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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. und Fuchsberger, Christian
(2017)
1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
Scientific Reports 2017 (7), S. 45040.
Veröffentlichungsdatum dieses Volltextes: 25 Jan 2018 12:05
Artikel
DOI zum Zitieren dieses Dokuments: 10.5283/epub.36582
Zusammenfassung
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.
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| Dokumentenart | Artikel | ||||
| Titel eines Journals oder einer Zeitschrift | Scientific Reports | ||||
| Verlag: | Nature | ||||
|---|---|---|---|---|---|
| Ort der Veröffentlichung: | LONDON | ||||
| Band: | 2017 | ||||
| Nummer des Zeitschriftenheftes oder des Kapitels: | 7 | ||||
| Seitenbereich: | S. 45040 | ||||
| Datum | 28 April 2017 | ||||
| Institutionen | Medizin > Abteilung für Nephrologie Medizin > Institut für Epidemiologie und Präventivmedizin > Lehrstuhl für Genetische Epidemiologie | ||||
| Identifikationsnummer |
| ||||
| Stichwörter / Keywords | GLOMERULAR-FILTRATION-RATE; COMMUNITY-BASED POPULATION; WIDE ASSOCIATION; SERUM CREATININE; GENOTYPE IMPUTATION; LINKAGE ANALYSIS; DISEASE; VARIANTS; INTEGRATION; HERITABILITY; | ||||
| Dewey-Dezimal-Klassifikation | 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin | ||||
| Status | Veröffentlicht | ||||
| Begutachtet | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden | Ja | ||||
| URN der UB Regensburg | urn:nbn:de:bvb:355-epub-365822 | ||||
| Dokumenten-ID | 36582 |
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