Evidence map›Paper›PMID 30555508›Full record

ArticleFrontiers in genetics2018

Structural Equation Modeling and Whole-Genome Scans Uncover Chromosome Regions and Enriched Pathways for Carcass and Meat Quality in Beef.

Joel D Leal-Gutiérrez, Fernanda M Rezende, Mauricio A Elzo, Dwain Johnson, Francisco Peñagaricano, Raluca G Mateescu

Abstract read
In one paragraph

Article in Frontiers in genetics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Comparative Analysis of Recursive and Alternative Modelling Approaches Considering Body Condition Score for Genetic Evaluation of Mature Cow Weight.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2026
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  5. The association ofArchives animal breeding · 2025
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  8. Genomic structural equation modelling provides a whole-system approach for the future crop breeding.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Joel D Leal-GutiérrezDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.
Fernanda M RezendeDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.
Mauricio A ElzoDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.
Dwain JohnsonDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.
Francisco PeñagaricanoDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.
Raluca G MateescuDepartment of Animal Sciences, University of Florida, Gainesville, FL, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

cellular compartmentalizationcellular differentiationcellular proliferationfat depositionpostmortem proteolysis

Identifiers

PMID30555508
PMCPMC6282042

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.