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

DI Chasman, C Fuchsberger, C Pattaro, A Teumer, CA Boger, K Endlich, M Olden, MH Chen, A Tin, D Taliun, M Li, XY Gao, M Gorski, Q Yang, C Hundertmark, MC Foster, CM O'Seaghdha, N Glazer, Aaron Isaacs, CT LiuAV Smith, JR O'Connell, M Struchalin, T Tanaka, G (Guo) Li, AD Johnson, HJ Gierman, MF Feitosa, SJ Hwang, EJ Atkinson, K Lohman, MC Cornelis, A Johansson, A Tonjes, Abbas Dehghan, JC Lambert, EG Holliday, R Sorice, Z Kutalik, T Lehtimaki, T Esko, H Deshmukh, S Ulivi, AY Chu, F Murgia, S Trompet, M Imboden, S Coassin, G Pistis, TB Harris, LJ (Lenore) Launer, T Aspelund, G Eiriksdottir, BD Mitchell, E Boerwinkle, Heléna Schmidt, M Cavalieri, M Rao, F Hu, Ayse Demirkan, Ben Oostra, M de Andrade, ST Turner, JZ (Jing Zhong) Ding, JS Andrews, BI Freedman, F Giulianini, W Koenig, T Illig, C Meisinger, C Gieger, L Zgaga, T Zemunik, M Boban, C Minelli, HE Wheeler, W Igl, G Zaboli, SH Wild, AF Wright, H Campbell, D Ellinghaus, U Nothlings, G Jacobs, R Biffar, F Ernst, G Homuth, HK Kroemer, M Nauck, S Stracke, U Volker, H Volzke, P Kovacs, M Stumvoll, R Magi, Bert Hofman, André Uitterlinden, Fernando Rivadeneira, YS Aulchenko, O Polasek, N Hastie, V Vitart, C Helmer, JJ Wang, B Stengel, D Ruggiero, S Bergmann, M Kahonen, J Viikari, T Nikopensius, M Province, S Ketkar, H Colhoun, A Doney, A Robino, BK Kramer, L Portas, I Ford, BM Buckley, M Adam, GA Thun, B Paulweber, M Haun, C Sala, P Mitchell, M Ciullo, SK Kim, P Vollenweider, O Raitakari, A Metspalu, C Palmer, P Gasparini, M Pirastu, JW Jukema, NM Probst-Hensch, F Kronenberg, D Toniolo, V Gudnason, AR Shuldiner, J Coresh, R Schmidt, L Ferrucci, DS Siscovick, Cornelia Duijn, IB Borecki, SLR Kardia, YM Liu, GC Curhan, I Rudan, U Gyllensten, JF Wilson, A Franke, PP Pramstaller, R Rettig, I Prokopenko, JCM Witteman, C Hayward, PM Ridker, A Parsa, M Bochud, IM Heid, WHL Kao, CS Fox, A Kottgen

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59 Citations (Scopus)


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.
Original languageUndefined/Unknown
Pages (from-to)5329-5343
Number of pages15
JournalHuman Molecular Genetics
Issue number24
Publication statusPublished - 2012

Research programs

  • EMC MM-01-39-09-A
  • EMC NIHES-01-64-01
  • EMC NIHES-01-64-02

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