Top gwas signal summary

Last updated: 2024-08-29

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/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_AA.csv	output/logistic_surv_comparison/top_signal_AA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_COA.csv	output/logistic_surv_comparison/top_signal_COA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_AOA.csv	output/logistic_surv_comparison/top_signal_AOA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_AA.csv	output/logistic_surv_comparison/top_signal_LR_AA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_COA.csv	output/logistic_surv_comparison/top_signal_LR_COA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_AOA.csv	output/logistic_surv_comparison/top_signal_LR_AOA.csv
/Users/nicholeyang/Downloads/survival-data-analysis/output/top_signal.pdf	output/top_signal.pdf

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library(stringr)

logistic.aoa <- list.files(path="/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/aoa", pattern = "\\.glm\\.logistic$", full.names = TRUE)
logistic.coa <- list.files(path="/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/coa", pattern = "\\.glm\\.logistic$", full.names = TRUE)

logistic <- list.files(path="/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/all", pattern = "\\.glm\\.logistic$", full.names = TRUE)

dir = "/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv"
surv.aoa <- list.files(path = dir, pattern = "^aoa.*\\.rds$", full.names = TRUE)
surv.coa <- list.files(path = dir, pattern = "^coa.*\\.rds$", full.names = TRUE)
surv <- list.files(path = dir, pattern = "^all.*\\.rds$", full.names = TRUE)

1. All asthma

## Based on logistic pvalue
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.all = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.all) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic)){
  path = logistic[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/all_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.logistic$P)
  snp = gwas.logistic$ID[indx]
  pval.all[i, ] = c(region, snp, -log10(gwas.logistic$P[indx]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

## Based on survival pvalue
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.all2 = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.all2) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic)){
  path = logistic[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/all_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.surv$p.value.spa)
  snp = gwas.surv$ID[indx]
  pval.all2[i, ] = c(region, snp, -log10(gwas.logistic$P[which(gwas.logistic$ID == snp)]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

snp_list = rbind(pval.all, pval.all2)
write.csv(snp_list, "/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_AA.csv")

2. COA

## logistic signal
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.coa = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.coa) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic.coa)){
  path = logistic.coa[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/coa_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.logistic$P)
  snp = gwas.logistic$ID[indx]
  pval.coa[i, ] = c(region, snp, -log10(gwas.logistic$P[indx]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

## survival signals
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.coa2 = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.coa2) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic.coa)){
  path = logistic.coa[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/coa_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.surv$p.value.spa)
  snp = gwas.surv$ID[indx]
  pval.coa2[i, ] = c(region, snp, -log10(gwas.logistic$P[which(gwas.logistic$ID == snp)]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

snp_list = rbind(pval.coa, pval.coa2)
write.csv(snp_list, "/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_COA.csv")

3. AOA

## logistic signals
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.aoa = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.aoa) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic.aoa)){
  path = logistic.aoa[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/aoa_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.logistic$P)
  snp = gwas.logistic$ID[indx]
  pval.aoa[i, ] = c(region, snp, -log10(gwas.logistic$P[indx]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

## survival signals
pattern <- "chr[0-9XYM]+_[0-9]+_[0-9]+"
pval.aoa2 = data.frame(matrix(NA, nrow = length(logistic), ncol = 4))
colnames(pval.aoa2) = c("region", "snp", "logistic", "coxph")
for (i in 1:length(logistic.aoa)){
  path = logistic.aoa[i]
  region = str_extract(path, pattern)
  gwas.logistic = read.csv(path, header = TRUE, sep = "\t")
  gwas.surv = readRDS(paste0(dir, "/aoa_gwas_", region, ".rds"))
  gwas.surv = data.frame(gwas.surv)
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) unlist(strsplit(rownames(gwas.surv)[i], "_"))[1])
  indx = which.min(gwas.surv$p.value.spa)
  snp = gwas.surv$ID[indx]
  pval.aoa2[i, ] = c(region, snp, -log10(gwas.logistic$P[which(gwas.logistic$ID == snp)]),
                   -log10(gwas.surv$p.value.spa[which(gwas.surv$ID == snp)]))
}

snp_list = rbind(pval.aoa, pval.aoa2)
write.csv(snp_list, "/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_AOA.csv")

Likelihood ratios of top signals

res.AA = read.csv("/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_AA.csv", row.names = "X")

res.COA = read.csv("/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_COA.csv", row.names = "X")

res.AOA = read.csv("/Users/nicholeyang/Downloads/survival-data-analysis/output/logistic_surv_comparison/top_signal_LR_AOA.csv", row.names = "X")

par(mfrow = c(1,3))

indx.remove = which(res.AA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
hist(res.AA$survival[-indx.remove] - res.AA$logistic.1[-indx.remove], main = "AA",
     xlab = "CoxPH log(LR) - logistic log(LR)")


indx.remove = which(res.COA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
hist(res.COA$survival[-indx.remove] - res.COA$logistic.1[-indx.remove], main = "COA",
     xlab = "CoxPH log(LR) - logistic log(LR)")



indx.remove = which(res.AOA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
hist(res.AOA$survival[-indx.remove] - res.AOA$logistic.1[-indx.remove], main = "AOA",
     xlab = "CoxPH log(LR) - logistic log(LR)")

Version	Author	Date
82b913b	yunqiyang0215	2024-08-28
c0e1aa5	yunqiyang0215	2024-08-28
2241d7c	yunqiyang0215	2024-08-15
9e4ae0d	yunqiyang0215	2024-07-29

pdf("/Users/nicholeyang/Downloads/survival-data-analysis/output/top_signal.pdf", height = 4, width = 8)
par(mfrow = c(1,3))

indx.remove = which(res.AA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
plot(res.AA$logistic.1[-indx.remove], res.AA$survival[-indx.remove], pch = 20, xlab = "Logistic log(LR)", ylab = "CoxPH log(LR)", main = "AA")
abline(a = 0, b = 1, col = "red")

print(paste0(sum(res.AA$logistic.1[-indx.remove] < res.AA$survival[-indx.remove]), " out of ", length(res.AA$logistic.1[-indx.remove]), " signals has survival log(LR) higher than logistic log(LR)."))

[1] "16 out of 18 signals has survival log(LR) higher than logistic log(LR)."

indx.remove = which(res.COA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
plot(res.COA$logistic.1[-indx.remove], res.COA$survival[-indx.remove], pch = 20, xlab = "Logistic log(LR)", ylab = "CoxPH log(LR)", main = "COA")
abline(a = 0, b = 1, col = "red")

print(paste0(sum(res.COA$logistic.1[-indx.remove] < res.COA$survival[-indx.remove]), " out of ", length(res.COA$logistic.1[-indx.remove]), " signals has survival log(LR) higher than logistic log(LR)."))

[1] "10 out of 18 signals has survival log(LR) higher than logistic log(LR)."

indx.remove = which(res.AOA$region %in% c("chr2_236400001_242193529", "chr12_54500001_56200000", "chr19_31900001_35100000"))
plot(res.AOA$logistic.1[-indx.remove], res.AOA$survival[-indx.remove], pch = 20, xlab = "Logistic log(LR)", ylab = "CoxPH log(LR)", main = "AOA")
abline(a = 0, b = 1, col = "red")

print(paste0(sum(res.AOA$logistic.1[-indx.remove] < res.AOA$survival[-indx.remove]), " out of ", length(res.AOA$logistic.1[-indx.remove]), " signals has survival log(LR) higher than logistic log(LR)."))

[1] "11 out of 18 signals has survival log(LR) higher than logistic log(LR)."

par(mfrow = c(1,3))
plot(res.AA$logistic, res.AA$coxph)
abline(a = 0, b = 1, col = "red")
plot(res.AOA$logistic, res.AOA$coxph)
abline(a = 0, b = 1, col = "red")
plot(res.COA$logistic, res.COA$coxph)
abline(a = 0, b = 1, col = "red")

sessionInfo()

R version 4.1.1 (2021-08-10)
Platform: x86_64-apple-darwin20.6.0 (64-bit)
Running under: macOS Monterey 12.0.1

Matrix products: default
BLAS:   /usr/local/Cellar/openblas/0.3.18/lib/libopenblasp-r0.3.18.dylib
LAPACK: /usr/local/Cellar/r/4.1.1_1/lib/R/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] stringr_1.4.0   workflowr_1.6.2

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     rstudioapi_0.13  whisker_0.4      knitr_1.36      
 [5] magrittr_2.0.1   R6_2.5.1         rlang_1.1.1      fastmap_1.1.0   
 [9] fansi_0.5.0      highr_0.9        tools_4.1.1      xfun_0.27       
[13] utf8_1.2.2       cli_3.6.1        git2r_0.28.0     jquerylib_0.1.4 
[17] htmltools_0.5.5  ellipsis_0.3.2   rprojroot_2.0.2  yaml_2.2.1      
[21] digest_0.6.28    tibble_3.1.5     lifecycle_1.0.3  later_1.3.0     
[25] sass_0.4.4       vctrs_0.6.3      promises_1.2.0.1 fs_1.5.0        
[29] cachem_1.0.6     glue_1.4.2       evaluate_0.14    rmarkdown_2.11  
[33] stringi_1.7.5    bslib_0.4.1      compiler_4.1.1   pillar_1.9.0    
[37] jsonlite_1.7.2   httpuv_1.6.3     pkgconfig_2.0.3

Top gwas signal summary

Yunqi Yang

7/3/2024

1. All asthma

2. COA

3. AOA

Likelihood ratios of top signals