Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies
© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology.
| Publié dans: | Journal of experimental botany. - 1985. - 73(2022), 16 vom: 12. Sept., Seite 5460-5473 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2022
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| Accès à la collection: | Journal of experimental botany |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Zea mays L Association mapping candidate genes genomic selection inbred line panel linkage disequilibrium population structure |
| Résumé: | © The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology. The identification of genomic regions associated with root traits and the genomic prediction of untested genotypes can increase the rate of genetic gain in maize breeding programs targeting roots traits. Here, we combined two maize association panels with different genetic backgrounds to identify single nucleotide polymorphisms (SNPs) associated with root traits, and used a genome-wide association study (GWAS) and to assess the potential of genomic prediction for these traits in maize. For this, we evaluated 377 lines from the Ames panel and 302 from the Backcrossed Germplasm Enhancement of Maize (BGEM) panel in a combined panel of 679 lines. The lines were genotyped with 232 460 SNPs, and four root traits were collected from 14-day-old seedlings. We identified 30 SNPs significantly associated with root traits in the combined panel, whereas only two and six SNPs were detected in the Ames and BGEM panels, respectively. Those 38 SNPs were in linkage disequilibrium with 35 candidate genes. In addition, we found higher prediction accuracy in the combined panel than in the Ames or BGEM panel. We conclude that combining association panels appears to be a useful strategy to identify candidate genes associated with root traits in maize and improve the efficiency of genomic prediction |
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| Description: | Date Completed 14.09.2022 Date Revised 04.09.2024 published: Print Citation Status MEDLINE |
| ISSN: | 1460-2431 |
| DOI: | 10.1093/jxb/erac236 |