GCPBayes pipeline: a tool for exploring pleiotropy at the gene level
| dc.rights.license | open | en_US |
| dc.contributor.author | ASGARI, Yazdan | |
| dc.contributor.author | SUGIER, Pierre-Emmanuel | |
| hal.structure.identifier | Bordeaux population health [BPH] | |
| dc.contributor.author | BAGHFALAKI, Taban | |
| dc.contributor.author | LUCOTTE, Elise | |
| dc.contributor.author | KARIMI, Mojgan | |
| dc.contributor.author | SEDKI, Mohammed | |
| dc.contributor.author | NGO, Amélie | |
| dc.contributor.author | LIQUET, Benoit | |
| dc.contributor.author | TRUONG, Thérèse | |
| dc.date.accessioned | 2023-11-07T14:01:53Z | |
| dc.date.available | 2023-11-07T14:01:53Z | |
| dc.date.issued | 2023-09-01 | |
| dc.identifier.issn | 2631-9268 | en_US |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/184659 | |
| dc.description.abstractEn | Cross-phenotype association using gene-set analysis can help to detect pleiotropic genes and inform about common mechanisms between diseases. Although there are an increasing number of statistical methods for exploring pleiotropy, there is a lack of proper pipelines to apply gene-set analysis in this context and using genome-scale data in a reasonable running time. We designed a user-friendly pipeline to perform cross-phenotype gene-set analysis between two traits using GCPBayes, a method developed by our team. All analyses could be performed automatically by calling for different scripts in a simple way (using a Shiny app, Bash or R script). A Shiny application was also developed to create different plots to visualize outputs from GCPBayes. Finally, a comprehensive and step-by-step tutorial on how to use the pipeline is provided in our group's GitHub page. We illustrated the application on publicly available GWAS (genome-wide association studies) summary statistics data to identify breast cancer and ovarian cancer susceptibility genes. We have shown that the GCPBayes pipeline could extract pleiotropic genes previously mentioned in the literature, while it also provided new pleiotropic genes and regions that are worthwhile for further investigation. We have also provided some recommendations about parameter selection for decreasing computational time of GCPBayes on genome-scale data. | |
| dc.language.iso | EN | en_US |
| dc.rights | Attribution-NonCommercial 3.0 United States | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/us/ | * |
| dc.title.en | GCPBayes pipeline: a tool for exploring pleiotropy at the gene level | |
| dc.title.alternative | NAR Genom Bioinform | en_US |
| dc.type | Article de revue | en_US |
| dc.identifier.doi | 10.1093/nargab/lqad065 | en_US |
| dc.subject.hal | Sciences du Vivant [q-bio]/Santé publique et épidémiologie | en_US |
| dc.identifier.pubmed | 37416786 | en_US |
| dc.description.sponsorshipEurope | European Union’s Horizon 2020 research and innovation programme | en_US |
| bordeaux.journal | NAR Genomics and Bioinformatics | en_US |
| bordeaux.page | lqad065 | en_US |
| bordeaux.volume | 5 | en_US |
| bordeaux.hal.laboratories | Bordeaux Population Health Research Center (BPH) - UMR 1219 | en_US |
| bordeaux.issue | 3 | en_US |
| bordeaux.institution | Université de Bordeaux | en_US |
| bordeaux.institution | INSERM | en_US |
| bordeaux.team | BIOSTAT | en_US |
| bordeaux.peerReviewed | oui | en_US |
| bordeaux.inpress | non | en_US |
| bordeaux.identifier.funderID | Ligue Contre le Cancer | en_US |
| bordeaux.import.source | pubmed | |
| hal.popular | non | en_US |
| hal.audience | Internationale | en_US |
| hal.export | false | |
| workflow.import.source | pubmed | |
| dc.rights.cc | Pas de Licence CC | en_US |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=NAR%20Genomics%20and%20Bioinformatics&rft.date=2023-09-01&rft.volume=5&rft.issue=3&rft.spage=lqad065&rft.epage=lqad065&rft.eissn=2631-9268&rft.issn=2631-9268&rft.au=ASGARI,%20Yazdan&SUGIER,%20Pierre-Emmanuel&BAGHFALAKI,%20Taban&LUCOTTE,%20Elise&KARIMI,%20Mojgan&rft.genre=article |
