Weighted Interaction SNP Hub R Package
First install WGCNA and dependencies:
### Installing WGCNA first install.packages(c("matrixStats", "Hmisc", "splines", "foreach", "doParallel", "fastcluster", "dynamicTreeCut", "survival")) source("http://bioconductor.org/biocLite.R") biocLite() biocLite(c("GO.db", "preprocessCore", "impute")) install.packages("WGCNA")
Then use simple installation using source:
You can also install using devtools install_github():
### Instaling devtools install.packages(c("devtools","curl", "httr")) ### Install WISH library("devtools") install_github("QSG-Group/WISH")
Finally if none of that works install dependencies manually:
### Instaling devtools install.packages(c("devtools","curl", "httr")) ### Install Rest of Dependencies install.packages(c("doParallel", "foreach","fastcluster", "Rcpp", "RcppEigen", "data.table", "corrplot", "heatmap3", "flashClust", "bigmemory", "parallel", "ggplot2")) ### Install WISH source("https://install-github.me/QSG-Group/WISH") # or library("devtools") install_github("QSG-Group/WISH")
For the reference manual see the WISH.pdf file
The files test.ped, test.tped and test_pheno.txt show how you have to structure your input files and allow you to test the comands.
For using WISH you need to have your genotype data in the plink format. See here for information on the plink data format:
Once you have a ped file you must make sure that is is 0,1,2 coded. This can be done using the plink recode function:
plink --file <input ped file> --recode12
Further you need a transposed ped file. This is create with the following command:
plink --file <input 0,1,2 coded ped file> --recode --transpose
The next step is to generat a genotype matrix in R using thegenerat.genotype() function. This will transform the alleles into single genotypes. Note that at this stage you should give a list of selected SNPs IDs or associated p-values for filtering if you have not previously filtered your data (see the generate.genotype() documentation for details on filtering. We would recomend to somewhat maximise the number of interactions calculated, with 10.000-20.000 SNPs being fairly easy with to run with ~10-15 cores, and more is possible with strong computing facilities.
We recommend prefiltering your data using a main effect filter. For example you can run a simple GWAS using plink:
plink --file <ped file basename> --linear --o <output basename>
The computed p-values can be used in later steps as filter.
Loading data into R
The functions in the WISH R package accept both filepaths or data frames as input. To load the data easily into R use following commands:
library(data.table) ped <- fread(<filepath to pedfile>, data.table = F) tped <- fread(<filepath to tpedfile>, data.table = F)
If memory load is a problem it is recommended to use the file paths, as the working enviroment is duplicated when using multiple threads. There is no strict guideline for the p-value threshold, but given fairly standard server computing facilites using a p-value that filters down to 10.000-20.000 variants is reasonable.
genotype <-generate.genotype(<input ped>,<input tped>,gwas.id=<selected list of id>,gwas.p=<p-values of input SNPs>)
warning If you have more than about 1 million SNPs you must either fast.read = F which will slow down the loading time significantly. You can also increase your stacklimit using ulimit in the command line,but do this only if you know what you are doing.
There is the option of applying LD filtering using the LD_blocks function after generating the genotype matrix. Note that this requires that the input data is sorted by chromosome and coordinate to work properly. Read the manual discription of the function for more detail.
LD_genotype<-LD_blocks(genotype) genotype <- LD_genotype$genotype
After generating the genotypes file it is recomended to run a test run to estimate run time of the epistatic interaction calculation based on available computing setup:
epistatic.correlation(<phenotype dataframe>, genotype,threads = <number of cores available> ,test=T)
This will give you an order of magnitude of the expected run time given your input, but not exact time. The next step is to run the analysis: We recommend using simple=F for better results:
correlations<-epistatic.correlation(<phenotype dataframe>, genotype,threads = <number of cores available> ,test=F,simple=F)
Once you have calculated epistatic correlations you can get a coarse grained overview of the results using the genome.interaction() function:
genome.interaction(<input tped file>, correlations)
Finally to create modules of interacting markers use the generate.modules function:
The modules object includes a large range of outputs from the network analysis.
warning If you have epistatic correlation coefficients values that are strong outliers this can heavily affect this step or even make it fail. Thus it is recommended to set those coefficients to 0, for example if we only want coefficients between 1000 and -1000:
correlations$Coefficients[correlations$Coefficients > 1000 | correlations$Coefficients < -1000] <-0
The range of desired values is individual depending on the properties of each dataset. It is also a good idea to set NA values to 0, as sometimes the linear models are not computable.
To do trait module correlation fist extract eigen values:
ME<-moduleEigengenes(genotype, colors= modules$modulecolors,softPower = modules$power.estimate)
Then we can calculate the module trait correlations:
Haja Kadarmideen, email@example.com Victor A. O Carmelo, firstname.lastname@example.org Quantitative genomics, bioinformatics and computational biology group Department of Applied Mathematics and Computer Science Technical University of Denmark
Lisette J.A. Kogelman and Haja N.Kadarmideen (2014).
Weighted Interaction SNP Hub (WISH) network method for building genetic
Networks for complex diseases and traits using whole genome genotype data.
BMC Systems Biology 8(Suppl 2):S5.
Victor A. O. Carmelo, Lisette J. A. Kogelman, Majbritt Busk Madsen & Haja N. Kadarmideen(2018) WISH-R– a fast and efficient tool for construction of epistatic networks for complex traits and diseases BMC Bioinformatics volume 19, Article number: 277 https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2291-2