diff --git a/exec/00_gp_slurm_job_wrapper-LUCERNE.sh b/exec/00_gp_slurm_job_wrapper-LUCERNE.sh
new file mode 100755
index 0000000..f84ef8d
--- /dev/null
+++ b/exec/00_gp_slurm_job_wrapper-LUCERNE.sh
@@ -0,0 +1,95 @@
+#!/bin/bash
+
+### Load the conda environment
+module load Miniconda3/22.11.1-1
+conda init bash
+source ~/.bashrc
+conda activate genomic_selection
+
+################################################################
+### TOP-LEVEL SLURM ARRAY JOB SUBMISSION SCRIPT
+### Please edit the input variables below to match your dataset:
+################################################################
+
+### Input variables (use the absolute path to files to be precise)
+### (1) R matrix object with n rows corresponding to samples, and p columns corresponding to the markers or loci. 
+###     Should have no missing data or else will be imputed via mean value imputation.
+GENOTYPE_DATA_RDS='/group/pasture/Jeff/lucerne/workdir/FINAL-IMPUTED-noTrailingAllele-filteredSNPlist.Rds'
+### (2) Tab-delimited phenotype file where column 1: sample names, column 2: population name, columns 3 and so on refers to the phenotype values of one trait per column.
+###     Headers for the columns should be named appropriately, e.g. ID, POP, TRAIT1, TRAIT2, etc.
+###     Missing values are allowed for samples whose phenotypes will be predicted by the best model identified within the population they belong to.
+###     Missing values may be coded as empty cells, -, NA, na, NaN, missing, and/or MISSING.
+PHENOTYPE_DATA_TSV='/group/pasture/Jeff/lucerne/workdir/Lucerne_PhenomicsDB_2024-05-27-BiomassPredicted.tsv'
+### (3) Number of folds for k-fold cross-validation.
+KFOLDS=5
+### (4) Number of replications of the k-fold cross-validation each representing a random sorting of the samples hence yielding different ways of partitioning the data.
+NREPS=5
+### (5) Full path to the location of the executable Rscript gp.R
+DIR='/group/pasture/Jeff/gp/exec'
+
+### Check if the genotype file exists
+if [ ! -f $GENOTYPE_DATA_RDS ]
+then
+    echo "Error: The genotype file: $GENOTYPE_DATA_RDS does not exist. Are you specifying the full path? Is the name correct?"
+    exit 101
+fi
+### Check if the phenotype file exists
+if [ ! -f $PHENOTYPE_DATA_TSV ]
+then
+    echo "Error: The phenotype file: $PHENOTYPE_DATA_TSV does not exist. Are you specifying the full path? Is the name correct?"
+    exit 102
+fi
+### Check if the genotype file is a valid Rds file
+echo 'args = commandArgs(trailingOnly=TRUE)
+geno = suppressWarnings(tryCatch(readRDS(args[1]), error=function(e){print("Error loading genotype file.")}))
+' > test_geno_rds.R
+if [ $(Rscript test_geno_rds.R $GENOTYPE_DATA_RDS | grep -i "error" | wc -l) -eq 1 ]
+then
+    echo "Error: The genotype file: $GENOTYPE_DATA_RDS is not an Rds file."
+    exit 103
+fi
+rm test_geno_rds.R
+### Check if the phenotype file is formatted according to the required specifications
+echo 'args = commandArgs(trailingOnly=TRUE)
+pheno = suppressWarnings(tryCatch(read.delim(args[1], sep="\t", header=TRUE), error=function(e){print("Error loading phenotype file.")}))
+' > test_pheno_rds.R
+if [ $(Rscript test_pheno_rds.R $PHENOTYPE_DATA_TSV | grep -i "error" | wc -l) -eq 1 ]
+then
+    echo "Error: The phenotype file: $GENOTYPE_DATA_RDS is not formatted according to specifications. It should be tab-delimited and a header line must be present."
+    exit 104
+fi
+rm test_pheno_rds.R
+### Check if the genomic_selection repo folder exists
+if [ ! -d $DIR ]
+then
+    echo "Error: The genotype_selection directory: $DIR does not exist. Are you specifying the full path? Is the name correct?"
+    exit 105
+fi
+### Check if the genomic_selection repo belongs to the user
+if [ ! -w $DIR ]
+then
+    echo "Error: You do not have permission to write in the genotype_selection directory: $DIR. Did you clone the genomic_selection repository into a directory you have write-access to?"
+    exit 106
+fi
+### Check if the genomic_selection repo has contains the slurm array job submission script
+if [ ! -f ${DIR}/01_gp_slurm_job.sh ]
+then
+    echo "Error: The genotype_selection directory: $DIR does not contain the script: 01_gp_slurm_job.sh. Are you sure this is the genomic_selection repo directory?"
+    exit 107
+fi
+### Initialise the output directory which will contain all the output Rds files across populations and traits
+if [ ! -d ${DIR}/output ]
+then
+    mkdir ${DIR}/output
+fi
+### Submit an array of jobs equivalent to the number of traits in the phenotype file
+cd $DIR/
+N_TRAITS=$(echo $(head -n1 $PHENOTYPE_DATA_TSV | awk '{print NF}') - 2 | bc)
+N_POPS=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | wc -l)
+sbatch --array 1-$(echo "${N_TRAITS} * ${N_POPS}" | bc) \
+    01_gp_slurm_job-LUCERNE.sh \
+        ${GENOTYPE_DATA_RDS} \
+        ${PHENOTYPE_DATA_TSV} \
+        ${KFOLDS} \
+        ${NREPS} \
+        ${DIR}
diff --git a/exec/00_gp_slurm_job_wrapper-RYEGRASS.sh b/exec/00_gp_slurm_job_wrapper-RYEGRASS.sh
new file mode 100755
index 0000000..574382e
--- /dev/null
+++ b/exec/00_gp_slurm_job_wrapper-RYEGRASS.sh
@@ -0,0 +1,95 @@
+#!/bin/bash
+
+### Load the conda environment
+module load Miniconda3/22.11.1-1
+conda init bash
+source ~/.bashrc
+conda activate genomic_selection
+
+################################################################
+### TOP-LEVEL SLURM ARRAY JOB SUBMISSION SCRIPT
+### Please edit the input variables below to match your dataset:
+################################################################
+
+### Input variables (use the absolute path to files to be precise)
+### (1) R matrix object with n rows corresponding to samples, and p columns corresponding to the markers or loci. 
+###     Should have no missing data or else will be imputed via mean value imputation.
+GENOTYPE_DATA_RDS='/group/pasture/Jeff/ryegrass/workdir/STR_NUE_WUE_HS-1717536141.3435302.3200855812-IMPUTED.Rds'
+### (2) Tab-delimited phenotype file where column 1: sample names, column 2: population name, columns 3 and so on refers to the phenotype values of one trait per column.
+###     Headers for the columns should be named appropriately, e.g. ID, POP, TRAIT1, TRAIT2, etc.
+###     Missing values are allowed for samples whose phenotypes will be predicted by the best model identified within the population they belong to.
+###     Missing values may be coded as empty cells, -, NA, na, NaN, missing, and/or MISSING.
+PHENOTYPE_DATA_TSV='/group/pasture/Jeff/ryegrass/workdir/STR_NUE_WUE_PhenomicsDB_2024-05-20-BiomassPredictedAndGroudtruth.tsv'
+### (3) Number of folds for k-fold cross-validation.
+KFOLDS=5
+### (4) Number of replications of the k-fold cross-validation each representing a random sorting of the samples hence yielding different ways of partitioning the data.
+NREPS=5
+### (5) Full path to the location of the executable Rscript gp.R
+DIR='/group/pasture/Jeff/gp/exec'
+
+### Check if the genotype file exists
+if [ ! -f $GENOTYPE_DATA_RDS ]
+then
+    echo "Error: The genotype file: $GENOTYPE_DATA_RDS does not exist. Are you specifying the full path? Is the name correct?"
+    exit 101
+fi
+### Check if the phenotype file exists
+if [ ! -f $PHENOTYPE_DATA_TSV ]
+then
+    echo "Error: The phenotype file: $PHENOTYPE_DATA_TSV does not exist. Are you specifying the full path? Is the name correct?"
+    exit 102
+fi
+### Check if the genotype file is a valid Rds file
+echo 'args = commandArgs(trailingOnly=TRUE)
+geno = suppressWarnings(tryCatch(readRDS(args[1]), error=function(e){print("Error loading genotype file.")}))
+' > test_geno_rds.R
+if [ $(Rscript test_geno_rds.R $GENOTYPE_DATA_RDS | grep -i "error" | wc -l) -eq 1 ]
+then
+    echo "Error: The genotype file: $GENOTYPE_DATA_RDS is not an Rds file."
+    exit 103
+fi
+rm test_geno_rds.R
+### Check if the phenotype file is formatted according to the required specifications
+echo 'args = commandArgs(trailingOnly=TRUE)
+pheno = suppressWarnings(tryCatch(read.delim(args[1], sep="\t", header=TRUE), error=function(e){print("Error loading phenotype file.")}))
+' > test_pheno_rds.R
+if [ $(Rscript test_pheno_rds.R $PHENOTYPE_DATA_TSV | grep -i "error" | wc -l) -eq 1 ]
+then
+    echo "Error: The phenotype file: $GENOTYPE_DATA_RDS is not formatted according to specifications. It should be tab-delimited and a header line must be present."
+    exit 104
+fi
+rm test_pheno_rds.R
+### Check if the genomic_selection repo folder exists
+if [ ! -d $DIR ]
+then
+    echo "Error: The genotype_selection directory: $DIR does not exist. Are you specifying the full path? Is the name correct?"
+    exit 105
+fi
+### Check if the genomic_selection repo belongs to the user
+if [ ! -w $DIR ]
+then
+    echo "Error: You do not have permission to write in the genotype_selection directory: $DIR. Did you clone the genomic_selection repository into a directory you have write-access to?"
+    exit 106
+fi
+### Check if the genomic_selection repo has contains the slurm array job submission script
+if [ ! -f ${DIR}/01_gp_slurm_job.sh ]
+then
+    echo "Error: The genotype_selection directory: $DIR does not contain the script: 01_gp_slurm_job.sh. Are you sure this is the genomic_selection repo directory?"
+    exit 107
+fi
+### Initialise the output directory which will contain all the output Rds files across populations and traits
+if [ ! -d ${DIR}/output ]
+then
+    mkdir ${DIR}/output
+fi
+### Submit an array of jobs equivalent to the number of traits in the phenotype file
+cd $DIR/
+N_TRAITS=$(echo $(head -n1 $PHENOTYPE_DATA_TSV | awk '{print NF}') - 2 | bc)
+N_POPS=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | wc -l)
+sbatch --array 1-$(echo "${N_TRAITS} * ${N_POPS}" | bc) \
+    01_gp_slurm_job-RYEGRASS.sh \
+        ${GENOTYPE_DATA_RDS} \
+        ${PHENOTYPE_DATA_TSV} \
+        ${KFOLDS} \
+        ${NREPS} \
+        ${DIR}
diff --git a/exec/01_gp_slurm_job-LUCERNE.sh b/exec/01_gp_slurm_job-LUCERNE.sh
new file mode 100755
index 0000000..f646369
--- /dev/null
+++ b/exec/01_gp_slurm_job-LUCERNE.sh
@@ -0,0 +1,120 @@
+#!/bin/bash
+#SBATCH --job-name="GS"
+#SBATCH --account="dbiopast2"   ### EDIT ME: Pick the appropriate account name, e.g. dbiopast1 or dbiopast2
+#SBATCH --ntasks=1              ### LEAVE ME:Request a single task as we will be submitting this as an array job where each job corresponds to a trait
+#SBATCH --cpus-per-task=32      ### EDIT ME: Parallelisation across replications, folds and models (more cpu means faster execution time but probably longer time to wait for the Slurm scheduler to find resources to allocate to the job)
+#SBATCH --mem=400G              ### EDIT ME: Proportional to the input data (will need to test the appropriate memory required, hint use `seff ${JOBID}`)
+#SBATCH --time=7-0:0:00         ### EDIT ME: Proportional to the input data, number of folds, replications, and models to be used
+###################################################################################################
+### Edit the Slurm settings above to match your requirements. 
+###################################################################################################
+
+###################################################################################################
+### The variables below will be exported from `00_gs_slurm_job_wrapper.sh`:
+###################################################################################################
+### Input variables (use the absolute path to files to be precise)
+### (1) R matrix object with n rows corresponding to samples, and p columns corresponding to the markers or loci. 
+###     Should have no missing data or else will be imputed via mean value imputation.
+# GENOTYPE_DATA_RDS='/group/pasture/Jeff/genomic_selection/tests/grape.rds'
+GENOTYPE_DATA_RDS=$1
+### (2) Tab-delimited phenotype file where column 1: sample names, column 2: population name, columns 3 and so on refers to the phenotype values of one trait per column.
+###     Headers for the columns should be named appropriately, e.g. ID, POP, TRAIT1, TRAIT2, etc.
+###     Missing values are allowed for samples whose phenotypes will be predicted by the best model identified within the population they belong to.
+###     Missing values may be coded as empty cells, -, NA, na, NaN, missing, and/or MISSING.
+# PHENOTYPE_DATA_TSV='/group/pasture/Jeff/genomic_selection/tests/grape_pheno.txt'
+PHENOTYPE_DATA_TSV=$2
+### (3) Number of folds for k-fold cross-validation.
+# KFOLDS=5
+KFOLDS=$3
+### (4) Number of replications of the k-fold cross-validation each representing a random sorting of the samples hence yielding different ways of partitioning the data.
+# NREPS=3
+NREPS=$4
+### (5) Full path to the location of the executable Rscript gp.R
+# DIR='/group/pasture/Jeff/gp/exec'
+DIR=$5
+###################################################################################################
+### Edit the code below, if and only if you have read the documentation or familiar with `src/*.R`:
+###################################################################################################
+### Define the trait and population to include
+N_POPS=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | wc -l)
+TRAIT_IDX=$(echo "((${SLURM_ARRAY_TASK_ID}-1) / ${N_POPS}) + 1" | bc)
+POP_IDX=$(echo "${SLURM_ARRAY_TASK_ID} % ${N_POPS}" | bc)
+if [ "${POP_IDX}" -eq 0 ]
+then
+    POP_IDX=${N_POPS}
+fi
+COLUMN_ID=$(echo 2 + ${TRAIT_IDX} | bc)
+TRAIT=$(head -n1 $PHENOTYPE_DATA_TSV | cut -f${COLUMN_ID})
+POP=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | head -n${POP_IDX} | tail -n1)
+### Skip leave-one-population-out cross-validation if there is only one population
+if [ "${N_POPS}" -eq 1 ]
+then
+    BOOL_ACROSS=FALSE
+else
+    if [ "${POP_IDX}" -eq 1 ]
+    then
+        BOOL_ACROSS=TRUE
+    else
+        BOOL_ACROSS=FALSE
+    fi
+fi
+### Output directories
+DIR_OUT_MAIN=${DIR}/output
+DIR_OUT=${DIR_OUT_MAIN}/output-${TRAIT}-${POP}
+mkdir $DIR_OUT
+### Log messages
+echo JOB_${SLURM_ARRAY_TASK_ID}-TRAIT_${TRAIT}-POP_${POP} > ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+echo "==========================================
+-------------------------------------------
+	    Job Info
+-------------------------------------------
+SLURM_JOB_ID = $SLURM_JOB_ID
+SLURM_JOB_NAME = $SLURM_JOB_NAME
+SLURM_JOB_NODELIST = $SLURM_JOB_NODELIST
+SLURM_SUBMIT_HOST = $SLURM_SUBMIT_HOST
+SLURM_SUBMIT_DIR = $SLURM_SUBMIT_DIR
+SLURM_NTASKS = $SLURM_NTASKS
+SLURM_ARRAY_TASK_ID = $SLURM_ARRAY_TASK_ID
+SLURM_MEM_PER_NODE = $(echo "$SLURM_MEM_PER_NODE / (2^10)" | bc) GB
+SLURM_CPUS_PER_TASK = $SLURM_CPUS_PER_TASK
+-------------------------------------------
+	    Variables
+-------------------------------------------
+GENOTYPE_DATA_RDS	: $GENOTYPE_DATA_RDS
+PHENOTYPE_DATA_TSV	: $PHENOTYPE_DATA_TSV
+KFOLDS		        : $KFOLDS
+NREPS		        : $NREPS
+TRAIT		        : $TRAIT
+POPULATION          : $POP
+-------------------------------------------
+	    Output directory
+-------------------------------------------
+${DIR_OUT}
+==========================================" >> ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+
+### Load the conda environment
+module load Miniconda3/22.11.1-1
+conda init bash
+source ~/.bashrc
+conda activate genomic_selection
+
+### Run within and across population replicated k-fold cross-validation and prediction of missing phenotypes
+time \
+Rscript ${DIR}/gp.R \
+    --fname-geno $GENOTYPE_DATA_RDS \
+    --fname-pheno $PHENOTYPE_DATA_TSV \
+    --population $POP \
+    --dir-output $DIR_OUT \
+    --pheno-idx-col-y $COLUMN_ID \
+    --bool-within TRUE \
+    --bool-across $BOOL_ACROSS \
+    --n-folds $KFOLDS \
+    --n-reps $NREPS \
+    --bool-parallel TRUE \
+    --max-mem-Gb $(echo "$SLURM_MEM_PER_NODE / (2^10)" | bc) \
+    --n-threads $SLURM_CPUS_PER_TASK \
+    --verbose TRUE >> ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+### Clean-up
+mv ${DIR_OUT}/GENOMIC_PREDICTIONS_OUTPUT-*.Rds ${DIR_OUT_MAIN}
+mv ${DIR_OUT}/job_info-${TRAIT}-${POP}.log ${DIR_OUT_MAIN}
+rm -R ${DIR_OUT}
diff --git a/exec/01_gp_slurm_job-RYEGRASS.sh b/exec/01_gp_slurm_job-RYEGRASS.sh
new file mode 100755
index 0000000..5a32a1e
--- /dev/null
+++ b/exec/01_gp_slurm_job-RYEGRASS.sh
@@ -0,0 +1,117 @@
+#!/bin/bash
+#SBATCH --job-name="GS"
+#SBATCH --account="dbiopast1"   ### EDIT ME: Pick the appropriate account name, e.g. dbiopast1 or dbiopast2
+#SBATCH --ntasks=1              ### LEAVE ME:Request a single task as we will be submitting this as an array job where each job corresponds to a trait
+#SBATCH --cpus-per-task=32      ### EDIT ME: Parallelisation across replications, folds and models (more cpu means faster execution time but probably longer time to wait for the Slurm scheduler to find resources to allocate to the job)
+#SBATCH --mem=250G              ### EDIT ME: Proportional to the input data (will need to test the appropriate memory required, hint use `seff ${JOBID}`)
+#SBATCH --time=5-0:0:00         ### EDIT ME: Proportional to the input data, number of folds, replications, and models to be used
+###################################################################################################
+### Edit the Slurm settings above to match your requirements. 
+###################################################################################################
+
+###################################################################################################
+### The variables below will be exported from `00_gs_slurm_job_wrapper.sh`:
+###################################################################################################
+### Input variables (use the absolute path to files to be precise)
+### (1) R matrix object with n rows corresponding to samples, and p columns corresponding to the markers or loci. 
+###     Should have no missing data or else will be imputed via mean value imputation.
+# GENOTYPE_DATA_RDS='/group/pasture/Jeff/genomic_selection/tests/grape.rds'
+GENOTYPE_DATA_RDS=$1
+### (2) Tab-delimited phenotype file where column 1: sample names, column 2: population name, columns 3 and so on refers to the phenotype values of one trait per column.
+###     Headers for the columns should be named appropriately, e.g. ID, POP, TRAIT1, TRAIT2, etc.
+###     Missing values are allowed for samples whose phenotypes will be predicted by the best model identified within the population they belong to.
+###     Missing values may be coded as empty cells, -, NA, na, NaN, missing, and/or MISSING.
+# PHENOTYPE_DATA_TSV='/group/pasture/Jeff/genomic_selection/tests/grape_pheno.txt'
+PHENOTYPE_DATA_TSV=$2
+### (3) Number of folds for k-fold cross-validation.
+# KFOLDS=5
+KFOLDS=$3
+### (4) Number of replications of the k-fold cross-validation each representing a random sorting of the samples hence yielding different ways of partitioning the data.
+# NREPS=3
+NREPS=$4
+### (5) Full path to the location of the executable Rscript gp.R
+# DIR='/group/pasture/Jeff/gp/exec'
+DIR=$5
+###################################################################################################
+### Edit the code below, if and only if you have read the documentation or familiar with `src/*.R`:
+###################################################################################################
+### Define the trait and population to include
+N_POPS=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | wc -l)
+TRAIT_IDX=$(echo "((${SLURM_ARRAY_TASK_ID}-1) / ${N_POPS}) + 1" | bc)
+POP_IDX=$(echo "${SLURM_ARRAY_TASK_ID} % ${N_POPS}" | bc)
+if [ "${POP_IDX}" -eq 0 ]
+then
+    POP_IDX=${N_POPS}
+fi
+COLUMN_ID=$(echo 2 + ${TRAIT_IDX} | bc)
+TRAIT=$(head -n1 $PHENOTYPE_DATA_TSV | cut -f${COLUMN_ID})
+POP=$(tail -n+2 $PHENOTYPE_DATA_TSV | cut -f2 - | sort | uniq | head -n${POP_IDX} | tail -n1)
+### Skip leave-one-population-out cross-validation if there is only one population
+if [ "${N_POPS}" -eq 1 ]
+then
+    BOOL_ACROSS=FALSE
+else
+    if [ "${POP_IDX}" -eq 1 ]
+    then
+        BOOL_ACROSS=TRUE
+    else
+        BOOL_ACROSS=FALSE
+    fi
+fi
+### Output directories
+DIR_OUT_MAIN=${DIR}/output
+DIR_OUT=${DIR_OUT_MAIN}/output-${TRAIT}-${POP}
+mkdir $DIR_OUT
+### Log messages
+echo JOB_${SLURM_ARRAY_TASK_ID}-TRAIT_${TRAIT}-POP_${POP} > ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+echo "==========================================
+-------------------------------------------
+	    Job Info
+-------------------------------------------
+SLURM_JOB_ID = $SLURM_JOB_ID
+SLURM_JOB_NAME = $SLURM_JOB_NAME
+SLURM_JOB_NODELIST = $SLURM_JOB_NODELIST
+SLURM_SUBMIT_HOST = $SLURM_SUBMIT_HOST
+SLURM_SUBMIT_DIR = $SLURM_SUBMIT_DIR
+SLURM_NTASKS = $SLURM_NTASKS
+SLURM_ARRAY_TASK_ID = $SLURM_ARRAY_TASK_ID
+-------------------------------------------
+	    Variables
+-------------------------------------------
+GENOTYPE_DATA_RDS	: $GENOTYPE_DATA_RDS
+PHENOTYPE_DATA_TSV	: $PHENOTYPE_DATA_TSV
+KFOLDS		        : $KFOLDS
+NREPS		        : $NREPS
+TRAIT		        : $TRAIT
+-------------------------------------------
+	    Output directory
+-------------------------------------------
+${DIR_OUT}
+==========================================" >> ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+
+### Load the conda environment
+module load Miniconda3/22.11.1-1
+conda init bash
+source ~/.bashrc
+conda activate genomic_selection
+
+### Run within and across population replicated k-fold cross-validation and prediction of missing phenotypes
+time \
+Rscript ${DIR}/gp.R \
+    --fname-geno $GENOTYPE_DATA_RDS \
+    --fname-pheno $PHENOTYPE_DATA_TSV \
+    --population $POP \
+    --dir-output $DIR_OUT \
+    --pheno-idx-col-y $COLUMN_ID \
+    --bool-within TRUE \
+    --bool-across $BOOL_ACROSS \
+    --n-folds $KFOLDS \
+    --n-reps $NREPS \
+    --bool-parallel TRUE \
+    --max-mem-Gb $SLURM_MEM_PER_NODE \
+    --n-threads $SLURM_CPUS_PER_TASK \
+    --verbose TRUE >> ${DIR_OUT}/job_info-${TRAIT}-${POP}.log
+### Clean-up
+mv ${DIR_OUT}/GENOMIC_PREDICTIONS_OUTPUT-*.Rds ${DIR_OUT_MAIN}
+mv ${DIR_OUT}/job_info-${TRAIT}-${POP}.log ${DIR_OUT_MAIN}
+rm -R ${DIR_OUT}
diff --git a/exec/tests/grape.Rds b/exec/tests/grape.Rds
new file mode 100644
index 0000000..af372fb
Binary files /dev/null and b/exec/tests/grape.Rds differ
diff --git a/exec/tests/grape_pheno.tsv b/exec/tests/grape_pheno.tsv
new file mode 100644
index 0000000..1c54973
--- /dev/null
+++ b/exec/tests/grape_pheno.tsv
@@ -0,0 +1,78 @@
+entry	group	dummy_phenotype	dummy_phenotype_with_10_missing
+Grape1:MERGE	g_1	23.5381374964476	NA
+Grape10:MERGE	g_1	24.9505720936119	NA
+Grape11:MERGE	g_1	15.7444879989717	NA
+Grape12:MERGE	g_3	-8.8343125638148	NA
+Grape13:MERGE	g_1	-11.284077978673	NA
+Grape14:MERGE	g_3	-37.3410707860039	NA
+Grape15:MERGE	g_1	-1.18212393031896	NA
+Grape17:MERGE	g_1	12.3516621535621	NA
+Grape18:MERGE	g_3	-10.8056228420199	NA
+Grape19:MERGE	g_3	-11.7909281333106	NA
+Grape20:MERGE	g_2	10.48317064716	9.4001503902491
+Grape21:MERGE	g_2	23.6551276315743	22.2187556729557
+Grape22:MERGE	g_1	14.6029702123532	14.0372493658364
+Grape25:MERGE	g_1	2.8072056841089	2.33220395060562
+Grape26:MERGE	g_2	3.7270643827174	5.50198554606952
+Grape27:MERGE	g_3	-20.6208074511543	-19.4892609942684
+Grape28:MERGE	g_2	20.422323694328	20.9825637100559
+Grape29:MERGE	g_3	-33.2413126506808	-32.9697239867334
+Grape30:MERGE	g_1	16.5638297556618	16.1186110404052
+Grape31:MERGE	g_1	-7.10375239741587	-4.6403711079849
+Grape32:MERGE	g_1	0.476191573415302	0.806247548928381
+Grape33:MERGE	g_3	-20.3610942590788	-19.3197573910514
+Grape34:MERGE	g_3	-1.34859340149026	0.379198167724654
+Grape35:MERGE	g_2	22.3661507357147	22.0708946325877
+Grape36:MERGE	g_3	26.9938827278852	25.5617171711754
+Grape37:MERGE	g_3	-27.4729421545443	-27.0436487491887
+Grape39:MERGE	g_3	-27.7195074206229	-26.9231772902085
+Grape40:MERGE	g_3	-17.6988813540433	-17.7301851620682
+Grape41:MERGE	g_1	-32.2424066439846	-32.8796564586587
+Grape42:MERGE	g_3	-0.967561756212053	-1.79107514329331
+Grape43:MERGE	g_3	-9.15299047869434	-9.16703243533913
+Grape44:MERGE	g_1	31.853190869054	31.954228258615
+Grape46:MERGE	g_3	-6.41719697490732	-7.50964492695065
+Grape48:MERGE	g_3	-8.31118727319531	-8.83553546107298
+Grape49:MERGE	g_2	24.3663357347868	23.8613712460964
+Grape50:MERGE	g_1	-23.4341187873299	-23.2404551626634
+Grape51:MERGE	g_1	-11.4992708226544	-10.7563901387753
+Grape52:MERGE	g_2	22.373253229066	22.7557855519618
+Grape53:MERGE	g_3	-27.1507616278279	-27.7742428017575
+Grape54:MERGE	g_3	-1.54733323054998	-0.291318202295207
+Grape55:MERGE	g_3	-15.9786029255577	-15.9026641699913
+Grape56:MERGE	g_1	9.33095922525655	10.1150489547282
+Grape57:MERGE	g_3	2.55775066919576	2.83343376676098
+Grape58:MERGE	g_3	-8.09163225225368	-8.8147000677584
+Grape59:MERGE	g_1	0.56961338698426	-0.151881036361276
+Grape6:MERGE	g_1	1.97264560331551	1.27725033862341
+Grape60:MERGE	g_2	-4.6523292012708	-4.80815516483193
+Grape61:MERGE	g_3	19.02553332324	17.5577571486201
+Grape62:MERGE	g_3	5.88985405364896	5.32928266218876
+Grape63:MERGE	g_3	-34.2937223684746	-33.8649083386099
+Grape64:MERGE	g_3	-18.0263552536873	-20.24300860618
+Grape65:MERGE	g_3	-8.0207977603204	-8.76610998881978
+Grape66:MERGE	g_3	-29.1846929821102	-30.0502534438325
+Grape67:MERGE	g_2	25.1040393385362	24.2893367981688
+Grape68:MERGE	g_2	10.1165418631329	10.3972309725717
+Grape69:MERGE	g_2	-7.88224633095993	-7.87403903207285
+Grape7:MERGE	g_1	19.4824353747125	20.2255653900461
+Grape70:MERGE	g_2	-25.6976364833835	-25.5965710238528
+Grape71:MERGE	g_1	2.81878293988589	2.43511194607758
+Grape72:MERGE	g_3	-7.40346626250736	-8.56397859721165
+Grape73:MERGE	g_2	-6.01540741082493	-4.42950012584943
+Grape74:MERGE	g_3	-17.2218594103819	-16.3104089105095
+Grape76:MERGE	g_2	-7.73653912111139	-7.60728785534578
+Grape77:MERGE	g_3	15.7098023079321	14.68283469026
+Grape78:MERGE	g_3	-37.4150884849509	-38.4335205568808
+Grape81:MERGE	g_1	-0.974746633551515	0.0804291000092735
+Grape82:MERGE	g_2	0.601595206707301	-0.450001264212616
+Grape83:MERGE	g_3	-4.68263476495642	-4.66306993654109
+Grape84:MERGE	g_1	-17.1410904504135	-16.9154353983312
+Grape86:MERGE	g_3	-2.58196632267392	-3.09016256286464
+Grape88:MERGE	g_2	-18.0221099828036	-18.0599542703923
+Grape89:MERGE	g_3	-19.2395785475012	-19.9079522501793
+Grape9:MERGE	g_3	-32.5585104928072	-32.215564536882
+Grape91:MERGE	g_2	18.5913047145126	18.3214670018996
+Grape92:MERGE	g_3	14.9985431863512	15.0413089451049
+Grape93:MERGE	g_3	-5.69969950790176	-6.85431361620004
+Grape95:MERGE	g_1	-4.52066738467827	-3.88032575399918