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Sowadan O, Xu S, Li Y, Muleke EM, Sitoe HM, Dang X, Jiang J, Dong H, Hong D. Genome-Wide Association Analysis Unravels New Quantitative Trait Loci (QTLs) for Eight Lodging Resistance Constituent Traits in Rice ( Oryza sativa L.). Genes (Basel) 2024; 15:105. [PMID: 38254994 PMCID: PMC10815206 DOI: 10.3390/genes15010105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/13/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
Lodging poses a significant challenge to rice yield, prompting the need to identify elite alleles for lodging resistance traits to improve cultivated rice varieties. In this study, a natural population of 518 rice accessions was examined to identify elite alleles associated with plant height (PH), stem diameter (SD), stem anti-thrust (AT/S), and various internode lengths (first (FirINL), second (SecINL), third (ThirINL), fourth (ForINL), and fifth (FifINL) internode lengths). A total of 262 SSR markers linked to these traits were uncovered through association mapping in two environmental conditions. Phenotypic evaluations revealed striking differences among cultivars, and genetic diversity assessments showed polymorphisms across the accessions. Favorable alleles were identified for PH, SD, AT/S, and one to five internode lengths, with specific alleles displaying considerable effects. Noteworthy alleles include RM6811-160 bp on chromosome 6 (which reduces PH) and RM161-145 bp on chromosome 5 (which increases SD). The study identified a total of 42 novel QTLs. Specifically, seven QTLs were identified for PH, four for SD, five for AT/S, five for FirINL, six for SecINL, five for ThirINL, six for ForINL, and four for FifINL. QTLs qAT/S-2, qPH2.1, qForINL2.1, and qFifINL exhibited the most significant phenotypic variance (PVE) of 3.99% for the stem lodging trait. AT/S, PH, ForINL, and FifINL had additive effects of 5.31 kPa, 5.42 cm, 4.27 cm, and 4.27 cm, respectively, offering insights into eight distinct cross-combinations for enhancing each trait. This research suggests the potential for crossbreeding superior parents based on stacked alleles, promising improved rice cultivars with enhanced lodging resistance to meet market demands.
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Affiliation(s)
- Ognigamal Sowadan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
| | - Shanbin Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
| | - Yulong Li
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
- Institute of Crop Research, Anhui Academy of Agricultural Sciences, Hefei 230031, China
| | - Everlyne Mmbone Muleke
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
- Department of Agriculture and Land Use Management, School of Agriculture, Veterinary Sciences and Technology, Masinde Muliro University of Science and Technology, Kakamega P.O. Box 190-50100, Kenya
| | - Hélder Manuel Sitoe
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
- Faculty of Agronomy and Biological Sciences, Púngue University, P.O. Box 323, Manica 2202, Mozambique
| | - Xiaojing Dang
- Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei 230031, China; (X.D.); (J.J.)
| | - Jianhua Jiang
- Institute of Rice Research, Anhui Academy of Agricultural Sciences, Hefei 230031, China; (X.D.); (J.J.)
| | - Hui Dong
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
| | - Delin Hong
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Nanjing Agricultural University, Nanjing 210095, China; (O.S.); (S.X.); (Y.L.); (E.M.M.); (H.M.S.); (H.D.)
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Zuffo LT, DeLima RO, Lübberstedt T. Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5460-5473. [PMID: 35608947 PMCID: PMC9467658 DOI: 10.1093/jxb/erac236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Accepted: 06/06/2022] [Indexed: 05/13/2023]
Abstract
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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Affiliation(s)
- Leandro Tonello Zuffo
- Corteva Agriscience, Rio Verde, GO, Brazil
- Department of Agronomy, Universidade Federal de Viçosa, Viçosa, MG, Brazil
- Department of Agronomy, Iowa State University, Ames, IA, USA
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