Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis
Abstract Background 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 b...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-01-01
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| Series: | BMC Medicine |
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| Online Access: | https://doi.org/10.1186/s12916-022-02723-4 |
| _version_ | 1797952478320263168 |
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| author | Heejin Jin Ye An Kim Young Lee Seung-hyun Kwon Ah Ra Do Sujin Seo Sungho Won Je Hyun Seo |
| author_facet | Heejin Jin Ye An Kim Young Lee Seung-hyun Kwon Ah Ra Do Sujin Seo Sungho Won Je Hyun Seo |
| author_sort | Heejin Jin |
| collection | DOAJ |
| description | Abstract Background 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. |
| first_indexed | 2024-04-10T22:46:58Z |
| format | Article |
| id | doaj.art-5164b5dde9e646348bcdf6cdf5c09be0 |
| institution | Directory Open Access Journal |
| issn | 1741-7015 |
| language | English |
| last_indexed | 2024-04-10T22:46:58Z |
| publishDate | 2023-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medicine |
| spelling | doaj.art-5164b5dde9e646348bcdf6cdf5c09be02023-01-15T12:14:03ZengBMCBMC Medicine1741-70152023-01-0121111510.1186/s12916-022-02723-4Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysisHeejin Jin0Ye An Kim1Young Lee2Seung-hyun Kwon3Ah Ra Do4Sujin Seo5Sungho Won6Je Hyun Seo7Institute of Health and Environment, Seoul National UniversityDivision of Endocrinology, Department of Internal Medicine, Veterans Health Service Medical CenterVeterans Medical Research Institute, Veterans Health Service Medical CenterVeterans Medical Research Institute, Veterans Health Service Medical CenterInterdisciplinary Program of Bioinformatics, College of National Sciences, Seoul National UniversityDepartment of Public Health Science, Graduate School of Public Health, Seoul National UniversityInstitute of Health and Environment, Seoul National UniversityVeterans Medical Research Institute, Veterans Health Service Medical CenterAbstract Background 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.https://doi.org/10.1186/s12916-022-02723-4Diabetic kidney diseaseGWASGenetic variantsPredictionMicrovascular complications |
| spellingShingle | Heejin Jin Ye An Kim Young Lee Seung-hyun Kwon Ah Ra Do Sujin Seo Sungho Won Je Hyun Seo Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis BMC Medicine Diabetic kidney disease GWAS Genetic variants Prediction Microvascular complications |
| title | Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis |
| title_full | Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis |
| title_fullStr | Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis |
| title_full_unstemmed | Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis |
| title_short | Identification of genetic variants associated with diabetic kidney disease in multiple Korean cohorts via a genome-wide association study mega-analysis |
| title_sort | identification of genetic variants associated with diabetic kidney disease in multiple korean cohorts via a genome wide association study mega analysis |
| topic | Diabetic kidney disease GWAS Genetic variants Prediction Microvascular complications |
| url | https://doi.org/10.1186/s12916-022-02723-4 |
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