What is JASS?
JASS is a python package that handles the computation of joint statistics over sets of selected GWAS results, and the interactive exploration of the results through a web interface or static graph (https://jass.pasteur.fr/index.html). JASS is a highly versatile tool that can be used either as :
an online web service, or
a command line tool that one can use independently for development purposes.
More precisely, The generation of joint statistics over a set of selected studies, and the generation of static plots to display the results, is easily performed using the command line interface. These functionalities can also be accessed through a web application embedded in the python package, which also enables the exploration of the results through a dynamic JavaScript interface.The JASS analysis module handles the data processing, going from the import of the data up to the computation of the joint statistics and the generation of the various static plots to illustrate the results.
In this documentation, we cover first the steps required for installing the software, and illustrate its usage through a reproducible example to guide the users.
We also briefly describe in the next section the pre-processing of raw GWAS data which can be performed through a companion script provided on behalf of the JASS package.
For method details and application inspiration check out our publications with JASS or its accompanying packages (RAISS):
JASS application paper [JLM+21]
JASS computational architecture [JLG+20]
RAISS [JSPA19]
Tomaz Berisa and Joseph K. Pickrell. Approximately independent linkage disequilibrium blocks in human populations. Bioinformatics, 32(2):283–285, 2015. doi:10.1093/bioinformatics/btv546.
Hanna Julienne, Vincent Laville, Zachary R McCaw, Zihuai He, Vincent Guillemot, Carla Lasry, Andrey Ziyatdinov, Cyril Nerin, Amaury Vaysse, Pierre Lechat, and others. Multitrait gwas to connect disease variants and biological mechanisms. PLoS genetics, 17(8):e1009713, 2021.
Hanna Julienne, Pierre Lechat, Vincent Guillemot, Carla Lasry, Chunzi Yao, Robinson Araud, Vincent Laville, Bjarni Vilhjalmsson, Hervé Ménager, and Hugues Aschard. Jass: command line and web interface for the joint analysis of gwas results. NAR Genomics and Bioinformatics, 2(1):lqaa003, 2020.
Hanna Julienne, Huwenbo Shi, Bogdan Pasaniuc, and Hugues Aschard. RAISS: robust and accurate imputation from summary statistics. Bioinformatics, 35(22):4837–4839, 06 2019. URL: https://doi.org/10.1093/bioinformatics/btz466, arXiv:https://academic.oup.com/bioinformatics/article-pdf/35/22/4837/30706731/btz466.pdf, doi:10.1093/bioinformatics/btz466.
Florian Privé. Optimal linkage disequilibrium splitting. Bioinformatics, 38(1):255–256, 07 2021. URL: https://doi.org/10.1093/bioinformatics/btab519, arXiv:https://academic.oup.com/bioinformatics/article-pdf/38/1/255/41891000/btab519.pdf, doi:10.1093/bioinformatics/btab519.
Yuka Suzuki, Hervé Ménager, Bryan Brancotte, Raphaël Vernet, Cyril Nerin, Christophe Boetto, Antoine Auvergne, Christophe Linhard, Rachel Torchet, Pierre Lechat, and others. Trait selection strategy in multi-trait gwas: boosting snps discoverability. bioRxiv, 2023.