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Gray cells (with negative -log10 Simple Sum p-values)
correspond to gene-tissue pairs with no Simple Sum (SS)
P-value.
The exact reason for the absence of a SS P-value can be gleaned
in the table below
Please note that we leave up to the user to determine the threshold of significance among the datasets that passed the first-stage significance test. For example, if a user selected 3 tissues and 4 genes for testing, and 3 other secondary datasets (a total of 3 × 4 + 3 = 15 tests) and among these, 6 datasets were tested for colocalization, then one would conservatively choose to consider a Bonferroni-corrected p-value threshold of 0.05 / 6 = 8.3 × 10-3. Thus, Simple Sum colocalization tests above this threshold would be considered as significant.
-1 values correspond to gene-tissue pairs with no eQTL data (likely due to little or no expression)
-2 values correspond to gene-tissue pairs that did not pass the Bonferroni-corrected first stage testing for signficance among the secondary datasets chosen
-3 values correspond to gene-tissue pairs where the Simple Sum P-value computation failed, likely due to insufficient SNPs
Please note that we leave up to the user to determine the threshold of significance among the datasets that passed the first-stage significance test. For example, if a user selected 3 tissues and 4 genes for testing, and 3 other secondary datasets (a total of 3 × 4 + 3 = 15 tests) and among these, 6 datasets were tested for colocalization, then one would conservatively choose to consider a Bonferroni-corrected p-value threshold of 0.05 / 6 = 8.3 × 10-3. Thus, Simple Sum colocalization tests above this threshold would be considered as significant.
-1 values correspond to gene-tissue pairs with no eQTL data (likely due to little or no expression)
-2 values correspond to gene-tissue pairs that did not pass the Bonferroni-corrected first stage testing for signficance among the secondary datasets chosen
-3 values correspond to gene-tissue pairs where the Simple Sum P-value computation failed, likely due to insufficient SNPs
Please note that we leave up to the user to determine the threshold of significance among the datasets that passed the first-stage significance test. For example, if a user selected 3 tissues and 4 genes for testing, and 3 other secondary datasets (a total of 3 × 4 + 3 = 15 tests) and among these, 6 datasets were tested for colocalization, then one would conservatively choose to consider a Bonferroni-corrected p-value threshold of 0.05 / 6 = 8.3 × 10-3. Thus, Simple Sum colocalization tests above this threshold would be considered as significant.