Description Usage Arguments Value Examples

Once the preliminary work is done using "GA.prelim()", this function tests a specifc region/gene. Single SNP analyses are also incorporated.

1 2 |

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

`G` |
Genetic variants in the target region/gene, an m*q matrix where m is the subject ID and q is the total number of genetic variables. Note that the number of rows in Z should be same as the number of subjects. |

`Gsub.id` |
The subject id corresponding to the genotype matrix, an m dimensional vector. This is in order to match the phenotype and genotype matrix. The default is NULL, where the order is assumed to be matched with Y, X and time. |

`weights` |
Can be a numeric vector of weights for genetic variants (The length should be same as the number of genetic variants in the set.), or pre-determined weights: "beta" (beta weights as in SKAT paper), "rare" (restrited to MAF<0.01), "common" (restrited to MAF>0.01). The default is NULL, where the flat weights are applied. |

`B` |
Number of Bootstrap replicates. The default is 5000. |

`B.coef` |
Direct import of Bootstrap coefficients, an m by B matrix. This is in order to efficiently implement the Bootstrap step. The default is NULL. |

`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; "Best guess" uses the genotype with highest probability. |

`p.value` |
P-value of the set based generalized score type test. |

`p.single` |
P-values of the incorporated single SNP analyses |

`n.marker` |
number of heterozygous SNPs in the SNP set. |

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | ```
## GA.prelim does the preliminary data management.
# Input: Y, time, X (covariates)
## GA.test tests a region.
# Input: G (genetic variants) and result of GEI.prelim
library(LGEWIS)
# Load data example
# Y: outcomes, n by 1 matrix where n is the total number of observations
# X: covariates, n by p matrix
# time: describe longitudinal structure, n by 2 matrix
# G: genotype matrix, m by q matrix where m is the total number of subjects
data(LGEWIS.example)
Y<-LGEWIS.example$Y
X<-LGEWIS.example$X
time<-LGEWIS.example$time
G<-LGEWIS.example$G
# Preliminary data management
result.prelim<-GA.prelim(Y,time,X=X)
# test with 5000 bootstrap replicates
result<-GA.test(result.prelim,G,B=5000)
``` |

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