Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease

Osborne, Amy J.; Bierzynska, Agnieszka; Colby, Elizabeth; Andag, Uwe; Kalra, Philip A.; Radresa, Olivier; Skroblin, Philipp; Taal, Maarten W.; Welsh, Gavin I.; Saleem, Moin A.; Campbell, Colin

Abstract

Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.

Citation

Osborne, A. J., Bierzynska, A., Colby, E., Andag, U., Kalra, P. A., Radresa, O., …Campbell, C. (2024). Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease. npj Systems Biology and Applications, 10(1), Article 28. https://doi.org/10.1038/s41540-024-00350-8

• Journal Article Type: Article
• Acceptance Date: Feb 20, 2024
• Online Publication Date: Mar 9, 2024
• Publication Date: Mar 9, 2024
• Deposit Date: Mar 15, 2024
• Publicly Available Date: Mar 15, 2024
• Journal: npj Systems Biology and Applications
• Electronic ISSN: 2056-7189
• Publisher: Nature Publishing Group
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Article Number: 28
• DOI: https://doi.org/10.1038/s41540-024-00350-8
• Keywords: Computational biology and bioinformatics; Genetics; Molecular medicine; Nephrology; Systems biology
• Public URL: https://nottingham-repository.worktribe.com/output/32450383
• Publisher URL: https://www.nature.com/articles/s41540-024-00350-8
• Additional Information: Received: 4 September 2023; Accepted: 20 February 2024; First Online: 9 March 2024; : M.W.T. reports consulting fees from Boehringer Ingelheim, honoraria from Bayer and support to attend conferences from Bayer and a leadership role in the International Society of Nephrology. No competing interests to declare from the other authors.