RICOPILI: Rapid Imputation for COnsortias PIpeLIne

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 Contact sripke@broadinstitute.org Supplementary information Supplementary data are available at Bioinformatics online.

Towards Reproducible Brain-Wide Association Studies

“…For example, genomics researchers realized that a handful of well-characterized genes were likely false discoveries resulting from underpowered studies 21 . Large consortia efforts in genomics have led to sample sizes exceeding 1 million individuals 36 with standardized methodological pipelines 37 . These large discovery sets are used to train whole-genome models that are subsequently used to answer more detailed questions in independent samples 18 .…”

Genetic comorbidity between major depression and cardio‐metabolic traits, stratified by age at onset of major depression

“…PGC cohorts were genotyped following their local protocols. Individual genotype data for all PGC cohorts was processed using the PGC "Ricopili" pipeline for standardized quality control, imputation and analysis (https://github.com/Nealelab/ricopili) (Lam et al, 2019).…”

Minimal phenotyping yields genome-wide association signals of low specificity for major depression

“…We obtained permissions from 20 cohorts with sample sizes greater than 500, among which 17 recorded endorsement of DSM-5 criteria A for MDD (Supplementary Note and Supplementary Table 21). We obtained PRS from GWAS for each definition of depression in UK Biobank, using LD-clumped (LD r 2 < 0.1) independent SNPs with P values of associations below 8 thresholds (P < 10 -4 , 0.001, 0.01, 0.05, 0.1, 0.2, 0.5 and 1), and predicted MDD status in the 20 PGC cohorts using the Ricopili pipeline 82 . We obtained Nagelkerke's r 2 between the PRS and MDD status, AUC of the prediction, and variance of MDD status explained by the PRS for each cohort.…”