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client/pages/index.vue

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-          The JASS web interface efficiently compute multi-trait genome-wide association study (GWAS) and enable user to interactively explore results.
-          Multi-trait GWAS can increase statistical power by leveraging pleiotropy, but also can deepen our understanding of SNPs functional effect (for a detailed explanation see the citation box below).
-          Currently this website host a total of <b>{{ getTotalPhenotypes.toLocaleString() }}</b> phenotypes available from <b>{{ getTotalTables.toLocaleString() }}</b> ancestries for analysis with the Omnibus test.
-          All GWAS have been pre-processed using the <a href="https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline">JASS pre-processing pipeline</a> and imputation of missing statistics has been conducted using the <a href="https://gitlab.pasteur.fr/statistical-genetics/raiss">RAISS software</a>, resulting in a total of <b>{{ getTotalSNP.toLocaleString() }}</b> SNPs available for analysis.
-          To analyze data in your own facility and/or access supplementary joint analysis tests, please download and install the <a href="https://statistical-genetics.pages.pasteur.fr/jass/">JASS python package.</a>
+          During the past decade, the statistical genetic community discovered and replicated thousands of variant-trait associations in human populations. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes.
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+          In practice, conducting multi-trait GWAS can become a hurdle due to missing values, heterogeneous data format, and computational cost. JASS (Joint Analysis of Summary Statistics) webserver solves all these practical barriers allowing investigators to conduct multi-trait GWAS interactively. Through this website, you can investigate <b>{{ getTotalPhenotypes.toLocaleString() }}</b> traits spanning several clinical domains and <b>{{ getTotalTables.toLocaleString() }}</b> ancestries. The results page displays genetic correlation matrices between traits, downloadable Manhattan plots and new associations table.
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+          Our databases have been curated and imputed using the python package <a href="https://gitlab.pasteur.fr/statistical-genetics/jass_preprocessing">JASS preprocessing</a> (data QC and harmonization) and <a href="https://gitlab.pasteur.fr/statistical-genetics/raiss">RAISS</a> (imputation of summary statistics). To conduct multi-trait GWAS on your own data refers to the <a href="https://statistical-genetics.pages.pasteur.fr/jass/">JASS python package documentation</a>. A Nextflow pipeline encapsulating summary statistic curation, imputation, and multi-trait GWAS is available <a href="https://gitlab.pasteur.fr/statistical-genetics/jass_suite_pipeline">here</a>.
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