RICOPILI: Rapid Imputation for COnsortias PIpeLIne
| Autor: | Manuel Mattheisen, Benjamin M. Neale, Georgia Panagiotaropoulou, Oleksandr Frei, Jackie Goldstein, Max Lam, Nora Skarabis, Hunna J. Watson, Nina Roth Mota, W. De Witte, Chun Chieh Fan, Stephan Ripke, Vassily Trubetskoy, Swapnil Awasthi, Niamh Mullins, Hailiang Huang, Raymond K. Walters, Mark J. Daly, Robert Karlsson |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
Database Computer science Genomics Correlation and dependence Genome-wide association study computer.software_genre 03 medical and health sciences 0302 clinical medicine Polygenic risk score Imputation (statistics) computer 030217 neurology & neurosurgery Imputation (genetics) 030304 developmental biology Genetic association |
| DOI: | 10.1101/587196 |
| Popis: | MotivationGenome-wide association study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biological insights for several complex traits. RICOPILI, an open sourced Perl-based pipeline was developed to address the challenges of rapidly processing large scale multi-cohort GWAS studies including quality control, imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing (HPC) environments.SummaryRICOPILI was created as the Psychiatric Genomics Consortium (PGC) pipeline for GWAS and has been adopted by other users. The pipeline features i) technical and genomic quality control in case-control and trio cohorts ii) genome-wide phasing and imputation iv) association analysis v) meta-analysis vi) polygenic risk scoring and vii) replication analysis. Notably, a major differentiator from other GWAS pipelines, RICOPILI leverages on automated parallelization and cluster job management approaches for rapid production of imputed genome-wide data. A comprehensive meta-analysis of simulated GWAS data has been incorporated demonstrating each step of the pipeline. This includes all of the associated visualization plots, to allow ease of data interpretation and manuscript preparation. Simulated GWAS datasets are also packaged with the pipeline for user training tutorials and developer work.Availability and ImplementationRICOPILI has a flexible architecture to allow for ongoing development and incorporation of newer available algorithms and is adaptable to various HPC environments (QSUB, BSUB, SLURM and others). Specific links for genomic resources are either directly provided in this paper or via tutorials and external links. The central location hosting scripts and tutorials is found at this URL:https://sites.google.com/a/broadinstitute.org/RICOPILI/homeContactsripke@broadinstitute.orgSupplementary informationSupplementary data are available. |
| Databáze: | OpenAIRE |
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