Association mapping for general combining ability with yield, plant height and ear height using F1 population in maize

Abstract

General combining ability (GCA) is an important index for inbred lines breeding of maize. To identify the genetic loci of GCA and associated agronomic traits, an association analysis with 195 SSRs was made in phenotypic traits of 240 F1 derived from 120 elite inbred lines containing current breeding resources of maize crossed with 2 testers (Zheng58 and Chang7-2) in two places in 2018. All of the 20 association loci detected for grain yield (GY), plant height (PH), ear height (EH) and GCA for the three traits in two places could explain a phenotypic variation range of 7.31%-9.29%. Among the 20 association loci, 9 (7.31%-9.04%) were associated with GY, 4 (7.22%-8.91%) were related to GCA of GY, 1 (7.56%) was associated with PH, and 3 (7.53%-8.96%) were related to EH. In addition, 3 loci (9.14%-9.29%) were associated with GCA of PH whereas no locus was identified for GCA of EH. In the comparison of the association loci detected in Baoding and Handan, interestingly, one locus (7.69% and 8.11%) was identified in both environments and one locus (7.52% and 7.82%) was identified for yield and GCA of yield. Therefore, the identification of GY-, PH-, EH- and GCA-related association loci could not only provide references for high yield breeding of maize, but also help us comprehend the relationships among GY, agricultural traits and GCA.

Genome-Wide Association Analysis Unravels New Quantitative Trait Loci (QTLs) for Eight Lodging Resistance Constituent Traits in Rice (Oryza sativa L.)

“…Gene diversity, summary statistics, allele frequency, number of alleles per locus, and the polymorphism information content (PIC) were assessed using Power Marker V3.25, as described in the following citations [28][29][30][31]. To determine the genetic relationships between individuals, pairwise relatedness coefficients (K, kinship matrix) were computed using SPAGeDi55 (Spatial Pattern Analysis of Genetic Diversity).…”

Genome-Wide Association Analysis Unravels New QTLs for Eight Lodging Resistance Constituent Traits in Rice (<em>Oryza sativa</em> L.)

“…Gene diversity, summary statistics, allele frequency, number of alleles per locus, and the polymorphism information content (PIC) were assessed using Power Marker V3.25, as described in the following citations [29][30][31][32][33]. To determine the genetic relationships between individuals, pairwise relatedness coefficients (K, kinship matrix) were computed using SPAGeDi55 (Spatial Pattern Analysis of Genetic Diversity).…”

Combining datasets for maize root seedling traits increases the power of GWAS and genomic prediction accuracies

“…Genome-wide association study (GWAS) is a powerful tool for gene mapping in plants and animals, and has been widely used for genetic dissection of complex quantitative traits in many major crop species ( Xiao et al , 2017 ; Liu and Yan, 2019 ; Cortes et al , 2021 ). In maize, GWAS has been successfully employed to understand the genetic basis of, and to identify candidate genes related to, flowering time and plant height ( Vanous et al , 2018 ; Li et al , 2020 ; Zheng et al , 2021a ), stalk traits ( Zhang et al , 2018 ; Mazaheri et al , 2019 ), tassel architecture ( Wu et al , 2016 ; Y. Wang et al , 2019 ), nitrogen use efficiency ( Morosini et al , 2017 ; Ertiro et al , 2020 ), low-phosphorus tolerance ( Xu et al , 2018 ; Q. J. Wang et al , 2019 ), cold tolerance ( Yi et al , 2021 ), drought tolerance ( Zaidi et al , 2016 ; Yuan et al , 2019 ; Liu and Qin, 2021 ), grain yield and yield-related traits ( Zhang et al , 2020a ; Yang et al , 2020 ; Ma and Cao, 2021 ), and grain quality ( Zhang et al , 2020b ; Zheng et al , 2021b ; Zhou et al , 2021 ). Moreover, several candidate genes involved in genetic control of root-related traits have been identified in maize by a GWAS approach.…”