Abstract
ABSTRACT: 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.
Highlights
Rice (Oryza sativa) is one of the most important crops in the world
Average results and the respective estimated standard deviations relative to molecular heritability, predictive ability, and regression coefficient between genomic value and phenotypic value associated with the Delta-p and Genomic Best Linear Unbiased Predictor (G-BLUP) methods, as well as predictive ability and regression coefficient between the estimated genomic value through the Delta-p/G-BLUP index and phenotypic value are shown in table 1
The average results and respective standard deviations relative to molecular heritability, predictive ability, and regression coefficient between genomic value and phenotypic value associated with Bayesian methods (BLASSO and BayesCpi), as well as predictive ability and regression coefficient between the estimated genomic value through the index (Delta-p/Bayesian Least Absolute Shrinkage and Selection Operator (BLASSO) index and Delta-p/BayesCpi index) and phenotypic value are shown in table 2
Summary
Rice (Oryza sativa) is one of the most important crops in the world. Increased rice production has played key roles in food security, especially in developing countries in Asia and Africa (CHEN, 2017). The production of this crop is approximately 12,327.8 thousand tons (CONAB, 2018). Despite supplying world’s current population, it is estimated that by 2050, rice production in the world must increase from 60 to 110% to meet population demand (GODFRAY et al, 2010; TILMAN et al, 2011 RAY et al, 2013). V.49, n.6, considering improvements in yield over existing varieties. According to SPINDEL et al (2015), because the process is extremely time-consuming, using conventional breeding and selection methods, it takes ten years on average for elite varieties to be developed and identified
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