Maximizing genetic gain for rice (Oryza sativa L.) grain yield by implementing genomic selection
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Abstract
Improving a quantitative trait like grain yield in rice using conventional breeding approaches is time- and resource-demanding. Utilizing genomic selection for improving grain yield in rice is assumed to be promising. Our objective in the present investigation was to integrate genomic selection approach into the current breeding program to improve genetic gain. A founder population genotyped with novel genomic markers was used as a training population. The training population was phenotyped over three years for grain yield. A bi-parental population developed from parents from the founder population genotyped with the same markers was used as the testing population. Four different predictive models were used on the training population at different marker densities. The results indicate that poor marker density leads to poor predictive abilities among all models. Increasing marker density improves the prediction ability; however, the increment in predictive ability over the mid-density of markers was relatively low. The candidate genotypes selected based on predicted performance in the testing population showed a 20% higher genetic gain over the testing population, a 16% higher gain over the training population, and a 150% higher gain over the mid-parent value. The mid-density markers covering the rice genome uniformly are sufficient to implement genomic selection in rice. Integrating genomic selection into ongoing breeding programs would benefit the breeder in selecting potential candidates for improving grain yield in rice.
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References
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