| Title: | Combine Multiple GWAS by Using Gene-Environment Interactions |
|---|---|
| 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. |
| Authors: | Youfei Yu [aut], Alexander Rix [aut, cre] |
| Maintainer: | Alexander Rix <[email protected]> |
| License: | GPL-3 |
| Version: | 0.9.1 |
| Built: | 2024-11-11 05:45:12 UTC |
| Source: | https://github.com/umich-cphds/subgxe |
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.
pasta(p.values, study.sizes, cor)pasta(p.values, study.sizes, cor)
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, |
A list containing the joint p value and the test statistic, which contains the optimal subset.
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
# 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$selected.subset# grab synthetic study for example data("studies") n.studies <- 5 study.sizes <- c(nrow(studies[[1]]), nrow(studies[[2]]), nrow(studies[[3]]), nrow(studies[[4]]), nrow(studies[[5]])) study.pvals <- rep(0, n.studies) # Correlations of p-values among the studies. # In this case the studies were generated independently so its just I cor.matrix <- diag(1, n.studies) # load the lrtest() function to conduct the likelihood ratio test # Used just to generate the input p-values, not required in pasta itself. library(lmtest) for(i in 1:n.studies) { # model with gene(G) by environment(E) interaction model <- glm(D ~ G + E + GbyE, data = studies[[i]], family = binomial) # model without G and GE interaction null.model <- glm(D ~ E, data = studies[[i]], family = binomial) # likelihood ratio test from the package lmtest study.pvals[i] = lmtest::lrtest(null.model, model)[2, 5] } pasta <- pasta(study.pvals, study.sizes, cor.matrix) pasta$p.pasta pasta$test.statistic$selected.subset
Synthetic data for subgxe
studiesstudies
A list of 5 data.frames with 12000 observations
(6000 cases, 6000 controls) on 4 variables:
Disease status. Numeric 0-1
Genetic variant. Numeric 0-1
Exposure. Numeric 0-1
G * E. Either 1 or 0.