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)...

Full description

Bibliographic Details
Main Authors: Joel D. Leal-Gutiérrez, Fernanda M. Rezende, Mauricio A. Elzo, Dwain Johnson, Francisco Peñagaricano, Raluca G. Mateescu
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Genetics
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fgene.2018.00532/full
id doaj-50661abca27345cab5791d75d219921e
record_format Article
spelling 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
work_keys_str_mv AT joeldlealgutierrez structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT fernandamrezende structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT fernandamrezende structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT mauricioaelzo structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT dwainjohnson structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT franciscopenagaricano structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT franciscopenagaricano structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
AT ralucagmateescu structuralequationmodelingandwholegenomescansuncoverchromosomeregionsandenrichedpathwaysforcarcassandmeatqualityinbeef
_version_ 1716747680179290112