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Agronomic traits are important determinants to rice yield, which are controlled by complex genetic factors as well as genotype by environment(G×E) interaction effects. The G×E effects for agronomic traits of rice have been dissected with various approaches, but not with the current available approach, the association studies. In this study, a total of 32 655 single nucleotide polymorphisms were used to carry out associations with 14 agronomic traits among 20 rice accessions in two environments. The G×E interaction effects for all the agronomic traits were at highly significant levels(P < 0.01), accounting for 3.4%–22.3% of the total sum of squares except for the length of brown rice. Twenty three putative quantitative trait loci(QTLs), including five previously known and several new promising associations, were identified for 10 of 14 traits. Analysis of the relationships between the traits for which QTLs and the genotype effects could be identified suggested that the higher the genotypic effect, the higher the possibility to identify QTLs for the given trait. The new QTLs detected in this study will facilitate dissection of the complex agronomic traits and may give insight into the G ×E effects with association mapping.
Agronomic traits are important determinants to rice yield, which are controlled by complex genetic factors as well as genotype by environment (G × E) interaction effects. The G × E effects for agronomic traits of rice have been dissected with various approaches, but not with the current available approach, the association studies. In this study, a total of 32 655 single nucleotide polymorphisms were used to carry out associations with 14 agronomic traits among 20 rice accessions in two environments. The G × E interaction effects for all the agronomic traits accounting for 3.4% -22.3% of the total sum of squares except for the length of brown rice. Twenty three putative quantitative trait loci (QTLs), including five previously known and several new promising associations, were identified for 10 of 14 traits. Analysis of the relationships between the traits for which QTLs and the genotype effects could be identified suggest that the higher the g enotypic effect, the higher the possibility to identify QTLs for the given trait. The new QTLs detected in this study will facilitate dissection of the complex agronomic traits and may give insight into the G × E effects with association mapping.