README.md

In transbioZI/Gimpute: Gimpute: An efficient genetic data processing and imputation pipeline

Gimpute: An efficient genetic data processing and imputation pipeline

Function

In order to ensure the reliability and reproducibility of genome-wide association study (GWAS) data, and enable meta-analysis across cohorts from different genotyping arrays. we set up an efficient, automatic and comprehensive genotype data processing and imputation pipeline termed Gimpute. It consists of pre-processing (genetic variant information updating/matching/liftOver, quality control of genetic variants and study samples, the alignment of variants to the imputation references), pre-phasing and imputation, as well as post-imputation quality control.

Installation

Install Gimpute in R: ```{r eval=FALSE} install.packages("devtools") library("devtools") install_github("transbioZI/Gimpute", build_vignettes=TRUE)

Gimpute runs on any 64-bit x86 Linux distribution. Additional dependencies are described in the [tutorial](https://github.com/transbioZI/Gimpute/blob/master/vignettes/GimputeTutorial.Rmd).

### Tutorial
The detailed instruction is explained in the [Gimpute tutorial](https://github.com/transbioZI/Gimpute/blob/master/vignettes/GimputeTutorial.Rmd), along with a complete [running example](https://github.com/transbioZI/Gimpute/blob/master/tests/runTests.R). 

The best view of the tutorial is in HTML format with a table of contents by executing the following R codes after you have downloaded the tutorial (GimputeTutorial.Rmd in vignettes directory).

```{r eval=FALSE}
install.packages("rmarkdown")
library("rmarkdown")
render("GimputeTutorial.Rmd")

Citation

Chen, J., et al. (2018). Gimpute: an efficient genetic data imputation pipeline. Bioinformatics.

transbioZI/Gimpute documentation built on April 10, 2022, 4:20 a.m.