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Researchers discover new genetic associations with chronic kidney disease

In a recent study published in Nature Communications, researchers identified new rare variants and genes associated with chronic kidney disease. They performed exome-wide association studies (ExWAS) of over 400,000 individuals using whole-exome sequencing (WES) data from the UK Biobank.

Understanding chronic kidney disease

Chronic kidney disease (CKD) is a prevalent condition affecting around 10% of adults globally. Previous genetic studies have aimed to identify rare, pathogenic variants or common susceptibility variants for CKD using genome-wide association studies (GWAS). However, while GWASs have discovered hundreds of associations between common genotypes and kidney function they cannot comprehensively investigate rare coding variants.

The glomerular filtration rate (GFR) is a critical measurement in evaluating kidney function, but it is not feasible to measure directly in population-based studies. Instead, GFR is estimated from biomarker measurements and demographic information, with serum creatinine being the most commonly used biomarker. Recently developed GFR estimating equations also include cystatin C, which is less dependent on muscle mass, and serum urea and urate levels, which are strongly reflective of kidney function. By understanding these different biomarkers and utilizing genomic data, researchers can study CKD using GWASs or in the case of this study, ExWASs.

Discovering genes

While previous studies had limited power, the availability of WES data from the UK Biobank has allowed the researchers in this study to comprehensively study CKD, conducting ExWASs of over 400,000 individuals. The study discovered rare variants and genes associated with measures of kidney function and disease, characterized across different kidney phenotypes and clinical definitions of CKD and identified highly expressed genes in specific cell types and tissues.

In total, the study identified associations at 158 unique single variants and 57 genes with gene-level tests, all driven by rare variants. They also distinguished genes that are likely involved in biomarker metabolism from those truly related to reduced kidney function or kidney damage, and detected numerous genes and variants known to cause monogenic kidney diseases when mutated. Additionally, the study established a previously unreported eGFR-associated frameshift variant in CLDN10 as a functional allele. The genetic architecture of the studied kidney function markers showed substantial overlap between genes identified in association with serum creatinine, cystatin C, and urea. In total, the study identified 105 unique genes and characterized 174 rare variant-associations and 83 gene-associations for five measures of kidney function and disease.

The power of big data

The approach used in this study, which combined whole-exome sequencing and imputation-based analysis, allowed for the replication of numerous known kidney disease-causing variants and genes, as well as the identification of new variants and genes not previously linked to kidney function and disease. The large amount of data provided by the UK Biobank enabled the researchers to conduct an in-depth analysis, resulting in the discovery of previously unknown associations. This study underscores the power of large-scale genetic studies, which can reveal insights into the genetic underpinnings of complex diseases like kidney function and CKD.

However, one notable limitation of this study is that it focused on individuals of European descent, which may limit its generalizability to other populations. It is crucial to have diversity in genetic studies to ensure that findings can be generalized to different populations. Identified variants and genes may not apply to other ethnic groups and it is therefore important to replicate these findings in other diverse populations to establish the broader relevance of the results. Additionally, while the study identified associations between genetic variants and kidney function markers, it is still unclear how these variants mechanistically contribute to kidney disease, which will require further research.

More on these topics

Exome / Genomics / Whole-exome sequencing

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