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RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

Max Lam1,2,3,4,5, Swapnil Awasthi1,6, Hunna J Watson7,8,9

  • 1Stanley Center for Psychiatric Research, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.

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|August 9, 2019
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Summary
This summary is machine-generated.

RICOPILI is an efficient, open-source pipeline for processing large-scale genome-wide association studies (GWAS). It automates quality control, imputation, and analysis, accelerating genetic discovery for complex traits.

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Area of Science:

  • Genetics and Genomics
  • Computational Biology
  • Statistical Genetics

Background:

  • Genome-wide association studies (GWAS) are crucial for understanding complex traits.
  • Processing large-scale, multi-cohort GWAS data presents significant computational challenges.
  • Existing pipelines may lack efficiency or adaptability for diverse high-performance computing environments.

Purpose of the Study:

  • To develop RICOPILI, an open-sourced Perl-based pipeline for efficient processing of large-scale GWAS data.
  • To address challenges in quality control, imputation, and downstream analyses for multi-cohort GWAS.
  • To provide a computationally efficient and portable solution for genetic research.

Main Methods:

  • Developed RICOPILI, a Perl-based pipeline integrating automated parallelization and cluster job management.
  • Implemented features for technical and genomic quality control (QC) in various cohort types.
  • Included genome-wide phasing, imputation, association analysis, meta-analysis, polygenic risk scoring, and replication analysis.

Main Results:

  • RICOPILI demonstrates computational efficiency and portability across diverse high-performance computing (HPC) environments.
  • The pipeline successfully automates key GWAS steps, including rapid production of imputed genome-wide data.
  • Integrated meta-analysis of simulated GWAS data validates each pipeline step with visualization plots.

Conclusions:

  • RICOPILI provides a robust and flexible platform for large-scale GWAS, facilitating data interpretation and manuscript preparation.
  • Its adaptable architecture supports ongoing development and integration of new algorithms.
  • The pipeline is a valuable tool for the psychiatric genomics community and beyond, aiding genetic discovery.