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

摘要/Abstract

摘要： 为进一步探索传统线性回归模型难以捕捉基因型与表型之间复杂关系的不足,本研究旨在利用机器学习整合组学数据提升基因组预测的准确性。本研究基于具有基因型与转录组数据的数据集：1)华西牛数据集涉及宰前活重、胴体重和净肉重3个主要经济性状；2)水稻数据集包含单株产量、每穗粒数和千粒重3个农艺性状。采用五折交叉验证，以皮尔逊相关系数评估育种值估计的准确性。首先比较了基于单一组学数据作为输入时的预测表现，随后基于自编码器构建隐含矩阵作为关系矩阵用于模型建模。结果表明，使用转录组数据替代基因组数据作为输入可以提升模型的预测能力。在水稻和华西牛数据集分别提高了44.2%和27.4%，进一步地，将隐含矩阵用于建模后，模型预测准确性相较基因组关系矩阵在水稻和华西牛中分别提升了4.10%和6.81%。相关性分析表明，隐含矩阵与原始组学数据之间存在较强的非线性关系。将转录组作为模型输入，结合自编码器构建的关系矩阵，可有效提升选种选育的准确性，为育种工作的持续改进提供参考依据。

关键词: 多组学数据, 特征提取, 机器学习, 基因组预测

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

中图分类号:

S813.3

骞里, 梁忙, 邓天宇, 杜丽丽, 李柯安宁, 邱诗元, 薛青青, 张路培, 高雪, 徐凌洋, 郑彩宏, 李俊雅, 高会江. 基于自编码器整合转录组数据提升基因组预测的准确性[J]. 畜牧兽医学报, 2025, 56(9): 4410-4421.

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.

参考文献

[1] SCHAEFFER L R. Strategy for applying genome-wide selection in dairy cattle[J]. J Anim Breed Genet, 2006, 123: 218-223.
[2] WIGGANS G R,CARRILLO J A. Genomic selection in United States dairy cattle[J]. Front Genet, 2022, 13: 994466.
[3] GARCÍA-RUIZ A, COLE J B, VANRADEN P M, et al. Changes in genetic selection differentials and generation intervals in US Holstein dairy cattle as a result of genomic selection[J]. Proc Natl Acad Sci U S A, 2016, 113: E3995-4004.
[4] WIGGANS G R, COLE J B, HUBBARD S M, et al. Genomic selection in dairy cattle: The USDA experience[J]. Annu Rev Anim Biosci, 2017, 5: 309-327.
[5] CAMPOS G S, CARDOSO F F, GOMES C C G, et al. Development of genomic predictions for Angus cattle in Brazil incorporating genotypes from related American sires[J]. J Anim Sci, 2022, 1:100.
[6] ZHANG Z, LIU J, DING X, et al. Best linear unbiased prediction of genomic breeding values using a trait-specific marker-derived relationship matrix[J]. PLoS One, 2010, 5(9); E12648.
[7] EDWARDS S M, SØRENSEN I F, SARUP P, et al. Genomic prediction for quantitative traits is improved by mapping variants to gene ontology categories in Drosophila melanogaster[J]. Genetics, 2016, 203: 1871-1883.
[8] MEUWISSEN T, EIKJE L S,GJUVSLAND A B. GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values[J]. Genet Sel Evol, 2024, 56: 17.
[9] LOPES M S, BOVENHUIS H, VAN SON M, et al. Using markers with large effect in genetic and genomic predictions[J]. J Anim Sci, 2017, 95: 59-71.
[10] MISZTAL I, LEGARRA A,AGUILAR I. Computing procedures for genetic evaluation including phenotypic, full pedigree, and genomic information[J]. J Dairy Sci, 2009, 92: 4648-4655.
[11] MÄNTYSAARI E A, EVANS R D,STRANDÉN I. Efficient single-step genomic evaluation for a multibreed beef cattle population having many genotyped animals[J]. J Anim Sci, 2017, 95: 4728-4737.
[12] XU Y, ZHANG Y, CUI Y, et al. GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection[J]. Brief Bioinform, 2024, 25(5):385.
[13] WU B, XIONG H, ZHUO L, et al. Multi-view BLUP: a promising solution for post-omics data integrative prediction[J]. J Genet Genomics, 2025, 52: 839-847.
[14] GESTEIRO N, MALVAR R A, BUTRÓN A, et al. Genome-wide association study and genomic predictions for hydroxycinnamate concentrations in maize stover[J]. J Agric Food Chem, 2025, 73: 2289-2298.
[15] XU H, WANG Z, WANG F, et al. Genome-wide association study and genomic selection of spike-related traits in bread wheat[J]. Theor Appl Genet, 2024, 137: 131.
[16] XU R, YANG Q, LIU Z, et al. Genome-wide association analysis and genomic prediction of salt tolerance trait in soybean germplasm[J]. Front Plant Sci, 2024, 15: 1494551.
[17] LIU K, YIN Y, WANG B, et al. Integrating significant SNPs identified by GWAS for genomic prediction of the number of ribs and carcass length in Suhuai pigs[J]. Animals (Basel), 2025, 15(3): 412.
[18] ZHAO Z, NIU Q, WU T, et al. Comparative analysis of genomic prediction for production traits using genomic annotation and a genome-wide association study at sequencing levels in beef cattle[J]. Agriculture, 2024, 14: 2255.
[19] TU T C, LIN C J, LIU M C, et al. Genomic prediction and genome-wide association study for growth-related traits in Taiwan Country chicken[J]. Animals (Basel), 2025, 15(3): 376.
[20] CRICK F. Central dogma of molecular biology[J]. Nature, 1970, 227: 561-563.
[21] DE LOS CAMPOS G, HICKEY J M, PON WONG R, et al. Whole-genome regression and prediction methods applied to plant and animal breeding[J]. Genetics, 2013, 193: 327-345.
[22] KANG M, KO E,MERSHA T B. A roadmap for multi-omics data integration using deep learning[J]. Brief Bioinform, 2022, 23(1): bbab454.
[23] FRISCH M, THIEMANN A, FU J, et al. Transcriptome-based distance measures for grouping of germplasm and prediction of hybrid performance in maize[J]. Theor Appl Genet, 2010, 120: 441-450.
[24] ZENKE-PHILIPPI C, THIEMANN A, SEIFERT F, et al. Prediction of hybrid performance in maize with a ridge regression model employed to DNA markers and mRNA transcription profiles[J]. BMC Genomics, 2016, 17: 262.
[25] RITCHIE M D, HOLZINGER E R, LI R, et al. Methods of integrating data to uncover genotype-phenotype interactions[J]. Nat Rev Genet, 2015, 16: 85-97.
[26] ZHU J, SOVA P, XU Q, et al. Stitching together multiple data dimensions reveals interacting metabolomic and transcriptomic networks that modulate cell regulation[J]. PLoS Biol, 2012, 10: e1001301.
[27] AN B, LIANG M, CHANG T, et al. KCRR: a nonlinear machine learning with a modified genomic similarity matrix improved the genomic prediction efficiency[J]. Brief Bioinform, 2021, 22(6): bbab132.
[28] LI M, HALL T, MACHUGH D E, et al. KPRR: a novel machine learning approach for effectively capturing nonadditive effects in genomic prediction[J]. Brief Bioinform, 2024, 26(1): bbae683.
[29] WANG J, ZONG W, SHI L, et al. Using mixed kernel support vector machine to improve the predictive accuracy of genome selection[J]. Journal of Integrative Agriculture, 2024, 3: 083.
[30] THORSRUD J A, EVANS K M, QUIGLEY K C, et al. Performance comparison of genomic best linear unbiased prediction and four machine learning models for estimating genomic breeding values in working dogs[J]. Animals (Basel), 2025, 15(3): 408.
[31] VAN DER LAAN L, PARMLEY K, SAADATI M, et al. Genomic and phenomic prediction for soybean seed yield, protein, and oil[J]. Plant Genome, 2025, 18: e70002.
[32] HANSEN P B, RUUD A K, DE LOS CAMPOS G, et al. Integration of DNA methylation and transcriptome data improves complex trait prediction in Hordeum vulgare[J]. Plants (Basel), 2022, 11(17): 2190.
[33] KNOCH D, WERNER C R, MEYER R C, et al. Multi-omics-based prediction of hybrid performance in canola[J]. Theor Appl Genet, 2021, 134: 1147-1165.
[34] XU F, CHE Z, QIAO J, et al. Integrating gene expression data into single-step method (ssBLUP) improves genomic prediction accuracy for complex traits of Duroc×Erhualian F(2) pig population[J]. Curr Issues Mol Biol, 2024, 46: 13713-13724.
[35] ZHAO L, TANG P, LUO J, et al. Genomic prediction with NetGP based on gene network and multi-omics data in plants[J]. Plant Biotechnol J, 2025, 23: 1190-1201.
[36] WANG K, ABID M A, RASHEED A, et al. DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants[J]. Mol Plant, 2023, 16: 279-293.
[37] MA W, QIU Z, SONG J, et al. A deep convolutional neural network approach for predicting phenotypes from genotypes[J]. Planta, 2018, 248: 1307-1318.
[38] WU C, ZHANG Y, YING Z, et al. A transformer-based genomic prediction method fused with knowledge-guided module[J]. Brief Bioinform, 2023, 25(1): bbad438.
[39] ZOU Q, TAI S, YUAN Q, et al. Large-scale crop dataset and deep learning-based multi-modal fusion framework for more accurate G×E genomic prediction[J]. Computers and Electronics in Agriculture, 2025, 230: 109833.
[40] HUA J, XING Y, WU W, et al. Single-locus heterotic effects and dominance by dominance interactions can adequately explain the genetic basis of heterosis in an elite rice hybrid[J]. Proc Natl Acad Sci U S A, 2003, 100: 2574-2579.
[41] YU H, XIE W, WANG J, et al. Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers[J]. PLoS One, 2011, 6: e17595.
[42] WANG J, YU H, WENG X, et al. An expression quantitative trait loci-guided co-expression analysis for constructing regulatory network using a rice recombinant inbred line population[J]. J Exp Bot, 2014, 65: 1069-1079.
[43] HU X, XIE W, WU C, et al. A directed learning strategy integrating multiple omic data improves genomic prediction[J]. Plant Biotechnol J, 2019, 17: 2011-2020.
[44] HUMPHREYS R K, PUTH M T, NEUHÄUSER M, et al. Underestimation of Pearson’s product moment correlation statistic[J]. Oecologia, 2019, 189: 1-7.
[45] TRIPATHI Y M, CHATLA S B, CHANG Y I, et al. A nonlinear correlation measure with applications to gene expression data[J]. PLoS One, 2022, 17: e0270270.
[46] WANG X, SHI S, WANG G, et al. Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs[J]. J Anim Sci Biotechnol, 2022, 13: 60.
[47] GONDRO C, VAN DER WERF J,HAYES B. Genome-wide association studies and genomic prediction[M]. Springer, 2013.
DOI:10.1007/978-1-62703-447-0.
[48] XIA P P, ZHANG L,LI F Z. Learning similarity with cosine similarity ensemble[J]. Inform Sci, 2015, 307: 39-52.
[49] AZODI C B, PARDO J, VANBUREN R, et al. Transcriptome-based prediction of complex traits in maize[J]. Plant Cell, 2020, 32: 139-151.
[50] XIE Z, XU X, LI L, et al. Residual networks without pooling layers improve the accuracy of genomic predictions[J]. Theor Appl Genet, 2024, 137: 138.
[51] AZODI C B, BOLGER E, MCCARREN A, et al. Benchmarking parametric and machine learning models for genomic prediction of complex traits[J]. G3 (Bethesda), 2019, 9: 3691-3702.
[52] ZHANG H, XI Q, ZHANG F, et al. Application of deep learning in cancer prognosis prediction model[J]. Technol Cancer Res Treat, 2023, 22: 15330338231199287.
[53] WANG P, LEHTI-SHIU M D, LOTRECK S, et al. Prediction of plant complex traits via integration of multi-omics data[J]. Nat Commun, 2024, 15: 6856.
[54] CUI Y, LI R, LI G, et al. Hybrid breeding of rice via genomic selection[J]. Plant Biotechnol J, 2020, 18: 57-67.
[55] VABALAS A, GOWEN E, POLIAKOFF E, et al. Machine learning algorithm validation with a limited sample size[J]. PLoS One, 2019, 14: e0224365.
[56] VAN SMEDEN M, MOONS K G, DE GROOT J A, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria[J]. Stat Methods Med Res, 2019, 28: 2455-2474.
[57] ALWOSHEEL A, VAN CRANENBURGH S,CHORUS C G. Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis[J]. J Choice Modell, 2018, 28: 167-182.