Description Usage Arguments Value References Examples
This function provides the p-value for a joint test of association between a phenotype and a set of genetic variants (SNPs) using the Adaptive Rank Truncated Product method [1] after a global test for the best mode of inheritance of every SNP. The final gene-p-value is obtained from the permutational null distribution of the test statistic.
1 2 |
data |
Data frame containing the variables in the model. The first column is the dependent variable which must be a binary variable defined as factor (in case-control studies, the usual codification is 1 for cases and 0 for controls). SNP values may be codified in a numerical form (0,1,2) denoting the number of minor alleles, or using a character form where the two alleles are specified, without spaces, tabs or any other symbol between the two alleles. |
B |
Number of permutations considered in the permutational procedure. |
K |
Integer that indicates the maximum truncation point. |
gene_list |
File that provides the name of the set (for instance, gene) where each SNP belongs. This file has two columns: the SNP-Id ("Id"), and the Gene-Id ("Gene"). The SNP-Id must have the same label as the colnames of the data file. |
Gene |
Name of the gene that we want to analyze. The default value is Gene= "all" that indicates that the p-values of all SNPs in the database are to be combined. In this case it is not necessary to specify the gene_list file. In other case, we need to specify the name of the gene, for instance, Gene = "Gene1", and also the gene_list file. |
addit |
logical to determine if only an additive inheritance model should be considered in the global Test or, conversely, if we want to consider all possible inheritance models (dominant, recessive, log-additive and co-dominant). By default, addit = FALSE. |
covariable |
Data frame containing the covariables in the model. Each column represents one covariable. By default, covariable=NULL. |
family |
This can be a character string naming a family distribution. By default, family=binomial. |
List with the following components:
nPerm |
Number of permutations. |
Gene |
Considered Gene. |
Trunkpoint |
Considered truncation point. |
Kopt |
Optimal truncation point. |
genevalue |
gene-pvalue. |
[3] Yu, K. Li, Q. Bergen, A.W. Pfeiffer, R.M. Rosenberg, P.S. Caporaso, N. Kraft, P. and Chatterjee,N. (2009). Pathway analysis by adaptive combination of P-values. Genet, Epidemiol. December; 33(8): 700-709.
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 | # load the included example dataset.
# This is a simulated case/control study data set
# with 2000 patients (1000 cases / 1000 controls)
# and 10 SNPs, where all of them have
# a direct association with the outcome:
data(data)
#globalARTP(data, B=1000, K=10, Gene="all", addit = FALSE)
# it may take some time,
# hence the result of this example is included:
data(ans11)
# You can test:
globalARTP(data, B=1, K=10, Gene="all", addit = FALSE)
# We consider that the first four SNPs
# are included in "Gene1",
# and the other six SNPs
# are included in "Gene2":
data(gene_list)
#globalARTP(data, B=1000, K=10, gene_list=gene_list, Gene="Gene1", addit = FALSE)
# it may take some time,
# hence the result of this example is included:
data(ans1)
# You can test:
globalARTP(data, B=1, K=10, gene_list=gene_list, Gene="Gene1", addit = FALSE)
|
$nPerm
[1] 1
$Trunkpoint
[1] 10
$Kopt
[1] 1
$genevalue
[1] 0.5
$nPerm
[1] 1
$Gene
[1] "Gene1"
$Trunkpoint
[1] 4
$Kopt
[1] 1
$genevalue
[1] 0.5
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.