Abstract
Hypertension is regarded as a multifactorial disease with a modest contribution of genetic factors and strongly affected by environmental factors. Recent genome-wide association studies have identified specific loci associated with high blood pressure (BP) and hypertension. This study aimed to examine the association between the genetic risk score (GRS), a linear function of multiple single nucleotide polymorphisms (SNPs) associated with hypertension, and high BP and prevalent hypertension at baseline examination and to evaluate the efficacy of the GRS for predicting incident hypertension with longitudinal data in Korean subjects. Data for 8,556 participants, aged 40 to 69, in a community-based cohort study were analyzed. Unweighted GRS (cGRS) and weighted GRS (wGRS) were constructed from 4 SNPs related to high BP or hypertension in previous genome-wide association and its replication studies for the Korean middle-aged population. Cross-sectional analysis (n=8,556) revealed that cGRS was significantly associated with prevalent hypertension (odds ratio=1.15 per risk allele; 95%CI, 1.09-1.20). Additionally, the odds ratios (ORs) of prevalent hypertension for those who in medium and the highest tertile compared with those who in the lowest tertile of wGRS were 1.31 (95% CI, 1.15-1.50) and 1.59 (95%CI, 1.38-1.82), respectively. In a longitudinal analysis (n=5,632), participants in the highest tertile of wGRS had a 1.22-fold (OR=1.22, 95%CI, 1.02‒1.46) greater risk of incident hypertension relative to those in the lowest tertile, after adjusting for a number of confounding factors. However, wGRS topped with traditional risk factors had no significant effect on discrimination ability (c-statistics with and without wGRS were 0.811 and 0.810, P=0.1057). But, reclassification analysis showed that the addition of GRS to the model with conventional risk factors led to about 9% significant increment in category-free net reclassification improvement. GRSs based on 4 SNPs were independently associated with hypertension and may provide a statistically significant improvement over the existing model for prediction of incident hypertension.