Bridge: a GUI Software for Genetic Risk Prediction

Bridge is built for both designing and analyzing a risk prediction model. In the design stage, it provides an estimated classification accuracy of the model and determines the sample size required to verify this accuracy. In the analysis stage, it adopts a robust and powerful algorithm for forming the risk prediction model.

Reference: Ye C and Lu Q. Bridge: a GUI package for genetic risk prediction. BMC genetics. 2013; 14:122.

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File name Download Description
Bridge Bridge_1.0.zip R GUI software for genetic risk prediction (version 1.0)
Installing script install.R A R script to install Bridge software. In order to appropriate install Bridge, we suggest users to first install a recent R version (>= 3.0.0.)
Bridge vignette Bridge.pdf User manual (version 1.0)

 

GWGGI: Genome-Wide Gene-Gene Interaction Analysis

GWGGI is C++ software for genome-wide gene-gene interaction analyses . GWGGI utilizes tree-based algorithms to search a large number of genetic markers for disease-associated joint association with the consideration of high-order interactions, and then uses non-parametric statistics to test the joint association.

Reference: Wei C and Lu Q. GWGGI: software for genome-wide gene-gene interaction analysis. BMC genetics. 2014; 15:101.

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Platform File Version
Unix gwggi.unix.zip v1.0
Windows gwggi.win.zip v1.0
C++ source gwggi.code.zip v1.0

 

GSU: A Generalized Association Test Based on U Statistics

GSU is a software package for testing the association of a set of sequencing variants (e.g., variants in a gene) with univariate or multivariate responses.It is developed based on a non-parametric statistics, and thus is computationally effecient and can accommodate various types of responses with unknown distributions.

Reference: Wei C and Lu Q. A generalized association test based on U statistics. Bioinformatics. 2017; 33(13):1963-1971.

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Platform File Version
Unix gsu.unix.zip v1.0
Windows gsu.win.zip v1.0
Readme Readme.txt v1.0

 

GGRF: R code for genetic association analysis of sequencing Data by using a generalized genetic random field method

GGRF utilizes a generalized genetic random field method for the statistical analysis of sequencing data. It accommodates a variety of disease phenotypes (e.g., quantitative and binary phenotypes), and can be applied to small-scale sequencing data without need for small-sample adjustment.

Reference: Li M, He Z, Zhang M, Zhan X, Wei C, Elston RC and Lu Q. A generalized genetic random field method for the genetic association analysis of sequencing data. Genetic epidemiology. 2014; 38(3):242-53.

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File name Download Description
GGRF function.r R code
Example Example.zip a simple example of running the program
Readme Readme.txt Readme file

 

CAR: R code for a conditional autoregressive model accounting for genetic heterogeneity

CAR is proposed for genetic association analysis considering genetic heterogeneity. It has certain advantages when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals. It can also be applied to small-scale sequencing data without a small-sample adjustment.

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File name Download Description
CAR Code.zip R code
Example Example.R a simple example of running the CAR program