A Study of Maize Genotype-Environment Interaction Based on Deep K-Means Clustering Neural Network
文献类型： 外文期刊
作者： Bai, Longpeng 1 ; Wang, Kaiyi 2 ; Zhang, Qiusi 2 ; Zhang, Qi 2 ; Wang, Xiaofeng 2 ; Pan, Shouhui 2 ; Zhang, Liyang 5 ; He, Xuliang 2 ; Li, Ran 2 ; Zhang, Dongfeng 2 ; Han, Yanyun 2 ;
作者机构： 1.Shanghai Ocean Univ, Key Lab Fisheries Informat, Minist Agr & Rural Affairs, Hucheng Ring Rd 999, Shanghai 201306, Peoples R China
2.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China
3.Natl Innovat Ctr Digital Seed Ind, Beijing 100097, Peoples R China
4.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China
5.SDIC Seed Technol Co Ltd, Beijing 100034, Peoples R China
6.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China
7.Heilongjiang Univ, Coll Modern Agr & Ecol Environm, Harbin 150080, Peoples R China
关键词： small ecological region delineation; deep k-means clustering neural network; genotype by environment interaction
期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.8 ）
ISSN：
年卷期： 2025 年 15 卷 4 期
页码：
收录情况： SCI
摘要： The phenotype (P) of a crop is determined by the genotype (G), environment (E), and genotype-by-environment (G x E) interaction, expressed as P = G + E + G x E. Thus, studying G x E interactions is essential for phenotypic research. Traditional methods of crop phenotypes and adaptability based on G x E interaction analysis, based on large ecological regions, fail to account for year-to-year environmental changes and the blurring of region boundaries, leading to inaccurate insights into the relationship between genotypes and environmental factors. To address these issues, this study divided the research area into small ecological regions through the clustering of meteorological data, providing a more accurate framework for studying G x E interactions in maize. To ascertain the optimal method for ecological region delineation, the yield variance (SYV), the Davies-Bouldin Index (DBI), and the Silhouette Index (SI) were used to evaluate and compare the performance of the K-Means, Autoencoder K-Means (Ae-KM), and Deep K-Means Clustering Neural Network (DKMCNN) methodologies. The DKMCNN surpassed other methodologies and was selected for delineation. Based on this delineation result, the interactions between genotypes and the environment on maize were investigated and clarified using genome-wide association analysis (GWAS) and analysis of variance (ANOVA). Ultimately, through the analysis of maize field trial data from 2020 to 2021, we identified up to 108 single-nucleotide polymorphisms (SNPs) in 2020 and 153 SNPs in 2021 that exerted significant effects on maize yield and exhibited strong correlations with environmental factors, including temperature, cumulative precipitation, and cumulative sunshine duration.
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