Journal of the American Society of Nephrology : JASN · 2026
Genetic Landscape of Kidney Failure in a Korean Transplant Cohort: Genome-Wide Association and Multi-Polygenic Risk Score Analyses.
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Jeon Hee Jung, Jang Hye-Mi, Park Yi Seul, Kim Sung Min, Kim Bong-Jo, Kim Hyung Woo, Jeong Jong Cheol, Park Seokwoo, Park Borae G, Kim Myoung Soo, Park Hyun-Young, Kim Young Jin, Yang Jaeseok, Korean Organ Transplantation Registry (KOTRY) Study Group
Аннотация
KEY POINTS: Genome-wide association study identified multiple genetic loci associated with kidney failure and its subtypes in Korean cohorts. Multiple polygenic risk scores improved the prediction of kidney failure beyond basic available clinical factors. Genetic architecture of kidney failure differed from that of CKD driven mainly by kidney function traits.
BACKGROUND: Kidney failure represents the final, irreversible stage of CKD, yet its genetic architecture remains incompletely defined compared with CKD. Although previous studies largely focused on kidney function traits, the genetic determinants of kidney failure itself and its etiologic subtypes are poorly understood. Therefore, this study aimed to identify genetic loci associated with kidney failure and its subtypes and to evaluate the predictive performance of multiple polygenic risk scores (multi-PRSs) for stratifying kidney failure risk.
METHODS: We performed a large-scale genome-wide association study using a dataset of 2355 patients with kidney failure across three subtypes defined by primary disease, along with 152,131 controls. Polygenic risk scores for type 2 diabetes, hypertension, eGFR, and CKD were calculated, and a multi-PRS model was derived.
RESULTS: The genome-wide association study identified multiple kidney failure-associated loci, including HLA-DRB1 for all kidney failure and glomerulonephritis as the primary disease, and COL24A1 for hypertensive kidney failure at genome-wide significance ( P < 5×10 -8 ), implicating distinct immune- and hypertension-related pathways in kidney failure pathogenesis. Polygenic risk score analysis revealed that genetically distinct components of type 2 diabetes, hypertension, CKD, and eGFR were significantly linked to kidney failure risk across subtypes (odds ratio=1.1-2.5). A combined multi-PRS model demonstrated superior predictive performance (odds ratio=1.5-2.5). Comparison with CKD cohorts, predominantly influenced by eGFR-related genetics, uncovered unique kidney failure-specific genetic signatures, highlighting kidney failure as a genetically heterogeneous and multifactorial disease beyond CKD.
CONCLUSIONS: This study identified subtype-specific genetic loci and demonstrated that multi-PRS models improve kidney failure risk prediction beyond basic available clinical factors.
BACKGROUND: Kidney failure represents the final, irreversible stage of CKD, yet its genetic architecture remains incompletely defined compared with CKD. Although previous studies largely focused on kidney function traits, the genetic determinants of kidney failure itself and its etiologic subtypes are poorly understood. Therefore, this study aimed to identify genetic loci associated with kidney failure and its subtypes and to evaluate the predictive performance of multiple polygenic risk scores (multi-PRSs) for stratifying kidney failure risk.
METHODS: We performed a large-scale genome-wide association study using a dataset of 2355 patients with kidney failure across three subtypes defined by primary disease, along with 152,131 controls. Polygenic risk scores for type 2 diabetes, hypertension, eGFR, and CKD were calculated, and a multi-PRS model was derived.
RESULTS: The genome-wide association study identified multiple kidney failure-associated loci, including HLA-DRB1 for all kidney failure and glomerulonephritis as the primary disease, and COL24A1 for hypertensive kidney failure at genome-wide significance ( P < 5×10 -8 ), implicating distinct immune- and hypertension-related pathways in kidney failure pathogenesis. Polygenic risk score analysis revealed that genetically distinct components of type 2 diabetes, hypertension, CKD, and eGFR were significantly linked to kidney failure risk across subtypes (odds ratio=1.1-2.5). A combined multi-PRS model demonstrated superior predictive performance (odds ratio=1.5-2.5). Comparison with CKD cohorts, predominantly influenced by eGFR-related genetics, uncovered unique kidney failure-specific genetic signatures, highlighting kidney failure as a genetically heterogeneous and multifactorial disease beyond CKD.
CONCLUSIONS: This study identified subtype-specific genetic loci and demonstrated that multi-PRS models improve kidney failure risk prediction beyond basic available clinical factors.