diff --git a/jass/models/inittable.py b/jass/models/inittable.py
index a7cd1b5e16f70139d89f22ada36a185a78144715..f49fef2e3ad05c98ac271895238e8d7da3eb4232 100644
--- a/jass/models/inittable.py
+++ b/jass/models/inittable.py
@@ -9,6 +9,7 @@ import re
 import glob
 import logging
 from pandas import HDFStore, DataFrame, read_csv, concat, options, read_hdf
+import h5py
 import pandas as pd
 # create (or open) an hdf5 file and opens in append mode
 import numpy as np
@@ -29,10 +30,13 @@ class InitMeta(object):
 def get_inittable_meta(file_name):
     init_store = HDFStore(file_name, mode='r')
     nb_snps = init_store.get_storer("SumStatTab").nrows
-    name=f"Name of {file_name.split('/')[-1]}"
-    desc=f"Description {file_name.split('/')[-1]}"
     init_store.close()
     nb_phenotypes = read_hdf(file_name, "PhenoList").shape[0]
+    
+    f = h5py.File(file_name, mode='r')
+    name=f.attrs['title']
+    desc=f.attrs["description"]
+    f.close()
     return dict(
         nb_snps=int(nb_snps),
         nb_phenotypes=int(nb_phenotypes),
@@ -40,6 +44,15 @@ def get_inittable_meta(file_name):
         desc=desc,
     )
 
+def add_inittable_meta(file_name, title, description):
+    """
+    add description in hdf5 attributes 
+    """
+    f = h5py.File(file_name, mode='a')
+    f.attrs['title'] = title
+    f.attrs["description"] = description
+    f.close()
+ 
 def get_gwasname(file_name):
     return "_".join(os.path.basename(file_name).split("_")[0:3])
 
@@ -51,7 +64,6 @@ def check_if_SNP_unique(z_gwas_chrom):
         )
         raise IOError(msg)
 
-
 def get_gwas_dict(input_data_path):
     gwas_dict = {}
     # retrieve all files corresponding to glob patterns
diff --git a/jass/test/data_real/summary.csv b/jass/test/data_real/summary.csv
old mode 100755
new mode 100644
diff --git a/jass/test/data_test1/initTable.hdf5 b/jass/test/data_test1/initTable.hdf5
index 904ed8c837cac132e7e8795377c2dd559e40ee4a..a6bea734cfee2da3ed7eb8b419ff703895375c29 100644
Binary files a/jass/test/data_test1/initTable.hdf5 and b/jass/test/data_test1/initTable.hdf5 differ
diff --git a/jass/test/data_test2/initTable.hdf5 b/jass/test/data_test2/initTable.hdf5
index 035a9cc84f3df0ba479ce53a8ebd55b6f7953833..4737c04b0bda81e993626705b0325d71944700f0 100644
Binary files a/jass/test/data_test2/initTable.hdf5 and b/jass/test/data_test2/initTable.hdf5 differ
diff --git a/scripts/hdf5_add_attributes.py b/scripts/hdf5_add_attributes.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b5ab65729e6a37de281310a0bcb2d3783daccfa
--- /dev/null
+++ b/scripts/hdf5_add_attributes.py
@@ -0,0 +1,23 @@
+
+from jass.models.inittable import get_inittable_meta, add_inittable_meta
+# we need python package h5py to read/write .hdf5 file
+
+
+
+if __name__ == "__main__":
+
+    title = 'Curated GWAS summary statistics on African ancestry on 19 blood count traits and glycemic traits (hg38)'
+    des = 'Genome wide curated summary statistics on 19 blood count traits and glycemic traits' \
+          'File format is the inittable format intended to be used with the Joint Analysis of Summary Statistics (JASS), which allows to perform multi-trait GWAS:' \
+          'https://gitlab.pasteur.fr/statistical-genetics/jass' \
+          'GWAS of hematological traits originate from Chen et al paper and were downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/32888493#study_panel). GWAS of glycemic traits come from the (18) study downloadable from GWAS Catalog (https://www.ebi.ac.uk/gwas/publications/34059833).'
+    hdf5_file = '/pasteur/zeus/projets/p02/GGS_JASS/jass_pipeline_dev_copie/jass/jass/test/data_test2/initTable.hdf5'
+
+    add_inittable_meta(hdf5_file, title, des)
+
+    print(get_inittable_meta(hdf5_file))
+
+
+
+
+