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To circumvent the problem of multiple testing due to the large number of statistical tests performed simultaneously in the association study, the false discovering rate was controlled and permutation P-values were computed with FBAT program (1,000,000 permutation tests were performed).
The differentially expressed miRNAs were determined by edgeR software using generalized linear models with FDR (false discovering rate) value less than 0.05 [ 58].The expression levels of miRNAs in each group were estimated by FPKM method.
P-values of multiple comparisons analysis made on continuous or discontinuous (classifications) variables of impulsivity were adjusted using the False Discovering Rate (FDR) controlling procedures [ 46].
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Multiple-comparison adjustment was further applied using Benjamini and Hochberg's false discover rate (FDR) method [19].
The genes, exhibiting ≥2-fold change in expression with the false discover rate (FDR) ≤0.01, were considered as 'differentially expressed' and further examined to identify affected pathways.
Benjamini and Hochberg's method was used in order to control the false discover rate [41].
Each method produces a regularized t-statistic from which a false discover rate (FDR) can be calculated to identify the most likely DE genes.
The statistical significance (false discover rate (FDR), q-value) and the ratio of the changes in expression was calculated using Significance Analysis of Microarray (SAM) software [14] following LOWESS normalization.
Both the minimum p-value for all tagging SNPs within the gene and minimum false discover rate (FDR) adjusted p-values for the three SNP haplowalk sliding window for VDR and RXRA were significantly associated with RCC risk.
False discover rate was employed for multiple test correction (Benjamini and Hochberg 1995).
Such procedure will be carried to avoid false discovered rate due to multiple comparisons.
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