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Table 1 Data set simulation parameters, prevalence = 0.5

From: Improving machine learning reproducibility in genetic association studies with proportional instance cross validation (PICV)

  Scenario 1 Scenario 2 Scenario 3
SNP1 MAF: 0.1 0.2 0.2
SNP2 MAF: 0.1 0.1 0.2
Penetrance: 0.493 0.531 0.522 0.507 0.480 0.556 0.514 0.481 0.425
0.526 0.387 0.410 0.471 0.590 0.249 0.467 0.544 0.674
0.611 0.008 0.358 0.485 0.532 0.482 0.539 0.447 0.304
  Scenario 4 Scenario 5 Scenario 6
SNP1 MAF: 0.3 0.3 0.3
SNP2 MAF: 0.1 0.2 0.3
Penetrance: 0.513 0.494 0.456 0.488 0.525 0.450 0.481 0.533 0.446
0.438 0.530 0.696 0.527 0.455 0.562 0.525 0.468 0.513
0.520 0.475 0.506 0.478 0.458 0.814 0.483 0.470 0.734
  Scenario 7 Scenario 8 Scenario 9
SNP1 MAF: 0.4 0.4 0.4
SNP2 MAF: 0.1 0.2 0.3
Penetrance: 0.484 0.501 0.535 0.490 0.523 0.455 0.502 0.523 0.425
0.570 0.494 0.359 0.512 0.468 0.568 0.499 0.472 0.588
0.545 0.551 0.245 0.565 0.395 0.668 0.495 0.503 0.501
  Scenario 10 Scenario 11 Scenario 12
SNP1 MAF: 0.4 0.5 0.5
SNP2 MAF: 0.4 0.1 0.2
Penetrance: 0.476 0.535 0.449 0.306 0.333 0.341 0.476 0.521 0.482
0.506 0.473 0.568 0.428 0.314 0.256 0.521 0.472 0.536
0.536 0.503 0.410 0.322 0.198 0.595 0.715 0.392 0.502
  Scenario 13 Scenario 14 Scenario 15
SNP1 MAF: 0.5 0.5 0.5
SNP2 MAF: 0.3 0.4 0.5
Penetrance: 0.500 0.520 0.459 0.422 0.515 0.547 0.440 0.560 0.440
0.477 0.480 0.563 0.548 0.491 0.470 0.522 0.484 0.509
0.608 0.482 0.429 0.531 0.492 0.485 0.515 0.472 0.542