Computational protocol: TNFAIP3 Gene Polymorphisms Are Associated with Response to TNF Blockade in Psoriasis

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Protocol publication

[…] The Michigan and Toronto samples were compared for a variety of epidemiological and phenotypic variables using two-sample tests for the equality of the means. For variables expressed as a percentage, Fisher’s exact test was used. All other variables except age were first transformed using the optimal Box-Cox power transform (power of 0.5 for age at PsV onset and duration of PsV, 0.7 for age at PsA onset, 0.2 for duration of PsA, and −0.1 for BMI) in order to achieve near-normality of the residuals of the appropriate regression model. Means for age, age at onset of PsV, and age at onset of PsA were then tested with the two-sample t-test for unequal variances. Means for duration of PsV, duration of PsA, and BMI were tested with a multiple regression model that included linear (duration PsA), linear and quadratic (BMI), or linear, quadratic, and cubic (duration PsV) terms for age and that used a Huber-White heteroscedasticity consistent covariance matrix.Data were analyzed for linear trend of association between drug response and genotypes and haplotypes of two SNPs in TNFAIP3 using logistic regression, where drug response was coded as a binary variable (0 = poor, 1 = good), and the genotype predictor variable was expressed as the dosage of the SNP allele or two SNP haplotype. Association between drug response and several covariates (worst-ever TBSA, BMI, age at onset, and PsA) was also tested with logistic regression. The Box-Tidwell procedure was applied to association testing of TBSA, BMI, and age at onset to determine whether a power transformation was necessary to achieve a linear relationship between the logit of drug response and the covariate, which is an important assumption of logistic regression. In all cases, the deviation between a model using the optimal linearizing power transformation of the covariate and a model using the untransformed covariate was nonsignificant, so untransformed covariates were used in all tests. Age at onset, the only covariate to show significant association with drug response, was included in all tests assessing association between drug response and genotypes and haplotypes of the two SNPs in TNFAIP3. Interaction between age at onset and SNP genotype was assessed with a likelihood ratio test applied to nested logistic regression models with and without a multiplicative interaction term. Conditional haplotype-based association testing was performed by comparing the odds of disease for pairs of haplotypes that have different alleles for the SNP under scrutiny but the same allele for the other SNP.For individual cohorts, nominal significance of association was assessed using 100,000 random permutations of the drug response variable. A special set of 100,000 restricted permutations of drug response labels was generated to determine significance that was corrected for multiple testing of eight single marker tests (4 drug treatments × 2 SNPs), twelve individual haplotype tests (4 drugs × 3 haplotypes), or four omnibus haplotype tests (4 drugs × 1 omnibus test). Responses for all SNP–drug combinations were permuted as intact vectors to maintain all dependencies among responses, and response vectors were permuted randomly within strata formed by groups of patients receiving the same combination of drugs to ensure constant sample size among all permutations in the presence of missing genotype data. The best association p-value among all tests for each iteration of the permutation procedure was saved, the saved p-values were sorted into ascending order, and the corrected p-value determined as the fractional rank within this vector of the best observed p-value across all tests.Meta-analysis was used to test for association across both cohorts combined. For single markers, standard fixed and random-effects models were used. Asymptotic p-values were reported for nominal significance, and the restricted permutations generated for the Michigan and Toronto samples were used to determine significance corrected for multiple testing. For haplotypes, fixed-effects meta-analysis of both cohorts was formulated as a logistic regression model with sample cohort as an additional covariate, and permutations were used to assess both nominal and corrected significance. Heterogeneity of ORs between the two samples was tested with Cochran’s Q statistic and the I2 heterogeneity index. All association analyses were carried out with version 1.0.7 of PLINK ().Power calculations were carried out with version 3.1.2 of G*Power () using an exact unconditional test of allelic association, which under Hardy-Weinberg equilibrium is essentially equivalent to the permutational version of the logistic regression test used in this study. Risk allele frequencies and the ratio of good to poor responders in the underlying population were estimated from the Toronto sample; the expected ORs for association were estimated from the Michigan sample. A type I error rate of 0.021 was used to compute power of association tests that are corrected for multiple testing, which is equivalent to a corrected significance of 0.05 when testing single SNPs in the Toronto sample. […]

Pipeline specifications

Software tools PLINK, G*Power
Applications Miscellaneous, GWAS
Organisms Homo sapiens
Diseases Arthritis, Rheumatoid, Celiac Disease, Diabetes Mellitus, Lupus Erythematosus, Systemic, Neoplasms, Psoriasis, Arthritis, Psoriatic