Genetics and stem cells

Genetics and kidney disease

Scientists have made a “revolutionary” discovery that could help explain the causes of kidney disease, according to BBC News. The news comes from a new study that checked the DNA of over 90,000 people, comparing the presence of specific DNA variants to kidney function. It found that 13 variants are associated with altered kidney function.

Dr Jim Wilson, a geneticist at the University of Edinburgh who worked on the study, told the BBC that the results are a “very critical first step” towards greater understanding of the biology behind chronic kidney disease (CKD). He also reiterated the early nature of the discovery, adding that “transferring what we've found into clinical benefits will take some years."

This research drew together data from several genetic studies to identify new DNA variants associated with kidney function and CKD. However, the findings suggest that other genetic variants and environmental factors may affect the risk of this disease. This well-conducted research furthers our understanding of the complex genetic basis of healthy kidney functioning, but more research is needed before the findings can be applied to treatment or diagnosis of kidney disease.

Where did the story come from?

The research was carried out by Dr Anna Kottgen and colleagues from John Hopkins University, as well as a consortium of researchers from academic and medical institutions around the world. The study was published in the peer-reviewed medical journal Nature Genetics.

The BBC accurately reported the methods and findings of this important research.

What kind of research was this?

This was a meta-analysis of genome-wide association studies, in which researchers compared how often particular DNA variants occurred in people with CKD to how often they occurred in people without the disease. Genome-wide association studies are a form of case-control study and are a way to assess how particular genes are associated with disease in a large number of people.

What did the research involve?

In total, the researchers had DNA data for over 90,000 individuals of European ancestry available for pooling. During the first part of this two-part study, the researchers performed a meta-analysis of the results from different studies which featured a total of 67,093 individuals. The purpose of this meta-analysis was to determine whether any genetic variations were more common in individuals who had CKD.

Twenty different samples sets contributed to the first part of the experiment, all of which had been assessed for some measure of kidney function (levels of serum creatinine [eGFRcrea] or serum cystatin c [eGFRcys], or diagnosis of CKD [defined as eGFRcrea less than 60 ml/min/1.73 m2]). This population sample featured 5,807 people with CKD. The pooling of results from separate studies into a single analysis increases the study’s ability to detect associations between gene variants and disease markers.

It is common for genome-wide association studies to attempt to replicate their findings in a separate, second sample. The process was performed in this study by investigating whether the variants that were significantly linked with disease in the first part of the study were also significantly linked with disease in a separate population. There were 22,503 people available for the replication sample. These were drawn from 14 cohort studies and were pooled in the same way as in the first part of the analyses. All participants were of European ancestry.

The researchers say that because levels of creatinine in the blood are affected by both creatinine production and the efficient functioning of the kidneys, the measure of a substance called eGFRcys is the best measure of true renal function. They used this measure to confirm which of their significant associations was most likely to be linked to kidney function. Because both diabetes and hypertension increase the risk of CKD, the researchers also performed an additional analysis comparing people in groups with and without these conditions.

What were the basic results?

Following the meta-analysis in the first part of the study, the researchers found 28 DNA variants that were associated with any of the three measures of kidney function. The findings confirmed five previously known associations but also identified 23 new ones. In the second study of the 23 new variants, 20 of them were significantly associated with markers of kidney disease.

After investigating associations with eGFRcys, the researchers noted that they identified 13 new variants linked to renal function and 7 linked to creatinine metabolism. Three of the five previously identified variants were also found to be linked to eGFRcys. Together, these 16 variants account for only 1.4% of the variation in eGFRcys seen in the samples. There was no difference in associations when the authors analysed the participants separately by presence or absence of diabetes or hypertension.

How did the researchers interpret the results?

The researchers concluded that many common genetic variants are associated with markers of kidney function. This highlights the role of different genes in the variety of functions that are required to maintain healthy kidneys.


This meta-analysis of genome-wide association studies increases the available information about the biology of kidney function. As the researchers themselves say, the results further our understanding of the biologic mechanisms of kidney function, and identify important genes involved in a variety of metabolic and functional processes in the kidney. The study was well conducted, using recognised methods in this field of research, and the results are reliable. The researchers verified their initial findings in a separate population, further increasing the reliability of the results.

Importantly, the identified DNA variants accounted for just 1.4% of the variations in eGFRcys levels in these populations, indicating that other genetic and environmental factors may be related to the risk of poor kidney function. Further research will be needed to translate these findings into ways to diagnose or treat CKD.

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