Field Crop 2024, Vol.7, No.6, 325-333 http://cropscipublisher.com/index.php/fc 325 Research Insight Open Access Meta-Analysis of Yield-Related Genetic Markers in Cotton Shanjun Zhu, Mengting Luo Institute of Life Science, Jiyang College of Zhejiang A&F University, Zhuji, 311800, Zhejiang, China Corresponding email: mengting.luo@jicat.org Field Crop, 2024, Vol.7, No.6 doi: 10.5376/fc.2024.07.0033 Received: 08 Nov., 2024 Accepted: 12 Dec., 2024 Published: 25 Dec., 2024 Copyright © 2024 Zhu and Luo, 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. Preferred citation for this article: Zhu S.J., and Luo M.T., 2024, Meta-analysis of yield-related genetic markers in cotton, Field Crop, 7(6): 325-333 (doi: 10.5376/fc.2024.07.0033) Abstract This study presents a meta-analysis of yield-related genetic markers in cotton, focusing on their historical development, current applications, and future prospects. Advances in genetic marker technologies, including the use of SSRs, SNPs, and high-throughput sequencing, are explored, alongside their association with key yield traits such as boll size, lint weight, and plant height. Marker-assisted selection (MAS) and genomic selection (GS) are evaluated for their roles in improving breeding efficiency. A case study highlights the successful implementation of MAS in cotton breeding programs and the associated challenges. This analysis identifies significant markers and consistent quantitative trait loci (QTLs) across diverse studies, providing insights into emerging trends and practical applications. The study concludes by emphasizing the integration of advanced technologies, the importance of collaborative research, and actionable recommendations to enhance the role of genetic markers in sustainable cotton breeding. These findings offer valuable guidance for researchers and breeders aiming to address future challenges in cotton productivity. Keywords Cotton breeding; Genetic markers; Marker-assisted selection; Yield improvement; Quantitative trait loci (QTLs) 1 Introduction Cotton (Gossypium spp.) is a crucial crop globally, serving as a primary source of natural fiber for the textile industry and contributing significantly to the economies of many countries (Hussain et al., 2019; Zhang et al., 2019). It is cultivated extensively worldwide, with India being one of the largest areas for cotton cultivation, although it faces challenges in productivity (Joshi et al., 2023). The economic importance of cotton is underscored by its role in providing employment and supporting industries related to textiles and agriculture (Gu et al., 2020). Enhancing cotton yield is fraught with challenges, particularly due to environmental stresses such as drought, which significantly impact production. The genetic erosion of cotton due to a narrow genetic base further complicates breeding efforts aimed at improving yield (Hussain et al., 2019). Additionally, the variability in environmental conditions across different regions necessitates the development of cotton varieties that can maintain high yields under diverse conditions (Baytar et al., 2018; Gu et al., 2020). Genetic markers, particularly quantitative trait loci (QTLs), play a pivotal role in understanding the genetic basis of yield-related traits in cotton. These markers help in identifying genes associated with fiber quality and yield, facilitating marker-assisted selection (MAS) in breeding programs (Said et al., 2013; Li et al., 2016; Liu et al., 2022). Recent studies have identified numerous QTLs linked to yield and fiber quality traits, providing insights into the genetic architecture of these traits and aiding in the development of improved cotton varieties (Xia et al., 2014; Qin et al., 2015; Zhang et al., 2019). This study attempts to consolidate existing research on yield-related genetic markers in cotton to identify consistent QTLs that can be utilized in breeding programs, discuss the integration of data from multiple studies to provide a comprehensive understanding of the genetic factors influencing yield traits, and provide an overview of strategies to enhance the efficiency of breeding aimed at improving cotton yield and quality. The findings are expected to offer valuable insights for cotton breeders and contribute to the development of high-yielding, resilient cotton varieties.
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