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

Nature Reviews Nephrology volume 16, pages 628–640 (2020)Cite this article

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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.

Key points

  • Genome-wide association studies (GWAS) have identified a large number of nucleotide variations that show strong and reproducible association with kidney-related traits such as serum and urine metabolites, estimated glomerular filtration rate, albuminuria and hypertension.

  • More than 95% of disease-associated GWAS signals are located in non-coding regions of the genome, which can be identified by analysing chromatin accessibility and conformation, DNA methylation and transcription factor binding.

  • Disease-associated genetic variants seem to be located in cell type-specific gene regulatory regions, where they modulate disease risk by quantitatively altering gene transcript levels.

  • The use of advanced computational approaches to combine datasets orthogonal to GWAS data, including molecular quantitative trait studies, single-cell transcriptomics and epigenetic information, is necessary for the prioritization of GWAS variants.

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Fig. 1: Conceptual model for translating genetic variants into disease mechanisms.
Fig. 2: Causal variant prioritization.
Fig. 3: Computational integration of GWAS and molecular QTLs.

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Acknowledgements

K.S. is supported by NIH National Institute of Diabetes and Digestive and Kidney Diseases grants R01DK076077, R01 DK087635 and DP3 DK108220.

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Authors and Affiliations

  1. Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

    Katie Marie Sullivan & Katalin Susztak

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  1. Katie Marie Sullivan
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  2. Katalin Susztak
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Both authors wrote, reviewed or edited the manuscript before submission. K.M.S. researched data for the article and K.S. made substantial contributions to the discussion of content.

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Correspondence to Katalin Susztak.

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Nature Reviews Nephrology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Related links

Encyclopedia of DNA Elements (ENCODE): https://www.encodeproject.org

Genotype–Tissue Expression (GTEx) Portal: https://gtexportal.org/home/

International Human Epigenome Consortium (IHEC): http://ihec-epigenomes.org

NEPH eQTL browser: http://nephqtl.org

NIH Roadmap Epigenomics Project: http://www.roadmapepigenomics.org

Susztaklab Human Kidney eQTL atlas: http://susztaklab.com/eqtl/

Glossary terms

Secondary chromatin structure

The structure formed by the folding of primary chromatin.

Fine mapping

The process by which a variant is assigned to a complex trait.

Quantitative trait loci

(QTL) Genetic loci that are associated with variation in a phenotype.

Imputation

The inclusion of genotypes that are not directly measured, by estimating the missing data based on reference genome datasets and SNP linkage.

Haplotype

A set of genetic polymorphisms that are inherited together.

Promoter

The DNA sequences on which transcription is initiated.

Enhancer

A DNA sequence that is bound by transcription factors to increase the transcription of a gene.

Insulator

A DNA sequence that limits chromatin activation.

Transposable elements

DNA sequences whose position can change within the genome.

Cis-regulatory elements

Regions of non-coding DNA that affect the expression of nearby genes.

Massively parallel reporter assays

High-throughput experiments, in which the transcriptional activities of many regulatory sequences can be determined.

Alternative splicing

A process by which multiple transcripts are produced from a single gene.

Posterior probability

The probability of an event occurring based on information from a prior event.

Mendelian randomization

A technique that uses genetic data to make causative inference.

Orthogonal datasets

Independent and uncorrelated datasets.

Cellular encapsulation

A technique whereby cells are entrapped in a spherical semipermeable polymeric membrane.

Cellular deconvolution

The estimation of gene expression data for individual cell types within a bulk expression dataset.

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Sullivan, K.M., Susztak, K. Unravelling the complex genetics of common kidney diseases: from variants to mechanisms. Nat Rev Nephrol 16, 628–640 (2020). https://doi.org/10.1038/s41581-020-0298-1

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