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Construction of a high-density genetic map using genotyping by sequencing (GBS) for quantitative trait loci (QTL) analysis of three plant morphological traits in upland cotton (Gossypium hirsutum L.)
- Qi, Haikun, Wang, Ning, Qiao, Wenqing, Xu, Qinghua, Zhou, Hong, Shi, Jianbin, Yan, Gentu, Huang, Qun
- Euphytica 2017 v.213 no.4 pp. 83
- Gossypium hirsutum, chromosome mapping, chromosomes, cotton, crop production, crops, fruiting, genotyping by sequencing, grains, marker-assisted selection, mechanical harvesting, mechanization, parents, plant breeding, quantitative trait loci, single nucleotide polymorphism, subsidies, China
- In recent years, the production costs of cotton (Gossypium hirsutum L.) in China have continued to rise, and this has been accompanied by relatively low productivity, diminished enthusiasm of Chinese farmers for planting cotton, and the difficulty caused by high subsidies as well as the high degree of mechanized harvesting for competing crops like grains. Therefore, it is urgent to improve the level of mechanization and the scale of cotton production in China. Morphological traits play an important role in the mechanized harvesting of cotton. Plant height (PH), height of the first fruiting branch node (HFFBN), and the number of vegetative shoot (NOVS) are key cotton morphological traits that influence mechanical harvesting. The genetic basis of PH, HFFBN, and NOVS were examined in the Z571 and CCRI 49 parents as well as 188 individuals comprising the F₂ mapping population. This F₂ population was examined using genotyping by sequencing (GBS) with 5571 high-density polymorphism single nucleotide polymorphism (SNP) markers to construct a genetic linkage map comprised of 3187 polymorphic markers. The genetic map spanned 3828.551 cM, with an average distance of 0.687 cM between markers. The complete interval mapping method identified 17 quantitative trait loci (QTL) for PH, HFFBN, and NOVS located on chromosomes 3, 4, 5, 7, 9, 17, 19, 23, and 25. Our study provides an efficient approach for fast detection of QTL underlying complex trait variation with high accuracy, thus providing preliminary information that can improve the efficiency of subsequent machine cotton picking through breeding and molecular marker-assisted selection methods.