The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
RICOPILI: Rapid Imputation for COnsortias PIpeLIne
Open accessPublished version of a preprint
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“We used FUSION44 and EpiXcan45 as tests of robustness of the SMR results.”
From Mapping genomic loci implicates genes and synaptic biology in schizophrenia (2022) · cites this paper as [44]
In the citing paper
“We performed systematic quality control on the raw genotyping data to filter out both unqualified samples and SNPs using the Ricopili pipeline for case-control groups43.”
From A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression (2023) · cites this paper as [43]
In the citing paper
“Association analyses of each cohort were performed with logistic regression in the Ricopili pipeline and age, sex and ten principal components were used as covariates in the association analysis to correct for the population stratification43.”
From A regulatory variant at 19p13.3 is associated with primary biliary cholangitis risk and ARID3A expression (2023) · cites this paper as [43]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
Self-citationA global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
Self-citationPrincipal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Self-citationBiological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577
LD Score regression distinguishes confounding from polygenicity in genome-wide association studies
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Bulik-Sullivan, B. K., Loh, P.-R., Finucane, H. K., Ripke, S., Yang, J., Patterson, N., Daly, M. J., Price, A. L., & Neale, B. M. (2015) · Nature Genetics · doi:10.1038/ng.3211 · Cited by 5,559
Self-citationMETAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205
Howie, B. N., Donnelly, P., & Marchini, J. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000529 · Cited by 3,928
Next-generation genotype imputation service and methods
Das, S., Forer, L., Schönherr, S., Sidore, C., Locke, A. E., Kwong, A., Vrieze, S. I., Chew, E. Y., Levy, S., McGue, M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3656 · Cited by 3,808
A reference panel of 64,976 haplotypes for genotype imputation
the Haplotype Reference Consortium (2016) · Nature Genetics · doi:10.1038/ng.3643 · Cited by 2,836
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479
A linear complexity phasing method for thousands of genomes
Delaneau, O., Marchini, J., & Zagury, J.-F. (2012) · Nature Methods · doi:10.1038/nmeth.1785 · Cited by 1,850
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737
Genetic architectures of psychiatric disorders: the emerging picture and its implications
Sullivan, P. F., Daly, M. J., & O'Donovan, M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3240 · Cited by 1,177
Self-citationHou, L., Kellis, M., Liu, Y., Park, Y., & Rinaldi, N. J. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.34944/dspace/4648 · Cited by 1,082
Psychiatric Genomics: An Update and an Agenda
Sullivan, P. F., Agrawal, A., Bulik, C. M., Andreassen, O. A., Børglum, A. D., Breen, G., Cichon, S., Edenberg, H. J., Faraone, S. V., Gelernter, J., et al. (2018) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17030283 · Cited by 676
HAPGEN2: simulation of multiple disease SNPs
Su, Z., Marchini, J., & Donnelly, P. (2011) · Bioinform. · doi:10.1093/bioinformatics/btr341 · Cited by 352
Fast and accurate long-range phasing in a UK Biobank cohort
Loh, P.-R., Palamara, P. F., & Price, A. L. (2016) · Nature Genetics · doi:10.1038/ng.3571 · Cited by 342
Translating genome-wide association findings into new therapeutics for psychiatry
Breen, G. D., Li, Q. S., Roth, B. L., O’Donnell, P., Didriksen, M., Dolmetsch, R. E., O’Reilly, P. F., Gaspar, H. A., Manji, H. K., Hüebel, C., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4411 · Cited by 116
Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69
Flagged first, then by citations
Cited by
Sorted by citations
Mapping genomic loci implicates genes and synaptic biology in schizophrenia
Trubetskoy, V., Pardiñas, A. F., Qi, T., Panagiotaropoulou, G., Awasthi, S., Bigdeli, T. B., Bryois, J., Chen, C.-Y., Dennison, C. A., Hall, L. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04434-5 · Cited by 2,964
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849
Mullins, N., Forstner, A. J., O’Connell, K. S., Coombes, B., Coleman, J. R. I., Qiao, Z., Als, T. D., Bigdeli, T. B., Børte, S., Bryois, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00857-4 · Cited by 1,517
Self-citationImproving polygenic prediction in ancestrally diverse populations
Ruan, Y., Lin, Y.-F., Feng, Y.-C. A., Chen, C.-Y., Lam, M., Guo, Z., Initiatives, S. G. A., Ahn, Y. M., Akiyama, K., Arai, M., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01054-7 · Cited by 707
Self-citationComparative genetic architectures of schizophrenia in East Asian and European populations
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Lam, M., Indonesia Schizophrenia Consortium, Genetic REsearch on schizophreniA neTwork-China and the Netherlands (GREAT-CN), Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0512-x · Cited by 655
Self-citation
Metadata
- Authors
- Lam, M., Awasthi, S., Watson, H. J., Goldstein, J., Panagiotaropoulou, G., Trubetskoy, V., Karlsson, R., Frei, O., Fan, C.-C., De Witte, W., Mota, N. R., Mullins, N., Brügger, K., Lee, S. H., Wray, N. R., Skarabis, N., Huang, H., Neale, B., Daly, M. J., Mattheisen, M., Walters, R., & Ripke, S.
- Affiliations
- Broad Institute (US)
- University of North Carolina at Chapel Hill (US)
- Karolinska Institutet (SE)
- University of Oslo (NO)
- Department of Human Genetics, Donders Institute for Brain , Cognition and Behaviour
- Radboud University Nijmegen (NL)
- Icahn School of Medicine at Mount Sinai (US)
- University of Bergen (NO)
- University of South Australia (AU)
- The University of Queensland (AU)
- Charité – Universitätsmedizin Berlin (DE)
- Aarhus University (DK)
- Corresponding author
- Ripke, Stephan
- ORCID
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-08
- Volume
- 36
- Issue
- 3
- Pages
- 930-933
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC7868045
- OpenAlex
- W2967846176
- Cited by
- 315
- References
- 20
- Citations by year
- 2026: 39 · 2025: 49 · 2024: 56 · 2023: 57 · 2022: 47 · 2021: 36 · 2020: 23 · 2019: 7
- FWCI
- 17.01
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by-nc)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion, cc-by-nc)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by-nc)
- Radboud Repository (Radboud University) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (U01 MH109528, U01 MH1095320)
- National Medical Research Council (NMRC/TCR/003, MH095: 003/008-1014, NMRC/CG/004/2013, NMRC/SEEDFD/019/2017)
- NIH/NIMH (U01MH109528)
- Broad Institute of MIT and Harvard (691099)
- German Research Foundation (RI2846/1-1)
- NIH
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genotype imputation, quality control, psychiatric genomics, polygenic risk scoring, replication analysis, meta-analysis, high-performance computing, parallelization, Perl
- MeSH terms
- Algorithms, Software, Genome, Genomics, Genome-Wide Association Study
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
