Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef
Structural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2)...
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doaj-50661abca27345cab5791d75d219921e2020-11-24T21:14:20ZengFrontiers Media S.A.Frontiers in Genetics1664-80212018-11-01910.3389/fgene.2018.00532407904Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in BeefJoel D. Leal-Gutiérrez0Fernanda M. Rezende1Fernanda M. Rezende2Mauricio A. Elzo3Dwain Johnson4Francisco Peñagaricano5Francisco Peñagaricano6Raluca G. Mateescu7Department of Animal Sciences, University of Florida, Gainesville, FL, United StatesDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesFaculdade de Medicina Veterinária, Universidade Federal de Uberlândia, Uberlândia, BrazilDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesUniversity of Florida Genetics Institute, University of Florida, Gainesville, FL, United StatesDepartment of Animal Sciences, University of Florida, Gainesville, FL, United StatesStructural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2) to perform whole-genome scans for these latent variables in order to identify genomic regions and individual genes with both direct and indirect effects. A total of 726 steers from an Angus-Brahman multibreed population with records for 22 phenotypes were used. A total of 480 animals were genotyped with the GGP Bovine F-250. The single-step genomic best linear unbiased prediction method was used to estimate the amount of genetic variance explained for each latent variable by chromosome regions of 20 adjacent SNP-windows across the genome. Three types of genetic effects were considered: (1) direct effects on a single latent phenotype; (2) direct effects on two latent phenotypes simultaneously; and (3) indirect effects. The final structural model included carcass quality as an independent latent variable and meat quality as a dependent latent variable. Carcass quality was defined by quality grade, fat over the ribeye and marbling, while the meat quality was described by juiciness, tenderness and connective tissue, all of them measured through a taste panel. From 571 associated genomic regions (643 genes), each one explaining at least 0.05% of the additive variance, 159 regions (179 genes) were associated with carcass quality, 106 regions (114 genes) were associated with both carcass and meat quality, 242 regions (266 genes) were associated with meat quality, and 64 regions (84 genes) were associated with carcass quality, having an indirect effect on meat quality. Three biological mechanisms emerged from these findings: postmortem proteolysis of structural proteins and cellular compartmentalization, cellular proliferation and differentiation of adipocytes, and fat deposition.https://www.frontiersin.org/article/10.3389/fgene.2018.00532/fullcellular compartmentalizationcellular differentiationcellular proliferationfat depositionpostmortem proteolysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Joel D. Leal-Gutiérrez Fernanda M. Rezende Fernanda M. Rezende Mauricio A. Elzo Dwain Johnson Francisco Peñagaricano Francisco Peñagaricano Raluca G. Mateescu |
spellingShingle |
Joel D. Leal-Gutiérrez Fernanda M. Rezende Fernanda M. Rezende Mauricio A. Elzo Dwain Johnson Francisco Peñagaricano Francisco Peñagaricano Raluca G. Mateescu Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef Frontiers in Genetics cellular compartmentalization cellular differentiation cellular proliferation fat deposition postmortem proteolysis |
author_facet |
Joel D. Leal-Gutiérrez Fernanda M. Rezende Fernanda M. Rezende Mauricio A. Elzo Dwain Johnson Francisco Peñagaricano Francisco Peñagaricano Raluca G. Mateescu |
author_sort |
Joel D. Leal-Gutiérrez |
title |
Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef |
title_short |
Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef |
title_full |
Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef |
title_fullStr |
Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef |
title_full_unstemmed |
Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef |
title_sort |
structural equation modeling and whole-genome scans uncover chromosome regions and enriched pathways for carcass and meat quality in beef |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Genetics |
issn |
1664-8021 |
publishDate |
2018-11-01 |
description |
Structural equation models involving latent variables are useful tools for formulating hypothesized models defined by theoretical variables and causal links between these variables. The objectives of this study were: (1) to identify latent variables underlying carcass and meat quality traits and (2) to perform whole-genome scans for these latent variables in order to identify genomic regions and individual genes with both direct and indirect effects. A total of 726 steers from an Angus-Brahman multibreed population with records for 22 phenotypes were used. A total of 480 animals were genotyped with the GGP Bovine F-250. The single-step genomic best linear unbiased prediction method was used to estimate the amount of genetic variance explained for each latent variable by chromosome regions of 20 adjacent SNP-windows across the genome. Three types of genetic effects were considered: (1) direct effects on a single latent phenotype; (2) direct effects on two latent phenotypes simultaneously; and (3) indirect effects. The final structural model included carcass quality as an independent latent variable and meat quality as a dependent latent variable. Carcass quality was defined by quality grade, fat over the ribeye and marbling, while the meat quality was described by juiciness, tenderness and connective tissue, all of them measured through a taste panel. From 571 associated genomic regions (643 genes), each one explaining at least 0.05% of the additive variance, 159 regions (179 genes) were associated with carcass quality, 106 regions (114 genes) were associated with both carcass and meat quality, 242 regions (266 genes) were associated with meat quality, and 64 regions (84 genes) were associated with carcass quality, having an indirect effect on meat quality. Three biological mechanisms emerged from these findings: postmortem proteolysis of structural proteins and cellular compartmentalization, cellular proliferation and differentiation of adipocytes, and fat deposition. |
topic |
cellular compartmentalization cellular differentiation cellular proliferation fat deposition postmortem proteolysis |
url |
https://www.frontiersin.org/article/10.3389/fgene.2018.00532/full |
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