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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.