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Analysis of the genetic basis of fiber-related traits and flowering time in upland cotton using machine learning

文献类型： 外文期刊

作者： Li, Weinan 1 ; Zhang, Mingjun 2 ; Fan, Jingchao 3 ; Yang, Zhaoen 2 ; Peng, Jun 1 ; Zhang, Jianhua 1 ; Lan, Yubin 4 ; Chai, Mao 2 ;

作者机构： 1.Chinese Acad Agr Sci, Nanfan Res Inst, Sanya 572024, Hainan, Peoples R China

2.Chinese Acad Agr Sci, State Key Lab Cotton Biobreeding & Integrated Util, Inst Cotton Res, Anyang 455000, Henan, Peoples R China

3.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China

4.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou 510642, Guangdong, Peoples R China

期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：4.2； 五年影响因子：4.9 ）

ISSN： 0040-5752

年卷期： 2025 年 138 卷 1 期

页码：

收录情况： SCI

摘要： Cotton is an important crop for fiber production, but the genetic basis underlying key agronomic traits, such as fiber quality and flowering days, remains complex. While machine learning (ML) has shown great potential in uncovering the genetic architecture of complex traits in other crops, its application in cotton has been limited. Here, we applied five machine learning models-AdaBoost, Gradient Boosting Regressor, LightGBM, Random Forest, and XGBoost-to identify loci associated with fiber quality and flowering days in cotton. We compared two SNP dataset down-sampling methods for model training and found that selecting SNPs with an Fscale value greater than 0 outperformed randomly selected SNPs in terms of model accuracy. We further performed machine learning quantitative trait loci (mlQTLs) analysis for 13 traits related to fiber quality and flowering days. These mlQTLs were then compared to those identified through genome-wide association studies (GWAS), revealing that the machine learning approach not only confirmed known loci but also identified novel QTLs. Additionally, we evaluated the effect of population size on model accuracy and found that larger population sizes resulted in better predictive performance. Finally, we proposed candidate genes for the identified mlQTLs, including two argonaute 5 proteins, Gh_A09G104100 and Gh_A09G104400, for the FL3/FS2 locus, as well as GhFLA17 and Syntaxin-121 (Gh_D09G143700) for the FSD09_2/FED09_2 locus. Our findings demonstrate the efficacy of machine learning in enhancing the identification of genetic loci in cotton, providing valuable insights for improving cotton breeding strategies.

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