Table1_Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle.XLSX

Data integration using hierarchical analysis based on the central dogma or common pathway enrichment analysis may not reveal non-obvious relationships among omic data. Here, we applied factor analysis (FA) and Bayesian network (BN) modeling to integrate different omic data and complex traits by latent variables (production, carcass, and meat quality traits). A total of 14 latent variables were identified: five for phenotype, three for miRNA, four for protein, and two for mRNA data. Pearson correlation coefficients showed negative correlations between latent variables miRNA 1 (mirna1) and miRNA 2 (mirna2) (−0.47), ribeye area (REA) and protein 4 (prot4) (−0.33), REA and protein 2 (prot2) (−0.3), carcass and prot4 (−0.31), carcass and prot2 (−0.28), and backfat thickness (BFT) and miRNA 3 (mirna3) (−0.25). Positive correlations were observed among the four protein factors (0.45–0.83): between meat quality and fat content (0.71), fat content and carcass (0.74), fat content and REA (0.76), and REA and carcass (0.99). BN presented arcs from the carcass, meat quality, prot2, and prot4 latent variables to REA; from meat quality, REA, mirna2, and gene expression mRNA1 to fat content; from protein 1 (prot1) and mirna2 to protein 5 (prot5); and from prot5 and carcass to prot2. The relations of protein latent variables suggest new hypotheses about the impact of these proteins on REA. The network also showed relationships among miRNAs and nebulin proteins. REA seems to be the central node in the network, influencing carcass, prot2, prot4, mRNA1, and meat quality, suggesting that REA is a good indicator of meat quality. The connection among miRNA latent variables, BFT, and fat content relates to the influence of miRNAs on lipid metabolism. The relationship between mirna1 and prot5 composed of isoforms of nebulin needs further investigation. The FA identified latent variables, decreasing the dimensionality and complexity of the data. The BN was capable of generating interrelationships among latent variables from different types of data, allowing the integration of omics and complex traits and identifying conditional independencies. Our framework based on FA and BN is capable of generating new hypotheses for molecular research, by integrating different types of data and exploring non-obvious relationships.

基于中心法则或常见通路富集分析的层级分析用于组学数据整合时，往往无法揭示组学数据间非直观的关联关系。本研究采用因子分析（Factor Analysis, FA）与贝叶斯网络（Bayesian Network, BN）建模方法，通过潜变量整合不同组学数据与复杂性状（包括生产性状、胴体性状与肉品质性状）。最终共识别出14个潜变量：表型组对应5个，miRNA组对应3个，蛋白质组对应4个，mRNA组对应2个。皮尔逊相关系数分析结果显示，miRNA 1（mirna1）与miRNA 2（mirna2）间呈负相关（-0.47）；眼肌面积（Ribeye Area, REA）与蛋白质4（prot4）间呈负相关（-0.33），与蛋白质2（prot2）间呈负相关（-0.3）；胴体性状与prot4间呈负相关（-0.31），与prot2间呈负相关（-0.28）；背膘厚度（Backfat Thickness, BFT）与miRNA 3（mirna3）间呈负相关（-0.25）。4个蛋白质潜变量间呈正相关（相关系数范围0.45~0.83）：肉品质与脂肪含量间相关系数为0.71，脂肪含量与胴体性状间为0.74，脂肪含量与REA间为0.76，REA与胴体性状间为0.99。贝叶斯网络显示存在如下有向边：从胴体性状、肉品质、prot2与prot4潜变量指向REA；从肉品质、REA、mirna2与mRNA1基因表达指向脂肪含量；从蛋白质1（prot1）与mirna2指向蛋白质5（prot5）；从prot5与胴体性状指向prot2。蛋白质潜变量间的关联关系为解析这些蛋白质对REA的调控作用提供了新的研究假说。该网络还揭示了miRNA与伴肌动蛋白（nebulin）间的关联。REA作为网络中的核心节点，可调控胴体性状、prot2、prot4、mRNA1与肉品质，表明REA可作为肉品质的良好评价指标。miRNA潜变量、BFT与脂肪含量间的关联，反映了miRNA对脂质代谢的调控作用。mirna1与由伴肌动蛋白亚型构成的prot5间的关联，则有待进一步研究验证。因子分析通过识别潜变量降低了数据的维度与复杂度；贝叶斯网络则可构建不同类型数据对应的潜变量间的互作关系，实现组学数据与复杂性状的整合，并识别条件独立关系。本研究提出的基于因子分析与贝叶斯网络的整合框架，可通过整合多类型组学数据并挖掘非直观的关联关系，为分子研究提供全新的研究假说。