Evaluation of Genetic Associations with Clinical Phenotypes of Kidney Stone Disease.

BACKGROUND AND OBJECTIVE

Previous studies have reported a strong genetic contribution to kidney stone risk. This study aims to identify genetic associations of kidney stone disease within a large-scale electronic health record system.

METHODS

We performed genome-wide association studies (GWASs) for nephrolithiasis from genotyped samples of 5571 cases and 83 692 controls. This analysis included a primary GWAS focused on nephrolithiasis and subsequent subgroup GWASs stratified by stone composition types. For significant risk variants, we performed association analyses with stone composition and first-time 24-h urine parameters. To assess disease severity, we investigated the associations with age at first stone diagnosis, age at first stone-related procedure, and time between first and second stone-related procedures.

KEY FINDINGS AND LIMITATIONS

The primary GWAS analysis identified ten significant loci, all located on chromosome 16 within coding regions of the gene. The strongest signal was rs28544423 (odds ratio 1.17, 95% confidence interval 1.11-1.23,  = 2.7 × 10). In subgroup GWASs stratified by six kidney stone composition subtypes, 19 significant loci were identified including two loci in coding regions (brushite; , rs79970906 and rs4725104). The single nucleotide polymorphism rs28544423 was associated with differences in 24-h excretion of urinary analytes, and the minor allele was positively associated with calcium oxalate dihydrate stone composition  < 0.05). No associations were found between variants and disease severity. Limitations include an omitted variable bias and a misclassification bias.

CONCLUSIONS AND CLINICAL IMPLICATIONS

We replicated germline variants associated with kidney stone disease risk at and reported novel variants associated with stone composition Genetic variants of are associated with differences in 24-h urine parameters and stone composition, but not disease severity.

PATIENT SUMMARY

We identify genetic variants linked to kidney stone disease within an electronic health record (EHR) system. These findings suggest a role for the EHR to enable a precision-medicine approach for stone disease.