Consistent Genetic Loci Across Different Case Definitions of Kidney Stone Disease: GWAS Findings from the UK Biobank 500K Cohort
Clinical Genetics and Therapeutics
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Primary Categories:
- Clinical- Pediatric
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Secondary Categories:
- Clinical- Pediatric
Introduction:
Kidney stone disease is a prevalent condition affecting 1 in 8 Americans, posing a significant health and economic burden. Genetic influences are substantial, with heritability estimates ranging from 45% to 50%. Several genome-wide association studies (GWAS) have been conducted, but their results vary, partly due to differing definitions of kidney stone cases. This study aims to conduct three GWAS using big data from the UK Biobank, employing three different case definitions to compare the results.
Methods:
We used data from the UK Biobank, including 502,189 participants. Kidney stone cases were defined using three criteria, in reference to previous publications: (1) inpatient records (narrowest), (2) inpatient plus general practice (GP) records (broader), and (3) inpatient, GP, and self-reported history (broadest). GWAS was conducted using REGENIE v3.2.8, with genotype and imputation data based on the UK BiLEVE Axiom and UK Biobank Axiom arrays. Post-GWAS analysis involved FUMA for annotation and visualization. Whole genome sequencing was used to validate significant associations.
Results:
The number of kidney stone cases varied by definition: narrowest (8,626), broader (10,828), and broadest (12,270). The GWAS results revealed the number of SNPs reaching genome-wide significance (p < 5 × 10^-8): 839 (narrowest), 826 (broader), and 775 (broadest). The number of genome-wide significant index SNPs were: 22 (narrowest), 19 (broader), and 15 (broadest). Significant genes/loci were: 16 (narrowest), 14 (broader), and 12 (broadest). Across the definitions, 11 genes/loci were consistently associated with kidney stones: ALPL, DGKD, RGS14, SLC34A1, KCNK5, DGKH, WDR72, PDILT/UMOD, CYP24A1, CLDN14, and RTDR1/BCR. TRPM6 was unique to the inpatient cohort; CLDN16 and PLCB1 were significant in the inpatient + GP cohort, and GRXCR1 was significant in the broadest cohort.
Conclusion:
Our study demonstrates that different case definitions influence genetic associations with kidney stones. Despite these differences, 11 genes were consistently implicated, providing valuable insights into the genetic architecture of kidney stones and highlighting potential therapeutic targets.
Kidney stone disease is a prevalent condition affecting 1 in 8 Americans, posing a significant health and economic burden. Genetic influences are substantial, with heritability estimates ranging from 45% to 50%. Several genome-wide association studies (GWAS) have been conducted, but their results vary, partly due to differing definitions of kidney stone cases. This study aims to conduct three GWAS using big data from the UK Biobank, employing three different case definitions to compare the results.
Methods:
We used data from the UK Biobank, including 502,189 participants. Kidney stone cases were defined using three criteria, in reference to previous publications: (1) inpatient records (narrowest), (2) inpatient plus general practice (GP) records (broader), and (3) inpatient, GP, and self-reported history (broadest). GWAS was conducted using REGENIE v3.2.8, with genotype and imputation data based on the UK BiLEVE Axiom and UK Biobank Axiom arrays. Post-GWAS analysis involved FUMA for annotation and visualization. Whole genome sequencing was used to validate significant associations.
Results:
The number of kidney stone cases varied by definition: narrowest (8,626), broader (10,828), and broadest (12,270). The GWAS results revealed the number of SNPs reaching genome-wide significance (p < 5 × 10^-8): 839 (narrowest), 826 (broader), and 775 (broadest). The number of genome-wide significant index SNPs were: 22 (narrowest), 19 (broader), and 15 (broadest). Significant genes/loci were: 16 (narrowest), 14 (broader), and 12 (broadest). Across the definitions, 11 genes/loci were consistently associated with kidney stones: ALPL, DGKD, RGS14, SLC34A1, KCNK5, DGKH, WDR72, PDILT/UMOD, CYP24A1, CLDN14, and RTDR1/BCR. TRPM6 was unique to the inpatient cohort; CLDN16 and PLCB1 were significant in the inpatient + GP cohort, and GRXCR1 was significant in the broadest cohort.
Conclusion:
Our study demonstrates that different case definitions influence genetic associations with kidney stones. Despite these differences, 11 genes were consistently implicated, providing valuable insights into the genetic architecture of kidney stones and highlighting potential therapeutic targets.