Description Usage Arguments Value Examples

Once the preliminary work is done by "WGScan.prelim()", this function scan a target region. This function is often used for candidate region analyses.

1 2 3 | ```
WGScan.Region(result.prelim,G,pos,Gsub.id=NULL,Z=NULL,MAF.weights='beta',
test='combined',window.size=c(5000,10000,15000,20000,25000,50000),MAF.threshold=1,
impute.method='fixed')
``` |

`result.prelim` |
The output of function "WGScan.prelim()" |

`G` |
Genetic variants in the target region, an n*p matrix where n is the subject ID and p is the total number of genetic variants. |

`pos` |
The positions of genetic variants, an p dimensional vector. Each position corresponds to a column in the genotype matrix. |

`Gsub.id` |
The subject id corresponding to the genotype matrix, an n dimensional vector. Each ID corresponds to a row in the genotype matrix. This is used to match phenotype with genotype. The default is NULL, where the matched phenotype and genotype matrices are assumed. |

`Z` |
Weight matrix for functional annotations, an p*q matrix where p is the total number of genetic variables and q is the number of weights. This is used to incorperate functional annotations. The default is NULL, where minor allele frequency weighted (see MAF.weights) dispersion and/or burden tests are applied. |

`MAF.weights` |
Minor allele frequency based weight. Can be 'beta' to up-weight rare variants or 'equal' for a flat weight. The default is 'beta'. |

`test` |
Can be 'dispersion', 'burden' or 'combined'. The test is 'combined', both dispersion and burden tests are applied. The default is 'combined'. |

`window.size` |
Candidate window sizes in base pairs. The default is c(5000,10000,15000,20000,25000,50000). Note that extemely small window size (e.g. 1) requires large sample size. |

`MAF.threshold` |
Threshold for minor allele frequency. Variants above MAF.threshold are ignored. The default is 1. |

`impute.method` |
Choose the imputation method when there is missing genotype. Can be "random", "fixed" or "bestguess". Given the estimated allele frequency, "random" simulates the genotype from binomial distribution; "fixed" uses the genotype expectation; "bestguess" uses the genotype with highest probability. |

`n.marker` |
Number of tested variants in the window (heterozygous variants below MAF threshold). |

`window.summary` |
Results for all windows. Each row presents a window, including chromosome number, start position, end position,dispersion p-value(s), burden p-values(s). |

`M` |
Estimated number of effective tests. |

`threshold` |
Estimated threshold, 0.05/M. This threshold is for windows tested in this particular region. |

`p.value` |
P-value of entire region. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## WGScan.prelim does the preliminary data management.
# Input: Y, X (covariates)
## WGScan.Region scans a region.
# Input: G (genetic variants), pos (position) Z (weights) and result of WGScan.prelim
library(WGScan)
# Load data example
# Y: outcomes, n by 1 matrix where n is the total number of observations
# X: covariates, n by d matrix
# G: genotype matrix, n by p matrix where n is the total number of subjects
# pos: positions of genetic variants, p dimention vector
# Z: functional annotation matrix, p by q matrix
data(WGScan.example)
Y<-WGScan.example$Y;X<-WGScan.example$X
G<-WGScan.example$G;pos<-WGScan.example$pos
Z<-WGScan.example$Z
# Preliminary data management
result.prelim<-WGScan.prelim(Y,X=X,out_type="C",B=5000)
# Scan the region with functional annotations defined in Z
result<-WGScan.Region(result.prelim,G,pos,Z=Z)
``` |

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