New insights into genomic selection through population-based non-parametric prediction methods

Lima, Leísa Pires; Azevedo, Camila Ferreira; Resende, Marcos Deon Deon Vilela de; Silva, Fabyano F.; Suela, Matheus Massariol; Nascimento, Moysés; Viana, José Marcelo Soriano

doi:10.1590/1678-992x-2017-0351

Cited by 3 publications

(2 citation statements)

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“…Several methods, such as Bayesian methods, Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO), BayesCpi, and the mixed-model method, Genomic Best Linear Unbiased Predictor (G-BLUP), have been extensively applied to GWS and are recommended for genomic prediction (DE LOS CAMPOS et al, 2012, AZEVEDO et al, 2015. However, new methodologies have been proposed, such as the method called Delta-p (RESENDE, 2015;LIMA et al, 2019), which does not demand an iterative computational method and consequently, does not require evaluation regarding the convergence of results. Such methodology divides the estimation population into two subpopulations, one associated with higher phenotypic values and the other associated with lower phenotypic values.…”

Section: Introductionmentioning

confidence: 99%

“…Effects of the markers were estimated non-parametrically using the difference between the allelic frequencies and the genetic gain associated with these two subpopulations. With the goal of combining good properties of different methodologies, LIMA et al (2019) proposed the use of a genomic index, called the Delta-p/G-BLUP index, which combines estimated genomic values obtained by Delta-p and G-BLUP. The Delta-p/G-BLUP index was more accurate than G-BLUP in genomic prediction.…”

Section: Introductionmentioning

confidence: 99%

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Combined index of genomic prediction methods applied to productivity

et al. 2019

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Rice cultivation has great national and global importance, being one of the most produced and consumed cereals in the world and the primary food for more than half of the world’s population. Because of its importance as food, developing efficient methods to select and predict genetically superior individuals in reference to plant traits is of extreme importance for breeding programs. The objective of this research was to evaluate and compare the efficiency of the Delta-p, G-BLUP (Genomic Best Linear Unbiased Predictor), BayesCpi, BLASSO (Bayesian Least Absolute Shrinkage and Selection Operator), Delta-p/G-BLUP index, Delta-p/BayesCpi index, and Delta-p/BLASSO index in the estimation of genomic values and the effects of single nucleotide polymorphisms on phenotypic data associated with rice traits. Use of molecular markers allowed high selective efficiency and increased genetic gain per unit time. The Delta-p method uses the concept of change in allelic frequency caused by selection and the theoretical concept of genetic gain. The Index is based on the principle of combined selection, using the information regarding the additive genomic values predicted via G-BLUP, BayesCpi, BLASSO, or Delta-p. These methods were applied and compared for genomic prediction using nine rice traits: flag leaf length, flag leaf width, panicles number per plant, primary panicle branch number, seed length, seed width, amylose content, protein content, and blast resistance. Delta-p/G-BLUP index had higher predictive abilities for the traits studied, except for amylose content trait in which the method with the highest predictive ability was BayesCpi, being approximately 3% greater than that of the Delta-p/G-BLUP index.

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confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combined index of genomic prediction methods applied to productivity

et al. 2019

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Genomic Prediction: Progress and Perspectives for Rice Improvement

Bartholome

Prakash

Cobb³

2022

Methods in Molecular Biology

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Genomic prediction can be a powerful tool to achieve greater rates of genetic gain for quantitative traits if thoroughly integrated into a breeding strategy. In rice as in other crops, the interest in genomic prediction is very strong with a number of studies addressing multiple aspects of its use, ranging from the more conceptual to the more practical. In this chapter, we review the literature on rice (Oryza sativa) and summarize important considerations for the integration of genomic prediction in breeding programs. The irrigated breeding program at the International Rice Research Institute is used as a concrete example on which we provide data and R scripts to reproduce the analysis but also to highlight practical challenges regarding the use of predictions. The adage “To someone with a hammer, everything looks like a nail” describes a common psychological pitfall that sometimes plagues the integration and application of new technologies to a discipline. We have designed this chapter to help rice breeders avoid that pitfall and appreciate the benefits and limitations of applying genomic prediction, as it is not always the best approach nor the first step to increasing the rate of genetic gain in every context.

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Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications

et al. 2021

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The world population is expected to be larger and wealthier over the next few decades and will require more animal products, such as milk and beef. Tropical regions have great potential to meet this growing global demand, where pasturelands play a major role in supporting increased animal production. Better forage is required in consonance with improved sustainability as the planted area should not increase and larger areas cultivated with one or a few forage species should be avoided. Although, conventional tropical forage breeding has successfully released well-adapted and high-yielding cultivars over the last few decades, genetic gains from these programs have been low in view of the growing food demand worldwide. To guarantee their future impact on livestock production, breeding programs should leverage genotyping, phenotyping, and envirotyping strategies to increase genetic gains. Genomic selection (GS) and genome-wide association studies play a primary role in this process, with the advantage of increasing genetic gain due to greater selection accuracy, reduced cycle time, and increased number of individuals that can be evaluated. This strategy provides solutions to bottlenecks faced by conventional breeding methods, including long breeding cycles and difficulties to evaluate complex traits. Initial results from implementing GS in tropical forage grasses (TFGs) are promising with notable improvements over phenotypic selection alone. However, the practical impact of GS in TFG breeding programs remains unclear. The development of appropriately sized training populations is essential for the evaluation and validation of selection markers based on estimated breeding values. Large panels of single-nucleotide polymorphism markers in different tropical forage species are required for multiple application targets at a reduced cost. In this context, this review highlights the current challenges, achievements, availability, and development of genomic resources and statistical methods for the implementation of GS in TFGs. Additionally, the prediction accuracies from recent experiments and the potential to harness diversity from genebanks are discussed. Although, GS in TFGs is still incipient, the advances in genomic tools and statistical models will speed up its implementation in the foreseeable future. All TFG breeding programs should be prepared for these changes.

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New insights into genomic selection through population-based non-parametric prediction methods

Cited by 3 publications

References 23 publications

Combined index of genomic prediction methods applied to productivity

Combined index of genomic prediction methods applied to productivity

Genomic Prediction: Progress and Perspectives for Rice Improvement

Genomic Selection in Tropical Forage Grasses: Current Status and Future Applications

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