ABC | Volume 111, Nº1, July 2018

Original Article Pereira et al. Genes and coronary artery disease risk Arq Bras Cardiol. 2018; 111(1):50-61 inclusion of one non-template control (NTC) in each plate of 96 wells. All SNPs TaqMan assays had blind duplicates accounting for 20% of all samples. Some SNP genotypes were randomly confirmed by conventional direct DNA sequencing, as 10-15% of all samples were re-amplified for sequencing. Call rates for SNPs in the GRS were 98%-100% and a minimum 95% call rate was set for quality control. Computation of the GENETIC RISK SCORE We have tested several models to construct the GRS using both non-weighted and weighted scores, taking into consideration each pattern of inheritance for each gene locus. An additive score (AGRS) was generated, i.e., for each one of the 31 variants a score of 0, 1, and 2 was defined as there were 0, 1 or 2 risk alleles, by calculating the accumulated sum of the risk alleles in these variants. Each individual could be assigned a GRS of 0-62. Additionally, a multiplicative GRS (MGRS) was calculated by multiplying the relative risk for each genotype. Validation of the risk score calculation was performed in a random sample of 597 patients (20%). Statistical analysis Categorical variables were expressed by frequencies and percentages and compared by the Chi-squared test or Fisher’s exact test. Continuous variables were expressed as mean ± standard deviation (SD) or median (1st quartile – 3rd quartile) and compared by Student's t-test (unpaired) or Mann-Whitney, as appropriate. The Kolmogorov-Smirnov test and the Levene´s test were used to test the assumption of normality and the homogeneity of the variables. All analyses were considered significant when p values were less than 0.05. Binary logistic regression was used to determine the combined and separate effects of the variables on the risk for angiographic CAD. GRS was modeled using as a continuous variable and as quartiles, using the first quartile as the reference category. Multivariate analyses were used to adjust for 7 covariates also reported to be associated with CAD. We plotted receiver operating characteristic (ROC) curves and calculated the area under the curve (AUC) for logistic regression models including TRFs without and with GRS (quartiles). Pairwise comparison of ROC curves was performed using the Delong test. 11 The model calibration was tested with Hosmer-Lemeshow goodness-of-fit test. A P-value less than 0.05 was considered statistically significant. Collinearity between the variables was measured by assessment of tolerance and variance inflation factor (VIF). Associations of SNPs with CAD were considered significant at p < 0.05 and in aggregate with GRS models at p < 0.0015 applying Bonferroni correction. For MAF of 30%, the study had 70% power to detect an OR for CAD of 1.3 and > 90% for OR ≥ 1.35, for 2-sided alpha of < 0.05 for 2,000 cases and 1,000 controls. Power calculations used G power Statistical Power Analyses. The potential of GRS to improve individual risk stratification then was measured using the net reclassification improvement (NRI) method, 12 defined as the percentage of subjects in each subgroup changing categories when the new model of GRS (in quartiles) was added. The integrated discrimination improvement (IDI), defined as the incremental improvement prognostic value of GRS, was compared between cases and controls. NRI was computed by categorical and non‑categorical (continuous) variables using the PredictABEL package available in R software (version 3.2.0). Statistical analyses were performed using SPSS version 19.0 (IBM), MedCalc version 13.3.3.0 and R software version 3.1.2. Results Baseline characteristics of the population Table 1 shows the baseline characteristics of our population. As expected, cases and controls showed no significant differences concerning gender and age, since this was a selection criterion. Higher frequency of dyslipidemia, diabetes, hypertension, physical inactivity, smoking habit, alcohol consumption, and family history of premature cardiovascular disease was found in CAD patients when compared to the controls (p < 0.0001). Also, PWV, BMI and waist-to-height ratio were higher in cases than in controls, with statistical significance (p < 0.05) (Table 1). The other biochemical variables analyzed such as hemoglobin, leucocytes, fibrinogen, homocysteine and hs-CRP > 3 showed significantly higher levels in the coronary patients group when compared to the controls (p < 0.05) (Table 1). Computation and analysis of Genetic Risk Score Deviation from Hardy-Weinberg equilibrium for the 33 genotypes at individual loci were assessed using the Chi‑squared test and p < 0.002 with Bonferroni correction for all SNPs included. LPA gene variant was excluded for further analyses due to its low Hardy-Weinberg p-value (p < 0.002). Linkage disequilibrium for the mutually adjusted SNPs within the genes was studied. CDKN2B gene was excluded because of the strong linkage disequilibrium with another selected SNP, rs1333049, which resides in the 9p21 region. The remaining 31 SNPs were included for further analysis (Supplementary Table 1). In this study, the MGRS had the highest AUC value for assessing the risk for CAD disease with a specificity of 62.3% and sensitivity of 54% (data not shown) and therefore this model was computed in the subsequent analyzes (Supplementary Table 2). The MGRS of 31 SNPs was significantly higher in CAD cases than in controls (0.67 ± 0.73 vs 0.48 ± 0.53; p < 0.0001), even by quartile and gender discrimination (Table 2). A normal distribution of risk alleles in the total sample set including cases and controls is shown in Figure 1. While CAD patients exhibited lower GRS values, risk alleles were more prevalent in this group than in controls. In CAD patients, a mean of 27 risk alleles was seen in 52% of the individuals, and a mean of 26 risk alleles was found in 53% of controls (Figure 1). When analyzed in deciles, GRS showed that the increase in the number of risk alleles was significantly associated with CAD 52