Odyssey: A semi-automated pipeline for phasing, imputation, and analysis of genome-wide genetic data

Abstract

Background: Genome imputation, admixture resolution and genome-wide association analyses are timely and computationally intensive processes with many composite and requisite steps. Analysis time increases further when building and installing the run programs required for these analyses. For scientists that may not be as versed in programing language, but want to perform these operations hands on, there is a lengthy learning curve to utilize the vast number of programs available for these analyses. Results: In an effort to streamline the entire process with easy-to-use steps for scientists working with big data, the Odyssey pipeline was developed. Odyssey is a simplified, efficient, semi-automated genome-wide imputation and analysis pipeline, which prepares raw genetic data, performs pre-imputation quality control, phasing, imputation, post-imputation quality control, population stratification analysis, and genome-wide association with statistical data analysis, including result visualization. Odyssey is a pipeline that integrates programs such as PLINK, SHAPEIT, Eagle, IMPUTE, Minimac, and several R packages, to create a seamless, easy-to-use, and modular workflow controlled via a single user-friendly configuration file. Odyssey was built with compatibility in mind, and thus utilizes the Singularity container solution, which can be run on Linux, MacOS, and Windows platforms. It is also easily scalable from a simple desktop to a High-Performance System (HPS). Conclusion: Odyssey facilitates efficient and fast genome-wide association analysis automation and can go from raw genetic data to genome: phenome association visualization and analyses results in 3-8 h on average, depending on the input data, choice of programs within the pipeline and available computer resources. Odyssey was built to be flexible, portable, compatible, scalable, and easy to setup. Biologists less familiar with programing can now work hands on with their own big data using this easy-to-use pipeline.