Cargando…
An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs
Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we...
Autores principales: | , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8567094/ https://www.ncbi.nlm.nih.gov/pubmed/34745221 http://dx.doi.org/10.3389/fgene.2021.748070 |
_version_ | 1784594161677107200 |
---|---|
author | Liu, Xianxian Zhang, Junjie Xiong, Xinwei Chen, Congying Xing, Yuyun Duan, Yanyu Xiao, Shijun Yang, Bin Ma, Junwu |
author_facet | Liu, Xianxian Zhang, Junjie Xiong, Xinwei Chen, Congying Xing, Yuyun Duan, Yanyu Xiao, Shijun Yang, Bin Ma, Junwu |
author_sort | Liu, Xianxian |
collection | PubMed |
description | Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig’s muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10(−4)) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10(−5)), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality. |
format | Online Article Text |
id | pubmed-8567094 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85670942021-11-05 An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs Liu, Xianxian Zhang, Junjie Xiong, Xinwei Chen, Congying Xing, Yuyun Duan, Yanyu Xiao, Shijun Yang, Bin Ma, Junwu Front Genet Genetics Understanding the genetic factors behind meat quality traits is of great significance to animal breeding and production. We previously conducted a genome-wide association study (GWAS) for meat quality traits in a White Duroc × Erhualian F2 pig population using Illumina porcine 60K SNP data. Here, we further investigate the functional candidate genes and their network modules associated with meat quality traits by integrating transcriptomics and GWAS information. Quantitative trait transcript (QTT) analysis, gene expression QTL (eQTL) mapping, and weighted gene co-expression network analysis (WGCNA) were performed using the digital gene expression (DGE) data from 493 F2 pig’s muscle and liver samples. Among the quantified 20,108 liver and 23,728 muscle transcripts, 535 liver and 1,014 muscle QTTs corresponding to 416 and 721 genes, respectively, were found to be significantly (p < 5 × 10(−4)) correlated with 22 meat quality traits measured on longissiums dorsi muscle (LM) or semimembranosus muscle (SM). Transcripts associated with muscle glycolytic potential (GP) and pH values were enriched for genes involved in metabolic process. There were 42 QTTs (for 32 genes) shared by liver and muscle tissues, of which 10 QTTs represent GP- and/or pH-related genes, such as JUNB, ATF3, and PPP1R3B. Furthermore, a genome-wide eQTL mapping revealed a total of 3,054 eQTLs for all annotated transcripts in muscle (p < 2.08 × 10(−5)), including 1,283 cis-eQTLs and 1771 trans-eQTLs. In addition, WGCNA identified five modules relevant to glycogen metabolism pathway and highlighted the connections between variations in meat quality traits and genes involved in energy process. Integrative analysis of GWAS loci, eQTL, and QTT demonstrated GALNT15/GALNTL2 and HTATIP2 as strong candidate genes for drip loss and pH drop from postmortem 45 min to 24 h, respectively. Our findings provide valuable insights into the genetic basis of meat quality traits and greatly expand the number of candidate genes that may be valuable for future functional analysis and genetic improvement of meat quality. Frontiers Media S.A. 2021-10-21 /pmc/articles/PMC8567094/ /pubmed/34745221 http://dx.doi.org/10.3389/fgene.2021.748070 Text en Copyright © 2021 Liu, Zhang, Xiong, Chen, Xing, Duan, Xiao, Yang and Ma. 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 Liu, Xianxian Zhang, Junjie Xiong, Xinwei Chen, Congying Xing, Yuyun Duan, Yanyu Xiao, Shijun Yang, Bin Ma, Junwu An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title | An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title_full | An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title_fullStr | An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title_full_unstemmed | An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title_short | An Integrative Analysis of Transcriptome and GWAS Data to Identify Potential Candidate Genes Influencing Meat Quality Traits in Pigs |
title_sort | integrative analysis of transcriptome and gwas data to identify potential candidate genes influencing meat quality traits in pigs |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8567094/ https://www.ncbi.nlm.nih.gov/pubmed/34745221 http://dx.doi.org/10.3389/fgene.2021.748070 |
work_keys_str_mv | AT liuxianxian anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT zhangjunjie anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xiongxinwei anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT chencongying anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xingyuyun anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT duanyanyu anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xiaoshijun anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT yangbin anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT majunwu anintegrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT liuxianxian integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT zhangjunjie integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xiongxinwei integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT chencongying integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xingyuyun integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT duanyanyu integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT xiaoshijun integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT yangbin integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs AT majunwu integrativeanalysisoftranscriptomeandgwasdatatoidentifypotentialcandidategenesinfluencingmeatqualitytraitsinpigs |