Genome wide selection in Citrus breeding

Góis, Itamara Bomfim; Borém, Aluízio; Cristofani–Yaly, Mariângela; Resende, Marcos Deon Vilela de; Azevedo, Camila Ferreira; Bastianel, Marinês; Novelli, Valdenice M.; Machado, Marcos Antônio

doi:10.4238/gmr15048863

Cited by 21 publications

(14 citation statements)

References 35 publications

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“…The results for ha2 and r gy showed a high positive association, with a correlation coefficient of 88%. As observed in this work, the association between predictive capacity and heritability has been reported by other researchers (Cavalcanti et al, 2012; Gois et al, 2016). Prediction bias for most of the evaluated traits showed a b estimate close to the unit.…”

Section: Discussionsupporting

confidence: 89%

“…The selective accuracy estimates of GS (r gg ) were of low to moderate magnitude (Resende and Duarte, 2007). Selective accuracy (r gg ) refers to the correlation between the true genotypic value of the genetic treatment and that estimated or predicted from the phenotypic information (Gois et al, 2016). The adequate r gg values are close to the unit.…”

Section: Discussionmentioning

confidence: 99%

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Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

et al. 2019

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Genomic Selection (GS) has allowed the maximization of genetic gains per unit time in several annual and perennial plant species. However, no GS studies have addressed Coffea arabica, the most economically important species of the genus Coffea. Therefore, this study aimed (i) to evaluate the applicability and accuracy of GS in the prediction of the genomic estimated breeding value (GEBV); (ii) to estimate the genetic parameters; and (iii) to evaluate the time reduction of the selection cycle by GS in Arabica coffee breeding. A total of 195 Arabica coffee individuals, belonging to 13 families in generation of F2, susceptible backcross and resistant backcross, were phenotyped for 18 agronomic traits, and genotyped with 21,211 SNP molecular markers. Phenotypic data, measured in 2014, 2015, and 2016, were analyzed by mixed models. GS analyses were performed by the G-BLUP method, using the RKHS (Reproducing Kernel Hilbert Spaces) procedure, with a Bayesian algorithm. Heritabilities and selective accuracies were estimated, revealing moderate to high magnitude for most of the traits evaluated. Results of GS analyses showed the possibility of reducing the cycle time by 50%, maximizing selection gains per unit time. The effect of marker density on GS analyses was evaluated. Genomic selection proved to be promising for C. arabica breeding. The agronomic traits presented high complexity for they are controlled by several QTL and showed low genomic heritabilities, evidencing the need to incorporate genomic selection methodologies to the breeding programs of this species.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

et al. 2019

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“…Similar results were obtained in a recent study in which a biparental rubber tree population with 189 and 143 clones of the cross PB260 × RRIM600 was used; the population was genotyped with a limited number of markers (332 simple sequence repeat markers) (Cros et al, 2019), which resulted in a GS accuracy of 0.53. Other plant species have also been evaluated, with GS accuracies reaching moderate to high values (0.59 and 0.91) in a family of 180 Citrus clones (Gois et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions

et al. 2019

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Several genomic prediction models combining genotype × environment (G×E) interactions have recently been developed and used for genomic selection (GS) in plant breeding programs. G×E interactions reduce selection accuracy and limit genetic gains in plant breeding. Two data sets were used to compare the prediction abilities of multienvironment G×E genomic models and two kernel methods. Specifically, a linear kernel, or GB (genomic best linear unbiased predictor [GBLUP]), and a nonlinear kernel, or Gaussian kernel (GK), were used to compare the prediction accuracies (PAs) of four genomic prediction models: 1) a single-environment, main genotypic effect model (SM); 2) a multienvironment, main genotypic effect model (MM); 3) a multienvironment, single-variance G×E deviation model (MDs); and 4) a multienvironment, environment-specific variance G×E deviation model (MDe). We evaluated the utility of genomic selection (GS) for 435 individual rubber trees at two sites and genotyped the individuals via genotyping-by-sequencing (GBS) of single-nucleotide polymorphisms (SNPs). Prediction models were used to estimate stem circumference (SC) during the first 4 years of tree development in conjunction with a broad-sense heritability (H 2) of 0.60. Applying the model (SM, MM, MDs, and MDe) and kernel method (GB and GK) combinations to the rubber tree data revealed that the multienvironment models were superior to the single-environment genomic models, regardless of the kernel (GB or GK) used, suggesting that introducing interactions between markers and environmental conditions increases the proportion of variance explained by the model and, more importantly, the PA. Compared with the classic breeding method (CBM), methods in which GS is incorporated resulted in a 5-fold increase in response to selection for SC with multienvironment GS (MM, MDe, or MDs). Furthermore, GS resulted in a more balanced selection response for SC and contributed to a reduction in selection time when used in conjunction with traditional genetic breeding programs. Given the rapid advances in genotyping methods and their declining costs and given the overall costs of large-scale progeny testing and shortened breeding cycles, we expect GS to be implemented in rubber tree breeding programs.

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“…Recently, GWAS studies were performed for quality traits (Curtolo et al, 2017; Minamikawa et al, 2017). Gois et al (2016), providing evidences of the potential of genome wide selection in citrus for fruit acid and sugar contents. However, the learning and applications populations were limited to one parental combination.…”

Section: Selection Of Improved Cultivarsmentioning

confidence: 99%

Facing Climate Change: Biotechnology of Iconic Mediterranean Woody Crops

et al. 2019

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The Mediterranean basin is especially sensitive to the adverse outcomes of climate change and especially to variations in rainfall patterns and the incidence of extremely high temperatures. These two concurring adverse environmental conditions will surely have a detrimental effect on crop performance and productivity that will be particularly severe on woody crops such as citrus, olive and grapevine that define the backbone of traditional Mediterranean agriculture. These woody species have been traditionally selected for traits such as improved fruit yield and quality or alteration in harvesting periods, leaving out traits related to plant field performance. This is currently a crucial aspect due to the progressive and imminent effects of global climate change. Although complete genome sequence exists for sweet orange ( Citrus sinensis ) and clementine ( Citrus clementina ), olive tree ( Olea europaea ) and grapevine ( Vitis vinifera ), the development of biotechnological tools to improve stress tolerance still relies on the study of the available genetic resources including interspecific hybrids, naturally occurring (or induced) polyploids and wild relatives under field conditions. To this respect, post-genomic era studies including transcriptomics, metabolomics and proteomics provide a wide and unbiased view of plant physiology and biochemistry under adverse environmental conditions that, along with high-throughput phenotyping, could contribute to the characterization of plant genotypes exhibiting physiological and/or genetic traits that are correlated to abiotic stress tolerance. The ultimate goal of precision agriculture is to improve crop productivity, in terms of yield and quality, making a sustainable use of land and water resources under adverse environmental conditions using all available biotechnological tools and high-throughput phenotyping. This review focuses on the current state-of-the-art of biotechnological tools such as high throughput –omics and phenotyping on grapevine, citrus and olive and their contribution to plant breeding programs.

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Genome wide selection in Citrus breeding

Cited by 21 publications

References 35 publications

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

Early Selection Enabled by the Implementation of Genomic Selection in Coffea arabica Breeding

Genomic Selection in Rubber Tree Breeding: A Comparison of Models and Methods for Managing G×E Interactions

Facing Climate Change: Biotechnology of Iconic Mediterranean Woody Crops

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