Open AccessA roadmap for the genetic analysis of renal aging
Author(s) -
Noordmans Gerda A.,
Hillebrands JanLuuk,
Goor Harry,
Korstanje Ron
Publication year - 2015
Publication title -
aging cell
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.103
H-Index - 140
eISSN - 1474-9726
pISSN - 1474-9718
DOI - 10.1111/acel.12378
Subject(s) - biology , genome wide association study , candidate gene , disease , genetics , genetic association , kidney disease , population , gene , computational biology , bioinformatics , single nucleotide polymorphism , pathology , genotype , medicine , endocrinology , environmental health
Summary Several studies show evidence for the genetic basis of renal disease, which renders some individuals more prone than others to accelerated renal aging. Studying the genetics of renal aging can help us to identify genes involved in this process and to unravel the underlying pathways. First, this opinion article will give an overview of the phenotypes that can be observed in age‐related kidney disease. Accurate phenotyping is essential in performing genetic analysis. For kidney aging, this could include both functional and structural changes. Subsequently, this article reviews the studies that report on candidate genes associated with renal aging in humans and mice. Several loci or candidate genes have been found associated with kidney disease, but identification of the specific genetic variants involved has proven to be difficult. CUBN , UMOD , and SHROOM 3 were identified by human GWAS as being associated with albuminuria, kidney function, and chronic kidney disease ( CKD ). These are promising examples of genes that could be involved in renal aging, and were further mechanistically evaluated in animal models. Eventually, we will provide approaches for performing genetic analysis. We should leverage the power of mouse models, as testing in humans is limited. Mouse and other animal models can be used to explain the underlying biological mechanisms of genes and loci identified by human GWAS . Furthermore, mouse models can be used to identify genetic variants associated with age‐associated histological changes, of which Far2, Wisp2, and Esrrg are examples. A new outbred mouse population with high genetic diversity will facilitate the identification of genes associated with renal aging by enabling high‐resolution genetic mapping while also allowing the control of environmental factors, and by enabling access to renal tissues at specific time points for histology, proteomics, and gene expression.