Implementing meta - analysis from genome - wide association studies for pork quality traits
Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a metaanalysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining z-scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [CAPN1] and calpastatin [CAST]), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit [PRKAG3]). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial [ACSF3]) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [GYS1] and ferritin, light polypeptide [FTL]). Thus, implementation of MA GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.
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Subjects: | CANDIDATE GENES, GENOME - WIDE ASSOCIATION, MEAT QUALITY, META - ANALYSIS, PIGS, , |
Online Access: | http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46307 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
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CANDIDATE GENES GENOME - WIDE ASSOCIATION MEAT QUALITY META - ANALYSIS PIGS CANDIDATE GENES GENOME - WIDE ASSOCIATION MEAT QUALITY META - ANALYSIS PIGS |
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CANDIDATE GENES GENOME - WIDE ASSOCIATION MEAT QUALITY META - ANALYSIS PIGS CANDIDATE GENES GENOME - WIDE ASSOCIATION MEAT QUALITY META - ANALYSIS PIGS Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, D. A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro Implementing meta - analysis from genome - wide association studies for pork quality traits |
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Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a metaanalysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining z-scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [CAPN1] and calpastatin [CAST]), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit [PRKAG3]). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial [ACSF3]) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [GYS1] and ferritin, light polypeptide [FTL]). Thus, implementation of MA GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations. |
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CANDIDATE GENES GENOME - WIDE ASSOCIATION MEAT QUALITY META - ANALYSIS PIGS |
author |
Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, D. A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro |
author_facet |
Bernal Rubio, Yeni Liliana Gualdrón Duarte, José Luis Bates, R. O. Ernst, C. W. Nonneman, D. Rohrer, G. A. King, D. A. Shackelford, S. D. Wheeler, T. L. Cantet, Rodolfo Juan Carlos Steibel, Juan Pedro |
author_sort |
Bernal Rubio, Yeni Liliana |
title |
Implementing meta - analysis from genome - wide association studies for pork quality traits |
title_short |
Implementing meta - analysis from genome - wide association studies for pork quality traits |
title_full |
Implementing meta - analysis from genome - wide association studies for pork quality traits |
title_fullStr |
Implementing meta - analysis from genome - wide association studies for pork quality traits |
title_full_unstemmed |
Implementing meta - analysis from genome - wide association studies for pork quality traits |
title_sort |
implementing meta - analysis from genome - wide association studies for pork quality traits |
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http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46307 http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber= |
work_keys_str_mv |
AT bernalrubioyenililiana implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT gualdronduartejoseluis implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT batesro implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT ernstcw implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT nonnemand implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT rohrerga implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT kingda implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT shackelfordsd implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT wheelertl implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT cantetrodolfojuancarlos implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits AT steibeljuanpedro implementingmetaanalysisfromgenomewideassociationstudiesforporkqualitytraits |
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KOHA-OAI-AGRO:463072022-04-13T10:00:01Zhttp://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=46307http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=http://ceiba.agro.uba.ar/cgi-bin/koha/opac-detail.pl?biblionumber=AAGImplementing meta - analysis from genome - wide association studies for pork quality traitsBernal Rubio, Yeni LilianaGualdrón Duarte, José LuisBates, R. O.Ernst, C. W.Nonneman, D.Rohrer, G. A.King, D. A.Shackelford, S. D.Wheeler, T. L.Cantet, Rodolfo Juan CarlosSteibel, Juan Pedrotextengapplication/pdfPork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a metaanalysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining z-scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [CAPN1] and calpastatin [CAST]), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit [PRKAG3]). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial [ACSF3]) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [GYS1] and ferritin, light polypeptide [FTL]). Thus, implementation of MA GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.Pork quality plays an important role in the meat processing industry. Thus, different methodologies have been implemented to elucidate the genetic architecture of traits affecting meat quality. One of the most common and widely used approaches is to perform genome-wide association (GWA) studies. However, a limitation of many GWA in animal breeding is the limited power due to small sample sizes in animal populations. One alternative is to implement a metaanalysis of GWA (MA-GWA) combining results from independent association studies. The objective of this study was to identify significant genomic regions associated with meat quality traits by performing MA-GWA for 8 different traits in 3 independent pig populations. Results from MA-GWA were used to search for genes possibly associated with the set of evaluated traits. Data from 3 pig data sets (U.S. Meat Animal Research Center, commercial, and Michigan State University Pig Resource Population) were used. A MA was implemented by combining z-scores derived for each SNP in every population and then weighting them using the inverse of estimated variance of SNP effects. A search for annotated genes retrieved genes previously reported as candidates for shear force (calpain-1 catalytic subunit [CAPN1] and calpastatin [CAST]), as well as for ultimate pH, purge loss, and cook loss (protein kinase, AMP-activated, γ 3 noncatalytic subunit [PRKAG3]). In addition, novel candidate genes were identified for intramuscular fat and cook loss (acyl-CoA synthetase family member 3 mitochondrial [ACSF3]) and for the objective measure of muscle redness, CIE a* (glycogen synthase 1, muscle [GYS1] and ferritin, light polypeptide [FTL]). Thus, implementation of MA GWA allowed integration of results for economically relevant traits and identified novel genes to be tested as candidates for meat quality traits in pig populations.CANDIDATE GENESGENOME - WIDE ASSOCIATIONMEAT QUALITYMETA - ANALYSISPIGSJournal of animal science |