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Abstract: FR-PO302

Canonical Correlation Analysis to Identify Single Nucleotide Polymorphisms in CKD

Session Information

Category: Genetic Diseases of the Kidneys

  • 1102 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

Genetic risk for chronic kidney disease (CKD) is associated with the two complementary kidney function biomarkers, estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN). Within this context, genome-wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) associated with each biomarker, as well as SNPs likely relevant for kidney function based on their effect size directions. However, it has not yet been investigated which SNPs show a joint statistical association with, or shared genetic basis for, both kidney function biomarkers considered together in a multivariate analysis.

Methods

To identify SNPs correlated with both eGFR and BUN jointly, we applied canonical correlation analysis (CCA) to two CKD genotype datasets (European ancestry NURTuRE-CKD and Salford Kidney Study) and metaCCA to the wider context of three larger publicly available GWAS summary statistics datasets (European ancestry CKDGen, United Kingdom Household Longitudinal Study, and BioBank Japan). SNPs that showed a significant correlation with both eGFR and BUN, using Bonferroni-correction, were gene-annotated based on location (Annovar) and kidney tissue expression quantitative trait loci (eQTL; Qtlizer). Functional enrichment and gene overlap was assessed.

Results

For the CKDGen European ancestry dataset, of the 122 SNPs previously reported as likely relevant for kidney function by Wuttke et al. 2019, we found that 97 SNPs (80%) showed a significant correlation with both eGFR and BUN jointly using metaCCA. For the metaCCA-identified pruned SNPs that were in exons or kidney tissue eQTL-associated, the genes showed significant functional enrichment for proximal tubule transport, low molecular weight proteinuria, abnormal renal physiology and renal tubular atrophy, and others. Between the metaCCA-identified genes for the CKDGen (54 genes) and BioBank Japan (7 genes) datasets, despite the genetic ancestry difference, three genes overlapped (FGF5, GOSR2, MUC1). For the two smaller CCA-analysed datasets, the top SNPs showed p-values < 9E-3 but none were significant based on the Bonferroni correction. Gene annotations for these top SNPs overlapped with some metaCCA-identified genes, including SLC7A9 and GATM.

Conclusion

This powerful methodology identifies novel SNPs correlated with kidney function in CKD.