Tina Levstek , Bojan Vujkovac , Albina Nowak , João-Paulo Oliveira , Gabriela Dostálová , Aleš Linhart , Markéta Šafaříková , Gheona Altarescu , Katarina Trebušak Podkrajšek
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引用次数: 0
Abstract
Fabry nephropathy, a common complication of Fabry disease (FD), presents with a heterogeneous clinical phenotype. To date, genetic biomarkers associated with the progression of Fabry nephropathy have not been thoroughly investigated. The aim of our study was therefore to identify genetic variants associated with nephropathy progression in FD.
A total of 300 Caucasian patients were enrolled and stratified into two groups based on their estimated glomerular filtration rate (eGFR). Whole-exome sequencing was performed, and variants in a curated panel of 190 genes related to podocyte homeostasis were subjected to bioinformatic analysis.
We identified six genetic variants with a false discovery rate (FDR) below 0.05. Five of these variants were located in non-coding regions: the 3′ untranslated region (UTR) (YRDC: rs7686, TPPP: rs28364690), the 5’ UTR (SNCA: rs1372518), an upstream region (COL4A2: rs7320419), and a non-coding exon (DLC1: rs10888175). Each of these variants was associated with an odds ratio less than one, suggesting a potential protective effect against nephropathy progression. In contrast, the coding variant rs749735949 in the FAT1 gene (FDR = 0.032) was associated with an odds ratio of 14.9, indicating a markedly increased risk of progressive nephropathy in carriers.
These findings suggest that rs749735949 in FAT1 may serve as a predictive biomarker for accelerated eGFR decline, and could support the identification of biological targets for the prevention and improved management of Fabry nephropathy.
期刊介绍:
Molecular Genetics and Metabolism contributes to the understanding of the metabolic and molecular basis of disease. This peer reviewed journal publishes articles describing investigations that use the tools of biochemical genetics and molecular genetics for studies of normal and disease states in humans and animal models.