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Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank

Journal of Human Genetics volume 68, pages 55–64 (2023)Cite this article

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Abstract

Chronic kidney disease (CKD) is a syndrome characterized by a gradual loss of kidney function with decreased estimated glomerular filtration rate (eGFR), which may be accompanied by an increase in the urine albumin-to-creatinine ratio (UACR). Although trans-ethnic genome-wide association studies (GWASs) have been conducted for kidney-related traits, there have been few analyses in the Japanese population, especially for the UACR trait. In this study, we conducted a GWAS to identify loci related to multiple kidney-related traits in Japanese individuals. First, to detect loci associated with CKD, eGFR, and UACR, we performed separate GWASs with the following two datasets: 475 cases of CKD diagnosed at seven university hospitals and 3471 healthy subjects (dataset 1) and 3664 cases of CKD-suspected individuals with eGFR <60 ml/min/1.73 m2 or urinary protein ≥ 1+ and 5952 healthy subjects (dataset 2). Second, we performed a meta-analysis between these two datasets and detected the following associated loci: 10 loci for CKD, 9 loci for eGFR, and 22 loci for UACR. Among the loci detected, 22 have never been reported previously. Half of the significant loci for CKD were shared with those for eGFR, whereas most of the loci associated with UACR were different from those associated with CKD or eGFR. The GWAS of the Japanese population identified novel genetic components that were not previously detected. The results also suggest that the group primarily characterized by increased UACR possessed genetically different features from the group characterized by decreased eGFR.

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Acknowledgements

This study was supported by AMED under Grant Number 21km0405210s. We would like to thank Editage (www.editage.com) for English language editing.

Author information

Authors and Affiliations

  1. Division of Nephrology and Endocrinology, The University of Tokyo, Tokyo, Japan

    Yuka Sugawara, Yosuke Hirakawa & Masaomi Nangaku

  2. Department of Nephrology and Hypertension, Kawasaki Medical School, Okayama, Japan

    Hajime Nagasu & Naoki Kashihara

  3. Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan

    Akira Narita, Seizo Koshiba, Gen Tamiya & Masayuki Yamamoto

  4. Department of Nephrology, Rheumatology, Endocrinology and Metabolism, Okayama University, Okayama, Japan

    Akihiro Katayama & Jun Wada

  5. Department of Nephrology and Laboratory Medicine, Kanazawa University, Ishikawa, Japan

    Miho Shimizu & Takashi Wada

  6. Department of Nephrology, Hypertension & Strokology, Kyushu University, Fukuoka, Japan

    Hiromasa Kitamura & Toshiaki Nakano

  7. Department of Nephrology, Kyoto University, Kyoto, Japan

    Hideki Yokoi & Motoko Yanagita

  8. Division of Clinical Nephrology and Rheumatology, Niigata University, Niigata, Japan

    Shin Goto & Ichiei Narita

  9. The Advanced Research Center for Innovations in Next-Generation Medicine (INGEM), Tohoku University, Sendai, Japan

    Seizo Koshiba

  10. Graduate School of Medicine, Tohoku University, Sendai, Japan

    Gen Tamiya & Masayuki Yamamoto

  11. Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan

    Gen Tamiya

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  1. Yuka Sugawara
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Contributions

YS, YH, HN, SK, MY, and NK conceptualized the study. YS, YH, HN, AK, JW, MS, TW, HK, TN, HK, MY, SG, IN, and MN conducted SNP array study in each institution. AN and GT conducted GWAS. MY and NK supervised the study. YS and AN wrote the manuscript and the other authors revised the manuscript.

Corresponding author

Correspondence to Naoki Kashihara.

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Supplementary information

Supplementary Figure 1. Principal component analysis (PCA) plot of (A) dataset 1 and (B) dataset 2

Supplementary Figure 2. Quantile-quantile (Q-Q) plot for the studied kidney function-related traits

Supplementary Figure 3. Manhattan plot of CKD risk loci using the results from REGENIE

Supplementary Figure 4. Zoomed-in plots of the regions associated with eGFR levels

Supplementary Figure 5. Genome-wide Cochran’s Q test for heterogeneity of SNP effects between the two datasets

Supplementary Table 1. Associations of index SNPs for CKD risk in each dataset

Supplementary Table 2. Associations of index SNPs for the level of eGFR in each dataset

Supplementary Table 3. Associations of index SNPs for the level of UACR in each dataset

10038_2022_1094_MOESM9_ESM.pdf

Supplementary Table 4. Comparison of the loci significantly detected in the present study for the CKD trait with those detected in the past studies conducted in Japanese population

10038_2022_1094_MOESM10_ESM.pdf

Supplementary Table 5. Comparison of the loci significantly detected in the present study for the eGFR trait with those detected in the past studies conducted in Japanese or East Asian population

10038_2022_1094_MOESM11_ESM.pdf

Supplementary Table 6. Comparison of the loci significantly detected in the present study for the UACR trait with those included in GWAS Catalog

Supplementary information

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Sugawara, Y., Hirakawa, Y., Nagasu, H. et al. Genome-wide association study of the risk of chronic kidney disease and kidney-related traits in the Japanese population: J-Kidney-Biobank. J Hum Genet 68, 55–64 (2023). https://doi.org/10.1038/s10038-022-01094-1

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