SKATBinary_Single | R Documentation |

This function computes p-values of single variant test using the firth and efficient resampling methods.

SKATBinary_Single(Z, obj, method.bin="Hybrid" , impute.method = "bestguess", is_check_genotype=TRUE, is_dosage = FALSE , missing_cutoff=0.15, max_maf=1, estimate_MAF=1 , N.Resampling=2*10^6, seednum=100, epsilon=10^-6)

`Z` |
a numeric genotype vector. Each genotype should be coded as 0, 1, 2, and 9 (or NA) for AA, Aa, aa, and missing, where A is a major allele and a is a minor allele. |

`obj` |
output object from SKAT_Null_Model. |

`method.bin` |
a type of method to compute a p-value (default="Hybrid"). See details. |

`impute.method` |
a method to impute missing genotypes (default= "bestguess"). |

`is_check_genotype` |
a logical value indicating whether to check the validity of the genotype matrix Z (default= TRUE). See SKAT page for details. |

`is_dosage` |
a logical value indicating whether the matrix Z is a dosage matrix. If it is TRUE, SKAT will ignore “is_check_genotype”. |

`missing_cutoff` |
a cutoff of the missing rates of SNPs (default=0.15). Any SNPs with missing rates higher than the cutoff will be excluded from the analysis. |

`max_maf` |
a cutoff of the maximum minor allele frequencies (MAF) (default=1, no cutoff). Any SNPs with MAF > cutoff will be excluded from the analysis. |

`estimate_MAF` |
a numeric value indicating how to estimate MAFs for the weight calculation and the missing genotype imputation. See SKAT page for details |

`N.Resampling` |
a number of resampling to be conducted to get p-values (default=2 *10^6). |

`seednum` |
a seed number for random number generation (default=100). If NULL, no seed number will be assigned. |

`epsilon` |
a precision level (default=10^-6). |

This function implements three methods (method.bin) to compute p-values: 1) Efficient resampling (ER); 2) Firth biased adjusted likelihood ratio test (Firth); and 3) Hybrid. "Hybrid" selects a method based on the total minor allele count (MAC), the number of individuals with minor alleles (m), and the degree of case-control imbalance.

Adaptive ER (ER.A) is not implemented yet.

If seednum is not NULL, set.seed(seednum) function is used to specify seeds to get the same p-values of ER based methods for different runs. Therefore, please set seednum=NULL, if you do not want to set seeds.

`p.value` |
p-value. It will be the mid p-value if ER is used to compute the p-value. |

`p.value.standard` |
(ER only) standard p-value. |

`p.value.resampling` |
p-values from resampled outcome. You can obtain it when n.Resampling in SKAT_Null_Model was > 0. See the SKAT_Null_Model. |

`p.value.standard.resampling` |
(ER only) standard p-values from resampled outcome. |

`m` |
the number of individuals with minor alleles. |

`MAP` |
the minimum possible p-values. It is available when the method.bin="ER" and m is sufficiently small. |

`MAC` |
the total minor allele count (MAC). |

`n.total` |
(ER only) the number of resampling to be generated to get the p-value. It can be smaller than N.Resampling when the total number of configurations of case-controls among individuals with minor alleles are smaller than N.Resampling. |

`is.accurate` |
logical value for the accuracy of the p-value. If it is false, more resampling is needed to accurately estimate the p-value. |

`method.bin` |
a type of method to be used to compute the p-value. |

Seunggeun Lee

Lee, S., Fuchsberger, C., Kim, S., Scott, L. (2015)
An efficient resampling method for calibrating single and gene-based rare variant association analysis in case-control studies.
*Biostatistics*, in press.

data(SKATBinary.example) Z<-SKATBinary.example$Z obj<-SKAT_Null_Model(y ~ x1 + x2, out_type="D", data=SKATBinary.example) out = SKATBinary_Single(Z[,1], obj) # p-value out$p.value # MAP out$MAP # method used to compute p-value (method.bin) out$method.bin # # Use firth method to compute p-value SKATBinary_Single(Z[,1], obj, method.bin="Firth")$p.value

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