Unravelling the complex genetics of common kidney diseases: from variants to mechanisms

Abstract

Genome-wide association studies (GWAS) have identified hundreds of loci associated with kidney-related traits such as glomerular filtration rate, albuminuria, hypertension, electrolyte and metabolite levels. However, these impressive, large-scale mapping approaches have not always translated into an improved understanding of disease or development of novel therapeutics. GWAS have several important limitations. Nearly all disease-associated risk loci are located in the non-coding region of the genome and therefore, their target genes, affected cell types and regulatory mechanisms remain unknown. Genome-scale approaches can be used to identify associations between DNA sequence variants and changes in gene expression (quantified through bulk and single-cell methods), gene regulation and other molecular quantitative trait studies, such as chromatin accessibility, DNA methylation, protein expression and metabolite levels. Data obtained through these approaches, used in combination with robust computational methods, can deliver robust mechanistic inferences for translational exploitation. Understanding the genetic basis of common kidney diseases means having a comprehensive picture of the genes that have a causal role in disease development and progression, of the cells, tissues and organs in which these genes act to affect the disease, of the cellular pathways and mechanisms that drive disease, and of potential targets for disease prevention, detection and therapy.

Figure 1.. Epigenome annotation methods for causal variant prioritization

a)Genetic effects on the epigenome are an important component of the genetic risk of a disease. | DNase I hypersensitivity (DHS) analysis, identifies DNA that is not tightly wound around histones, ie is in an area of open chromatin. These DNA elements are more sensitive to enzymatic digestion by DNase1, In contrast, DNA which is tightly wrapped in nucleosomes is more resistant to digestion. The DNAse digested ends are then enriched and sequenced. (1A) b)ATAC-seq uses a hyperactive Tn5 transposase, which is an RNase that inserts sequencing adapters into open regions of the genome during a process called tagmentation. The tagged DNA fragments are then purified, PCR-amplified and sequenced using next-generation sequencing.(1B) c) Modifications to histone proteins, such as methylation, acetylation and trimethylation, can be detected using Chromatin Immunoprecipitation (ChIP) based methods. These modifications can then be used as indicators of the chromatin state which is associated with gene activation or repression. d) Analysis of Chromatin Conformation. Chromatin conformation experiments examine which pairs of DNA loci are in contact with each other, genome wide, and allows long distance contacts to be elucidated. The process involves DNA digestion, biotin labelling and formation of ligation products, which are then sequenced. ^, These methods of epigenome annotation, identify cis-regulatory elements (CREs), which contain causal variants and associate with gene expression.

Figure 2.. Function validation of causal variants

Several methods can be used to validate causal variants and elucidate the underlying mechanisms that link the sequence variant to disease risk — variant-to-function analysis. Modelling risk variants using Crispr-Cas9 system in single cells or in 3D structures (organoids) and animal models (zebra fish and mice) can provide information on the effect of a sequence variant on gene expression levels. Analyses of healthy tissue, which can be microdissected into glomeruli and tubules to generate kidney tissue compartment-specific data, or analysed by single-cell technologies can provide information on homeostatic gene regulation, including information on the cell types that express genes whose expression associates with sequence variants. Further validation of the potential mechanisms underlying the modulation of disease risk can obtained by analysing samples obtained from patient with kidney disease.

Figure 3.. Quantitative trait analysis for causal gene prioritization

Expression of Quantitative trait analysis determines the association between genetic variations and gene expression in the same tissue samples. a| In this example, GWAS identified an association between the sequence variant T/T and chronic kidney disease. This variant is located in an open area of chromatin within a region in which the lysine (K) at position 27 of histone 3 (H3) is acetylated (ac) H3K27ac, which is a DNA enhancer and therefore suggests active gene transcription. The T/T sequence variant affects binding of the transcription factor to the enhancer and results in a decrease in the expression of DAB adaptor protein 2 (DAB2) compared with the T/A and A/A variants. In turn, this decrease in DAB2 expression correlates with a decreased of estimated glomerular filtration rate. b| Genetic correlation can result from direct causality, in which a variant has a direct effect on a quantitative trait, but it can also result from pleiotropy, in which a variant affects multiple traits, or linkage, which refers to the existence of two variants that are associated by linkage disequilibrium (that is, a non-random association of alleles at different loci) and have independent effects on different phenotypes. These different underlying causes of genetic correlation complicate the identification of causal variants. c| [ Integration of GWAS–molecular QTLs (molQTLs). Overlap between a GWAS variant, an eQTL and a chromatin accessibility QTL (caQTL) indicates that the sequence variant not only correlates with a disease risk trait (for example, eGFR) but also with gene expression and chromatin accessibility. This overlap potentially indicates that the sequence variant is located in gene regulatory region (that is, an area of open chromatin) where it can modulate expression of a causal gene involved in disease risk.