The marker allele frequency p can be derived through an independent population survey, or, if taking controls as a random sample of the population in regard to the marker genotypes, approximately estimated from controls as p ^ = n 21 + 0.5 n 22 / n 2, where n2 = n21 + n22 + n23.
The results of the analysis summarized in Additional file 7 show that use of marker allele frequency estimates from control samples does not result in any notable difference in the false positive rate and test power from use of the true marker allele frequencies.
Phenotypic values of the controls were decomposed as follow: where μ control is the average value of the controls, is the effect of the year of experimentation estimated from the control plots (l = 2004, 2005), is the sub-block effect (p = 124) at year l, is the seed lot (s = ' Initial', ' Commercial'), and is the residual, with n indexing the replicates sharing the same sub-block.
Data were generated using Etienne's algorithm [58] with parameters θ = 6 and m = 0.039, to approximate the parameters estimated from the Control data (with estimates of θ of 4.9 and 7.7 for the mZSM and lZSM distributions, respectively).
Genomic binding sites were identified from ChIP-Seq data using local Poisson background models estimated from IgG control data as described previously.
Six unique haplotypes were estimated from the control population, revealing five common haplotypes occurring at a frequency of 5% or more (Table 4).
The proportions of cadavers sporulating were significantly less than expected based upon the number of ants estimated (from the control data) to have died from other causes (F1,17 = 8.55, P = 0.0095).
For the cDNA data, conservative threshold values for differential expression were chosen to minimize the false-positive and false-negative rates estimated from Scorecard control probes printed on the arrays.
This suggests that the mean Q30 ORP error rate of ~5×10-5 ~5×10-5estimatedhe control data (Table 3) was not conservative enough to eliminate all frome posithee variant controlpossibly dataTableR errors.
Moreover, we compared the size of the off-transient recorded in both TBPH LOF lines with that estimated from the control regression line which revealed 15 and 25% reduction in the off-transient in TBPH−/− and GMR>TBPH-RNAi, respectively.
