Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection
Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphi...
Ausführliche Beschreibung
Autor*in: |
Pungpapong, Vitara [verfasserIn] |
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E-Artikel |
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Englisch |
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2011 |
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Anmerkung: |
© Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
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Übergeordnetes Werk: |
Enthalten in: BMC proceedings - London : BioMed Central, 2007, 5(2011), Suppl 9 vom: 29. Nov. |
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Übergeordnetes Werk: |
volume:5 ; year:2011 ; number:Suppl 9 ; day:29 ; month:11 |
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DOI / URN: |
10.1186/1753-6561-5-S9-S5 |
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520 | |a Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. | ||
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10.1186/1753-6561-5-S9-S5 doi (DE-627)SPR028444817 (SPR)1753-6561-5-S9-S5-e DE-627 ger DE-627 rakwb eng Pungpapong, Vitara verfasserin aut Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. Minor Allele Frequency (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Genetic Region (dpeaa)DE-He213 Causal SNPs (dpeaa)DE-He213 GAW17 Data (dpeaa)DE-He213 Wang, Libo aut Lin, Yanzhu aut Zhang, Dabao aut Zhang, Min aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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 5 2011 Suppl 9 29 11 |
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10.1186/1753-6561-5-S9-S5 doi (DE-627)SPR028444817 (SPR)1753-6561-5-S9-S5-e DE-627 ger DE-627 rakwb eng Pungpapong, Vitara verfasserin aut Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. Minor Allele Frequency (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Genetic Region (dpeaa)DE-He213 Causal SNPs (dpeaa)DE-He213 GAW17 Data (dpeaa)DE-He213 Wang, Libo aut Lin, Yanzhu aut Zhang, Dabao aut Zhang, Min aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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 5 2011 Suppl 9 29 11 |
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10.1186/1753-6561-5-S9-S5 doi (DE-627)SPR028444817 (SPR)1753-6561-5-S9-S5-e DE-627 ger DE-627 rakwb eng Pungpapong, Vitara verfasserin aut Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. Minor Allele Frequency (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Genetic Region (dpeaa)DE-He213 Causal SNPs (dpeaa)DE-He213 GAW17 Data (dpeaa)DE-He213 Wang, Libo aut Lin, Yanzhu aut Zhang, Dabao aut Zhang, Min aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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 5 2011 Suppl 9 29 11 |
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10.1186/1753-6561-5-S9-S5 doi (DE-627)SPR028444817 (SPR)1753-6561-5-S9-S5-e DE-627 ger DE-627 rakwb eng Pungpapong, Vitara verfasserin aut Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. Minor Allele Frequency (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Genetic Region (dpeaa)DE-He213 Causal SNPs (dpeaa)DE-He213 GAW17 Data (dpeaa)DE-He213 Wang, Libo aut Lin, Yanzhu aut Zhang, Dabao aut Zhang, Min aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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 5 2011 Suppl 9 29 11 |
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10.1186/1753-6561-5-S9-S5 doi (DE-627)SPR028444817 (SPR)1753-6561-5-S9-S5-e DE-627 ger DE-627 rakwb eng Pungpapong, Vitara verfasserin aut Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. Minor Allele Frequency (dpeaa)DE-He213 Rare Variant (dpeaa)DE-He213 Genetic Region (dpeaa)DE-He213 Causal SNPs (dpeaa)DE-He213 GAW17 Data (dpeaa)DE-He213 Wang, Libo aut Lin, Yanzhu aut Zhang, Dabao aut Zhang, Min aut Enthalten in BMC proceedings London : BioMed Central, 2007 5(2011), Suppl 9 vom: 29. Nov. (DE-627)559080840 (DE-600)2411867-9 1753-6561 nnns volume:5 year:2011 number:Suppl 9 day:29 month:11 https://dx.doi.org/10.1186/1753-6561-5-S9-S5 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 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 5 2011 Suppl 9 29 11 |
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Genome-wide association analysis of GAW17 data using an empirical Bayes variable selection |
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Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
abstractGer |
Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
abstract_unstemmed |
Abstract Next-generation sequencing technologies enable us to explore rare functional variants. However, most current statistical techniques are too underpowered to capture signals of rare variants in genome-wide association studies. We propose a supervised coalescing of single-nucleotide polymorphisms to obtain gene-based markers that can stably reveal possible genetic effects related to rare alleles. We use a newly developed empirical Bayes variable selection algorithm to identify associations between studied traits and genetic markers. Using our novel method, we analyzed the three continuous phenotypes in the GAW17 data set across 200 replicates, with intriguing results. © Pungpapong et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( |
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score |
7.4000216 |