Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems t...
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Autor*in:	Teber, Erdahl T [verfasserIn] Liu, Jason Y Ballouz, Sara Fatkin, Diane Wouters, Merridee A

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Search Space Disease Gene Significant SNPs Enrichment Ratio Consensus Approach

Anmerkung:	© Teber et al; licensee BioMed Central Ltd. 2009. 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 (

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 10(2009), Suppl 1 vom: 30. Jan.
Übergeordnetes Werk:	volume:10 ; year:2009 ; number:Suppl 1 ; day:30 ; month:01

Links:	Volltext

DOI / URN:	10.1186/1471-2105-10-S1-S69

Katalog-ID:	SPR026855690

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520			\|a Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes.
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700	1		\|a Fatkin, Diane \|4 aut
700	1		\|a Wouters, Merridee A \|4 aut
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allfields	10.1186/1471-2105-10-S1-S69 doi (DE-627)SPR026855690 (SPR)1471-2105-10-S1-S69-e DE-627 ger DE-627 rakwb eng Teber, Erdahl T verfasserin aut Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teber et al; licensee BioMed Central Ltd. 2009. 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 ( Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213 Liu, Jason Y aut Ballouz, Sara aut Fatkin, Diane aut Wouters, Merridee A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), Suppl 1 vom: 30. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:Suppl 1 day:30 month:01 https://dx.doi.org/10.1186/1471-2105-10-S1-S69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 Suppl 1 30 01
spelling	10.1186/1471-2105-10-S1-S69 doi (DE-627)SPR026855690 (SPR)1471-2105-10-S1-S69-e DE-627 ger DE-627 rakwb eng Teber, Erdahl T verfasserin aut Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teber et al; licensee BioMed Central Ltd. 2009. 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 ( Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213 Liu, Jason Y aut Ballouz, Sara aut Fatkin, Diane aut Wouters, Merridee A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), Suppl 1 vom: 30. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:Suppl 1 day:30 month:01 https://dx.doi.org/10.1186/1471-2105-10-S1-S69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 Suppl 1 30 01
allfields_unstemmed	10.1186/1471-2105-10-S1-S69 doi (DE-627)SPR026855690 (SPR)1471-2105-10-S1-S69-e DE-627 ger DE-627 rakwb eng Teber, Erdahl T verfasserin aut Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teber et al; licensee BioMed Central Ltd. 2009. 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 ( Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213 Liu, Jason Y aut Ballouz, Sara aut Fatkin, Diane aut Wouters, Merridee A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), Suppl 1 vom: 30. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:Suppl 1 day:30 month:01 https://dx.doi.org/10.1186/1471-2105-10-S1-S69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 Suppl 1 30 01
allfieldsGer	10.1186/1471-2105-10-S1-S69 doi (DE-627)SPR026855690 (SPR)1471-2105-10-S1-S69-e DE-627 ger DE-627 rakwb eng Teber, Erdahl T verfasserin aut Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teber et al; licensee BioMed Central Ltd. 2009. 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 ( Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213 Liu, Jason Y aut Ballouz, Sara aut Fatkin, Diane aut Wouters, Merridee A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), Suppl 1 vom: 30. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:Suppl 1 day:30 month:01 https://dx.doi.org/10.1186/1471-2105-10-S1-S69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 Suppl 1 30 01
allfieldsSound	10.1186/1471-2105-10-S1-S69 doi (DE-627)SPR026855690 (SPR)1471-2105-10-S1-S69-e DE-627 ger DE-627 rakwb eng Teber, Erdahl T verfasserin aut Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Teber et al; licensee BioMed Central Ltd. 2009. 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 ( Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213 Liu, Jason Y aut Ballouz, Sara aut Fatkin, Diane aut Wouters, Merridee A aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 10(2009), Suppl 1 vom: 30. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:10 year:2009 number:Suppl 1 day:30 month:01 https://dx.doi.org/10.1186/1471-2105-10-S1-S69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2009 Suppl 1 30 01
language	English
source	Enthalten in BMC bioinformatics 10(2009), Suppl 1 vom: 30. Jan. volume:10 year:2009 number:Suppl 1 day:30 month:01
sourceStr	Enthalten in BMC bioinformatics 10(2009), Suppl 1 vom: 30. Jan. volume:10 year:2009 number:Suppl 1 day:30 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Search Space Disease Gene Significant SNPs Enrichment Ratio Consensus Approach
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Teber, Erdahl T @@aut@@ Liu, Jason Y @@aut@@ Ballouz, Sara @@aut@@ Fatkin, Diane @@aut@@ Wouters, Merridee A @@aut@@
publishDateDaySort_date	2009-01-30T00:00:00Z
hierarchy_top_id	326644814
id	SPR026855690
language_de	englisch
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author	Teber, Erdahl T
spellingShingle	Teber, Erdahl T misc Search Space misc Disease Gene misc Significant SNPs misc Enrichment Ratio misc Consensus Approach Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
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topic_title	Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies Search Space (dpeaa)DE-He213 Disease Gene (dpeaa)DE-He213 Significant SNPs (dpeaa)DE-He213 Enrichment Ratio (dpeaa)DE-He213 Consensus Approach (dpeaa)DE-He213
topic	misc Search Space misc Disease Gene misc Significant SNPs misc Enrichment Ratio misc Consensus Approach
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title	Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
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title_full	Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
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title_sort	comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
title_auth	Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
abstract	Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. © Teber et al; licensee BioMed Central Ltd. 2009. 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	Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. © Teber et al; licensee BioMed Central Ltd. 2009. 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	Background Automated candidate gene prediction systems allow geneticists to hone in on disease genes more rapidly by identifying the most probable candidate genes linked to the disease phenotypes under investigation. Here we assessed the ability of eight different candidate gene prediction systems to predict disease genes in intervals previously associated with type 2 diabetes by benchmarking their performance against genes implicated by recent genome-wide association studies. Results Using a search space of 9556 genes, all but one of the systems pruned the genome in favour of genes associated with moderate to highly significant SNPs. Of the 11 genes associated with highly significant SNPs identified by the genome-wide association studies, eight were flagged as likely candidates by at least one of the prediction systems. A list of candidates produced by a previous consensus approach did not match any of the genes implicated by 706 moderate to highly significant SNPs flagged by the genome-wide association studies. We prioritized genes associated with medium significance SNPs. Conclusion The study appraises the relative success of several candidate gene prediction systems against independent genetic data. Even when confronted with challengingly large intervals, the candidate gene prediction systems can successfully select likely disease genes. Furthermore, they can be used to filter statistically less-well-supported genetic data to select more likely candidates. We suggest consensus approaches fail because they penalize novel predictions made from independent underlying databases. To realize their full potential further work needs to be done on prioritization and annotation of genes. © Teber et al; licensee BioMed Central Ltd. 2009. 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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title_short	Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies
url	https://dx.doi.org/10.1186/1471-2105-10-S1-S69
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author2	Liu, Jason Y Ballouz, Sara Fatkin, Diane Wouters, Merridee A
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Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies

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