数字农科院2.0

GWAS and GS analysis revealed the selection and prediction efficiency for yield, plant morphological, and fiber quality in Gossypium barbadense

文献类型： 外文期刊

作者： Tao Yang;Honggang Wang;Jikun Song;Kang Zhao;Bo Pang;Yongpan Wang;Ping Luo;Weiwei Liang;Shunyu Shi;Jie Wang;Yifeng Lin;Jing Li;Zhenrui Wang;Yongqin Guo;Wenwei Gao

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期刊名称： Theoretical and Applied Genetics

ISSN： 0040-5752

年卷期： 2025 年 138 卷 7 期

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收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Key message: Genetic variation in a Gossypium barbadense population was revealed using resquencing. GWAS and RNA-seq on Gossypium barbadense population identified several candidate genes associated with fiber length, micronaire, and elongation. Abstract: Sea Island cotton (Gossypium barbadense), a premier tetraploid cotton species, is globally renowned for its fibers, which exhibit thermal expansion and contraction properties similar to those of animal fibers such as cashmere. Despite its significance, there remains a limited understanding of how genes influence primary traits across germplasms and the relationship between predictive factors identified through genomic selection (GS) technology and heritability. This study aimed to address this academic gap. A total of 203 Sea Island cotton accessions were incorporated for resequencing. Population evolution analysis revealed three distinct groups, which were largely shaped by geographical distribution and breeding objectives. Then, genome-wide association study (GWAS) was performed on 15 traits related to yield, fiber quality, and plant morphological, identifying a greater number of loci associated with fiber quality traits that exhibited higher broad sense heritability. Transcriptomic and gene expression analysis identified six key genes involved in regulating fiber length (GB_A05G1764 and GB_A05G1761), fiber micronaire (GB_A05G1895 and GB_A05G1771), and fiber elongation (GB_A05G1702 and GB_A05G1707). Furthermore, geographical and temporal analyses indicated that these traits underwent directional selection in Sea Island cotton. In addition, this study explored the effects of marker density, significant marker quantities, and population size on prediction accuracy using GS technology, finding that traits with higher broad sense heritability, such as fiber quality, achieved higher prediction accuracy, while those with lower broad sense heritability, such as plant morphological, showed reduced accuracy. This study provides an important reference for future GS breeding, in addition to deepening the scientific understanding of the genetic evolution of Sea Island cotton.

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