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Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness
Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and m...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386181/ https://www.ncbi.nlm.nih.gov/pubmed/35991540 http://dx.doi.org/10.3389/fgene.2022.935238 |
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author | Silva-Vignato, Bárbara Cesar, Aline Silva Mello Afonso, Juliana Moreira, Gabriel Costa Monteiro Poleti, Mirele Daiana Petrini, Juliana Garcia, Ingrid Soares Clemente, Luan Gaspar Mourão, Gerson Barreto Regitano, Luciana Correia de Almeida Coutinho, Luiz Lehmann |
author_facet | Silva-Vignato, Bárbara Cesar, Aline Silva Mello Afonso, Juliana Moreira, Gabriel Costa Monteiro Poleti, Mirele Daiana Petrini, Juliana Garcia, Ingrid Soares Clemente, Luan Gaspar Mourão, Gerson Barreto Regitano, Luciana Correia de Almeida Coutinho, Luiz Lehmann |
author_sort | Silva-Vignato, Bárbara |
collection | PubMed |
description | Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR < 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle. |
format | Online Article Text |
id | pubmed-9386181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93861812022-08-19 Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness Silva-Vignato, Bárbara Cesar, Aline Silva Mello Afonso, Juliana Moreira, Gabriel Costa Monteiro Poleti, Mirele Daiana Petrini, Juliana Garcia, Ingrid Soares Clemente, Luan Gaspar Mourão, Gerson Barreto Regitano, Luciana Correia de Almeida Coutinho, Luiz Lehmann Front Genet Genetics Understanding the architecture of gene expression is fundamental to unravel the molecular mechanisms regulating complex traits in bovine, such as intramuscular fat content (IMF) and backfat thickness (BFT). These traits are economically important for the beef industry since they affect carcass and meat quality. Our main goal was to identify gene expression regulatory polymorphisms within genomic regions (QTL) associated with IMF and BFT in Nellore cattle. For that, we used RNA-Seq data from 193 Nellore steers to perform SNP calling analysis. Then, we combined the RNA-Seq SNP and a high-density SNP panel to obtain a new dataset for further genome-wide association analysis (GWAS), totaling 534,928 SNPs. GWAS was performed using the Bayes B model. Twenty-one relevant QTL were associated with our target traits. The expression quantitative trait loci (eQTL) analysis was performed using Matrix eQTL with the complete SNP dataset and 12,991 genes, revealing a total of 71,033 cis and 36,497 trans-eQTL (FDR < 0.05). Intersecting with QTL for IMF, we found 231 eQTL regulating the expression levels of 117 genes. Within those eQTL, three predicted deleterious SNPs were identified. We also identified 109 eQTL associated with BFT and affecting the expression of 54 genes. This study revealed genomic regions and regulatory SNPs associated with fat deposition in Nellore cattle. We highlight the transcription factors FOXP4, FOXO3, ZSCAN2, and EBF4, involved in lipid metabolism-related pathways. These results helped us to improve our knowledge about the genetic architecture behind important traits in cattle. Frontiers Media S.A. 2022-08-04 /pmc/articles/PMC9386181/ /pubmed/35991540 http://dx.doi.org/10.3389/fgene.2022.935238 Text en Copyright © 2022 Silva-Vignato, Cesar, Afonso, Moreira, Poleti, Petrini, Garcia, Clemente, Mourão, Regitano and Coutinho. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Silva-Vignato, Bárbara Cesar, Aline Silva Mello Afonso, Juliana Moreira, Gabriel Costa Monteiro Poleti, Mirele Daiana Petrini, Juliana Garcia, Ingrid Soares Clemente, Luan Gaspar Mourão, Gerson Barreto Regitano, Luciana Correia de Almeida Coutinho, Luiz Lehmann Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title | Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title_full | Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title_fullStr | Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title_full_unstemmed | Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title_short | Integrative Analysis Between Genome-Wide Association Study and Expression Quantitative Trait Loci Reveals Bovine Muscle Gene Expression Regulatory Polymorphisms Associated With Intramuscular Fat and Backfat Thickness |
title_sort | integrative analysis between genome-wide association study and expression quantitative trait loci reveals bovine muscle gene expression regulatory polymorphisms associated with intramuscular fat and backfat thickness |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9386181/ https://www.ncbi.nlm.nih.gov/pubmed/35991540 http://dx.doi.org/10.3389/fgene.2022.935238 |
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