EAGLE is an R package which uses Allele Specific Expression (ASE) quantified via RNA-seq to detect gene by environment (GxE) interactions. The underlying idea is that observing an association between the level of allelic imbalance at an exonic SNP, and an environmental factor, is evidence of a cis-regulatory element modulating the transcriptomic response to the environmental perturbation. The underlying statistical model is a binomial Generalized Linear Mixed Model, with a random effect term used to account for count overdisperion. Model fitting is achieved using Non-conjugate Variational Message Passing.
The paper describing EAGLE has now been published:
Allele-specific expression reveals interactions between genetic variation and environment. Knowles, D. A; Davis, J. R; Edgington, H.; Raj, A.; Favé, M.; Zhu, X.; Potash, J. B; Weissman, M. M; Shi, J.; Levinson, D.; Awadalla, P.; Mostafavi, S.; Montgomery, S. B; and Battle, A. Nature Methods 2017.
An early bioRxiv preprint is available.
The code is on github.
eagle has been tested under the following architectures:
I have not tried compiling under Windows.
You will need the RcppEigen R package installed. In R run
install.packages("RcppEigen")
To download the code
git clone git@github.com:davidaknowles/eagle.git
To compile+install, in this directory run
R CMD INSTALL --build .
Alternatively run
# require(devtools)
devtools::install_github("davidaknowles/eagle")
For a simple example script on synthetic data look at test.R. For a slightly more involved/realistic example, including eQTLs and realistic filtering options look at big_test.R.