دورية أكاديمية
Genomic prediction using training population design in interspecific soybean populations.
| العنوان: | Genomic prediction using training population design in interspecific soybean populations. |
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| المؤلفون: | Beche E; Division of Plant Science, University of Missouri, Columbia, MO USA., Gillman JD; Plant Genetics Res. Unit, USDA-ARS, Columbia, MO USA., Song Q; Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD USA., Nelson R; Department of Crop Sciences, University of Illinois, and USDA-Agricultural Research Service (retired), 1101 W. Peabody Dr., Urbana, IL 61801 USA., Beissinger T; Division of Plant Breeding Methodology, Department of Crop Sciences, Georg-August-Universität, Göttingen, Germany., Decker J; Division of Animal Science, University of Missouri, Columbia, MO USA., Shannon G; Division of Plant Science, University of Missouri, Columbia, MO USA., Scaboo AM; Division of Plant Science, University of Missouri, Columbia, MO USA. |
| المصدر: | Molecular breeding : new strategies in plant improvement [Mol Breed] 2021 Feb 10; Vol. 41 (2), pp. 15. Date of Electronic Publication: 2021 Feb 10 (Print Publication: 2021). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Kluwer Academic Publishers Country of Publication: Netherlands NLM ID: 9506703 Publication Model: eCollection Cited Medium: Internet ISSN: 1572-9788 (Electronic) Linking ISSN: 13803743 NLM ISO Abbreviation: Mol Breed Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Publication: Dordrecht : Kluwer Academic Publishers Original Publication: Dordrecht ; Boston : Kluwer Academic Publishers, c1995- |
| مستخلص: | Agronomically important traits generally have complex genetic architecture, where many genes have a small and largely additive effect. Genomic prediction has been demonstrated to increase genetic gain and efficiency in plant breeding programs beyond marker-assisted selection and phenotypic selection. The objective of this study was to evaluate the impact of allelic origin, marker density, training population size, and cross-validation schemes on the accuracy of genomic prediction models in an interspecific soybean nested association mapping (NAM) panel. Three cross-validation schemes were used: (a) Within-Family (WF): training population and predictions are made exclusively within each family; (b) Across All families (AF): all the individuals from the three families were randomly assigned to either the training or validation set; (c) Leave one Family out (LFO): each family is predicted using a training set that contains the other two families. Predictive abilities increased with training population size up to 350 individuals, but no significant gains were noted beyond 250 individuals in the training population. The number of markers had a limited impact on the observed predictive ability across traits; increasing markers used in the model above 1000 revealed no significant increases in prediction accuracy. Predictive abilities for AF were not significantly different from the WF method, and predictive abilities across populations for the WF method had a range of 0.58 to 0.70 for maturity, protein, meal, and oil. Our results also showed encouraging prediction accuracies for grain yield (0.58-0.69) using the WF method. Partitioning genomic prediction between G. max and G. soja alleles revealed useful information to select material with a larger allele contribution from both parents and could accelerate allele introgression from exotic germplasm into the elite soybean gene pool. Supplementary Information: The online version contains supplementary material available at 10.1007/s11032-021-01203-6. Competing Interests: Conflict of interestThe authors declare no competing interests. (© The Author(s), under exclusive licence to Springer Nature B.V. part of Springer Nature 2021.) |
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| فهرسة مساهمة: | Keywords: Genetic diversity; Genomic estimated breeding values; Genomic prediction; Glycine soja; Grain yield; Soybean |
| تواريخ الأحداث: | Date Created: 20230613 Latest Revision: 20230928 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC10236090 |
| DOI: | 10.1007/s11032-021-01203-6 |
| PMID: | 37309481 |
| قاعدة البيانات: | MEDLINE |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 1572-9788 |
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| DOI: | 10.1007/s11032-021-01203-6 |