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Assessment of Genetic Heritability in Rice Breeding Lines Based on Morphological Traits and Caryopsis Ultrastructure.

ABSTRACT: Rice (Oryza sativa L) is a most important staple food crop of the world because more than half of the World's population is dependent on it for their livelihood. Global rice production must be doubled by 2050 to cope up with the situation of population growth. Narrow genetic base in the released varieties has made the improvement in plateaus. Widening the genetic base is necessary to overcome the yield barrier. Hybridization and pre-breeding has been carried out to broaden the genetic base. Heritability and genetic advances were measured in the F5 lines (Tulaipanji × IR64), F3 lines (Tulaipanji × IR64 × PB1460), and F3 lines (Badshabhog × Swarna sub1). Some of the breeding lines were showing promising field performance with high yield potentiality. Wide crosses were performed to widen the genetic base between (Ranjit × O. rufipogon) and (Badshabhog × O. rufipogon) and the heritability pattern of the morphological characteristics in the progeny lines was evaluated. Nutritional quality of the rice grain is totally dependent on the morphology and histological characteristics of the caryopsis which are genetically determined. Caryopses ultrastructural analyses were carried out in seventeen different rice breeding lines through SEM. SEM analysis showed distinguishing ultrastructure in respect to pericarp, testa, aleurone layer, protein bodies and starchy endosperm in the breeding lines with distinctive inheritance pattern. This study provides information about the cross compatibility of the wide hybridization and heritability measures of the morphological traits which may supplement the breeding program to break the yield plateaus.

SUBMITTER: Roy SC

PROVIDER: S-EPMC7210993 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Estimating genetic trends using historical data is an important parameter to check the success of the breeding programs. The estimated genetic trends can act as a guideline to target the appropriate breeding strategies and optimize the breeding program for improved genetic gains. In this study, 17 years of historical data from IRRI's rice drought breeding program was used to estimate the genetic trends and assess the breeding program's success. We also identified top-performing lines based on grain yield breeding values as an elite panel for implementing future population improvement-based breeding schemes. A two-stage approach of pedigree-based mixed model analysis was used to analyze the data and extract the breeding values and estimate the genetic trends for grain yield under non-stress, drought, and in combined data of non-stress and drought. Lower grain yield values were observed in all the drought trials. Heritability for grain yield estimates ranged between 0.20 and 0.94 under the drought trials and 0.43-0.83 under non-stress trials. Under non-stress conditions, the genetic gain of 0.21% (10.22 kg/ha/year) for genotypes and 0.17% (7.90 kg/ha/year) for checks was observed. The genetic trend under drought conditions exhibited a positive trend with the genetic gain of 0.13% (2.29 kg/ha/year) for genotypes and 0.55% (9.52 kg/ha/year) for checks. For combined analysis showed a genetic gain of 0.27% (8.32 kg/ha/year) for genotypes and 0.60% (13.69 kg/ha/year) for checks was observed. For elite panel selection, 200 promising lines were selected based on higher breeding values for grain yield and prediction accuracy of > 0.40. The breeding values of the 200 genotypes formulating the core panel ranged between 2366.17 and 4622.59 (kg/ha). A positive genetic rate was observed under all the three conditions; however, the rate of increase was lower than the required rate of 1.5% genetic gain. We propose a recurrent selection breeding strategy within the elite population with the integration of modern tools and technologies to boost the genetic gains in IRRI's drought breeding program. The elite breeding panel identified in this study forms an easily available and highly enriched genetic resource for future recurrent selection programs to boost the genetic gains.

Project description:Background Genomic selection is a powerful tool in plant breeding. By building a prediction model using a training set with markers and phenotypes, genomic estimated breeding values (GEBVs) can be used as predictions of breeding values in a target set with only genotype data. There is, however, limited information on how prediction accuracy of genomic prediction can be optimized. The objective of this study was to evaluate the performance of 11 genomic prediction models across species in terms of prediction accuracy for two traits with different heritabilities using several subsets of markers and training population proportions. Species studied were maize (Zea mays, L.), soybean (Glycine max, L.), and rice (Oryza sativa, L.), which vary in linkage disequilibrium (LD) decay rates and have contrasting genetic architectures. Results Correlations between observed and predicted GEBVs were determined via cross validation for three training-to-testing proportions (90:10, 70:30, and 50:50). Maize, which has the shortest extent of LD, showed the highest prediction accuracy. Amongst all the models tested, Bayes B performed better than or equal to all other models for each trait in all the three crops. Traits with higher broad-sense and narrow-sense heritabilities were associated with higher prediction accuracy. When subsets of markers were selected based on LD, the accuracy was similar to that observed from the complete set of markers. However, prediction accuracies were significantly improved when using a subset of total markers that were significant at P ≤ 0.05 or P ≤ 0.10. As expected, exclusion of QTL-associated markers in the model reduced prediction accuracy. Prediction accuracy varied among different training population proportions. Conclusions We conclude that prediction accuracy for genomic selection can be improved by using the Bayes B model with a subset of significant markers and by selecting the training population based on narrow sense heritability. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-022-03479-y.

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Assessment of Genetic Heritability in Rice Breeding Lines Based on Morphological Traits and Caryopsis Ultrastructure.

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