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Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic varia...

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Autores principales: Service, Susan K., Teslovich, Tanya M., Fuchsberger, Christian, Ramensky, Vasily, Yajnik, Pranav, Koboldt, Daniel C., Larson, David E., Zhang, Qunyuan, Lin, Ling, Welch, Ryan, Ding, Li, McLellan, Michael D., O'Laughlin, Michele, Fronick, Catrina, Fulton, Lucinda L., Magrini, Vincent, Swift, Amy, Elliott, Paul, Jarvelin, Marjo-Riitta, Kaakinen, Marika, McCarthy, Mark I., Peltonen, Leena, Pouta, Anneli, Bonnycastle, Lori L., Collins, Francis S., Narisu, Narisu, Stringham, Heather M., Tuomilehto, Jaakko, Ripatti, Samuli, Fulton, Robert S., Sabatti, Chiara, Wilson, Richard K., Boehnke, Michael, Freimer, Nelson B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907339/
https://www.ncbi.nlm.nih.gov/pubmed/24497850
http://dx.doi.org/10.1371/journal.pgen.1004147
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author Service, Susan K.
Teslovich, Tanya M.
Fuchsberger, Christian
Ramensky, Vasily
Yajnik, Pranav
Koboldt, Daniel C.
Larson, David E.
Zhang, Qunyuan
Lin, Ling
Welch, Ryan
Ding, Li
McLellan, Michael D.
O'Laughlin, Michele
Fronick, Catrina
Fulton, Lucinda L.
Magrini, Vincent
Swift, Amy
Elliott, Paul
Jarvelin, Marjo-Riitta
Kaakinen, Marika
McCarthy, Mark I.
Peltonen, Leena
Pouta, Anneli
Bonnycastle, Lori L.
Collins, Francis S.
Narisu, Narisu
Stringham, Heather M.
Tuomilehto, Jaakko
Ripatti, Samuli
Fulton, Robert S.
Sabatti, Chiara
Wilson, Richard K.
Boehnke, Michael
Freimer, Nelson B.
author_facet Service, Susan K.
Teslovich, Tanya M.
Fuchsberger, Christian
Ramensky, Vasily
Yajnik, Pranav
Koboldt, Daniel C.
Larson, David E.
Zhang, Qunyuan
Lin, Ling
Welch, Ryan
Ding, Li
McLellan, Michael D.
O'Laughlin, Michele
Fronick, Catrina
Fulton, Lucinda L.
Magrini, Vincent
Swift, Amy
Elliott, Paul
Jarvelin, Marjo-Riitta
Kaakinen, Marika
McCarthy, Mark I.
Peltonen, Leena
Pouta, Anneli
Bonnycastle, Lori L.
Collins, Francis S.
Narisu, Narisu
Stringham, Heather M.
Tuomilehto, Jaakko
Ripatti, Samuli
Fulton, Robert S.
Sabatti, Chiara
Wilson, Richard K.
Boehnke, Michael
Freimer, Nelson B.
author_sort Service, Susan K.
collection PubMed
description Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.
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spelling pubmed-39073392014-02-04 Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci Service, Susan K. Teslovich, Tanya M. Fuchsberger, Christian Ramensky, Vasily Yajnik, Pranav Koboldt, Daniel C. Larson, David E. Zhang, Qunyuan Lin, Ling Welch, Ryan Ding, Li McLellan, Michael D. O'Laughlin, Michele Fronick, Catrina Fulton, Lucinda L. Magrini, Vincent Swift, Amy Elliott, Paul Jarvelin, Marjo-Riitta Kaakinen, Marika McCarthy, Mark I. Peltonen, Leena Pouta, Anneli Bonnycastle, Lori L. Collins, Francis S. Narisu, Narisu Stringham, Heather M. Tuomilehto, Jaakko Ripatti, Samuli Fulton, Robert S. Sabatti, Chiara Wilson, Richard K. Boehnke, Michael Freimer, Nelson B. PLoS Genet Research Article Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20–30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5′ and 3′ untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants. Public Library of Science 2014-01-30 /pmc/articles/PMC3907339/ /pubmed/24497850 http://dx.doi.org/10.1371/journal.pgen.1004147 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
spellingShingle Research Article
Service, Susan K.
Teslovich, Tanya M.
Fuchsberger, Christian
Ramensky, Vasily
Yajnik, Pranav
Koboldt, Daniel C.
Larson, David E.
Zhang, Qunyuan
Lin, Ling
Welch, Ryan
Ding, Li
McLellan, Michael D.
O'Laughlin, Michele
Fronick, Catrina
Fulton, Lucinda L.
Magrini, Vincent
Swift, Amy
Elliott, Paul
Jarvelin, Marjo-Riitta
Kaakinen, Marika
McCarthy, Mark I.
Peltonen, Leena
Pouta, Anneli
Bonnycastle, Lori L.
Collins, Francis S.
Narisu, Narisu
Stringham, Heather M.
Tuomilehto, Jaakko
Ripatti, Samuli
Fulton, Robert S.
Sabatti, Chiara
Wilson, Richard K.
Boehnke, Michael
Freimer, Nelson B.
Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title_full Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title_fullStr Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title_full_unstemmed Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title_short Re-sequencing Expands Our Understanding of the Phenotypic Impact of Variants at GWAS Loci
title_sort re-sequencing expands our understanding of the phenotypic impact of variants at gwas loci
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3907339/
https://www.ncbi.nlm.nih.gov/pubmed/24497850
http://dx.doi.org/10.1371/journal.pgen.1004147
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