano Paludzyszyn Filho⁴  Paulo E. T. dos Santos⁴  Matias Kirst⁵  Janeo E. de Almeida Filho⁵  Márcio F. R. Resende⁶  Leandro G. Neves⁶ 

[1] Agronomy Department, University of Florida, 32611, Gainesville, FL, USA;Cell Biology Department, Molecular Biology Program, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, 70910-900, Brasília, DF, Brazil;EMBRAPA Genetic Resources and Biotechnology, Estação Parque Biológico, 70770-910, Brasília, DF, Brazil;Cell Biology Department, Molecular Biology Program, Biological Sciences Institute, University of Brasília, Campus Darcy Ribeiro, 70910-900, Brasília, DF, Brazil;EMBRAPA Genetic Resources and Biotechnology, Estação Parque Biológico, 70770-910, Brasília, DF, Brazil;Forest Genomics Laboratory, School of Forest Resources and Conservation, University of Florida, 32611, Gainesville, FL, USA;EMBRAPA Forestry, Estrada da Ribeira, Km 111, 83411-000, Colombo, PR, Brazil;Forest Genomics Laboratory, School of Forest Resources and Conservation, University of Florida, 32611, Gainesville, FL, USA;RAPiD Genomics LLC, 32601, Gainesville, FL, USA;

关键词: Genomic selection;    GWAS;    SNP genotyping;    Relatedness;    Tree breeding;    Eucalyptus benthamii;    E. pellita;   

DOI  :  10.1186/s12864-017-3920-2

 received in 2016-10-07, accepted in 2017-07-03,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe advent of high-throughput genotyping technologies coupled to genomic prediction methods established a new paradigm to integrate genomics and breeding. We carried out whole-genome prediction and contrasted it to a genome-wide association study (GWAS) for growth traits in breeding populations of Eucalyptus benthamii (n =505) and Eucalyptus pellita (n =732). Both species are of increasing commercial interest for the development of germplasm adapted to environmental stresses.ResultsPredictive ability reached 0.16 in E. benthamii and 0.44 in E. pellita for diameter growth. Predictive abilities using either Genomic BLUP or different Bayesian methods were similar, suggesting that growth adequately fits the infinitesimal model. Genomic prediction models using ~5000–10,000 SNPs provided predictive abilities equivalent to using all 13,787 and 19,506 SNPs genotyped in the E. benthamii and E. pellita populations, respectively. No difference was detected in predictive ability when different sets of SNPs were utilized, based on position (equidistantly genome-wide, inside genes, linkage disequilibrium pruned or on single chromosomes), as long as the total number of SNPs used was above ~5000. Predictive abilities obtained by removing relatedness between training and validation sets fell near zero for E. benthamii and were halved for E. pellita. These results corroborate the current view that relatedness is the main driver of genomic prediction, although some short-range historical linkage disequilibrium (LD) was likely captured for E. pellita. A GWAS identified only one significant association for volume growth in E. pellita, illustrating the fact that while genome-wide regression is able to account for large proportions of the heritability, very little or none of it is captured into significant associations using GWAS in breeding populations of the size evaluated in this study.ConclusionsThis study provides further experimental data supporting positive prospects of using genome-wide data to capture large proportions of trait heritability and predict growth traits in trees with accuracies equal or better than those attainable by phenotypic selection. Additionally, our results document the superiority of the whole-genome regression approach in accounting for large proportions of the heritability of complex traits such as growth in contrast to the limited value of the local GWAS approach toward breeding applications in forest trees.

【 授权许可】

CC BY   
© The Author(s). 2017

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