Meta-QTL analysis reveals genomic regions associated with fiber quality and other key traits in cotton

Authors: TOSHPULATOV, ABDULQAHHOR - Uzbekistan Academy Of Sciences; TURAEV, OZOD - Uzbekistan Academy Of Sciences; ISKANDAROV, ABDULLOH - Uzbekistan Academy Of Sciences; KHALIKOV, KUVANDIK - Uzbekistan Academy Of Sciences; ARSLANOVA, SEVARA - Uzbekistan Academy Of Sciences; SAFIULLINA, ASIYA - Uzbekistan Academy Of Sciences; KUDRATOVA, MUKHLISA - Uzbekistan Academy Of Sciences; ORIPOVA, BARNO - Uzbekistan Academy Of Sciences; RAFIEVA, FERUZA - Uzbekistan Academy Of Sciences; KHOLOVA, MADINA - Uzbekistan Academy Of Sciences; ERNAZAROVA, DILRABO - Uzbekistan Academy Of Sciences; KODIROV, DAVRON - Uzbekistan Academy Of Sciences; KURBANOV, ABDUBURKHAN - Uzbekistan Academy Of Sciences; ERJIGITOV, DOSTON - Uzbekistan Academy Of Sciences; KHIDIROV, MUKHAMMAD - Uzbekistan Academy Of Sciences; Yu, John; KUSHANOV, FAKHRIDDIN - Uzbekistan Academy Of Sciences

Submitted to: Plants
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/21/2025
Publication Date: 10/24/2025
Citation: Toshpulatov, A., Turaev, O., Iskandarov, A., Khalikov, K., Arslanova, S., Safiullina, A., Kudratova, M., Oripova, B., Rafieva, F., Kholova, M., Ernazarova, D., Kodirov, D., Kurbanov, A., Erjigitov, D., Khidirov, M., Yu, J., Kushanov, F. 2025. Meta-QTL analysis reveals genomic regions associated with fiber quality and other key traits in cotton. Plants. 14(21). Article e3252. https://doi.org/10.3390/plants14213252.
DOI: https://doi.org/10.3390/plants14213252

Interpretive Summary: Cultivated worldwide as an important cash crop, cotton (Gossypium L.) is valued for its natural fiber as well as protein and oil production. Recent advances in cotton genomic research provide opportunities to identify genetic factors conferring fiber quality and other valuable traits for improving agricultural productivity. In this study, we analyzed and discovered Meta-Quantitative Trait Loci (MQTL) clusters associated with fiber quality properties, resistance to biotic stressors, and morphological, physiological, and biochemical characteristics. The meta-analysis included 1,685 Quantitative Trait Loci (QTLs) collected from multiple studies, with a focus on fiber-related traits such as fiber length, maturity, and yield. These QTLs were then mapped onto a consensus genetic map, which resulted in 71 MQTL clusters across the cotton genome, with most clusters located on chromosomes A05, D05, and D09. Within the MQTL clusters, candidate genes were predicted and categorized into biological processes, molecular functions, and cellular components, including functional proteins that are linked to fiber quality. While further validation with different genetic populations is recommended to confirm these findings, they provide a valuable resource for cotton breeding, offering insights into genomic regions that could enhance fiber quality and yield.

Technical Abstract: Cotton (Gossypium L.) is a globally important crop, that is the primary source for its natural fiber and oil production. Research on the cotton genome, particularly concerning fiber quality traits, is crucial for improving agricultural productivity. This study aimed to identify Meta-QTL (MQTL) clusters associated with various cotton traits, including fiber quality, morphological, physiological, biochemical characteristics, and resistance to biotic stresses. A meta-analysis was conducted using 1,685 QTLs collected from multiple studies, with a focus on fiber-related traits such as fiber length, maturity, and yield. Using BioMercator V4.2.3, the QTLs were mapped onto a consensus map, and MQTLs were identified using a two-step algorithm based on the average confidence interval. The results revealed 71 MQTL clusters across the cotton genome, with significant clusters located on chromosomes A05, D05, and D09. Further analysis identified several candidate genes, including GhLAC-4 and GhUDP-glycosyltransferase, linked to fiber quality. This study provides a valuable resource for cotton breeding, offering insights into genomic regions that could enhance fiber quality and yield. However, further validation with different genetic populations is recommended to confirm these findings.