Abstract
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.
Highlights
Many economically important characteristics in livestock could be described by a set of individual traits which often are themselves quantitative in nature
The objectives of this study were to: (1) identify a theoretical model involving beef growth, carcass quality and meat quality that closely fits the variance-covariance structure present in the sample phenotypic data and (2) perform whole-genome scans for the latent variables constructed in the structural equation (SE) analysis to identify genomic regions with direct and indirect effects
Genomic DNA was extracted from blood samples using the DNeasy Blood & Tissue kit (Qiagen, Valencia, CA, United States) and stored at −20◦C
Summary
Many economically important characteristics in livestock could be described by a set of individual traits which often are themselves quantitative in nature. Deriving a network of unobserved latent variables from the individual measured traits can be achieved by applying structural equation (SE) modeling (Schumacker et al, 2010; Rosa et al, 2011). Latent variables are defined as variables that are not directly measurable but can be characterized from several observed phenotypes. Latent variable modeling allows to investigate complex phenomena, such as meat quality, reducing at the same time data dimensionality because many phenotypes are combined to represent few underlying concepts of interest. The SE analysis determines if the theoretical model is supported by the sample data, and the theoretical model can be modified and retested until a model fitting the sample data is acquired (Schumacker et al, 2010)
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