Integration of genome-wide association studies with biological knowledge identifies six novel genes related to kidney function.

Authors:
Daniel I Chasman, Christian Fuchsberger, Cristian Pattaro, Alexander Teumer, Carsten A Böger, Karlhans Endlich, Matthias Olden, Ming-Huei Chen, Adrienne Tin, Daniel Taliun, Man Li, Xiaoyi Gao, Mathias Gorski, Qiong Yang, Claudia Hundertmark, Meredith C Foster, Conall M O'Seaghdha, Nicole Glazer, Aaron Isaacs, Ching-Ti Liu, Albert V Smith, Jeffrey R O'Connell, Maksim Struchalin, Toshiko Tanaka, Guo Li, Andrew D Johnson, Hinco J Gierman, Mary F Feitosa, Shih-Jen Hwang, Elizabeth J Atkinson, Kurt Lohman, Marilyn C Cornelis, Asa Johansson, Anke Tönjes, Abbas Dehghan, Jean-Charles Lambert, Elizabeth G Holliday, Rossella Sorice, Zoltan Kutalik, Terho Lehtimäki, Tõnu Esko, Harshal Deshmukh, Sheila Ulivi, Audrey Y Chu, Federico Murgia, Stella Trompet, Medea Imboden, Stefan Coassin, Giorgio Pistis, - -, Tamara B Harris, Lenore J Launer, Thor Aspelund, Gudny Eiriksdottir, Braxton D Mitchell, Eric Boerwinkle, Helena Schmidt, Margherita Cavalieri, Madhumathi Rao, Frank Hu, Ayse Demirkan, Ben A Oostra, Mariza de Andrade, Stephen T Turner, Jingzhong Ding, Jeanette S Andrews, Barry I Freedman, Franco Giulianini, Wolfgang Koenig, Thomas Illig, Christa Meisinger, Christian Gieger, Lina Zgaga, Tatijana Zemunik, Mladen Boban, Cosetta Minelli, Heather E Wheeler, Wilmar Igl, Ghazal Zaboli, Sarah H Wild, Alan F Wright, Harry Campbell, David Ellinghaus, Ute Nöthlings, Gunnar Jacobs, Reiner Biffar, Florian Ernst, Georg Homuth, Heyo K Kroemer, Matthias Nauck, Sylvia Stracke, Uwe Völker, Henry Völzke, Peter Kovacs, Michael Stumvoll, Reedik Mägi, Albert Hofman, Andre G Uitterlinden, Fernando Rivadeneira, Yurii S Aulchenko, Ozren Polasek, Nick Hastie, Veronique Vitart, Catherine Helmer, Jie Jin Wang, Bénédicte Stengel, Daniela Ruggiero, Sven Bergmann, Mika Kähönen, Jorma Viikari, Tiit Nikopensius, Michael Province, Shamika Ketkar, Helen Colhoun, Alex Doney, Antonietta Robino, Bernhard K Krämer, Laura Portas, Ian Ford, Brendan M Buckley, Martin Adam, Gian-Andri Thun, Bernhard Paulweber, Margot Haun, Cinzia Sala, Paul Mitchell, Marina Ciullo, Stuart K Kim, Peter Vollenweider, Olli Raitakari, Andres Metspalu, Colin Palmer, Paolo Gasparini, Mario Pirastu, J Wouter Jukema, Nicole M Probst-Hensch, Florian Kronenberg, Daniela Toniolo, Vilmundur Gudnason, Alan R Shuldiner, Josef Coresh, Reinhold Schmidt, Luigi Ferrucci, David S Siscovick, Cornelia M van Duijn, Ingrid B Borecki, Sharon L R Kardia, Yongmei Liu, Gary C Curhan, Igor Rudan, Ulf Gyllensten, James F Wilson, Andre Franke, Peter P Pramstaller, Rainer Rettig, Inga Prokopenko, Jacqueline Witteman, Caroline Hayward, Paul M Ridker, Afshin Parsa, Murielle Bochud, Iris M Heid, W H Linda Kao, Caroline S Fox, Anna Köttgen
Year of publication:
2012
Volume:
21
Issue:
24
Issn:
0964-6906
Journal title abbreviated:
HUM MOL GENET
Journal title long:
Human molecular genetics
Impact factor:
5.985
Abstract: 
In conducting genome-wide association studies (GWAS), analytical approaches leveraging biological information may further understanding of the pathophysiology of clinical traits. To discover novel associations with estimated glomerular filtration rate (eGFR), a measure of kidney function, we developed a strategy for integrating prior biological knowledge into the existing GWAS data for eGFR from the CKDGen Consortium. Our strategy focuses on single nucleotide polymorphism (SNPs) in genes that are connected by functional evidence, determined by literature mining and gene ontology (GO) hierarchies, to genes near previously validated eGFR associations. It then requires association thresholds consistent with multiple testing, and finally evaluates novel candidates by independent replication. Among the samples of European ancestry, we identified a genome-wide significant SNP in FBXL20 (P = 5.6 × 10(-9)) in meta-analysis of all available data, and additional SNPs at the INHBC, LRP2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication and overall P-values of 4.5 × 10(-4)-2.2 × 10(-7). Neither the novel PLEKHA1 nor FBXL20 associations, both further supported by association with eGFR among African Americans and with transcript abundance, would have been implicated by eGFR candidate gene approaches. LRP2, encoding the megalin receptor, was identified through connection with the previously known eGFR gene DAB2 and extends understanding of the megalin system in kidney function. These findings highlight integration of existing genome-wide association data with independent biological knowledge to uncover novel candidate eGFR associations, including candidates lacking known connections to kidney-specific pathways. The strategy may also be applicable to other clinical phenotypes, although more testing will be needed to assess its potential for discovery in general.