Sentence examples for multiple testing yet from inspiring English sources

None of the associations between the 123 SNPs and baseline BMI in the subcohort was significant after accounting for multiple testing, yet three associations reached nominal significance (P<0.05) (LEPR SNP rs3790426 β = 0.18 kg/m2 per minor allele, P = 0.01; LEP SNP rs2278815 β = −0.14 kg/m2 per minor allele, P = 0.03; NUCB2 SNP rs10832763 β = 0.16 kg/m2 per minor allele, P = 0.03) (Table S2).

However, the most effective approach to correct for multiple tests has yet to be defined.

This would limit the utility of gene expression studies where the validity of any multiple testing procedure has yet to be ascertained [ 13].

Since phase-1 was an explorative phase no correction for multiple testing was applied yet and SNPs with a p-value <0.05 were studied in phase-2.

Yet, because of multiple testing, the consistency of results across all models and subsamples makes it unlikely that the observed associations arose by chance.

The use of a simulation approach is a simple yet comprehensive way to address multiple testing.

Similarly, three "alternative 5' splice site" and 11 "alternative 3' splice site" events showed nominal significance (p < 0.01), yet none of them retained significance after multiple testing correction.

A method or a strategy to apply multiple testing corrections accurately in a case-control candidate gene association study has not yet been established [ 67, 68].

Yet the use of multiple groups in quantile-based analysis surely exacerbates the tendency to multiple testing, and this is clearly what is seen in the literature [ 6, 7].

Significance testing of pairwise FST values was corrected for multiple testing using the B Y method FDR as modified by Narum (2006) as this method controls Type I errors yet provides improved power to differentiate populations over Bonferroni correction.

15 In this study we propose a simple yet effective strategy that avoids comparing probesets across experiments based on FDR values while still controlling for multiple testing.