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Meta-Analysis
. 2017 Apr 28:7:45040.
doi: 10.1038/srep45040.

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

Mathias Gorski  1   2 , Peter J van der Most  3 , Alexander Teumer  4 , Audrey Y Chu  5   6 , Man Li  7   8 , Vladan Mijatovic  9 , Ilja M Nolte  3 , Massimiliano Cocca  10   11 , Daniel Taliun  12 , Felicia Gomez  13 , Yong Li  14 , Bamidele Tayo  15 , Adrienne Tin  7 , Mary F Feitosa  13 , Thor Aspelund  16   17 , John Attia  18   19 , Reiner Biffar  20 , Murielle Bochud  21 , Eric Boerwinkle  22 , Ingrid Borecki  23 , Erwin P Bottinger  24 , Ming-Huei Chen  5 , Vincent Chouraki  25 , Marina Ciullo  26   27 , Josef Coresh  7 , Marilyn C Cornelis  28 , Gary C Curhan  29   30 , Adamo Pio d'Adamo  31 , Abbas Dehghan  32 , Laura Dengler  2 , Jingzhong Ding  33 , Gudny Eiriksdottir  16 , Karlhans Endlich  34 , Stefan Enroth  35 , Tõnu Esko  36 , Oscar H Franco  32 , Paolo Gasparini  37   38 , Christian Gieger  39   40   41 , Giorgia Girotto  37   38 , Omri Gottesman  24 , Vilmundur Gudnason  16   42 , Ulf Gyllensten  35 , Stephen J Hancock  18   43 , Tamara B Harris  44 , Catherine Helmer  45   46 , Simon Höllerer  1 , Edith Hofer  47   48 , Albert Hofman  32 , Elizabeth G Holliday  19 , Georg Homuth  49 , Frank B Hu  50 , Cornelia Huth  41   51 , Nina Hutri-Kähönen  52 , Shih-Jen Hwang  5 , Medea Imboden  53   54 , Åsa Johansson  35 , Mika Kähönen  55   56 , Wolfgang König  57   58   59 , Holly Kramer  15 , Bernhard K Krämer  60 , Ashish Kumar  53   54   61 , Zoltan Kutalik  21 , Jean-Charles Lambert  25 , Lenore J Launer  44 , Terho Lehtimäki  62   63 , Martin de Borst  64 , Gerjan Navis  64 , Morris Swertz  64 , Yongmei Liu  33 , Kurt Lohman  33 , Ruth J F Loos  24   65 , Yingchang Lu  24 , Leo-Pekka Lyytikäinen  62   63 , Mark A McEvoy  18 , Christa Meisinger  41 , Thomas Meitinger  66   67 , Andres Metspalu  36 , Marie Metzger  68 , Evelin Mihailov  36 , Paul Mitchell  69 , Matthias Nauck  70   71 , Albertine J Oldehinkel  72 , Matthias Olden  1   5 , Brenda Wjh Penninx  73 , Giorgio Pistis  10 , Peter P Pramstaller  12 , Nicole Probst-Hensch  53   54 , Olli T Raitakari  74   75 , Rainer Rettig  76 , Paul M Ridker  6   77 , Fernando Rivadeneira  78 , Antonietta Robino  38 , Sylvia E Rosas  79 , Douglas Ruderfer  24 , Daniela Ruggiero  26 , Yasaman Saba  80 , Cinzia Sala  10 , Helena Schmidt  80 , Reinhold Schmidt  47 , Rodney J Scott  81   82 , Sanaz Sedaghat  32 , Albert V Smith  16   42 , Rossella Sorice  26   27 , Benedicte Stengel  68 , Sylvia Stracke  83 , Konstantin Strauch  39   84 , Daniela Toniolo  10 , Andre G Uitterlinden  78 , Sheila Ulivi  38 , Jorma S Viikari  85   86 , Uwe Völker  49   71 , Peter Vollenweider  87 , Henry Völzke  4   71   88 , Dragana Vuckovic  37   38 , Melanie Waldenberger  40   41 , Jie Jin Wang  69 , Qiong Yang  89 , Daniel I Chasman  6   90   91 , Gerard Tromp  92 , Harold Snieder  3 , Iris M Heid  1 , Caroline S Fox  5 , Anna Köttgen  14   93 , Cristian Pattaro  12 , Carsten A Böger  2 , Christian Fuchsberger  12
Affiliations
Meta-Analysis

1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function

Mathias Gorski et al. Sci Rep. .

Erratum in

  • Corrigendum: 1000 Genomes-based meta-analysis identifies 10 novel loci for kidney function.
    Gorski M, Most PJV, Teumer A, Chu AY, Li M, Mijatovic V, Nolte IM, Cocca M, Taliun D, Gomez F, Li Y, Tayo B, Tin A, Feitosa MF, Aspelund T, Attia J, Biffar R, Bochud M, Boerwinkle E, Borecki I, Bottinger EP, Chen MH, Chouraki V, Ciullo M, Coresh J, Cornelis MC, Curhan GC, Adamo AP, Dehghan A, Dengler L, Ding J, Eiriksdottir G, Endlich K, Enroth S, Esko T, Franco OH, Gasparini P, Gieger C, Girotto G, Gottesman O, Gudnason V, Gyllensten U, Hancock SJ, Harris TB, Helmer C, Höllerer S, Hofer E, Hofman A, Holliday EG, Homuth G, Hu FB, Huth C, Hutri-Kähönen N, Hwang SJ, Imboden M, Johansson Å, Kähönen M, König W, Kramer H, Krämer BK, Kumar A, Kutalik Z, Lambert JC, Launer LJ, Lehtimäki T, de Borst MH, Navis G, Swertz M, Liu Y, Lohman K, Loos RJF, Lu Y, Lyytikäinen LP, McEvoy MA, Meisinger C, Meitinger T, Metspalu A, Metzger M, Mihailov E, Mitchell P, Nauck M, Oldehinkel AJ, Olden M, Wjh Penninx B, Pistis G, Pramstaller PP, Probst-Hensch N, Raitakari OT, Rettig R, Ridker PM, Rivadeneira F, Robino A, Rosas SE, Ruderfer D, Ruggiero D, Saba Y, Sala C, Schmidt H, Schmidt R, Scott RJ, Sedaghat S, Smith AV, Sorice R, Stengel B, Stracke S, Strauch K, Toniolo D, Uitterlinden AG, Ulivi S, Viikari… See abstract for full author list ➔ Gorski M, et al. Sci Rep. 2017 May 26;7:46835. doi: 10.1038/srep46835. Sci Rep. 2017. PMID: 28548086 Free PMC article.

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 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 × 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 whole-genome sequencing becomes feasible in large samples.

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Conflict of interest statement

Caroline S Fox became a Merck employee as of Dec 14, 2015 and Audrey Chu became a Merck Employee as of July 18, 2016. The majority of the work related to this manuscript was completed before that. Daniel I Chasman has received grant support for genotyping and analysis in the WGHS. Ingrid B Borecki became employed at Regeneron Pharmaceuticals, Inc. recently, after the majority of the work related to this manuscript was completed.

Figures

Figure 1
Figure 1. Manhattan Plot of the results of the 1000 Genome meta-analysis of eGFRcrea.
Shown are the (−log10) p-values by genomic position (GRCh build 37). Highlighted are the 10 novel loci identified with genome-wide significance (blue, annotated by nearest gene), the 39 previously published and confirmed (genome-wide significant) loci (green) and the 14 previously published loci that were not genome-wide significant in this analysis (orange).
Figure 2
Figure 2. Effects of the 1000 Genomes lead variants for all novel and known loci.
Shown are the effect sizes and minor allele frequencies (MAF) of the 1000 Genomes lead variants (variants with smallest p-value) in each of the 10 novel (blue), the 39 known genome-wide significant loci (green), and the 14 known loci that were not genome-wide significant in this analysis (orange). Additionally, the 80% power to detect such effects in a sample size of 110,000 subjects (as in this 1000 Genomes meta-analysis) is shown as a red line. A known locus is defined by the published lead variant ±1 Mb; a novel locus is defined by the 1000 Genome lead variant ±1 Mb.
See this image and copyright information in PMC

References

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