Meta-QTLs and candidate genes for kernel protein content in maize

Area of Science:

• Plant Genetics
• Agricultural Science
• Bioinformatics

Background:

• Maize kernel protein content is insufficient for nutritional needs, impacting feed efficiency and plant-based diets.
• Previous quantitative trait loci (QTL) studies identified many regions but lacked precision due to broad confidence intervals and variability.
• Identifying reliable candidate genes for protein enhancement in maize has been challenging.

Purpose of the Study:

• To conduct a comprehensive meta-QTL (MQTL) analysis to refine the genetic architecture of maize kernel protein content.
• To identify high-precision MQTLs and core candidate genes for improving protein accumulation in maize.
• To provide a foundation for functional genomics and marker-assisted breeding for enhanced maize protein.

Main Methods:

• Integrated data from 25 QTL studies, comprising 258 initial QTLs for maize kernel protein content.
• Constructed a high-density consensus genetic map using 23 genetic maps and 19,836 markers.
• Performed MQTL analysis, integrated genome-wide association studies (GWAS) data, and conducted homology analysis across cereal crops.

Main Results:

• Identified 67 MQTLs, reducing confidence interval size by an average of 2.51-fold.
• Achieved high mapping precision with 18 MQTLs having physical intervals <1 Mb.
• Validated MQTLs by colocation with 15 GWAS signals and identified 54 core candidate genes, including homologs with known roles in protein accumulation.

Conclusions:

• Meta-QTL analysis is effective in refining complex trait architecture and increasing mapping precision for maize kernel protein content.
• The identified MQTLs and candidate genes provide valuable resources for future functional studies and genetic improvement of maize protein.
• This study lays the groundwork for developing maize varieties with improved nutritional value and enhanced feed efficiency.