Analysis of genome-wide association study data using the protein knowledge base

<p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p< Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ballouz Sara [verfasserIn] Liu Jason Y [verfasserIn] Oti Martin [verfasserIn] Gaeta Bruno [verfasserIn] Fatkin Diane [verfasserIn] Bahlo Melanie [verfasserIn] Wouters Merridee A [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2011

Übergeordnetes Werk:	In: BMC Genetics - BMC, 2003, 12(2011), 1, p 98
Übergeordnetes Werk:	volume:12 ; year:2011 ; number:1, p 98

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2156-12-98

Katalog-ID:	DOAJ04225986X

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spelling	10.1186/1471-2156-12-98 doi (DE-627)DOAJ04225986X (DE-599)DOAJ8abda2e8345642efb5fad16526a4a860 DE-627 ger DE-627 rakwb eng QH426-470 Ballouz Sara verfasserin aut Analysis of genome-wide association study data using the protein knowledge base 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p< Genetics Liu Jason Y verfasserin aut Oti Martin verfasserin aut Gaeta Bruno verfasserin aut Fatkin Diane verfasserin aut Bahlo Melanie verfasserin aut Wouters Merridee A verfasserin aut In BMC Genetics BMC, 2003 12(2011), 1, p 98 (DE-627)326644938 (DE-600)2041497-3 14712156 nnns volume:12 year:2011 number:1, p 98 https://doi.org/10.1186/1471-2156-12-98 kostenfrei https://doaj.org/article/8abda2e8345642efb5fad16526a4a860 kostenfrei http://www.biomedcentral.com/1471-2156/12/98 kostenfrei https://doaj.org/toc/1471-2156 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 98
allfields_unstemmed	10.1186/1471-2156-12-98 doi (DE-627)DOAJ04225986X (DE-599)DOAJ8abda2e8345642efb5fad16526a4a860 DE-627 ger DE-627 rakwb eng QH426-470 Ballouz Sara verfasserin aut Analysis of genome-wide association study data using the protein knowledge base 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p< Genetics Liu Jason Y verfasserin aut Oti Martin verfasserin aut Gaeta Bruno verfasserin aut Fatkin Diane verfasserin aut Bahlo Melanie verfasserin aut Wouters Merridee A verfasserin aut In BMC Genetics BMC, 2003 12(2011), 1, p 98 (DE-627)326644938 (DE-600)2041497-3 14712156 nnns volume:12 year:2011 number:1, p 98 https://doi.org/10.1186/1471-2156-12-98 kostenfrei https://doaj.org/article/8abda2e8345642efb5fad16526a4a860 kostenfrei http://www.biomedcentral.com/1471-2156/12/98 kostenfrei https://doaj.org/toc/1471-2156 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 98
allfieldsGer	10.1186/1471-2156-12-98 doi (DE-627)DOAJ04225986X (DE-599)DOAJ8abda2e8345642efb5fad16526a4a860 DE-627 ger DE-627 rakwb eng QH426-470 Ballouz Sara verfasserin aut Analysis of genome-wide association study data using the protein knowledge base 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p< Genetics Liu Jason Y verfasserin aut Oti Martin verfasserin aut Gaeta Bruno verfasserin aut Fatkin Diane verfasserin aut Bahlo Melanie verfasserin aut Wouters Merridee A verfasserin aut In BMC Genetics BMC, 2003 12(2011), 1, p 98 (DE-627)326644938 (DE-600)2041497-3 14712156 nnns volume:12 year:2011 number:1, p 98 https://doi.org/10.1186/1471-2156-12-98 kostenfrei https://doaj.org/article/8abda2e8345642efb5fad16526a4a860 kostenfrei http://www.biomedcentral.com/1471-2156/12/98 kostenfrei https://doaj.org/toc/1471-2156 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 98
allfieldsSound	10.1186/1471-2156-12-98 doi (DE-627)DOAJ04225986X (DE-599)DOAJ8abda2e8345642efb5fad16526a4a860 DE-627 ger DE-627 rakwb eng QH426-470 Ballouz Sara verfasserin aut Analysis of genome-wide association study data using the protein knowledge base 2011 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p< Genetics Liu Jason Y verfasserin aut Oti Martin verfasserin aut Gaeta Bruno verfasserin aut Fatkin Diane verfasserin aut Bahlo Melanie verfasserin aut Wouters Merridee A verfasserin aut In BMC Genetics BMC, 2003 12(2011), 1, p 98 (DE-627)326644938 (DE-600)2041497-3 14712156 nnns volume:12 year:2011 number:1, p 98 https://doi.org/10.1186/1471-2156-12-98 kostenfrei https://doaj.org/article/8abda2e8345642efb5fad16526a4a860 kostenfrei http://www.biomedcentral.com/1471-2156/12/98 kostenfrei https://doaj.org/toc/1471-2156 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2011 1, p 98
language	English
source	In BMC Genetics 12(2011), 1, p 98 volume:12 year:2011 number:1, p 98
sourceStr	In BMC Genetics 12(2011), 1, p 98 volume:12 year:2011 number:1, p 98
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Genetics
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container_title	BMC Genetics
authorswithroles_txt_mv	Ballouz Sara @@aut@@ Liu Jason Y @@aut@@ Oti Martin @@aut@@ Gaeta Bruno @@aut@@ Fatkin Diane @@aut@@ Bahlo Melanie @@aut@@ Wouters Merridee A @@aut@@
publishDateDaySort_date	2011-01-01T00:00:00Z
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author	Ballouz Sara
spellingShingle	Ballouz Sara misc QH426-470 misc Genetics Analysis of genome-wide association study data using the protein knowledge base
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topic_title	QH426-470 Analysis of genome-wide association study data using the protein knowledge base
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title	Analysis of genome-wide association study data using the protein knowledge base
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title_full	Analysis of genome-wide association study data using the protein knowledge base
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title_sort	analysis of genome-wide association study data using the protein knowledge base
callnumber	QH426-470
title_auth	Analysis of genome-wide association study data using the protein knowledge base
abstract	<p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<Genome-wide association studies (GWAS) aim to identify causal variants and genes for complex disease by independently testing a large number of SNP markers for disease association. Although genes have been implicated in these studies, few utilise the multiple-hit model of complex disease to identify causal candidates. A major benefit of multi-locus comparison is that it compensates for some shortcomings of current statistical analyses that test the frequency of each SNP in isolation for the phenotype population versus control.</p< <p<Results</p< <p<Here we developed and benchmarked several protocols for GWAS data analysis using different <it<in-silico </it<gene prediction and prioritisation methodologies. We adopted a high sensitivity approach to the data, using less conservative statistical SNP associations. Multiple gene search spaces, either of fixed-widths or proximity-based, were generated around each SNP marker. We used the candidate disease gene prediction system <it<Gentrepid </it<to identify candidates based on shared biomolecular pathways or domain-based protein homology. Predictions were made either with phenotype-specific known disease genes as input; or without <it<a priori </it<knowledge, by exhaustive comparison of genes in distinct loci. Because <it<Gentrepid </it<uses biomolecular data to find interactions and common features between genes in distinct loci of the search spaces, it takes advantage of the multi-locus aspect of the data.</p< <p<Conclusions</p< <p<Results suggest testing multiple SNP-to-gene search spaces compensates for differences in phenotypes, populations and SNP platforms. Surprisingly, domain-based homology information was more informative when benchmarked against gene candidates reported by GWA studies compared to previously determined disease genes, possibly suggesting a larger contribution of gene homologs to complex diseases than Mendelian diseases.</p<
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Analysis of genome-wide association study data using the protein knowledge base

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