Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle
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 late...
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.948240/full |
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| author | Francisco José de Novais Haipeng Yu Aline Silva Mello Cesar Mehdi Momen Mirele Daiana Poleti Bruna Petry Gerson Barreto Mourão Luciana Correia de Almeida Regitano Gota Morota Luiz Lehmann Coutinho |
| author_facet | Francisco José de Novais Haipeng Yu Aline Silva Mello Cesar Mehdi Momen Mirele Daiana Poleti Bruna Petry Gerson Barreto Mourão Luciana Correia de Almeida Regitano Gota Morota Luiz Lehmann Coutinho |
| author_sort | Francisco José de Novais |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T17:50:59Z |
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| id | doaj.art-e91188cb95c64570bf91cd0cedf6e3e5 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-13T17:50:59Z |
| publishDate | 2022-10-01 |
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| series | Frontiers in Genetics |
| spelling | doaj.art-e91188cb95c64570bf91cd0cedf6e3e52022-12-22T02:36:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-10-011310.3389/fgene.2022.948240948240Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattleFrancisco José de Novais0Haipeng Yu1Aline Silva Mello Cesar2Mehdi Momen3Mirele Daiana Poleti4Bruna Petry5Gerson Barreto Mourão6Luciana Correia de Almeida Regitano7Gota Morota8Luiz Lehmann Coutinho9Department of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, BrazilDepartment of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesDepartment of Agri-Food Industry, Food and Nutrition, University of São Paulo, Piracicaba, BrazilDepartment of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesDepartment of Veterinary Medicine, School of Animal Science and Food Engineering, University of Sao Paulo, Pirassununga, BrazilDepartment of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, BrazilDepartment of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, BrazilEmbrapa Pecuária Sudeste, São Carlos, BrazilDepartment of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, United StatesDepartment of Animal Science, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, BrazilData 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.https://www.frontiersin.org/articles/10.3389/fgene.2022.948240/fullBayesian networkfactor analysismeat qualitylatent variablesomics data |
| spellingShingle | Francisco José de Novais Haipeng Yu Aline Silva Mello Cesar Mehdi Momen Mirele Daiana Poleti Bruna Petry Gerson Barreto Mourão Luciana Correia de Almeida Regitano Gota Morota Luiz Lehmann Coutinho Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle Frontiers in Genetics Bayesian network factor analysis meat quality latent variables omics data |
| title | Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle |
| title_full | Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle |
| title_fullStr | Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle |
| title_full_unstemmed | Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle |
| title_short | Multi-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattle |
| title_sort | multi omic data integration for the study of production carcass and meat quality traits in nellore cattle |
| topic | Bayesian network factor analysis meat quality latent variables omics data |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.948240/full |
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