Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches

Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysi...
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Autor*in:	Guo, Yuanmei [verfasserIn] Huang, Yixuan Hou, Lijuan Ma, Junwu Chen, Congying Ai, Huashui Huang, Lusheng Ren, Jun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Quantitative Trait Locus Backfat Thickness Fatness Trait Suggestive Locus Traditional Quantitative Trait Locus Mapping

Anmerkung:	© The Author(s) 2017

Übergeordnetes Werk:	Enthalten in: Genetics, selection, evolution - London : BioMed Central, 1989, 49(2017), 1 vom: 14. Feb.
Übergeordnetes Werk:	volume:49 ; year:2017 ; number:1 ; day:14 ; month:02

Links:	Volltext

DOI / URN:	10.1186/s12711-017-0295-4

Katalog-ID:	SPR026810719

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520			\|a Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations.
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allfields	10.1186/s12711-017-0295-4 doi (DE-627)SPR026810719 (SPR)s12711-017-0295-4-e DE-627 ger DE-627 rakwb eng Guo, Yuanmei verfasserin aut Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213 Huang, Yixuan aut Hou, Lijuan aut Ma, Junwu aut Chen, Congying aut Ai, Huashui aut Huang, Lusheng aut Ren, Jun (orcid)0000-0001-6664-3998 aut Enthalten in Genetics, selection, evolution London : BioMed Central, 1989 49(2017), 1 vom: 14. Feb. (DE-627)312849052 (DE-600)2012369-3 1297-9686 nnns volume:49 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s12711-017-0295-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 49 2017 1 14 02
spelling	10.1186/s12711-017-0295-4 doi (DE-627)SPR026810719 (SPR)s12711-017-0295-4-e DE-627 ger DE-627 rakwb eng Guo, Yuanmei verfasserin aut Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213 Huang, Yixuan aut Hou, Lijuan aut Ma, Junwu aut Chen, Congying aut Ai, Huashui aut Huang, Lusheng aut Ren, Jun (orcid)0000-0001-6664-3998 aut Enthalten in Genetics, selection, evolution London : BioMed Central, 1989 49(2017), 1 vom: 14. Feb. (DE-627)312849052 (DE-600)2012369-3 1297-9686 nnns volume:49 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s12711-017-0295-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 49 2017 1 14 02
allfields_unstemmed	10.1186/s12711-017-0295-4 doi (DE-627)SPR026810719 (SPR)s12711-017-0295-4-e DE-627 ger DE-627 rakwb eng Guo, Yuanmei verfasserin aut Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213 Huang, Yixuan aut Hou, Lijuan aut Ma, Junwu aut Chen, Congying aut Ai, Huashui aut Huang, Lusheng aut Ren, Jun (orcid)0000-0001-6664-3998 aut Enthalten in Genetics, selection, evolution London : BioMed Central, 1989 49(2017), 1 vom: 14. Feb. (DE-627)312849052 (DE-600)2012369-3 1297-9686 nnns volume:49 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s12711-017-0295-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 49 2017 1 14 02
allfieldsGer	10.1186/s12711-017-0295-4 doi (DE-627)SPR026810719 (SPR)s12711-017-0295-4-e DE-627 ger DE-627 rakwb eng Guo, Yuanmei verfasserin aut Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213 Huang, Yixuan aut Hou, Lijuan aut Ma, Junwu aut Chen, Congying aut Ai, Huashui aut Huang, Lusheng aut Ren, Jun (orcid)0000-0001-6664-3998 aut Enthalten in Genetics, selection, evolution London : BioMed Central, 1989 49(2017), 1 vom: 14. Feb. (DE-627)312849052 (DE-600)2012369-3 1297-9686 nnns volume:49 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s12711-017-0295-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 49 2017 1 14 02
allfieldsSound	10.1186/s12711-017-0295-4 doi (DE-627)SPR026810719 (SPR)s12711-017-0295-4-e DE-627 ger DE-627 rakwb eng Guo, Yuanmei verfasserin aut Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2017 Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213 Huang, Yixuan aut Hou, Lijuan aut Ma, Junwu aut Chen, Congying aut Ai, Huashui aut Huang, Lusheng aut Ren, Jun (orcid)0000-0001-6664-3998 aut Enthalten in Genetics, selection, evolution London : BioMed Central, 1989 49(2017), 1 vom: 14. Feb. (DE-627)312849052 (DE-600)2012369-3 1297-9686 nnns volume:49 year:2017 number:1 day:14 month:02 https://dx.doi.org/10.1186/s12711-017-0295-4 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 49 2017 1 14 02
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author	Guo, Yuanmei
spellingShingle	Guo, Yuanmei misc Quantitative Trait Locus misc Backfat Thickness misc Fatness Trait misc Suggestive Locus misc Traditional Quantitative Trait Locus Mapping Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
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topic_title	Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches Quantitative Trait Locus (dpeaa)DE-He213 Backfat Thickness (dpeaa)DE-He213 Fatness Trait (dpeaa)DE-He213 Suggestive Locus (dpeaa)DE-He213 Traditional Quantitative Trait Locus Mapping (dpeaa)DE-He213
topic	misc Quantitative Trait Locus misc Backfat Thickness misc Fatness Trait misc Suggestive Locus misc Traditional Quantitative Trait Locus Mapping
topic_unstemmed	misc Quantitative Trait Locus misc Backfat Thickness misc Fatness Trait misc Suggestive Locus misc Traditional Quantitative Trait Locus Mapping
topic_browse	misc Quantitative Trait Locus misc Backfat Thickness misc Fatness Trait misc Suggestive Locus misc Traditional Quantitative Trait Locus Mapping
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title	Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
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title_full	Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
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author_browse	Guo, Yuanmei Huang, Yixuan Hou, Lijuan Ma, Junwu Chen, Congying Ai, Huashui Huang, Lusheng Ren, Jun
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title_sort	genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
title_auth	Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
abstract	Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. © The Author(s) 2017
abstractGer	Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. © The Author(s) 2017
abstract_unstemmed	Background Genome-wide association studies (GWAS) have been extensively used to identify genomic regions associated with a variety of phenotypic traits in pigs. Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. The two significant SNPs on SSC7 and X had pleiotropic effects in the $ F_{2} $, Erhualian and Sutai populations. © The Author(s) 2017
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title_short	Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches
url	https://dx.doi.org/10.1186/s12711-017-0295-4
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author2	Huang, Yixuan Hou, Lijuan Ma, Junwu Chen, Congying Ai, Huashui Huang, Lusheng Ren, Jun
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Until now, most GWAS have explored single-trait association models. Here, we conducted both single- and multi-trait GWAS and a meta-analysis for nine fatness and growth traits on 2004 pigs from four diverse populations, including a White Duroc × Erhualian $ F_{2} $ intercross population and Chinese Sutai, Laiwu and Erhualian populations. Results We identified 44 chromosomal regions that were associated with the nine traits, including four genome-wide significant single nucleotide polymorphisms (SNPs) on SSC2 (SSC for Sus scrofa chromosome), 4, 7 and X. Compared to the single-population GWAS, the meta-analysis was less powerful for the identification of SNPs with population-specific effects but more powerful for the detection of SNPs with population-shared effects. Multiple-trait analysis reduced the power to detect trait-specific SNPs but significantly enhanced the power to identify common SNPs across traits. The SNP on SSC7 had pleiotropic effects on the nine traits in the $ F_{2} $ and Erhualian populations. Another pleiotropic SNP was observed on SSCX for these traits in the $ F_{2} $ and Sutai populations. Both population-specific and shared SNPs were identified in this study, thus reflecting the complex genetic architecture of pig growth and fatness traits. Conclusions We demonstrate that the multi-trait method and the meta-analysis on multiple populations can be used to increase the power of GWAS. 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Genome-wide detection of genetic markers associated with growth and fatness in four pig populations using four approaches

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