A systems view of genetics in chronic kidney disease

Abstract

A tight interplay of genetic predisposition and environmental factors define the onset and the rate of progression of chronic renal disease. We are seeing a rapid expansion of information about genetic loci associated with kidney function and complex renal disease. However, discovering the functional links that bridge the gap from genetic risk loci to disease phenotype is one of the main challenges ahead. Risk loci are currently assigned to a putative context using the functional annotation of the closest genes via a guilt-by-proximity approach. These approaches can be extended by strategies integrating genetic risk loci with kidney-specific, genome-wide gene expression. Risk loci-associated transcripts can be assigned a putative disease-specific function using gene expression coregulation networks. Ultimately, genotype-phenotype dependencies postulated from these associative approaches in humans need to be tested via genetic modification in model organisms. In this review, we survey strategies that employ human tissue-specific expression and the use of model organisms to identify and validate the functional relationship between genotype and phenotype in renal disease. Strategies to unravel how genetic risk and environmental factors orchestrate renal disease manifestation can be the first steps toward a more integrated, holistic approach urgently needed for chronic renal diseases.

Figure 1

Illustration of physiologic effects of genetic variants based on molecular systems commonly studied in systems biology. Groups of variants (a) directly affect regulatory (b) and proteomic (signaling) (c) machinery of the cell, leading to disruption in a metabolic pathway (d) that ultimately leads to a clinical trait (e) and the renal disease phenotype (f). Shown is a group of regulatory and non-synonymous SNPs and their effect: SNP A lies in the promoter region of a gene, modifying the binding characteristics of a transcription factor; similarly B modifies an enhancer or repressor for another gene; and C and D are non-synonymous, with C having an indirect regulatory effect because of its role as a transcription factor. The layers illustrate that different groups of variants may affect different parts of cellular physiology, but ultimately impact the same phenotype.

Figure 2

Use of expression and clinical traits to compute quantitative trait loci (QTL) in order to identify GWAS candidates with putative regulatory effect impacting clinical traits associated with the GWAS phenotype. Kidney specific eQTLs allow focus on tissue-specific regulatory effects, while clinical QTLs narrow further to those showing associations with measures of renal disease progression.

Figure 3

Use of clinical trait correlated tissue-specific expression to identify GWAS candidate genes and co-regulated genes relevant to a clinical condition.

Figure 4

Systems genetics strategy for studying systems effects of candidate variants. Various high-throughput technologies allow observation of the state of the molecular mechanism of the cell as quantitative measures of macromolecules (shown are RNA, proteins, and metabolites) that can be used in quantitative trait locus (QTL) analysis.