Genome-wide association studies and whole-genome prediction reveal the genetic architecture of KRN in maize

BMC Plant Biology

Yong-Xiang Li¹  Chunhui Li¹  Yu Li¹  Dengfeng Zhang¹  Yixin An¹  Lin Chen¹  Tianyu Wang¹  Yunsu Shi¹ 

[1] Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, 100081, Beijing, China;

关键词: Maize;    Kernel row number;    Genome-wide association study;    Quantitative trait nucleotide;    Whole-genome prediction;   

DOI  :  10.1186/s12870-020-02676-x

来源: Springer

【 摘 要 】

BackgroundKernel row number (KRN) is an important trait for the domestication and improvement of maize. Exploring the genetic basis of KRN has great research significance and can provide valuable information for molecular assisted selection.ResultsIn this study, one single-locus method (MLM) and six multilocus methods (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB and ISIS EM-BLASSO) of genome-wide association studies (GWASs) were used to identify significant quantitative trait nucleotides (QTNs) for KRN in an association panel including 639 maize inbred lines that were genotyped by the MaizeSNP50 BeadChip. In three phenotyping environments and with best linear unbiased prediction (BLUP) values, the seven GWAS methods revealed different numbers of KRN-associated QTNs, ranging from 11 to 177. Based on these results, seven important regions for KRN located on chromosomes 1, 2, 3, 5, 9, and 10 were identified by at least three methods and in at least two environments. Moreover, 49 genes from the seven regions were expressed in different maize tissues. Among the 49 genes, ARF29 (Zm00001d026540, encoding auxin response factor 29) and CKO4 (Zm00001d043293, encoding cytokinin oxidase protein) were significantly related to KRN, based on expression analysis and candidate gene association mapping. Whole-genome prediction (WGP) of KRN was also performed, and we found that the KRN-associated tagSNPs achieved a high prediction accuracy. The best strategy was to integrate all of the KRN-associated tagSNPs identified by all GWAS models.ConclusionsThese results aid in our understanding of the genetic architecture of KRN and provide useful information for genomic selection for KRN in maize breeding.

【 授权许可】

CC BY