Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis

BMC Medicine

Research Article

Sujin Seo¹  Ye An Kim²  Heejin Jin³  Sungho Won⁴  Ah Ra Do⁵  Je Hyun Seo⁶  Seung-hyun Kwon⁶  Young Lee⁶ 

[1] Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea;Division of Endocrinology, Department of Internal Medicine, Veterans Health Service Medical Center, Seoul, Korea;Institute of Health and Environment, Seoul National University, Seoul, Korea;Institute of Health and Environment, Seoul National University, Seoul, Korea;Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Korea;RexSoft Corps, Seoul, Korea;Interdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National University, Seoul, South Korea;Veterans Medical Research Institute, Veterans Health Service Medical Center, Jinhwangdo-ro 61-gil 53, Gangdong-gu, Seoul, Korea;

关键词: Diabetic kidney disease;    GWAS;    Genetic variants;    Prediction;    Microvascular complications;   

DOI  :  10.1186/s12916-022-02723-4

 received in 2022-07-11, accepted in 2022-12-28,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundThe pathogenesis of diabetic kidney disease (DKD) is complex, involving metabolic and hemodynamic factors. Although DKD has been established as a heritable disorder and several genetic studies have been conducted, the identification of unique genetic variants for DKD is limited by its multiplex classification based on the phenotypes of diabetes mellitus (DM) and chronic kidney disease (CKD). Thus, we aimed to identify the genetic variants related to DKD that differentiate it from type 2 DM and CKD.MethodsWe conducted a large-scale genome-wide association study mega-analysis, combining Korean multi-cohorts using multinomial logistic regression. A total of 33,879 patients were classified into four groups—normal, DM without CKD, CKD without DM, and DKD—and were further analyzed to identify novel single-nucleotide polymorphisms (SNPs) associated with DKD. Additionally, fine-mapping analysis was conducted to investigate whether the variants of interest contribute to a trait. Conditional analyses adjusting for the effect of type 1 DM (T1D)-associated HLA variants were also performed to remove confounding factors of genetic association with T1D. Moreover, analysis of expression quantitative trait loci (eQTL) was performed using the Genotype-Tissue Expression project. Differentially expressed genes (DEGs) were analyzed using the Gene Expression Omnibus database (GSE30529). The significant eQTL DEGs were used to explore the predicted interaction networks using search tools for the retrieval of interacting genes and proteins.ResultsWe identified three novel SNPs [rs3128852 (P = 8.21×10−25), rs117744700 (P = 8.28×10−10), and rs28366355 (P = 2.04×10−8)] associated with DKD. Moreover, the fine-mapping study validated the causal relationship between rs3128852 and DKD. rs3128852 is an eQTL for TRIM27 in whole blood tissues and HLA-A in adipose-subcutaneous tissues. rs28366355 is an eQTL for HLA-group genes present in most tissues.ConclusionsWe successfully identified SNPs (rs3128852, rs117744700, and rs28366355) associated with DKD and verified the causal association between rs3128852 and DKD. According to the in silico analysis, TRIM27 and HLA-A can define DKD pathophysiology and are associated with immune response and autophagy. However, further research is necessary to understand the mechanism of immunity and autophagy in the pathophysiology of DKD and to prevent and treat DKD.

【 授权许可】

CC BY   
© The Author(s) 2023

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