Scientia Agricultura Sinica ›› 2025, Vol. 58 ›› Issue (2): 203-213.doi: 10.3864/j.issn.0578-1752.2025.02.001

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Heterosis Groups Research in Maize Inbred Lines Based on Machine Learning

CAO ShiLiang1(), ZHANG JianGuo1, YU Tao1, YANG GengBin1, LI WenYue1, MA XueNa1, SUN YanJie4, HAN WeiBo2, TANG Gui3, SHAN DaPeng4   

  1. 1 Maize Institute, Heilongjiang Academy of Agricultural Sciences/National Engineering Laboratory for Maize (Harbin), Harbin 150086
    2 Institue of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin 150086
    3 Rural Revitalization Institute, Heilongjiang Academy of Agricultural Sciences, Harbin 150086
    4 Suihua Branch of Heilongjiang Academy of Agricultural Sciences, Suihua 152052, Heilongjiang
  • Received:2024-06-18 Accepted:2024-09-14 Online:2025-01-21 Published:2025-01-21

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Abstract:

【Objective】The objective of this study is to optimize the classification and discriminant method of maize heterotic groups, and provide guidance and reference for maize breeding practices.【Method】Solid-phase chips were used to genotype 60 waxy maize inbred lines, and high-quality SNP markers with different density were obtained through quality control. Population structure analysis and genetic distance clustering were used to classify the 60 waxy maize inbred lines into different groups, and the differences between different classification methods were compared. On this basis, random forest and support vector machine methods were used to sample and discriminate the results of different classification methods. Five-fold cross-validation was used for sampling, and the prediction accuracy of maize group classification based on different classification methods was compared.【Result】Using different quality control standards, 11 431 and 4 022 molecular markers were obtained, respectively. Based on these two molecular marker densities, 60 materials were divided into 5 and 4 clusters, respectively. When using 11 431 SNP markers, the population structure analysis and genetic distance clustering results showed that the intra-cluster sample consistency was 63.33%. When using 4 022 SNP markers for clustering, the intra-cluster sample consistency was 90.00%. The prediction accuracy results for discriminating maize inbred line clusters showed that the average prediction accuracy (91.43%) of Random Forest and Support Vector Machine using 4 022 markers were higher than that of 11 431 markers (86.25%). Among them, the highest prediction accuracy was achieved by Random Forest using 4 022 markers, with a prediction accuracy of 94.17%.【Conclusion】Clustering analysis ultimately divided 60 waxy maize inbred lines into 4 clusters. Sampling and cross-validation results using Random Forest and Support Vector Machine for cluster classification showed that Random Forest achieved higher prediction accuracy than Support Vector Machine.

Key words: maize, machine learning, cross-validation, clustering, discrimination analysis

Fig. 1

Quantitative distribution of molecular markers with two different densities on different chromosomes"

Fig. 2

Population structure based on 11 431 SNPs a: Line chart of cross validation error; b: Population structure diagram"

Fig. 3

Cluster analysis graph based on genetic distance A: Group A; B: Group B; C: Group C; D: Group D; E: Group E. The same as below"

Table 1

Comparison of grouping result between structure and cluster analysis"

分群方法
Clustering method		 A群
Group A		 B群
Group B		 C群
Group C		 D群
Group D		 E群
Group E
群体结构分群
STR		 N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N13, N15, N16, N32 N38, N39, N40, N41, N42, N43, N44 N14, N18, N19, N20, N21, N22,
N23, N24, N25, N26		 N17, N28, N29, N30, N31, N33, N34, N35, N36, N37, N48, N50, N52, N53, N54, N55, N56, N57, N58, N60 N27, N45, N46, N47, N49, N51, N59
遗传距离分群
GD		 N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N14, N16, N17, N32, N34 N38, N39, N40, N41, N42, N43, N44, N48, N50 N18, N19, N20, N21, N22, N23, N24, N25, N26, N27, N29, N31, N35, N37, N49, N51, N54, N59, N60 N13, N15, N28, N30, N33, N36, N45, N46, N47, N52, N53, N55, N56 N57, N58

Fig. 4

Population structures based on 4 022 SNPs a: Line chart of cross validation error; b: Population structure diagram"

Fig. 5

Cluster analysis graph based on genetic distance"

Table 2

Comparison of grouping result between structure and cluster analysis"

分群方法
Clustering method		 A群
Group A		 B群
Group B		 C群
Group C		 D群
Group D
群体结构分群
STR		 N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N16, N32, N13, N15, N14, N17 N38, N39, N40, N41, N42, N43, N44 N18, N19, N20, N21, N22, N23, N24, N25, N26, N29, N34 N45, N46, N47, N59, N51, N53, N27, N28, N30, N49, N56, N37, N31, N48, N54, N55, N52, N33, N50, N35, N36, N60, N57, N58
遗传距离分群
GD		 N1, N2, N3, N4, N5, N6, N7, N8, N9, N10, N11, N12, N16, N32, N13, N15, N14, N17 N38, N39, N40, N41, N42, N43, N44, N48, N50, N55, N57, N58 N18, N19, N20, N21, N22, N23, N24, N25, N29, N34 N26, N27, N28, N30, N31, N33, N35, N36, N37, N45, N46, N47, N49, N51, N52, N53, N54, N56, N59, N60

Table 3

Comparison of prediction accuracy of inbred line group discriminant (prediction accuracy/coefficient of variation)"

分群方法
Grouping methods		 预测方法
Prediction methods		 标记数
Marker number		 预测方法均值
Mean of prediction method (%)		 分群方法均值
Mean of grouping methods (%)
11431 4022
群体结构
STR		 随机森林RF 87.50%/0.11 94.17%/0.07 90.84 89.83
支持向量机SVM 87.22%/0.11 90.42%/0.10 88.82
遗传距离
GD		 随机森林RF 86.25%/0.11 94.17%/0.06 90.21 87.40
支持向量机SVM 84.03%/0.13 86.94%/0.10 85.49
均值Mean (%) 86.25 91.43

Fig. 6

Comparison of prediction accuracy of machine learning models under different classification methods a: Comparison of prediction accuracy between different molecular density; b: Cluster analysis based on genetic distance"

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