Abdelraheem A, Esmaeili N, O’Connell M, Zhang J. 2019. Progress and perspective on drought and salt stress tolerance in cotton. Industrial Crops and Products, 130, 118-129.

Abdelraheem A, Liu F, Song M, Zhang J F. 2017. A meta-analysis of quantitative trait loci for abiotic and biotic stress resistance in tetraploid cotton. Molecular Genetics and Genomics, 292, 1221-1235.

Acuña-Galindo M A, Mason R E, Subramanian N K, Hays D B. 2015. Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Science, 55, 477-492.

Akohoue F, Miedaner T. 2022. Meta-analysis and co-expression analysis revealed stable QTL and candidate genes conferring resistances to Fusarium and Gibberella ear rots while reducing mycotoxin contamination in maize. Frontiers in Plant Science, 13, 1050891.

An C, Jenkins J N, Wu J, Guo Y, McCarty J C. 2010. Use of fiber and fuzz mutants to detect QTL for yield components, seed, and fiber traits of upland cotton. Euphytica, 172, 21-34.

Anilkumar C, Sah R P, Muhammed Azharudheen T P, Behera S, Singh N, Prakash N R, Sunitha N C, Devanna B N, Marndi B C, Patra B C, Nair S K. 2022. Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification. Scientific Reports, 12, 13832.

Arcade A, Labourdette A, Falque M, Mangin B, Chardon F, Charcosset A, Joets J. 2004. BioMercator: Integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics, 20, 2324-2326.

Bournonville C F, Díaz-Ricci J C. 2011. Quantitative determination of superoxide in plant leaves using a modified NBT staining method. Phytochemical Analysis, 22, 268-271.

Castro D, Contreras L M, Kurz L, Wilkesman J. 2017. Detection of guaiacol peroxidase on electrophoretic gels. Methods in Molecular Biology, 1626, 199-204.

Cervilla L M, Blasco B, Ríos J J, Romero L, Ruiz J M. 2007. Oxidative stress and antioxidants in tomato (Solanum lycopersicum) plants subjected to boron toxicity. Annals of Botany, 100, 747-756.

Goffinet B, Gerber S. 2000. Quantitative trait loci: a meta-analysis. Genetics, 155, 463-473.

Darvasi A, Soller M. 1997. A simple method to calculate resolving power and confidence interval of QTL map location. Behavior Genetics, 27, 125-132.

Fan Y, Shabala S, Ma Y, Xu R, Zhou M. 2015. Using QTL mapping to investigate the relationships between abiotic stress tolerance (drought and salinity) and agronomic and physiological traits. BMC Genomics, 16, 43.

Giannopolitis C N, Ries S K. 1977. Superoxide dismutases: II. Purification and quantitative relationship with water-soluble protein in seedlings. Plant Physiology, 59, 315-318.

Guo B, Sleper D, Lu P, Shannon J, Nguyen H, Arelli P. 2006. QTLs associated with resistance to soybean cyst nematode in soybean: Meta-analysis of QTL locations. Crop Science, 46, 595-602.

Guo K, Chen T, Zhang P, Liu Y, Che Z, Shahinnia F, Yang D. 2023. Meta-QTL analysis and in-silico transcriptome assessment for controlling chlorophyll traits in common wheat. Plant Genome, 16, e20294.

Haigler C H, Betancur L, Stiff M R, Tuttle J R. 2012. Cotton fiber: A powerful single-cell model for cell wall and cellulose research. Frontiers in Plant Science, 3, 104.

Han Z, Ku L, Zhang Z, Zhang J, Guo S, Liu H, Zhao R, Ren Z, Zhang L, Su H, Dong L, Chen Y. 2014. QTLs for seed vigor-related traits identified in maize seeds germinated under artificial aging conditions. PLoS ONE, 9, e92535.

Hashimoto M, Endo T, Peltier G, Tasaka M, Shikanai T. 2003. A nucleus‐encoded factor, CRR2, is essential for the expression of chloroplast ndhB in Arabidopsis. The Plant Journal, 36, 541-549.

Hu Y, Chen J, Fang L, Zhang Z, Ma W, Niu Y, Ju L, Deng J, Zhao T, Lian J. 2019. Gossypium barbadense and Gossypium hirsutum genomes provide insights into the origin and evolution of allotetraploid cotton. Nature Genetics, 51, 739-748.

Hund A, Fracheboud Y, Soldati A, Frascaroli E, Salvi S, Stamp P. 2004. QTL controlling root and shoot traits of maize seedlings under cold stress. Theoretical and Applied Genetics, 109, 618-629.

Joshi G, Soe Y P, Palanog A, Hore T K, Nha C T, Calayugan M I, Inabangan-Asilo M A, Amparado A, Pandey I D, Cruz P C S, Hernandez J E, Swamy B P M. 2023. Meta-QTLs and haplotypes for efficient zinc biofortification of rice. Plant Genome, 16, e20315.

Kumar S, Singh V P, Saini D K, Sharma H, Saripalli G, Kumar S, Balyan H S, Gupta P K. 2021. Meta-QTLs, ortho-MQTLs, and candidate genes for thermotolerance in wheat (Triticum aestivum L.). Molecular Breeding, 41, 69.

Kumar V, Goyal V, Mandlik R, Kumawat S, Sudhakaran S, Padalkar G, Rana N, Deshmukh R, Roy J, Sharma T R, Sonah H. 2022. Pinpointing genomic regions and candidate genes associated with seed oil and protein content in soybean through an integrative transcriptomic and QTL Meta-analysis. Cells, 12, 97.

Liu H, Mullan D, Zhang C, Zhao S, Li X, Zhang A, Lu Z, Wang Y, Yan G. 2020. Major genomic regions responsible for wheat yield and its components as revealed by meta-QTL and genotype-phenotype association analyses. Planta, 252, 65.

Liu R, Gong J, Xiao X, Zhang Z, Li J, Liu A, Lu Q, Shang H, Shi Y, Ge Q, Iqbal M S, Deng X, Li S, Pan J, Duan L, Zhang Q, Jiang X, Zou X, Hafeez A, Chen Q, et al. 2018. GWAS analysis and QTL identification of fiber quality traits and yield components in upland cotton using enriched high-density SNP markers. Frontiers in Plant Science, 9, 1067.

Maryum Z, Luqman T, Nadeem S, Khan S, Wang B, Ditta A, Khan M K R. 2022. An overview of salinity stress, mechanism of salinity tolerance and strategies for its management in cotton. Frontiers in Plant Science, 13, 907937.

Parida A K, Dagaonkar V S, Phalak M S, Umalkar G V, Aurangabadkar L P. 2007. Alterations in photosynthetic pigments, protein and osmotic components in cotton genotypes subjected to short-term drought stress followed by recovery. Plant Biotechnology Reports, 1, 37-48.

Qian F, Jing J, Zhang Z, Chen S, Sang Z, Li W. 2023. GWAS and meta-QTL analysis of yield-related ear traits in maize. Plants (Basel), 12, 3806.

Quraishi U M, Pont C, Ain Q U, Flores R, Burlot L, Alaux M, Quesneville H, Salse J. 2017. Combined genomic and genetic data integration of major agronomical traits in bread wheat (Triticum aestivum L.). Frontiers in Plant Science, 8, 1843.

Ramel F, Sulmon C, Bogard M, Couée I, Gouesbet G. 2009. Differential patterns of reactive oxygen species and antioxidative mechanisms during atrazine injury and sucrose-induced tolerance in Arabidopsis thaliana plantlets. BMC Plant Biology, 9, 1-18.

Ramírez-Tejero J A, Jiménez-Ruiz J, Serrano A, Belaj A, León L, de la Rosa R, Mercado-Blanco J, Luque F. 2021. Verticillium wilt resistant and susceptible olive cultivars express a very different basal set of genes in roots. BMC Genomics, 22, 229.

Reinisch A J, Dong J M, Brubaker C L, Stelly D M, Wendel J F, Paterson A H. 1994. A detailed RFLP map of cotton, Gossypium hirsutum × Gossypium barbadense: Chromosome organization and evolution in a disomic polyploid genome. Genetics, 138, 829-847.

Rogers G M, Poore M H, Paschal J C. 2002. Feeding cotton products to cattle. Veterinary Clinics (Food Animal Practice), 18, 267-294.

Said J I, Lin Z, Zhang X, Song M, Zhang J. 2013. A comprehensive meta QTL analysis for fiber quality, yield, yield related and morphological traits, drought tolerance, and disease resistance in tetraploid cotton. BMC Genomics, 14, 776.

Said J I, Song M, Wang H, Lin Z, Zhang X, Fang D D, Zhang J. 2015. A comparative meta-analysis of QTL between intraspecific Gossypium hirsutum and interspecific G. hirsutum × G. barbadense populations. Molecular Genetics and Genomics, 290, 1003-1025.

Saini D K, Srivastava P, Pal N, Gupta P K. 2022. Meta-QTLs, ortho-meta-QTLs and candidate genes for grain yield and associated traits in wheat (Triticum aestivum L.). Theoretical and Applied Genetics, 135, 1049-1081.

Semagn K, Beyene Y, Warburton M L, Tarekegne A, Mugo S, Meisel B, Sehabiague P, Prasanna B M. 2013. Meta-analyses of QTL for grain yield and anthesis silking interval in 18 maize populations evaluated under water-stressed and well-watered environments. BMC Genomics, 14, 313.

Sosnowski O, Charcosset A, Joets J. 2012. BioMercator V3: An upgrade of genetic map compilation and quantitative trait loci meta-analysis algorithms. Bioinformatics, 28, 2082-2083.

Su J, Li D, Yuan W, Li Y, Ju J, Wang N, Ling P, Feng K, Wang C. 2024. Integrating RTM-GWAS and meta‑QTL data revealed genomic regions and candidate genes associated with the first fruit branch node and its height in upland cotton. Theoretical and Applied Genetics, 137, 207.

Sukumaran S, Reynolds M P, Sansaloni C. 2018. Genome-wide association analyses identify QTL hotspots for yield and component traits in durum wheat grown under yield potential, drought, and heat stress environments. Frontiers in Plant Science, 9, 81.

Veyrieras J B, Goffinet B, Charcosset A. 2007. MetaQTL: A package of new computational methods for the meta-analysis of QTL mapping experiments. BMC Bioinformatics, 8, 49.

Wang C X, Li M L, Zhang D G, Zhang X L, Liu J J, Su J J. 2024. Knockdown of the atypical protein kinase genes GhABC1K2-A05 and GhABC1K12-A07 make cotton more sensitive to salt and PEG stress. Journal of Integrative Agriculture, 23, 3370-3386.

Welcker C, Sadok W, Dignat G, Renault M, Salvi S, Charcosset A, Tardieu F. 2011. A common genetic determinism for sensitivities to soil water deficit and evaporative demand: Meta-analysis of quantitative trait Loci and introgression lines of maize. Plant Physiology, 157, 718-729.

Xie D F, Cheng R Y, Fu X, Zhang X Y, Price M, Lan Y L, Wang C B, He X J. 2021. A combined morphological and molecular evolutionary analysis of karst-environment adaptation for the genus Urophysa (Ranunculaceae). Frontiers in Plant Science, 12, 667988.

Xu J, Liu Y, Liu J, Cao M, Wang J, Lan H, Xu Y, Lu Y, Pan G, Rong T. 2012. The genetic architecture of flowering time and photoperiod sensitivity in maize as revealed by QTL review and meta analysis. Journal of Integrative Plant Biology, 54, 358-373.

Yang Y, Amo A, Wei D, Chai Y, Zheng J, Qiao P, Cui C, Lu S, Chen L, Hu Y G. 2021. Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat. Theoretical and Applied Genetics, 134, 3083-3109.

Yu J, Jung S, Cheng C H, Lee T, Zheng P, Buble K, Crabb J, Humann J, Hough H, Jones D, Campbell J T, Udall J, Main D. 2021. CottonGen: The community database for cotton genomics, genetics, and breeding research. Plants (Basel), 10, 2805.

Yuan W, Li Y, Zhang W, Ju J, Guo X, Yang J, Lin H, Wang C, Ma Q, Su J. 2025. Pinpointing MQTLs and candidate genes related to early maturity in upland cotton through the integration of meta‑analysis, RNA-seq, and VIGS approaches. Industrial Crops and Products, 223, 120195.

Zhang J, Yu J, Pei W, Li X, Said J, Song M, Sanogo S. 2015. Genetic analysis of Verticillium wilt resistance in a backcross inbred line population and a meta-analysis of quantitative trait loci for disease resistance in cotton. BMC Genomics, 16, 577.

Zhao L, Lü Y, Chen W, Yao J, Li Y, Li Q, Pan J, Fang S, Sun J, Zhang Y. 2020. Genome-wide identification and analyses of the AHL gene family in cotton (Gossypium). BMC Genomics, 21, 69.

Zhao X, Peng Y, Zhang J, Fang P, Wu B. 2018. Identification of QTLs and meta-QTLs for seven agronomic traits in multiple maize populations under well-watered and water-stressed conditions. Crop Science, 58, 507-520.