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For this study, we defined our outcome as "ever-hypertension" if an individual was hypertensive in any of the 4 examinations, and "never-hypertension" if hypertension criteria were never met in those 4 examinations. In this way, we created a single hypertension outcome based on the longitudinal structure of the data. The genetic analysis was focused on unrelated individuals. Gender, smoking habits, and age were selected as the main clinical predictors based on exploratory data analysis. Similar to the definition of outcome, smoking was defined as "ever-smokers" and "nonsmokers" based on multiple examinations. We first treated age as a continuous variable and detected its significant association with hypertension (odds ratio [OR] = 1.034; 95% confidence interval [CI]: 1.009, 1.059; p value = 0.0075). Then we examined the possible nonlinear relation between age and the defined hypertension outcome based on restricted ALPI cubic spline method [4] and found that the pattern of OR changed DNA Damage inhibitor as age changed. Finally, based on the cubic splines plot (Figure ?(Figure1),1), we dichotomized age at 55 years. Figure 1 Cubic splines plot for age Quality control of genotype data We focused on genome-wide association studies data of chromosome 3, and conducted quality control of genotype data using PLINK [5]. Thresholds for data quality control steps were set as follows: individual genotyping missing rate at 0.05, minor allele frequency at 0.1, missing rate per SNP at 0.05, and Hardy-Weinberg equilibrium at 1 �� 10?6. Heterozygosity rate was assessed for potential outliers. We merged our data set with HapMap [6] data and generated a multidimensional OTX015 in vivo scaling plot (Figure ?(Figure2).2). To adjust for population stratification effect, we used EIGENSTRAT [7,8] to conduct principal components analysis to explicitly model ancestry differences between individuals and obtained a principal component for each subject. Figure 2 Multidimensional scaling plot (outlier in red circle) Preliminary analysis and gene-based haplotype construction A logistic regression model was applied on association analysis for SNPs and the defined hypertension outcome with adjustment for covariates as well as principal component vectors obtained from the population stratification procedure. We first found some nominally significant SNPs (p