Genome-wide association studies for maize yield at three contrasting sites

• Autor(a) principal: Dashe, Dentsen Fortune
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: LOCUS Repositório Institucional da UFV
• Texto Completo: https://locus.ufv.br//handle/123456789/32095; https://doi.org/10.47328/ufvbbt.2024.001
• topic: Milho - Melhoramento genético; Mapeamento cromossômico - Métodos estatísticos; Genética e Melhoramento

In the present study, genome-wide association study (GWAS) was used to identify marker association in three different environments. The study analyzed the 2018 maize dataset from the genome-2-field (G2F) initiative for yield trait marker association using three bi-parental populations and a total of 936 hybrid individuals. The GWAS analysis was performed by implementing two models: generalized linear model (GLM) (population structure (Q) accounted for), and mixed linear model (MLM) (population structure (Q) and kinship (K) accounted for), in order to minimize spurious associations and control for types I and II error rates. A total of 791 significantly associated SNPs were detected for the yield trait (p>0.0001) but overlaps in SNPs were identified from the different models in each site and thereafter, a total of 39 unique loci overlapped from the 791 loci from both models in all three sites were noted, out of which 25 loci were located on chromosome 1, 1 on chromosome 3, 6 on chromosome 7 and 7 on chromosome 8. The presence of these unique loci across the two models and three diverse environments suggests their robustness and potential relevance and warrants further investigation into the specific genetic factors and molecular mechanisms underlying their association with maize yield across varying growing conditions. Therefore further validation study and fine mapping of these loci will provide valuable information for understanding the genetic and environmental components of grain yield in maize. Keywords: GWAS. Zea mays. Principal components. MLM. GLM.