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R/fGWAS: Functional GWAS package for R (Version 1.0)


The R/fGWAS Package (Functional Genome-wide Association Studies) is developed as a new method for genome-wide association studies based on a single SNP analysis [1]. The proceduce called by Bayesian Lasso model includes two steps, which is characterized by the production of a preconditioned reponse variable by a supervised principle component analysis and the formulation of Bayesian lasso for selecting a subset of significant SNPs. Based on this theoretical model, the package integrates two steps into one function to analyse the genotypical data and phenotypical data. The analysis flow can be started by the data loading, and then the data goes through the main function of the Bayesian Lasso model. Finally the results will be generated. By the result summarization, the information about significant SNP positions can be reported.

For more details, please refer to the document.

[1] Jiahan Li, Kiranmoy Das, Guifang fu, Runze Li and Rongling Wu. The Bayesian Lasso for Genome-wide Associations Studies. Bioinformatics (2010). First published online: December 14, 2010. doi: 10.1093/bioinformatics/btq688


The packages for Windows, Linux and Mac OS are available. The document also can be downloaded by the following link.


The R/fGWAS package depends on 3 packages, including mvtnorm, glmnet and bigmemory. Before R/fGWAS is installed, these packages should be installed in advance. After you download this package file, please type the following command or click the menu item "Install packages from local zip files".

Windows OS:

>install.packages("x:/fullpath/fgwas_1.0-1.zip", repos=NULL)

Linux/Mac OS:

>install.packages("/fullpath/ fgwas_1.0-1.tar", repos=NULL)

Before the package is used in R, It is necessary to loading package by the following command:

> library(fgwas)

After it is loaded, all functions within R/fGWAS will be readily available to the user.

Sample Script:

The following source shows how to call the main function in R.

dat1 <- LS.load_plink("bmi_chr1_t1000.tped", "bmi_chr1_t1000.tfam", "bmi_phenos.csv", 1 );
res1 <- LS.run(dat1);

dat2 <- LS.load_plink("bmi_chr2_t1000.tped", "bmi_chr2_t1000.tfam", "bmi_phenos.csv", 2 );
res2 <- LS.run(dat2);

ret <- LS.merge_result(res1, res2);

Sample Figures:

Last updated:

Version 1.0 01/19/2011

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