Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration

Gunjaca, Grgo; Boban, Mladen; Pehlic, Marina; Zemunik, Tatijana; Budimir, Danijela; Kolcic, Ivana; Lauc, Gordan; Rudan, Igor; Polasek, Ozren
February 2010
Croatian Medical Journal;Feb2010, Vol. 51 Issue 1, p23
Academic Journal
Aim To 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, LRRC16A, 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. Results The 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. Conclusion Despite 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.


Related Articles

  • Common Variants in SLC17A3 Gene Affect Intra-personal Variation in Serum Uric Acid Levels in Longitudinal Time Series. Polasek, Ozren; Jeroncic, Iris; Mulic, Rosanda; Klismanic, Zorana; Pehlic, Marina; Zemunik, Tatijana; Kolcic, Ivana // Croatian Medical Journal;Feb2010, Vol. 51 Issue 1, p32 

    Aim To investigate whether intra-personal variation in serum uric acid concentration is influenced by genes that were described to be associated with serum uric acid levels in cross-sectional studies. Methods The study included 92 participants from the isolated community of the Croatian island...

  • On Choosing Between Linear and Log-Linear Models. Seidman, David // Journal of Politics;May76, Vol. 38 Issue 2, p461 

    Discusses the problem of choosing between linear and log-linear models in investigations with more than one specification of a relationship. Equation to estimate the ordinary linear regression model; Solution when two regression models are explaining different sources of variation; Discussion...

  • Understanding linear and logistic regression analyses. Worster, Andrew; Fan, Jerome; Ismaila, Afisi // CJEM: Canadian Journal of Emergency Medicine;Mar2007, Vol. 9 Issue 2, p111 

    The article discusses various methods which helps to evaluate the relation between a clinical outcome of interest and one or more variables. The researchers have used the linear and logistic regression in order to determine the impact of the type of trauma team leader (TTL) to the emergency...

  • MODELING JUDGMENTS OF LINEAR EXTENT. Greene, Ernest; Frawley, William // Perceptual & Motor Skills;Jun2004 Part 1, Vol. 98 Issue 3, p1049 

    Subjects were asked to make judgments of linear extent, specifically, to assess and reproduce the span between dots, with distances that ranged from 0.5 to 8° of visual angle. The errors of judgment were modeled by regressing against linear and Fourier components, yielding a model for each of...

  • Association between SLC2A9 transporter gene variants and uric acid phenotypes in African American and white families. Rule, Andrew D.; de Andrade, Mariza; Matsumoto, Martha; Mosley, Tom H.; Kardia, Sharon; Turner, Stephen T. // Rheumatology;May2011, Vol. 50 Issue 5, p871 

    Objectives. SLC2A9 gene variants associate with serum uric acid in white populations, but little is known about African American populations. Since SLC2A9 is a transporter, gene variants may be expected to associate more closely with the fractional excretion of urate, a measure of renal tubular...

  • Robust linear regression methods in association studies. Lourenço, V. M.; Pires, A. M.; Kirst, M. // Bioinformatics;Mar2011, Vol. 27 Issue 6, p815 

    Motivation: It is well known that data deficiencies, such as coding/rounding errors, outliers or missing values, may lead to misleading results for many statistical methods. Robust statistical methods are designed to accommodate certain types of those deficiencies, allowing for reliable results...

  • Genotype-based changes in serum uric acid affect blood pressure. Parsa, Afshin; Brown, Eric; Weir, Matthew R; Fink, Jeffrey C; Shuldiner, Alan R; Mitchell, Braxton D; McArdle, Patrick F // Kidney International;Mar2012, Vol. 81 Issue 5, p502 

    Elevated serum levels of uric acid consistently correlate with hypertension, but the directionality of the association remains debated. To help define this relationship, we used a controlled setting within a homogeneous Amish community and the Mendelian randomization of a nonsynonymous coding...

  • Serum uric acid distribution according to SLC22A12 W258X genotype in a cross-sectional study of a general Japanese population. Hamajima, Nobuyuki; Naito, Mariko; Hishida, Asahi; Okada, Rieko; Asai, Yatami; Wakai, Kenji // BMC Medical Genetics;2011, Vol. 12 Issue 1, p33 

    Background: Although SLC22A12 258X allele was found among those with hypouricemia, it was unknown that serum uric acid distribution among those with SLC22A12 258X allele. This study examined serum uric acid (SUA) distribution according to SLC22A12 W258X genotype in a general Japanese population....

  • Covariance Structures of Linear Models. Gupta, A. K.; D. G. Kabe // Pakistan Journal of Statistics & Operation Research;Supplement, Vol. 7 Issue 2, p465 

    Young, Scariano, and Hallum (2005) study two univariate linear regression models, and establish a certain condition for the variances estimates to be equal; however, the condition is in error. They then apply this incorrect condition to some unknown completely randomized design model. The...


Read the Article


Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics