ArticlePloS one2018
Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Accelerating genetic gain through integrated genomic selection in crop plants.Journal of applied genetics · 2026Review
- Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan.Rice (New York, N.Y.) · 2025Article
- QTN detection and candidate gene identification for improved eating and cooking quality in rice using GWAS and PLS regression analysis.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Advances in genomic tools for plant breeding: harnessing DNA molecular markers, genomic selection, and genome editing.Biological research · 2024Review
- Impact of rice GENERAL REGULATORY FACTOR14h (GF14h) on low-temperature seed germination and its application to breeding.PLoS genetics · 2024Article
- Deciphering individual triticale grain weight patterns: A gaussian mixture model approach.PloS one · 2024Article
- Review
- Upcycling rice yield trial data using a weather-driven crop growth model.Communications biology · 2023Article
- Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals.Genes · 2023Review
- Independent control of organ number and distribution pattern in rice panicle.Frontiers in plant science · 2023Article
- Performance of Bayesian and BLUP alphabets for genomic prediction: analysis, comparison and results.Heredity · 2022Article
- Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice.Molecular breeding : new strategies in plant improvement · 2022Article
- Genomic Prediction: Progress and Perspectives for Rice Improvement.Methods in molecular biology (Clifton, N.J.) · 2022Review
- Genomic Selection: A Tool for Accelerating the Efficiency of Molecular Breeding for Development of Climate-Resilient Crops.Frontiers in genetics · 2022Review
- Landraces of temperate japonica rice have superior alleles for improving culm strength associated with lodging resistance.Scientific reports · 2020Article
- Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios.Scientific reports · 2020Article
- Wheat individual grain-size variance originates from crop development and from specific genetic determinism.PloS one · 2020Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Grain-filling ability is one of the factors that controls grain yield in rice (Oryza sativa L.). We developed a method for describing grain weight distribution, which is the probability density function of single grain weight in a panicle, using 128 Japanese rice varieties. With this method, we quantitively analyzed genotypic differences in grain-filling ability and used the grain weight distribution parameters for genomic prediction subject to genetic improvement in grain yield in rice. The novel description method could represent the observed grain weight distribution with five genotype-specific parameters of a mixture of two gamma distributions. The estimated genotype-specific parameters representing the proportion of filled grains had applicability to explain the grain filling ability of genotypes comparable to that of sink-filling rate and the conventionally measured proportion of filled grains, which suggested the efficiency and flexibility of grain weight distribution parameters to handle several genotypes. We revealed that perfectly filled grains have to be prioritized over partially filled grains for the optimum allocation of the source of yield in a panicle, from the analysis for obtaining an ideal shape of grain weight distribution. We conducted genomic prediction of grain weight distribution considering five genotype-specific parameters of the distribution as phenotypes relating to grain filling ability. The proportion of filled grains, average weight of filled grains, and variance of filled grain weight, which were considered to control grain yield to a certain degree, were predicted with accuracies of 0.30, 0.28, and 0.53, respectively. The proposed description method of grain weight distribution facilitated not only the investigation of the optimum allocation of nutrients in a panicle for realizing high grain-filling ability, but also allowed genomic selection of grain weight distribution.
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