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Updated: Jun 4, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Combined Genome-Wide Association Study and Linkage Analysis for Mining Candidate Genes for the Kernel Row Number in
Jiao Kong1, Fuyan Jiang2, Ranjan K Shaw2
1College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming 650201, China.
Abstract:
Kernel row number (KRN) is one of the key traits that significantly affect maize yield and productivity. Therefore, investigating the candidate genes and their functions in regulating KRN provides a theoretical basis and practical direction for genetic improvement in maize breeding, which is vital for increasing maize yield and understanding domestication. In this study, three recombinant inbred line (RIL) populations were developed using the parental lines AN20, YML1218, CM395, and Ye107, resulting in a multiparent population comprising a total of 490 F9 RILs. Phenotypic evaluation of the RILs for KRN was performed in three distinct environments. The heritability estimates of the RILs ranged from 81.40% to 84.16%. Genotyping-by-sequencing (GBS) of RILs identified 569,529 high-quality single nucleotide polymorphisms (SNPs). Combined genome-wide association study (GWAS) and linkage analyses revealed 120 SNPs and 22 quantitative trait loci (QTLs) which were significantly associated with KRN in maize. Furthermore, two novel candidate genes, Zm00001d042733 and Zm00001d042735, regulating KRN in maize were identified, which were located in close proximity to the significant SNP3-178,487,003 and overlapping the interval of QTL qKRN3-1. Zm00001d042733 encodes ubiquitin carboxyl-terminal hydrolase and Zm00001d042735 encodes the Arabidopsis Tóxicos en Levadura family of proteins. This study identified novel candidate loci and established a theoretical foundation for further functional validation of candidate genes. These findings deepen our comprehension of the genetic mechanisms that underpin KRN and offer potential applications of KRN-related strategies in developing maize varieties with higher yield.
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