The R package **VarExp** provides functions for the estimating of the percentage of phenotypic variance explained by genetic effects, interaction effects or jointly by both effects. This suite of functions are useful for meta-analysis designs where pooling individual genotype data is challenging. A pre-print article related to this work is available [here](bioRkiv link)
## Prerequisite
Library [**Rcurl**](https://cran.r-project.org/web/packages/RCurl/index.html) is required to run **VarExp**
## Installation
For now, **VarExp** can be installed only using package source. In R, after setting your working directory to *VarExp_0.1.0.tar.gz* location, type:
```r
install.packages("VarExp_0.1.0.tar.gz", repos = NULL, type = "source")
```
## Input format
Two input files are required.
* A file providing the meta-analysis results with the following mandatory columns:
+ the rs identifier of the variant
+ the chromosome number on which the variant is
+ the physical position of the variant (currently in NCBI Build B37)
+ the tested allele of the variant
+ the frequency of the allele A0
+ the regression coefficient of the main genetic effect
+ the regression coefficient of the interaction effect
```{r, echo = FALSE}
data("GWAS")
print(GWAS, row.names = FALSE)
```
* A file providing the summary statistics for the outcome and the exposure in each individual cohort included in the meta-analysis.
Mandatory columns of this file are:
+ the identifier of the cohort
+ the sample size of the cohort
+ the phenotype mean in the cohort
+ the standard deviation of the phenotype in the cohort
+ the exposure mean in the cohort
+ the standard deviation of the exposure in the cohort
```{r, echo = FALSE}
data("COHORT")
print(COHORT, row.names = FALSE)
```
Note that in the case of a binary exposure, the two latter columns can be replaced by a single column providing the count of exposed individuals in each cohort.
## Short tutorial
Data used in this tutorial are included in the ***VarExp*** package.
``` r
# Load the package
library(VarExp)
# Load the meta-analysis summary statistics file
data(GWAS)
# Load the cohort description file
data(COHORT)
# Compute the genotype correlation matrix from the reference panel
C <- getGenoCorMatrix(GWAS$RSID, GWAS$CHR, GWAS$POS, GWAS$A0, "EUR", pruning = FALSE)
# Make sure SNPs in the GWAS data and in the correlation matrix match
# Necessary if pruning = TRUE, otherwise should have no effect
GWAS <- checkInput(GWAS, colnames(C))
# Retrieve mean and variance of the exposure and the phenotype
Please note that this project is released with a [Contributor Code of Conduct](https://github.com/vincenla/VarExp/code-of-conduct.md). By participating in this project you agree to abide by its terms.
## License
This project is licensed under the MIT License - see the [LICENSE.md](https://github.com/vincentla/VarExp/) file for details
