Abstract: PO1330
A Multivariate Analysis of Genome-Wide Association (GWAS) Data to Identify Genes Associated with CKD
Session Information
- Genetic Diseases of the Kidneys: Non-Cystic - II
November 04, 2021 | Location: On-Demand, Virtual Only
Abstract Time: 10:00 AM - 12:00 PM
Category: Genetic Diseases of the Kidneys
- 1002 Genetic Diseases of the Kidneys: Non-Cystic
Authors
- Osborne, Amy J., University of Bristol, Bristol, Bristol, United Kingdom
- Bierzynska, Agnieszka, University of Bristol, Bristol, Bristol, United Kingdom
- Welsh, Gavin Iain, University of Bristol, Bristol, Bristol, United Kingdom
- Saleem, Moin, University of Bristol, Bristol, Bristol, United Kingdom
- Campbell, Ian Colin, University of Bristol, Bristol, Bristol, United Kingdom
Background
Chronic kidney disease (CKD), a major public health burden, is characterised by a progressive loss of nephron function which leads to an impaired ability to filter the blood. Genome-wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) and loci associated with the kidney function biomarkers estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN) by mostly univariate-based analyses. However, gene-based multivariate-SNP and multivariate-biomarker relationships have typically not been considered so far in this context. The purpose of this study was to highlight the additional insights gained from the statistical power of a multivariate-based approach to identify potential risk factor genes for CKD.
Methods
We used a multivariate statistical approach, canonical correlation analysis (CCA), to identify single nucleotide polymorphisms (SNPs) that showed significant correlation with estimated glomerular filtration rate (eGFR) and blood urea nitrate (BUN) taken jointly. Since attributes were in the form of three published GWAS summary statistics datasets, we used metaCCA. The SNPs were filtered using linkage disequilibrium-based pruning. For the significant SNPs and genes we identified, their functions, signalling pathways and cellular expression were investigated using gene set statistical enrichment analyses.
Results
For each of three published GWAS summary statistics datasets of both European and Japanese ancestry groups, we identified sets of 159, 246 and 181 protein-coding genes, respectively, that contained significant SNPs. Using gene set statistical enrichment analyses, these genes showed significant enrichment for kidney development processes, signalling pathways and kidney cell gene expression signatures. In addition, between all three datasets, we identified four significant genes (CBLB, MACROD2, MECOM and SHROOM3) that overlapped. Between two datasets, we identified a further four significant SNPs that overlapped.
Conclusion
By using a multivariate statistical approach, we have identified both previously reported and additional genes that contained SNPs statistically associated with kidney function. Overall, these findings provide new insights into SNPs and genes potentially involved in kidney function and CKD risk.
Funding
- Government Support – Non-U.S.