基于自编码器整合转录组数据提升基因组预测的准确性

doi:10.11843/j.issn.0366-6964.2025.09.023

Abstract

Abstract:

The study aimed to further address the limitations of traditional linear regression models in capturing the complex relationships between genotype and phenotype, and improve the accuracy of genomic prediction by integrating omics data using machine learning. This study was based on two datasets containing both genotype and transcriptome information: 1) The Huaxi cattle dataset involved 3 economically important traits: live weight, carcass weight, and net meat weight; 2) The rice dataset included 3 agronomic traits: yield, grain, and kilo-grain weight (KGW). Five-fold cross-validation was employed, and Pearson correlation coefficients were used to evaluate the accuracy of estimated breeding values. We first compared the prediction performance using single-omics data as input, and then applied an autoencoder to perform dimensionality reduction and construct latent matrices as new relationship matrix for model training. The results showed that using transcriptomic data instead of genomic data as model input improved prediction performance, with accuracy increases of 44.2% and 27.4% in the rice and Huaxi cattle datasets, respectively. Furthermore, incorporating latent matrices extracted via autoencoders further enhanced prediction accuracy by 4.10% in rice and 6.81% in Huaxi cattle compared to traditional genomic relationship matrix. Correlation analysis revealed that the latent matrix exhibited strong nonlinear relationships with the original omics data. Using transcriptomic data as model input and incorporating relationship matrices constructed via autoencoders can improve the accuracy of selection, provide valuable insights for sustained genetic improvement in breeding programs.

Key words: multi-omics data, features prescreening, machine learning, genomic prediction

CLC Number:

S813.3

QIAN Li, LIANG Mang, DENG Tianyu, DU Lili, LI Keanning, QIU Shiyuan, XUE Qingqing, ZHANG Lupei, GAO Xue, XU Lingyang, ZHENG Caihong, LI Junya, GAO Huijiang. Improving Genomic Prediction Accuracy via Auto-encoder-based Compression of Transcriptome Data[J]. Acta Veterinaria et Zootechnica Sinica, 2025, 56(9): 4410-4421.

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References 57

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数据集 Dataset	性状 Trait	输入数据类型 Input data type	模型 Model	α Alpha	λ Lambda	核函数 Kernel
华西牛 Huaxi cattle	宰前活重 Live weight	基因组Genome	KRR_G	0.5	0.1	rbf
	宰前活重 Live weight	转录组Transcriptome	KRR_T	0.5	0.1	rbf
	胴体重 Carcass weight	基因组Genome	KRR_G	0.5	0.1	rbf
	胴体重 Carcass weight	转录组Transcriptome	KRR_T	0.5	0.1	rbf
	净肉重 Net meat weight	基因组Genome	KRR_G	0.5	0.01	rbf
	净肉重 Net meat weight	转录组Transcriptome	KRR_T	0.4	0.1	rbf
水稻 Rice	单株产量 Yield	基因组Genome	KRR_G	1.0	default	rbf
	单株产量 Yield	转录组Transcriptome	KRR_T	1.0	default	rbf
	每穗粒数 Grain	基因组Genome	KRR_G	1.0	default	rbf
	每穗粒数 Grain	转录组Transcriptome	KRR_T	1.0	default	rbf
	千粒重 KGW	基因组Genome	KRR_G	1.0	default	rbf
	千粒重 KGW	转录组Transcriptome	KRR_T	1.0	default	rbf

数据集 Dataset	性状 Trait	模型 Model	学习率 Learning rate	训练轮次 Epoch	批大小 Batch size	各层神经元数 Neurons	激活函数 Activation
水稻 Rice	单株产量Yield	ANN_G	0.000 1	50	8	(128，16)	ReLU
	单株产量Yield	ANN_T	0.000 5	100	6	(256，32)	ReLU
	每穗粒数Grain	ANN_G	0.001	100	6	(128，16)	ReLU
	每穗粒数Grain	ANN_T	0.000 1	50	23	(128，16)	ReLU
	千粒重KGW	ANN_G	0.001	50	6	(128，16)	ReLU
	千粒重KGW	ANN_T	0.001	50	6	(128，16)	null
华西牛 Huaxi cattle	宰前活重Live weight	ANN_G	0.001	50	8	(128，16)	ReLU
	宰前活重Live weight	ANN_T	0.001	50	8	(128，16)	ReLU
	胴体重Carcass weight	ANN_G	0.001	50	8	(128，16)	ReLU
	胴体重Carcass weight	ANN_T	0.001	50	8	(128，16)	ReLU
	净肉重Net meat weight	ANN_G	0.001	50	8	(128，16)	ReLU
	净肉重Net meat weight	ANN_T	0.001	50	6	(128，16)	ReLU

模块 Block	输入维度 Input dimension	隐藏维度 Hidden dimension	输出维度 Output dimension	训练轮次 Epoch	批大小 Batch size	学习率 Learning rate
编码器Encoder	218/210	128	64	50	8	0.1
解码器Decoder	64	128	218/210	50	8	0.1

数据集 Dataset	性状 Trait	α Alpha	λ Lambda	核函数 Kernel
水稻 Rice	单株产量Yield	0.3	0.001	rbf
	每穗粒数Grain	0.3	0.001	rbf
	千粒重KGW	0.3	0.001	rbf
华西牛 Huaxi cattle	宰前活重Live weight	0.4	0.2	rbf
	胴体重Carcass weight	0.4	0.2	rbf
	净肉重Net meat weight	5	0.01	rbf

数据集 Dataset	性状 Trait	个体数 Count	遗传力 Heritability	表型平均值 Mean	最小值 Minimum	中位数 Median	最大值 Maximum
水稻 Rice	单株产量Yield	210	0.74	25.88±4.40	9.96	26.35	36.99
	每穗粒数Grain	210	0.91	99.42±19.20	50.18	98.59	149.33
	千粒重KGW	210	0.97	24.41±2.51	18.03	24.51	29.92
华西牛 Huaxi cattle	宰前活重Live weight	218	0.44	692.68±69.81	514.40	684.00	868.60
	胴体重Carcass weight	218	0.34	379.95±40.12	268.30	375.40	476.20
	净肉重Net meat weight	148	0.49	324.88±37.27	229.30	321.40	412.10

Improving Genomic Prediction Accuracy via Auto-encoder-based Compression of Transcriptome Data

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数据集 Dataset	性状 Trait	模型/% Lasso	模型/% KRR	模型/% ANN	平均预测准确性/% Mean prediction accuracy
水稻 Rice	单株产量Yield	206.3	53.9	49.6	103.3
	每穗粒数Grain	45.8	21.9	32.9	33.5
	千粒重KGM	-9.6	0.0	-3.2	-4.3
	平均预测准确性Mean prediction accuracy	80.8	25.3	26.4	44.2
华西牛 Huaxi cattle	宰前活重Live weight	47.6	45.6	36.4	43.2
	胴体重Carcass weight	56.3	27.4	8.3	30.7
	净肉重Net meat weight	5.5	7.3	12.6	8.5
	平均预测准确性Mean prediction accuracy	36.5	26.8	19.1	27.4

数据集 Dataset	性状 Trait	模型 GBLUP	模型 KRR_G	模型 ANN_G	模型 KRR_LM
水稻 Rice	单株产量Yield	0.421	0.434	0.456	0.483
	每穗粒数Grain	0.626	0.625	0.541	0.641
	千粒重KGW	0.854	0.849	0.843	0.857
	平均预测准确性Mean prediction accuracy	0.634	0.636	0.613	0.660
华西牛 Huaxi cattle	宰前活重Live weight	0.222	0.185	0.239	0.216
	胴体重Carcass weight	0.164	0.167	0.180	0.205
	净肉重Net meat weght	0.188	0.154	0.159	0.191
	平均预测准确性Mean prediction accuracy	0.191	0.169	0.193	0.204

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