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Correction for multiple testing was implemented using the Benjamini-Hochberg false discovery rate (FDR), with adjusted p values of ≤0.1 deemed to be significant.
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Correction for multiple tests was implemented using the false discovery rate method (FDR; Benjamini and Hochberg 1995) using an alpha level of 0.05 to determine significance.
We used a significance level of α = 0.05 and no correction for multiple testing was implemented.
No associations were detected if a correction for multiple testing was implemented.
Bonferroni correction for multiple testing was implemented based on the number of SNPs analyzed per gene.
Correction for multiple testing was implemented with the Bonferroni method as well as with the false discovery rate approach [ 19].
Multiple imputation was implemented using the R package, 'mi' [27].
In summary, multiple testing corrections were applied at different levels: first, adjusting for testing multiple voxels in the whole brain was implemented using FWE correction; second, adjusting for testing multiple SNPs using Bonferroni correction, and finally, the gold standard of replication analysis was carried out to rule out any remaining false positive associations.
A correction for multiple testing was then implemented using Bonferroni [ 51] or False Discovery Rate (FDR) [ 51, 52] using the multtest R package [ 53].
Multiple applications are implemented using the CFA.
Two tests for recombination were implemented using the Datamonkey server, the first looking for a single recombination event (SBP test) and the second for multiple recombination events (GARD test).
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