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1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function
Gorski, Mathias, van der Most, Peter J., Teumer, Alexander, Chu, Audrey Y., Li, Man
, Mijatovic, Vladan, Nolte, Ilja M., Cocca, Massimiliano
, Taliun, Daniel, Gomez, Felicia, Li, Yong
, Tayo, Bamidele, Tin, Adrienne, Feitosa, Mary F., Aspelund, Thor
, Attia, John, Biffar, Reiner, Bochud, Murielle, Boerwinkle, Eric, Borecki, Ingrid, Bottinger, Erwin P., Chen, Ming-Huei, Chouraki, Vincent
, Ciullo, Marina
, Coresh, Josef, Cornelis, Marilyn C., Curhan, Gary C., d’Adamo, Adamo Pio
, Dehghan, Abbas, Dengler, Laura, Ding, Jingzhong, Eiriksdottir, Gudny, Endlich, Karlhans
, Enroth, Stefan
, Esko, Tõnu, Franco, Oscar H.
, Gasparini, Paolo, Gieger, Christian, Girotto, Giorgia, Gottesman, Omri, Gudnason, Vilmundur, Gyllensten, Ulf, Hancock, Stephen J., Harris, Tamara B., Helmer, Catherine, Höllerer, Simon, Hofer, Edith, Hofman, Albert, Holliday, Elizabeth G., Homuth, Georg, Hu, Frank B., Huth, Cornelia
, Hutri-Kähönen, Nina, Hwang, Shih-Jen, Imboden, Medea, Johansson, Åsa
, Kähönen, Mika
, König, Wolfgang, Kramer, Holly, Krämer, Bernhard K., Kumar, Ashish, Kutalik, Zoltan, Lambert, Jean-Charles
, Launer, Lenore J., Lehtimäki, Terho, de Borst, Martin, Navis, Gerjan, Swertz, Morris, Liu, Yongmei, Lohman, Kurt, Loos, Ruth J. F.
, Lu, Yingchang, Lyytikäinen, Leo-Pekka, McEvoy, Mark A., Meisinger, Christa, Meitinger, Thomas, Metspalu, Andres, Metzger, Marie, Mihailov, Evelin, Mitchell, Paul, Nauck, Matthias, Oldehinkel, Albertine J., Olden, Matthias, Penninx, Brenda W. J. H., Pistis, Giorgio, Pramstaller, Peter P., Probst-Hensch, Nicole, Raitakari, Olli T., Rettig, Rainer, Ridker, Paul M., Rivadeneira, Fernando, Robino, Antonietta, Rosas, Sylvia E., Ruderfer, Douglas, Ruggiero, Daniela, Saba, Yasaman, Sala, Cinzia, Schmidt, Helena, Schmidt, Reinhold, Scott, Rodney J., Sedaghat, Sanaz, Smith, Albert V.
, Sorice, Rossella, Stengel, Benedicte
, Stracke, Sylvia, Strauch, Konstantin, Toniolo, Daniela, Uitterlinden, Andre G., Ulivi, Sheila, Viikari, Jorma S., Völker, Uwe, Vollenweider, Peter, Völzke, Henry, Vuckovic, Dragana, Waldenberger, Melanie
, Jin Wang, Jie, Yang, Qiong, Chasman, Daniel I., Tromp, Gerard, Snieder, Harold, Heid, Iris M., Fox, Caroline S., Köttgen, Anna, Pattaro, Cristian, Böger, Carsten A. and Fuchsberger, Christian
(2017)
1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
Scientific Reports 2017 (7), p. 45040.
Date of publication of this fulltext: 25 Jan 2018 12:05
Article
DOI to cite this document: 10.5283/epub.36582
Abstract
HapMap imputed genome-wide association studies (GWAS) have revealed > 50 loci at which common variants with minor allele frequency > 5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete ...
HapMap imputed genome-wide association studies (GWAS) have revealed > 50 loci at which common variants with minor allele frequency > 5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 x 10(-8) previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until wholegenome sequencing becomes feasible in large samples.
Involved Institutions
Details
| Item type | Article | ||||
| Journal or Publication Title | Scientific Reports | ||||
| Publisher: | Nature | ||||
|---|---|---|---|---|---|
| Place of Publication: | LONDON | ||||
| Volume: | 2017 | ||||
| Number of Issue or Book Chapter: | 7 | ||||
| Page Range: | p. 45040 | ||||
| Date | 28 April 2017 | ||||
| Institutions | Medicine > Abteilung für Nephrologie Medicine > Institut für Epidemiologie und Präventivmedizin > Lehrstuhl für Genetische Epidemiologie | ||||
| Identification Number |
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| Keywords | GLOMERULAR-FILTRATION-RATE; COMMUNITY-BASED POPULATION; WIDE ASSOCIATION; SERUM CREATININE; GENOTYPE IMPUTATION; LINKAGE ANALYSIS; DISEASE; VARIANTS; INTEGRATION; HERITABILITY; | ||||
| Dewey Decimal Classification | 600 Technology > 610 Medical sciences Medicine | ||||
| Status | Published | ||||
| Refereed | Yes, this version has been refereed | ||||
| Created at the University of Regensburg | Yes | ||||
| URN of the UB Regensburg | urn:nbn:de:bvb:355-epub-365822 | ||||
| Item ID | 36582 |
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