Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes.

To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. He...
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Autor*in:	Jussi Naukkarinen [verfasserIn] Ida Surakka [verfasserIn] Kirsi H Pietiläinen [verfasserIn] Aila Rissanen [verfasserIn] Veikko Salomaa [verfasserIn] Samuli Ripatti [verfasserIn] Hannele Yki-Järvinen [verfasserIn] Cornelia M van Duijn [verfasserIn] H-Erich Wichmann [verfasserIn] Jaakko Kaprio [verfasserIn] Marja-Riitta Taskinen [verfasserIn] Leena Peltonen [verfasserIn] ENGAGE Consortium [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Übergeordnetes Werk:	In: PLoS Genetics - Public Library of Science (PLoS), 2005, 6(2010), 6, p e1000976
Übergeordnetes Werk:	volume:6 ; year:2010 ; number:6, p e1000976

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1371/journal.pgen.1000976

Katalog-ID:	DOAJ060370823

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520			\|a To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.
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author_variant	j n jn i s is k h p khp a r ar v s vs s r sr h y j hyj c m v d cmvd h e w hew j k jk m r t mrt l p lp e c ec
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allfields	10.1371/journal.pgen.1000976 doi (DE-627)DOAJ060370823 (DE-599)DOAJc2f8cc33a7ba400b8f4ea5c5142f2e00 DE-627 ger DE-627 rakwb eng QH426-470 Jussi Naukkarinen verfasserin aut Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity. Genetics Ida Surakka verfasserin aut Kirsi H Pietiläinen verfasserin aut Aila Rissanen verfasserin aut Veikko Salomaa verfasserin aut Samuli Ripatti verfasserin aut Hannele Yki-Järvinen verfasserin aut Cornelia M van Duijn verfasserin aut H-Erich Wichmann verfasserin aut Jaakko Kaprio verfasserin aut Marja-Riitta Taskinen verfasserin aut Leena Peltonen verfasserin aut ENGAGE Consortium verfasserin aut In PLoS Genetics Public Library of Science (PLoS), 2005 6(2010), 6, p e1000976 (DE-627)485248026 (DE-600)2186725-2 15537404 nnns volume:6 year:2010 number:6, p e1000976 https://doi.org/10.1371/journal.pgen.1000976 kostenfrei https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 kostenfrei http://europepmc.org/articles/PMC2880558?pdf=render kostenfrei https://doaj.org/toc/1553-7390 Journal toc kostenfrei https://doaj.org/toc/1553-7404 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_2522 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 6 2010 6, p e1000976
spelling	10.1371/journal.pgen.1000976 doi (DE-627)DOAJ060370823 (DE-599)DOAJc2f8cc33a7ba400b8f4ea5c5142f2e00 DE-627 ger DE-627 rakwb eng QH426-470 Jussi Naukkarinen verfasserin aut Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity. Genetics Ida Surakka verfasserin aut Kirsi H Pietiläinen verfasserin aut Aila Rissanen verfasserin aut Veikko Salomaa verfasserin aut Samuli Ripatti verfasserin aut Hannele Yki-Järvinen verfasserin aut Cornelia M van Duijn verfasserin aut H-Erich Wichmann verfasserin aut Jaakko Kaprio verfasserin aut Marja-Riitta Taskinen verfasserin aut Leena Peltonen verfasserin aut ENGAGE Consortium verfasserin aut In PLoS Genetics Public Library of Science (PLoS), 2005 6(2010), 6, p e1000976 (DE-627)485248026 (DE-600)2186725-2 15537404 nnns volume:6 year:2010 number:6, p e1000976 https://doi.org/10.1371/journal.pgen.1000976 kostenfrei https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 kostenfrei http://europepmc.org/articles/PMC2880558?pdf=render kostenfrei https://doaj.org/toc/1553-7390 Journal toc kostenfrei https://doaj.org/toc/1553-7404 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_2522 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 6 2010 6, p e1000976
allfields_unstemmed	10.1371/journal.pgen.1000976 doi (DE-627)DOAJ060370823 (DE-599)DOAJc2f8cc33a7ba400b8f4ea5c5142f2e00 DE-627 ger DE-627 rakwb eng QH426-470 Jussi Naukkarinen verfasserin aut Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity. Genetics Ida Surakka verfasserin aut Kirsi H Pietiläinen verfasserin aut Aila Rissanen verfasserin aut Veikko Salomaa verfasserin aut Samuli Ripatti verfasserin aut Hannele Yki-Järvinen verfasserin aut Cornelia M van Duijn verfasserin aut H-Erich Wichmann verfasserin aut Jaakko Kaprio verfasserin aut Marja-Riitta Taskinen verfasserin aut Leena Peltonen verfasserin aut ENGAGE Consortium verfasserin aut In PLoS Genetics Public Library of Science (PLoS), 2005 6(2010), 6, p e1000976 (DE-627)485248026 (DE-600)2186725-2 15537404 nnns volume:6 year:2010 number:6, p e1000976 https://doi.org/10.1371/journal.pgen.1000976 kostenfrei https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 kostenfrei http://europepmc.org/articles/PMC2880558?pdf=render kostenfrei https://doaj.org/toc/1553-7390 Journal toc kostenfrei https://doaj.org/toc/1553-7404 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_2522 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 6 2010 6, p e1000976
allfieldsGer	10.1371/journal.pgen.1000976 doi (DE-627)DOAJ060370823 (DE-599)DOAJc2f8cc33a7ba400b8f4ea5c5142f2e00 DE-627 ger DE-627 rakwb eng QH426-470 Jussi Naukkarinen verfasserin aut Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity. Genetics Ida Surakka verfasserin aut Kirsi H Pietiläinen verfasserin aut Aila Rissanen verfasserin aut Veikko Salomaa verfasserin aut Samuli Ripatti verfasserin aut Hannele Yki-Järvinen verfasserin aut Cornelia M van Duijn verfasserin aut H-Erich Wichmann verfasserin aut Jaakko Kaprio verfasserin aut Marja-Riitta Taskinen verfasserin aut Leena Peltonen verfasserin aut ENGAGE Consortium verfasserin aut In PLoS Genetics Public Library of Science (PLoS), 2005 6(2010), 6, p e1000976 (DE-627)485248026 (DE-600)2186725-2 15537404 nnns volume:6 year:2010 number:6, p e1000976 https://doi.org/10.1371/journal.pgen.1000976 kostenfrei https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 kostenfrei http://europepmc.org/articles/PMC2880558?pdf=render kostenfrei https://doaj.org/toc/1553-7390 Journal toc kostenfrei https://doaj.org/toc/1553-7404 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_2522 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 6 2010 6, p e1000976
allfieldsSound	10.1371/journal.pgen.1000976 doi (DE-627)DOAJ060370823 (DE-599)DOAJc2f8cc33a7ba400b8f4ea5c5142f2e00 DE-627 ger DE-627 rakwb eng QH426-470 Jussi Naukkarinen verfasserin aut Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes. 2010 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity. Genetics Ida Surakka verfasserin aut Kirsi H Pietiläinen verfasserin aut Aila Rissanen verfasserin aut Veikko Salomaa verfasserin aut Samuli Ripatti verfasserin aut Hannele Yki-Järvinen verfasserin aut Cornelia M van Duijn verfasserin aut H-Erich Wichmann verfasserin aut Jaakko Kaprio verfasserin aut Marja-Riitta Taskinen verfasserin aut Leena Peltonen verfasserin aut ENGAGE Consortium verfasserin aut In PLoS Genetics Public Library of Science (PLoS), 2005 6(2010), 6, p e1000976 (DE-627)485248026 (DE-600)2186725-2 15537404 nnns volume:6 year:2010 number:6, p e1000976 https://doi.org/10.1371/journal.pgen.1000976 kostenfrei https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 kostenfrei http://europepmc.org/articles/PMC2880558?pdf=render kostenfrei https://doaj.org/toc/1553-7390 Journal toc kostenfrei https://doaj.org/toc/1553-7404 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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_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_2522 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 6 2010 6, p e1000976
language	English
source	In PLoS Genetics 6(2010), 6, p e1000976 volume:6 year:2010 number:6, p e1000976
sourceStr	In PLoS Genetics 6(2010), 6, p e1000976 volume:6 year:2010 number:6, p e1000976
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Genetics
isfreeaccess_bool	true
container_title	PLoS Genetics
authorswithroles_txt_mv	Jussi Naukkarinen @@aut@@ Ida Surakka @@aut@@ Kirsi H Pietiläinen @@aut@@ Aila Rissanen @@aut@@ Veikko Salomaa @@aut@@ Samuli Ripatti @@aut@@ Hannele Yki-Järvinen @@aut@@ Cornelia M van Duijn @@aut@@ H-Erich Wichmann @@aut@@ Jaakko Kaprio @@aut@@ Marja-Riitta Taskinen @@aut@@ Leena Peltonen @@aut@@ ENGAGE Consortium @@aut@@
publishDateDaySort_date	2010-01-01T00:00:00Z
hierarchy_top_id	485248026
id	DOAJ060370823
language_de	englisch
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author	Jussi Naukkarinen
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topic_title	QH426-470 Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes
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title	Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes.
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title_sort	use of genome-wide expression data to mine the "gray zone" of gwa studies leads to novel candidate obesity genes
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title_auth	Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes.
abstract	To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.
abstractGer	To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.
abstract_unstemmed	To get beyond the "low-hanging fruits" so far identified by genome-wide association (GWA) studies, new methods must be developed in order to discover the numerous remaining genes that estimates of heritability indicate should be contributing to complex human phenotypes, such as obesity. Here we describe a novel integrative method for complex disease gene identification utilizing both genome-wide transcript profiling of adipose tissue samples and consequent analysis of genome-wide association data generated in large SNP scans. We infer causality of genes with obesity by employing a unique set of monozygotic twin pairs discordant for BMI (n = 13 pairs, age 24-28 years, 15.4 kg mean weight difference) and contrast the transcript profiles with those from a larger sample of non-related adult individuals (N = 77). Using this approach, we were able to identify 27 genes with possibly causal roles in determining the degree of human adiposity. Testing for association of SNP variants in these 27 genes in the population samples of the large ENGAGE consortium (N = 21,000) revealed a significant deviation of P-values from the expected (P = 4x10(-4)). A total of 13 genes contained SNPs nominally associated with BMI. The top finding was blood coagulation factor F13A1 identified as a novel obesity gene also replicated in a second GWA set of approximately 2,000 individuals. This study presents a new approach to utilizing gene expression studies for informing choice of candidate genes for complex human phenotypes, such as obesity.
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title_short	Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes.
url	https://doi.org/10.1371/journal.pgen.1000976 https://doaj.org/article/c2f8cc33a7ba400b8f4ea5c5142f2e00 http://europepmc.org/articles/PMC2880558?pdf=render https://doaj.org/toc/1553-7390 https://doaj.org/toc/1553-7404
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Use of genome-wide expression data to mine the "Gray Zone" of GWA studies leads to novel candidate obesity genes.

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