Visual Abstract
Abstract
Background A family history of urolithiasis is associated with a more than doubling of urolithiasis risk, and a twin study estimating 56% heritability of the condition suggests a pivotal role for host genetic factors. However, previous genome-wide association studies (GWAS) have identified only six risk-related loci.
Methods To identify novel urolithiasis-related loci in the Japanese population, we performed a large-scale GWAS of 11,130 cases and 187,639 controls, followed by a replication analysis of 2289 cases and 3817 controls. Diagnosis of urolithiasis was confirmed either by a clinician or using medical records or self-report. We also assessed the association of urolithiasis loci with 16 quantitative traits, including metabolic, kidney-related, and electrolyte traits (such as body mass index, lipid storage, eGFR, serum uric acid, and serum calcium), using up to 160,000 samples from BioBank Japan.
Results The analysis identified 14 significant loci, including nine novel loci. Ten regions showed a significant association with at least one quantitative trait, including metabolic, kidney-related, and electrolyte traits, suggesting a common genetic basis for urolithiasis and these quantitative traits. Four novel loci were related to metabolic traits, obesity, hypertriglyceridemia, or hyperuricemia. The remaining ten loci were associated with kidney- or electrolyte-related traits; these may affect crystallization. Weighted genetic risk score analysis indicated that the highest risk group (top 20%) showed an odds ratio of 1.71 (95% confidence interval, 1.42 to 2.06) - 2.13 (95% confidence interval, 2.00 to 2.27) compared with the reference group (bottom 20%).
Conclusions Our findings provide evidence that host genetic factors related to regulation of metabolic and crystallization pathways contribute to the development of urolithiasis.
Urolithiasis is a common obstructive urinary disorder, and calcium oxalate crystal is the major constituent. Urolithiasis causes severe, acute pain with frequent recurrence rate. Therefore, its prevention and management are important health issues. The prevalence of urolithiasis is estimated to be 10%–15% in men and 7% in women, and its prevalence has increased by 70%–85% between the 1980s and the 2000s in both the United States and Japan.1,2 The recurrence rate of urolithiasis is 30%–50% within 10 years after the initial episode 3 and occasionally leads to severe complications, such as pyelonephritis or AKI. Therefore, the identification of risk factors is important for the management of urolithiasis. Environmental factors, such as lifestyle, obesity,2,4 hypertension,5 and diabetes,6 are associated with an increased risk of urolithiasis. In addition, a positive family history increases the disease risk by 2.57-fold among the Japanese population,7 and a twin study estimated the heritability of urolithiasis to be 56%,8 suggesting a pivotal role for host genetic factors. Monogenic diseases causing hypercalciuria (CLCN5, SLC34A1, NKCC2, ROMK, and CaSR9), distal tubular acidosis (SLC4A1), hyperoxaluria (AGTXT), and cystinuria (SLC3A1 and SLC7A9) are associated with familial urolithiasis syndrome.9 In addition, three previous genome-wide association studies (GWAS) in European and Japanese populations identified six common genetic factors on 21q22.13 (CLDN14),10 5q35.3, 7p14.3, 13q14.1,11 1p36.12 (ALPL), and 3q21.1 (CASR).12 These loci were shown to be quantitative loci for alkaline phosphatase and serum phosphate (1p36.12), parathyroid hormone and urinary magnesium-to-calcium ratio (5q35.3 and 21q22.13), bone mineral density (7p14.3 and 21q22.13), serum calcium (13q14.1 and 3q21.1), and kidney function (5q35.3). These suggest that the underlying mechanism of these traits is the regulation of cations, kidney function, and mineralization. To further elucidate the genetic factors related to urolithiasis, we conducted a GWAS using more than 13,000 Japanese cases and 190,000 control samples.
Methods
Study Participants
The characteristics of each cohort are shown in Table 1. DNA samples of 12,846 patients with nephrolithiasis and 162,394 controls were obtained from BioBank Japan (Supplemental Table 1).13,14 About 270,000 individuals diagnosed with any of 51 diseases including nephrolithiasis were enrolled in the BioBank Japan project during 2003 and 2018.13,14 Samples from BioBank Japan with or without nephrolithiasis were used as cases and controls, respectively. The 28,867 controls (screening 1) were from four population-based cohorts, including the Japan Public Health Center–based Prospective Study,15 the Japan Multi-Institutional Collaborative Cohort study,16 Iwate Tohoku Medical Megabank Organization, and Tohoku Medical Megabank Organization.17 A total of 573 patients with nephrolithiasis and 195 healthy controls were recruited at the Nagoya City University18 (Replication stage, Table 1). The diagnosis of nephrolithiasis in patients from Nagoya City University (n=573) and BioBank Japan at screening 1 (n=6246) was confirmed by a clinician. The diagnosis of nephrolithiasis was on the basis of clinical symptoms, imaging (x-ray, ultrasound examination, and/or computed tomography), and laboratory tests according to diagnostic guidelines by the Japanese Urological Association and European Association of Urology.19 At screening 2, cases (n=4884) and the replication (n=1716) were selected from samples in BioBank Japan, on the basis of medical history obtained by questionnaire or medical records. There were no exclusion criteria, and we used all available samples at the time of experiment. Analyses of 16 quantitative traits were conducted using up to 160,000 samples from BioBank Japan in the previous studies (Supplemental Table 1).20,21 Genomic DNA was extracted from peripheral blood leukocytes using a standard method. All participants provided written/oral informed consent, and the project was approved by the ethical committees at each institute with approval number of 25-1-0417 and H17-16.
Characteristics of study population
Genotyping and Imputation
The strategy of our screening is shown in Figure 1. At screening 1, we used clinically confirmed urolithiasis cases and healthy controls. At screening 2, we selected 4884 urolithiasis cases and 158,772 controls from BioBank Japan on the basis of medical records or self-reporting. In previous studies,20,21 all 11,130 urolithiasis cases and 187,639 controls (screening 1 and 2) were genotyped with an Illumina HumanOmniExpressExome BeadChip or a combination of the Illumina HumanOmniExpress and HumanExome BeadChips (Table 1). Genotyping results are available at the National Bioscience Database Center (NBDC) human database (https://humandbs.biosciencedbc.jp/en/hum0014-v12) under dataset identifier JGAS00000000114. We excluded (1) samples with a call rate <0.98, (2) samples from closely related individuals identified by identity-by-descent analysis, (3) sex-mismatched samples with a lack of information, and (4) samples from non-East Asian outliers identified by principal component analysis of the studied samples and the three major reference populations (Africans, Europeans, and East Asians) in the International HapMap Project.22 We then applied standard quality-control criteria for variants, excluding those with (1) a single nucleotide polymorphism (SNP) call rate <0.99, (2) minor allele frequency <1%, and (3) Hardy–Weinberg equilibrium P value <1.0×10−6. We prephased the genotypes with MaCH23 and imputed dosages with minimac and the 1000 Genomes Project Phase 1 (version 3) East Asian reference haplotypes.24 Imputed SNPs with an imputation quality R-squared<0.4 were excluded from the subsequent association analysis.
Urolithiasis GWAS using 11,130 Japanese cases and 187,639 controls identifies 14 loci. A total of 13,419 cases and 191,456 controls are used in this study. Seventeen SNPs in 14 regions clear genome-wide significant threshold (P<5×10−8) in the meta-analysis of screening 1 and screening 2. A meta-analysis of screening 1, screening 2, and replication identifies 14 urolithiasis regions, including nine novel regions.
GWAS
We conducted a GWAS using a logistic regression model by incorporating age, sex, and the top ten principal components as covariates. Meta-analysis of screening 1 and screening 2 was conducted by METAL.25 Heterogeneity between studies was examined using Cochran Q test.26 To estimate the genetic correlation, a bivariate linkage disequilibrium (LD) score regression27 was conducted using the results from the GWAS of screening 1 and screening 2 with the LD scores for the East Asian population.21 We calculated the genomic inflation factor λGenomic control (GC) in R. λGC adjusted to a sample size of 1000 (λ1000) was calculated using the following formula,28 as large sample sizes cause inflated λGC values29: λ1000=1+(1−λobserved)×(1/ncases+1/ncontrols)/(1/1000cases+1/1000controls). A quantile-quantile plot was drawn using R. A Manhattan plot of the associations was constructed by plotting −log10 (P values) against chromosome position using R. We generated regional plots with LocusZoom (version 1.3).30 A forest plot was drawn using R.
Replication Study
Among the genome-wide significant SNPs with P<5×10−8 from the meta-analysis of screening 1 and screening 2, we selected 17 SNPs within 14 loci by LD analysis (r2<0.2). We genotyped 17 SNPs using 2320 urolithiasis cases and 3961 controls by the multiplex PCR-based Invader assay (Third Wave Technologies). The meta-analysis of screening 1, screening 2, and the replication was conducted using METAL in the same way as in the screening step. The threshold of heterogeneity was P<0.05/17.
Pleiotropy Analysis
The GWAS results for metabolic traits (body mass index [BMI], total cholesterol (TC), HDL cholesterol, LDL cholesterol, triglyceride (TG), blood sugar (BS), and hemoglobin A1c), kidney-related traits (BUN, serum creatinine [sCr], eGFR, and uric acid [UA]), and electrolytes (sodium [Na], potassium [K], chloride [Cl], calcium [Ca], and phosphate [P]) were used in the pleiotropy analysis.21 To assess the colocalization of causal SNPs, we conducted a conditional logistic regression analysis with conditioning of the top SNPs of the Japanese quantitative trait loci (QTL) GWAS. The tested SNPs were selected to be located within 1 Mb of the typing SNP. We downloaded the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS catalog (version 1.0.1).
Expression QTL Analysis
Expression data for specific tissues obtained from the GTEx Portal were used to evaluate whether the variants in the genomic loci identified in this study affect gene expression (expression QTL analysis).
Subgroup Analysis
We used the participants in screening 1 for the subgroup analysis. Each subgroup was analyzed using a logistic regression model with an adjustment for sex and age. Control samples were the same as those used in screening 1. Then, comparisons between subgroups were performed using a logistic regression model with an adjustment for sex and age.
Weighted Genetic Risk Score
A total of 17 associated SNPs (P<5.0×10−8 from the meta-analysis of screening 1 and screening 2) were used. The weighted genetic risk score (wGRS) model was established to incorporate the estimate (weight) from the meta-analysis of screening 1 and screening 2 for each of the 17 associated SNPs. The cumulative risk scores were calculated by multiplying the weight of each SNP by the frequency of the risk alleles for the SNP carried by the individual, and the sum across the total number of SNPs was considered. Subsequently, the risk scores were classified into five quantiles on the basis of wGRS. P values, odds ratios, and 95% confidence intervals were evaluated using the first quantile as the reference.
Statistical Analyses
We conducted GWAS and subgroup analysis by logistic regression model using PLINK. Meta-analysis was conducted using METAL. The wGRS was evaluated by Fisher exact test using R. Summary statistics and primary genotyping data that support the findings of this study can be found at NBDC under the accession code hum0014 (http://humandbs.biosciencedbc.jp/).
Results
GWAS of Urolithiasis
A total of 6246 patients with urolithiasis and 28,867 controls (screening 1) and 4884 urolithiasis cases and 158,772 controls (screening 2) were analyzed in the screening stage. All samples were genotyped using the Illumina OmniExpressExome or OmniExpress+HumanExome BeadChip in previous analyses (Figure 1, Table 1).20,21,31 We excluded samples after performing a standard quality control procedure. Then, we selected 509,872 SNPs for genome-wide imputation (minor allele frequency≥0.01, Hardy Weinberg Equilibrium≥1×10−6, and call rate ≥0.99) and obtained imputed dosages of 6,603,247 SNPs (R-squared≥0.4). We tested the association with urolithiasis using a logistic regression analysis with age, sex, and the top ten principal components as covariates. A meta-analysis of screening 1 and screening 2 indicated that 822 SNPs in 14 genomic regions, including five previously reported loci, showed significant association with P value of <5×10−8 (Figure 2, Supplemental Table 2). The genomic inflation factor λ and λ1000 were 1.164 and 1.008, respectively (Supplemental Figure 1).28 The LD score regression analysis28 of screening 1 and screening 2 revealed a genetic correlation score of 0.92, indicating that the results were consistent between the two studies.
GWAS identifies 14 loci including nine novel loci. The genome-wide P values of 6,603,247 autosomal SNPs in 11,130 cases and 187,639 controls from the meta-analysis of screening 1 and screening 2 are shown. Closed arrowheads (n=5; 1p36.12, 5q35.3, 7p14.3, 13q14.11, and 21q22.13) and open arrowheads (n=9; 2p23.2–3, 6p21.2, 6p12.3, 6q23.2, 16p12.3, 16q12.2, 17q23.2, 19p13.12, and 20q13.2) indicate known and newly identified loci, respectively. The red horizontal line represents the genome-wide significance threshold of P=5.0×10−8.
We selected 17 SNPs in 14 genomic regions with significant associations (P<5×10−8) in the replication analysis using the LD (r2<0.2) and conditioned analysis adjusted by lead SNPs in each region (Supplemental Figure 2). We substituted the top-ranked SNPs rs6667242, rs11746443, rs3798519, and rs74956940 on 1p36.12, 5q35.3, 6p12.3, and 19p13.12, respectively, for rs1697420, rs10866705, rs62405419, and rs2241358, respectively, because we could not design probes for the Invader assay. These 17 SNPs were analyzed using independent Japanese samples comprising 2289 cases and 3817 controls18 by a multiplex PCR-based Invader assay32 (Supplemental Table 3). The results showed that the risk alleles were consistent, and the effect sizes were similar among the three studies (screening 1, screening 2, and replication) for 17 SNPs. A meta-analysis revealed that 16 SNPs within 14 regions including nine novel regions exceeded the genome-wide significant threshold (P<5×10−8)33,34 (Supplemental Table 4, Table 2). These SNPs included GCKR-C2orf16-ZNF512-CCDC121-GPN1-SUPT7L-SLC4A1AP-MRPL33-RBKS (2p23.2–3), SAYSD1-KCNK5 (6p21.2), TFAP2D-TFAP2B (6p12.3), EPB41L2 (6q23.2), PDILT (16p12.3), FTO (16q12.2), BCAS3-TBX2-C17orf82 (17q23.2), PKN1-PTGER1-GIPC1 (19p13.12), and BCAS1 (20q13.2). rs219780 on CLDN14, identified in the first urolithiasis GWAS in the European population,10 is monomorphic among the Asian population and was not analyzed in our study. However, our study found significant association of rs7277076 on CLDN14, which is in strong LD with rs219780 (D’=0.97 and r2=0.33) in the European population. In addition, six previously identified SNPs in five regions showed a significant association (P=0.03–2.76×10−18; Supplemental Table 5). Thus these loci are common urolithiasis loci among Asian and European populations.
The result of association analysis of urolithiasis in each stage
Urolithiasis is a complex disease that is regulated by many factors, such as serum/urinary cations, urate, kidney function, obesity, humoral factors, dietary factors, and hydration. To further investigate the roles of these genetic factors in the pathogenesis of urolithiasis, we analyzed the association of 17 variants with 16 quantitative traits in three categories, metabolic (n=7), kidney-related (n=4), and electrolyte (n=5) (Supplemental Tables 1 and 6), that were analyzed in our previous studies.20,21 Interestingly, ten of the 14 regions showed a significant association with at least one of 16 quantitative traits, including metabolic (2p23.2–3, 6p12.3, and 16q12.2), kidney-related (2p23.3, 5q35.3, 6p12.3, 13q14.11, 16p12.3, 16q12.2, 17q23.2, and 21q22.13), and electrolyte traits (1p36.12, 2p23.3, 5q35.3, 17q23.2, and 20q13.2) (P<5×10−8) (Supplemental Figures 3 and 4, Supplemental Tables 7–9). Among the associations, 2p23.3 was associated with seven traits across all three categories (TC, TG, BS, sCr, eGFR, UA, and Cl). Moreover, 6p12.3 (BMI and BUN) and 16q12.2 (BMI and BUN) showed pleiotropy across metabolic and kidney-related traits, whereas 5q35.3 (sCr, eGFR, and P) and 17q23.2 (sCr, eGFR, UA, K, Cl, and Ca) showed pleiotropy across kidney-related traits and electrolytes (Figure 3). The regional plots of ten pleiotropic loci are shown in Supplemental Figure 5. All ten regions exhibited a similar pattern of association between the QTLs and urolithiasis. The results of a conditioned analysis adjusted by lead SNPs on the basis of our previous QTL analysis20,21 revealed the colocalization of causal variants between urolithiasis and quantitative traits (Supplemental Figure 5).
Association of 10 urolithiasis loci with Metabolic trait, Kidney-related trait, and electrolyte. Venn diagram showing 14 regions that are associated with 16 quantitative traits in three categories, including metabolic, kidney-related, and electrolyte.
We conducted a subgroup analysis using clinical information, such as recurrent stones, stone location, positive family history, and history of gout (Supplemental Figure 6). As a result, rs35747824 and rs7277076 were significantly associated with recurrent stones, whereas rs35747824 and rs13006480 were associated with kidney stones and history of gout.
We also conducted subgroup analysis (BMI <25 or ≥25, and men/women) using samples in screening 1. As a result, most of SNPs exhibited similar results in each subgroup without significant heterogeneity (Supplemental Figure 7, Supplemental Tables 10 and 11). Interestingly, SNP rs6667242 on 1p36.12 (ALPL), rs13006480 on 2p23.2 (MRPL33), and rs6928986 on 6q23.2 (EPB41L2) showed stronger effect in the low BMI group, women, and men, respectively (Q<0.05). These results suggested the potential interaction of these variations with hormonal and lifestyle factors.
We then conducted logistic regression analysis of 17 SNPs using BMI as a covariate (Supplemental Table 12). Twelve regions among 14 indicated the significant association even after adjusted by BMI. 16q12.2 (FTO) and 20q13.2 (BCAS1) exhibited marginal association with P value of 1.97×10−7 and 1.20×10−7, respectively, but the odds ratios were not remarkably affected. We also assessed the potential SNP–SNP interactions. Nine among 138 combinations showed a P value of <0.05 (0.003–0.04); however, none of them were considered to clear significance threshold after multiple testing correction (Supplemental Table 13).
To evaluate the cumulative effects of genetic variants on urolithiasis risk, a wGRS was used. We used the 17 significant SNPs that cleared the GWAS significance threshold in the screening stage and their corresponding weights from the meta-analysis of the screening stage. Individuals were partitioned into quintile groups on the basis of the wGRS. As a result, the highest risk group (top quantile) showed an odds ratio of 2.13 (95% confidence interval, 2.00 to 2.27) and 1.71 (95% confidence interval, 1.42 to 2.06) compared with the reference group (first quantile) in the screening stage and replication stage, respectively (Supplemental Table 14). We also evaluated the association of these variations with the mRNA expression of nearby genes. rs1106357, rs1260326, and rs3798519 were associated with ALPL, GCKR, and TFAP2B, respectively (Supplemental Figure 8). In addition to rs6667242, rs1106357 was also associated with urolithiasis, serum ALP level, and serum P levels (Supplemental Figure 9). These results suggested an underlying molecular mechanism whereby these variations regulate the risk of urolithiasis.
Discussion
To the best of our knowledge, this study included the largest number of urolithiasis cases (n=13,419). We found that nine novel loci were associated with the risk of urolithiasis in the Japanese population. The association of these risk alleles with several quantitative traits suggests potential pathogenic mediators. Four of nine novel risk loci were associated with higher BMI or triglycerides (2p23.3, 6p12.3, and 16q12.2) and/or higher serum uric acid level (2p23.3 and 17q23.2) (Figure 3, Supplemental Figures 3 and 4).35,36 Obesity, hyperlipidemia, and metabolic syndrome are well established risk factors for urolithiasis.37–40 Metabolic syndrome is also associated with hyperuricemia,41,42 and one recent study has shown a direct association between hyperuricemia and urolithiasis, at least in men.43 FTO variations on 16q12.2 suppress the expression of IRX3 and IRX5 and subsequently increase body weight by promoting lipid storage.44 The risk allele on 6p12.3 was associated with higher expression of TFAP2B (Supplemental Figure 8C), and TFAP2B promotes the enlargement of adipocytes.45 GCKR functions as a negative regulator of GCK, and the risk allele on 2p23.3 would suppress GCKR function via amino acid substitution and/or its transcriptional repression (Supplemental Figure 8B) and subsequently enhance the activity of GCK,46 which promotes insulin secretion and triglyceride synthesis.47 Previously, 17q23.2 was shown to be associated with hyperuricemia,21,36,48 although the underlying molecular mechanism has not been elucidated. Thus, these four loci could potentially promote the formation of urolithiasis through the regulation of metabolic traits, such as obesity and increased uric acid production (Supplemental Figure 10).
The majority of patients with urolithiasis have calcium-containing stones and hypercalciuria. Four previously reported loci were shown to be associated with higher urine Ca concentration (5q35.3, 7q14.3, 13q14.11, and 21q22.13)49–51 and familial urolithiasis (SLC34A1 on 5q35.3).52 Three of the new risk loci we have identified also appear to regulate calcium metabolism. Vitamin D and parathyroid hormone increase serum Ca and hence urine Ca level through the regulation of intestinal Ca absorption and bone mineral resorption.53,54 rs13041834 on 20q13.2 is located near CYP24A1, which encodes an enzyme that inactivates both 25- and 1,25-dihydroxyvitamin D, and its risk allele was associated with higher 25-hydroxyvitamin D 55,56 and serum Ca.57 rs6928986 on 6q23.2 is located within EPB41L2, which regulates the localization and activity of PTHR.58 rs74956940 on 19p13.12 is located within the PTGER1 gene that was shown to regulate urine concentrating ability.59 CLDN14 on 21q22.13 is a member of claudin family and encodes an integral membrane protein and a component of tight junction strands that decrease paracellular permeability of cations, including Ca2+.60 SNPs on CLDN14 were associated with kidney stones, magnesium-to-calcium ratio in urine, and reduced bone mineral density.49 Thus, seven out of 14 urolithiasis loci likely increase kidney stone risk by regulating serum and urine calcium concentration.
Finally, we speculate that 1p36.12, 16p12.3, and 6p21.2 might regulate the crystallization step. The risk allele of rs6667242 on 1p36.12 was associated with a high ALP level and low serum P level (Supplemental Figures 8A and 9). ALPL hydrolyzes extracellular inorganic pyrophosphate, which normally functions in the urine as an inhibitor of calcium oxalate and calcium phosphate crystallization.61,62 ALPL also increases renal phosphate excretion.63 Thus, this risk allele would be predicted to both increase urine phosphate and decrease the level of crystallization inhibitor, thereby increasing the urolithiasis risk. rs35747824 on 16p12.3 is located within UMOD, which encodes uromodulin, the most abundant protein in urine and a known inhibitor of the crystallization of Ca in urine.64 rs1544935 on 6p21.2 is located within KCNK5, which encodes a potassium channel. KCNK5 knockout mice exhibit proximal renal tubular acidosis and, in response to bicarbonate loading, excrete an alkaline urine,65 which would be predicted to reduce the solubility product of calcium phosphate.66 So this variant may regulate stone formation by regulating urine pH.
Taken together, all 14 loci are associated with the regulation of either the metabolic or crystallization pathway (Supplemental Figure 10). Nearly half of case samples were selected from BioBank Japan on the basis of self-reporting and/or medical record. However, we observed consistent results between screening 1 (cases confirmed by physicians) and screening 2 (cases selected by self-reporting/medical record) with a genetic correlation score of 0.92, suggesting low level of heterogeneity between two groups. All samples were Japanese in this study, and only eight out of 17 SNPs showed significant association (P<0.05) in the replication study, probably because of the small sample size. In addition, no information was available on stone composition or 24-hour urine values. Therefore, replication analyses using independent cohorts with different ethnic background and detailed clinical information are necessary. In addition, the roles of these variations need to be evaluated by functional genomics in future studies. We hope our findings will contribute to the elucidation of the molecular pathology of urolithiasis and the implementation of personalized medical care for this disease.
Disclosures
None.
Supplemental Materials
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2018090942/-/DCSupplemental.
Supplemental Figure 1. Quantile-quantile plot for imputed GWAS of urolithiasis in the Japanese population.
Supplemental Figure 2. Regional plots of 17 loci for urolithiasis before and after condition.
Supplemental Figure 3. Forest plots for risk variants in the 17 urolithiasis risk loci.
Supplemental Figure 4. Summary of pleiotropy analysis.
Supplemental Figure 5. Regional plots of ten pleiotropic loci before and after condition.
Supplemental Figure 6. Subgroup analysis of the 17 urolithiasis risk loci.
Supplemental Figure 7. Forest plots for risk variants in the 17 urolithiasis risk loci.
Supplemental Figure 8. Expression QTL analysis.
Supplemental Figure 9. Forest plots for risk variants of rs6667242 and rs1106357 with serum ALP and P.
Supplemental Figure 10. Putative effector genes and functions.
Supplemental Table 1. Characteristics of samples from BioBank Japan.
Supplemental Table 2. List of 822 SNPs with a P value of <5×10−8 in meta-analysis of screening 1 and screening 2.
Supplemental Table 3. The results of association analysis of urolithiasis in the replication stage.
Supplemental Table 4. The results of association analysis of urolithiasis in each stage.
Supplemental Table 5. Association of reported SNPs in our GWAS dataset.
Supplemental Table 6. List of quantitative traits.
Supplemental Table 7. Overview of the identified loci and their pleiotropy in metabolic traits.
Supplemental Table 8. Overview of the identified loci and their pleiotropy in kidney-related traits.
Supplemental Table 9. Overview of the identified loci and their pleiotropy in electrolyte traits.
Supplemental Table 10. The association of SNPs with urolithiasis in BMI≥25 and BMI<25.
Supplemental Table 11. The association of SNPs with urolithiasis in men and women.
Supplemental Table 12. The result of association analysis of urolithiasis in each stage using age, sex, ten principal components, and BMI as covariates.
Supplemental Table 13. Interactions among 17 associated SNPs.
Supplemental Table 14. wGRS analysis of 17 associated SNPs.
Acknowledgments
We thank all of the participants in this study. We are grateful to the staff of BioBank Japan, Tohoku Medical Megabank, Iwate Tohoku Medical Megabank, Japan Multi-Institutional Collaborative Cohort, and Japan Public Health Center-based Prospective Study for their outstanding assistance. We also thank Satoyo Oda and Yoshiyuki Yukawa for their technical assistance.
C. Tanikawa, Y. Kamatani, M. Kubo, and K. Matsuda conceived and planned the experiments. C. Tanikawa, M. Usami, and Y. Momozawa carried out the experiments. C. Tanikawa, Y. Kamatani, C. Terao, and A. Takahashi carried out statistical analysis. K. Suzuki, S. Ogishima, A. Shimizu, M. Satoh, K. Matsuo, H. Mikami, M. Naito, K. Wakai, T. Yamaji, N. Sawada, M. Iwasaki, S. Tsugane, K. Kohri, T. Yasui, Y. Murakami, M. Kubo, and K. Matsuda contributed to sample preparation and data analysis. C. Tanikawa, Y. Kamatani, A.S.L. Yu, and K. Matsuda contributed to the interpretation of the results. C. Tanikawa, A.S.L. Yu, and K. Matsuda took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.
This study was partially supported by the BioBank Japan project and the Tohoku Medical Megabank project, which is supported by the Ministry of Education, Culture, Sports, Sciences and Technology Japan and the Japan Agency for Medical Research and Development. The Japan Public Health Center-based Prospective Study has been supported by the National Cancer Research and Development Fund since 2011 and was supported by a grant-in-aid for cancer research from the Ministry of Health, Labour and Welfare of Japan from 1989 to 2010. The Japan Multi-Institutional Collaborative Cohort study was supported by grants-in-aid for scientific research for priority areas of cancer (17015018) and innovative areas (221S0001) and a Japan society for the promotion of science (JSPS) KAKENHI grant (16H06277) from the Japan Ministry of Education, Science, Sports, Culture and Technology. This study was also supported by JSPS KAKENHI grant 25293168 (to K. Matsuo).
Footnotes
Published online ahead of print. Publication date available at www.jasn.org.
- Copyright © 2019 by the American Society of Nephrology
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