Computational protocol: Increased coagulation activity and genetic polymorphisms in the F5, F10 and EPCR genes are associated with breast cancer: a case-control study

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Protocol publication

[…] We used a SNP tagging approach to avoid genotyping redundant SNPs. By using a minor allele frequency (MAF) criterion of ≥10% and pairwise r2 ≥ 0.8 as a cut-off for proxies, 39 SNPs were selected in the following gene regions: F2 (n = 3), F3 (TF) (n = 4), F5 (n = 10), F7 (n = 2), F10 (n = 9), TFPI (n = 9), and EPCR (n = 2). The tag-SNP selection was performed using the Tagger program (, []) implemented in Haploview v. 4.2 and genotype data from the Caucasian population (Utah residents with ancestry from northern and western Europe) from the HapMap project release 27, phase III on NCBI B36 assembly, dbSNPb126. Factor V Leiden (rs6025) and the prothrombin G20210A (rs1799963) polymorphisms were also included in the SNP selection. Hence, the final SNP selection consisted of 41 SNPs that were genotyped in both cases and controls.Individuals with ≥50% missing genotypes and SNPs with <97% call rates were excluded for further analysis. SNPs that deviated from Hardy-Weinberg equilibrium in the exact test were also excluded (significance threshold P < 0.001).One SNP failed genotyping in all individuals (TFPI rs3213739), two SNPs had genotyping call rates <97% (F7 rs1475931 and TFPI rs2041778), and one SNP was out of Hardy Weinberg equilibrium in controls (EPCR rs867186). Hence, after filtering, 37 of the 41 genotyped SNPs remained for further analysis. [...] All statistical analyzes were performed using SPSS statistical software (version 21.0; SPSS Inc., Chicago, IL, USA) and PLINK v.1.07 ( between each SNP and breast cancer were analyzed using an allelic chi-square (χ2) test with 1 degree of freedom. The false discovery rate (FDR) procedure described by Benjamini & Hochberg [] was used to correct for multiple testing.Odds ratios (ORs), 95% confidence intervals (CIs), and P-values were determined for the genotypes of the SNPs that were significant at the 5% level and had a FDR < 0.25 in the allelic test. Binary logistic regression under the additive risk model was applied with case/control status as the dependent variable, and genotypes (coded 0, 1, 2 for each extra risk allele) as the categorical independent variables. Risk alleles were defined as the alleles being more prevalent among cases, thus ORs >1 were obtained.Independence between SNP associations was tested by conditional analysis in PLINK, where the allelic dosage for a given SNP was added as a covariate in a binary logistic regression model (additive model). The E-M algorithm was used to estimate haplotype frequencies, and haplotype-based association analysis was conducted using binary logistic regression (additive model). Haploview v. 4.2 was used for creating linkage disequilibrium (LD) plots, and the SNAP tool [] was used to obtain pair-wise LD measurements. The Alamut software (v. 2.0) was used to predict if any of the associated SNPs, or their proxies, could affect splicing.The plasma levels of hemostatic parameters were compared between cases and controls using t-test when normally distributed, or the non-parametric Mann-Whitney when the distribution was skewed. Tests with P < 0.05 were considered significantly different. Logistic regression was used to determine the associations with breast cancer status, or ER, PR, HR, and triple negative status, for either high or low levels of each of the hemostatic parameters. Case and control subjects were dichotomized according to either the 10th or the 90th percentiles of the hemostatic parameters’ plasma levels (defined in controls). The group with levels above the 10th percentile or below the 90th percentile served as the reference group.Genotype-phenotype correlations were evaluated by the Kruskal-Wallis test. For tests with P < 0.05, follow-up pairwise comparisons were conducted using Mann Whitney U testing with Bonferroni correction, and genotype-phenotype pairs with at least one significant pairwise test were reported. For correlations with the APC resistant phenotype, factor V Leiden carriers were excluded due to the established role for the factor V Leiden variant in APC resistance. […]

Pipeline specifications

Software tools Tagger, Haploview, SPSS, PLINK
Applications Miscellaneous, GWAS
Organisms Homo sapiens
Diseases Blood Coagulation Disorders, Breast Neoplasms, Neoplasms, Activated Protein C Resistance
Chemicals Estrogens, Progesterone