Computational protocol: Screening toll-like receptor markers to predict latent tuberculosis infection and subsequent tuberculosis disease in a Chinese population

Similar protocols

Protocol publication

[…] The data were double entered on a spreadsheet (Microsoft Excel) and any discrepancies were checked with the original questionnaire data to ensure data consistency. The clinical and demographic characteristics were compared among the three groups (PTB, LTBI, and HC) with ANOVA for continuous variables and with the χ2 test or Fisher’s exact test for categorical variables. p < 0.05 was considered significant.The allele and genotype frequencies of each polymorphism were determined by direct counting. The genotype distributions for each polymorphism were then tested for Hardy–Weinberg equilibrium values with the χ2 test. The genotype and allele frequencies of the different groups were compared by calculating the odds ratios and 95% confidence intervals (CI) in a conditional logistic regression model (STATA version 9.0; College Station, TX). The linkage disequilibrium (LD) coefficients D’ and r2 were then calculated for the multi-locus polymorphisms studied in TLR2 and TLR4 to determine any co-segregation. The associations between the haplotypes and LTBI or TB were tested by calculating the logistic regression (adjustments) statistic and the corresponding p values and odds ratios (ORs) with 95% confidence intervals (CIs) using the SNPStats software (http://bioinfo.iconcologia.net/SNPstats/) []. The relationships between the TLR polymorphisms and the risk of PTB or LTBI were evaluated with the nonparametric MDR method []. Each best model was tested for its accuracy, cross-validation consistency, and significance level, determined with permutation testing, testing accuracy, and testing OR (95% CI) in the MDR analysis. Cross-validation consistency was defined as the number of cross-validation replicates (partitions) in which the same n-locus model was chosen as the best model (i.e., the number of replicates in which the classification error was minimized). Bonferroni corrections were applied to multiple comparisons. The level of significant was p < 0.003125 (0.05/16). […]

Pipeline specifications

Software tools Stata, snpStats
Application GWAS
Organisms Homo sapiens
Diseases Tuberculosis, Tuberculosis, Pulmonary, Latent Tuberculosis