Identification of Stable Meta-QTLs and Candidate Genes Underlying Fiber Quality and Agronomic Traits in Cotton

TL;DR

This study identifies stable genetic regions and candidate genes in cotton that influence fiber quality and stress tolerance, offering tools for crop improvement.

Contribution

The study introduces 14 novel Meta-QTLs and identifies 75 candidate genes linked to fiber traits and stress resilience in cotton.

Findings

Integration of 2162 high-confidence QTLs led to the identification of 75 Meta-QTLs across the cotton genome.

14 MQTLs were newly reported, with some linked to fiber quality and stress tolerance traits.

Candidate gene analysis revealed 75 genes, 38 of which are associated with lignin catabolism, flavin binding, and stress responses.

Abstract

Cotton is a globally important crop, with fiber quality traits governed by complex quantitative trait loci (QTL). However, the utility of QTL data is often limited due to inconsistencies across studies. This study conducted a comprehensive Meta-QTL (MQTL) analysis by integrating 2864 QTLs from 50 independent studies published between 2000 and 2024. Of these, 2162 high-confidence QTLs were projected onto a consensus genetic map using BioMercator V4.2.3, resulting in the identification of 75 MQTLs across the cotton genome. These MQTLs exhibited significantly reduced confidence intervals and enhanced statistical support, with 14 MQTLs reported for the first time. Several MQTLs, including MQTLchr7-1, MQTLchr14-1, and MQTLchr24-1, were identified as stable clusters harboring key fiber quality and stress tolerance traits. Candidate gene analysis within select MQTL regions revealed 75 genes, 38 of which were annotated with significant gene ontology terms related to lignin catabolism, flavin binding, and stress responses. Notably, GhLAC-4, GhCTL2, and UDP-glycosyltransferase 92A1 were highlighted for their potential roles in fiber development and abiotic stress tolerance. These findings provide a refined genomic framework for cotton improvement and offer valuable resources for marker-assisted selection (MAS) and functional genomics aimed at enhancing fiber quality, yield, and stress resilience in cotton breeding programs.

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