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A PS score [Equation (3) in Section 2] was calculated for each variant, where lower values corresponded to a more significant relationship with the disease. The red distribution represents matching scores from binding events and the blue distribution represents matching scores from non-binding events. A P-value was calculated for each of the 51 SNPs by using the percentile of their priority scores in the null distributio

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Then, four intersects from these binding patterns corresponds to the candidate SNP, including fn,tf(SVR), fn,tf(SVA), fb,tf(SVR) and fb,tf(SVA). Intuitively, if one specific SNP were predicted to cause the matching score to shift from within the non-binding distribution to within the binding distribution, or vice versa, then this SNP could contribute to the gain or loss of binding affinity, respectively. For each TFBS, we calculated the distributions of matching scores for both binding and non-binding events (Fig. 2A and Section 2). A P-value was calculated for each of the 51 promoter SNPs based on their priority score PS(V) [Equation (3)], using permutation analysis of 1000 iterations. Then, we used a range of different thresholds, ranking SNPs/mutations from the lowest to highest PS scores, to select the positive mutations (scores lower than the threshold) which are recognized by regSNPs as being causally related to disease as well as negative mutations (scores higher than the threshold

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