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Table 3 Power of RGC-corrected tests- nominal level 0.05, K = 200, a = (300, 200), b = (200, 300).

From: Correcting for cryptic relatedness by a regression-based genomic control method

F

MAF

Model

T 0

T 1/2

T 1

0.01

p = 0.2

DOM(f0 = 0.1, f1 = f2 = 0.15)

0.134

0.791

0.857

0.777

  

ADD(f0 = 0.1, f1 = 0.17, f2 = 0.24)

0.401

0.805

0.781

0.734

  

REC(f0 = f1 = 0.1, f2 = 0.2)

0.803

0.378

0.130

0.728

 

p = 0.4

DOM(f0 = 0.1, f1 = f2 = 0.15)

0.179

0.704

0.852

0.780

  

ADD(f0 = 0.1, f1 = 0.14, f2 = 0.18)

0.701

0.905

0.856

0.866

  

REC(f0 = f1 = 0.1, f2 = 0.2)

0.995

0.936

0.418

0.990

0.02

p = 0.2

DOM(f0 = 0.1, f1 = f2 = 0.15)

0.129

0.682

0.767

0.674

  

ADD(f0 = 0.1, f1 = 0.17, f2 = 0.24)

0.362

0.698

0.689

0.636

  

REC(f0 = f1 = 0.1, f2 = 0.2)

0.753

0.333

0.130

0.687

 

p = 0.4

DOM(f0 = 0.1, f1 = f2 = 0.15)

0.179

0.704

0.852

0.780

  

ADD(f0 = 0.1, f1 = 0.14, f2 = 0.18)

0.655

0.853

0.811

0.809

  

REC(f0 = f1 = 0.1, f2 = 0.2)

0.987

0.876

0.403

0.974

  1. The frequencies of null markers are randomly selected from [0.1, 0.5].