Discovery of genomic regions associated with grain yield and agronomic traits in Bi-parental populations of maize ( Zea mays . L) Under optimum and low nitrogen conditions

• Published in: Frontiers in genetics Vol. 14; p. 1266402
• Main Authors: Kimutai, Collins, Ndlovu, Noel, Chaikam, Vijay, Ertiro, Berhanu Tadesse, Das, Biswanath, Beyene, Yoseph, Kiplagat, Oliver, Spillane, Charles, Prasanna, Boddupalli M, Gowda, Manje
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 26.10.2023
• Subjects: Genetics; grain yield; low soil nitrogen; maize; quantitative trait loci (QTL); sub-Saharan Africa; quantitative trait loci (QTL); low soil nitrogen; sub-Saharan Africa; grain yield; maize
• ISSN: 1664-8021; 1664-8021
• DOI: 10.3389/fgene.2023.1266402

Low soil nitrogen levels, compounded by the high costs associated with nitrogen supplementation through fertilizers, significantly contribute to food insecurity, malnutrition, and rural poverty in maize-dependent smallholder communities of sub-Saharan Africa (SSA). The discovery of genomic regions associated with low nitrogen tolerance in maize can enhance selection efficiency and facilitate the development of improved varieties. To elucidate the genetic architecture of grain yield (GY) and its associated traits (anthesis-silking interval (ASI), anthesis date (AD), plant height (PH), ear position (EPO), and ear height (EH)) under different soil nitrogen regimes, four F maize populations were evaluated in Kenya and Zimbabwe. GY and all the traits evaluated showed significant genotypic variance and moderate heritability under both optimum and low nitrogen stress conditions. A total of 91 quantitative trait loci (QTL) related to GY (11) and other secondary traits (AD (26), PH (19), EH (24), EPO (7) and ASI (4)) were detected. Under low soil nitrogen conditions, PH and ASI had the highest number of QTLs. Furthermore, some common QTLs were identified between secondary traits under both nitrogen regimes. These QTLs are of significant value for further validation and possible rapid introgression into maize populations using marker-assisted selection. Identification of many QTL with minor effects indicates genomic selection (GS) is more appropriate for their improvement. Genomic prediction within each population revealed low to moderately high accuracy under optimum and low soil N stress management. However, the accuracies were higher for GY, PH and EH under optimum compared to low soil N stress. Our findings indicate that genetic gain can be improved in maize breeding for low N stress tolerance by using GS.