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Supplemental Proof 1 Prospective and retrospective classifiers are equivalent.

Supplemental Proof 2 Direction of prospective bias.

Supplemental Example 1 Example of direction of prospective bias

Supplemental Table 1 Model specifications for single locus models

Supplemental Table 2 Model specifications for two-locus models

Supplemental Table 3 Full simulation results for BP, BR, and variance measures for CEMDR, CEBOOT N = 100, 1000 for all models with MAF = 0.5.

Supplemental Table 4 Simulation results for BP and variance for CEadj for all models with MAF = 0.5.

Supplemental Table 5 Simulation results for BP, BR, and variance measures for CEMDR, CEBOOT N = 100 for dominant and recessive models with MAF = 0.5 and 0.25.

Supplemental Table 6 Results for CEBOOT with error in the estimate &pcirc;D, with &pcirc;D estimated for each dataset from n = 100, 500 subjects.

Supplemental Table 7 Results for CEBOOT with error in the estimate &pcirc;D, with &pcirc;D both under- and over-estimated for each model from n = 100, 500 subjects.

Supplemental Figure 1 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with N = 100, and CEadj with known prevalence for the single locus model with dominant risk.

Supplemental Figure 2 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with N = 100, and CEadj with known prevalence for the single locus model with recessive risk.

Supplemental Figure 3 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with N = 100, and CEadj with known prevalence for the two-locus XOR risk model.

Supplemental Figure 4 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with N = 100, and CEadj with known prevalence for the two-locus ZZ risk model.

Supplemental Figure 5 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with N = 100, and CEadj with known prevalence for the two-locus Box risk model.

Supplemental Figure 6 Bias (left) and variance (right) of MDR and bootstrap estimates of prediction error by prevalence comparing MAF = 0.5 and 0.25 for the single locus model with dominant risk.

Supplemental Figure 7 Bias (left) and variance (right) of MDR and bootstrap estimates of prediction error by prevalence comparing MAF = 0.5 and 0.25 for the single locus model with recessive risk.

Supplemental Figure 8 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the single locus model with dominant risk.

Supplemental Figure 9 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the single locus model with recessive risk.

Supplemental Figure 10 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the single locus model with additive risk.

Supplemental Figure 11 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the two-locus XOR risk model.

Supplemental Figure 12 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the two-locus ZZ risk model.

Supplemental Figure 13 Prospective bias (left) and variance (right) of CEMDR, CEBOOT with known prevalence, and CEBOOT with inaccurate prevalence estimates for each dataset from np = 100, 500 subjects for the two-locus Box risk model.

Supplemental Figure 14 Prospective bias for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with dominant risk.

Supplemental Figure 15 Variance for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with dominant risk.

Supplemental Figure 16 Prospective bias for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with recessive risk.

Supplemental Figure 17 Variance for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with recessive risk.

Supplemental Figure 18 Prospective bias for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with additive risk.

Supplemental Figure 19 Variance for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the single locus model with additive risk.

Supplemental Figure 20 Prospective bias for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the two-locus XOR risk model.

Supplemental Figure 21 Variance for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the two-locus XOR risk model.

Supplemental Figure 22 Prospective bias for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the two-locus Box risk model.

Supplemental Figure 23 Variance for CEBOOT with under-estimated (left) and over-estimated (right) prevalence from np = 100, 500 subjects compared with CEBOOT with known prevelance and CEMDR for the two-locus Box risk model.

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