T-GEN (Transcriptome-mediated identification of disease-associated Genes with Epigenetic aNnotation) is a framework to identify disease-associated genes leveraging epigenetic information. The preprint can be found at Leveraging funcational annotation to identify genes associated with complex diseases.
The software is developed and tested in Linux and Mac OS environments.
- R-3.6.1
- varbvs
- psych
install.packages("devtools")
library(devtools)
install_github("pcarbo/varbvs",subdir = "varbvs-R")
install.packages("psych")
library("psych")
library("varbvs")
source("code/tgen.R")
source("code/tgenpve.R")
source("code/tgennorm.R")
source("code/tgennormupdate.R")
environment(tgen) <- asNamespace('varbvs')
environment(tgenpve) <- asNamespace('varbvs')
environment(tgennorm) <- asNamespace('varbvs')
environment(tgennormupdate) <- asNamespace('varbvs')
# fit the tgen model
load("./data/data.Rdata")
md = tgen(X = x,y=y,Z=NULL,annot = annotat.array,family="gaussian")We trained the gene expression imputation models using GTEx and epigenetic information from Roadmap Epigenomics project. The pre-trained imputation models in 26 tissues can be found at https://drive.google.com/drive/folders/15Lrox4FzmmAWw82yfQH2vj7rhuMgO0HF?usp=sharing.
We used the code from MetaXcan to conduct the gene-disease association test. More specifically, the fourth step of MetaXcan.
Part of the gene expression imputation code is modified from varbvs. The test code is from [MetaXcan] (https://github.com/hakyimlab/MetaXcan). We thank the authors for sharing the code.
Liu et al. (2020). Leveraging funcational annotation to identify genes associated with complex diseases. bioRxiv, 529297. Link