Ma Wenlong, Zheng Weigang, Qin Shenghua, Wang Chao, Lei Bowen, Liu Yuwen
State Key Laboratory of Genome and Multi-omics Technologies, Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China.
Gigascience. 2025 Jan 6;14. doi: 10.1093/gigascience/giaf083.
Genomic selection, which leverages genomic information to predict the breeding value of individuals, has dramatically accelerated the improvement of economically important traits. The growing availability of multiomics data in agricultural species offers an unprecedented opportunity to enrich this process with prior biological knowledge. However, fully harnessing these rich data sources for accurate phenotype prediction in genomic selection remains in its early stages.
In this study, we present DeepAnnotation, a novel interpretable genomic selection model designed for phenotype prediction by integrating comprehensive multiomics functional annotations using deep learning. To capture the complex information flow from genotype to phenotype, DeepAnnotation aligns multiomics biological annotations with sequential network layers in a deep learning architecture, mirroring the natural regulatory cascade from genotype to intermediate molecular phenotypes-such as cis-regulatory elements, genes, and gene modules-and ultimately to phenotypes of economic traits. Comparing against 7 classical models (rrBLUP, LightGBM, KAML, BLUP, BayesR, MBLUP, and BayesRC), DeepAnnotation demonstrated significantly superior prediction accuracy (Pearson correlation coefficient increased by 6.4% to 120.0%) and computational efficiency for 3 pork production traits (lean meat percentage, loin muscle depth, and back fat thickness) using a dataset of 1,700 training Duroc boars and 240 independent validation individuals, each genotyped for 11,633,164 single-nucleotide polymorphisms (SNPs), particularly in identifying top-performing individuals. Furthermore, the interpretability embedded within our framework enables the identification of potential causal SNPs and the exploration of their mediated molecular mechanisms underlying trait variation.
DeepAnnotation is an open-source, interpretable deep learning approach for phenotype prediction, leveraging comprehensive multiomics functional annotations. Freely accessible via GitHub and Docker, it provides a valuable tool for researchers and practitioners in genomic selection.
基因组选择利用基因组信息预测个体的育种值，极大地加速了经济重要性状的改良。农业物种中多组学数据的日益丰富为利用先验生物学知识丰富这一过程提供了前所未有的机会。然而，在基因组选择中充分利用这些丰富的数据源进行准确的表型预测仍处于早期阶段。
在本研究中，我们提出了DeepAnnotation，这是一种新颖的可解释基因组选择模型，旨在通过深度学习整合全面的多组学功能注释来进行表型预测。为了捕捉从基因型到表型的复杂信息流，DeepAnnotation将多组学生物学注释与深度学习架构中的顺序网络层对齐，反映了从基因型到中间分子表型（如顺式调控元件、基因和基因模块），最终到经济性状表型的自然调控级联。与7种经典模型（rrBLUP、LightGBM、KAML、BLUP、BayesR、MBLUP和BayesRC）相比，使用1700头杜洛克种公猪的训练数据集和240个独立验证个体（每个个体针对11,633,164个单核苷酸多态性（SNP）进行基因分型），DeepAnnotation在预测3个猪肉生产性状（瘦肉率、腰大肌深度和背膘厚度）时表现出显著更高的预测准确性（皮尔逊相关系数提高了6.4%至120.0%）和计算效率，特别是在识别表现最佳的个体方面。此外，我们框架中嵌入的可解释性能够识别潜在的因果SNP，并探索其介导的性状变异分子机制。
DeepAnnotation是一种用于表型预测的开源、可解释的深度学习方法，利用了全面的多组学功能注释。通过GitHub和Docker可免费获取，它为基因组选择的研究人员和从业者提供了一个有价值的工具。
