Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration
Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration
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DOI：
10.3325/cmj.2010.51.23
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发表时间：
2010-02-01
影响因子：
1.9
通讯作者：
Polasek, Ozren
中科院分区：
文献类型：
--
作者：
Gunjaca, Grgo;Boban, Mladen;Polasek, Ozren
目的探讨基因组信息在个体血清尿酸浓度预测中的价值。方法对3个人群样本进行了调查：来自亚得里亚海岛屿社区的维斯（n = 980）和科尔库拉（n = 944），以及来自斯普利特市的一般人群（n = 507）。血清尿酸浓度与基于8个先前描述的基因的遗传风险评分相关：PDZK 1，GCKR，SLC 2A 9，ABCG 2，LRRC 16 A，SLC 17 A1，SLC 16 A9和SLC 22 A12，由总共16个单核苷酸多态性（SNP）代表。结果CART预测尿酸的最重要变量是男性的遗传风险评分和女性的年龄。任何单一SNP预测血清尿酸浓度的变异百分比在0.0%-2.0%之间。遗传风险评分对男性尿酸变异的解释率为0.1%~ 2.5%，女性为3.9%~ 4.9%。最高百分比的方差时，年龄，性别和遗传风险评分被用作预测因子，总方差的30.9%，在汇总analysis.Conclusion尽管整体解释方差的百分比低，尿酸似乎是最具预测性的人类数量性状的基础上，目前可用的SNP信息。遗传风险评分的使用是遗传流行病学中一种有价值的方法，与单一SNP方法相比，它增加了基于基因组信息的人类数量性状的可预测性。
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 Korcula (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.