Cut region for finemapping

Last updated: 2024-08-27

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Knit directory: survival-data-analysis/

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File	Version	Author	Date	Message
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Rmd	d9c6599	yunqiyang0215	2024-06-18	wflow_publish("analysis/region_cut.Rmd")
html	ebeca15	yunqiyang0215	2024-06-18	Build site.
Rmd	2b1f395	yunqiyang0215	2024-06-18	wflow_publish("analysis/region_cut.Rmd")

Description:

This file cut the regions from survival GWAS results to create smaller regions for fine-mapping. We first find the top signal (smallest pvalue.spa) based on the preferred analysis, and select the region +- 250kb around the top signal.

regions = c("chr1_150600001_155100000",
            "chr10_6600001_12200000",
            "chr11_75500001_77400000", 
            "chr15_59000001_63400000",
            "chr17_33500001_39800000",
            "chr2_102100001_105300000",
            "chr6_30500001_32100000",
            "chr12_46000001_48700000", 
            "chr2_143400001_147900000")

analysis.prefer = c("coa", "aa", "aa", "aa","coa", "aa", "aa", "aoa", "aoa")
dist = 250000

for (i in 1:length(regions)){
  reg = regions[i]
  if (analysis.prefer[i] == "aa"){
    path.surv = paste0("/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv/all_gwas_", reg, ".rds")
    path.logit = paste0("/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/all/gwas_", reg, ".asthma.glm.logistic")
  }
  if (analysis.prefer[i] == "coa"){
    path.surv = paste0("/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv/coa_gwas_", reg, ".rds")
    path.logit = paste0("/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/coa/gwas_", reg, ".asthma_coa.glm.logistic")
  }
  if (analysis.prefer[i] == "aoa"){
    path.surv = paste0("/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv/aoa_gwas_", reg, ".rds")
    path.logit = paste0("/Users/nicholeyang/Downloads/survivalsusie/data/gwas_logistic_out/aoa/gwas_", reg, ".asthma_aoa.glm.logistic")
  }
  gwas.surv = data.frame(readRDS(path.surv))
  gwas.logit = read.csv(path.logit, header = TRUE, sep = "\t")
  gwas.surv$ID = sapply(1:nrow(gwas.surv), function(i) strsplit(rownames(gwas.surv)[i], "_")[[1]][1])
  gwas.surv$variant = rownames(gwas.surv)
  dat = merge(gwas.surv, gwas.logit, by = "ID")
  top = which(dat$p.value.spa == min(dat$p.value.spa, na.rm = TRUE))
  pos_start = dat$POS[top] - dist
  pos_end = dat$POS[top] + dist
  indx = which(dat$POS > pos_start & dat$POS < pos_end)
  print(paste0("There are ", length(dat$variant[indx]), " SNPs remain in ", reg))
  write.table(dat$variant[indx],
              paste0("/Users/nicholeyang/Downloads/survivalsusie/data/asthma_finemap_snplist202408/sub_", reg, '.txt'))
}

[1] "There are 1333 SNPs remain in chr1_150600001_155100000"
[1] "There are 1651 SNPs remain in chr10_6600001_12200000"
[1] "There are 1628 SNPs remain in chr11_75500001_77400000"
[1] "There are 1558 SNPs remain in chr15_59000001_63400000"
[1] "There are 1212 SNPs remain in chr17_33500001_39800000"
[1] "There are 1821 SNPs remain in chr2_102100001_105300000"
[1] "There are 9934 SNPs remain in chr6_30500001_32100000"
[1] "There are 1595 SNPs remain in chr12_46000001_48700000"
[1] "There are 1190 SNPs remain in chr2_143400001_147900000"

Previous way of cutting:

select the top signal + surrounding SNPs based on all asthma;
select the top signal + surrounding SNPs based on AOA.

par(mfrow = c(4,2))
dir = "/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv"
for (reg in regions){
  file_path = paste0(dir, "/all_gwas_", reg, ".rds")
  gwas = data.frame(readRDS(file_path))
  print(paste0("There are ", nrow(gwas), " SNPs in ", reg))
  top = which(gwas$p.value.spa == min(gwas$p.value.spa))
  
  start = top - 2500
  end = top + 2500
  
  if (reg == "chr1_150600001_155100000"){ start = top - 1500}
  if (reg == "chr11_75500001_77400000"){ 
    start = top - 1000
    end = top + 1000
    }
  if (reg == "chr15_59000001_63400000") {
    start = top - 500
    end = top + 500
  }
  if (reg == "chr17_33500001_39800000"){start = top - 1500}
  
  start = max(1, start)
  end = min(nrow(gwas), end)
  print(paste0("There are ", nrow(gwas[start:end, ]), " SNPs remain in ", reg))
  plot(-log10(gwas[start:end, ]$p.value.spa),pch = 20, cex = 0.5, main = reg)
  write.table(rownames(gwas[start:end, ]),
              paste0("/Users/nicholeyang/Downloads/survivalsusie/data/asthma_finemap_snplist/sub_", reg, '.txt'))
}

[1] "There are 10507 SNPs in chr1_150600001_155100000"
[1] "There are 4001 SNPs remain in chr1_150600001_155100000"

[1] "There are 21347 SNPs in chr10_6600001_12200000"
[1] "There are 5001 SNPs remain in chr10_6600001_12200000"

[1] "There are 5352 SNPs in chr11_75500001_77400000"
[1] "There are 2001 SNPs remain in chr11_75500001_77400000"

[1] "There are 13682 SNPs in chr15_59000001_63400000"
[1] "There are 1001 SNPs remain in chr15_59000001_63400000"

[1] "There are 16832 SNPs in chr17_33500001_39800000"
[1] "There are 4001 SNPs remain in chr17_33500001_39800000"

[1] "There are 8043 SNPs in chr2_102100001_105300000"
[1] "There are 4780 SNPs remain in chr2_102100001_105300000"

[1] "There are 14915 SNPs in chr6_30500001_32100000"
[1] "There are 5001 SNPs remain in chr6_30500001_32100000"

[1] "There are 7941 SNPs in chr12_46000001_48700000"
[1] "There are 3580 SNPs remain in chr12_46000001_48700000"

Version	Author	Date
ebeca15	yunqiyang0215	2024-06-18

[1] "There are 9647 SNPs in chr2_143400001_147900000"
[1] "There are 5001 SNPs remain in chr2_143400001_147900000"

reg = "chr12_46000001_48700000"
file_path = "/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv/aoa_gwas_chr12_46000001_48700000.rds"
gwas = data.frame(readRDS(file_path))
start = 5500
end = 7000
plot(-log10(gwas[start:end, ]$p.value.spa),pch = 20, cex = 0.5, main = reg)

Version	Author	Date
c417e68	yunqiyang0215	2024-06-26
ebeca15	yunqiyang0215	2024-06-18

write.table(rownames(gwas[start:end, ]),
              paste0("/Users/nicholeyang/Downloads/survivalsusie/data/asthma_finemap_snplist/sub_", reg, '.txt'))

reg = "chr2_143400001_147900000"
file_path = "/Users/nicholeyang/Downloads/survivalsusie/result/gwas_surv/aoa_gwas_chr2_143400001_147900000.rds"
gwas = data.frame(readRDS(file_path))
start = 5000
end = 7000
plot(-log10(gwas[start:end, ]$p.value.spa),pch = 20, cex = 0.5, main = reg)

write.table(rownames(gwas[start:end, ]),
              paste0("/Users/nicholeyang/Downloads/survivalsusie/data/asthma_finemap_snplist/sub_", reg, '.txt'))

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] 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        stringr_1.4.0    tools_4.1.1     
[13] xfun_0.27        utf8_1.2.2       cli_3.6.1        git2r_0.28.0    
[17] jquerylib_0.1.4  htmltools_0.5.5  ellipsis_0.3.2   rprojroot_2.0.2 
[21] yaml_2.2.1       digest_0.6.28    tibble_3.1.5     lifecycle_1.0.3 
[25] later_1.3.0      sass_0.4.4       vctrs_0.6.3      promises_1.2.0.1
[29] fs_1.5.0         cachem_1.0.6     glue_1.4.2       evaluate_0.14   
[33] rmarkdown_2.11   stringi_1.7.5    bslib_0.4.1      compiler_4.1.1  
[37] pillar_1.9.0     jsonlite_1.7.2   httpuv_1.6.3     pkgconfig_2.0.3

Cut region for finemapping

Yunqi Yang

6/18/2024

Description:

Previous way of cutting: