Collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information.
To install the development version of CoMM, it's easiest to use the 'devtools' package. Note that REMI depends on the 'Rcpp' package, which also requires appropriate setting of Rtools and Xcode for Windows and Mac OS/X, respectively.
#install.packages("devtools")
library(devtools)
install_github("gordonliu810822/CoMM")
The 'CoMM' vignette will provide a good start point for the genetic analysis using CoMM package. The following help page will also provide quick references for CoMM package and the example command lines:
library(CoMM)
package?CoMM
All the simulation results can be reproduced by using the code at simulation. Before running simulation to reproduce the results, please familarize yourself with CoMM using 'CoMM' vignette. Simulation results can be reproduced using simulation.R with a batch script nscc_sim.txt.
The simulation results of CoMM-S2 can be reproduced by the code of file in the simulation. The introduction of usage of CoMM-S2 is in the 'CoMM' vignette, please refer to the code file of CoMM-S2 CoMM_ S2_ simulation_power.R and CoMM_ S2_ simulation_t1e.R in the vignette to reproduce the simulation results. The excel tables for the results of analysis of 14 GWAS summary statistics can be found at gene_significance4_centering_excel.zip
- Can Yangc, Xiang Wanc, Xinyi Lin, Mengjie Chen, Xiang Zhou, Jin Liu+. (2018) CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory information, Bioinformatics, 35(10), 1644–1652.
- Yi Yang, Xingjie Shi, Yuling Jiao, Jian Huang, Min Chen, Xiang Zhou, Lei Sun, Xinyi Lin, Can Yang, Jin Liu+. (2019) CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies, Bioinformatics, btz880.
This package is developed and maintained by Yi Yang ([email protected]) and Jin Liu ([email protected]).