The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results.
To install and load the package in R
library(devtools) install_github("MathieuEmily/GeneGeneInteR") library(GeneGeneInteR)
Importation of genotypes with ImportFile function Supported format are pedfile, PLINK, VCF (4.0) file, or genotypes imputed by IMPUTE2.
#### Example of ped format with 17 genes ped <- system.file("extdata/example.ped", package="GeneGeneInteR") info <- system.file("extdata/example.info", package="GeneGeneInteR") posi <- system.file("extdata/example.txt", package="GeneGeneInteR") dta <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t") ## Importation of the phenotype resp <- system.file("extdata/response.txt", package="GeneGeneInteR") Y <- read.csv(resp, header=FALSE)
Prior to the statistical analysis, dataset can be modified by applying filters to the SNPs (snpMatrixScour function) or by imputing missing genotypes (imputeSnpMatrix function). A subset of genes can also be selected with the select.snps function.
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 are removed. No filtering is applied regarding missing data (call.rate=1). dta <- snpMatrixScour(dta$snpX,genes.info = dta$genes.info, min.maf = 0.05, min.eq = 1e-3, call.rate = 1) ## Imputation of the missing genotypes dta <- imputeSnpMatrix(dta$snpX,dta$genes.info) ## Selection of a subset of 12 genes dta <- selectSnps(dta$snpX, dta$genes.info, c("bub3","CDSN","Gc","GLRX","PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
Gene-based gene-gene interaction analysis can be performed by testing each pair of genes in the datatset (function GGI). 10 methods are implemented in the GeneGeneInteR package to test a pair of genes: - 6 Gene-Gene multidimensional methods - Principal Components Analysis - PCA - Canonical Correlation Analysis - CCA - Kernel Canonical Correlation Analysis - KCCA - Composite Linkage Disequilibrium - CLD - Partial Least Square Path Modeling - PLSPM - Gene-Based Information Gain Method - GBIGM - 4 Gene-Gene interaction methods based on SNP-SNP interaction testing: - Minimum p-value test - minP - Gene Association Test using Extended Simes procedure - GATES - Truncated Tail Strength test - tTS - Truncated p-value Product test - tProd .
## Testing for all pair of genes with the CLD method GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="CLD")
Visualization of the results can be performed through either a matrix display (GGI.plot) or a network output (draw.network).
## Plot of the results with default values plot(GGI.res) ## Plot of the results with a threshold and an ordering of the genes. plot(GGI.res,threshold=0.1,hclust.order=TRUE) ## Example of network with default threshold 0.05 plot(GGI.res,method="network") ## Example of network with threshold 0.01 where genes with no interaction are not plotted (plot.nointer=FALSE) plot(GGI.res,threshold=0.1,plot.nointer=FALSE,method="network")
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