Uncovering microsatellite markers associated with agronomic traits of South Sudan landrace maize

• Published in: Genes & genomics 2023, 45(12), , pp.1587-1598
• Main Authors: Mathiang, Emmanuel Andrea, Park, Hyeon, Jang, So Jung, Cho, Jungeun, Heo, Tae Hyeon, Lee, Ju Kyong
• Format: Article
• Language: English
• Subjects: Alleles; Animal Genetics and Genomics; Association analysis; Biological diversity; Biomedical and Life Sciences; Corn; Crop yields; Edible Grain - genetics; Ethylenediaminetetraacetic acid; Gene Frequency; Genetic aspects; Genetic diversity; Genetic markers; Genotypes; Human Genetics; Leaves; Life Sciences; Marker-assisted selection; Microbial Genetics and Genomics; Microsatellite Repeats - genetics; Microsatellites; Phenotype; Plant Genetics and Genomics; Population genetics; Population structure; Research Article; South Sudan; Zea mays - genetics; 생물학
• ISSN: 1976-9571; 2092-9293; 2092-9293
• DOI: 10.1007/s13258-023-01465-8

Background Maize has great importance in South Sudan as the most cultivated cereal after sorghum; however, numerous challenges are encountered in its production. To raise maize production, it is critical to exploit the wealth of its genetic variation for grain yield enhancement. Objective This study aimed to conduct association analysis to identify specific simple sequence repeat (SSR) markers associated with quantitative agronomic traits. Methods Genetic variation and population structure were investigated among 31 maize accessions by association analysis using 50 SSR markers and seven quantitative agronomic traits. Results The genotypes exhibited abundant genetic variation, and 418 alleles were detected with an average of 8.4 alleles per locus. The average genetic diversity, major allele frequency, and polymorphic information content were 0.754, 0.373, and 0.725, respectively. The population structure based on 50 SSR markers divided the maize accessions into two main groups and an admixed group without considering their descent. Association analysis was performed using a general linear model (Q GLM) and a mixed linear model (Q + K MLM). Q GLM detected 44 trait-marker associations involving 23 SSR markers. Q + K MLM detected four marker-trait associations involving three SSR markers (umc2286, umc1303, umc1429) associated with days to tasseling, days to silking, leaf length, and leaf width. Conclusions The detected significant SSR markers related to agronomic traits could be useful for future genetic studies. Additionally, markers affecting several agronomic traits and overlapped SSR markers require further testing on a wide range of genotypes prior to their consideration as candidate markers for marker assisted selection for South Sudan maize improvement.