RICOPILI: Rapid Imputation for COnsortias PIpeLIne

Max Lam; Swapnil Awasthi; Hunna J Watson; Jackie Goldstein; Georgia Panagiotaropoulou; Vassily Trubetskoy; Robert Karlsson; Oleksander Frei; Chun-Chieh Fan; Ward De Witte; Nina R Mota; Niamh Mullins; Kim Brügger; S Hong Lee; Naomi R Wray; Nora Skarabis; Hailiang Huang; Benjamin Neale; Mark J Daly; Manuel Mattheisen; Raymond Walters; Stephan Ripke

doi:10.1093/bioinformatics/btz633

RICOPILI: Rapid Imputation for COnsortias PIpeLIne

Bioinformatics. 2020 Feb 1;36(3):930-933. doi: 10.1093/bioinformatics/btz633.

Authors

Max Lam^{1

2

3

4

5}, Swapnil Awasthi^{1

6}, Hunna J Watson^{7

8

9}, Jackie Goldstein^{1

2}, Georgia Panagiotaropoulou^{1

6}, Vassily Trubetskoy^{1

6}, Robert Karlsson¹⁰, Oleksander Frei¹¹, Chun-Chieh Fan¹¹, Ward De Witte¹², Nina R Mota^{12

13}, Niamh Mullins¹⁴, Kim Brügger¹⁵, S Hong Lee¹⁶, Naomi R Wray^{17

18}, Nora Skarabis⁶, Hailiang Huang^{1

2}, Benjamin Neale^{1

2}, Mark J Daly^{1

2}, Manuel Mattheisen^{19

20

21}, Raymond Walters^{1

2}, Stephan Ripke^{1

2

6}

Affiliations

¹ Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
² Analytic and Translational Genetics Unit, Massachusetts General Hospital, Cambridge, MA 02114, USA.
³ Division of Psychiatry Research, The Zucker Hillside Hospital, Northwell Health, Glen Oaks, NY 11004, USA.
⁴ Research Division, Institute of Mental Health Singapore, Singapore 539747, Singapore.
⁵ Human Genetics, Genome Institute of Singapore, Singapore 138672, Singapore.
⁶ Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, 10117 Berlin, Germany.
⁷ Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁸ Division of Paediatrics, School of Medicine, The University of Western Australia, Perth, WA 6009, Australia.
⁹ School of Psychology, Curtin University, Perth, WA 6102, Australia.
¹⁰ Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden.
¹¹ NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Ullevål Hospital, N-0424 Oslo, Norway.
¹² Department of Human Genetics, Donders Institute for Brain, Cognition and Behaviour.
¹³ Department of Psychiatry, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands.
¹⁴ Pamela Sklar Division of Psychiatric Genomics, Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
¹⁵ Department of Informatics, University of Bergen, 5020 Bergen, Norway.
¹⁶ Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, South Australia 5000, Australia.
¹⁷ Institute for Molecular Bioscience, The University of Queensland, QLD 4072, Australia.
¹⁸ Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia.
¹⁹ Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital Würzburg, Würzburg, Germany.
²⁰ Department of Clinical Neuroscience, Centre for Psychiatric Research, Karolinska Institutet, Stockholm, Sweden.
²¹ Department of Biomedicine, Aarhus University, Aarhus, Denmark.

Abstract

Summary: 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.

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.

Supplementary information: Supplementary data are available at Bioinformatics online.

Publication types

Meta-Analysis
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Genome
Genome-Wide Association Study*
Genomics
Software*

Abstract

Publication types

MeSH terms

Grants and funding