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.
|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.
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.
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.
|Example||Example.zip||a simple example of running the program|
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.