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...

Descripción completa

Detalles Bibliográficos
Autores principales: Liu, Xianxian, Zhang, Junjie, Xiong, Xinwei, Chen, Congying, Xing, Yuyun, Duan, Yanyu, Xiao, Shijun, Yang, Bin, Ma, Junwu
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