diff --git a/eva_submission/nextflow/accession_and_load.nf b/eva_submission/nextflow/accession_and_load.nf
index ba4b041..406d1a8 100644
--- a/eva_submission/nextflow/accession_and_load.nf
+++ b/eva_submission/nextflow/accession_and_load.nf
@@ -139,8 +139,18 @@ workflow {
                 .combine(normalise_vcf.out.vcf_tuples, by:0)
                 .map{tuple(it[0], it[9], it[2], it[3], it[4], it[5], it[6], it[7], it[8])}   // vcf_filename, normalised vcf, fasta, analysis_accession, db_name, vep_version, vep_cache_version, vep_species, aggregation
         load_variants_vcf(normalised_vcfs_ch)
-        run_vep_on_variants(normalised_vcfs_ch, load_variants_vcf.out.variant_load_complete)
-        calculate_statistics_vcf(normalised_vcfs_ch, load_variants_vcf.out.variant_load_complete)
+        // Ensure that all the load are completed before the VEP and calculate statistics starts
+        vep_ch = normalised_vcfs_ch
+                .groupTuple(by: [4, 5, 6, 7] ) // group by db_name, vep_version, vep_cache_version, vep_species
+                .map{tuple(it[4], it[5], it[6], it[7])}
+        run_vep_on_variants(vep_ch, load_variants_vcf.out.variant_load_complete.collect())
+        normalised_vcfs_ch.view()
+        stats_ch = normalised_vcfs_ch
+                   .groupTuple(by: [3, 4, 8])  // group by analysis_accession, db_name, aggregation
+                   .map{tuple(it[3], it[4], it[8], it[1])} // analysis_accession, db_name, aggregation, grouped normalised_vcf_files
+        stats_ch.view()
+
+        calculate_statistics_vcf(stats_ch, load_variants_vcf.out.variant_load_complete.collect())
 
         if (!is_human_study) {
             vcf_files_dbname = Channel.fromPath(params.valid_vcfs)
@@ -348,7 +358,8 @@ process run_vep_on_variants {
     vep_version.trim() != "" && vep_cache_version.trim() != ""
 
     input:
-    tuple val(vcf_filename), val(vcf_file), val(fasta), val(analysis_accession), val(db_name), val(vep_version), val(vep_cache_version), val(vep_species), val(aggregation)
+    //tuple val(vcf_filename), val(vcf_file), val(fasta), val(analysis_accession), val(db_name), val(vep_version), val(vep_cache_version), val(vep_species), val(aggregation)
+    tuple val(db_name), val(vep_version), val(vep_cache_version), val(vep_species)
     val variant_load_complete
 
     output:
@@ -360,11 +371,7 @@ process run_vep_on_variants {
     def pipeline_parameters = ""
 
     pipeline_parameters += " --spring.batch.job.names=annotate-variants-job"
-
-    pipeline_parameters += " --input.vcf.aggregation=" + aggregation.toString().toUpperCase()
-    pipeline_parameters += " --input.vcf=" + vcf_file.toRealPath().toString()
-    pipeline_parameters += " --input.vcf.id=" + analysis_accession.toString()
-    pipeline_parameters += " --input.fasta=" + fasta.toString()
+    pipeline_parameters += " --input.vcf.id="  // The whole study will be annotated again
 
     pipeline_parameters += " --spring.data.mongodb.database=" + db_name.toString()
 
@@ -395,7 +402,7 @@ process calculate_statistics_vcf {
     aggregation.toString() == "none"
 
     input:
-    tuple val(vcf_filename), val(vcf_file), val(fasta), val(analysis_accession), val(db_name), val(vep_version), val(vep_cache_version), val(vep_species), val(aggregation)
+    tuple val(analysis_accession), val(db_name), val(aggregation), val(vcf_files)
     val variant_load_complete
 
     output:
@@ -410,9 +417,8 @@ process calculate_statistics_vcf {
     pipeline_parameters += " --spring.batch.job.names=calculate-statistics-job"
 
     pipeline_parameters += " --input.vcf.aggregation=" + aggregation.toString().toUpperCase()
-    pipeline_parameters += " --input.vcf=" + vcf_file.toRealPath().toString()
+    pipeline_parameters += " --input.vcf=" + file(vcf_files[0]).toRealPath().toString() // If there are multiple file only use the first
     pipeline_parameters += " --input.vcf.id=" + analysis_accession.toString()
-    pipeline_parameters += " --input.fasta=" + fasta.toString()
 
     pipeline_parameters += " --spring.data.mongodb.database=" + db_name.toString()