Genomic-polygenic and polygenic predictions for nine ultrasound and carcass traits in Angus-Brahman multibreed cattle using three sets of genotypes

Elzo, Mauricio A.; Mateescu, Raluca G.; Johnson, D. D.; Scheffler, J. M.; Carr, Chad; Rae, D. O.; Wasdin, J. G.; Driver, M. D.; Driver, Joseph

doi:10.1016/j.livsci.2017.05.027

Cited by 18 publications

(28 citation statements)

References 32 publications

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“…The estimated heritabilities for the latent phenotypes carcass quality and taste quality were 0.46 ± 0.10 and 0.36 ± 0.10, respectively, and their genetic correlation was 0.87 ± 0.09. Genetic parameters for individual growth, carcass quality, and meat palatability traits have been estimated in the present population and reported previously ( Elzo et al, 2012 , 2013 , 2015a , 2016 , 2017 ).…”

Section: Resultssupporting

confidence: 53%

“…Therefore, growth rate can be considered an important factor with direct effect on carcass composition and a fast growth rate resulting from a high plane of nutrition would lead to animals fattening earlier ( Lawrie, 1998 ; del Campo et al, 2008 ). Low to medium genetic and phenotypic correlations among growth, carcass quality and meat quality traits, such as hot carcass weight and fat over the ribeye, hot carcass weight and shear force, marbling and tenderness have been reported in different breeds ( Johnston et al, 2003 ; Reverter et al, 2003 ; Mateescu et al, 2015 ; Elzo et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Resultssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…Genetic trends in the evaluated period were estimated by the relationship of the GV obtained by GPM with the non-parameterized smoothed effect of the year of birth, using a generalized additive model (gam). Only GVs with an accuracy (formula described by Elzo et al, 2017 ) greater than 0.3 were considered. The gam was:…”

Section: Methodsmentioning

confidence: 99%

Genetic parameters and trends for growth traits in Blanco Orejinegro cattle

Toro

Burgos-Paz

Elzo

et al. 2020

Translational Animal Science

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Since 1940, efforts have been made to preserve the Blanco Orejinegro (BON) cattle breed by maintaining gene banks. Nine years ago, a BON genetic improvement program was implemented to increase genetic gain based on control of productivity and the use of performance tests, polygenic and genomic evaluations, and selection indices. The objective of this study was to estimate genetic parameters and trends for growth traits by using polygenic (PM) and genomic-polygenic (GPM) models. Productive information from years 1980 to 2019 was used. The following data were used: 7304 birth weight (BW) records, 1281 records of body weight adjusted to 120 days (W120), 4791 records of weight adjusted to 240 days (W240), 3339 records of weight adjusted to 480 days (W480), and 1364 records of weight adjusted to 720 days (W720). The relationship matrix included 13612 pure animals belonging to the Colombian Corporation for Agricultural Research - AGROSAVIA and the Producer Network program. A total of 1224 individuals were genotyped with chips of different densities and all were imputed up to 50932 SNPs. Polygenic and genomic-polygenic models were used to estimate genetic parameters and correlations between the genetic values estimated by each model. A generalized additive model with smoothing was used to estimate trends of genetic values from 1980 to 2019. Heritabilities between 0.29 and 0.40, genetic correlations between 0.13 and 0.94, and phenotypic correlations between 0.23 and 0.72 were observed. Close-to-zero genetic growth was observed for BW and W120. Genetic trends for the other traits were positive, with higher growth in the last seven years. The heritabilities observed in this population indicate that the traits measured would respond to selection. Greater genetic progress can be achieved in W240, W480 and W720 by continuing with genetic evaluations and developing performance tests and strategies to make genetic material readily available to producers.

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“…Quincey Farms, Chiefland, Florida), where they were provided a standard feedlot diet based on corn, protein, vitamins, and minerals until they reached a subcutaneous fat thickness over the ribeye of approximately 1.27 cm [43]. Cattle were transported to a commercial processing facility (FPL Food LLC., Augusta, Georgia) one day prior to harvest.…”

Section: Discussionmentioning

confidence: 99%

Identification of eQTLs and sQTLs associated with meat quality in beef

Gutierrez

Elzo

Mateescu

2019

Preprint

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Background: Transcription has a substantial genetic control and genetic dissection of gene expression could help us understand the genetic architecture of complex phenotypes such as meat quality in cattle. The objectives of the present research were: 1) to perform eQTL and sQTL mapping analyses for meat quality traits in longissimus dorsi muscle; 2) to uncover genes whose expression is influenced by local or distant genetic variation; 3) to identify expression and splicing hot spots; and 4) to uncover genomic regions affecting the expression of multiple genes. Results: Eighty steers were selected for phenotyping, genotyping and RNA-seq evaluation. A panel of traits related to meat quality was recorded in longissimus dorsi muscle. Information on 112,042 SNPs and expression data on 8,588 autosomal genes and 87,770 exons from 8,467 genes were included in an expression and splicing quantitative trait loci (QTL) mapping (eQTL and sQTL, respectively). A gene, exon and isoform differential expression analysis previously carried out in this population identified 1,352 genes, referred to as DEG, as explaining part of the variability associated with meat quality traits. The eQTL and sQTL mapping was performed using a linear regression model in the R package Matrix eQTL. Genotype and year of birth were included as fixed effects, and population structure was accounted for by including as a covariate the first PC from a PCA analysis on genotypic data. The identified QTLs were classified as cis or trans using 1 Mb as the maximum distance between the associated SNP and the gene being analyzed. A total of 8,377 eQTLs were identified, including 75.6% trans, 10.4% cis, 12.5% DEG trans and 1.5% DEG cis; while 11,929 sQTLs were uncovered: 66.1% trans, 16.9% DEG trans, 14% cis and 3% DEG cis. Twenty-seven expression master regulators and 13 splicing master regulators were identified and were classified as membrane-associated or cytoskeletal proteins, transcription factors or DNA methylases. These genes could control the expression of other genes through cell signaling or by a direct transcriptional activation/repression mechanism. Conclusion: In the present analysis, we show that eQTL and sQTL mapping makes possible positional identification of gene and isoform expression regulators.

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Genomic-polygenic and polygenic predictions for nine ultrasound and carcass traits in Angus-Brahman multibreed cattle using three sets of genotypes

Cited by 18 publications

References 32 publications

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

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

Genetic parameters and trends for growth traits in Blanco Orejinegro cattle

Identification of eQTLs and sQTLs associated with meat quality in beef

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