The genotype data of cases and controls was analyzed for deviations from Hardy-Weinberg equilibrium by the Haploview v4.1 software [44] . The differences in allele and genotype distributions between severe sepsis and control groups were compared using x 2 -test or Fisher's exact test when appropriate. The test for association with genotypes used the global genotype test in the 362 contingency table. Allele frequencies of cases and controls were used to calculate the OR and the 95% CI. Multivariate logistic regression was used to adjust for potential confounding factors. When comparing severe sepsis group to sepsis group, we entered the genotypes or alleles in the multivariate models controlling for the confounding variables including age, gender, history of diseases, source of infection, APACHE II and SOFA scores. When comparing severe sepsis patients to healthy controls, age and gender were included in the multivariate models. The Bonferroni method was used to correct for multiple comparisons where applicable. The power analysis was performed using the Genetic Power Calculator web tool [45] . A two tailed Pvalue of ,0.05 was considered statistically significant, whereas a value of corrected P,(0.05/number of tests) was considered significant after Bonferroni correction. Continuous variables were described as either a mean 6 standard deviation, or as a median with interquartile range. TNF-a serum levels between individuals with different rs1800629 genotypes (AA+GA vs. GG) were compared by Student's t-test. To determine whether an association with rs1800629 genotypes might depend on other potential confounding factors for TNF-a serum levels, we investigated the association of rs1800629 genotypes by adding the polymorphisms to a linear regression model controlling for age, gender and APACHE II scores. The software used for statistical calculations was SPSS 15.0 (SPSS Inc., Chicago, IL, USA) unless specified.