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Detection of MicroRNA Expression in the Kidneys of Immunoglobulin A Nephropathic Mice
Published on: July 8, 2020
Genetic study of immunoglobulin A nephropathy: From research to clinical application
Ming Li1,2, Xueqing Yu1,2,3
1Department of Nephrology, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Abstract:
Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis throughout the world and a major cause of end-stage renal disease among East Asian population. It is widely considered that genetic factors play an important role in the pathogenesis of IgAN. The genetic studies of IgAN, is from the association studies of candidate genes and family-based designs, to the genome-wide association studies. Altogether there are five large genome-wide association studies performed, which have identified multiple susceptibility loci for IgAN, including several novel loci found in Chinese population. The discovery of these susceptibility loci has provided important insight into the potential biological mechanisms and pathways that influence genetic risk to IgAN. In addition, genetic interaction and structural variation (such as copy number variation) studies were also conducted to identify more variants associated with IgAN and disease progression. The genetic studies of IgAN have made great achievements in recent years. Most of susceptibility loci discovered up to date contain genes involved in the response to mucosal pathogens, suggesting that intestinal immune network for IgA production may be involved in the pathogenesis of IgAN. While the genetic studies of the complex diseases remain to be challenging, new genetic and analytical techniques and methods, especially next-generation sequencing-based studies, need to be applied to advance the genetic studies of IgAN. More importantly, we need to identify the genetic factors which influence the clinical phenotypes and renal progression to end-stage renal disease. This will require the integration of genomic data with other omics profiles (e.g. transcriptomics, metabolomics and immunomics) in patients with long-term clinical follow-up data to better understand the factors underlying inter-individual variability, not only in disease susceptibility, but also in the long-term prognosis and healthcare requirements.
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