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Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis

Author

Listed:
  • Sarah E. Graham

    (University of Michigan)

  • Jonas B. Nielsen

    (University of Michigan)

  • Matthew Zawistowski

    (University of Michigan)

  • Wei Zhou

    (University of Michigan)

  • Lars G. Fritsche

    (University of Michigan)

  • Maiken E. Gabrielsen

    (Norwegian University of Science and Technology
    Norwegian University of Science and Technology)

  • Anne Heidi Skogholt

    (Norwegian University of Science and Technology
    Norwegian University of Science and Technology
    Norwegian University of Science and Technology)

  • Ida Surakka

    (University of Michigan)

  • Whitney E. Hornsby

    (University of Michigan)

  • Damian Fermin

    (University of Michigan)

  • Daniel B. Larach

    (University of Michigan)

  • Sachin Kheterpal

    (University of Michigan)

  • Chad M. Brummett

    (University of Michigan)

  • Seunggeun Lee

    (University of Michigan)

  • Hyun Min Kang

    (University of Michigan)

  • Goncalo R. Abecasis

    (University of Michigan)

  • Solfrid Romundstad

    (Norwegian University of Science and Technology
    Levanger Hospital, Health Trust Nord-Trøndelag)

  • Stein Hallan

    (Norwegian University of Science and Technology
    St Olav Hospital)

  • Matthew G. Sampson

    (University of Michigan)

  • Kristian Hveem

    (Norwegian University of Science and Technology
    Norwegian University of Science and Technology
    Norwegian University of Science and Technology)

  • Cristen J. Willer

    (University of Michigan
    University of Michigan
    University of Michigan)

Abstract

Chronic kidney disease (CKD) is a growing health burden currently affecting 10–15% of adults worldwide. Estimated glomerular filtration rate (eGFR) as a marker of kidney function is commonly used to diagnose CKD. We analyze eGFR data from the Nord-Trøndelag Health Study and Michigan Genomics Initiative and perform a GWAS meta-analysis with public summary statistics, more than doubling the sample size of previous meta-analyses. We identify 147 loci (53 novel) associated with eGFR, including genes involved in transcriptional regulation, kidney development, cellular signaling, metabolism, and solute transport. Additionally, sex-stratified analysis identifies one locus with more significant effects in women than men. Using genetic risk scores constructed from these eGFR meta-analysis results, we show that associated variants are generally predictive of CKD with only modest improvements in detection compared with other known clinical risk factors. Collectively, these results yield additional insight into the genetic factors underlying kidney function and progression to CKD.

Suggested Citation

  • Sarah E. Graham & Jonas B. Nielsen & Matthew Zawistowski & Wei Zhou & Lars G. Fritsche & Maiken E. Gabrielsen & Anne Heidi Skogholt & Ida Surakka & Whitney E. Hornsby & Damian Fermin & Daniel B. Larac, 2019. "Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09861-z
    DOI: 10.1038/s41467-019-09861-z
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    Cited by:

    1. Markus Scholz & Katrin Horn & Janne Pott & Matthias Wuttke & Andreas Kühnapfel & M. Kamal Nasr & Holger Kirsten & Yong Li & Anselm Hoppmann & Mathias Gorski & Sahar Ghasemi & Man Li & Adrienne Tin & J, 2024. "X-chromosome and kidney function: evidence from a multi-trait genetic analysis of 908,697 individuals reveals sex-specific and sex-differential findings in genes regulated by androgen response element," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Bradley Jermy & Kristi Läll & Brooke N. Wolford & Ying Wang & Kristina Zguro & Yipeng Cheng & Masahiro Kanai & Stavroula Kanoni & Zhiyu Yang & Tuomo Hartonen & Remo Monti & Julian Wanner & Omar Yousse, 2024. "A unified framework for estimating country-specific cumulative incidence for 18 diseases stratified by polygenic risk," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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