Multi-parent mapping reveals epistatic architecture and elite haplotypes for multi-trait improvement in cotton

Area of Science:

• Plant genetics
• Crop breeding
• Genomics

Background:

• Cotton domestication has led to genetic trade-offs between yield and fiber quality, limiting simultaneous trait improvement.
• Cryptic loci and genetic interactions remain underexplored in conventional breeding programs.

Purpose of the Study:

• To develop a Multi-Parent Advanced Generation Intercross (MAGIC) population for high-resolution genetic dissection in cotton.
• To construct an integrative framework for optimizing multiple agronomic traits and resolving genetic trade-offs.
• To identify genomic regions shaped by modern breeding selection in Xinjiang cotton cultivars.

Main Methods:

• Development of an eight-parent MAGIC population (319 recombinant inbred lines) and integration with 318 Xinjiang cotton cultivars.
• High-resolution genomic analyses using SNP-based and identity-by-descent mapping to identify significant quantitative trait loci (sQTLs and hQTLs).
• Genome-wide epistasis analysis to uncover cryptic loci and population genomic analyses of breeding selection.

Main Results:

• Identification of 111 sQTLs and 15 hQTLs associated with 19 agronomic traits across multiple environments.
• Discovery of over 3,000 cryptic loci, revealing complex genetic interactions influencing trait variation.
• Identification of 520 genomic regions under selection in modern cotton breeding and validation of a multi-trait optimization framework.

Conclusions:

• MAGIC populations offer dual utility for high-resolution genetic dissection and practical breeding applications.
• The presented integrative framework provides a scalable strategy for addressing genetic trade-offs.
• This approach facilitates coordinated multi-trait improvement in cotton and other essential crops.