Department of Economics and Business Economics

JASS: command line and web interface for the joint analysis of GWAS results

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DOI

  • Hanna Julienne, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Pierre Lechat, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Vincent Guillemot, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Carla Lasry, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Chunzi Yao, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Robinson Araud, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Vincent Laville, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Bjarni Vilhjalmsson
  • Hervé Ménager, Department of Computational Biology-USR 3756 CNRS, Institut Pasteur, 75015 Paris, France.
  • ,
  • Hugues Aschard, Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. erobinso@hsph.harvard.edu.

Genome-wide association study (GWAS) has been the driving force for identifying association between genetic variants and human phenotypes. Thousands of GWAS summary statistics covering a broad range of human traits and diseases are now publicly available. These GWAS have proven their utility for a range of secondary analyses, including in particular the joint analysis of multiple phenotypes to identify new associated genetic variants. However, although several methods have been proposed, there are very few large-scale applications published so far because of challenges in implementing these methods on real data. Here, we present JASS (Joint Analysis of Summary Statistics), a polyvalent Python package that addresses this need. Our package incorporates recently developed joint tests such as the omnibus approach and various weighted sum of Z-score tests while solving all practical and computational barriers for large-scale multivariate analysis of GWAS summary statistics. This includes data cleaning and harmonization tools, an efficient algorithm for fast derivation of joint statistics, an optimized data management process and a web interface for exploration purposes. Both benchmark analyses and real data applications demonstrated the robustness and strong potential of JASS for the detection of new associated genetic variants. Our package is freely available at https://gitlab.pasteur.fr/statistical-genetics/jass.

Original languageEnglish
Article numberlqaa003
JournalNAR genomics and bioinformatics
Volume2
Issue1
Number of pages12
ISSN2631-9268
DOIs
Publication statusPublished - Mar 2020

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