Identification of Quality-Related Genomic Regions and Candidate Genes in Silage Maize by Combining GWAS and Meta-Analysis

TL;DR

This study combines genetic analysis methods to find genes and regions in silage maize that affect its quality traits like protein and starch content.

Contribution

The study introduces a combined GWAS and meta-analysis approach to identify key genomic regions and candidate genes for silage maize quality.

Findings

27 significant SNPs and 87 consensus QTLs were identified, with 7 linked to multiple quality traits.

One SNP overlapped with a QTL interval related to crude protein, fiber, and starch content.

300 and 5669 candidate genes were predicted through GWAS and meta-analysis, highlighting key metabolic pathways.

Abstract

Enhancing quality traits is a primary objective in silage maize breeding programs. The use of genome-wide association studies (GWAS) for quality traits, in combination with the integration of genetic resources, presents an opportunity to identify crucial genomic regions and candidate genes influencing silage maize quality. In this study, a GWAS was conducted on 580 inbred lines of silage maize, and a meta-analysis was performed on 477 quantitative trait loci (QTLs) from 34 studies. The analysis identified 27 significant single nucleotide polymorphisms (SNPs) and 87 consensus QTLs (cQTLs), with 7 cQTLs associated with multiple quality traits. By integrating the SNPs identified through association mapping, one SNP was found to overlap with the cQTL interval related to crude protein, neutral detergent fiber, and starch content. Furthermore, enrichment analysis predicted 300 and 5669 candidate genes through GWAS and meta-analysis, respectively, highlighting pathways such as cellular metabolism, the biosynthesis of secondary metabolites, ribosome function, carbon metabolism, protein processing in the endoplasmic reticulum, and amino acid biosynthesis. The examination of 13 candidate genes from three co-located regions revealed Zm00001d050977 as a cytochrome P450 family gene, while the other 2 genes primarily encode proteins involved in stress responses and other biological pathways. In conclusion, this research presents a methodology combining GWAS and meta-analysis to identify genomic regions and potential genes influencing quality traits in silage maize. These findings serve as a foundation for the identification of significant QTLs and candidate genes crucial for improving silage maize quality.

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