Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels

Abstract

Elevated serum urate levels cause gout and correlate with cardiometabolic diseases via poorly understood mechanisms. We performed a trans-ancestry genome-wide association study of serum urate in 457,690 individuals, identifying 183 loci (147 previously unknown) that improve the prediction of gout in an independent cohort of 334,880 individuals. Serum urate showed significant genetic correlations with many cardiometabolic traits, with genetic causality analyses supporting a substantial role for pleiotropy. Enrichment analysis, fine-mapping of urate-associated loci and colocalization with gene expression in 47 tissues implicated the kidney and liver as the main target organs and prioritized potentially causal genes and variants, including the transcriptional master regulators in the liver and kidney, HNF1A and HNF4A. Experimental validation showed that HNF4A transactivated the promoter of ABCG2, encoding a major urate transporter, in kidney cells, and that HNF4A p.Thr139Ile is a functional variant. Transcriptional coregulation within and across organs may be a general mechanism underlying the observed pleiotropy between urate and cardiometabolic traits.

Figure 1 ∣. Trans-ethnic GWAS meta-analysis identifies 183 loci associated with serum urate.

Outer ring: Dot size represents the genetic effect size of the index SNP at each labeled locus on serum urate. Blue band: −log₁₀(two-sided meta-analysis P-value) for association with serum urate (n = 457,690), by chromosomal position (GRCh37 (hg19) reference build). Red line indicates genome-wide significance (P = 5 × 10⁻⁸). Blue gene labels indicate novel loci, gray labels loci reported in previous GWAS of serum urate. Green band: −log₁₀(two-sided meta-analysis P-value) for association with gout (n = 763,813), by chromosomal position. Red line indicates genome-wide significance (P = 5 × 10⁻⁸). Inner band: Dots represent index SNPs with significant heterogeneity and are color-coded according to its source: green for ancestry-related heterogeneity (P_anc-het < 2.7 × 10⁻⁴ (0.05/183)), red for residual heterogeneity (P_res-het < 2.7 × 10⁻⁴), and yellow for both (P_anc-het and P_res-het < 2.7 × 10⁻⁴). Loci are labeled with the gene closest to the index SNP. P_anc-het and P_res-het were generated by MR-MEGA (Methods).

Figure 2 ∣. A genetic risk score (GRS) for serum urate improves gout risk prediction.

a, Histogram of the urate GRS among 334,880 European ancestry participants of the UK Biobank. The y-axes show the number of individuals (left) and the prevalence of gout (right), the x-axis shows categories of the urate GRS. The units on the x-axis represent genetically predicted serum urate levels (mg/dl) compared to individuals without any urate-increasing alleles. b, Age- and sex-adjusted odds ratio of gout (y-axis) by GRS category (x-axis) among 334,880 European-ancestry participants of the UK Biobank, comparing each category to the most prevalent category (4.74 < GRS ≤ 5.02) with error bars representing 95% confidence intervals; * denotes logistic regression two-sided P-value < 0.05, ** denotes P < 5 × 10⁻¹⁰, and *** P < 5 × 10⁻¹⁰⁰. c, Comparison of the receiver operating characteristic (ROC) curves of different prediction models of gout: genetic (GRS only; red), demographic (age + sex; green), and combined (GRS + age + sex; blue). y-axis: sensitivity, x-axis: specificity. At the optimal cut points determined by the maximum of the Youden’s index, the sensitivity of the combined model was 84% and specificity was 68%.

Figure 3 ∣. Serum urate shows widespread genetic correlations with cardio-metabolic risk factors and diseases.

The Circos plot shows significant genome-wide genetic correlations between serum urate and 214 complex traits or diseases (genetic correlation P < 6.6 × 10⁻⁵ = 0.05/748 traits tested), with bar height proportional to the genetic correlation coefficient (r_g) estimate for each trait and coloring according to its direction (dark blue, r_g > 0; light blue, r_g < 0). Traits and diseases are labeled on the outside of the plot and grouped into nine different categories. Each category is color-coded (inner ring, inset). The greatest genetic correlation was observed with gout (r_g = 0.92, P = 3.3 × 10⁻⁷⁰). Genetic correlations with multiple cardio-metabolic risk factors and diseases reflect their known directions from observational studies. The serum urate association statistics for estimating genetic correlations were from the European-ancestry meta-analysis (n = 288,649).

Figure 4 ∣. Genes expressed in urate-associated loci are enriched in kidney tissue and pathways.

a, Grouped physiological systems (x-axis) that were tested individually for enrichment of expression of genes in urate-associated loci among European-ancestry individuals (n = 288,649) using DEPICT are shown as a bar plot, with the −log₁₀(enrichment P-value) on the y-axis. Significantly enriched systems are labeled and highlighted in blue (enrichment false discovery rate (FDR) < 0.01). b, Correlated (r > 0.2) meta-gene sets that were strongly enriched (enrichment FDR < 0.01) for genes mapping into urate-associated loci among European-ancestry individuals (n = 288,649). Thickness of the edges represents the magnitude of the correlation coefficient, node size, color and intensity represent the number of clustered gene sets, gene set origin, and enrichment P-value, respectively.

Figure 5 ∣. Prioritization of p.Thr139Ile at HNF4A and functional study of HNF4A regulation of ABCG2 transcription.

a, Graph shows credible set size (x-axis) against the posterior probability of association (PPA; y-axis) for each of 1,453 SNPs with PPA > 1% in 114 99% credible sets. Triangles mark missense SNPs, with size proportional to their Combined Annotation Dependent Depletion (CADD) score. Blue triangles indicate missense variants mapping into small (≤ 5 SNPs) credible sets or with high PPA (≥ 50%). b, Predicted HNF1A or HNF4A binding sites in the promoter region of ABCG2 using LASAGNA 2.0, the consensus affinity sequence, and the P-value of likely matches based on nucleotide position within a consensus transcription factor binding site (Methods). c, Relative luciferase activity and transactivation of ABCG2 promoter in cells transfected with variable amount of HNF1A or HNF4A constructs (mean (line) ± s.e.m. (whiskers), n = 3 independent experiments, P-values calculated with ordinary one-way ANOVA with Tukey’s multiple comparison test). d, Position of p.Thr139Ile (T139I) in DNA binding domain/hinge region within HNF4A homodimer structure (PDB 4IQR). e, Relative luciferase activity and transactivation of ABCG2 promoter in cells transfected with variable amount of constructs (ng’s of transfected DNA) of wild-type HNF4A (threonine) or isoleucine at position 139 (± s.e.m., n = 3 independent experiments, P-values calculated with ordinary one-way ANOVA with Tukey’s multiple comparison test).

Figure 6 ∣. Co-localization of urate-association signals with gene expression in cis in kidney tissues.

Serum urate association signals identified among European ancestry individuals (n = 288,649) were tested for co-localization with all eQTLs where the eQTL cis-window overlapped (±100 kb) the index SNP. Genes with ≥1 positive co-localization (posterior probability of one common causal variant, H4, ≥ 0.80) in a kidney tissue are illustrated with the respective index SNP and transcript (y-axis). Co-localizations across all tissues (x-axis) are illustrated as dots, where the size of the dots indicates the posterior probability of the co-localization. Negative co-localizations (posterior probability of H4 < 0.80) are marked in gray, while the positive co-localizations are color-coded relative to the change in expression with a color gradient as indicated in the legend.