Accelerating biomass improvement is a major goal of
- Award ID(s):
- 2019077
- NSF-PAR ID:
- 10357567
- Date Published:
- Journal Name:
- Frontiers in Genetics
- Volume:
- 12
- ISSN:
- 1664-8021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency ofMiscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits inMiscanthus sacchariflorus . We evaluated a diversity panel with 590 accessions ofM. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reportedMiscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits inM. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings ofM. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings ofM. sacchariflorus . This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement ofM. sacchariflorus . -
Abstract BACKGROUND Pea (
Pisum sativum ) is a prevalent cool‐season crop that produces seeds valued for their high protein content. Modern cultivars have incorporated several traits that improved harvested yield. However, progress toward improving seed quality has received less emphasis, in part due to the lack of tools for easily and rapidly measuring seed traits. In this study we evaluated the accuracy of single‐seed near‐infrared spectroscopy (NIRS) for measuring pea‐seed weight, protein, and oil content. A total of 96 diverse pea accessions were analyzed using both single‐seed NIRS and wet chemistry methods. To demonstrate field relevance, the single‐seed NIRS protein prediction model was used to determine the impact of seed treatments and foliar fungicides on the protein content of harvested dry peas in a field trial.RESULTS External validation of partial least squares (PLS) regression models showed high prediction accuracy for protein and weight (R2= 0.94 for both) and less accuracy for oil (R2= 0.74). Single‐seed weight was weakly correlated with protein and oil content in contrast with previous reports. In the field study, the single‐seed NIRS predicted protein values were within 10 mg g−1of an independent analytical reference measurement and were sufficiently precise to detect small treatment effects.
CONCLUSION The high accuracy of protein and weight estimation show that single‐seed NIRS could be used in the dual selection of high‐protein, high‐weight peas early in the breeding cycle, allowing for faster genetic advancement toward improved pea nutritional quality. © 2020 Society of Chemical Industry
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Summary Hybrid breeding is the main strategy for improving productivity in many crops, especially in rice and maize. Genomic hybrid breeding is a technology that uses whole‐genome markers to predict future hybrids. Predicted superior hybrids are then field evaluated and released as new hybrid cultivars after their superior performances are confirmed. This will increase the opportunity of selecting true superior hybrids with minimum costs. Here, we used genomic best linear unbiased prediction to perform hybrid performance prediction using an existing rice population of 1495 hybrids. Replicated 10‐fold cross‐validations showed that the prediction abilities on ten agronomic traits ranged from 0.35 to 0.92. Using the 1495 rice hybrids as a training sample, we predicted six agronomic traits of 100 hybrids derived from half diallel crosses involving 21 parents that are different from the parents of the hybrids in the training sample. The prediction abilities were relatively high, varying from 0.54 (yield) to 0.92 (grain length). We concluded that the current population of 1495 hybrids can be used to predict hybrids from seemingly unrelated parents. Eventually, we used this training population to predict all potential hybrids of cytoplasm male sterile lines from 3000 rice varieties from the 3K Rice Genome Project. Using a breeding index combining 10 traits, we identified the top and bottom 200 predicted hybrids. SNP genotypes of the training population and parameters estimated from this training population are available for general uses and further validation in genomic hybrid prediction of all potential hybrids generated from all varieties of rice.
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