TY - JOUR
T1 - GCPBayes pipeline
T2 - a tool for exploring pleiotropy at the gene level
AU - Asgari, Yazdan
AU - Sugier, Pierre Emmanuel
AU - Baghfalaki, Taban
AU - Lucotte, Elise
AU - Karimi, Mojgan
AU - Sedki, Mohammed
AU - Ngo, Amélie
AU - Liquet, Benoit
AU - Truong, Thérèse
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85165208735&partnerID=8YFLogxK
U2 - 10.1093/nargab/lqad065
DO - 10.1093/nargab/lqad065
M3 - Article
AN - SCOPUS:85165208735
SN - 2631-9268
VL - 5
JO - NAR Genomics and Bioinformatics
JF - NAR Genomics and Bioinformatics
IS - 3
M1 - lqad065
ER -