| Title: | Combine Multiple GWAS by Using Gene-Environment Interactions |
| Version: | 0.9.0 |
| Description: | Classical methods for combining summary data from genome-wide association studies (GWAS) only use marginal genetic effects and power can be compromised in the presence of heterogeneity. 'subgxe' is a R package that implements p-value assisted subset testing for association (pASTA), a method developed by Yu et al. (2019) <doi:10.1159/000496867>. pASTA generalizes association analysis based on subsets by incorporating gene-environment interactions into the testing procedure. |
| License: | GPL-3 |
| URL: | https://github.com/umich-cphds/subgxe |
| BugReports: | https://github.com/umich-cphds/subgxe/issues |
| Suggests: | lmtest, knitr, rmarkdown |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2019-06-14 11:26:19 UTC; alexrix |
| Author: | Youfei Yu [aut], Alexander Rix [cre] |
| Maintainer: | Alexander Rix <alexrix@umich.edu> |
| Repository: | CRAN |
| Date/Publication: | 2019-06-14 13:30:03 UTC |
pasta for multi-phenotype analysis
Description
Search for the subset that yields the strongest evidence of association and calculate the meta-analytic p-value, possibly in the presence of gene-environmental interaction.
Usage
pasta(p.values, study.sizes, cor)
Arguments
p.values |
The p.value of each study. |
study.sizes |
The sample size of each study. |
cor |
The correlation matrix of the studies. For example, if each study
is independent, |
Value
A list containing the joint p value and the test statistic, which contains the optimal subset.
References
Yu Y, Xia L, Lee S, Zhou X, Stringham H, M, Boehnke M, Mukherjee B: Subset-Based Analysis Using Gene-Environment Interactions for Discovery of Genetic Associations across Multiple Studies or Phenotypes. Hum Hered 2019. doi: 10.1159/000496867
Examples
# grab synthetic study for example
data("studies")
n.studies <- 5
study.sizes <- c(nrow(studies[[1]]), nrow(studies[[2]]), nrow(studies[[3]]),
nrow(studies[[4]]), nrow(studies[[5]]))
study.pvals <- rep(0, n.studies)
# Correlations of p-values among the studies.
# In this case the studies were generated independently so its just I
cor.matrix <- diag(1, n.studies)
# load the lrtest() function to conduct the likelihood ratio test
# Used just to generate the input p-values, not required in pasta itself.
library(lmtest)
for(i in 1:n.studies) {
# model with gene(G) by environment(E) interaction
model <- glm(D ~ G + E + GbyE, data = studies[[i]], family = binomial)
# model without G and GE interaction
null.model <- glm(D ~ E, data = studies[[i]], family = binomial)
# likelihood ratio test from the package lmtest
study.pvals[i] = lmtest::lrtest(null.model, model)[2, 5]
}
pasta <- pasta(study.pvals, study.sizes, cor.matrix)
pasta$p.pasta
pasta$test.statistic$selected.subset
Synthetic data for subgxe
Description
Synthetic data for subgxe
Usage
studies
Format
A list of 5 data.frames with 12000 observations
(6000 cases, 6000 controls) on 4 variables:
- D
Disease status. Numeric 0-1
- G
Genetic variant. Numeric 0-1
- E
Exposure. Numeric 0-1
- GbyE
G * E. Either 1 or 0.