Trans-ancestry GWAS identifies 59 loci and improves risk prediction and fine-mapping for kidney stone disease

Abstract

Kidney stone disease is a multifactorial disease with increasing incidence worldwide. Trans-ancestry GWAS has become a popular strategy to dissect genetic structure of complex traits. Here, we conduct a large trans-ancestry GWAS meta-analysis on kidney stone disease with 31,715 cases and 943,655 controls in European and East Asian populations. We identify 59 kidney stone disease susceptibility loci, including 13 novel loci and show similar effects across populations. Using fine-mapping, we detect 1612 variants at these loci, and pinpoint 25 causal signals with a posterior inclusion probability >0.5 among them. At a novel locus, we pinpoint TRIOBP gene and discuss its potential link to kidney stone disease. We show that a cross-population polygenic risk score, PRS-CSx_EAS&EUR, exhibits superior predictive performance for kidney stone disease than other polygenic risk scores constructed in our study. Relative to individuals in the third quintile of PRS-CSx_EAS&EUR, those in the lowest and highest quintiles exhibit distinct kidney stone disease risks with odds ratios of 0.57 (0.51-0.63) and 1.83 (1.68-1.98), respectively. Our results suggest that kidney stone disease patients with higher polygenic risk scores are younger at onset. In summary, our study advances the understanding of kidney stone disease genetic architecture and improves its genetic predictability.

Fig. 1. Study design and workflow.

GWAS genome-wide association study, BBJ Biobank Japan, CKB China Kadoorie Biobank, UKB UK Biobank, CADD combined annotation-dependent depletion, AUC areas under the receiver operating curves, PRS polygenic risk score, KSD kidney stone disease. *Referred that the EUR-specific meta-analysis was obtained in our previous study.

Fig. 2. Trans-ancestry GWAS meta-analysis for KSD.

A Manhattan plot for P-values of variants in trans-ancestry GWAS meta-analysis. The gray dashed line indicated genome-wide significance level of 5 × 10⁻⁸. Red and black diamonds represented the novel and reported genetic susceptibility loci for KSD, respectively. B Comparison of lead variant effects at 59 identified loci between EAS-specific meta-analysis (left) and EUR-specific meta-analysis (right) with trans-ancestry meta-analysis. These points were colored based on their significances from corresponding GWAS meta-analyses. Source data are provided as a Source Data file. C Comparison of lead variant effects at 59 identified loci between EAS-specific with EUR-specific meta-analyses. These points were colored based on their significances from corresponding GWAS meta-analyses. The ρ_c referred to Lin’s concordance correlation coefficient. Variants that reached the genome-wide significance level in both EAS and EUR GWAS meta-analyses were labeled. Source data are provided as a Source Data file.

Fig. 3. Fine-mapping results for KSD by using MeSuSiE and SuSiE.

A Box plot of SNP number of credible sets identified by MeSuSiE and SuSiE. The central line, bounds of box and whiskers represented to the median value, quartiles and the minima and maxima, respectively. Source data are provided as a Source Data file. B Bar plot of the number of causal signals with PIP > 0.5 detected by MeSuSiE and SuSiE. Source data are provided as a Source Data file. C LocusZoom plot displayed the fine-mapping results on KSD from different methods in a genomic region on chromosome 5. The left column showed Manhattan plots of EUR, EAS and trans-ancestry GWAS meta-analyses results in this region. The genome-wide significance threshold was 5 × 10^-8, indicated by the black dashed lines. The most significant signal in EUR and EAS populations was labeled. The right column showed LocusZoom plots, displaying the PIP of signals calculated by SuSiE in EUR and EAS populations and by MeSuSiE. The last row showed annotated genes in this genomic region. For the signals in the detected credible sets, we used an upper triangle to represent a European-specific signal, a lower triangle to represent an East Asian ancestry-specific signal and a diamond to represent a shared signal. PIP of 0.5 indicated a potential causal signal, whether shared or ancestry-specific. PIP posterior inclusion probability, EUR European, EAS East Asian.

Fig. 4. Trans-ancestry PRS-CSx improved predictive performance and discrimination ability for KSD risk.

A Comparison of pseudo-R² and AUCs for cov-only model and four PRS models. B Comparison of KSD risk by four PRSs. The central points and error bars represented ORs and the 95% confidence intervals, respectively. Source data are provided as a Source Data file. C Predictive performance of PRS in relation to known non-genetic KSD related factors. The central points and error bars represented AUC values and 95% confidence intervals, respectively. Source data are provided as a Source Data file. D Cumulative hazard curve of KSD according to stratified PRS-CSx_EAS&EUR. The models constructed for Figure A and Figure B were adjusted for age, sex, and PC1-10, with an additional adjustment for non-genetic KSD-related factors for Figure D. All analyses were completed in testing dataset, including 4996 KSD cases and 199,409 controls. AUC area under receiver operating curve, cov-only covariates-only prediction model, OR odd ratio, HDL high-density lipoprotein, TG triglycerides. The glucose referred to the fasting glucose, and the calcium, urate, and vitamin D referred to the blood calcium, urate and vitamin D concentrations.