diff --git a/client/assets/css/global.css b/client/assets/css/global.css
index ebeb0bc0479742ccb74fad159b586b9165b42f42..db428185c471606f2b69e0bf167a3b5cff8dac2d 100644
--- a/client/assets/css/global.css
+++ b/client/assets/css/global.css
@@ -92,7 +92,6 @@
   width: 75%;
   margin: 0 auto;
   padding: 30px 0px 30px 0px;
-  text-align: center !important;
 }
 
 .card-title {
diff --git a/client/pages/index.vue b/client/pages/index.vue
index facaef2744c9f903f3adad0ce396e948c8927959..f79463c3c71682def4899b20a3b25995c67eb532 100644
--- a/client/pages/index.vue
+++ b/client/pages/index.vue
@@ -1,12 +1,14 @@
 <template>
   <div>
-    <h1 class="main_title"> Joint Analysis of Summary Statistics </h1>
+    <h1 class="main_title"> Welcome to JASS v2.1, a web interface for the Joint Analysis of GWAS Summary Statistics</h1>
     <p class="main_intro">
-      The JASS web interface efficiently compute multi-trait genome-wide association study (GWAS) and enable user to interacively explore results.
-      <br>JASS 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).
-      <br>Currently this website host <b> {{initmeta.nb_phenotypes}} traits </b>available 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>{{initmeta.nb_snps}} SNPs</b> 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.
+      The JASS web interface allows for a fast derivation of the joint tests of selected genome-wide association study (GWAS) summary statistics. The current version allows to perform two multivariate tests, 
+      <ol><li>an omnibus K degree of freedom test, and </li>
+      <li>an 1 degree of freedom weighted sum of individual statistics with weights defined as the loadings of the first principal component of the genetic correlation matrix. </li> 
+      </ol>
+      For more advance applications, please use the command line version. For details on the methodology, please refer to the citations below.
+      <br>
+      <br>The online version currently includes XXX publicly available GWAS classified into XX biological categories. 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>{{initmeta.nb_snps}} SNPs</b>.
     </p>
 
    <v-container fluid>
diff --git a/client/pages/phenotypes.vue b/client/pages/phenotypes.vue
index 5fb317eb42982e3a1af7f68de142bfee5c08089b..71047fcc340e3083a35d06f53d80378623c19455 100644
--- a/client/pages/phenotypes.vue
+++ b/client/pages/phenotypes.vue
@@ -2,13 +2,12 @@
   <div>
         <div style="text-align:center; margin:30px;">
   <h1>
-    Genome Wide Analysis
-  </h1>
+    Genome-wide analysis parameters</h1>
   <p style="width:80%; margin: 0 auto;">
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla vitae sapien at ante eleifend aliquet. Etiam cursus et ante non ultrices. Cras vel sollicitudin urna. Aliquam consectetur massa nec augue consequat, ac commodo est suscipit. Nulla sollicitudin risus eget orci feugiat vulputate. Maecenas ut feugiat massa. Nam euismod, augue a ullamcorper interdum, ipsum urna blandit nisl, cursus vehicula leo ante ac nisi. Sed semper pulvinar nisi ut dictum. In cursus velit nec eros sagittis aliquet. Donec interdum volutpat massa viverra fermentum.
+Please select the traits you want to analyze jointly with the omnibus test. A research box to the right allows you to query our current database. The maximum number of trait that can be analyzed jointly is 64 traits. Computation time for a large number of trait can take up to half an hour.
   </p>
 </div>
-    <h2>List of phenotypes</h2>
+    <h2>Select the GWAS to be analyzed jointly:</h2>
     <div id="app">
       <v-app>
         <v-data-table