Description Usage Arguments Value Examples

Once the model under the null model is fitted using "null.LGRF()", this function tests a specifc region/gene.

1 2 |

`Z` |
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. |

`result.null` |
The output of function "null.LGRF()" |

`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. |

`interGXT` |
Whether to incorperate the gene-time interaction effect. Incorperating this effect can improve power if there is any gene-time interaction, but has slight power loss otherwise. The default is FALSE. *Please note that the second column of time should be included as a covairate when interGXT is TRUE. |

`similarity` |
Choose the similarity measurement for the genetic variants. Can be either "GR" for genetic relationship or "IBS" for identity by state. The default is "GR" for better computational efficiency. |

`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 LGRF test. |

`n.marker` |
number of tested 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 25 26 27 28 29 30 31 32 33 | ```
## null.LGRF fits the null model.
# Input: Y, time, X (covariates)
## test.LGRF tests a region and give p-value.
# Input: Z (genetic variants) and result of null.longGRF
library(LGRF)
# 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
# Z: genotype matrix, m by q matrix where m is the total number of subjects
data(LGRF.example)
Y<-LGRF.example$Y;time<-LGRF.example$time;X<-LGRF.example$X;Z<-LGRF.example$Z
# Fit the null model
result.null<-null.LGRF(Y,time,X=cbind(X,time[,2]))
# *Please note that the second column of time should be included as a covairate if
# the gene by time interaction effect will be incorperated.
# The LGRF-G test
pLGRF_G<-test.LGRF(Z,result.null)
# The LGRF-GT test
pLGRF_GT<-test.LGRF(Z,result.null,interGXT=TRUE)
# The LGRF-G test using the IBS similarity
pLGRF_G_IBS<-test.LGRF(Z,result.null,similarity="IBS")
# The LGRF-GT test, main effect is modeled using the IBS similarity
pLGRF_GT_IBS<-test.LGRF(Z,result.null,interGXT=TRUE,similarity="IBS")
``` |

LGRF documentation built on May 2, 2019, 10:59 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.