A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n = 31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

A clustering approach to improve our understanding of the genetic and phenotypic complexity of chronic kidney disease

Chronic kidney disease (CKD) is a complex disorder that causes a gradual loss of kidney function, affecting approximately 9.1% of the world's population. Here, we use a soft-clustering algorithm to deconstruct its genetic heterogeneity. First, we selected 322 CKD-associated independent genetic variants from published genome-wide association studies (GWAS) and added association results for 229 traits from the GWAS catalog. We then applied nonnegative matrix factorization (NMF) to discover overlapping clusters of related traits and variants. We computed cluster-specific polygenic scores and validated each cluster with a phenome-wide association study (PheWAS) on the BioMe biobank (n=31,701). NMF identified nine clusters that reflect different aspects of CKD, with the top-weighted traits signifying areas such as kidney function, type 2 diabetes (T2D), and body weight. For most clusters, the top-weighted traits were confirmed in the PheWAS analysis. Results were found to be more significant in the cross-ancestry analysis, although significant ancestry-specific associations were also identified. While all alleles were associated with a decreased kidney function, associations with CKD-related diseases (e.g., T2D) were found only for a smaller subset of variants and differed across genetic ancestry groups. Our findings leverage genetics to gain insights into the underlying biology of CKD and investigate population-specific associations.

Quantitative analysis of the intra- and inter-subject variability of the whole salivary proteome

BACKGROUND AND OBJECTIVE

Interest in human saliva is increasing for disease-specific biomarker discovery studies. However, protein composition of whole saliva can grossly vary with physiological and environmental factors over time and it comprises human as well as bacterial proteins.

MATERIAL AND METHODS

We compared intra- and inter-subject variabilities using complementary gel-based (two-dimensional difference gel electrophoresis, 2-D DIGE) and gel-free (liquid chromatography tandem mass spectrometry, LC-MS/MS) proteomics profiling of saliva. Unstimulated whole saliva of four subjects was examined at three different time-points (08.00 h, 12.00 h and 17.00 h) and variability of the saliva proteome was analyzed on two successive days by LC-MS/MS.

RESULTS

In the 2-D DIGE experiment, the median coefficient of variation (CV) for intra-subject variability was significantly lower (CV of 0.39) than that for inter-subject variability (CV of 0.57; CV of technical replicates 0.17). LC-MS/MS data confirmed the significantly lower variation within subjects over time (CV of 0.37) than the inter-subject variability (CV of 0.53; CV of technical replicates 0.11), and that the inter-subject variability was not time-dependent.

CONCLUSION

Both techniques revealed similar trends of variations on technical, intra- and inter-subject level but provided peptide and protein focused information and should thus be used as complementary approaches. The data presented indicate that 2-D DIGE as well as LC-MS/MS approaches are suitable for biomarker screening in saliva.