Meta-analysis of 28,141 individuals identifies common variants within five new loci that influence uric acid concentrations

Abstract

Elevated serum uric acid levels cause gout and are a risk factor for cardiovascular disease and diabetes. To investigate the polygenetic basis of serum uric acid levels, we conducted a meta-analysis of genome-wide association scans from 14 studies totalling 28,141 participants of European descent, resulting in identification of 954 SNPs distributed across nine loci that exceeded the threshold of genome-wide significance, five of which are novel. Overall, the common variants associated with serum uric acid levels fall in the following nine regions: SLC2A9 (p = 5.2x10(-201)), ABCG2 (p = 3.1x10(-26)), SLC17A1 (p = 3.0x10(-14)), SLC22A11 (p = 6.7x10(-14)), SLC22A12 (p = 2.0x10(-9)), SLC16A9 (p = 1.1x10(-8)), GCKR (p = 1.4x10(-9)), LRRC16A (p = 8.5x10(-9)), and near PDZK1 (p = 2.7x10(-9)). Identified variants were analyzed for gender differences. We found that the minor allele for rs734553 in SLC2A9 has greater influence in lowering uric acid levels in women and the minor allele of rs2231142 in ABCG2 elevates uric acid levels more strongly in men compared to women. To further characterize the identified variants, we analyzed their association with a panel of metabolites. rs12356193 within SLC16A9 was associated with DL-carnitine (p = 4.0x10(-26)) and propionyl-L-carnitine (p = 5.0x10(-8)) concentrations, which in turn were associated with serum UA levels (p = 1.4x10(-57) and p = 8.1x10(-54), respectively), forming a triangle between SNP, metabolites, and UA levels. Taken together, these associations highlight additional pathways that are important in the regulation of serum uric acid levels and point toward novel potential targets for pharmacological intervention to prevent or treat hyperuricemia. In addition, these findings strongly support the hypothesis that transport proteins are key in regulating serum uric acid levels.

Figure 1. Genome-wide association results.

Manhattan plots showing significance of association of all SNPs in the meta-analysis for (A) men and women combined, (B) men only, and (C) women only. SNPs are plotted on the x-axis according to their position on each chromosome against association with uric acid concentrations on the y-axis (shown as −log₁₀ p-value).

Figure 2. Regional association plots of nine urate loci.

P-value plots showing the association signals in the region of (A) PDZK1 on chromosome 1, (B) GCKR on chromosome 2, (C) SLC2A9 on chromosome 4, (D) ABCG2 on chromosome 4, (E) LRRC16A on chromosome 6, (F) SLC17A1 on chromosome 6, (G) SLC16A9 on chromosome 10, (H) SLC22A11 on chromosome 11, and (I) SLC22A12 on chromosome 11. −log₁₀ p-values are plotted as a function of genomic position (NCBI Build 36). Large diamonds in red indicate the most significant SNP in the region while other SNPs in the region are given as colour-coded smaller diamonds. Red diamonds indicate high correlation with the lead SNP (r2>0.8), orange diamonds indicate moderate correlation with the most significant SNP (0.5<r2<0.8), yellow indicates markers in weak correlation with the most significant SNP (0.2<r2<0.5), white indicates no correlation with the most significant SNP (r2<0.2). Estimated recombination rates (HapMap Phase II) are given in light blue, genes as well as the direction of transcription (NCBI) are displayed by green bars.