Izvorni znanstveni članak
Predictive Value of 8 Genetic Loci for Serum Uric Acid Concentration
Grgo Gunjača
; University of Split School of Medicine, Split, Croatia
Mladen Boban
; University of Split School of Medicine, Split, Croatia
Marina Pehlić
; University of Split School of Medicine, Split, Croatia
Tatijana Zemunik
; University of Split School of Medicine, Split, Croatia
Danijela Budimir
; University of Split School of Medicine, Split, Croatia
Ivana Kolčić
; Andrija Štampar School of Public Health, Medical School, University of Zagreb, Zagreb, Croatia
Gordan Lauc
; University of Osijek, School of Medicine, Osijek, Croatia
Igor Rudan
; Department of Public Health Sciences, The University of Edinburgh Medical School, Teviot Place, Edinburgh, UK
Ozren Polašek
; Andrija Štampar School of Public Health, Medical School, University of Zagreb, Zagreb, Croatia
Sažetak
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.
Ključne riječi
uric acid; prediction; data mining; SNP; genome-wide association; variance
Hrčak ID:
53503
URI
Datum izdavanja:
15.2.2010.
Posjeta: 1.269 *