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This was despite a dramatically lower significance threshold for individual tests after multiple testing alpha adjustment in the pairwise search.
Because of multiple testing, alpha level of 0.05 was divided by three for a more stringent alpha level (Bonferroni adjustment).
However, when tested individually, none of the GVs achieved an association P-value less than the adapted (i.e., for multiple testing) alpha of 1 × 10−8.
After correcting for multiple testing (alpha level 0.05/9 = 0.0056) only the differences on psychological distress (p = .004) and neuroticism (p < .001) remained significant.
Similar(56)
Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches.
In view of multiple testing, an alpha of 0.01 was used to test the hypotheses.
Due to multiple testing, an alpha of.01 was used to evaluate results.
Due to multiple testing, an alpha level of 1% was adopted as the threshold for statistical significance.
As such, Bayesian model selection offers solutions to analytical problems related to multiple testing, such as alpha inflation and loss of power after correcting the alpha level (Klugkist, van Wesel, & Bullens, 2011).
In the enrichment analysis we discovered as much as 51 KEGG pathways to be significant following adjustment for multiple testing at an alpha level of 0.05.
The Bonferroni correction was used in cases of multiple testing to prevent alpha-inflation.
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