Computational protocol: The Association between Polymorphisms in the MRPL4 and TNF-α Genes and Susceptibility to Allergic Rhinitis

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[…] The majority of the tag SNPs (tSNPs) were chosen from the Hapmap database according to the following selection strategy: Firstly, the International Haplotype Mapping (HapMap) ( SNP databases were used to select tSNPs in the ICAM-1, NF-κB, TNF-α and MRPL4 genes region, and the screened region extended 10 kilobases upstream of the annotated transcription start site and downstream at the end of the last each gene exon. The tSNPs were selected to extract most of the genetic information in the region using the CHB genotyping data from the HapMap database (HapMap data rel 27 Phase II +III, Feb2009) . From this dataset, genotyping data for 20 tSNPs were obtained and loaded in the Haploview software version 4.1 . Secondly, tSNPs were then selected using a pairwise tagging algorithm setting the Hardy-Weinberg p value, minor allele frequency (MAF) and r2 thresholds at 0.01, 0.05 and 0.8, respectively. The linkage disequilibrium (LD) pattern of each gene in the CHB population exhibited strong LD in several groups of tSNPs (r2≥0.8), indicating that most common SNPs can be captured by a subset of tagging SNPs. Consequently, we choose 14 SNPs, including rs5498, rs281432, rs281428, rs1059840, rs8104608, rs11668618, rs13117745, rs230530, rs3774963, rs1598861, rs118882, rs1800629, rs3093668, and rs1799964 to represent the four genes loci for eventual genotyping.Single nucleotide polymorphism genotyping DNA was isolated from peripheral blood leukocytes and collected in EDTA-treated tubes, using commercial DNA Isolation Kits for Mammalian Blood (Roche, Indianapolis, USA). Isolated DNA from the blood was stored at 4°C and used in further investigations within 2 days of isolation. The majority of the selected SNP genotyping was performed with the Sequenom MassARRAYiPLEX Gold platform (Sequenom, San Diego, California) according to the manufacturer’s instructions. The polymerase chain reaction (PCR) and extension primers were designed using MassARRAY Assay Design 3.1 software (). Genotyping was performed without knowledge of the case or control status. A 10% random sample was tested in duplicate by different investigators to test the reproducibility of the assay; which was shown to be 100%. [...] Data were initially processed for suitability for further statistical evaluation using the Haploview version 4.1 software. Hardy-Weinberg equilibrium (HWE) of each SNP was assessed in controls only and a threshold P<0.05 was regarded to indicate deviation from HWE. In addition, we assessed the MAF, non-missing genotype percentage and other criteria in the AR cases as well as controls to filter the data. Among them, minor allele frequency (MAF) and non-missing genotype percentage thresholds were set at <0.001 and <95%, respectively.Differences in frequencies of the alleles and genotypes between the AR subjects and control subjects were evaluated using the chi-square test and a P-value of 0.05 was considered significant. A correction of allele distribution χ2 test was made by using 100,000 permutation testing. The Global P values (2 degrees of freedom) were also calculated when we compared the genotype frequencies between cases and controls using a χ2 test. Akaike’s information criteria (AIC) were used to select the most parsimonious genetic model for each SNP. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by unconditional logistic regression analysis, adjusted for age and gender. Moreover, tests for trend were done by including genotypes as an ordinal variable in regression models (df = 1) to obtain P values for trend (two-sided). These analyses were conducted using the STATA statistical package (version 11.0; Stata Corp LP, College Station, TX, USA).A multistage strategy was employed for analysing gene–gene interactions. Firstly we first used the multifactor dimensionality reduction (MDR) method to detect and characterize locus–locus and gene–gene interaction models . Interaction dendrograms and graphs based on entropy (measurement of randomness) estimates were subsequently employed to confirm, visualize, and interpret the interactions models identified by MDR.The MDR approach , , applies a constructive induction algorithm that creates a new attribute by pooling genotypes from multiple SNPs. This method includes a combined cross-validation/permutation-testing procedure that minimizes false-positive results by multiple examinations of the data. Models that are true-positives are likely to be generalized to independent datasets and will have estimated testing accuracies of greater than 0.5. In addition to the testing accuracy, we also employed the cross validation consistency (CVC), a measure of how many times out of 10 divisions of the data that MDR found the same best model. Among this set of best multifactor models, the combination of genetic factors that maximized the testing accuracy and/or the highest CVC was selected and further evaluated using permutation testing. Moreover, age and gender were included as environmental factors for MDR analysis. The MDR analysis was performed by using version 1.0.0 of the open-source MDR software package freely available online ( statistical power for the study was calculated using G*Power 2 software ( […]

Pipeline specifications

Software tools Haploview, G*Power
Applications Miscellaneous, GWAS
Organisms Homo sapiens
Diseases Rhinitis, Allergic, Seasonal, Neoplasms