Simulation Scenario | Statistics | Mean of FPR in Deleterious Model | SD of FPR in Deleterious Model | Focal Window TPR in Mild-Pos Model | Focal Window TPR in Strong-Pos Model |
---|---|---|---|---|---|

Model_0; Chr11Max; r = 1e-9 | pI | 0.354 | 0.047 | 0.900 | 1.000 |

RD | 0.204 | 0.048 | 0.521 | 0.569 | |

U50 | 0.117 | 0.037 | 0.438 | 0.432 | |

Q95 | 0.437 | 0.051 | 0.875 | 1.000 | |

Model 0, Chr11Max; r = hg19 | pI | 0.229 | 0.086 | 0.885 | 1.000 |

RD | 0.134 | 0.061 | 0.577 | 0.648 | |

U50 | 0.081 | 0.046 | 0.365 | 0.444 | |

Q95 | 0.121 | 0.034 | 0.637 | 0.752 | |

Model_h; Chr11Max; r = hg19 | pI | 0.087 | 0.108 | 0.967 | 1.000 |

RD | 0.098 | 0.117 | 1.000 | 0.654 | |

U50 | 0.097 | 0.036 | 0.767 | 0.500 | |

Q95 | 0.099 | 0.120 | 1.000 | 0.933 |

For the deleterious model, we computed the false positive rates (FPRs) in 50-kb nonoverlapping windows using the most extreme 5% value from the neutral distribution as the critical value, and show the mean FPR in the third column. For the AI models (Mild-Pos and Strong Pos), we computed the TPRs using the same neutral cutoff value in all windows, and show the TPR in the window that contains the adaptive mutation (“Focal TPR”). Note that a properly calibrated null model should have a FPR of 0.05.