Decoding the genetic basis of ear‐related traits in maize (Zea mays L.) using linkage mapping, association mapping and genomic prediction

Author:

Chang Liguo1ORCID,He Kunhui1,Liu Jianchao1

Affiliation:

1. Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, Ministry of Agriculture and Rural Affairs, College of Agronomy Northwest A&F University Yangling Shaanxi China

Abstract

AbstractAs important yield components, the genetic analysis of ear‐related traits could provide a theoretical basis for maize breeding. Here, we reported the comprehensive genetic architecture of five ear‐related traits using 150 recombinant inbred lines (RIL) populations derived from the cross between Xu178 and K12. Besides, two sets of association populations were used to dissect genetic loci of five traits by genome‐wide association study (GWAS). A total of 32 QTLs of ear‐related traits were detected in the linkage mapping. Based on the mixed linear model (MLM), a total of 117 significant SNP markers of ear‐related traits were detected. Furthermore, a combined GWAS and linkage mapping analysis revealed 51 significant SNP markers fell within the confidence interval of QTLs. A total of seven co‐located significant SNP markers among different traits were found. Finally, six important candidate genes related to grain development were screened out. In addition, through haplotype analysis, two favourable haplotypes were found on chromosome 4, which could increase row number per ear (RNE), kernel number per row (KNR) and grain yield per plant (GYP) to a certain extent. Compared with the random model, the prediction accuracy of genomic prediction (GP) was improved in different degrees by considering the significant SNP markers as fixed effects. The stable genetic QTLs, candidate genes and favourable haplotypes found in this study are valuable resources, which will provide theoretical reference for high‐yield breeding of maize. Taken together, the research results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Wiley

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