Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

Abstract

AimTo investigate the value of genomic information in prediction of individual serum uric acid concentrations.Methods Three population samples were investigated: from isolated Adriatic island communities of Vis (n = 980) and Korčula (n = 944), and from general population of the city of Split (n = 507). Serum uric acid concentration was correlated with the genetic risk score based on 8 previously described genes: PDZK1, GCKR, SLC2A9, ABCG2, LR-RC16A, SLC17A1, SLC16A9, and SLC22A12, represented by a total of 16 single-nucleotide polymorphisms (SNP). The data were analyzed using classification and regression tree (CART) and general linear modeling. ResultsThe most important variables for uric acid prediction with CART were genetic risk score in men and age in women. The percent of variance for any single SNP in predicting serum uric acid concentration varied from 0.0%-2.0%. The use of genetic risk score explained 0.1%-2.5% of uric acid variance in men and 3.9%-4.9% in women. The highest percent of variance was obtained when age, sex, and genetic risk score were used as predictors, with a total of 30.9% of variance in pooled analysis. ConclusionDespite overall low percent of explained variance, uric acid seems to be among the most predictive human quantitative traits based on the currently available SNP information. The use of genetic risk scores is a valuable approach in genetic epidemiology and increases the predictability of human quantitative traits based on genomic information compared with single SNP approach. Uric acid is the end product of purine metabolism in humans and higher primates and was initially believed to be a highly potent risk factor for disease development (1). This was confirmed in different studies that reported increased levels of serum uric acid in metabolic disorders and cancer (2-4). However, recent studies have reported possible beneficial effects of uric acid, especially in cardiovascular and neurological diseases of late onset (5,6). Possible protective effects are due to a strong antioxidant properties of uric acid (7). All these findings make uric acid a highly interesting research target in both epidemiology and genetics.Efforts in the search for the genetic basis of uric acid concentrations have been focused on several candidate genes (8,9) and the recently described SLC2A9 has been found to be a gene with a major effect in humans, especially in women (10,11). SLC2A9, also known as Glut 9, is a member of the SLC2A facilitative glucose transporter family, which has an important role in the sugars metabolism (12). The gene encodes a putative transporter of 540 amino acids, which is closely related to several more members of the same gene family, with 44% and 38% resemblance to Glut5 and Glut1, respectively (12). The protein consists of 12 transmembrane domains, sugar motifs, and other signatures which facilitate sugars transport (13). The long form of this protein in humans is most strongly expressed in basolateral membranes of proximal renal tubular cells, the liver, and placenta, w...

Associations of the uric acid related genetic variants in SLC2A9 and ABCG2 loci with coronary heart disease risk

“…Recent genome-wide association studies (GWASs) identified multiple genetic loci associated with serum uric acid concentrations [ 9 , 13 , 18 , 26 , 27 ]. Our previous GWAS also confirmed two reported loci SLC2A9 (solute carrier family 2, facilitated glucose transporter member 9, 4p16.1) [ 18 , 22 , 26 , 28 , 29 ] and ABCG2 (ATP-binding cassette, sub-family G, member 2, 4q22) [ 12 , 30 , 31 ] positively associated with serum uric acid levels in a Chinese population [ 32 ]. Several studies investigated the associations between the genetic variants in the uric acid related loci of SLC2A9 and ABCG2 and the risk of CHD among Europeans [ 12 , 22 , 25 , 33 , 34 ] and found no association between them.…”

Effect of Genetic Polymorphisms on Development of Gout

“…In type 2 diabetes, a disease commonly associated with gout, the C statistics of the prediction models using genetic information alone have been reported to range from 0.54 to 0.63 20,27,28,29 . The genetic aspects of uric acid have been indicated to be a good example of the polygenic trait theory 33 , and all of the genetic variants in this study have been shown to be associated with SUA level. Minimal effects of genetic variants for prediction have also been reported for obesity and premature coronary disease 30,31 .…”

Genetic variability related to serum uric acid concentration and risk of Parkinson's disease

“…Similarly, the GRS based in 8 loci located in almost the same genes than those in our study was also shown to correlate with serum UA levels in a recent meta-analysis of GWAS, 10 as well as in a Croatian population in a different study. 11 Our results, interpreted in the framework of Mendelian Randomization (MR), support the existence of a real connection between serum UA and PD risk, reasonably ruling out the possibility of confounding and reverse causation. Indeed, according to the MR approach, when a causal association exists between risk factor (UA) and disease endpoint (PD), the genetic variant that governs that risk factor should have a commensurate association with the disease itself.…”

“…Both sexes, analyzed separately, showed a significant inverse correlation between GRS and serum UA levels in men (r 2 5 20.18, P 5 .03; adjusted by age, P 5 .03), and these data were less significant in women (r 2 5 20.17, P 5 .06; adjusted by age, P 5 .08). The correlation The GRS represents the number of risk alleles related to low serum uric acid levels, divided into first tertile (2-7), second tertile (8)(9), and third tertile (10)(11)(12)(13)(14)(15). Maximum GRS after exclusion of SLC22A11 was 16.…”