Evidence map›Paper›PMID 34510797›Full record

ArticleThe plant genome2021

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.

Maribel Cruz, Juan David Arbelaez, Katherine Loaiza, Juan Cuasquer, Juan Rosas, Eduardo Graterol

Abstract read
In one paragraph

Article in The plant genome, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

  1. Article
  2. Article
  3. 110 years of rice breeding at LSU: realized genetic gains and future optimization.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025
    Article
  4. Article
  5. Contrasting Alleles ofPlants (Basel, Switzerland) · 2024
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  7. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maribel CruzFLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia.
Juan David ArbelaezDep. of Crop Sciences, Univ. of Illinois, Urbana-Champaign, Turner Hall N-211|1102 S. Goodwin Ave. | 046, Urbana, IL, 61801, USA.ORCID 0000-0002-5803-7872
Katherine LoaizaFLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia.
Juan CuasquerCIAT (International Center for Tropical Agriculture), Kilómetro 17 Recta Cali, Palmira, CP, Cali, Valle del Cauca, 763537, Colombia.
Juan RosasINIA (Instituto Nacional de Investigación Agropecuaria), Ruta 8 Km. 281/33000, Treinta y Tres, Uruguay.
Eduardo GraterolFLAR (Fondo Latinoamericano para Arroz de Riego), CIAT (International Center for Tropical Agriculture), Kilómetro 17 c, CP, Cali, Valle del Cauca, 763537, Colombia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

OryzaCookingGenome-Wide Association StudyLatin AmericaPlant BreedingResearch Design

Identifiers

PMID34510797
PMCPMC12807091

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.