RICOPILI: Rapid Imputation for COnsortias PIpeLIne.
paper
Cited
Public
- Authors
- Lam, Max; Awasthi, Swapnil; Watson, Hunna J; Goldstein, Jackie; Panagiotaropoulou, Georgia; Trubetskoy, Vassily; Karlsson, Robert; Frei, Oleksander; Fan, Chun-Chieh; De Witte, Ward; Mota, Nina R; Mullins, Niamh; BrΓΌgger, Kim; Lee, S Hong; Wray, Naomi R; Skarabis, Nora; Huang, Hailiang; Neale, Benjamin; Daly, Mark J; Mattheisen, Manuel; Walters, Raymond; Ripke, Stephan
- Year
- 2020
- Journal
- Bioinformatics (Oxford, England)
- PMID
- 31393554
- DOI
- 10.1093/bioinformatics/btz633
- PMCID
- PMC7868045
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.
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| A polygenic risk score analysis of ASD and ADHD across emotion recognition subtypes. | Waddington F et al. | β | 2021 | β |
| BIGwas: Single-command quality control and association testing for multi-cohort and biobank-scale GWAS/PheWAS data. | KΓ€ssens JC et al. | β | 2021 | β |
| Cerebrospinal fluid proteomics targeted for central nervous system processes in bipolar disorder. | GΓΆteson A et al. | β | 2021 | β |
| Dissecting the Association Between Inflammation, Metabolic Dysregulation, and Specific Depressive Symptoms: A Genetic Correlation and 2-Sample Mendelian Randomization Study. | Kappelmann N et al. | β | 2021 | β |
| Functional co-activation of the default mode network in APOE Ξ΅4-carriers: A replication study. | Mentink LJ et al. | β | 2021 | β |
| Genetic risk of clozapine-induced leukopenia and neutropenia: a genome-wide association study. | Chen J et al. | β | 2021 | β |
| Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. | Mullins N et al. | β | 2021 | β |
| Genome-wide association study of patients with a severe major depressive episode treated with electroconvulsive therapy. | Clements CC et al. | β | 2021 | β |
| Investigating an in silico approach for prioritizing antidepressant drug prescription based on drug-induced expression profiles and predicted gene expression. | Shoaib M et al. | β | 2021 | β |
| Large-scale collaboration in ENIGMA-EEG: A perspective on the meta-analytic approach to link neurological and psychiatric liability genes to electrophysiological brain activity. | Smit DJA et al. | β | 2021 | β |
| Leveraging both individual-level genetic data and GWAS summary statistics increases polygenic prediction. | AlbiΓ±ana C et al. | β | 2021 | β |
| Mapping relationships between ADHD genetic liability, stressful life events, and ADHD symptoms in healthy adults. | Li T et al. | β | 2021 | β |
| Neuropsychiatric Genetics of Psychosis in the Mexican Population: A Genome-Wide Association Study Protocol for Schizophrenia, Schizoaffective, and Bipolar Disorder Patients and Controls. | Camarena B et al. | β | 2021 | β |
| No evidence that vitamin D is able to prevent or affect the severity of COVID-19 in individuals with European ancestry: a Mendelian randomisation study of open data. | Amin HA et al. | β | 2021 | β |
| Polygenic Heterogeneity Across Obsessive-Compulsive Disorder Subgroups Defined by a Comorbid Diagnosis. | Strom NI et al. | β | 2021 | β |
| Polygenic risk scores for alcohol involvement relate to brain structure in substance-naΓ―ve children: Results from the ABCD study. | Hatoum AS et al. | β | 2021 | β |
| Potential Genetic Overlap Between Insomnia and Sleep Symptoms in Major Depressive Disorder: A Polygenic Risk Score Analysis. | Melhuish Beaupre LM et al. | β | 2021 | β |
| Relationships between apparent cortical thickness and working memory across the lifespan - Effects of genetics and socioeconomic status. | Krogsrud SK et al. | β | 2021 | β |
| Risk variants and polygenic architecture of disruptive behavior disorders in the context of attention-deficit/hyperactivity disorder. | Demontis D et al. | β | 2021 | β |
| Sex-specific effects of polygenic risk for schizophrenia on lifespan cognitive functioning in healthy individuals. | Koch E et al. | β | 2021 | β |
| Synaptic processes and immune-related pathways implicated in Tourette syndrome. | Tsetsos F et al. | β | 2021 | β |
| The role of glucocorticoid receptor gene in the association between attention deficit-hyperactivity disorder and smaller brain structures. | Bandeira CE et al. | β | 2021 | β |
| Aggression based genome-wide, glutamatergic, dopaminergic and neuroendocrine polygenic risk scores predict callous-unemotional traits. | Ruisch IH et al. | β | 2020 | β |
| A large-scale genome-wide association study meta-analysis of cannabis use disorder. | Johnson EC et al. | β | 2020 | β |
| Genetic comorbidity between major depression and cardio-metabolic traits, stratified by age at onset of major depression. | Hagenaars SP et al. | β | 2020 | β |
| Genetic Liability for Depression, Social Factors and Their Interaction Effect in Depressive Symptoms and Depression Over Time in Older Adults. | Stringa N et al. | β | 2020 | β |
| Genetic liability to major depression and risk of childhood asthma. | Liu X et al. | β | 2020 | β |
| Genome-wide meta-analysis of problematic alcohol use in 435,563 individuals yields insights into biology and relationships with other traits. | Zhou H et al. | β | 2020 | β |
| Genomic influences on self-reported childhood maltreatment. | Dalvie S et al. | β | 2020 | β |
| Heterogeneity and Polygenicity in Psychiatric Disorders: A Genome-Wide Perspective. | Wendt FR et al. | β | 2020 | β |
| Integrative Genomic Enrichment Analysis Identified the Brain Regions and Development Stages Related to Anorexia Nervosa and Obsessive-Compulsive Disorder. | Cheng B et al. | β | 2020 | β |
| Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium. | Polimanti R et al. | β | 2020 | β |
| Minimal phenotyping yields genome-wide association signals of low specificity for major depression. | Cai N et al. | β | 2020 | β |
| Polygenic Risk Score Contribution to Psychosis Prediction in a Target Population of Persons at Clinical High Risk. | Perkins DO et al. | β | 2020 | β |
| The Longitudinal Aging Study Amsterdam: cohort update 2019 and additional data collections. | Hoogendijk EO et al. | β | 2020 | β |
| Comparative genetic architectures of schizophrenia in East Asian and European populations. | Lam M et al. | β | 2019 | β |
| Genetic risk scores for major psychiatric disorders and the risk of postpartum psychiatric disorders. | Bauer AE et al. | β | 2019 | β |