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Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean

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Title Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean
 
Creator Cruz, Maribel
Arbeláez, Juan David
Loaiza, Katherine
Cuasquer, Juan
Rosas, Juan
Graterol, Eduardo
 
Subject genetic parameters
phenotypic variation
genetic correlation
research evaluation
genetic markers
variación fenotípica
parámetros genéticos
correlación genética
 
Description Rice (Oryza sativa L.) grain quality is a set of complex interrelated traits that include grain milling, appearance, cooking, and edible properties. As consumer preferences in Latin America and the Caribbean evolve, determining what traits best capture regional grain quality preferences is fundamental for breeding and cultivar release. In this study, a genome-wide association study (GWAS), marker-assisted selection (MAS), and genomic selection (GS) were evaluated to help guide the development of new breeding strategies for rice grain quality improvement. For this purpose, 284 rice lines representing over 20 yr of breeding in Latin America and the Caribbean were genotyped and phenotyped for 10 different traits including grain milling, appearance, cooking, and edible quality traits. Genetic correlations among the 10 traits ranged from −0.83 to 0.85. A GWAS identified 19 significant marker/trait combinations associated with eight grain quality traits. Four functional markers, three located in the Waxy and one in the starch synthase IIa genes, were significantly associated with six grain-quality traits. These markers individually explained 51–75% of the phenotypic variance depending on the trait, clearly indicating their potential utility for MAS. Cross-validation studies to evaluate predictive abilities of four different GS models for each of the 10 quality traits were conducted and predictive abilities ranged from 0.3 to 0.72. Overall, the machine learning model random forest had the highest predictive abilities and was especially effective for traits where large effect quantitative trait loci were identified. This study provides the foundation for deploying effective molecular breeding strategies for grain quality in Latin American rice breeding programs.
 
Date 2021-09-12
2021-09-15T07:36:05Z
2021-09-15T07:36:05Z
 
Type Journal Article
 
Identifier Cruz, M.; Arbeláez, J.D.; Loaiza, K.; Cuasquer, Juan; Rosas, J.; Graterol, E. (2021) Genetic and phenotypic characterization of rice grain quality traits to define research strategies for improving rice milling, appearance, and cooking qualities in Latin America and the Caribbean. Plant Genome e20134 On-line First Paper (12 September 2021) 20 p. ISSN: 1940-3372
1940-3372
https://hdl.handle.net/10568/114976
https://doi.org/10.1002/tpg2.20134
 
Language en
 
Rights CC-BY-4.0
Open Access
 
Format 20 p.
application/pdf
 
Publisher Wiley
 
Source Plant Genome