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Nat Commun. 2019 Apr 23;10(1):1847. doi: 10.1038/s41467-019-09861-z.

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

Author information

1
Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, 48109, MI, USA.
2
Department of Biostatistics: Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, MI, USA.
3
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA.
4
K.G. Jebsen Center for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, 7491, Norway.
5
Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, 7491, Norway.
6
Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, 7491, Norway.
7
Department of Pediatrics: Pediatric Nephrology, University of Michigan, Ann Arbor, 48109, MI, USA.
8
Department of Anesthesiology, University of Michigan, Ann Arbor, 48109, MI, USA.
9
Department of Internal Medicine, Levanger Hospital, Health Trust Nord-Trøndelag, Levanger, 7600, Norway.
10
Department of Nephrology, St Olav Hospital, Trondheim, 7491, Norway.
11
K.G. Jebsen Center for Genetic Epidemiology, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, 7491, Norway. kristian.hveem@ntnu.no.
12
Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, 7491, Norway. kristian.hveem@ntnu.no.
13
HUNT Research Centre, Department of Public Health and General Practice, Norwegian University of Science and Technology, Levanger, 7600, Norway. kristian.hveem@ntnu.no.
14
Department of Internal Medicine: Cardiology, University of Michigan, Ann Arbor, 48109, MI, USA. cristen@umich.edu.
15
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, MI, USA. cristen@umich.edu.
16
Department of Human Genetics, University of Michigan, Ann Arbor, 48109, MI, USA. cristen@umich.edu.

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.

PMID:
31015462
PMCID:
PMC6478837
DOI:
10.1038/s41467-019-09861-z
[Indexed for MEDLINE]
Free PMC Article

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