Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Dec 21;14(1):6.
doi: 10.3390/metabo14010006.

Genome-Wide Association (GWAS) Applied to Carcass and Meat Traits of Nellore Cattle

Hugo Borges Dos Reis  1 , Minos Esperândio Carvalho  1 , Rafael Espigolan  2 , Mirele Daiana Poleti  1 , Dewison Ricardo Ambrizi  1 , Mariana Piatto Berton  3 , José Bento Sterman Ferraz  1 , Elisângela Chicaroni de Mattos Oliveira  1 , Joanir Pereira Eler  1
Affiliations

Genome-Wide Association (GWAS) Applied to Carcass and Meat Traits of Nellore Cattle

Hugo Borges Dos Reis et al. Metabolites. .

Abstract

The meat market has enormous importance for the world economy, and the quality of the product offered to the consumer is fundamental for the success of the sector. In this study, we analyzed a database which contained information on 2470 animals from a commercial farm in the state of São Paulo, Brazil. Of this total, 2181 animals were genotyped, using 777,962 single-nucleotide polymorphisms (SNPs). After quality control analysis, 468,321 SNPs provided information on the number of genotyped animals. Genome-wide association analyses (GWAS) were performed for the characteristics of the rib eye area (REA), subcutaneous fat thickness (SFT), shear force at 7 days' ageing (SF7), and intramuscular fat (IMF), with the aid of the single-step genomic best linear unbiased prediction (ssGBLUP) method, with the purpose of identifying possible genomic windows (~1 Mb) responsible for explaining at least 0.5% of the genetic variance of the traits under analysis (≥0.5%). These genomic regions were used in a gene search and enrichment analyses using MeSH terms. The distributed heritability coefficients were 0.14, 0.20, 0.18, and 0.21 for REA, SFT, SF7, and IMF, respectively. The GWAS results indicated significant genomic windows for the traits of interest in a total of 17 chromosomes. Enrichment analyses showed the following significant terms (FDR ≤ 0.05) associated with the characteristics under study: for the REA, heat stress disorders and life cycle stages; for SFT, insulin and nonesterified fatty acids; for SF7, apoptosis and heat shock proteins (HSP27); and for IMF, metalloproteinase 2. In addition, KEGG (Kyoto encyclopedia of genes and genomes) enrichment analysis allowed us to highlight important metabolic pathways related to the studied phenotypes, such as the growth hormone synthesis, insulin-signaling, fatty acid metabolism, and ABC transporter pathways. The results obtained provide a better understanding of the molecular processes involved in the expression of the studied characteristics and may contribute to the design of selection strategies and future studies aimed at improving the productivity of Nellore cattle.

Keywords: beef cattle; genomic selection; intramuscular fat; ssGBLUP; tenderness.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Manhattan plots of the posterior means of the percentages of genetic variance explained by each window of 1 Mb SNPs along the 29 autosomal chromosomes for the analyzed carcass quality traits: (a) rib eye area (REA); (b) subcutaneous fat thickness (SFT). Red lines indicate the threshold for considering significant SNP windows (0.5%). Colors indicate different chromosomes.
Figure 2
Figure 2
Manhattan plots of the posterior means of the percentages of genetic variance explained by each window of 1 Mb SNPs along the 29 autosomal chromosomes for the analyzed carcass quality traits: (a) shear force at 7 days’ ageing (SF7); (b) intramuscular fat (IMF). Red lines indicate the threshold for considering significant SNP windows (0.5%). Colors indicate different chromosomes.
Figure 3
Figure 3
KEGG enrichment analysis: (a) comparative pathway analysis for REA, SFT, SF, and IMF groups. “GeneRatio” indicates the proportion of genes within a specific pathway relative to the total number of genes enriched (expressed as a fraction in parentheses beside the group name). The “P.adjust” value represents statistical significance, with smaller values depicted in red and larger values in blue. (b) KEGG network analysis of enriched genes. Nodes in the network represent pathways, interconnected with genes belonging to those pathways. Central pathways or genes with higher connectivity are highlighted as they play a more pivotal role in this network. These improvements make the text clearer and more concise, ensuring that the information is presented in a more organized and reader-friendly manner. Size represents the number of genes in that pathway.
Figure 3
Figure 3
KEGG enrichment analysis: (a) comparative pathway analysis for REA, SFT, SF, and IMF groups. “GeneRatio” indicates the proportion of genes within a specific pathway relative to the total number of genes enriched (expressed as a fraction in parentheses beside the group name). The “P.adjust” value represents statistical significance, with smaller values depicted in red and larger values in blue. (b) KEGG network analysis of enriched genes. Nodes in the network represent pathways, interconnected with genes belonging to those pathways. Central pathways or genes with higher connectivity are highlighted as they play a more pivotal role in this network. These improvements make the text clearer and more concise, ensuring that the information is presented in a more organized and reader-friendly manner. Size represents the number of genes in that pathway.
Figure 4
Figure 4
GO enrichment analysis for biological processes (BPs). Comparative pathway analysis of REA, SFT, SF, and IMF groups. “GeneRatio” indicates the proportion of genes within a specific pathway relative to the total number of genes enriched (expressed as a fraction in parentheses beside the group name). The “P.adjust” value represents statistical significance, with smaller values depicted in red and larger values in blue. BP is indicative of the processes behind the gene function.
Figure 5
Figure 5
GO enrichment analysis for cellular components (CCs). Comparative pathway analysis of REA, SFT, SF, and IMF groups. “GeneRatio” indicates the proportion of genes within a specific pathway relative to the total number of genes enriched (expressed as a fraction in parentheses beside the group name). The “P.adjust” value represents statistical significance, with smaller values depicted in red and larger values in blue. CCs suggest where a function occurs, and molecular function indicates the action of a product.
Figure 6
Figure 6
GO enrichment analysis for molecular function (MF). Comparative pathway analysis of REA, SFT, SF, and IMF Groups. “GeneRatio” indicates the proportion of genes within a specific pathway relative to the total number of genes enriched (expressed as a fraction in parentheses beside the group name). The “P.adjust” value represents statistical significance, with smaller values depicted in red and larger values in blue. Molecular functions are the actions of a product.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Brazilian Beff . Paper Knowledge: Toward a Media History of Documents. Volume 7. Duke University Press; Durham, NC, USA: 2022. Abiec BEEF REPORT Perfil Da Pecuária No Brasil 2022; pp. 1–70.
    1. Henchion M.M., Mccarthy M., Resconi V.C. Beef Quality Attributes: A Systematic Review of Consumer Perspectives. MESC. 2017;128:1–7. doi: 10.1016/j.meatsci.2017.01.006. - DOI - PubMed
    1. Webb E.C., Neill H.A.O. The Animal Fat Paradox and Meat Quality. Meat Sci. 2008;80:28–36. doi: 10.1016/j.meatsci.2008.05.029. - DOI - PubMed
    1. Frank D., Ball A., Hughes J., Krishnamurthy R., Piyasiri U., Stark J., Watkins P., Warner R. Sensory and Flavor Chemistry Characteristics of Australian Beef: In Fl Uence of Intramuscular Fat, Feed, and Breed. J. Agric. Food Chem. 2016;64:4299–4311. doi: 10.1021/acs.jafc.6b00160. - DOI - PubMed
    1. Sainz R.D., Araujo F.R.C. Tipificação de Carcaças de Bovinos e Suínos; Proceedings of the Congresso Brasileiro de Ciencia e Tecnologia de Carne; São Pedro, SP, Brazil. 2001.