Computational protocol: Large Scale Association Analysis for Drug Addiction: Results from SNP to Gene

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Protocol publication

[…] We took several steps in testing the associations between genetic variants (SNP or gene) and substance dependenice. First, the P value of each SNP was evaluated by the logistic regression, and then the correlation coefficients (r 2) of all SNP pairs were calculated. The computation was performed in PLINK software (version 1.07) []. In the second step, we implemented the gene-based analysis in the open-source tool: Knowledge-Based Mining System for Genome-Wide Genetic Studies (KGG, version 2.0) [] based on the association test results and LD files obtained from PLINK. Simes procedure (GATES) was employed in the gene-based association test []. Specifically, assume that m SNPs are assigned to a gene; an association test such as through the traditional logistic regression or linear regression is used to examine the association between the phenotype and each single SNP. This step yields mP values for m SNPs. GATES combines the available mP values within a gene by using a modified Simes test to give a gene-based P value. The summary P value is defined as (1)PG=Min⁡⁡(mep(j)me(j)), where p (j) is the jth smallest P value among the m SNPs; m e is the effective number of independent P values among m SNPs within the gene, and m e(j) is the effective number of independent P values among the top j SNPs. The effective number of independent P values was derived by accounting for the LD structure among the specified SNPs; we refer to [] on the calculation.In order to compare the performance of the SNP-based and gene-based methods, in the SNP-based method, we selected those SNPs whose P values were less than 1.0E − 5 and then mapped them into the corresponding genes. This allows us to compare the susceptible genes identified by both methods discussed above. […]

Pipeline specifications

Software tools PLINK, KGG
Application GWAS
Diseases Substance-Related Disorders
Chemicals Nitroprusside