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The JASS web interface allows for a fast derivation of multi-trait genome-wide association study (GWAS) summary statistics.
JASS can increase statistical power by leveraging pleiotropy, but also can deepen our understanding of SNPs functional effect (for a detailed explanation see the tab citation).
The current online version of JASS allows for a multi-trait GWAS from ~150 GWAS using the omnibus test (see next page for the complete list).
All GWAS have been pre-processed using the <ahref="https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline">JASS pre-processing pipeline</a> and imputation of missing statistics has been conducted using the <ahref="https://gitlab.pasteur.fr/statistical-genetics/raiss">RAISS software</a>, resulting in a total of XXXX SNPs available for analysis.
To analyze data in your own facility and/or access supplementary joint analysis tests, please download and install the JASS python package.
Run JASS on a set of traits genome wide, Explore result interactively and download results summary or full genome wide results.
Can take up to ~30 to run depending on the number of trait selected.
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Run Genome Wide Analysis
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<v-card-title>Region Analysis</v-card-title>
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Run JASS on a region of interest, Explore result interactively and download results summary or full genome wide results.
Will run quick
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Run Region Analysis
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<h2> About </h2>
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Jass is developed by the Statistical Genetics group in collaboration with the HUB
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<v-card-title>Cite JASS</v-card-title>
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When using JASS, please cite the following two papers. If your analysis relies on public summary statistic please include the original publication in references.
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<ul>
<li>
JASS: command line and web interface for the joint analysis of GWAS results
Hanna Julienne, Pierre Lechat, Vincent Guillemot, Carla Lasry, Chunzi Yao, Robinson Araud, Vincent Laville, Bjarni Vilhjalmsson, Hervé Ménager, Hugues Aschard
in: NAR Genomics and Bioinformatics, Volume 2, Issue 1, March 2020, lqaa003, https://doi.org/10.1093/nargab/lqaa003
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<li>
Multitrait genetic-phenotype associations to connect disease variants and biological mechanisms
Hanna Julienne, Vincent Laville, Zachary R. McCaw, Zihuai He, Vincent Guillemot, Carla Lasry, Andrey Ziyatdinov, Amaury Vaysse, Pierre Lechat, Hervé Ménager, Wilfried Le Goff, Marie-Pierre Dube, Peter Kraft, Iuliana Ionita-Laza, Bjarni J. Vilhjálmsson, Hugues Aschard
