Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and agronomic traits under drought and optimum conditions in maize

作者：Manigben Kulai Amadu, Yoseph Beyene, Vijay Chaikam, Pangirayi Tongoona, Eric Yirenkyi Danquah, Beatrice Elohor Ifie, Juan Burgueño, B. M. Prasanna, Manje Gowda · 发表于：BMC Plant Biology · 年份：2025 · DOI：10.1186/s12870-025-06135-3 · 被引用次数：14 · 研究领域：Genetic Mapping and Diversity in Plants and Animals、Genetic and phenotypic traits in livestock、Genetics and Plant Breeding

BACKGROUND: Drought is a major abiotic stress in sub-Saharan Africa, impacting maize growth and development leading to severe yield loss. Drought tolerance is a complex trait regulated by multiple genes, making direct grain yield selection ineffective. To dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted on a panel of 236 maize lines testcrossed and evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO, and FARMCPU) from mrMLM and GAPIT R packages. Genomic prediction with RR-BLUP model was applied on BLUEs across locations under optimum and drought conditions. RESULTS: ≥ 10%). Furthermore, 43 candidate genes were discovered and annotated. Two major candidate genes, Zm00001eb041070 closely associated with grain yield near peak QTN, qGY_DS1.1 (S1_216149215) and Zm00001eb364110 closely related to anthesis-silking interval near peak QTN, qASI_DS8.2 (S8_167256316) were identified, encoding AP2-EREBP transcription factor 60 and TCP-transcription factor 20, respectively under drought stress. Haplo-pheno analysis identified superior haplotypes for qGY_DS1.1 (S1_216149215) associated with the higher grain yield under drought stress. Genomic prediction revealed moderate to high prediction accuracies under optimum and drought conditions. CONCLUSION: The lines carr...