The correspondence of the determined allele and genotype frequencies to Hardy–Weinberg equilibrium (HWE) was estimated with the chi-square test. The logistic regression approach with adjustment for covariates was applied to analyze associations of the genetic variants with XFG and POAG. The three standard genetic models were assumed: additive, recessive, and dominant. The following covariates were applied: age, body mass index (BMI), systolic and diastolic blood pressures as quantitative variables and a family history of glaucoma, the presence of essential hypertension, heart atherosclerosis, heart ischemia, and diabetes mellitus (either type I or type II) as qualitative variables (Table 1 ). Adjustment for multiple comparisons for several SNPs was performed using the adaptive permutation test [19 (link)], and that for the number of genetic models (three) and groups compared (three), using the Bonferroni correction. Thus, the aggregated level of significance was set at pperm<0.006 (0.05/9). Quanto 1.2.4 (Hydra 2009) was used to compute statistical power for each SNP.
The confidence intervals algorithm at D’>0.8 as implemented inHaploView v.4.2 was applied to identify haplotype blocks. Adjustment for multiple comparisons was performed using the permutation test (1,000 permutations). Taking account of the number of haplotypes studied (seven) and the number of cohorts (three), the aggregate significance level after the Bonferroni correction was set at pperm<0.0024 (0.05/21). The computations above were performed using the respective algorithms implemented in the gPLINK v. 2.050 software.
The confidence intervals algorithm at D’>0.8 as implemented in
