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

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dc.contributor.authorde Novais, Francisco Joseen
dc.contributor.authorYu, Haipengen
dc.contributor.authorCesar, Aline Silva Melloen
dc.contributor.authorMomen, Mehdien
dc.contributor.authorPoleti, Mirele Daianaen
dc.contributor.authorPetry, Brunaen
dc.contributor.authorMourao, Gerson Barretoen
dc.contributor.authorRegitano, Luciana Correia de Almeidaen
dc.contributor.authorMorota, Gotaen
dc.contributor.authorCoutinho, Luiz Lehmannen
dc.date.accessioned2023-04-27T12:45:01Zen
dc.date.available2023-04-27T12:45:01Zen
dc.date.issued2022-10en
dc.description.abstractData 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.en
dc.description.notesThe experiments were financially supported by "Fundacao de Amparo a Pesquisa do Estado de Sao Paulo," Brazil (FAPESP grants 12/23638-8 and 19/04089-2). This work was conducted while FJN was visiting Virginia Polytechnic Institute and State University, supported by the Coordinating Agency for Advanced Training of Graduate Personnel (CAPES grant: 88887.367967/2019-00).en
dc.description.sponsorshipFundacao de Amparo a Pesquisa do Estado de Sao Paulo," Brazil (FAPESP) [12/23638-8, 19/04089-2]; Coordinating Agency for Advanced Training of Graduate Personnel (CAPES) [88887.367967/2019-00]en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.3389/fgene.2022.948240en
dc.identifier.eissn1664-8021en
dc.identifier.other948240en
dc.identifier.pmid36338989en
dc.identifier.urihttp://hdl.handle.net/10919/114814en
dc.identifier.volume13en
dc.language.isoenen
dc.publisherFrontiersen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectBayesian networken
dc.subjectfactor analysisen
dc.subjectmeat qualityen
dc.subjectlatent variablesen
dc.subjectomics dataen
dc.titleMulti-omic data integration for the study of production, carcass, and meat quality traits in Nellore cattleen
dc.title.serialFrontiers in Geneticsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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