TY  - JA
AU  - Kimutai,C.
AU  - Ndlovu,N.
AU  - Chaikam,V.
AU  - Tadesse,B.
AU  - Das,B.
AU  - Beyene,Y.
AU  - Kiplagat,O.
AU  - Spillane,C.
AU  - Prasanna,B.M.
AU  - Gowda,M.
TI  - 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
SN  - 1664-8021
PY  - 2023///
CY  - Switzerland
PB  - Frontiers
KW  - Grain
KW  - AGROVOC
KW  - Nitrogen
KW  - Soil chemicophysical properties
KW  - Maize
KW  - Quantitative Trait Loci
KW  - Africa South of Sahara
N1  - Peer review; Open Access
N2  - 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 F3 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
UR  - https://hdl.handle.net/10883/22839
DO  - https://doi.org/10.3389/fgene.2023.1266402
T2  - Frontiers in Genetics
ER  -