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Hu M, Tian H, Yang K, Ding S, Hao Y, Xu R, Zhang F, Liu H, Zhang D. Comprehensive Evaluation and Selection of 192 Maize Accessions from Different Sources. PLANTS (BASEL, SWITZERLAND) 2024; 13:1397. [PMID: 38794467 PMCID: PMC11125448 DOI: 10.3390/plants13101397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/15/2024] [Accepted: 05/02/2024] [Indexed: 05/26/2024]
Abstract
In the period 2022-2023, an analysis of fourteen phenotypic traits was conducted across 192 maize accessions in the Aral region of Xinjiang. The Shannon-Wiener diversity index was employed to quantify the phenotypic diversity among the accessions. Subsequently, a comprehensive evaluation of the index was performed utilizing correlation analysis, principal component analysis (PCA) and cluster analysis. The results highlighted significant findings: (1) A pronounced diversity was evident across the 192 maize accessions, accompanied by complex interrelationships among the traits. (2) The 14 phenotypic traits were transformed into 3 independent indicators through principal component analysis: spike factor, leaf width factor, and number of spikes per plant. (3) The 192 materials were divided into three groups using cluster analysis. The phenotypes in Group III exhibited the best performance, followed by those in Group I, and finally Group II. The selection of the three groups can vary depending on the breeding objectives. This study analysed the diversity of phenotypic traits in maize germplasm resources. Maize germplasm was categorised based on similar phenotypes. These findings provide theoretical insights for the study of maize accessions under analogous climatic conditions in Alar City, which lay the groundwork for the efficient utilization of existing germplasm as well as the development and selection of new varieties.
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Affiliation(s)
- Mengting Hu
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Huijuan Tian
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Kaizhi Yang
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Shuqi Ding
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Ying Hao
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Ruohang Xu
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Fulai Zhang
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Hong Liu
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
| | - Dan Zhang
- College of Agriculture, Tarim University, Alar 843300, China; (M.H.); (H.T.); (K.Y.); (S.D.); (Y.H.); (R.X.); (F.Z.); (H.L.)
- Key Laboratory of Genetic Improvement and Efficient Production for Specialty Crops in Arid Southern Xinjiang of Xinjiang Corps, Alar 843300, China
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Adu GB, Awuku FJ, Garcia-Oliveira AL, Amegbor IK, Nelimor C, Nboyine J, Karikari B, Atosona B, Manigben KA, Aboyadana PA. DArTseq-based SNP markers reveal high genetic diversity among early generation fall armyworm tolerant maize inbred lines. PLoS One 2024; 19:e0294863. [PMID: 38630672 PMCID: PMC11023204 DOI: 10.1371/journal.pone.0294863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 11/09/2023] [Indexed: 04/19/2024] Open
Abstract
Diversity analysis using molecular markers serves as a powerful tool in unravelling the intricacies of inclusivity within various populations and is an initial step in the assessment of populations and the development of inbred lines for host plant resistance in maize. This study was conducted to assess the genetic diversity and population structure of 242 newly developed S3 inbred lines using 3,305 single nucleotide polymorphism (SNP) markers and to also assess the level of homozygosity achieved in each of the inbred lines. A total of 1,184 SNP markers were found highly informative, with a mean polymorphic information content (PIC) of 0.23. Gene diversity was high among the inbred lines, ranging from 0.04 to 0.50, with an average of 0.27. The residual heterozygosity of the 242 S3 inbred lines averaged 8.8%, indicating moderately low heterozygosity levels among the inbred lines. Eighty-four percent of the 58,322 pairwise kinship coefficients among the inbred lines were near zero (0.00-0.05), with only 0.3% of them above 0.50. These results revealed that many of the inbred lines were distantly related, but none were redundant, suggesting each inbred line had a unique genetic makeup with great potential to provide novel alleles for maize improvement. The admixture-based structure analysis, principal coordinate analysis, and neighbour-joining clustering were concordant in dividing the 242 inbred lines into three subgroups based on the pedigree and selection history of the inbred lines. These findings could guide the effective use of the newly developed inbred lines and their evaluation in quantitative genetics and molecular studies to identify candidate lines for breeding locally adapted fall armyworm tolerant varieties in Ghana and other countries in West and Central Africa.
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Affiliation(s)
| | | | - Ana Luisa Garcia-Oliveira
- International Maize and Wheat Improvement Center (CIMMYT), Nairobi, Kenya
- Department of Molecular Biology, College of Biotechnology, CCS Haryana Agricultural University, Hisar, Haryana, India
| | - Isaac Kodzo Amegbor
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
- Faculty of Natural and Agricultural Sciences, Department of Plant Breeding, University of the Free State, Bloemfontein, South Africa
| | - Charles Nelimor
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Jerry Nboyine
- CSIR-Savanna Agricultural Research Institute, Nyankpala, Ghana
| | - Benjamin Karikari
- Department of Agricultural Biotechnology, Faculty of Agriculture, Food and Consumer Sciences, University for Development Studies, Tamale, Ghana
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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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