diff --git a/.gitignore b/.gitignore
index a2866ec7..4c9a5c7d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,5 +1,8 @@
 config_used/
-pipeline/resources/
+pipeline/resources/*
+!pipeline/resources/software/prscs
+!pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/ldblk_1kg_chr23.hdf5
+!pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/snpinfo_1kg_hm3
 pipeline/.snakemake/
 pipeline/test_data/
 pipeline/logs/*
diff --git a/Scripts/format_target/format_target.R b/Scripts/format_target/format_target.R
index 50d57725..056938b1 100644
--- a/Scripts/format_target/format_target.R
+++ b/Scripts/format_target/format_target.R
@@ -69,20 +69,40 @@ if(nrow(target_snp) < nrow(ref)*0.9){
 # Determine target genome build
 ###################
 
+# Check what build(s) are in the reference file
+build_columns <- grep("BP_GRCh", names(ref), value = TRUE)
+cat("Available builds in reference:", build_columns, "\n")
+
 target_build <- detect_build( ref = ref,
                               targ = target_snp,
                               log_file = log_file)
 
-###################
-# Extract overlapping variants in plink format and insert RSIDs
-###################
+# Fallback if detection fails (for Maria's purpose only)
+if (is.na(target_build)) {
+  warning("Could not detect genome build from target and reference. Defaulting to GRCh37.")
+  target_build <- "GRCh37"
+}
+
+bp_col <- paste0("BP_", target_build)
+if (!(bp_col %in% names(ref))) {
+  stop(paste("Column", bp_col, "not found in reference. Available columns:", paste(names(ref), collapse = ", ")))
+}
 
 # Subset relevent build from ref
 ref_subset<-ref[, c("CHR","SNP",paste0("BP_",target_build),"A1","A2","IUPAC"), with=F]
 names(ref_subset)[names(ref_subset) == paste0("BP_",target_build)]<-'BP'
 
+###################
+# Extract overlapping variants in plink format and insert RSIDs
+###################
+
+#Ensure target matches ref if in X format
+target_snp$CHR[target_snp$CHR == "X"] <- "23"
+target_snp$CHR<-as.numeric(target_snp$CHR)
+
 # Merge target and ref by CHR and BP
 ref_target<-merge(target_snp, ref_subset, by=c('CHR','BP'))
+nrow(ref_target)
 
 # Insert IUPAC codes
 names(ref_target)[names(ref_target) == 'IUPAC']<-'IUPAC.y'
@@ -91,6 +111,7 @@ ref_target$IUPAC.x<-snp_iupac(ref_target$A1.x, ref_target$A2.x)
 # Only retain variants that are non-ambiguous SNPs in the target
 # Some might have been retained due to matching CHR and BP position with SNPs in reference
 ref_target<-ref_target[!is.na(ref_target$IUPAC.x),]
+nrow(ref_target)
 
 # Identify variants that need to be flipped
 flip <- detect_strand_flip(ref_target$IUPAC.x, ref_target$IUPAC.y)
@@ -111,6 +132,9 @@ if(nrow(ref_target) < nrow(ref)*0.7){
 write.table(ref_target$SNP.x, paste0(tmp_dir,'/extract_list_1.txt'), col.names = F, row.names = F, quote=F)
 write.table(ref_target$SNP.y, paste0(tmp_dir,'/extract_list_2.txt'), col.names = F, row.names = F, quote=F)
 
+cat("After write.table, extract_list_2.txt contents:\n")
+print(readLines(paste0(tmp_dir,'/extract_list_2.txt'), n = 6))
+
 # First extract variants based on original ID
 if(opt$format == 'plink1'){
   system(paste0(opt$plink2," --bfile ",opt$target, " --extract ", tmp_dir,"/extract_list_1.txt --make-pgen 'pvar-cols=' --threads 1 --out ", tmp_dir,"/subset"))
@@ -138,6 +162,8 @@ if(sum(names(targ_psam) %in% c('FID', 'IID')) == 1){
 # Now edit bim file to update IDs to reference IDs
 targ_pvar<-fread(paste0(tmp_dir,'/subset.pvar'))
 names(targ_pvar)<-c('CHR','BP','SNP','A2','A1')
+targ_pvar$CHR[targ_pvar$CHR == "X"] <- "23"
+targ_pvar$CHR<-as.numeric(targ_pvar$CHR)
 
 # Update SNP with reference SNP value based on CHR:BP:IUPAC in the previously matched ref and target data
 targ_pvar$IUPAC<-snp_iupac(targ_pvar$A1, targ_pvar$A2)
diff --git a/docs/pipeline_1kg_hgdp_prep.Rmd b/docs/pipeline_1kg_hgdp_prep.Rmd
index c663ffe1..8210567f 100644
--- a/docs/pipeline_1kg_hgdp_prep.Rmd
+++ b/docs/pipeline_1kg_hgdp_prep.Rmd
@@ -45,8 +45,8 @@ We are going to:
 
 ```{bash}
 # 1KG+HGDP data
-cd /users/k1806347/oliverpainfel/Data/hgdp_1kg
-wget https://ftp.ebi.ac.uk/pub/databases/spot/pgs/resources/pgsc_HGDP+1kGP_v1.tar.zst
+cd /scratch/users/k19054740/hgdp_1kg
+wget https://ftp.ebi.ac.uk/pub/databases/spot/pgs/resources/pgsc_HGDP+1kGP_v1.tar.zst #This is ~15GB
 tar --use-compress-program=unzstd -xvf pgsc_HGDP+1kGP_v1.tar.zst
 unzstd GRCh37_HGDP+1kGP_ALL.pvar.zst
 
@@ -54,6 +54,77 @@ unzstd GRCh37_HGDP+1kGP_ALL.pvar.zst
 wget https://storage.googleapis.com/gcp-public-data--gnomad/release/3.1.2/vcf/genomes/gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz
 gunzip -c gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz
 
+#hm3 full (build36)
+wget https://www.broadinstitute.org/files/shared/mpg/hapmap3/hapmap3_r1_b36_fwd_consensus.qc.poly.recode.ped.bz2
+wget https://www.broadinstitute.org/files/shared/mpg/hapmap3/hapmap3_r1_b36_fwd_consensus.qc.poly.recode.map.bz2
+bunzip2 hapmap3_r1_b36_fwd_consensus.qc.poly.recode.ped.bz2
+bunzip2 hapmap3_r1_b36_fwd_consensus.qc.poly.recode.map.bz2
+
+# We need builds 37 and 39 POS for all these SNPs
+chmod +x /scratch/prj/sgdp_strdep/maria/genetic/liftOver
+# For b36 (hg18) to b37 (hg19)
+wget http://hgdownload.soe.ucsc.edu/goldenPath/hg18/liftOver/hg18ToHg19.over.chain.gz
+# For b37 (hg19) to b39 (hg38)
+wget http://hgdownload.soe.ucsc.edu/goldenPath/hg19/liftOver/hg19ToHg38.over.chain.gz
+# Make .bed for X
+awk 'BEGIN{OFS="\t"} $1==23 {print "chr23", $4-1, $4, $2}' hapmap3_r1_b36_fwd_consensus.qc.poly.recode.map > chr23.b36.bed
+sed 's/^chr23/chrX/' chr23.b36.bed > chrX.b36.bed
+# Make corresponding new .ped for X
+module load plink/1.9-beta6.10-gcc-13.2.0
+plink --file hapmap3_r1_b36_fwd_consensus.qc.poly.recode --chr X --recode --out chrX_only
+
+#Run liftOver x2
+/scratch/prj/sgdp_strdep/maria/genetic/liftOver chrX.b36.bed hg18ToHg19.over.chain.gz chr23.b37.bed unMapped.b36to37.txt
+/scratch/prj/sgdp_strdep/maria/genetic/liftOver chr23.b37.bed hg19ToHg38.over.chain.gz chr23.b39.bed unMapped.b37to39.txt
+
+***
+
+# Convert to .rds to match ref files for 1-22
+```{r}
+# Read X .bed (SNP order & info)
+map <- read.table("/scratch/users/k19054740/hgdp_1kg/chrX.b36.bed", stringsAsFactors=FALSE, header=FALSE)
+colnames(map) <- c("CHR", "GenDist", "BP_GRCh36", "SNP")
+map$CHR <- ifelse(map$CHR == "chrX", "23", map$CHR)
+
+# Read .ped (genotypes start at column 7)
+ped <- read.table("/scratch/users/k19054740/hgdp_1kg/chrX_only.ped", stringsAsFactors=FALSE, header=FALSE)
+# Extract alleles from original hm3 .ped (build36)
+allele_cols <- as.character(unlist(ped[1, 7:ncol(ped)]))
+A1 <- allele_cols[seq(1, length(allele_cols), by=2)]
+A2 <- allele_cols[seq(2, length(allele_cols), by=2)]
+alleles_df <- data.frame(SNP = map$SNP, A1 = A1, A2 = A2, stringsAsFactors = FALSE)
+
+# Read build 36, 37 and 39 .bed files and get positions
+read_bed <- function(file) {
+  bed <- read.table(file, stringsAsFactors = FALSE, header = FALSE)
+  colnames(bed) <- c("CHR", "start", "end", "SNP")
+  bed$BP <- bed$start + 1
+  return(bed[, c("SNP", "CHR", "BP")])
+}
+
+b36 <- read_bed("/scratch/users/k19054740/hgdp_1kg/chrX.b36.bed")
+b37 <- read_bed("/scratch/users/k19054740/hgdp_1kg/chr23.b37.bed")
+b38 <- read_bed("/scratch/users/k19054740/hgdp_1kg/chr23.b39.bed")
+
+colnames(b36)[3] <- "BP_GRCh36"
+colnames(b37)[3] <- "BP_GRCh37"
+colnames(b38)[3] <- "BP_GRCh38"
+
+# Merge all POS by SNP
+pos_df <- merge(b36[, c("SNP", "BP_GRCh36")], 
+                b37[, c("SNP", "BP_GRCh37")], by = "SNP", all = TRUE)
+pos_df <- merge(pos_df, b38[, c("SNP", "BP_GRCh38")], by = "SNP", all = TRUE)
+
+# Merge alleles with positions
+final_df <- merge(pos_df, alleles_df, by = "SNP", all.x = TRUE)
+# Add chromosome info from original .map
+final_df$CHR <- map$CHR[match(final_df$SNP, map$SNP)]
+
+# Reorder columns
+final_df <- final_df[, c("CHR", "SNP", "BP_GRCh36", "BP_GRCh37", "BP_GRCh38", "A1", "A2")]
+
+#Write
+saveRDS(final_df, "/scratch/users/k19054740/software/GenoPred/pipeline/resources/data/ref/ref.chr23.rds")
 ```
 
 ***
@@ -61,34 +132,35 @@ gunzip -c gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz
 # Format and output required reference files
 
 ```{r}
-
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+setwd('/scratch/users/k19054740/software/GenoPred/pipeline')
 source('../functions/misc.R')
 source_all('../functions')
 library(data.table)
 library(GenoUtils)
 
 # Read in variant data for 1KG+HGDP
-pvar <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL.pvar', nThread=5)
+pvar <- fread('/scratch/users/k19054740/hgdp_1kg/GRCh37_HGDP+1kGP_ALL.pvar', nThread=5)
 
-# Remove unneeded data and variant that aren't SNPs
+# Remove unneeded data and variants that aren't SNPs
 pvar$INFO<-NULL
 pvar$FILTER<-NULL
 pvar<-pvar[nchar(pvar$REF) == 1,]
 pvar<-pvar[nchar(pvar$ALT) == 1,]
 names(pvar)<-c('CHR','BP','SNP','A1','A2')
-pvar<-pvar[pvar$CHR %in% 1:22,]
+pvar$CHR[pvar$CHR == "X"] <- "23"
+pvar<-pvar[pvar$CHR %in% 1:23,]
 pvar$CHR<-as.numeric(pvar$CHR)
 
 # Read in the hapmap snp data
-# Use the previously prepared 1KG reference data
+# Use the previously prepared 1KG reference data for X 
 hm3<-NULL
-for(i in 1:22){
-  hm3<-rbind(hm3, readRDS(paste0('/users/k1806347/oliverpainfel/test/GenoPred/pipeline/resources/data/ref/ref.chr',i,'.rds')))
+for(i in 23){
+  hm3<-rbind(hm3, readRDS(paste0('/scratch/users/k19054740/software/GenoPred/pipeline/resources/data/ref/ref.chr',i,'.rds')))
 }
 
-hm3<-hm3[,c('CHR','SNP','BP_GRCh36','BP_GRCh37','BP_GRCh38','A1','A2'), with=F]
+hm3<-hm3[,c('CHR','SNP','BP_GRCh36','BP_GRCh37','BP_GRCh38','A1','A2')]
 names(hm3) <- c('CHR','SNP','BP_GRCh36','BP','BP_GRCh38','A1','A2')
+hm3$CHR<-as.numeric(hm3$CHR)
 
 # Merge by chromosome and BP data
 pvar_hm3<-merge(pvar, hm3, by=c('CHR','BP'))
@@ -97,30 +169,30 @@ pvar_hm3<-merge(pvar, hm3, by=c('CHR','BP'))
 pvar_hm3$IUPAC.x<-snp_iupac(pvar_hm3$A1.x, pvar_hm3$A2.x)
 pvar_hm3$IUPAC.y<-snp_iupac(pvar_hm3$A1.y, pvar_hm3$A2.y)
 flip <- detect_strand_flip(pvar_hm3$IUPAC.x, pvar_hm3$IUPAC.y)
-sum(flip) # 0
+sum(flip) # 16 here not 0 
 
 # Retain variants that match IUPAC
 pvar_hm3<-pvar_hm3[pvar_hm3$IUPAC.x == pvar_hm3$IUPAC.y,]
 
 # Save intermediate file
-write.table(pvar_hm3,'/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3_snps.txt', col.names = T, row.names = F, quote = F)
+write.table(pvar_hm3,'/scratch/users/k19054740/hgdp_1kg/hgdp_1kg_hm3_snps.txt', col.names = T, row.names = F, quote = F)
 
 # Extract hm3 SNPs from plink2 files
-dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred')
-write.table(pvar_hm3$SNP.x, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist', col.names=F, row.names=F, quote=F)
-system('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL --make-pgen --extract /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3 --threads 5')
+dir.create('/scratch/users/k19054740/hgdp_1kg/genopred')
+write.table(pvar_hm3$SNP.x, '/scratch/users/k19054740/hgdp_1kg/genopred/extract.snplist', col.names=F, row.names=F, quote=F)
+system('plink2 --pfile /scratch/users/k19054740/hgdp_1kg/GRCh37_HGDP+1kGP_ALL --make-pgen --extract /scratch/users/k19054740/hgdp_1kg/genopred/extract.snplist --out /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3 --threads 5')
 
 # Insert RSIDs into new plink files
-pvar <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.pvar')
+pvar <- fread('/scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.pvar')
 pvar <- pvar[, c('#CHROM','POS','ID','REF','ALT'), with=F]
 pvar_hm3$ID <- pvar_hm3$SNP.x
 pvar[pvar_hm3, on=.(ID), SNP := i.SNP.y]
 pvar<-pvar[, c('#CHROM','POS','SNP','REF','ALT'), with=F]
 names(pvar)<-c('#CHROM','POS','ID','REF','ALT')
-fwrite(pvar, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.pvar', col.names=T, row.names=F, quote=F, sep=' ')
+fwrite(pvar, '/scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.pvar', col.names=T, row.names=F, quote=F, sep=' ')
 
 # Remove hard filtered individuals
-samp_meta<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz')
+samp_meta<-fread('/scratch/users/k19054740/hgdp_1kg/gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz')
 samp_meta<-samp_meta[grepl("hard_filtered\":false", samp_meta$gnomad_sample_filters), ]
 
 # Remove PCA outliers
@@ -138,7 +210,7 @@ for(i in 1:nrow(samp_meta)){
 }
 
 # Restrict table to individuals in the genetic data
-psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.psam')
+psam <- fread('/scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.psam')
 psam<-psam[psam$`#IID` %in% samp_meta$s,]
 kin_dat<-kin_dat[kin_dat$s %in% psam$`#IID` & kin_dat$ID %in% psam$`#IID`, ]
 relations<-data.frame(id_1=kin_dat$s, id_2=kin_dat$ID, kin=kin_dat$kin)
@@ -153,43 +225,43 @@ relations$Factor<-relations$kin
 relations$ID<-relations$id_1
 relations<-relations[, c('ID', 'Pair', 'Factor')]
 relations<-relations[order(relations$Pair), ]
-fwrite(relations, file='/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt', sep=' ', quote=F)
+fwrite(relations, file='/scratch/users/k19054740/hgdp_1kg/genopred/relations.txt', sep=' ', quote=F)
 
-system('/scratch/prj/ukbiobank/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt -t 0.05 -s 1 > /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+system('/scratch/prj/ukbiobank/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated -r /scratch/users/k19054740/hgdp_1kg/genopred/relations.txt -t 0.05 -s 1 > /scratch/users/k19054740/hgdp_1kg/genopred/relations_remove.txt')
 
-remove<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+remove<-fread('/scratch/users/k19054740/hgdp_1kg/genopred/relations_remove.txt')
 
 # Create keep file of unrelaed individuals
 psam_unrel <- psam[!(psam$`#IID` %in% remove$V2),]
 nrow(psam_unrel) # 3313 - This is 65 less than in the preprint Table S3. Maybe Greedy related is worse?
 
-write.table(psam_unrel[, '#IID', with=F], '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep', col.names=T, row.names=F, quote=F)
+write.table(psam_unrel[, '#IID', with=F], '/scratch/users/k19054740/hgdp_1kg/genopred/unrel.keep', col.names=T, row.names=F, quote=F)
 
 # Split by chromosome and retain unrelated individuals
-for(i in 1:22){
-  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3 --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep --chr ',i,' --make-pgen --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i))
+for(i in 1:23){
+system(paste0('plink2 --pfile /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3 --keep /scratch/users/k19054740/hgdp_1kg/genopred/unrel.keep --chr ',i,' --make-pgen --out /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i))
 }
 
 # Create keep files and calculate allele frequencies for each population
 # Population data is stored withint the psam file
-psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr22.psam')
+psam <- fread('/scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr22.psam')
 pop_dat<-psam[, c('#IID','SuperPop')]
 names(pop_dat)<-c('#IID','POP')
-write.table(pop_dat, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt', row.names=F, col.names=T, quote=F)
+write.table(pop_dat, '/scratch/users/k19054740/hgdp_1kg/genopred/pop.txt', row.names=F, col.names=T, quote=F)
 
-dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/')
+dir.create('/scratch/users/k19054740/hgdp_1kg/genopred/keep_files/')
 for(pop in unique(pop_dat$POP)){
-  write.table(pop_dat[pop_dat$POP == pop, '#IID', with=F], paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep'), col.names=F, row.names=F, quote=F)
-  dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop), recursive = T)
-  for(i in 1:22){
-    system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg_hm3.chr', i))
+  write.table(pop_dat[pop_dat$POP == pop, '#IID', with=F], paste0('/scratch/users/k19054740/hgdp_1kg/genopred/keep_files/',pop,'.keep'), col.names=F, row.names=F, quote=F)
+  dir.create(paste0('/scratch/users/k19054740/hgdp_1kg/genopred/freq_files/', pop), recursive = T)
+  for(i in 23){
+    system(paste0('plink2 --pfile /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --keep /scratch/users/k19054740/hgdp_1kg/genopred/keep_files/', pop, '.keep --chr ',i, ' --freq --out /scratch/users/k19054740/hgdp_1kg/genopred/freq_files/', pop, "/hgdp_1kg_hm3.chr", i))
   }
 }
 
 # Create frequency files across all reference individuals (TRANS)
-dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
-for(i in 1:22){
-  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg_hm3.chr', i))
+dir.create(paste0('/scratch/users/k19054740/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
+for(i in 23){
+  system(paste0('plink2 --pfile /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --chr ',i,' --freq --out /scratch/users/k19054740/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg_hm3.chr', i))
 }
 
 ####
@@ -199,12 +271,12 @@ for(i in 1:22){
 pvar_hm3<-pvar_hm3[,c('CHR','BP','SNP.y','A1.x','A2.x','BP_GRCh36','BP_GRCh38'), with=F]
 names(pvar_hm3)<-c('#CHROM','POS_GRCh37','ID','ALT','REF','POS_GRCh36','POS_GRCh38')
 
-for(chr in 1:22){
+for(chr in 1:23){
     ref<-pvar_hm3[pvar_hm3$`#CHROM` == chr,]
     ref$IUPAC<-snp_iupac(ref$ALT, ref$REF)
     for(pop in unique(pop_dat$POP)){
         # Read in reference frequency data
-        freq<-fread(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg_hm3.chr', chr,'.afreq'))
+        freq<-fread(paste0('/scratch/users/k19054740/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg_hm3.chr', chr,'.afreq'))
 
         # The freq files have come from the reference files, so we can assume they are on the same strand
         freq_match<-merge(ref, freq[,c('ID','ALT','REF','ALT_FREQS'), with=F], by=c('ID','ALT','REF'))
@@ -218,24 +290,22 @@ for(chr in 1:22){
 
     ref<-ref[,c("#CHROM","ID","POS_GRCh36","POS_GRCh37","POS_GRCh38","ALT","REF","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP))), with=F]
     names(ref)<-c("CHR","SNP","BP_GRCh36","BP_GRCh37","BP_GRCh38","A1","A2","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP)))
-    saveRDS(ref, file = paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',chr,'.rds'))
+    saveRDS(ref, file = paste0('/scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',chr,'.rds'))
 }
 
 # Create keeplist
 keep_list<-data.frame(POP=unique(pop_dat$POP),
                       PATH=paste0('resources/data/ref/keep_files/',unique(pop_dat$POP),'.keep'))
 
-write.table(keep_list, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref.keep.list', col.names=F, row.names=F, quote=F)
+write.table(keep_list, '/scratch/users/k19054740/hgdp_1kg/genopred/ref.keep.list', col.names=F, row.names=F, quote=F)
 
 ```
 
-***
-
 ## Package for zenodo
 
 <ref_dir>
-├── ref.chr<1-22>.<pgen/pvar/psam>
-├── ref.chr<1-22>.rds
+├── ref.chr<1-23>.<pgen/pvar/psam>
+├── ref.chr<1-23>.rds
 ├── ref.pop.txt (#IID, POP - with header)
 ├── ref.keep.list (POP and PATH - without header)
 ├── keep_files
@@ -246,13 +316,12 @@ write.table(keep_list, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
 
 ```{bash}
 
-# Copy over all the relavent files
-mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
+# Copy over all the relevant files to main dir
+mkdir scratch/users/k19054740/hgdp_1kg/genopred/ref
 
-for chr in $(seq 1 22); do
-  for file in $(echo pgen pvar psam rds); do
-    cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
-  done
+for chr in $(seq 1 23); do
+for file in $(echo pgen pvar psam rds); do
+    cp /scratch/users/k19054740/hgdp_1kg/genopred/hgdp_1kg_hm3.chr23.${file} /scratch/users/k19054740/hgdp_1kg/genopred/ref/ref.chr23.${file}
 done
 
 cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.pop.txt
@@ -264,7 +333,7 @@ cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/*.keep /users
 cp -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
 
 for pop in $(ls /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files); do
- for chr in $(seq 1 22); do
+ for chr in $(seq 1 23); do
   mv /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg_hm3.chr${chr}.afreq /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/ref.${pop}.chr${chr}.afreq
   rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg_hm3.chr${chr}.log
  done
@@ -276,6 +345,283 @@ cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref.keep.list /users/k18
 cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
 tar -czvf genopred_1kg_hgdp.tar.gz ref
 
+
+```
+
+***
+# Create subset of Pan UKB LD scores
+
+More information on the data: https://pan.ukbb.broadinstitute.org/downloads/index.html
+Original citation: Pan-UKB team. https://pan.ukbb.broadinstitute.org. 2020.
+
+```{bash}
+
+mkdir -p /users/k1806347/k19054740/ld_scores
+cd /users/k1806347/oliverpainfel/Data/ld_scores
+wget --no-check-certificate https://pan-ukb-us-east-1.s3.amazonaws.com/ld_release/UKBB.ALL.ldscore.tar.gz
+tar -xvf UKBB.ALL.ldscore.tar.gz
+rm UKBB.ALL.ldscore.tar.gz
+for population in $(echo EUR EAS CSA AMR MID AFR); do
+  mv UKBB.ALL.ldscore/UKBB.$population.l2.M_5_50 UKBB.$population.rsid.l2.M_5_50
+  mv UKBB.ALL.ldscore/UKBB.$population.rsid.l2.ldscore.gz UKBB.$population.rsid.l2.ldscore.gz
+done
+rm -r UKBB.ALL.ldscore
+cd ..
+tar -czvf ld_scores.tar.gz ld_scores
+
+```
+
+***
+
+############# NOT RUN 
+# Make dense version of the reference data
+
+We will create a reference dataset that is not restricted to HapMap3 variants, to allow for denser coverage, and broader applicability to the PGS catalogue.
+
+```{r}
+
+setwd('/scratch/users/k19054740/software/GenoPred/pipeline')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+library(GenoUtils)
+
+# Read in variant data for 1KG+HGDP
+pvar <- fread('/scratch/users/k19054740/hgdp_1kg/GRCh37_HGDP+1kGP_ALL.pvar', nThread=5)
+
+# Extract RSID information
+pvar$RSID <- gsub('.*gnomad_ID=', '', pvar$INFO)
+pvar$RSID[grepl('AF', pvar$RSID)] <- NA
+
+# Remove unneeded data and variant that aren't unambiguous SNPs and don't have RSID
+pvar$INFO<-NULL
+pvar$FILTER<-NULL
+pvar<-pvar[nchar(pvar$REF) == 1,]
+pvar<-pvar[nchar(pvar$ALT) == 1,]
+names(pvar)<-c('CHR','BP','SNP','A1','A2') #Note: no rsid here
+pvar$CHR[pvar$CHR == "X"] <- "23"
+pvar$CHR<-as.numeric(pvar$CHR)
+pvar<-pvar[pvar$CHR %in% 1:23,]
+pvar<-pvar[!is.na(pvar$RSID),]
+pvar$IUPAC<-snp_iupac(pvar$A1, pvar$A2)
+pvar<-pvar[pvar$IUPAC %in% c('R','Y','K','M'),]
+
+# Liftover data to GRCh38
+make_executable <- function(exe) {
+  Sys.chmod(exe, mode = (file.info(exe)$mode | "111"))
+}
+
+snp_modifyBuild_offline<-function (info_snp, liftOver, chain){
+  tmp_folder<-tempdir()
+
+  if (!all(c("chr", "pos") %in% names(info_snp)))
+    stop2("Please use proper names for variables in 'info_snp'. Expected %s.",
+          "'chr' and 'pos'")
+  liftOver <- normalizePath(liftOver)
+  make_executable(liftOver)
+  BED <- tempfile(fileext = ".BED")
+  info_BED <- with(info_snp, data.frame(paste0("chr", chr),
+                                        pos0 = pos - 1L, pos, id = seq_len(nrow(info_snp))))
+  fwrite(info_BED, BED, col.names = FALSE, sep = " ")
+  lifted<-paste0(tmp_folder,'/tmp.lifted')
+  unmapped<-paste0(tmp_folder,'/tmp.unmapped')
+  system(paste(liftOver, BED, chain, lifted, unmapped))
+  new_pos <- fread(lifted)
+  bad <- grep("^#", readLines(unmapped), value = TRUE, invert = TRUE)
+  print(paste0(length(bad)," variants have not been mapped."))
+  info_snp$pos <- NA
+  info_snp$pos[new_pos$V4] <- new_pos$V3
+  info_snp
+}
+
+names(pvar) <- c('chr','pos','snp','a1','a2','rsid','iupac')
+
+pvar_grch38<-snp_modifyBuild_offline(pvar, liftOver='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/software/liftover/liftover', chain='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/data/liftover/hg19ToHg38.over.chain.gz')
+
+pvar_grch36<-snp_modifyBuild_offline(pvar, liftOver='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/software/liftover/liftover', chain='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/data/liftover/hg19ToHg18.over.chain.gz')
+
+pvar[pvar_grch36, on=.(snp), pos_grch36 := i.pos]
+pvar[pvar_grch38, on=.(snp), pos_grch38 := i.pos]
+
+rm(pvar_grch36)
+rm(pvar_grch38)
+gc()
+
+pvar <- pvar[, c('chr','snp','rsid','a1','a2','iupac','pos_grch36','pos','pos_grch38'), with=F]
+names(pvar) <- c('CHR','SNP','RSID','A1','A2','IUPAC','BP_GRCh36','BP_GRCh37','BP_GRCh38')
+
+pvar<-pvar[complete.cases(pvar),]
+
+# Save intermediate file
+fwrite(pvar,'/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_snps.txt', quote = F, sep = ' ', na='NA')
+
+pvar<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_snps.txt')
+
+# Remove variants with duplicate RSIDs (retaining first instance)
+pvar <- pvar[!duplicated(pvar$RSID),]
+
+# Extract SNPs from plink2 files
+dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred')
+write.table(pvar$SNP, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist', col.names=F, row.names=F, quote=F)
+system('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL --make-pgen --extract /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg --threads 5')
+
+# Insert RSIDs into new plink files
+pvar_subset <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.pvar')
+pvar_subset <- pvar_subset[, c('#CHROM','POS','ID','REF','ALT'), with=F]
+pvar$ID <- pvar$SNP
+pvar_subset[pvar, on=.(ID), SNP := i.RSID]
+pvar_subset<-pvar_subset[, c('#CHROM','POS','SNP','REF','ALT'), with=F]
+names(pvar_subset)<-c('#CHROM','POS','ID','REF','ALT')
+fwrite(pvar_subset, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.pvar', col.names=T, row.names=F, quote=F, sep=' ')
+
+# Remove hard filtered individuals
+samp_meta<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz')
+samp_meta<-samp_meta[grepl("hard_filtered\":false", samp_meta$gnomad_sample_filters), ]
+
+# Remove PCA outliers
+samp_meta<-samp_meta[grepl("outlier\":false", samp_meta$hgdp_tgp_meta), ]
+
+# Remove relatives (we will use the sample meta-data for this - Estimated using PC-relate)
+library(jsonlite)
+kin_dat <- NULL
+for(i in 1:nrow(samp_meta)){
+  tmp <- fromJSON(samp_meta$relatedness_inference[i])
+  if(is.data.frame(tmp$related_samples)){
+    tmp$related_samples$ID<-samp_meta$s[i]
+    kin_dat<-rbind(kin_dat, tmp$related_samples)
+  }
+}
+
+# Restrict table to individuals in the genetic data
+psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.psam')
+psam<-psam[psam$`#IID` %in% samp_meta$s,]
+kin_dat<-kin_dat[kin_dat$s %in% psam$`#IID` & kin_dat$ID %in% psam$`#IID`, ]
+relations<-data.frame(id_1=kin_dat$s, id_2=kin_dat$ID, kin=kin_dat$kin)
+
+# Use GreedyRelated to find maximum unrelated set
+relations$Pair <- apply(relations, 1, function(row) {
+  # Construct a unique identifier for each pair regardless of order
+  paste(sort(c(row['id_1'], row['id_2'])), collapse = "_")
+})
+relations$Pair<-as.numeric(factor(relations$Pair))
+relations$Factor<-relations$kin
+relations$ID<-relations$id_1
+relations<-relations[, c('ID', 'Pair', 'Factor')]
+relations<-relations[order(relations$Pair), ]
+fwrite(relations, file='/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt', sep=' ', quote=F)
+
+system('/users/k1806347/oliverpainfel/Software/GreedyRelated/bin/GreedyRelated -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt -t 0.05 -s 1 > /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+
+remove<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+
+# Create keep file of unrelated individuals
+psam_unrel <- psam[!(psam$`#IID` %in% remove$V2),]
+nrow(psam_unrel) # 3313 - This is 65 less than in the preprint Table S3. Maybe Greedy related is worse?
+
+write.table(psam_unrel[, '#IID', with=F], '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep', col.names=T, row.names=F, quote=F)
+
+# Split by chromosome and retain unrelated individuals
+for(i in 1:23){
+  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep --chr ',i,' --make-pgen --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i))
+}
+
+# Create keep files and calculate allele frequencies for each population
+# Population data is stored withint the psam file
+psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr22.psam')
+pop_dat<-psam[, c('#IID','SuperPop')]
+names(pop_dat)<-c('#IID','POP')
+write.table(pop_dat, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt', row.names=F, col.names=T, quote=F)
+
+dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/')
+for(pop in unique(pop_dat$POP)){
+  write.table(pop_dat[pop_dat$POP == pop, '#IID', with=F], paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep'), col.names=F, row.names=F, quote=F)
+  dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop), recursive = T)
+  for(i in 1:23){
+    system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg.chr', i))
+  }
+}
+
+# Create frequency files across all reference individuals (TRANS)
+dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
+for(i in 1:23){
+  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg.chr', i))
+}
+
+####
+# Create .rds files for SNP stats
+####
+
+info_subset <- pvar[pvar$RSID %in% pvar_subset$ID, ]
+info_subset<-info_subset[,c('CHR','BP_GRCh37','RSID','A1','A2','BP_GRCh36','BP_GRCh38','IUPAC'), with=F]
+names(info_subset)<-c('#CHROM','POS_GRCh37','ID','ALT','REF','POS_GRCh36','POS_GRCh38','IUPAC')
+
+for(chr in 1:23){
+    ref<-info_subset[info_subset$`#CHROM` == chr,]
+    for(pop in unique(pop_dat$POP)){
+        # Read in reference frequency data
+        freq<-fread(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg.chr', chr,'.afreq'))
+
+        # The freq files have come from the reference files, so we can assume they are on the same strand
+        freq_match<-merge(ref, freq[,c('ID','ALT','REF','ALT_FREQS'), with=F], by=c('ID','ALT','REF'))
+        freq_swap<-merge(ref, freq[,c('ID','ALT','REF','ALT_FREQS'), with=F], by.x=c('ID','ALT','REF'), by.y=c('ID','REF','ALT'))
+        freq_swap$ALT_FREQS<-1-freq_swap$ALT_FREQS
+        tmp_freq<-rbind(freq_match, freq_swap)
+        tmp_freq<-tmp_freq[match(ref$ID, tmp_freq$ID),]
+
+        ref[[paste0('REF.FRQ.',pop)]]<-tmp_freq$ALT_FREQS
+    }
+
+    ref<-ref[,c("#CHROM","ID","POS_GRCh36","POS_GRCh37","POS_GRCh38","ALT","REF","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP))), with=F]
+    names(ref)<-c("CHR","SNP","BP_GRCh36","BP_GRCh37","BP_GRCh38","A1","A2","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP)))
+    saveRDS(ref, file = paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',chr,'.rds'))
+}
+
+```
+***
+
+## Package for zenodo
+
+<ref_dir>
+├── ref.chr<1-23>.<pgen/pvar/psam>
+├── ref.chr<1-23>.rds
+├── ref.pop.txt (#IID, POP - with header)
+├── ref.keep.list (POP and PATH - without header)
+├── keep_files
+│   └──<POP>.keep (#IID - with header)
+└── freq_files
+    └──<POP>
+        └──ref.<POP>.chr<CHR>.afreq # PLINK2 .afreq format
+
+```{bash}
+
+# Copy over all the relevant files
+mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
+
+for chr in $(seq 1 23); do
+  for file in $(echo pgen pvar psam rds); do
+    cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
+  done
+done
+
+cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.pop.txt
+
+mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files
+
+cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/*.keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/
+
+cp -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
+
+for pop in $(ls /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files); do
+ for chr in $(seq 1 23); do
+  mv /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.afreq /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/ref.${pop}.chr${chr}.afreq
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.log
+ done
+done
+
+# Compress folder and upload to zenodo
+cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
+tar -czvf genopred_1kg_hgdp_dense.tar.gz ref
+
 ```
 
 ***
@@ -329,7 +675,8 @@ pvar$FILTER<-NULL
 pvar<-pvar[nchar(pvar$REF) == 1,]
 pvar<-pvar[nchar(pvar$ALT) == 1,]
 names(pvar)<-c('CHR','BP','SNP','A1','A2','RSID')
-pvar<-pvar[pvar$CHR %in% 1:22,]
+pvar$CHR[pvar$CHR == "X"] <- "23"
+pvar<-pvar[pvar$CHR %in% 1:23,]
 pvar$CHR<-as.numeric(pvar$CHR)
 pvar<-pvar[!is.na(pvar$RSID),]
 pvar$IUPAC<-snp_iupac(pvar$A1, pvar$A2)
@@ -450,7 +797,7 @@ nrow(psam_unrel) # 3313 - This is 65 less than in the preprint Table S3. Maybe G
 write.table(psam_unrel[, '#IID', with=F], '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep', col.names=T, row.names=F, quote=F)
 
 # Split by chromosome and retain unrelated individuals
-for(i in 1:22){
+for(i in 1:23){
   system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep --chr ',i,' --make-pgen --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i))
 }
 
@@ -465,14 +812,14 @@ dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/')
 for(pop in unique(pop_dat$POP)){
   write.table(pop_dat[pop_dat$POP == pop, '#IID', with=F], paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep'), col.names=F, row.names=F, quote=F)
   dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop), recursive = T)
-  for(i in 1:22){
+  for(i in 1:23){
     system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg.chr', i))
   }
 }
 
 # Create frequency files across all reference individuals (TRANS)
 dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
-for(i in 1:22){
+for(i in 1:23){
   system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg.chr', i))
 }
 
@@ -484,7 +831,7 @@ info_subset <- pvar[pvar$RSID %in% pvar_subset$ID, ]
 info_subset<-info_subset[,c('CHR','BP_GRCh37','RSID','A1','A2','BP_GRCh36','BP_GRCh38','IUPAC'), with=F]
 names(info_subset)<-c('#CHROM','POS_GRCh37','ID','ALT','REF','POS_GRCh36','POS_GRCh38','IUPAC')
 
-for(chr in 1:22){
+for(chr in 1:23){
     ref<-info_subset[info_subset$`#CHROM` == chr,]
     for(pop in unique(pop_dat$POP)){
         # Read in reference frequency data
@@ -510,8 +857,8 @@ for(chr in 1:22){
 ## Package for zenodo
 
 <ref_dir>
-├── ref.chr<1-22>.<pgen/pvar/psam>
-├── ref.chr<1-22>.rds
+├── ref.chr<1-23>.<pgen/pvar/psam>
+├── ref.chr<1-23>.rds
 ├── ref.pop.txt (#IID, POP - with header)
 ├── ref.keep.list (POP and PATH - without header)
 ├── keep_files
@@ -522,10 +869,10 @@ for(chr in 1:22){
 
 ```{bash}
 
-# Copy over all the relavent files
+# Copy over all the relevant files
 mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
 
-for chr in $(seq 1 22); do
+for chr in $(seq 1 23); do
   for file in $(echo pgen pvar psam rds); do
     cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
   done
@@ -540,7 +887,7 @@ cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/*.keep /users
 cp -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
 
 for pop in $(ls /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files); do
- for chr in $(seq 1 22); do
+ for chr in $(seq 1 23); do
   mv /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.afreq /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/ref.${pop}.chr${chr}.afreq
   rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.log
  done
@@ -550,4 +897,4 @@ done
 cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
 tar -czvf genopred_1kg_hgdp_dense.tar.gz ref
 
-```
+```
\ No newline at end of file
diff --git a/functions/constants.R b/functions/constants.R
index ff8899c9..2e974d2e 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -1,5 +1,5 @@
 # Set range of chromosome numbers to use by default
-CHROMS <- 1:22
+CHROMS <- 1:23
 
 # Coordinates of high ld regions in build GRCh37
 long_ld_coord<-do.call(rbind, list(
@@ -47,4 +47,3 @@ pgs_methods_noneur <- c('ptclump','lassosum','lassosum2','megaprs','prscs','dbsl
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
 pgs_group_methods <- c('prscsx','xwing')
-
diff --git a/functions/pgs.R b/functions/pgs.R
index ac45a2dd..75fe55ad 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -202,7 +202,7 @@ read_geno <- function(target, format) {
 }
 
 # Read in PLINK .bim file
-read_bim<-function(dat, chr = 1:22){
+read_bim<-function(dat, chr = 1:23){
   bim<-NULL
   for(i in chr){
     bim<-rbind(bim, fread(paste0(dat, i,'.bim')))
@@ -230,7 +230,7 @@ read_pop_data <- function(x){
 }
 
 # Read in PLINK2 .pvar file
-read_pvar<-function(dat, chr = 1:22){
+read_pvar<-function(dat, chr = 1:23){
   pvar<-NULL
   for(i in chr){
     pvar<-rbind(pvar, fread(paste0(dat, i,'.pvar')))
@@ -269,13 +269,13 @@ remove_regions<-function(dat, regions){
 }
 
 # Read in GWAS summary statistics
-read_sumstats<-function(sumstats, chr = 1:22, log_file = NULL, extract = NULL, req_cols = c('CHR','BP','A1','A2','BETA','SE','P','FREQ')){
+read_sumstats<-function(sumstats, chr = 1:23, log_file = NULL, extract = NULL, req_cols = c('CHR','BP','A1','A2','BETA','SE','P','FREQ')){
   gwas<-fread(sumstats)
 
   log_add(log_file = log_file, message = paste0('sumstats contains ',nrow(gwas),' variants.'))
 
   # Retain requested chromosomes
-  if(!all(1:22 %in% chr)){
+  if(!all(1:23 %in% chr)){
     if(!('CHR' %in% names(gwas))){
       stop('Cannot filter sumstats by chromosome when CHR column is not present.')
     }
@@ -352,7 +352,7 @@ ldsc <- function(sumstats, ldsc, hm3_snplist, munge_sumstats, ld_scores, pop_pre
 }
 
 # Match A1 and A2 match a reference
-allele_match<-function(sumstats, ref_bim, chr = 1:22){
+allele_match<-function(sumstats, ref_bim, chr = 1:23){
   sumstats_ref<-merge(sumstats, ref_bim[, c('SNP','A1','A2'), with=F], by = 'SNP')
   swap_index <- sumstats_ref$A1.x == sumstats_ref$A2.y & sumstats_ref$A2.x == sumstats_ref$A1.y
   sumstats_ref$BETA[swap_index] <- -sumstats_ref$BETA[swap_index]
@@ -366,7 +366,7 @@ allele_match<-function(sumstats, ref_bim, chr = 1:22){
 }
 
 # Run DBSLMM
-dbslmm <- function(dbslmm, plink = plink, ld_blocks, chr = 1:22, bfile, sumstats, h2, h2f = 1, nsnp, nindiv, log_file = NULL, ncores=1){
+dbslmm <- function(dbslmm, plink = plink, ld_blocks, chr = 1:23, bfile, sumstats, h2, h2f = 1, nsnp, nindiv, log_file = NULL, ncores=1){
   # Create temp directory
   tmp_dir<-tempdir()
 
@@ -414,7 +414,7 @@ dbslmm <- function(dbslmm, plink = plink, ld_blocks, chr = 1:22, bfile, sumstats
 }
 
 # Calculate predictor correlations using LDAK
-ldak_pred_cor<-function(bfile, ldak, n_cores, chr = 1:22, keep = NULL){
+ldak_pred_cor<-function(bfile, ldak, n_cores, chr = 1:23, keep = NULL){
   tmp_dir<-tempdir()
   tmp_file<-tempfile()
 
@@ -436,7 +436,7 @@ ldak_pred_cor<-function(bfile, ldak, n_cores, chr = 1:22, keep = NULL){
 }
 
 # Read in external score file
-read_score <- function(score, chr = 1:22, log_file = NULL){
+read_score <- function(score, chr = 1:23, log_file = NULL){
 	# Read in score file
 	score <- fread(score)
 
diff --git a/functions/plink.R b/functions/plink.R
index d79f349e..4219fbfb 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -1,7 +1,7 @@
 #!/usr/bin/Rscript
-
+print("Using chromosome X")
 # Make a subset of plink1 binaries
-plink_subset<-function(plink=NULL, plink2=NULL, chr = 1:22, keep = NULL, extract = NULL, bfile=NULL, pfile=NULL, out, memory = 4000, threads = 1, make_bed = F){
+plink_subset<-function(plink=NULL, plink2=NULL, chr = 1:23, keep = NULL, extract = NULL, bfile=NULL, pfile=NULL, out, memory = 4000, threads = 1, make_bed = F){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -56,7 +56,7 @@ plink_subset<-function(plink=NULL, plink2=NULL, chr = 1:22, keep = NULL, extract
 }
 
 # Return a list of variants surviving QC using plink
-plink_qc_snplist<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, keep = NULL, chr = 1:22, threads = 1, memory = 4000, geno = NULL, maf = NULL, hwe = NULL){
+plink_qc_snplist<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, keep = NULL, chr = 1:23, threads = 1, memory = 4000, geno = NULL, maf = NULL, hwe = NULL){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -114,7 +114,7 @@ plink_qc_snplist<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, keep
 }
 
 # Merge plink files
-plink_merge<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:22, extract = NULL, keep = NULL, flip = NULL, make_bed =F, memory = 4000, out, threads = 1){
+plink_merge<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:23, extract = NULL, keep = NULL, flip = NULL, make_bed =F, memory = 4000, out, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -188,7 +188,7 @@ plink_merge<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:2
 }
 
 # Perform PCA using plink files
-plink_pca<-function(bfile=NULL, pfile=NULL, chr = 1:22, plink2, extract = NULL, keep = NULL, flip = NULL, memory = 4000, n_pc = 6, threads = 1){
+plink_pca<-function(bfile=NULL, pfile=NULL, chr = 1:23, plink2, extract = NULL, keep = NULL, flip = NULL, memory = 4000, n_pc = 6, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -231,7 +231,7 @@ plink_pca<-function(bfile=NULL, pfile=NULL, chr = 1:22, plink2, extract = NULL,
 }
 
 # Performing LD pruning
-plink_prune<-function(bfile=NULL, pfile=NULL, keep = NULL, plink=NULL, plink2=NULL, chr = 1:22, extract = NULL, memory = 4000, threads = 1){
+plink_prune<-function(bfile=NULL, pfile=NULL, keep = NULL, plink=NULL, plink2=NULL, chr = 1:23, extract = NULL, memory = 4000, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -292,7 +292,7 @@ plink_prune<-function(bfile=NULL, pfile=NULL, keep = NULL, plink=NULL, plink2=NU
 }
 
 # Peforming LD-based clumping
-plink_clump<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:22, sumstats, keep = NULL, memory = 4000, log_file = NULL, threads = 1){
+plink_clump<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:23, sumstats, keep = NULL, memory = 4000, log_file = NULL, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -356,7 +356,7 @@ plink_clump<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:2
 }
 
 # Generate kinship matrix and identify unrelated individuals (>2nd degree)
-plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:22, plink2='plink2', out, keep=NULL, threads = 1){
+plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:23, plink2='plink2', out, keep=NULL, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
@@ -410,7 +410,7 @@ plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:22, plink2=
   system(paste0('tail -n +2 ', tmp_dir, '/merged.king.cutoff.in.id > ', out, '.unrelated.keep'))
 }
 
-plink_score<-function(bfile=NULL, pfile=NULL, score, keep=NULL, extract=NULL, chr=1:22, frq=NULL, plink2=NULL, threads=1, fbm = F){
+plink_score<-function(bfile=NULL, pfile=NULL, score, keep=NULL, extract=NULL, chr=1:23, frq=NULL, plink2=NULL, threads=1, fbm = F){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }
diff --git a/functions/sumstat_cleaner.R b/functions/sumstat_cleaner.R
new file mode 100644
index 00000000..113ac025
--- /dev/null
+++ b/functions/sumstat_cleaner.R
@@ -0,0 +1,872 @@
+#' Interpret the header of GWAS summary statistics
+#'
+#' This function interprets the header of GWAS (Genome-Wide Association Studies) summary statistics. It reads the header, matches column names to a predefined dictionary, and provides a data frame indicating the original and interpreted column names, whether each column is kept or ignored, the reason for ignoring any columns, and a description of each interpreted column.
+#'
+#' @param sub_ss A data frame or matrix containing GWAS summary statistics, where the column names represent different data fields.
+#'
+#' @return A data frame with columns 'Original' (original column names from `sub_ss`), 'Interpreted' (standardized column names based on a predefined dictionary), 'Keep' (logical indicating whether the column is kept), 'Reason' (reason for ignoring a column, if applicable), and 'Description' (description of the interpreted column).
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' header_info <- head_interp(raw_sumstats_1)
+#' print(header_info)
+head_interp<-function(sub_ss){
+    message("head_interp() function started")  
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(sub_ss)) {
+    sub_ss <- data.table::as.data.table(sub_ss)
+  }
+
+  # Read in the header and interpret column names
+  sub_header<-names(sub_ss)
+
+  # Remove columns that are all NA
+  sub_ss_comp<-sub_ss[,apply(sub_ss, 2, function(x) !(all(is.na(x)))), with=F]
+  sub_header_comp<-names(sub_ss_comp)
+
+  int_header <- sub_header_comp
+  for(i in names(ss_head_dict)){
+    int_header[toupper(int_header) %in% ss_head_dict[[i]]] <- i
+  }
+  int_header[!(toupper(int_header) %in% unlist(ss_head_dict))]<-NA
+
+  # Show original and interpreted header
+  header_interp <- data.frame(Original = sub_header_comp,
+                              Interpreted = int_header)
+
+  # Show columns that are ignored due to be irrelevant, duplicated, or all NA
+  header_interp$Keep<-!(
+    !(int_header %in% names(ss_head_dict)) |
+      duplicated(int_header)
+  )
+
+  # Insert reason it was ignored
+  header_interp$Reason<-NA
+  header_interp$Reason[!(int_header %in% names(ss_head_dict))]<-'Not recognised'
+  header_interp$Reason[duplicated(int_header)]<-'Duplicated'
+
+  # Show columns ignored due to missingness
+  for(i in sub_header[!(sub_header %in% header_interp$Original)]){
+    header_interp<-rbind(header_interp, data.frame(Original = i,
+                                                   Interpreted = NA,
+                                                   Keep=F,
+                                                   Reason = 'First 1000 rows NA'))
+  }
+
+  header_interp[is.na(header_interp)]<-'NA'
+  header_interp$Keep<-as.character(header_interp$Keep)
+
+  # Insert description of each column after interpretation
+  header_labels<-data.frame(Interpreted=c('SNP','CHR','BP','A1','A2','P','OR','BETA','Z','SE','N','N_CAS','N_CON','NEF','FREQ','FRQ_A','FREQ_U','INFO'),
+                            Description=c("RSID for variant",
+                                          "Chromosome number",
+                                          "Base pair position",
+                                          "Allele 1 (effect allele)",
+                                          "Allele 2",
+                                          "P-value of association",
+                                          "Odds ratio effect size",
+                                          "BETA effect size",
+                                          "Z-score",
+                                          "Standard error of log(OR) or BETA",
+                                          "Total sample size",
+                                          "Number of cases",
+                                          "Number of controls",
+                                          "Effective sample size",
+                                          "Allele frequency",
+                                          "Allele frequency in cases",
+                                          "Allele frequency in controls",
+                                          "Imputation quality"))
+
+  header_interp<-merge(header_interp, header_labels, by='Interpreted', all.x=T)
+  header_interp$Keep<-factor(header_interp$Keep, levels=c('TRUE','FALSE'))
+  header_interp<-header_interp[order(header_interp$Keep),]
+  header_interp<-header_interp[,c('Original','Interpreted','Keep','Reason','Description')]
+
+  return(header_interp)
+}
+
+#' Check GWAS sumstat header is valid and update based on interpreted meaning
+#'
+#' This function checks if the header of a GWAS (Genome-Wide Association Studies) summary statistics dataset is valid and updates it based on interpreted meanings. It first interprets the header using `head_interp`, then logs the interpretation, checks for the presence of required columns, logs any errors or warnings, and finally updates the summary statistics dataset with the interpreted header.
+#'
+#' @param sumstats A data frame containing GWAS summary statistics where the column names are expected to be in the header.
+#' @param log_file An optional string specifying the path and name of the log file where messages about the header interpretation and validation will be recorded. If NULL, messages are printed to the console.
+#'
+#' @return A data frame of GWAS summary statistics with updated and validated column names based on the interpreted header.
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' formatted_ss_data <- format_header(raw_sumstats_1)
+#' print(formatted_ss_data)
+format_header<-function(sumstats, log_file = NULL){
+
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(sumstats)) {
+    sumstats <- data.table::as.data.table(sumstats)
+  }
+
+  header_interpretation<-head_interp(sumstats[1:1000,])
+  header_interpretation<-header_interpretation[match(names(sumstats), header_interpretation$Original),]
+
+  log_add(log_file = log_file, message = '---------------')
+
+  if(is.null(log_file)){
+    print.data.frame(header_interpretation, row.names=F, quote=F, right=F)
+  } else {
+    sink(file = log_file, append = T)
+    print.data.frame(header_interpretation, row.names=F, quote=F, right=F)
+    sink()
+  }
+
+  log_add(log_file = log_file, message = '---------------')
+
+  # Throw an error if required columns are not available
+  error<-F
+  messages <- NULL
+  if(!all(c('A1','A2') %in% header_interpretation$Interpreted)){
+    messages <- c(messages, "Error: Missing A1 and A2 information.")
+    error<-T
+  }
+  if(!any(c('OR','BETA','Z') %in% header_interpretation$Interpreted)){
+    messages <- c(messages, "Error: Effect size (BETA, OR, Z) must be present.")
+    error<-T
+  }
+  if(!('SNP' %in% header_interpretation$Interpreted) & !(all(c('CHR','BP') %in% header_interpretation$Interpreted))){
+    messages <- c(messages, "Error: Either SNP, or CHR and BP, must be present.")
+    error<-T
+  }
+  if(any(c('N_CAS','N_CON') %in% header_interpretation$Interpreted) & sum(c('N_CAS','N_CON') %in% header_interpretation$Interpreted) == 1 & !('N' %in% names(sumstats))){
+    messages <- c(messages, "Error: Either N, or both N_CAS and N_CON must be present.")
+    error<-T
+  }
+  if(any(c('FRQ_A','FRQ_U') %in% header_interpretation$Interpreted) & sum(c('FRQ_A','FRQ_U') %in% header_interpretation$Interpreted) == 1){
+    messages <- c(messages, "Warning: Both FRQ_A and FRQ_U must be present for either to be considered.")
+  }
+
+  if(!is.null(messages)){
+    log_add(log_file = log_file, message = messages)
+
+    if(error){
+      stop(paste(messages, sep='\n'))
+    }
+  }
+
+  # Remove columns that are not interpreted
+  sumstats <- sumstats[, as.logical(header_interpretation$Keep), with=F]
+
+  # Update sumstat header
+  names(sumstats)<-header_interpretation$Interpreted[as.logical(header_interpretation$Keep)]
+
+  # Check whether SNP contains RSIDs or is CHR:BP:A1:A2
+  if('SNP' %in% names(sumstats)){
+    if(all(grepl("^\\d+:[0-9]+:.+:.+$", sumstats$SNP))){
+      log_add(log_file = log_file, message = 'SNP column contains CHR:BP:A1:A2 information.')
+      snp_dat<-data.table(do.call(rbind, strsplit(sumstats$SNP, ':')))
+      if(!('CHR' %in% names(sumstats))){
+        log_add(log_file = log_file, message = 'CHR information extracted from SNP column.')
+        sumstats$CHR<-snp_dat$V1
+      }
+      if(!('BP' %in% names(sumstats))){
+        log_add(log_file = log_file, message = 'BP information extracted from SNP column.')
+        sumstats$BP<-snp_dat$V2
+      }
+      log_add(log_file = log_file, message = 'SNP column has been removed.')
+      sumstats$SNP <- NULL
+    }
+  }
+
+  return(sumstats)
+}
+
+#' Calculate average allele frequency across cases and controls
+#'
+#' This function calculates the average allele frequency across cases and controls in GWAS (Genome-Wide Association Study) summary statistics. It accounts for different scenarios based on the available data, such as when case and control counts are available, or when a sampling fraction is provided. The function also logs the method used for the calculation.
+#'
+#' @param sumstats A data frame containing GWAS summary statistics, which should include columns for allele frequencies in cases (`FRQ_A`), controls (`FRQ_U`), and optionally the number of cases (`N_CAS`) and controls (`N_CON`).
+#' @param sampling Optional; a numeric value representing the sampling fraction of cases in the study population. If not provided or NA, and `N_CAS` and `N_CON` are missing, the function assumes equal case and control numbers.
+#' @param log_file Optional; a string specifying the path and name of the log file to record the process of the function. If NULL or not provided, no logging is performed.
+#'
+#' @return Returns a numeric vector representing the average allele frequency across cases and controls. If allele frequency information is not available in `sumstats`, it returns NULL.
+#' @export
+#'
+#' @examples
+#' mean_freq <- calc_mean_freq(clean_sumstats_1, sampling = 0.5)
+#' print(mean_freq)
+calc_mean_freq<-function(sumstats, sampling = NA, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(sumstats)) {
+    sumstats <- data.table::as.data.table(sumstats)
+  }
+
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(sumstats)) {
+    sumstats <- data.table::as.data.table(sumstats)
+  }
+
+  if(all(c('FRQ_A','FRQ_U') %in% names(sumstats))){
+    if(all(c('FRQ_A','FRQ_U','N_CAS','N_CON') %in% names(sumstats))){
+      FREQ<- ((sumstats$FRQ_A*sumstats$N_CAS) + (sumstats$FRQ_U*sumstats$N_CON))/(sumstats$N_CAS + sumstats$N_CON)
+      log_add(log_file = log_file, message = 'Average allele frequency calculated weighted by N_CAS and N_CON.')
+    }
+    if(all(c('FRQ_A','FRQ_U') %in% names(sumstats)) & any(!(c('FRQ_A','FRQ_U') %in% names(sumstats))) & !(is.na(sampling))){
+      FREQ<- (sumstats$FRQ_A*sampling) + (sumstats$FRQ_U*(1-sampling))
+      log_add(log_file = log_file, message = 'Average allele frequency calculated weighted by sampling fraction.')
+    }
+    if(all(c('FRQ_A','FRQ_U') %in% names(sumstats)) & any(!(c('FRQ_A','FRQ_U') %in% names(sumstats))) & is.na(sampling)){
+      FREQ<- mean(sumstats$FRQ_A, sumstats$FRQ_U)
+      log_add(log_file = log_file, message = 'Average allele frequency calculated assuming N_CAS == N_CON.')
+    }
+  } else {
+    FREQ<-NULL
+  }
+
+  return(FREQ)
+}
+
+#' Create IUPAC Codes for pairs of SNP alleles
+#'
+#' This function takes two vectors representing pairs of alleles for SNP (Single Nucleotide Polymorphism) pairs and converts them into IUPAC (International Union of Pure and Applied Chemistry) codes. It returns an error message if the input vectors are of different lengths.
+#'
+#' @param x A character vector representing the first nucleotide of the allele pairs. Possible values are 'A', 'T', 'C', 'G'.
+#' @param y A character vector of the same length as `x`, representing the second nucleotide of the allele pairs. Possible values are 'A', 'T', 'C', 'G'.
+#'
+#' @return A character vector of IUPAC codes corresponding to the input SNP allele pairs. If `x` and `y` are different lengths, the function prints an error message and does not return a value.
+#' @export
+#'
+#' @examples
+#' iupac_codes <- snp_iupac(clean_sumstats_1$A1, clean_sumstats_1$A2)
+snp_iupac<-function(x=NA, y=NA){
+  if(length(x) != length(y)){
+    print('x and y are different lengths')
+  } else {
+    iupac<-rep(NA, length(x))
+    iupac[x == 'A' & y =='T' | x == 'T' & y =='A']<-'W'
+    iupac[x == 'C' & y =='G' | x == 'G' & y =='C']<-'S'
+    iupac[x == 'A' & y =='G' | x == 'G' & y =='A']<-'R'
+    iupac[x == 'C' & y =='T' | x == 'T' & y =='C']<-'Y'
+    iupac[x == 'G' & y =='T' | x == 'T' & y =='G']<-'K'
+    iupac[x == 'A' & y =='C' | x == 'C' & y =='A']<-'M'
+    return(iupac)
+  }
+}
+
+#' Detect the Genome Build of Target Using Reference Chromosome and Base Pair Information
+#'
+#' This function compares target SNP data with reference data to determine the genome build of the target. It checks chromosome and base pair concordance across different builds.
+#'
+#' @param ref A data frame containing reference SNP data with chromosome, base pair, and other relevant information.
+#' @param targ A data frame containing target SNP data to be compared against the reference.
+#' @param log_file An optional path to a log file where messages will be recorded.
+#' @param overlap_thresh A numeric value specifying the minimum overlap proportion required to consider a genome build as a good match. The overlap is typically expressed as the fraction of shared SNPs between the target and reference datasets. A higher threshold ensures stricter matching, while a lower threshold allows for more flexibility when data quality or completeness is lower.
+#'
+#' @return Returns the detected genome build as a character string, or NA if the build cannot be determined.
+#' @export
+#'
+#' @examples
+#' reference_data_path <- gsub('22.rds', '',
+#'   system.file("extdata", "ref.chr22.rds", package = "GenoUtils"))
+#' ref <- readRDS(paste0(reference_data_path,'22.rds'))
+#' targ <- clean_sumstats_1[clean_sumstats_1$CHR == 22,]
+#' detected_build <- detect_build(ref, targ)
+detect_build<-function(ref, targ, log_file = NULL, overlap_thresh=0.2){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(ref)) {
+    ref <- data.table::as.data.table(ref)
+  }
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(!(all(c('CHR','BP') %in% names(targ)))){
+    stop('CHR and BP columns must be present in targ.\n')
+  }
+  if(!(all(c("CHR","A1","A2") %in% names(targ)) & any(grepl('BP_GRCh', names(ref))))){
+    stop('CHR, A1, A2 and BP coordinates must be present in ref.\n')
+  }
+
+  # Insert IUPAC codes if missing
+  if(!('IUPAC' %in% names(targ))){
+    targ$IUPAC <- snp_iupac(targ$A1, targ$A2)
+  }
+  if(!('IUPAC' %in% names(ref))){
+    ref$IUPAC <- snp_iupac(ref$A1, ref$A2)
+  }
+
+  builds<-as.numeric(gsub('BP_GRCh','',names(ref)[grepl('BP_GRCh', names(ref))]))
+
+  # Check target-ref condordance of BP across builds
+  ref$CHR<-as.character(ref$CHR)
+  targ$CHR<-as.character(targ$CHR)
+
+  matched<-list()
+  build_overlap<-NULL
+  target_build<-NA
+  for(build_i in builds){
+    matched<-merge(targ, ref, by.x=c('CHR','BP','IUPAC'), by.y=c('CHR',paste0('BP_GRCh', build_i),'IUPAC'))
+    overlap_target <- nrow(matched) / nrow(targ)  # Proportion of matched SNPs in target
+    overlap_ref <- nrow(matched) / nrow(ref)      # Proportion of matched SNPs in reference
+
+    overlap<-nrow(matched)/nrow(targ)
+    build_overlap<-rbind(build_overlap, data.frame(build = build_i,
+                                                   nsnp = nrow(matched),
+                                                   overlap_target = overlap_target,
+                                                   overlap_ref = overlap_ref))
+    log_add(log_file = log_file, message = paste0('GRCh', build_i, ' match: ', round(overlap_target * 100, 2), '% (Target), ', round(overlap_ref * 100, 2), '% (Ref)'))
+  }
+
+  best_build <- build_overlap[which.max(build_overlap$overlap_target), ]
+
+  # Set a threshold where either the target or reference overlap is greater than 10%
+  if (best_build$overlap_target > overlap_thresh | best_build$overlap_ref > overlap_thresh) {
+    target_build <- paste0('GRCh', best_build$build)
+  }
+
+  return(target_build)
+}
+
+#' Convert SNP Alleles to Their Complementary Alleles
+#'
+#' This function converts each allele in a given vector to its complementary allele.
+#'
+#' @param x A character vector containing alleles ('A', 'T', 'C', 'G').
+#'
+#' @return A character vector with the complementary alleles.
+#' @export
+#'
+#' @examples
+#' complementary_alleles <- snp_allele_comp(c('A', 'C', 'G', 'T'))
+snp_allele_comp<-function(x=NA){
+  x_new<-x
+  x_new[x == 'A']<-'T'
+  x_new[x == 'T']<-'A'
+  x_new[x == 'G']<-'C'
+  x_new[x == 'C']<-'G'
+  x_new[!(x %in% c('A','T','G','C'))]<-NA
+  return(x_new)
+}
+
+#' Calculate Effective Sample Size
+#'
+#' This function calculates the effective sample size for a study based on the number of cases and controls.
+#'
+#' @param ncas The number of cases in the study.
+#' @param ncon The number of controls in the study.
+#'
+#' @return Returns the effective sample size as a numeric value.
+#' @export
+#'
+#' @examples
+#' effective_size <- neff(1000, 1500)
+neff<-function(ncas, ncon){
+  return(4/((1/ncas)+(1/ncon)))
+}
+
+#' Remove Ambiguous SNPs from Data
+#'
+#' This function filters out SNPs with ambiguous IUPAC codes (R, Y, K, M) from the given data.
+#'
+#' @param dat A data frame containing SNP data with IUPAC codes or alleles (A1, A2).
+#'
+#' @return A data frame with ambiguous SNPs removed.
+#' @export
+#'
+#' @examples
+#' cleaned_data <- remove_ambig(clean_sumstats_1)
+remove_ambig<-function(dat){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(dat)) {
+    dat <- data.table::as.data.table(dat)
+  }
+
+  if(!('IUPAC' %in% names(dat))){
+    if(!(all(c('A1','A2') %in% names(dat)))){
+      stop('Either IUPAC, or A1 and A2 must be present')
+    } else {
+      iupac_codes <- snp_iupac(dat$A1, dat$A2)
+      subset_condition <- iupac_codes %in% c('R', 'Y', 'K', 'M')
+    }
+  } else {
+    subset_condition <- dat$IUPAC %in% c('R', 'Y', 'K', 'M')
+  }
+
+  return(dat[subset_condition, ])
+}
+
+#' Detect Strand Flips in SNP Data
+#'
+#' This function identifies SNPs in the target that are on the opposite strand compared to the reference.
+#'
+#' @param targ A vector of IUPAC codes for the target SNPs.
+#' @param ref A vector of IUPAC codes for the reference SNPs.
+#'
+#' @return A logical vector indicating which SNPs are flipped.
+#' @export
+#'
+#' @examples
+#' flipped_snps <- detect_strand_flip(iupac_codes$target, iupac_codes$reference)
+detect_strand_flip<-function(targ, ref){
+  flipped<-(
+      (!is.na(targ) & !is.na(ref)) &
+      (
+        (targ == 'R' & ref == 'Y') |
+        (targ == 'Y' & ref == 'R') |
+        (targ == 'K' & ref == 'M') |
+        (targ == 'M' & ref == 'K')
+      )
+    )
+
+  return(flipped)
+}
+
+#' Harmonize Target Data with Reference Data
+#'
+#' This function harmonizes target SNP data with reference SNP data by alleles, and either chromosome and base pair, or SNP ID. It accounts for strand flips between the reference and target.
+#'
+#' @param targ A data frame of target SNP data to be harmonized.
+#' @param ref_rds The path to reference data files in RDS format.
+#' @param population The reference population matching the GWAS sample. This option is used to determine ancestry-matched reference allele frequencies.
+#' @param log_file An optional path to a log file where messages will be recorded.
+#' @param chr An optional numeric vector indicating chromosomes to be processed.
+#'
+#' @return A harmonized data frame of target SNP data.
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' # Get path and prefix to example ref_rds data
+#' reference_data_path <- gsub( '22.rds','',
+#'   system.file("extdata", "ref.chr22.rds", package = "GenoUtils"))
+#' harmonised_data <- ref_harmonise(clean_sumstats_1, reference_data_path, 'EUR')
+#' print(head(harmonised_data))
+ref_harmonise<-function(targ, ref_rds, population, log_file = NULL, chr = 1:23){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  # Insert IUPAC in targ if not already present
+  if(!any(names(targ) == 'IUPAC')){
+    targ$IUPAC <- snp_iupac(targ$A1, targ$A2)
+  }
+
+  ref_tmp<-readRDS(file = paste0(ref_rds, max(chr), '.rds'))
+  if(!(all(c('CHR','A1','A2','IUPAC') %in% names(ref_tmp)) & any(grepl('BP_GRCh', names(ref_tmp))))){
+    stop('CHR, A1, A2, IUPAC and BP coordinates must be present in ref_rds files.\n')
+  }
+
+  if(!(any(grepl('REF.FRQ', names(ref_tmp))))){
+    stop('There are no REF.FRQ.<POP> columns.\n')
+  }
+
+  ref_pops <- gsub('REF.FRQ.', '', names(ref_tmp)[grepl('REF.FRQ', names(ref_tmp))])
+  if(!(population %in% ref_pops)){
+    stop(paste0('Specified reference population must be one of the following: ', paste0(ref_pops, collapse=', '),'\n'))
+  }
+
+  # Check whether CHR and BP information are present
+  chr_bp_avail<-sum(c('CHR','BP') %in% names(targ)) == 2
+
+  # Check whether RSIDs are available for majority of SNPs in GWAS
+  rsid_avail<-(sum(grepl('rs', targ$SNP)) > 0.9*length(targ$SNP))
+
+  # Remove REF.FREQ column from targ if present
+  if(any(names(targ) == 'REF.FREQ')){
+    targ$REF.FREQ<-NULL
+  }
+
+  targ_matched<-NULL
+  flip_logical_all<-NULL
+  target_build<-NA
+
+  if(chr_bp_avail){
+    log_add(log_file = log_file, message = 'Merging sumstats with reference using CHR, BP, A1, and A2')
+
+    ###
+    # Determine build
+    ###
+
+    ref_tmp<-readRDS(file = paste0(ref_rds, max(targ$CHR), '.rds'))
+    target_build <- detect_build( ref = ref_tmp,
+                                  targ = targ[targ$CHR == max(targ$CHR),],
+                                  log_file = log_file)
+
+    if(!is.na(target_build)){
+      for(i in chr){
+
+        # Read reference data
+        ref_i<-readRDS(file = paste0(ref_rds,i,'.rds'))
+
+        # Retain only non-ambiguous SNPs
+        ref_i<-remove_ambig(ref_i)
+
+        # Rename columns prior to merging with target
+        names(ref_i)<-paste0('REF.',names(ref_i))
+        names(ref_i)[names(ref_i) == paste0('REF.REF.FRQ.',population)]<-'REF.FREQ'
+        ref_i$BP<-ref_i[[paste0('REF.BP_',target_build)]]
+        ref_i<-ref_i[, c('REF.CHR','REF.SNP','BP','REF.BP_GRCh37','REF.A1','REF.A2','REF.IUPAC','REF.FREQ'), with=F]
+
+        # Subset chromosome i from target
+        targ_i<-targ[targ$CHR == i,]
+
+        # Merge target and reference by BP
+        ref_target<-merge(targ_i, ref_i, by='BP')
+
+        # Identify targ-ref strand flips, and flip target
+        flip_logical<-detect_strand_flip(targ = ref_target$IUPAC, ref = ref_target$REF.IUPAC)
+        flip_logical_all<-c(flip_logical_all, flip_logical)
+
+        flipped<-ref_target[flip_logical,]
+        flipped$A1<-snp_allele_comp(flipped$A1)
+        flipped$A2<-snp_allele_comp(flipped$A2)
+        flipped$IUPAC<-snp_iupac(flipped$A1, flipped$A2)
+
+        # Identify SNPs that have matched IUPAC
+        matched<-ref_target[ref_target$IUPAC == ref_target$REF.IUPAC,]
+        matched<-rbind(matched, flipped)
+
+        # Flip REF.FREQ if alleles are swapped
+        matched$REF.FREQ[matched$A1 != matched$REF.A1]<-1-matched$REF.FREQ[matched$A1 != matched$REF.A1]
+
+        # Retain reference SNP, REF.FREQ, and REF.BP_GRCh37 data
+        matched<-matched[, names(matched) %in% c('CHR','REF.BP_GRCh37','REF.SNP','A1','A2','BETA','SE','OR','Z','FREQ','REF.FREQ','N','INFO','P'), with=F]
+        names(matched)[names(matched) == 'REF.SNP']<-'SNP'
+        names(matched)[names(matched) == 'REF.BP_GRCh37']<-'BP'
+
+        targ_matched<-rbind(targ_matched, matched)
+      }
+    }
+
+    if(is.na(target_build) & !(rsid_avail)){
+      log_add(log_file = log_file, message = 'Error: Target build could not be determined and SNP IDs unavailable.')
+      stop('Target build could not be determined and SNP IDs unavailable.\n')
+    }
+    if(is.na(target_build) & rsid_avail){
+      log_add(log_file = log_file, message = 'Target build could not be determined from CHR and BP data.')
+    }
+  }
+
+  if(is.na(target_build) & rsid_avail){
+    log_add(log_file = log_file, message = 'Using SNP, A1 and A2 to merge with the reference.')
+
+    for(i in chr){
+
+      # Read reference data
+      ref_i<-readRDS(file = paste0(ref_rds,i,'.rds'))
+
+      # Retain only non-ambiguous SNPs
+      ref_i<-remove_ambig(ref_i)
+
+      # Rename columns prior to merging with target
+      names(ref_i)<-paste0('REF.',names(ref_i))
+      names(ref_i)[names(ref_i) == paste0('REF.REF.FRQ.',population)]<-'REF.FREQ'
+      ref_i<-ref_i[, c('REF.CHR','REF.SNP','REF.BP_GRCh37','REF.A1','REF.A2','REF.IUPAC','REF.FREQ'), with=F]
+
+      # Merge target and reference by SNP ID
+      ref_target<-merge(targ, ref_i, by.x='SNP', by.y='REF.SNP')
+
+      # Identify targ-ref strand flips, and flip target
+      flip_logical<-detect_strand_flip(targ = ref_target$IUPAC, ref = ref_target$REF.IUPAC)
+      flip_logical_all<-c(flip_logical_all, flip_logical)
+
+      flipped<-ref_target[flip_logical,]
+      flipped$A1<-snp_allele_comp(flipped$A1)
+      flipped$A2<-snp_allele_comp(flipped$A2)
+      flipped$IUPAC<-snp_iupac(flipped$A1, flipped$A2)
+
+      # Identify SNPs that have matched IUPAC
+      matched<-ref_target[ref_target$IUPAC == ref_target$REF.IUPAC,]
+      matched<-rbind(matched, flipped)
+
+      # Flip REF.FREQ if alleles are swapped
+      matched$REF.FREQ[matched$A1 != matched$REF.A1]<-1-matched$REF.FREQ[matched$A1 != matched$REF.A1]
+
+      # Retain reference CHR and BP_GRCh37 data
+      matched<-matched[, names(matched) %in% c('REF.CHR','REF.BP_GRCh37','SNP','A1','A2','BETA','SE','OR','Z','FREQ','REF.FREQ','N','INFO','P'), with=F]
+      names(matched)[names(matched) == 'REF.CHR']<-'CHR'
+      names(matched)[names(matched) == 'REF.BP_GRCh37']<-'BP'
+
+      targ_matched<-rbind(targ_matched, matched)
+    }
+  }
+
+  log_add(log_file = log_file, message = paste0('After matching variants to the reference, ',nrow(targ_matched),' variants remain.'))
+  log_add(log_file = log_file, message = paste0(sum(flip_logical_all), ' variants were flipped to match reference.'))
+
+  return(targ_matched)
+}
+
+#' Filter Variants Based on INFO Score Threshold
+#'
+#' This function removes variants from the target data that have an INFO score below the specified threshold.
+#'
+#' @param targ A data frame of SNP data with an INFO column.
+#' @param thresh A numeric threshold for the INFO score.
+#' @param log_file An optional path to a log file where messages will be recorded.
+#'
+#' @return A filtered data frame of SNP data.
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' filtered_data <- filter_info(clean_sumstats_1, 0.8)
+filter_info<-function(targ, thresh, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(sum(names(targ) == 'INFO') == 1){
+    targ<-targ[targ$INFO >= thresh,]
+    log_add(log_file = log_file, message = paste0('After removal of SNPs with INFO < ',thresh,', ',nrow(targ),' variants remain.'))
+  } else {
+    log_add(log_file = log_file, message = 'INFO column is not present.')
+  }
+  return(targ)
+}
+
+#' Filter Variants Based on Minor Allele Frequency (MAF) Threshold
+#'
+#' This function removes variants from the target dataset that have a minor allele frequency (MAF) below a specified threshold. It uses either the reported MAF (`FREQ` column) or the reference MAF (`REF.FREQ` column), depending on the `ref` argument.
+#'
+#' @param targ A data frame containing SNP data.
+#' @param thresh A numeric threshold for MAF.
+#' @param ref Logical, if TRUE, uses the `REF.FREQ` column for filtering, otherwise uses `FREQ`.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#'
+#' @return A filtered data frame with variants meeting the MAF threshold.
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' filtered_data <- filter_maf(targ = clean_sumstats_1, thresh = 0.05, ref = FALSE)
+filter_maf<-function(targ, thresh, ref = F, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(ref == F){
+    if(any(names(targ) == 'FREQ')){
+      targ<-targ[targ$FREQ >= thresh & targ$FREQ <= (1-thresh),]
+      log_add(log_file = log_file, message = paste0('After removal of SNPs with reported MAF < ',thresh,', ',nrow(targ),' variants remain.'))
+    } else {
+      log_add(log_file = log_file, message = 'FREQ  column is not present.')
+    }
+  } else {
+    if(any(names(targ) == 'REF.FREQ')){
+      targ<-targ[targ$REF.FREQ >= thresh & targ$REF.FREQ <= (1-thresh),]
+      log_add(log_file = log_file, message = paste0('After removal of SNPs with reference MAF < ',thresh,', ',nrow(targ),' variants remain.'))
+    } else {
+      log_add(log_file = log_file, message = 'REF.FREQ  column is not present.')
+    }
+  }
+
+  return(targ)
+}
+
+#' Remove Variants with Discrepancy in Reported and Reference Allele Frequencies
+#'
+#' This function filters out variants from the target dataset where the absolute difference between reported and reference allele frequencies exceeds a specified threshold. Optionally, it can generate a plot of these discrepancies.
+#'
+#' @param targ A data frame containing SNP data with both reported and reference allele frequencies.
+#' @param thresh A numeric threshold for the absolute frequency difference.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#' @param plot_file Optional; path to save a plot file visualizing the frequency discrepancies.
+#'
+#' @return A filtered data frame with variants within the specified frequency discrepancy threshold.
+#' @export
+#'
+#' @import data.table
+#' @import grDevices
+#' @import graphics
+
+#' @examples
+#' filtered_data <- discord_maf( targ = clean_sumstats_1,
+#'                               thresh = 0.01,
+#'                               plot_file = paste0(tempdir(),"/plot.png"))
+discord_maf<-function(targ, thresh, log_file = NULL, plot_file = NA){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(!(any(names(targ) %in% 'REF.FREQ'))) {
+    stop('REF.FREQ column must be present in targ')
+  }
+
+  if(sum(names(targ) == 'FREQ') == 1){
+    targ$diff<-abs(targ$FREQ-targ$REF.FREQ)
+
+    if(!is.na(plot_file)){
+      png(plot_file, units='px', res=300, width=1200, height=1200)
+      plot(targ$REF.FREQ[targ$diff > thresh],targ$FREQ[targ$diff > thresh], xlim=c(0,1), ylim=c(0,1), xlab='Reference Allele Frequency', ylab='Sumstat Allele Frequency')
+      abline(coef = c(0,1))
+      dev.off()
+    }
+
+    targ<-targ[targ$diff < thresh,]
+    targ$diff<-NULL
+
+    log_add(log_file = log_file, message = paste0('After removal of SNPs with absolute MAF difference of < ',thresh,', ',nrow(targ),' variants remain.'))
+  } else {
+    log_add(log_file = log_file, message = 'Reported MAF column is not present, so discordance with reference cannot be determined.')
+  }
+  return(targ)
+}
+
+#' Filter Variants Based on Sample Size Deviation
+#'
+#' This function removes variants from the target dataset where the sample size (`N`) is more than a specified number of standard deviations away from the median sample size.
+#'
+#' @param targ A data frame containing SNP data with a sample size column (`N`).
+#' @param n_sd The number of standard deviations from the median to use as the threshold.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#'
+#' @return A filtered data frame with variants within the specified sample size range.
+#' @export
+#'
+#' @import data.table
+#' @import stats
+#'
+#' @examples
+#' filtered_data <- filter_n(targ = clean_sumstats_1, n_sd = 3)
+filter_n <- function(targ, n_sd = 3, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(length(unique(targ$N)) > 1){
+    thresh <- n_sd*sd(targ$N)
+    targ <- targ[targ$N < median(targ$N) + thresh & targ$N > median(targ$N) - thresh,]
+
+    log_add(log_file = log_file, message = paste0('After removal of SNPs with N > ',median(targ$N)+thresh,' or < ',median(targ$N)-thresh,', ',nrow(targ),' variants remain.'))
+  } else {
+    log_add(log_file = log_file, message = 'N column is not present or invariant.')
+  }
+  return(targ)
+}
+
+#' Convert Odds Ratios or Z-scores to Beta Coefficients
+#'
+#' This function calculates Beta coefficients for SNP data, using either available odds ratios (OR) or Z-scores and allele frequencies, if Beta coefficients are not already present.
+#'
+#' @param targ A data frame containing SNP data with OR or Z-scores.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#'
+#' @return The input data frame with added `BETA` column.
+#' @export
+#'
+#' @import data.table
+#'
+#' @examples
+#' updated_data <- insert_beta(targ = clean_sumstats_1)
+insert_beta <- function(targ, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  # If OR is present but BETA is not, convert OR to logOR and name it BETA
+  if('OR' %in% names(targ) & !('BETA' %in% names(targ))){
+    targ$BETA<-log(targ$OR)
+    log_add(log_file = log_file, message = 'BETA column inserted based on log(OR).')
+  }
+
+  # If Z is present but BETA is not, calculate it based on sample size, allele frequency and Z score
+  if('Z' %in% names(targ) & !('BETA' %in% names(targ))){
+    if('FREQ' %in% names(targ)){
+      frq_tmp<-targ$FREQ
+      log_add(log_file = log_file, message = 'BETA and SE column inserted based on Z, FREQ, and N.')
+    } else {
+      frq_tmp<-targ$REF.FREQ
+      log_add(log_file = log_file, message = 'BETA and SE column inserted based on Z, REF.FREQ, and N')
+    }
+
+    targ$SE <- 1/sqrt((2*frq_tmp)*(1-(frq_tmp))*(targ$N + (targ$Z^2)))
+    targ$BETA <- targ$Z * targ$SE
+  }
+  return(targ)
+}
+
+#' Compute Standard Error from Beta and P-value
+#'
+#' This function calculates the standard error (SE) for each SNP's Beta coefficient, based on its P-value, if SE is not already present.
+#'
+#' @param targ A data frame containing SNP data with Beta coefficients and P-values.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#'
+#' @return The input data frame with added `SE` column.
+#' @export
+#'
+#' @import data.table
+#' @import stats
+#'
+#' @examples
+#' updated_data <- insert_se(targ = clean_sumstats_1)
+insert_se <- function(targ, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(sum(names(targ) == 'SE') == 0){
+    if(any(!(c('BETA','P') %in% names(targ)))){
+      stop('BETA and P columns must be present to compute SE.\n')
+    }
+
+    z_tmp<-abs(qnorm(targ$P/2))
+    targ$SE<-abs(targ$BETA/z_tmp)
+
+    log_add(log_file = log_file, message = 'SE column inserted based on BETA and P.')
+  }
+  return(targ)
+}
+
+#' Adjust P-values to Avoid Genomic Control Artifacts
+#'
+#' This function checks for and adjusts P-values that may have been affected by genomic control. It recalculates P-values using available standard errors and Beta coefficients.
+#'
+#' @param targ A data frame containing SNP data with Beta coefficients, standard errors, and P-values.
+#' @param log_file Optional; path to a log file where messages will be recorded.
+#'
+#' @return The input data frame with adjusted P-values.
+#' @export
+#'
+#' @import data.table
+#' @import stats
+#'
+#' @examples
+#' # Read in example clean sumstats
+#' adjusted_data <- avoid_gc(targ = clean_sumstats_1)
+avoid_gc<-function(targ, log_file = NULL){
+  # Convert data.frame to data.table if necessary
+  if (is.data.frame(targ)) {
+    targ <- data.table::as.data.table(targ)
+  }
+
+  if(sum(names(targ) == 'SE') == 1){
+    targ$Z<-targ$BETA/targ$SE
+    targ$P_check<-2*pnorm(-abs(targ$Z))
+    targ$Z<-NULL
+
+    if(abs(mean(targ$P[!is.na(targ$P_check)]) - mean(targ$P_check[!is.na(targ$P_check)])) > 0.01){
+      targ$P<-targ$P_check
+      targ$P_check<-NULL
+      log_add(log_file = log_file, message = 'Genomic control detected. P-value recomputed using BETA and SE.')
+    } else {
+      log_add(log_file = log_file, message = 'Genomic control was not detected.')
+      targ$P_check<-NULL
+    }
+  } else {
+    log_add(log_file = log_file, message = 'SE column is not present, genomic control cannot be detected.')
+  }
+  return(targ)
+}
diff --git a/pipeline/envs/analysis.yaml b/pipeline/envs/analysis.yaml
index 107b96f2..bf300d3b 100644
--- a/pipeline/envs/analysis.yaml
+++ b/pipeline/envs/analysis.yaml
@@ -72,6 +72,7 @@ dependencies:
   - functools32=3.2.3.2
   - futures=3.3.0
   - gcc_impl_linux-64=13.2.0
+  - gdown
   - gettext=0.21.1
   - gflags=2.2.2
   - gfortran_impl_linux-64=13.2.0
@@ -254,7 +255,7 @@ dependencies:
   - r-diagram=1.6.5
   - r-diffobj=0.3.5
   - r-digest=0.6.33
-  - r-domc=1.3.8
+  - r-domc
   - r-doparallel=1.0.17
   - r-dorng=1.8.6
   - r-dotcall64=1.1_0
diff --git a/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/ldblk_1kg_chr23.hdf5 b/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/ldblk_1kg_chr23.hdf5
new file mode 100644
index 00000000..bf0c0723
Binary files /dev/null and b/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/ldblk_1kg_chr23.hdf5 differ
diff --git a/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/snpinfo_1kg_hm3 b/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/snpinfo_1kg_hm3
new file mode 100644
index 00000000..057ec0e5
--- /dev/null
+++ b/pipeline/resources/data/prscs_ref/1kg/ldblk_1kg_eur/snpinfo_1kg_hm3
@@ -0,0 +1,1130031 @@
+CHR	SNP	BP	A1	A2	MAF
+1	rs3131972	752721	A	G	0.161000
+1	rs3131969	754182	A	G	0.128200
+1	rs1048488	760912	C	T	0.160000
+1	rs12562034	768448	A	G	0.092450
+1	rs4040617	779322	G	A	0.118300
+1	rs4970383	838555	A	C	0.238600
+1	rs4475691	846808	T	C	0.183900
+1	rs1806509	853954	C	A	0.396600
+1	rs7537756	854250	G	A	0.192800
+1	rs13302982	861808	A	G	0.024850
+1	rs4040604	863124	G	T	0.024850
+1	rs2340587	864938	G	A	0.235600
+1	rs28576697	870645	C	T	0.292200
+1	rs1110052	873558	G	T	0.288300
+1	rs7523549	879317	T	C	0.039760
+1	rs3748592	880238	A	G	0.047710
+1	rs3748593	880390	A	C	0.037770
+1	rs2272756	882033	A	G	0.249500
+1	rs2340582	882803	A	G	0.047710
+1	rs4246503	884815	A	G	0.047710
+1	rs3748594	886384	A	G	0.038770
+1	rs3748595	887560	A	C	0.051690
+1	rs3748597	888659	T	C	0.046720
+1	rs13303106	891945	A	G	0.366800
+1	rs13303010	894573	G	A	0.098410
+1	rs3935066	900730	G	A	0.090460
+1	rs6696281	903104	T	C	0.050700
+1	rs28504611	908414	T	C	0.025840
+1	rs6694632	916834	G	A	0.404600
+1	rs13303118	918384	G	T	0.410500
+1	rs2341354	918573	A	G	0.401600
+1	rs6665000	924898	C	A	0.042740
+1	rs2341362	927309	T	C	0.042740
+1	rs9777703	928836	C	T	0.042740
+1	rs9777939	929190	A	G	0.042740
+1	rs1891910	932457	A	G	0.193800
+1	rs35940137	940203	A	G	0.051690
+1	rs3121567	943468	T	C	0.039760
+1	rs3128117	944564	C	T	0.392600
+1	rs2465126	947034	G	A	0.044730
+1	rs2341365	948692	G	A	0.044730
+1	rs15842	948921	T	C	0.042740
+1	rs1891906	950243	C	A	0.385700
+1	rs6657048	957640	T	C	0.013920
+1	rs2710888	959842	T	C	0.367800
+1	rs2710875	977780	C	T	0.081510
+1	rs17160776	985900	T	C	0.010930
+1	rs9803031	987200	C	T	0.085490
+1	rs13303287	987670	T	G	0.066600
+1	rs2465136	990417	C	T	0.321100
+1	rs2488991	994391	G	T	0.126200
+1	rs7526076	998395	A	G	0.243500
+1	rs3934834	1005806	T	C	0.168000
+1	rs3766192	1017197	C	T	0.451300
+1	rs3766191	1017587	T	C	0.165000
+1	rs9442372	1018704	A	G	0.440400
+1	rs10907177	1021346	G	A	0.180900
+1	rs3737728	1021415	A	G	0.261400
+1	rs9442398	1021695	A	G	0.259400
+1	rs6689308	1029805	G	A	0.167000
+1	rs6687776	1030565	T	C	0.166000
+1	rs6678318	1030633	A	G	0.166000
+1	rs9651273	1031540	A	G	0.272400
+1	rs11260595	1039098	A	C	0.028830
+1	rs6671356	1040026	C	T	0.134200
+1	rs6604968	1041700	G	A	0.135200
+1	rs4970405	1048955	G	A	0.099400
+1	rs12726255	1049950	G	A	0.134200
+1	rs4970409	1053452	A	G	0.104400
+1	rs7548798	1060174	T	C	0.293200
+1	rs7540009	1060235	A	G	0.028830
+1	rs17160824	1060608	A	G	0.103400
+1	rs11807848	1061166	C	T	0.394600
+1	rs12757754	1061262	G	A	0.102400
+1	rs9442373	1062638	C	A	0.430400
+1	rs2298217	1064979	T	C	0.123300
+1	rs12145826	1066029	A	G	0.081510
+1	rs9442380	1087683	T	C	0.060640
+1	rs7553429	1090557	C	A	0.028830
+1	rs4970362	1094738	A	G	0.349900
+1	rs9660710	1099342	A	C	0.047710
+1	rs4970420	1106473	A	G	0.209700
+1	rs4442317	1106784	C	T	0.211700
+1	rs4970421	1108637	A	G	0.045730
+1	rs11260542	1110019	G	A	0.085490
+1	rs9442387	1110586	C	T	0.358800
+1	rs12092254	1113121	A	G	0.085490
+1	rs1320565	1119858	T	C	0.077530
+1	rs1320571	1120431	A	G	0.051690
+1	rs3813204	1121014	A	G	0.127200
+1	rs4297230	1121341	T	C	0.128200
+1	rs11260549	1121794	A	G	0.127200
+1	rs4314833	1122024	C	T	0.118300
+1	rs11260554	1129672	T	G	0.118300
+1	rs11260556	1130206	A	G	0.203800
+1	rs10907175	1130727	C	A	0.084490
+1	rs9729550	1135242	C	A	0.251500
+1	rs3819001	1138913	C	T	0.055670
+1	rs11466691	1139862	C	T	0.017890
+1	rs1815606	1140435	T	G	0.308200
+1	rs11466681	1141387	T	C	0.050700
+1	rs11721	1152631	A	C	0.081510
+1	rs2887286	1156131	C	T	0.175900
+1	rs3813199	1158277	A	G	0.088470
+1	rs6603781	1158631	A	G	0.139200
+1	rs6671424	1160665	A	G	0.088470
+1	rs3766186	1162435	A	C	0.087480
+1	rs7515488	1163804	T	C	0.146100
+1	rs11260562	1165310	A	G	0.059640
+1	rs7528416	1171249	C	T	0.087480
+1	rs6697886	1173611	A	G	0.129200
+1	rs4970364	1174282	C	T	0.253500
+1	rs6675798	1176597	C	T	0.096420
+1	rs2297864	1177630	G	A	0.094430
+1	rs11260566	1178245	T	C	0.094430
+1	rs12093154	1178925	A	G	0.094430
+1	rs3818646	1191870	T	C	0.065610
+1	rs7524470	1192515	G	A	0.031810
+1	rs12072310	1193723	C	A	0.047710
+1	rs11804831	1194804	C	T	0.191800
+1	rs11260570	1201640	C	T	0.031810
+1	rs6667923	1206343	A	C	0.031810
+1	rs3737722	1209636	G	A	0.066600
+1	rs11260577	1210471	C	T	0.082500
+1	rs6689813	1210963	C	T	0.043740
+1	rs6685064	1211292	T	C	0.066600
+1	rs3753340	1217058	A	G	0.023860
+1	rs6603788	1218086	T	C	0.081510
+1	rs12077700	1219775	T	C	0.029820
+1	rs2144440	1220136	G	A	0.049700
+1	rs6662635	1220425	G	A	0.015900
+1	rs13306651	1226063	A	G	0.015900
+1	rs1749951	1231656	A	G	0.037770
+1	rs604618	1232319	A	G	0.075550
+1	rs1739855	1233941	C	T	0.082500
+1	rs2477782	1237036	C	T	0.083500
+1	rs11586188	1237829	A	G	0.041750
+1	rs11260584	1239339	T	G	0.075550
+1	rs1536168	1241529	A	G	0.044730
+1	rs12103	1247494	T	C	0.187900
+1	rs12142199	1249187	G	A	0.194800
+1	rs34054191	1250957	A	G	0.032800
+1	rs4970432	1254136	G	A	0.079520
+1	rs1810745	1257593	A	G	0.060640
+1	rs3845293	1258246	A	C	0.080520
+1	rs35424002	1269986	A	G	0.043740
+1	rs3855955	1276077	A	G	0.299200
+1	rs182532	1287040	T	C	0.033800
+1	rs307380	1287127	G	A	0.077530
+1	rs34389364	1295323	G	A	0.079520
+1	rs34876581	1295403	T	C	0.080520
+1	rs2765023	1297065	T	C	0.101400
+1	rs2765021	1297422	C	T	0.077530
+1	rs17160669	1305561	T	C	0.107400
+1	rs2765015	1308982	A	G	0.025840
+1	rs2765033	1310924	T	C	0.032800
+1	rs2649589	1311716	G	A	0.090460
+1	rs2649588	1314015	C	T	0.090460
+1	rs2477777	1314172	C	T	0.090460
+1	rs1240743	1333436	C	A	0.404600
+1	rs35242196	1333598	A	C	0.092450
+1	rs2291889	1335218	A	G	0.029820
+1	rs1240707	1335302	T	C	0.084490
+1	rs1240708	1335790	G	A	0.181900
+1	rs17160977	1341185	G	A	0.010930
+1	rs12089560	1356550	G	A	0.084490
+1	rs1695824	1365570	C	A	0.218700
+1	rs873927	1366830	G	A	0.218700
+1	rs1240703	1368599	A	C	0.014910
+1	rs1312568	1385211	G	A	0.182900
+1	rs1240732	1404809	G	A	0.079520
+1	rs1153105	1415099	C	T	0.182900
+1	rs819976	1421531	A	C	0.092450
+1	rs819980	1425700	C	T	0.099400
+1	rs6690515	1447325	A	G	0.121300
+1	rs6669795	1450947	C	A	0.050700
+1	rs9439458	1451380	C	T	0.142100
+1	rs9439462	1462766	T	C	0.050700
+1	rs12032637	1465382	G	A	0.052680
+1	rs6683520	1466828	T	C	0.037770
+1	rs1987191	1467485	C	T	0.038770
+1	rs3118505	1472047	T	C	0.068590
+1	rs1571150	1474304	A	C	0.333000
+1	rs7290	1477244	C	T	0.306200
+1	rs3766180	1478153	C	T	0.306200
+1	rs3766178	1478180	C	T	0.306200
+1	rs7517401	1483010	A	G	0.349900
+1	rs2031709	1485984	A	C	0.019880
+1	rs3128342	1486834	A	C	0.454300
+1	rs1887284	1487059	A	G	0.019880
+1	rs7531530	1489670	T	C	0.286300
+1	rs3820075	1490559	G	A	0.286300
+1	rs3766170	1497008	C	T	0.346900
+1	rs3766169	1497201	C	A	0.304200
+1	rs2296716	1497824	T	C	0.123300
+1	rs9439468	1499298	G	A	0.346900
+1	rs6603791	1500941	G	A	0.345900
+1	rs6603793	1505255	T	C	0.345900
+1	rs7519837	1510801	T	C	0.327000
+1	rs6687029	1519068	C	A	0.306200
+1	rs6604983	1521595	G	A	0.121300
+1	rs4648786	1521805	G	A	0.441400
+1	rs28414855	1526270	T	G	0.023860
+1	rs11552172	1534614	C	T	0.229600
+1	rs4648611	1550702	A	G	0.293200
+1	rs12748433	1559971	C	T	0.037770
+1	rs28635343	1560103	T	C	0.168000
+1	rs28464684	1566160	T	C	0.036780
+1	rs28456011	1567206	A	G	0.163000
+1	rs6604981	1599161	A	G	0.036780
+1	rs6699975	1603434	T	G	0.488100
+1	rs4074196	1611995	G	A	0.447300
+1	rs4074197	1612111	C	T	0.432400
+1	rs7520934	1615348	C	A	0.450300
+1	rs28487995	1619541	A	G	0.201800
+1	rs3737622	1620860	A	G	0.399600
+1	rs909823	1647686	A	C	0.078530
+1	rs2294489	1664124	T	C	0.092450
+1	rs4648764	1665613	G	A	0.486100
+1	rs7407	1684472	C	T	0.495000
+1	rs34306661	1687625	C	T	0.419500
+1	rs2281173	1688192	A	G	0.096420
+1	rs2235536	1689164	T	C	0.411500
+1	rs2235537	1689762	C	T	0.421500
+1	rs35672141	1691050	T	C	0.483100
+1	rs1107910	1692321	C	T	0.437400
+1	rs876029	1695574	G	T	0.068590
+1	rs5021859	1696659	A	G	0.068590
+1	rs7531583	1706160	A	G	0.263400
+1	rs34238514	1706735	T	C	0.192800
+1	rs12041925	1707740	G	T	0.231600
+1	rs12044597	1708801	A	G	0.496000
+1	rs10907194	1712230	T	C	0.230600
+1	rs7534617	1713869	G	T	0.232600
+1	rs742359	1715011	A	C	0.231600
+1	rs2272908	1721479	C	T	0.495000
+1	rs3737628	1722932	C	T	0.497000
+1	rs9660180	1723031	G	A	0.497000
+1	rs9970652	1725626	T	C	0.193800
+1	rs16825336	1745726	A	G	0.093440
+1	rs12742323	1746694	T	G	0.192800
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diff --git a/pipeline/resources/software/prscs b/pipeline/resources/software/prscs
new file mode 160000
index 00000000..621fdc80
--- /dev/null
+++ b/pipeline/resources/software/prscs
@@ -0,0 +1 @@
+Subproject commit 621fdc80daac56c93d9528eb1a1187f7b1fc9afb
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 64ebb653..e7752448 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -41,7 +41,7 @@ def get_chr_range(testing):
     val = int(testing[-2:])
     return range(val, val + 1)
   else:
-    return range(1, 23)
+    return range(1, 24)
 
 # Create function to check whether path of gwas or score exist
 def check_target_paths(df, chr):
@@ -100,16 +100,12 @@ def check_config_parameters(config):
 
     missing_params = []
     for param in required_params:
-        val = config.get(param)
-        if val is None:
+        if config.get(param) is None:
             missing_params.append(param)
 
     if missing_params:
         print("Error: Missing parameters in user-specified and default config files:", missing_params)
         sys.exit(1)
-        
-    if config.get("config_file") == "NA":
-        print("Warning: No user specified config file was provided.")
 
 # Check the config
 check_config_parameters(config)
@@ -274,70 +270,48 @@ else:
 # Set ldpred2 reference path
 if config['ldpred2_ldref'] == 'NA':
   ldpred2_ldref=f"{resdir}/data/ldpred2_ref"
-    
-  if 'ldpred2' in config['pgs_methods']:
-    # Check if gwas_list contains invalid populations
-    valid_pops = {'EUR'}
-    invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
-  
-    if invalid_pops:
-      raise ValueError(
-        f"Default ldpred2 reference data is only available for EUR populations. For other populations, please provide your own ldpred2 reference data using the ldpred2_ldref parameter. Download links to LDpred2 reference data for EUR, EAS and AFR populations can be found in this section of the website: https://opain.github.io/GenoPred/pipeline_readme.html#Specifying_alternative_reference_data_for_PGS_methods"
-      )
-
 else:
   ldpred2_ldref=config['ldpred2_ldref']
 
-  # Check the ldpred2 ldref data is present for the required populations in the gwas_list
-  if 'ldpred2' in config['pgs_methods']:
-    for pop in gwas_list_df['population'].unique():
-      path = f"{ldpred2_ldref}/{pop}"
-      # Check if map.rds file exists
-      map_file = os.path.join(path, "map.rds")
-      if not os.path.exists(map_file):
-        print(f"File not found: {map_file}")
-        raise FileNotFoundError(f"Required file not found: {map_file}. LDpred2 reference data must include map.rds for all populations.")
-  
-      # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
-      for chr in range(1, 23):
-        ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
-        if not os.path.exists(ld_file):
-          print(f"File not found: {ld_file}")
-          raise FileNotFoundError(f"Required file not found: {ld_file}. LDpred2 reference data must include files for all chromosomes.")
+# Check the ldpred2 ldref data is present for the required populations in the pgwas_list
+if 'ldpred2' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{ldpred2_ldref}/{pop}"
+    # Check if map.rds file exists
+    map_file = os.path.join(path, "map.rds")
+    if not os.path.exists(map_file):
+      print(f"File not found: {map_file}")
+      raise FileNotFoundError(f"Required file not found: {map_file}. LDpred2 reference data must include map.rds for all populations.")
+
+    # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
+    for chr in range(1, 23):
+      ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
+      if not os.path.exists(ld_file):
+        print(f"File not found: {ld_file}")
+        raise FileNotFoundError(f"Required file not found: {ld_file}. LDpred2 reference data must include files for all chromosomes.")
 
 # Set sbayesr reference path
 if config['sbayesr_ldref'] == 'NA':
   sbayesr_ldref=f"{resdir}/data/gctb_ref/ukbEURu_hm3_shrunk_sparse/ukbEURu_hm3_v3_50k_chr"
-  
-  if 'sbayesr' in config['pgs_methods']:
-    # Check if gwas_list contains invalid populations
-    valid_pops = {'EUR'}
-    invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
-  
-    if invalid_pops:
-      raise ValueError(
-        f"Default sbayesr reference data is only available for EUR populations. For other populations, please provide your own sbayesr reference data using the sbayesr_ldref parameter."
-      )
-
 else:
   sbayesr_ldref=config['sbayesr_ldref']
 
-  # Check the sbayesr ldref data is present for the required populations in the gwas_list
-  if 'sbayesr' in config['pgs_methods']:
-    for pop in gwas_list_df['population'].unique():
-      path = f"{sbayesr_ldref}/{pop}"
-      # Check if map.rds file exists
-      map_file = os.path.join(path, "map.rds")
-      if not os.path.exists(map_file):
-        print(f"File not found: {map_file}")
-        raise FileNotFoundError(f"Required file not found: {map_file}. SBayesR reference data must include map.rds for all populations.")
-  
-      # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
-      for chr in range(1, 23):
-        ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
-        if not os.path.exists(ld_file):
-          print(f"File not found: {ld_file}")
-          raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
+# Check the sbayesr ldref data is present for the required populations in the gwas_list
+if 'sbayesr' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{sbayesr_ldref}/{pop}"
+    # Check if map.rds file exists
+    map_file = os.path.join(path, "map.rds")
+    if not os.path.exists(map_file):
+      print(f"File not found: {map_file}")
+      raise FileNotFoundError(f"Required file not found: {map_file}. SBayesR reference data must include map.rds for all populations.")
+
+    # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
+    for chr in range(1, 23):
+      ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
+      if not os.path.exists(ld_file):
+        print(f"File not found: {ld_file}")
+        raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
 
 # Set quickprs reference path
 if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickprs" in config["pgs_methods"]:
@@ -470,7 +444,7 @@ def check_pgs_methods(x):
         return
 
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "lassosum2", "megaprs", "quickprs", "xwing", "prscsx", "bridgeprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "bridgeprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
@@ -484,7 +458,7 @@ check_pgs_methods(config['pgs_methods'])
 # Check valid tlprs_methods are specified
 def check_tlprs_methods(config):
     valid_tlprs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "lassosum2", "megaprs", "quickprs"
+        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs"
     }
 
     # Check if 'tlprs_methods' is empty
@@ -501,7 +475,7 @@ check_tlprs_methods(config)
 # Check valid leopard_methods are specified
 def check_leopard_methods(config):
     valid_leopard_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "lassosum2", "megaprs","quickprs"
+        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
     }
 
     # Check if 'leopard_methods' is empty
@@ -912,18 +886,17 @@ rule download_sbayesrc_annot:
     {{
       rm -r -f {resdir}/data/sbayesrc_annot; \
       mkdir -p {resdir}/data/sbayesrc_annot; \
-      gdown 1-dUPvduYB1zZewsItCNKcM7RvOAOojlP -O {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip; \
+      wget --no-check-certificate -O {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/Annotation/annot_baseline2.2.zip; \
       unzip {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip -d {resdir}/data/sbayesrc_annot; \
       rm {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip
     }} > {log} 2>&1
     """
 
 # Download SBayesRC reference data
-# Links to developer gdrive
-sbayesrc_ref_dev_urls = {
-    'EUR': '1HkxMT2UJKK__TfqNcZQoO7Q9XGnVtI28',
-    'EAS': '1qytY1H3ZracD2OoyqjsPjsTjxl9qdQ-c',
-    'AFR': '1kPOp2mc1odJBInd66sC40aKNwOhC--5A'
+sbayesrc_ref_urls = {
+    'EUR': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbEUR_HM3.zip',
+    'EAS': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbEAS_HM3.zip',
+    'AFR': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbAFR_HM3.zip'
 }
 
 rule download_sbayesrc_ref:
@@ -934,16 +907,16 @@ rule download_sbayesrc_ref:
   log:
     f"{resdir}/data/logs/download_sbayesrc_ref-{{population}}.log"
   params:
-    id=lambda w: sbayesrc_ref_dev_urls.get(w.population)
+    url=lambda w: sbayesrc_ref_urls.get(w.population)
   shell:
     """
     {{
       mkdir -p {resdir}/data/sbayesrc_ref; \
       rm -r -f {resdir}/data/sbayesrc_ref/{wildcards.population}; \
-      gdown {params.id} -O {resdir}/data/sbayesrc_ref/{wildcards.population}.xz; \
-      tar -xJf {resdir}/data/sbayesrc_ref/{wildcards.population}.xz -C {resdir}/data/sbayesrc_ref/; \
-      mv {resdir}/data/sbayesrc_ref/ukb{wildcards.population}_HM3 {resdir}/data/sbayesrc_ref/{wildcards.population}; \
-      rm {resdir}/data/sbayesrc_ref/{wildcards.population}.xz
+      wget --no-check-certificate -O {resdir}/data/sbayesrc_ref/{wildcards.population}.zip {params.url}; \
+      unzip {resdir}/data/sbayesrc_ref/{wildcards.population}.zip -d {resdir}/data/sbayesrc_ref/{wildcards.population}; \
+      rm {resdir}/data/sbayesrc_ref/{wildcards.population}.zip; \
+      mv {resdir}/data/sbayesrc_ref/{wildcards.population}/ukb{wildcards.population}_HM3/* {resdir}/data/sbayesrc_ref/{wildcards.population}/
     }} > {log} 2>&1
     """
 
@@ -1063,9 +1036,9 @@ rule download_ldak_map:
     {{
       rm -r {resdir}/data/ldak_map; \
       mkdir -p {resdir}/data/ldak_map; \
-      gdown 1mtw5Mx-F-Ws7lKLFqMh6nN4OrDGZTkZG -O {resdir}/data/ldak_map.tar.gz; \
-      tar -zxvf {resdir}/data/ldak_map.tar.gz -C {resdir}/data/; \
-      rm {resdir}/data/ldak_map.tar.gz
+      wget --no-check-certificate -O {resdir}/data/ldak_map/genetic_map_b37.zip https://www.dropbox.com/s/slchsd0uyd4hii8/genetic_map_b37.zip; \
+      unzip {resdir}/data/ldak_map/genetic_map_b37.zip -d {resdir}/data/ldak_map/; \
+      rm {resdir}/data/ldak_map/genetic_map_b37.zip
     }} > {log} 2>&1
     """
     
@@ -1205,7 +1178,7 @@ rule download_default_ref:
     {{
       rm -r {resdir}/data/ref; \
       mkdir -p {resdir}/data/ref; \
-      gdown --id 1vYH6V-7F68Ji1vy9TaH0ysjmdYJFef-f -O {resdir}/data/ref/genopred_1kg_hgdp.tar.gz; \
+      gdown --id 1vYH6V-7F68Ji1vy9TaH0ysjmdYJFef-f -O resources/data/ref/genopred_1kg_hgdp.tar.gz; \
       tar -xzvf {resdir}/data/ref/genopred_1kg_hgdp.tar.gz -C {resdir}/data/ref/; \
       mv {resdir}/data/ref/ref/* {resdir}/data/ref/; \
       rm -r {resdir}/data/ref/ref; \
@@ -1331,19 +1304,19 @@ rule download_xwing_software:
     rules.install_xpass.output,
     rules.install_genoutils_xwing.output
   output:
-    f"{resdir}/software/xwing/block_partition.txt"
+    "resources/software/xwing/block_partition.txt"
   conda:
     "../envs/xwing.yaml"
   benchmark:
-    f"{resdir}/data/benchmarks/download_xwing_software.txt"
+    "resources/data/benchmarks/download_xwing_software.txt"
   log:
-    f"{resdir}/data/logs/download_xwing_software.log"
+    "resources/data/logs/download_xwing_software.log"
   shell:
     """
     {{
-      rm -r -f {resdir}/software/xwing; \
-      git clone https://github.com/opain/X-Wing {resdir}/software/xwing; \
-      cd {resdir}/software/xwing; \
+      rm -r -f resources/software/xwing; \
+      git clone https://github.com/opain/X-Wing resources/software/xwing; \
+      cd resources/software/xwing; \
       git reset --hard e9fcc264266e0e884323311816bfe20053fd3f7a
     }} > {log} 2>&1
     """
@@ -1621,4 +1594,3 @@ rule get_all_resources:
     rules.get_quickprs_resources.output
   output:
     touch(f"{resdir}/software/get_all_resources.done")
-
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 85351b16..c68d8429 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -58,14 +58,18 @@ if 'gwas_list' in config:
     shell:
       """
       sumstat_cleaner_script=$(Rscript -e 'cat(system.file("scripts", "sumstat_cleaner.R", package = "GenoUtils"))' 2>&1 | grep -Eo "/.*sumstat_cleaner.R")
-      Rscript $sumstat_cleaner_script \
+      Rscript -e "library(GenoUtils);
+        CHROMS <- 1:23;
+        args <- commandArgs(trailingOnly=TRUE);
+        source('$sumstat_cleaner_script', local=TRUE);" \
         --sumstats {params.path} \
         --n {params.n} \
         --ref_chr {refdir}/ref.chr \
         --population {params.population} \
         --output {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned \
         --test {params.testing} > {log} 2>&1
-      """
+      """    
+      
 
 rule sumstat_prep:
   input: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=gwas_list_df['name'])
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index f237ff46..57372ecf 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -129,4 +129,3 @@ rule target_pgs_all:
 rule target_pgs:
   input:
     expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done", name=target_list_df['name'])
-