RICOPILI: Rapid Imputation for COnsortias PIpeLIne

By Max Lam, S. Awasthi, Hunna J. Watson, J. Goldstein, G. Panagiotaropoulou, V. Trubetskoy, R. Karlsson, Oleksander Frei, C. Fan, Ward De Witte and 9 more. bioRxiv, 2019

309 citations recorded by the source corpus.

Motivation 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, imputation and downstream analyses. The pipeline is computationally efficient with portability to a wide range of high-performance computing (HPC) environments. Summary RICOPILI 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 Implementation RICOPILI 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/home Contact sripke@broadinstitute.org Supplementary information Supplementary data are available.

Subfields: Genetics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/20cb3625c2494b45db8d20bfe3951aa36be52192

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Citations in the corpus

• Comparative genetic architectures of schizophrenia in East Asian and European populations (2018, unknown): “Principal component analysis (PCA) was conducted across samples via imputed best guess genotypes to identify and remove overlapping samples across datasets, cryptic related samples and population outliers.”
• Genome-wide association studies in ancestrally diverse populations: opportunities, methods, pitfalls, and recommendations. (2019, methodology): “In all cases, it’s recommended that variants be filtered for imputation quality and allele frequency or count prior to GWAS ​(Lam et al., 2019; Winkler et al., 2014)​.”