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Table 3 Summary of t-statistic Z-scores from conditional logistic model fits

From: Conditional entropy in variation-adjusted windows detects selection signatures associated with expression quantitative trait loci (eQTLs)

Dataset Z-score H|H Z-s. |ΔDAF| Z-s. Fst Z-s. ΔiHH Z-s. iHS
Mangravite 2012 22 6/NA NA 0 8
Montgomery 2010A 13 3/5 -4 1 -2
Montgomery 2010B 8 2/4 -3 3 -2
Schadt 2007 2 1/1 -1 1 0
Stranger 2007 8 -2/4 -8 -3 -2
Veyrieras 2008 8 0/0 -13 -3 -1
Zeller 2010 19 2/7 -7 2 0
  1. For each Dataset, the average value of the Z-score across all Models (1-5 from Table 2, rounded to the nearest whole number.) Two averages are given |ΔDAF| - Models 2 and 5 before the slash; Models 1, 3 and 4 after the slash. For H|H, a positive number indicates a positive value is a strong predictor for eQTLs, for the other measures, an extreme negative value indicates that a low log p-value is a strong predictor for eQTLs. ΔiHH and iHS are intended to be measures of positive selection. With the exception of |ΔDAF| (which changes substantially depending on whether Fst is included in the model), these Z-scores do not change greatly among Models 1-6 in Table 2, indicating that these predictors are largely independent.