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LOGODetect.R
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LOGODetect.R
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suppressMessages(require(snowfall))
suppressMessages(require(data.table))
suppressMessages(require(optparse))
suppressMessages(require(BEDMatrix))
suppressMessages(require(XPASS))
options(stringsAsFactors = F)
options(datatable.fread.datatable = F)
warnings('off')
############################### input of LOGODetect
option_list = list(
make_option('--sumstats', action = 'store', default = NA, type = 'character'),
make_option('--n_gwas', action = 'store', default = NA, type = 'character'),
make_option('--ref_dir', action = 'store', default = NA, type = 'character'),
make_option('--pop', action = 'store', default = NA, type = 'character'),
make_option('--ldsc_dir', action = 'store', default = NA, type = 'character'),
make_option('--block_partition', action = 'store', default = NA, type = 'character'),
make_option('--gc_snp', action = 'store', default = NA, type = 'character'),
make_option('--out_dir', action = 'store', default = NA, type = 'character'),
make_option('--chr', action = 'store', default = NA, type = 'integer'),
make_option('--n_cores', action = 'store', default = 20, type = 'numeric'),
make_option('--target_pop', action = 'store', default = NA, type = 'character'),
make_option('--n_topregion', action = 'store', default = NA, type = 'numeric'),
make_option('--max_nsnps', action = 'store', default = 2000, type = 'integer'),
make_option('--interval', action = 'store', default = 20, type = 'integer')
)
opt = parse_args(OptionParser(option_list=option_list))
sumstats = strsplit(opt$sumstats, ',')[[1]]
dat1 = data.frame(fread(sumstats[1]))
dat2 = data.frame(fread(sumstats[2]))
if(!is.na(opt$chr)){
chr = opt$chr
dat1 = dat1[dat1$CHR == chr, ]
dat2 = dat2[dat2$CHR == chr, ]
}
n_gwas = as.numeric(strsplit(opt$n_gwas, ',')[[1]])
n1 = n_gwas[1]
n2 = n_gwas[2]
ref_dir = opt$ref_dir
pop = strsplit(opt$pop, ',')[[1]]
if(length(pop) == 1){
npop = 1
pop1 = pop2 = pop
}else{
npop = 2
pop1 = pop[1]
pop2 = pop[2]
}
if(npop == 1){
ref = paste0(ref_dir, '/', pop, '/1000G_', pop, '_QC')
ref_bim = data.frame(fread(paste0(ref, '.bim')))
ref_bed = BEDMatrix(paste0(ref, '.bed'))
colnames(ref_bed) = ref_bim$V2
n_ref = nrow(ref_bed)
ldscore = data.frame(fread(paste0(ref_dir, '/', pop, '/', pop, '.l2.ldscore')))
}
if(npop == 2){
ref1 = paste0(ref_dir, '/', pop1, '/1000G_', pop1, '_QC')
ref2 = paste0(ref_dir, '/', pop2, '/1000G_', pop2, '_QC')
cov1 = paste0(ref_dir, '/', pop1, '/1000G_', pop1, '_QC_cov.txt')
cov2 = paste0(ref_dir, '/', pop2, '/1000G_', pop2, '_QC_cov.txt')
ref_bim = data.frame(fread(paste0(ref1, '.bim')))
ref1_bed = BEDMatrix(paste0(ref1, '.bed'))
ref2_bed = BEDMatrix(paste0(ref2, '.bed'))
colnames(ref1_bed) = colnames(ref2_bed) = ref_bim$V2
n_ref1 = nrow(ref1_bed)
n_ref2 = nrow(ref2_bed)
ldscore1 = data.frame(fread(paste0(ref_dir, '/', pop1, '/', pop1, '.l2.ldscore')))
ldscore2 = data.frame(fread(paste0(ref_dir, '/', pop2, '/', pop2, '.l2.ldscore')))
}
block = read.table(opt$block_partition, header = T)
ldsc_dir = opt$ldsc_dir
if(!is.na(opt$gc_snp)){
gc_snp = data.frame(fread(opt$gc_snp))[, 1]
}
out_dir = opt$out_dir
ncore = opt$n_cores
target_pop = opt$target_pop
n_topregion = opt$n_topregion
if(!dir.exists(out_dir)){
dir.create(out_dir)
}
Cn = opt$max_nsnps
inter = opt$interval
############################### QC
dat1 = dat1[complete.cases(dat1), ]
dat2 = dat2[complete.cases(dat2), ]
dat1$N = n1
dat2$N = n2
dat1$P = as.numeric(dat1$P)
dat2$P = as.numeric(dat2$P)
dat1$P[dat1$P < 1e-300] = 1e-300
dat2$P[dat2$P < 1e-300] = 1e-300
dat1$P[dat1$P > 1 - 1e-300] = 1 - 1e-300
dat2$P[dat2$P > 1 - 1e-300] = 1 - 1e-300
if('BETA' %in% colnames(dat1)){
dat1$Z = -qnorm(dat1$P/2) * sign(dat1$BETA)
}
if('BETA' %in% colnames(dat2)){
dat2$Z = -qnorm(dat2$P/2) * sign(dat2$BETA)
}
if('OR' %in% colnames(dat1)){
dat1$Z = -qnorm(dat1$P/2) * sign(log(dat1$OR))
}
if('OR' %in% colnames(dat2)){
dat2$Z = -qnorm(dat2$P/2) * sign(log(dat2$OR))
}
dat1 = dat1[dat1$Z != Inf & dat1$Z != -Inf, ]
dat2 = dat2[dat2$Z != Inf & dat2$Z != -Inf, ]
dat1 = dat1[dat1$A1 %in% c('A', 'C', 'T', 'G') & dat1$A2 %in% c('A', 'C', 'T', 'G'), ]
dat2 = dat2[dat2$A1 %in% c('A', 'C', 'T', 'G') & dat2$A2 %in% c('A', 'C', 'T', 'G'), ]
dat1 = dat1[! ((dat1$A1=="G")&(dat1$A2=="C") | (dat1$A1=="C")&(dat1$A2=="G") | (dat1$A1=="A")&(dat1$A2=="T") | (dat1$A1=="T")&(dat1$A2=="A")), ]
dat2 = dat2[! ((dat2$A1=="G")&(dat2$A2=="C") | (dat2$A1=="C")&(dat2$A2=="G") | (dat2$A1=="A")&(dat2$A2=="T") | (dat2$A1=="T")&(dat2$A2=="A")), ]
dat1 = dat1[, c('CHR', 'SNP', 'BP', 'A1', 'A2', 'P', 'N', 'Z')]
dat2 = dat2[, c('CHR', 'SNP', 'BP', 'A1', 'A2', 'P', 'N', 'Z')]
### remove duplicated SNPs
dat1 = dat1[! dat1$SNP %in% dat1$SNP[duplicated(dat1$SNP)], ]
dat2 = dat2[! dat2$SNP %in% dat2$SNP[duplicated(dat2$SNP)], ]
### extract SNPs overlapped with reference SNPs
if(npop == 1){
dat1_new = dat1[1, ][-1, ]
dat2_new = dat2[1, ][-1, ]
ref_bim_new = ref_bim[1, ][-1, ]
ref_bed_new = NULL
ldscore_new = ldscore[1, ][-1, ]
for(chr in 1:22){
f1 = dat1[dat1$CHR == chr, ]
f2 = dat2[dat2$CHR == chr, ]
ind = (ref_bim$V1 == chr)
ref_bim_chr = ref_bim[ind, ]
ref_bed_chr = ref_bed[, ind]
ldscore_chr = ldscore[ind, ]
f1 = f1[order(f1$BP), ]
f1 = f1[! f1$BP %in% f1$BP[duplicated(f1$BP)], ]
f1 = f1[f1$SNP %in% ref_bim_chr$V2 & f1$BP %in% ref_bim_chr$V4, ]
temp = ref_bim_chr[ref_bim_chr$V2 %in% f1$SNP, ]
pos = (f1$A1 == temp$V5)*(f1$A2 == temp$V6)
neg = (f1$A1 == temp$V6)*(f1$A2 == temp$V5)
flop = (neg == 1)
f1$A1[flop] = temp$V5[flop]
f1$A2[flop] = temp$V6[flop]
f1$Z[flop] = -f1$Z[flop]
ind = (pos + neg == 1)
f1 = f1[ind, ]
f2 = f2[order(f2$BP), ]
f2 = f2[! f2$BP %in% f2$BP[duplicated(f2$BP)], ]
f2 = f2[f2$SNP %in% ref_bim_chr$V2 & f2$BP %in% ref_bim_chr$V4, ]
temp = ref_bim_chr[ref_bim_chr$V2 %in% f2$SNP, ]
pos = (f2$A1 == temp$V5)*(f2$A2 == temp$V6)
neg = (f2$A1 == temp$V6)*(f2$A2 == temp$V5)
flop = (neg == 1)
f2$A1[flop] = temp$V5[flop]
f2$A2[flop] = temp$V6[flop]
f2$Z[flop] = -f2$Z[flop]
ind = (pos + neg == 1)
f2 = f2[ind, ]
overlap_snp = intersect(f1$SNP, f2$SNP)
f1 = f1[f1$SNP %in% overlap_snp, ]
f2 = f2[f2$SNP %in% overlap_snp, ]
dat1_new = rbind(dat1_new, f1)
dat2_new = rbind(dat2_new, f2)
ind = ref_bim_chr$V2 %in% overlap_snp
ref_bim_chr = ref_bim_chr[ind, ]
ref_bed_chr = ref_bed_chr[, ind]
ldscore_chr = ldscore_chr[ind, ]
ref_bim_new = rbind(ref_bim_new, ref_bim_chr)
ref_bed_new = cbind(ref_bed_new, ref_bed_chr)
ldscore_new = rbind(ldscore_new, ldscore_chr)
}
dat1 = dat1_new
dat2 = dat2_new
ref_bim = ref_bim_new
ref_bed = ref_bed_new
ldscore = ldscore_new
rm(dat1_new, dat2_new, ref_bim_new, ref_bed_new, ldscore_new)
}
if(npop == 2){
dat1_new = dat1[1, ][-1, ]
dat2_new = dat2[1, ][-1, ]
ref_bim_new = ref_bim[1, ][-1, ]
ref1_bed_new = NULL
ref2_bed_new = NULL
ldscore1_new = ldscore1[1, ][-1, ]
ldscore2_new = ldscore2[1, ][-1, ]
for(chr in 1:22){
f1 = dat1[dat1$CHR == chr, ]
f2 = dat2[dat2$CHR == chr, ]
ind = (ref_bim$V1 == chr)
ref_bim_chr = ref_bim[ind, ]
ref1_bed_chr = ref1_bed[, ind]
ref2_bed_chr = ref2_bed[, ind]
ldscore1_chr = ldscore1[ind, ]
ldscore2_chr = ldscore2[ind, ]
f1 = f1[order(f1$BP), ]
f1 = f1[! f1$BP %in% f1$BP[duplicated(f1$BP)], ]
f1 = f1[f1$SNP %in% ref_bim_chr$V2 & f1$BP %in% ref_bim_chr$V4, ]
temp = ref_bim_chr[ref_bim_chr$V2 %in% f1$SNP, ]
pos = (f1$A1 == temp$V5)*(f1$A2 == temp$V6)
neg = (f1$A1 == temp$V6)*(f1$A2 == temp$V5)
flop = (neg == 1)
f1$A1[flop] = temp$V5[flop]
f1$A2[flop] = temp$V6[flop]
f1$Z[flop] = -f1$Z[flop]
ind = (pos + neg == 1)
f1 = f1[ind, ]
f2 = f2[order(f2$BP), ]
f2 = f2[! f2$BP %in% f2$BP[duplicated(f2$BP)], ]
f2 = f2[f2$SNP %in% ref_bim_chr$V2 & f2$BP %in% ref_bim_chr$V4, ]
temp = ref_bim_chr[ref_bim_chr$V2 %in% f2$SNP, ]
pos = (f2$A1 == temp$V5)*(f2$A2 == temp$V6)
neg = (f2$A1 == temp$V6)*(f2$A2 == temp$V5)
flop = (neg == 1)
f2$A1[flop] = temp$V5[flop]
f2$A2[flop] = temp$V6[flop]
f2$Z[flop] = -f2$Z[flop]
ind = (pos + neg == 1)
f2 = f2[ind, ]
overlap_snp = intersect(f1$SNP, f2$SNP)
f1 = f1[f1$SNP %in% overlap_snp, ]
f2 = f2[f2$SNP %in% overlap_snp, ]
dat1_new = rbind(dat1_new, f1)
dat2_new = rbind(dat2_new, f2)
ind = ref_bim_chr$V2 %in% overlap_snp
ref_bim_chr = ref_bim_chr[ind, ]
ref1_bed_chr = ref1_bed_chr[, ind]
ref2_bed_chr = ref2_bed_chr[, ind]
ldscore1_chr = ldscore1_chr[ind, ]
ldscore2_chr = ldscore2_chr[ind, ]
ref_bim_new = rbind(ref_bim_new, ref_bim_chr)
ref1_bed_new = cbind(ref1_bed_new, ref1_bed_chr)
ref2_bed_new = cbind(ref2_bed_new, ref2_bed_chr)
ldscore1_new = rbind(ldscore1_new, ldscore1_chr)
ldscore2_new = rbind(ldscore2_new, ldscore2_chr)
}
dat1 = dat1_new
dat2 = dat2_new
ref_bim = ref_bim_new
ref1_bed = ref1_bed_new
ref2_bed = ref2_bed_new
ldscore1 = ldscore1_new
ldscore2 = ldscore2_new
rm(dat1_new, dat2_new, ref_bim_new, ref1_bed_new, ref2_bed_new, ldscore1_new, ldscore2_new)
}
############################### caluculate heritability and genetic correlation
if(npop == 1){
if(!dir.exists(paste0(out_dir, '/tmp_files'))){
dir.create(paste0(out_dir, '/tmp_files'))
}
if(!dir.exists(paste0(out_dir, '/tmp_files/ldsc'))){
dir.create(paste0(out_dir, '/tmp_files/ldsc'))
}
write.table(dat1, paste0(out_dir, '/tmp_files/dat1.txt'), row.names = F, col.names = T, quote = F)
write.table(dat2, paste0(out_dir, '/tmp_files/dat2.txt'), row.names = F, col.names = T, quote = F)
job = paste0('python ', ldsc_dir, '/munge_sumstats.py --sumstats ', out_dir, '/tmp_files/dat1.txt --chunksize 1000000 --out ', out_dir, '/tmp_files/ldsc/dat1_reformated --merge-alleles ', ref_dir, '/ldsc/w_hm3.snplist;
python ', ldsc_dir, '/munge_sumstats.py --sumstats ', out_dir, '/tmp_files/dat2.txt --chunksize 1000000 --out ', out_dir, '/tmp_files/ldsc/dat2_reformated --merge-alleles ', ref_dir, '/ldsc/w_hm3.snplist;
python ', ldsc_dir, '/ldsc.py --rg ', out_dir, '/tmp_files/ldsc/dat1_reformated.sumstats.gz,', out_dir, '/tmp_files/ldsc/dat2_reformated.sumstats.gz --ref-ld-chr ', ref_dir, '/ldsc/eur_w_ld_chr/ --w-ld-chr ', ref_dir, '/ldsc/eur_w_ld_chr/ --out ', out_dir, '/tmp_files/ldsc/ldsc_rg')
system(job, ignore.stdout = T)
}
if(npop == 2){
if(!dir.exists(paste0(out_dir, '/tmp_files'))){
dir.create(paste0(out_dir, '/tmp_files'))
}
if(!dir.exists(paste0(out_dir, '/tmp_files/XPASS'))){
dir.create(paste0(out_dir, '/tmp_files/XPASS'))
}
write.table(dat1, paste0(out_dir, '/tmp_files/dat1.txt'), row.names = F, col.names = T, quote = F)
write.table(dat2, paste0(out_dir, '/tmp_files/dat2.txt'), row.names = F, col.names = T, quote = F)
dat1_xpass = dat1[dat1$SNP %in% gc_snp, ]
dat2_xpass = dat2[dat2$SNP %in% gc_snp, ]
dat1_file = paste0(out_dir, '/tmp_files/XPASS/dat1_xpass.txt')
dat2_file = paste0(out_dir, '/tmp_files/XPASS/dat2_xpass.txt')
write.table(dat1_xpass, dat1_file, row.names = F, col.names = T, quote = F)
write.table(dat2_xpass, dat2_file, row.names = F, col.names = T, quote = F)
fit = XPASS(file_z1 = dat1_file, file_z2 = dat2_file,
file_ref1 = ref1, file_ref2 = ref2,
file_cov1 = cov1, file_cov2 = cov2,
file_predGeno = NULL, compPRS = F,
sd_method = "LD_block", compPosMean = F)
xpass = data.frame(fit$H)
write.table(xpass, paste0(out_dir, '/tmp_files/XPASS/XPASS_output.txt'), row.names = F, col.names = T, quote = F)
rm(dat1_xpass, dat2_xpass, fit)
gc()
}
############################### create tmp files in blocks
M = nrow(dat1)
if(!dir.exists(paste0(out_dir, '/tmp_files/block'))){
dir.create(paste0(out_dir, '/tmp_files/block'))
}
write.table(M, paste0(out_dir, '/tmp_files/block/M.txt'), row.names = F, col.names = F, quote = F)
if(npop == 1){
ldsc_input_check = F
Read = file(paste0(out_dir, '/tmp_files/ldsc/ldsc_rg.log'), 'r')
line = readLines(Read, n = 1)
while(length(line) > 0){
if( grepl('Heritability of phenotype 1', line) ){
ldsc_input_check = T
line = readLines(Read, n = 1)
line = readLines(Read, n = 1)
line = gsub(" ", " ", line)
line = gsub(" ", " ", line)
line = strsplit(line, ':')[[1]][2]
h2_1 = as.numeric(strsplit(line, " ")[[1]][2])
}
if( grepl('Heritability of phenotype 2', line) ){
line = readLines(Read, n = 1)
line = readLines(Read, n = 1)
line = gsub(" ", " ", line)
line = gsub(" ", " ", line)
line = strsplit(line, ':')[[1]][2]
h2_2 = as.numeric(strsplit(line, " ")[[1]][2])
}
if( grepl('Total Observed scale gencov', line) ){
line = gsub(" ", " ", line)
line = gsub(" ", " ", line)
line = strsplit(line, ':')[[1]][2]
gcov_total = as.numeric(strsplit(line, " ")[[1]][2])
line = readLines(Read, n = 1)
line = readLines(Read, n = 1)
intercept = as.numeric(strsplit(line, " ")[[1]][2])
}
line = readLines(Read, n = 1)
}
close(Read)
h2_snp_1 = h2_1/M
h2_snp_2 = h2_2/M
if(!ldsc_input_check){
stop('Error occurs reading heritability estimates from ldsc_rg.log')
}
for(block.ind in 1:nrow(block)){
chr = block$chr[block.ind]
bp_start = block$start[block.ind]
bp_stop = block$stop[block.ind]
ind = (ref_bim$V1 == chr & ref_bim$V4 >= bp_start & ref_bim$V4 <= bp_stop)
if(sum(ind) > 0){
dat1_block = dat1[ind, ]
dat2_block = dat2[ind, ]
ref_bim_block = ref_bim[ind, ]
ref_bed_block = ref_bed[, ind]
ldsc_block = ldscore$L2[ind]
ldsc_block = ldsc_block * (ldsc_block >= 1) + 1 * (ldsc_block < 1)
write.table(dat1_block, paste0(out_dir, '/tmp_files/block/dat1_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(dat2_block, paste0(out_dir, '/tmp_files/block/dat2_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(ref_bim_block, paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(ref_bed_block, paste0(out_dir, '/tmp_files/block/ref_bed_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(ldsc_block, paste0(out_dir, '/tmp_files/block/ldsc_block_', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
}
}
rm(f1, f2, ind, dat1, dat2, ldscore, ldscore_chr, ref_bim, ref_bim_chr, ref_bed, ref_bed_chr)
}
if(npop == 2){
h2_snp_1 = xpass$h1[1]/M
h2_snp_2 = xpass$h2[1]/M
for(block.ind in 1:nrow(block)){
chr = block$chr[block.ind]
bp_start = block$start[block.ind]
bp_stop = block$stop[block.ind]
ind = (ref_bim$V1 == chr & ref_bim$V4 >= bp_start & ref_bim$V4 <= bp_stop)
if(sum(ind) > 0){
dat1_block = dat1[ind, ]
dat2_block = dat2[ind, ]
ref_bim_block = ref_bim[ind, ]
ref1_bed_block = ref1_bed[, ind]
ref2_bed_block = ref2_bed[, ind]
ldscore1_block = ldscore1[ind, ]
ldscore2_block = ldscore2[ind, ]
ldsc1 = n1 * h2_snp_1 * ldscore1_block$L2 + (1 - h2_snp_1 * M)
ldsc2 = n2 * h2_snp_2 * ldscore2_block$L2 + (1 - h2_snp_2 * M)
xldsc_block = abs(ldsc1 * ldsc2)
write.table(dat1_block, paste0(out_dir, '/tmp_files/block/dat1_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(dat2_block, paste0(out_dir, '/tmp_files/block/dat2_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(ref_bim_block, paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(ref1_bed_block, paste0(out_dir, '/tmp_files/block/ref1_bed_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(ref2_bed_block, paste0(out_dir, '/tmp_files/block/ref2_bed_block_', block.ind, '.txt'), row.names = F, col.names = T, quote = F)
write.table(xldsc_block, paste0(out_dir, '/tmp_files/block/xldsc_block_', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
}
}
rm(f1, f2, ind, dat1, dat2, ldscore1, ldscore2, ldscore1_chr, ldscore2_chr, ref_bim, ref_bim_chr, ref1_bed, ref1_bed_chr, ref2_bed, ref2_bed_chr)
}
gc()
############################### scan statistic
n_montecarlo = 5000
# Cn = 2000
# inter = 20
if(npop == 1){
theta = c(0.5, 0.55, 0.6, 0.65, 0.7)
}
if(npop == 2){
theta = c(0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75)
}
thre = 1000
scan = function(z1, z2, ldsc, Cn, inter, le, ri, theta){
m = length(z1)
z1_time_z2 = c(0, cumsum(z1 * z2))
ldscore_sum = c(0, cumsum(ldsc))
if(m > inter){
Qmax = -Inf
window = seq(from = inter, to = floor(min(Cn, m)/inter)*inter, by = inter)
for(I in window){
j_count = ceiling((m-I+1)/inter)
qq = rep(0, j_count)
j = 1:j_count
qq = (z1_time_z2[(j-1)*inter+1 +I] - z1_time_z2[(j-1)*inter+1])/(ldscore_sum[(j-1)*inter+1 +I] - ldscore_sum[(j-1)*inter+1])^theta
qq = abs(qq)
Q = max(qq)
ind_Q = which.max(qq)
if(Qmax < Q){
Qmax = Q
low = (ind_Q-1)*inter + 1
high = (ind_Q-1)*inter + I
}
}
re = list()
re[[1]] = Qmax
re[[2]] = c(low + le - 1, high + le - 1)
return(re)
}
re = list()
re[[1]] = z1_time_z2[m + 1]/(ldscore_sum[m + 1])^theta
re[[2]] = c(le, ri)
return(re)
}
svd.try = function(x, nu, nv){
SVD = 'error'
while(length(SVD) == 1){
SVD = tryCatch(svd(x, nu, nv), error = function(e){ 'error' })
x = x + diag(mean(abs(x))/10000, nrow = nrow(x))
}
return(SVD)
}
simulate_zscore_helper = function(segment.partition, geno.block, n_ref, thre){
len = table(segment.partition$segment)
segment.count = max(segment.partition$segment)
if(segment.count == 1){
ld_diag = ld2_diag = list()
Sigma_p1 = Sigma_p2 = Sigma_V1 = Sigma_V2 = Sigma_D1 = Sigma_D2 = list()
snp_1 = segment.partition$SNP[segment.partition$segment == 1]
ld_diag[[1]] = cor(geno.block[, colnames(geno.block) %in% snp_1], use = 'pairwise.complete.obs')
ld_diag[[1]][is.na(ld_diag[[1]])] = 0
ld2_diag[[1]] = (n_ref-1)/(n_ref-2)*ld_diag[[1]]%*%ld_diag[[1]] - sum(len[1])/(n_ref-2)*ld_diag[[1]]
SVD = svd.try(ld_diag[[1]], nu = 0, nv = nrow(ld_diag[[1]]))
sv = SVD$d
Sigma_p1[[1]] = sum(sv>sv[1]/thre)
Sigma_D1[[1]] = sqrt(sv[1:Sigma_p1[[1]]])
Sigma_V1[[1]] = SVD$v[,1:Sigma_p1[[1]]]
SVD = svd.try(ld2_diag[[1]], nu = 0, nv = nrow(ld2_diag[[1]]))
sv = SVD$d
Sigma_p2[[1]] = sum(sv>sv[1]/thre)
Sigma_D2[[1]] = sqrt(sv[1:Sigma_p2[[1]]])
Sigma_V2[[1]] = SVD$v[,1:Sigma_p2[[1]]]
return(list(p1 = Sigma_p1, p2 = Sigma_p2, V1 = Sigma_V1, V2 = Sigma_V2, D1 = Sigma_D1, D2 = Sigma_D2))
}
if(segment.count > 1){
ld_diag = ld_offdiag = ld2_diag = ld2_offdiag = list()
C1 = C2 = Sigma_p1 = Sigma_p2 = Sigma_V1 = Sigma_V2 = Sigma_D1 = Sigma_D2 = list()
snp_1 = segment.partition$SNP[segment.partition$segment == 1]
snp_2 = segment.partition$SNP[segment.partition$segment == 2]
ld_diag[[1]] = cor(geno.block[, colnames(geno.block) %in% snp_1], use = 'pairwise.complete.obs')
ld_diag[[2]] = cor(geno.block[, colnames(geno.block) %in% snp_2], use = 'pairwise.complete.obs')
ld_offdiag[[1]] = cor(geno.block[, colnames(geno.block) %in% snp_1], geno.block[, colnames(geno.block) %in% snp_2], use = 'pairwise.complete.obs')
ld_diag[[1]][is.na(ld_diag[[1]])] = ld_diag[[2]][is.na(ld_diag[[2]])] = ld_offdiag[[1]][is.na(ld_offdiag[[1]])] = 0
ld2_diag[[1]] = (n_ref-1)/(n_ref-2)*( ld_diag[[1]]%*%ld_diag[[1]] + ld_offdiag[[1]]%*%t(ld_offdiag[[1]]) ) - sum(len[1:2])/(n_ref-2)*ld_diag[[1]]
ld2_offdiag[[1]] = (n_ref-1)/(n_ref-2)*( ld_diag[[1]]%*%ld_offdiag[[1]] + ld_offdiag[[1]]%*%ld_diag[[2]] ) - sum(len[1:2])/(n_ref-2)*ld_offdiag[[1]]
SVD = svd.try(ld_diag[[1]], nu = 0, nv = nrow(ld_diag[[1]]))
sv = SVD$d
Sigma_p1[[1]] = sum(sv>sv[1]/thre)
Sigma_D1[[1]] = sqrt(sv[1:Sigma_p1[[1]]])
Sigma_V1[[1]] = SVD$v[,1:Sigma_p1[[1]]]
SVD = svd.try(ld2_diag[[1]], nu = 0, nv = nrow(ld2_diag[[1]]))
sv = SVD$d
Sigma_p2[[1]] = sum(sv>sv[1]/thre)
Sigma_D2[[1]] = sqrt(sv[1:Sigma_p2[[1]]])
Sigma_V2[[1]] = SVD$v[,1:Sigma_p2[[1]]]
if(segment.count > 2){
for(j in 2:(segment.count-1)){
snp_1 = segment.partition$SNP[segment.partition$segment == j]
snp_2 = segment.partition$SNP[segment.partition$segment == j + 1]
ld_diag[[j+1]] = cor(geno.block[, colnames(geno.block) %in% snp_2], use = 'pairwise.complete.obs')
ld_offdiag[[j]] = cor(geno.block[, colnames(geno.block) %in% snp_1], geno.block[, colnames(geno.block) %in% snp_2], use = 'pairwise.complete.obs')
ld_diag[[j+1]][is.na(ld_diag[[j+1]])] = ld_offdiag[[j]][is.na(ld_offdiag[[j]])] = 0
ld2_diag[[j]] = (n_ref-1)/(n_ref-2)*( ld_diag[[j]]%*%ld_diag[[j]] + t(ld_offdiag[[j-1]])%*%ld_offdiag[[j-1]] + ld_offdiag[[j]]%*%t(ld_offdiag[[j]]) ) - sum(len[(j-1):(j+1)])/(n_ref-2)*ld_diag[[j]]
ld2_offdiag[[j]] = (n_ref-1)/(n_ref-2)*( ld_diag[[j]]%*%ld_offdiag[[j]] + ld_offdiag[[j]]%*%ld_diag[[j+1]] ) - sum(len[j:(j+1)])/(n_ref-2)*ld_offdiag[[j]]
if(j > 2){
ld_diag[[j-2]] = ld_offdiag[[j-2]] = ld2_diag[[j-2]] = ld2_offdiag[[j-2]] = 0
}
svd1 = svd.try(ld_diag[[j-1]], nu = 0, nv = nrow(ld_diag[[j-1]]))
sv1 = svd1$d
p1 = sum(sv1>sv1[1]/thre)
if(p1 == 1){
D1 = 1/sv1[1:p1]
}else{
D1 = diag(1/sv1[1:p1])
}
V1 = as.matrix(svd1$v[,1:p1])
C1[[j]] = t(ld_offdiag[[j-1]])%*%V1%*%D1%*%t(V1)
Sigma1 = ld_diag[[j]] - C1[[j]]%*%ld_offdiag[[j-1]]
svd2 = svd.try(ld2_diag[[j-1]], nu = 0, nv = nrow(ld2_diag[[j-1]]))
sv2 = svd2$d
p2 = sum(sv2>sv2[1]/thre)
if(p2 == 1){
D2 = 1/sv2[1:p2]
}else{
D2 = diag(1/sv2[1:p2])
}
V2 = as.matrix(svd2$v[,1:p2])
C2[[j]] = t(ld2_offdiag[[j-1]])%*%V2%*%D2%*%t(V2)
Sigma2 = ld2_diag[[j]] - C2[[j]]%*%ld2_offdiag[[j-1]]
SVD = svd.try(Sigma1, nu = 0, nv = nrow(Sigma1))
sv = SVD$d
Sigma_p1[[j]] = sum(sv>sv[1]/thre)
Sigma_D1[[j]] = sqrt(sv[1:Sigma_p1[[j]]])
Sigma_V1[[j]] = SVD$v[,1:Sigma_p1[[j]]]
SVD = svd.try(Sigma2, nu = 0, nv = nrow(Sigma2))
sv = SVD$d
Sigma_p2[[j]] = sum(sv>sv[1]/thre)
Sigma_D2[[j]] = sqrt(sv[1:Sigma_p2[[j]]])
Sigma_V2[[j]] = SVD$v[,1:Sigma_p2[[j]]]
}
}
j = segment.count
ld2_diag[[j]] = ld_diag[[j]]%*%ld_diag[[j]] + t(ld_offdiag[[j-1]])%*%ld_offdiag[[j-1]]
svd1 = svd.try(ld_diag[[j-1]], nu = 0, nv = nrow(ld_diag[[j-1]]))
sv1 = svd1$d
p1 = sum(sv1>sv1[1]/thre)
if(p1 == 1){
D1 = 1/sv1[1:p1]
}else{
D1 = diag(1/sv1[1:p1])
}
V1 = as.matrix(svd1$v[,1:p1])
C1[[j]] = t(ld_offdiag[[j-1]])%*%V1%*%D1%*%t(V1)
Sigma1 = ld_diag[[j]] - C1[[j]]%*%ld_offdiag[[j-1]]
svd2 = svd.try(ld2_diag[[j-1]], nu = 0, nv = nrow(ld2_diag[[j-1]]))
sv2 = svd2$d
p2 = sum(sv2>sv2[1]/thre)
if(p2 == 1){
D2 = 1/sv2[1:p2]
}else{
D2 = diag(1/sv2[1:p2])
}
V2 = as.matrix(svd2$v[,1:p2])
C2[[j]] = t(ld2_offdiag[[j-1]])%*%V2%*%D2%*%t(V2)
Sigma2 = ld2_diag[[j]] - C2[[j]]%*%ld2_offdiag[[j-1]]
SVD = svd.try(Sigma1, nu = 0, nv = nrow(Sigma1))
sv = SVD$d
Sigma_p1[[j]] = sum(sv>sv[1]/thre)
Sigma_D1[[j]] = sqrt(sv[1:Sigma_p1[[j]]])
Sigma_V1[[j]] = SVD$v[,1:Sigma_p1[[j]]]
SVD = svd.try(Sigma2, nu = 0, nv = nrow(Sigma2))
sv = SVD$d
Sigma_p2[[j]] = sum(sv>sv[1]/thre)
Sigma_D2[[j]] = sqrt(sv[1:Sigma_p2[[j]]])
Sigma_V2[[j]] = SVD$v[,1:Sigma_p2[[j]]]
return(list(C1 = C1, C2 = C2, p1 = Sigma_p1, p2 = Sigma_p2, V1 = Sigma_V1, V2 = Sigma_V2, D1 = Sigma_D1, D2 = Sigma_D2))
}
}
cal_qmax_1pop = function(bim, geno, n_ref, thre, n_montecarlo, n1, n2, h2_snp_1, h2_snp_2, intercept, M, ldsc, theta, Cn, inter){
### partition into segments
bim$segment = as.numeric(as.factor(ceiling(bim$V4/1000000)))
tab = which(table(bim$segment)>3000)
while(length(tab)>0){
ind = which(bim$segment == tab[1])
K = ceiling(length(ind)/3000)
quan = quantile(ind, seq(0, K)/K, type = 3)
for(i in 2:K){
bim$segment[(quan[i]+1):quan[i+1]] = bim$segment[(quan[i]+1):quan[i+1]] + 1/K*(i - 1)
}
bim$segment = as.numeric(as.factor(bim$segment))
tab = which(table(bim$segment)>3000)
}
tab = which(table(bim$segment)<10)
if(max(bim$segment) > 1){
while(length(tab)>0){
ind = which(bim$segment == tab[1])
if(min(ind) == 1){
bim$segment[ind] = bim$segment[max(ind) + 1]
}else{
bim$segment[ind] = bim$segment[min(ind) - 1]
}
bim$segment = as.numeric(as.factor(bim$segment))
tab = which(table(bim$segment)<10)
}
}
segment.partition = bim[, c(2, 7)]
colnames(segment.partition) = c('SNP', 'segment')
segment.count = max(segment.partition$segment)
### simulate zscore vectors under the null
pop.helper = simulate_zscore_helper(segment.partition, geno, n_ref, thre)
X = matrix(rnorm(n_montecarlo*3*pop.helper$p1[[1]]), nrow = pop.helper$p1[[1]], ncol = n_montecarlo*3)
pop_ss = t(pop.helper$V1[[1]]%*%(X*pop.helper$D1[[1]]))
X = matrix(rnorm(n_montecarlo*2*pop.helper$p2[[1]]), nrow = pop.helper$p2[[1]], ncol = n_montecarlo*2)
pop_tt = t(pop.helper$V2[[1]]%*%(X*pop.helper$D2[[1]]))
pop_SS = pop_ss
pop_TT = pop_tt
if(segment.count > 1){
for(j in 2:segment.count){
print(j)
X = matrix(rnorm(n_montecarlo*3*pop.helper$p1[[j]]), nrow = pop.helper$p1[[j]], ncol = n_montecarlo*3)
pop_ss_new = t(pop.helper$V1[[j]]%*%(X*pop.helper$D1[[j]])) + pop_ss%*%t(pop.helper$C1[[j]])
X = matrix(rnorm(n_montecarlo*2*pop.helper$p2[[j]]), nrow = pop.helper$p2[[j]], ncol = n_montecarlo*2)
pop_tt_new = t(pop.helper$V2[[j]]%*%(X*pop.helper$D2[[j]])) + pop_tt%*%t(pop.helper$C2[[j]])
print(j)
pop_SS = cbind(pop_SS, pop_ss_new)
pop_TT = cbind(pop_TT, pop_tt_new)
pop_ss = pop_ss_new
pop_tt = pop_tt_new
}
}
S1 = pop_SS[1:n_montecarlo, ]
S2 = pop_SS[(n_montecarlo + 1):(2*n_montecarlo), ]
S3 = pop_SS[(2*n_montecarlo + 1):(3*n_montecarlo), ]
T1 = pop_TT[1:n_montecarlo, ]
T2 = pop_TT[(n_montecarlo + 1):(2*n_montecarlo), ]
Z1 = sqrt(n1*h2_snp_1)*T1 + sqrt( max(1 - h2_snp_1*M - abs(intercept), 0) )*S1 + sqrt(abs(intercept))*S3
Z2 = sqrt(n2*h2_snp_2)*T2 + sqrt( max(1 - h2_snp_2*M - abs(intercept), 0) )*S2 + sqrt(abs(intercept))*sign(intercept)*S3
sd1 = sd2 = rep(0, n_montecarlo)
Qmax = matrix(0, nrow = n_montecarlo, ncol = length(theta))
for(i in 1:n_montecarlo){
z1 = Z1[i, ]
z2 = Z2[i, ]
m = length(z1)
sd1[i] = sqrt(mean(z1^2))
sd2[i] = sqrt(mean(z2^2))
for(j in 1:length(theta)){
re = scan(z1, z2, ldsc, Cn, inter, 1, m, theta[j])
Qmax[i, j] = as.numeric(re[[1]])
}
}
return(list(Qmax = Qmax, sd1 = sd1, sd2 = sd2))
}
cal_qmax_2pop = function(bim, geno1, geno2, n_ref1, n_ref2, thre, n_montecarlo, n1, n2, h2_snp_1, h2_snp_2, M, ldsc, theta, Cn, inter){
### partition into segments
bim$segment = as.numeric(as.factor(ceiling(bim$V4/1000000)))
tab = which(table(bim$segment)>3000)
while(length(tab)>0){
ind = which(bim$segment == tab[1])
K = ceiling(length(ind)/3000)
quan = quantile(ind, seq(0, K)/K, type = 3)
for(i in 2:K){
bim$segment[(quan[i]+1):quan[i+1]] = bim$segment[(quan[i]+1):quan[i+1]] + 1/K*(i - 1)
}
bim$segment = as.numeric(as.factor(bim$segment))
tab = which(table(bim$segment)>3000)
}
tab = which(table(bim$segment)<10)
if(max(bim$segment) > 1){
while(length(tab)>0){
ind = which(bim$segment == tab[1])
if(min(ind) == 1){
bim$segment[ind] = bim$segment[max(ind) + 1]
}else{
bim$segment[ind] = bim$segment[min(ind) - 1]
}
bim$segment = as.numeric(as.factor(bim$segment))
tab = which(table(bim$segment)<10)
}
}
segment.partition = bim[, c(2, 7)]
colnames(segment.partition) = c('SNP', 'segment')
segment.count = max(segment.partition$segment)
### simulate zscore vectors under the null
pop1.helper = simulate_zscore_helper(segment.partition, geno1, n_ref1, thre)
pop2.helper = simulate_zscore_helper(segment.partition, geno2, n_ref2, thre)
X = matrix(rnorm(n_montecarlo*pop1.helper$p1[[1]]), nrow = pop1.helper$p1[[1]], ncol = n_montecarlo)
pop1_ss = t(pop1.helper$V1[[1]]%*%(X*pop1.helper$D1[[1]]))
X = matrix(rnorm(n_montecarlo*pop1.helper$p2[[1]]), nrow = pop1.helper$p2[[1]], ncol = n_montecarlo)
pop1_tt = t(pop1.helper$V2[[1]]%*%(X*pop1.helper$D2[[1]]))
pop1_SS = pop1_ss
pop1_TT = pop1_tt
X = matrix(rnorm(n_montecarlo*pop2.helper$p1[[1]]), nrow = pop2.helper$p1[[1]], ncol = n_montecarlo)
pop2_ss = t(pop2.helper$V1[[1]]%*%(X*pop2.helper$D1[[1]]))
X = matrix(rnorm(n_montecarlo*pop2.helper$p2[[1]]), nrow = pop2.helper$p2[[1]], ncol = n_montecarlo)
pop2_tt = t(pop2.helper$V2[[1]]%*%(X*pop2.helper$D2[[1]]))
pop2_SS = pop2_ss
pop2_TT = pop2_tt
if(segment.count > 1){
for(j in 2:segment.count){
X = matrix(rnorm(n_montecarlo*pop1.helper$p1[[j]]), nrow = pop1.helper$p1[[j]], ncol = n_montecarlo)
pop1_ss_new = t(pop1.helper$V1[[j]]%*%(X*pop1.helper$D1[[j]])) + pop1_ss%*%t(pop1.helper$C1[[j]])
X = matrix(rnorm(n_montecarlo*pop1.helper$p2[[j]]), nrow = pop1.helper$p2[[j]], ncol = n_montecarlo)
pop1_tt_new = t(pop1.helper$V2[[j]]%*%(X*pop1.helper$D2[[j]])) + pop1_tt%*%t(pop1.helper$C2[[j]])
pop1_SS = cbind(pop1_SS, pop1_ss_new)
pop1_TT = cbind(pop1_TT, pop1_tt_new)
pop1_ss = pop1_ss_new
pop1_tt = pop1_tt_new
X = matrix(rnorm(n_montecarlo*pop2.helper$p1[[j]]), nrow = pop2.helper$p1[[j]], ncol = n_montecarlo)
pop2_ss_new = t(pop2.helper$V1[[j]]%*%(X*pop2.helper$D1[[j]])) + pop2_ss%*%t(pop2.helper$C1[[j]])
X = matrix(rnorm(n_montecarlo*pop2.helper$p2[[j]]), nrow = pop2.helper$p2[[j]], ncol = n_montecarlo)
pop2_tt_new = t(pop2.helper$V2[[j]]%*%(X*pop2.helper$D2[[j]])) + pop2_tt%*%t(pop2.helper$C2[[j]])
pop2_SS = cbind(pop2_SS, pop2_ss_new)
pop2_TT = cbind(pop2_TT, pop2_tt_new)
pop2_ss = pop2_ss_new
pop2_tt = pop2_tt_new
}
}
Z1 = sqrt(n1*h2_snp_1)*pop1_TT + sqrt(1 - h2_snp_1*M)*pop1_SS
Z2 = sqrt(n2*h2_snp_2)*pop2_TT + sqrt(1 - h2_snp_2*M)*pop2_SS
sd1 = sd2 = rep(0, n_montecarlo)
Qmax = matrix(0, nrow = n_montecarlo, ncol = length(theta))
for(i in 1:n_montecarlo){
z1 = Z1[i, ]
z2 = Z2[i, ]
m = length(z1)
sd1[i] = sqrt(mean(z1^2))
sd2[i] = sqrt(mean(z2^2))
for(j in 1:length(theta)){
re = scan(z1, z2, ldsc, Cn, inter, 1, m, theta[j])
Qmax[i, j] = as.numeric(re[[1]])
}
}
return(list(Qmax = Qmax, sd1 = sd1, sd2 = sd2))
}
apply.fun.1pop = function(block.ind){
library(data.table)
chr = block$chr[block.ind]
bp_start = block$start[block.ind]
bp_stop = block$stop[block.ind]
if(file.exists(paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt'))){
ref_bim_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt')))
ref_bed_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ref_bed_block_', block.ind, '.txt')))
ldsc_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ldsc_block_', block.ind, '.txt')))[, 1]
result = cal_qmax_1pop(bim = ref_bim_block, geno = ref_bed_block,
n_ref, thre, n_montecarlo,
n1, n2, h2_snp_1, h2_snp_2, intercept, M,
ldsc = ldsc_block, theta, Cn, inter)
write.table(result$Qmax, paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(result$sd1, paste0(out_dir, '/tmp_files/block/sd1_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(result$sd2, paste0(out_dir, '/tmp_files/block/sd2_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
}
}
apply.fun.2pop = function(block.ind){
library(data.table)
chr = block$chr[block.ind]
bp_start = block$start[block.ind]
bp_stop = block$stop[block.ind]
if(file.exists(paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt'))){
ref_bim_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ref_bim_block_', block.ind, '.txt')))
ref1_bed_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ref1_bed_block_', block.ind, '.txt')))
ref2_bed_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ref2_bed_block_', block.ind, '.txt')))
xldsc_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/xldsc_block_', block.ind, '.txt')))[, 1]
result = cal_qmax_2pop(bim = ref_bim_block, geno1 = ref1_bed_block, geno2 = ref2_bed_block,
n_ref1, n_ref2, thre, n_montecarlo,
n1, n2, h2_snp_1, h2_snp_2, M,
ldsc = xldsc_block, theta, Cn, inter)
write.table(result$Qmax, paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(result$sd1, paste0(out_dir, '/tmp_files/block/sd1_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
write.table(result$sd2, paste0(out_dir, '/tmp_files/block/sd2_block', block.ind, '.txt'), row.names = F, col.names = F, quote = F)
}
}
sfInit(parallel = TRUE, cpus = ncore)
sfLibrary(snowfall)
if(npop == 1){
sfExport('block', 'n_ref', 'thre', 'n_montecarlo', 'n1', 'n2', 'h2_snp_1', 'h2_snp_2', 'M', 'theta', 'Cn', 'inter', 'out_dir', 'intercept')
sfExport('scan', 'svd.try', 'simulate_zscore_helper', 'cal_qmax_1pop')
Result = sfLapply(1:nrow(block), apply.fun.1pop)
}
if(npop == 2){
sfExport('block', 'n_ref1', 'n_ref2', 'thre', 'n_montecarlo', 'n1', 'n2', 'h2_snp_1', 'h2_snp_2', 'M', 'theta', 'Cn', 'inter', 'out_dir')
sfExport('scan', 'svd.try', 'simulate_zscore_helper', 'cal_qmax_2pop')
Result = sfLapply(1:nrow(block), apply.fun.2pop)
}
sfStop()
############################### scan statistic on real GWAS data
whole_scan = function(z1, z2, ldsc, Cn, inter, thre, le, ri, theta){
re = scan(z1, z2, ldsc, Cn, inter, le, ri, theta)
if(re[[1]] <= thre){
return(list())
}
else{
a = re[[2]][1]
b = re[[2]][2]
if(a == le & b == ri){return(re)}
if(a == le & b < ri){
return( c( re , whole_scan(z1[(b+2-le):(ri+1-le)], z2[(b+2-le):(ri+1-le)], ldsc[(b+2-le):(ri+1-le)], Cn, inter, thre, b+1, ri, theta) ) )
}
if(a > le & b == ri){
return( c( whole_scan(z1[1:(a-le)], z2[1:(a-le)], ldsc[1:(a-le)], Cn, inter, thre, le, a-1, theta), re) )
}
return( c( whole_scan(z1[1:(a-le)], z2[1:(a-le)], ldsc[1:(a-le)], Cn, inter, thre, le, a-1, theta), re, whole_scan(z1[(b+2-le):(ri+1-le)], z2[(b+2-le):(ri+1-le)], ldsc[(b+2-le):(ri+1-le)], Cn, inter, thre, b+1, ri, theta) ) )
}
}
dat1 = data.frame(fread(paste0(out_dir, '/tmp_files/dat1.txt')))
dat2 = data.frame(fread(paste0(out_dir, '/tmp_files/dat2.txt')))
Result = list()
for(j in 1:length(theta)){
Result[[j]] = as.data.frame(matrix(0, nrow = 0, ncol = 7))
colnames(Result[[j]]) = c('chr', 'begin_pos', 'stop_pos', 'count', 'stat', 'pval', 'block')
}
n.block = 0
if(npop == 1){
for(block.ind in 1:nrow(block)){
if(file.exists(paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt'))){
n.block = n.block + 1
dat1_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/dat1_block_', block.ind, '.txt')))
dat2_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/dat2_block_', block.ind, '.txt')))
ldsc_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/ldsc_block_', block.ind, '.txt')))[, 1]
z1_block = dat1_block$Z
z2_block = dat2_block$Z
sd1_data = sqrt(mean(z1_block^2))
sd2_data = sqrt(mean(z2_block^2))
sd1_generated = fread(paste0(out_dir, '/tmp_files/block/sd1_block', block.ind, '.txt'))[, 1]
sd2_generated = fread(paste0(out_dir, '/tmp_files/block/sd2_block', block.ind, '.txt'))[, 1]
Qmax = as.matrix(read.table(paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt')))
q = rep(0, length(theta))
Qmax_scaled = matrix(0, nrow = n_montecarlo, ncol = length(theta))
for(j in 1:length(theta)){
Qmax_scaled[, j] = Qmax[, j]*(sd1_data * sd2_data)/(sd1_generated * sd2_generated)
q[j] = quantile(Qmax_scaled[, j], 0.95)
}
for(j in 1:length(theta)){
result = whole_scan(z1_block, z2_block, ldsc_block, Cn, inter, q[j], 1, length(z1_block), theta[j])
detect = c()
if(length(result)>0){
for(i in 1:(length(result)/2)){
ind.begin = result[[2*i]][1]
ind.stop = result[[2*i]][2]
count = ind.stop - ind.begin + 1
stat = sum(z1_block[ind.begin:ind.stop]*z2_block[ind.begin:ind.stop])/(sum(ldsc_block[ind.begin:ind.stop]))^theta[j]
pval = (sum(abs(stat) < Qmax_scaled[, j]) + 1)/(n_montecarlo + 1)
chr = dat1_block$CHR[1]
begin_pos = dat1_block$BP[ind.begin]
stop_pos = dat1_block$BP[ind.stop]
detect_new = c(chr, begin_pos, stop_pos, count, stat, pval, block.ind)
detect = rbind(detect, detect_new)
}
detect = data.frame(detect, row.names = NULL)
colnames(detect) = c('chr', 'begin_pos', 'stop_pos', 'count', 'stat', 'pval', 'block')
Result[[j]] = rbind(Result[[j]], detect)
}
}
}
}
}
if(npop == 2){
for(block.ind in 1:nrow(block)){
if(file.exists(paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt'))){
n.block = n.block + 1
dat1_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/dat1_block_', block.ind, '.txt')))
dat2_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/dat2_block_', block.ind, '.txt')))
xldsc_block = data.frame(fread(paste0(out_dir, '/tmp_files/block/xldsc_block_', block.ind, '.txt')))[, 1]
z1_block = dat1_block$Z
z2_block = dat2_block$Z
sd1_data = sqrt(mean(z1_block^2))
sd2_data = sqrt(mean(z2_block^2))
sd1_generated = fread(paste0(out_dir, '/tmp_files/block/sd1_block', block.ind, '.txt'))[, 1]
sd2_generated = fread(paste0(out_dir, '/tmp_files/block/sd2_block', block.ind, '.txt'))[, 1]
Qmax = as.matrix(read.table(paste0(out_dir, '/tmp_files/block/Qmax_block', block.ind, '.txt')))
q = rep(0, length(theta))
Qmax_scaled = matrix(0, nrow = n_montecarlo, ncol = length(theta))
for(j in 1:length(theta)){
Qmax_scaled[, j] = Qmax[, j]*(sd1_data * sd2_data)/(sd1_generated * sd2_generated)
q[j] = quantile(Qmax_scaled[, j], 0.95)
}
for(j in 1:length(theta)){
result = whole_scan(z1_block, z2_block, xldsc_block, Cn, inter, q[j], 1, length(z1_block), theta[j])
detect = c()
if(length(result)>0){
for(i in 1:(length(result)/2)){
ind.begin = result[[2*i]][1]
ind.stop = result[[2*i]][2]
count = ind.stop - ind.begin + 1
stat = sum(z1_block[ind.begin:ind.stop]*z2_block[ind.begin:ind.stop])/(sum(xldsc_block[ind.begin:ind.stop]))^theta[j]
pval = (sum(abs(stat) < Qmax_scaled[, j]) + 1)/(n_montecarlo + 1)
chr = dat1_block$CHR[1]
begin_pos = dat1_block$BP[ind.begin]
stop_pos = dat1_block$BP[ind.stop]
detect_new = c(chr, begin_pos, stop_pos, count, stat, pval, block.ind)
detect = rbind(detect, detect_new)
}
detect = data.frame(detect, row.names = NULL)
colnames(detect) = c('chr', 'begin_pos', 'stop_pos', 'count', 'stat', 'pval', 'block')
Result[[j]] = rbind(Result[[j]], detect)
}
}
}
}
}
for(j in 1:length(theta)){
n0 = nrow(Result[[j]])
if(n0 > 0){
Result[[j]] = Result[[j]][order(abs(Result[[j]]$stat), decreasing = T),]
Result[[j]] = Result[[j]][order(Result[[j]]$pval),]
Result[[j]]$testing_count = n.block
if(n0>1){
for(i in 2:n0){
temp = Result[[j]][1:(i-1), ]
testing_add = (sum(temp$block == Result[[j]]$block[i]))>0
Result[[j]]$testing_count[i:n0] = Result[[j]]$testing_count[i:n0] + testing_add
}
}
p_val = Result[[j]]$pval
BH = max(which(p_val<=0.05/Result[[j]]$testing_count*c(1:n0)))
Result[[j]]$pval_adj = Result[[j]]$pval * Result[[j]]$testing_count / c(1:n0)
if(BH>0){
Result[[j]] = Result[[j]][1:BH, c('chr', 'begin_pos', 'stop_pos', 'stat', 'pval', 'pval_adj')]
for(i in BH:1){
Result[[j]]$pval_adj[i] = min(Result[[j]]$pval_adj[i:BH])
}
}else{
Result[[j]] = Result[[j]][1, ][-1, ]
}
}
}
############################### calculate genetic covariane in aggregated regions
if(npop == 1){
if(!dir.exists(paste0(out_dir, '/tmp_files/S-LDSC'))){
dir.create(paste0(out_dir, '/tmp_files/S-LDSC'))
}
gcov = rep(0, length(theta))
for(j in 1:length(theta)){
if(nrow(Result[[j]])>0){
re = Result[[j]][, c('chr', 'begin_pos', 'stop_pos')]
re = re[order(re$begin_pos), ]
re = re[order(re$chr), ]
for(ch in 1:22){
annot = data.frame(fread(paste0(ref_dir, '/ldsc/1000g_eur/1000G_EUR_QC_chr', ch, '.bim')))
annot = annot[, 1:4]
colnames(annot) = c('CHR', 'SNP', 'CM', 'BP')
annot$C0 = 1
annot$C1 = 0
re_new = re[re$chr == ch, ]
if(nrow(re_new) > 0){
for(w in 1:nrow(re_new)){
annot$C0[re_new$begin_pos[w]<=annot$BP & annot$BP<=re_new$stop_pos[w]] = 0
annot$C1[re_new$begin_pos[w]<=annot$BP & annot$BP<=re_new$stop_pos[w]] = 1
}
}
write.table(annot, paste0(out_dir, '/tmp_files/S-LDSC/LOGODetect_theta_', theta[j], '_chr', ch, '.annot'), col.names = T, row.names = F, quote = F)
}
### S-LDSC
for(ch in 1:22){
job = paste0('python ', ldsc_dir, '/ldsc.py --l2 --bfile ', ref_dir, '/ldsc/1000g_eur/1000G_EUR_QC_chr', ch, ' --ld-wind-cm 1 --annot ', out_dir, '/tmp_files/S-LDSC/LOGODetect_theta_', theta[j], '_chr', ch, '.annot --out ', out_dir, '/tmp_files/S-LDSC/LOGODetect_theta_', theta[j], '_chr', ch, ' --print-snps ', ref_dir, '/ldsc/hapmap3_snps/hm.', ch, '.snp;')
system(job, ignore.stdout = T)
}