Shanjun Zhu , Mengting Luo

Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, China
Author    Correspondence author
Cotton Genomics and Genetics, 2025, Vol. 16, No. 5   doi: 10.5376/cgg.2025.16.0022
Received: 11 Jul., 2025    Accepted: 22 Aug., 2025    Published: 13 Sep., 2025

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Zhu S.J., and Luo M.T., 2025, Multi-trait GWAS for fiber quality and disease resistance in cotton, Cotton Genomics and Genetics, 16(5): 222-231 (doi: 10.5376/cgg.2025.16.0022)

 

Cotton is a globally important dual-purpose crop valued for its fiber yield, but both its yield and quality are severely impacted by a variety of pathogens. This study reviews the genetic architecture of fiber quality traits (such as strength, length, and fineness) and resistance to major diseases such as Verticillium wilt, Fusarium wilt, and bacterial wilt, focusing on potential genetic overlap and independence. We explore the methodological framework for multi-trait genome-wide association studies (MT-GWAS), highlighting statistical models such as multivariate linear mixed models and Bayesian methods, which outperform single-trait analyses by capturing pleiotropic loci and genetic correlations. We present key findings from cotton MT-GWAS, including the identification of co-localized QTLs, novel candidate genes, and genotype-by-environment interactions across multiple environmental datasets. We also highlight the integration of MT-GWAS with transcriptomic, metabolomic, epigenomic, and proteomic data, and the validation of functional genes using CRISPR, RNAi, and overexpression technologies. A case study demonstrates the practical application of MT-GWAS in a breeding program targeting fiber quality and disease resistance, enabling genetic validation and germplasm improvement. While MT-GWAS faces challenges such as population structure, statistical complexity, and translational gaps, advances in high-resolution phenotyping, pan-genomics, and predictive breeding strategies hold promise for broader application. This study highlights the potential of MT-GWAS to accelerate cotton improvement by revealing complex trait architecture and informing integrated breeding processes.

Multi-trait GWAS; Fiber quality; Disease resistance; Cotton breeding; Pleiotropy