Assessment of the Prediction Accuracy of Genomic Selection for Rice Amylose Content and Gel Consistency
Genomic selection is an effective method for accelerating the enhancement of plant agronomic traits. Currently, genotype acquisition mainly depends on resequencing and chip technology, and the cost and efficiency are still the key factors restricting the wide application of genomic selection breedin...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/2/336 |
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| Summary: | Genomic selection is an effective method for accelerating the enhancement of plant agronomic traits. Currently, genotype acquisition mainly depends on resequencing and chip technology, and the cost and efficiency are still the key factors restricting the wide application of genomic selection breeding. We explore the merits of Hyper-seq population sequencing technology in genomic selection breeding. Seven genomic selection models were constructed using 417 rice germplasm resources, and each model showed high prediction accuracy for amylose content (0.8316–0.8360) and gel consistency (0.7075–0.7235). We also constructed GBLUP models to explore how the marker number and population size affected prediction accuracy. With increased marker number and population size, prediction accuracy first increased significantly, then leveled off. Finally, through genome-wide association studies, SNPs were selected from five different significance levels for prediction accuracy studies. The results indicated that using markers that are significantly associated with traits greatly enhances the accuracy of prediction. |
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| ISSN: | 2073-4395 |