Open AccessRICOPILI: Rapid Imputation for COnsortias PIpeLIne
Author(s) -
Max Lam,
Swapnil Awasthi,
Hunna J. Watson,
Jackie Goldstein,
Γεωργία Παναγιωταροπούλου,
Vassily Trubetskoy,
Robert Karlsson,
Oleksander Frei,
Chun Fan,
Ward De Witte,
Nina Roth Mota,
Niamh Mullins,
Kim Brügger,
Sang Lee,
Naomi R. Wray,
Nora Skarabis,
Hailiang Huang,
Benjamin M. Neale,
Mark J. Daly,
Manuel Mattheisen,
Raymond Walters,
Stephan Ripke
Publication year - 2019
Publication title -
bioinformatics
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btz633
Subject(s) - computer science , genome wide association study , pipeline (software) , software portability , imputation (statistics) , data mining , benchmarking , data science , missing data , biology , machine learning , operating system , genetics , single nucleotide polymorphism , marketing , gene , genotype , business
Genome-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 (QC), imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing environments. RICOPILI was created as the Psychiatric Genomics Consortium pipeline for GWAS and adopted by other users. The pipeline features (i) technical and genomic QC 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 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.