基于G2PSE堆叠集成的全基因组选择方法

doi:10.3864/j.issn.0578-1752.2025.15.003

Abstract

Abstract:

【Objective】 Genomic selection (GS) is a core technology for predicting individual phenotypes or genetic values from genome-wide marker information, which has important theoretical value and practical significance in agricultural breeding and genetic research. However, high-dimensional feature redundancy and nonlinear relationship modeling are key challenges in genomic selection. A genotype to phenotype stacking ensemble (G2PSE) is proposed, aiming to improve the prediction accuracy and generalization ability, and provide an efficient solution for high-dimensional genomic data analysis. 【Method】 The G2PSE stacking ensemble model framework was constructed, incorporating ten-fold cross-validation, ensemble learning, feature selection (LAR algorithm), and feature enhancement strategies. The model employed random forests (RF), support vector regression (SVR), and gradient boosting regression (GBR) as base learners, with ordinary least squares regression (OLSR) as the meta-learner. Additionally, the impact of meta-learners such as random forest, support vector regression, and neural networks on model performance was evaluated. The G2PSE model consisted of three core submodels: (1) All-feature stacking ensemble (AFSE), which fully utilized all SNP features; (2) LAR-feature stacking ensemble (LFSE), which reduced redundant information through feature selection to improve generalization; (3) LAR-feature enhanced stacking ensemble (LFESE), which combined feature selection with enhancement strategies to optimize prediction capability in high-dimensional data environments. The performance of three feature enhancement variants (AFESE, HFESEⅠ, HFESEⅡ) was explored. Finally, the model was evaluated experimentally on multi-trait datasets of three species, namely wheat, soybean, and tilapia, and further evaluated on an independent test set using the Pepper203 dataset to validate the robustness of the model. 【Result】 The G2PSE model significantly outperformed traditional methods and single machine learning models in two metrics, Pearson correlation coefficient (PCC) and mean absolute error (MAE). Among the three core submodels, LFESE performed the best by combining the feature selection and enhancement strategies, LFSE reduced redundant information and enhanced the generalization ability by feature selection, and AFSE had a significant advantage in comprehensively capturing genotypic global information. In addition, the three feature enhancement variant models further validated the importance of feature quality compared to feature quantity in improving prediction performance. The experiments also showed that the linear regression model performed best in meta-learner selection, while the LFESE and LFSE submodels demonstrated a more balanced performance in terms of computational efficiency. And a reasonable feature selection threshold was crucial for model performance, where the optimal threshold for low-dimensional datasets was 10%-20%, while the optimal threshold for high-dimensional datasets was 1%. Finally, the evaluation on an independent test set proved that the LFESE submodel had the best generalization ability. 【Conclusion】 The G2PSE model significantly improves genomic selection prediction performance through ensemble learning, feature selection, and enhancement strategies.

Key words: genomic selection, stacking ensemble, feature selection, feature enhancement, agricultural breeding

ZHUANG RunJie, LIU HuiMing, WANG ShiYu, LÜ WanPing, WEN YongXian. Genomic Selection Method Based on G2PSE Stacking Ensemble[J].Scientia Agricultura Sinica, 2025, 58(15): 2960-2979.

Figures/Tables 12

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Table 4

Impact of different LAR screening thresholds on the prediction performance of the G2PSE model on Wheat2000"

性状 Traits	筛选SNP数目 Number of selected SNPs	LFSE模型 LFSE model	LFESE模型 LFESE model	HFESEⅠ 模型 HFESEⅠ model	HFESEⅡ 模型 HFESEⅡ model
TKW	337	0.713 (0.562)	0.802 (0.467)	0.710 (0.572)	0.669 (0.589)
	1685	0.710 (0.560)	0.750 (0.643)	0.522 (0.974)	0.667 (0.590)
	3371	0.704 (0.567)	0.694 (0.648)	0.404 (1.099)	0.666 (0.591)
	6742	0.704 (0.568)	0.689 (0.662)	0.373 (1.163)	0.666 (0.591)
	13483	0.702 (0.569)	0.691 (0.660)	0.368 (1.171)	0.666 (0.591)
	20225	0.700 (0.571)	0.724 (0.612)	0.365 (1.176)	0.666(0.591)
TW	337	0.674 (0.564)	0.778 (0.476)	0.709 (0.573)	0.602 (0.613)
	1685	0.665 (0.572)	0.655 (0.822)	0.528 (0.966)	0.600 (0.614)
	3371	0.661 (0.572)	0.635 (0.737)	0.374 (1.170)	0.599 (0.615)
	6742	0.657 (0.577)	0.627 (0.731)	0.373 (1.162)	0.599 (0.615)
	13483	0.658 (0.576)	0.627 (0.733)	0.368 (1.170)	0.599 (0.615)
	20225	0.659 (0.576)	0.628 (0.731)	0.366 (1.175)	0.600 (0.615)
GL	337	0.772 (0.478)	0.839 (0.410)	0.754 (0.510)	0.741 (0.502)
	1685	0.765 (0.486)	0.738 (0.648)	0.522 (0.947)	0.740 (0.504)
	3371	0.763 (0.489)	0.699 (0.654)	0.478 (1.007)	0.739 (0.504)
	6742	0.762 (0.490)	0.703 (0.643)	0.445 (1.031)	0.739 (0.504)
	13483	0.764 (0.488)	0.704 (0.642)	0.442 (1.036)	0.739 (0.504)
	20225	0.761 (0.490)	0.702 (0.644)	0.440 (1.039)	0.739 (0.504)
GW	337	0.750 (0.514)	0.812 (0.451)	0.712 (0.557)	0.732 (0.526)
	1685	0.745 (0.518)	0.743 (0.654)	0.497 (1.010)	0.731 (0.526)
	3371	0.740 (0.524)	0.746 (0.582)	0.420 (1.086)	0.731 (0.527)
	6742	0.741 (0.523)	0.716 (0.634)	0.402 (1.136)	0.731 (0.527)
	13483	0.741 (0.523)	0.714 (0.636)	0.401 (1.137)	0.731 (0.527)
	20225	0.742 (0.523)	0.716 (0.634)	0.400 (1.137)	0.731 (0.527)
GH	337	0.687 (0.567)	0.787 (0.483)	0.677 (0.586)	0.682 (0.567)
	1685	0.686 (0.566)	0.707 (0.705)	0.463 (1.043)	0.681 (0.568)
	3371	0.691 (0.561)	0.695 (0.654)	0.370 (1.170)	0.680 (0.569)
	6742	0.684 (0.566)	0.698 (0.645)	0.395 (1.125)	0.680 (0.569)
	13483	0.686 (0.565)	0.698 (0.645)	0.392 (1.129)	0.680 (0.569)
	20225	0.689 (0.563)	0.699 (0.644)	0.391 (1.132)	0.680 (0.569)
GP	337	0.626 (0.604)	0.746 (0.512)	0.627 (0.622)	0.515 (0.667)
	1685	0.609 (0.618)	0.662 (0.800)	0.414 (1.175)	0.513 (0.668)
	3371	0.604 (0.619)	0.616 (0.759)	0.358 (1.167)	0.512 (0.668)
	6742	0.603 (0.619)	0.617 (0.757)	0.365 (1.165)	0.512 (0.668)
	13483	0.603 (0.619)	0.618 (0.756)	0.357 (1.177)	0.512 (0.668)
	20225	0.602 (0.621)	0.615 (0.760)	0.352 (1.185)	0.512 (0.668)
SDS	337	0.663 (0.599)	0.796 (0.477)	0.694 (0.575)	0.525 (0.694)
	1685	0.656 (0.607)	0.763 (0.606)	0.580 (0.862)	0.523 (0.695)
	3371	0.644 (0.621)	0.714 (0.626)	0.468 (0.979)	0.520 (0.697)
	6742	0.644 (0.623)	0.713 (0.629)	0.488 (0.948)	0.518 (0.698)
	13483	0.643 (0.622)	0.712 (0.631)	0.487 (0.949)	0.521 (0.696)
	20225	0.644 (0.621)	0.707 (0.636)	0.440 (1.025)	0.520 (0.697)
PHT	337	0.501 (0.664)	0.697 (0.566)	0.553 (0.673)	0.279 (0.755)
	1685	0.477 (0.672)	0.626 (0.844)	0.422 (1.131)	0.278 (0.755)
	3371	0.449 (0.682)	0.520 (0.879)	0.334 (1.167)	0.275 (0.756)
	6742	0.452 (0.682)	0.512 (0.894)	0.294 (1.230)	0.276 (0.756)
	13483	0.449 (0.681)	0.515 (0.890)	0.293 (1.229)	0.275 (0.756)
	20225	0.451 (0.683)	0.517 (0.883)	0.311 (1.186)	0.275 (0.756)

Table 4

Table 5

Table 6

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数据集 Datasets	性状 Traits	个体数 Individuals	标记数 SNPs	遗传率 h²	数据来源 Data source
Wheat599	E1-GY	599	1279	0.832	https://github.com/AIBreeding/DNNGP?tab=readme-ov-file
	E2-GY	599	1279	0.729
	E3-GY	599	1279	0.689
	E4-GY	599	1279	0.711
Wheat2000	TKW	2000	33709	0.833	https://github.com/cma2015/DeepGS
	TW	2000	33709	0.754
	GL	2000	33709	0.881
	GW	2000	33709	0.848
	GH	2000	33709	0.839
	GP	2000	33709	0.625
	SDS	2000	33709	0.681
	PHT	2000	33709	0.434
Soy5014	HT	5014	4234	0.449	https://doi.org/10.1534/g3.116.032268
	R8	5014	4234	0.558
	YLD	5014	4234	0.485
Tilapia1125	HW	1125	32306	0.304	https://figshare.com/s/9b265a22b7e138c5a839
Pepper203	PHT	203	14922	0.610	https://bmcgenomdata.biomedcentral.com/articles/10.1186/s12863-023-01179-6
Pepper203	FT	203	14922	0.730

性状Traits	模型Model	条件数Condition number
HT	AFSE	14.823
	LFSE	16.598
	LFESE	490.901
	AFESE	5.436×10¹⁶
	HFESEⅠ	298.676
	HFESEⅡ	5.281×10¹⁶
R8	AFSE	7.709
	LFSE	8.837
	LFESE	495.610
	AFESE	5.506×10¹⁶
	HFESEⅠ	372.634
	HFESEⅡ	5.132×10¹⁶
YLD	AFSE	8.353
	LFSE	8.817
	LFESE	286.771
	AFESE	5.291×10¹⁶
	HFESEⅠ	286.676
	HFESEⅡ	5.489×10¹⁶

环境 Environment	筛选SNP数目 Number of selected SNPs	LFSE模型 LFSE model	LFESE模型 LFESE model	HFESEⅠ模型 HFESEⅠ model	HFESEⅡ模型 HFESEⅡ model
E1-GY	13	0.511(0.691)	0.489 (0.701)	0.581 (0.641)	0.297 (1.132)
	64	0.592 (0.634)	0.612 (0.616)	0.563 (0.655)	0.305 (1.112)
	128	0.604 (0.631)	0.636 (0.606)	0.543 (0.683)	0.321 (1.095)
	256	0.604 (0.625)	0.542 (0.670)	0.454 (1.251)	0.330 (1.074)
	512	0.597 (0.629)	0.407 (1.631)	0.333(1.728)	0.354 (1.030)
	767	0.596 (0.630)	0.129 (3.452)	0.079 (3.518)	0.353 (1.033)
E2-GY	13	0.429 (0.701)	0.467 (0.691)	0.518 (0.658)	0.339 (1.095)
	64	0.592 (0.624)	0.621 (0.622)	0.614 (0.625)	0.360 (1.033)
	128	0.607 (0.612)	0.698 (0.588)	0.681 (0.592)	0.373 (1.003)
	256	0.598 (0.622)	0.662 (0.634)	0.630 (0.662)	0.388 (0.968)
	512	0.564 (0.638)	0.521 (1.014)	0.409 (1.243)	0.431 (0.909)
	767	0.536 (0.648)	0.287 (2.026)	0.204 (2.484)	0.489 (0.838)
E3-GY	13	0.512 (0.678)	0.493 (0.681)	0.455 (0.692)	0.276 (1.070)
	64	0.512 (0.677)	0.498 (0.679)	0.501 (0.689)	0.274 (1.072)
	128	0.555 (0.655)	0.594 (0.628)	0.551 (0.672)	0.287 (1.059)
	256	0.511 (0.678)	0.496 (0.682)	0.523 (0.743)	0.280 (1.061)
	512	0.510 (0.681)	0.499 (0.679)	0.285 (1.394)	0.282 (1.061)
	767	0.510 (0.680)	0.496 (0.680)	0.132 (3.048)	0.279 (1.065)
E4-GY	13	0.457 (0.711)	0.477 (0.686)	0.514 (0.668)	0.291 (1.079)
	64	0.604 (0.630)	0.602 (0.633)	0.592 (0.644)	0.311 (1.047)
	128	0.639 (0.607)	0.653 (0.598)	0.648 (0.614)	0.319 (1.037)
	256	0.628 (0.607)	0.708 (0.594)	0.668 (0.637)	0.313 (1.044)
	512	0.586 (0.628)	0.434 (1.123)	0.403 (1.157)	0.323 (1.029)
	767	0.560 (0.641)	0.243 (2.231)	0.230 (2.387)	0.340 (1.011)

模型 Model	验证集Validation set		测试集Test set		差值绝对值Absolute difference
模型 Model	PCC	MAE	PCC	MAE	PCC	MAE
AFSE	0.639	0.566	0.520	0.580	0.119	0.014
LFSE	0.735	0.502	0.645	0.563	0.090	0.061
LFESE	0.693	0.544	0.734	0.517	0.041	0.027
AFESE	0.659	0.553	-0.023	0.690	0.682	0.137
HFESEⅠ	0.669	0.553	0.415	0.745	0.254	0.192
HFESEⅡ	0.659	0.554	0.527	0.625	0.132	0.071

模型 Model	验证集Validation set		测试集Test set		差值绝对值Absolute difference
模型 Model	PCC	MAE	PCC	MAE	PCC	MAE
AFSE	0.785	0.468	0.705	0.508	0.080	0.040
LFSE	0.806	0.444	0.756	0.501	0.050	0.057
LFESE	0.753	0.502	0.718	0.561	0.035	0.059
AFESE	0.769	0.467	0.434	0.701	0.335	0.234
HFESEⅠ	0.736	0.564	0.657	0.624	0.079	0.060
HFESEⅡ	0.768	0.469	0.712	0.521	0.056	0.052

Genomic Selection Method Based on G2PSE Stacking Ensemble

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