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

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dc.contributor.authorHeejin Jin-
dc.contributor.authorYe An Kim-
dc.contributor.authorYoung Lee-
dc.contributor.authorSeung‑hyun Kwon-
dc.contributor.authorAh Ra Do-
dc.contributor.authorSujin Seo-
dc.contributor.authorSungho Won-
dc.contributor.authorJe Hyun Seo-
dc.date.accessioned2023-01-27T08:00:28Z-
dc.date.available2023-01-27T08:00:28Z-
dc.date.issued2023-01-11-
dc.identifier.citationBMC Medicine, 21(16)ko_KR
dc.identifier.issn1741-7015-
dc.identifier.urihttps://hdl.handle.net/10371/189021-
dc.description.abstractBackground
The 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.
Methods
We 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.
Results
We 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.
Conclusions
We 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.
ko_KR
dc.description.sponsorshipThis study was supported by the Veterans Health Service Medical Center Research Grant (No. VHSMC20035) and National Research Foundation of Korea (NRF) grant, funded by the Korea government (Ministry of Science and ICT; No. 2022R1C1C1002929).ko_KR
dc.language.isoenko_KR
dc.publisherBMCko_KR
dc.subjectDiabetic kidney disease-
dc.subjectGWAS-
dc.subjectGenetic variants-
dc.subjectPrediction-
dc.subjectMicrovascular complications-
dc.titleIdentification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysisko_KR
dc.typeArticleko_KR
dc.identifier.doi10.1186/s12916-022-02723-4ko_KR
dc.citation.journaltitleBMC Medicineko_KR
dc.language.rfc3066en-
dc.rights.holderThe Author(s)-
dc.date.updated2023-01-15T04:10:55Z-
dc.citation.endpage15ko_KR
dc.citation.number16ko_KR
dc.citation.startpage1ko_KR
dc.citation.volume21ko_KR
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