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

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