Historical datasets support genomic selection models for the prediction of cotton fibre quality phenotypes across multiple environments

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Gapare, Washy ORCID ID icon; Liu, Shiming; Conaty, Warren ORCID ID icon; Zhu, Qian-Hao ORCID ID icon; Gillespie, Vanessa Gillespie; Llewellyn, Danny ORCID ID icon; Stiller, Warwick; Wilson, Iain ORCID ID icon


Journal Article

G3: Genes, Genomes, Genetics





Genomic selection (GS) has successfully been used in plant breeding to improve selection effi¬ciency and reduce breeding time and cost. However, there has not been a study to evaluate GS prediction models that may be used for genomic prediction of cotton breeding lines across multiple environments. In this study, we evaluated the performance of Bayes Ridge Regression, BayesA, BayesB, BayesC and Reproducing Kernel Hilbert Spaces regression models. We then extended the single-site GS model to accommodate genotype × environment interaction (G×E) using their covariance functions. Our study was based on a population of 215 upland cotton (Gossypium hirsutum) breeding lines which were evaluated for fibre length and strength at multiple locations in Australia and genotyped with 13,330 single nucleotide polymorphic (SNP) markers. BayesB, which assumes unique variance for each marker and a proportion of markers to have large effects, while most other markers have zero effect, was the preferred model. GS accuracy for fibre length based on a single-site model, ranged from 0.32 to 0.68, while that of fibre strength ranged from 0.25 to 0.45 using randomly selected sub-populations as the training population. Marker × Environment model showed a modest superiority over the across-site model that ignores G×E. The use of the GS Marker × Environment model could therefore identify which breeding lines have effects that are stable across environments and which ones are responsible for G×E and so reduce the amount of phenotypic screening required in cotton breeding programs to identify adaptable genotypes.


Genomic prediction; Genomic selection; Bayesian models; marker × environment interaction; Gossypium hirsutum



Journal article - Refereed


Gapare, Washy; Liu, Shiming; Conaty, Warren; Zhu, Qian-Hao; Gillespie, Vanessa Gillespie; Llewellyn, Danny; Stiller, Warwick; Wilson, Iain. Historical datasets support genomic selection models for the prediction of cotton fibre quality phenotypes across multiple environments. G3: Genes, Genomes, Genetics. 2018; 8(5 1):1721-1732. http://hdl.handle.net/102.100.100/87049?index=1

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