Last updated: 2020-11-20

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Rmd c2021d0 zouyuxin 2020-11-20 wflow_publish("analysis/bloodcells_regions.Rmd")

There are 248,980 individuals of white British ancestries with 16 blood cells phenotypes. The script to prepare the phenotypes and covariates is get_bloodcells. The filtering steps are also described here.

For genotype data, variants with imputation score (INFO) > 0.9, MAF > 1% are included in association studies.

The script to run GWAS is GWAS

For each phenotype, regions for fine-mapping are defined by greedily starting with the most significantly associated SNP, including SNPs within a window of 500kb centered at the SNP, until we include all significant SNPs (p < 5e-8). We merge ovelapping regions. We exclude HLA region (chr6: 25Mb - 36Mb). The steps are

  1. Find the most significantly associated SNP.

  2. Choose region +- 250kb around the SNP.

  3. Find the next most significantly associated SNP ouside the selected regions.

  4. Choose region +- 250kb around the SNP.

  5. Merge regions if they overlap. ...

When we select region across traits, we include all regions from each pheotype and merge overlapping regions. This produces some very large regions with more than 10000 SNPs.

library(data.table)
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:data.table':

    between, first, last
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union
pheno_names = c("WBC_count", "RBC_count", "Haemoglobin", "MCV", "RDW", "Platelet_count",
                "Plateletcrit", "PDW", "Lymphocyte_perc", "Monocyte_perc",
                "Neutrophill_perc", "Eosinophill_perc", "Basophill_perc",
                "Reticulocyte_perc", "MSCV", "HLR_perc")
trait_regions = list()
for(trait in pheno_names){
  # reg = fread(paste0('/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/regions/', trait, '_regions'))
  region = fread(paste0('~/Desktop/ukb-bloodcells/regions_raw/', trait, '_regions'))
  region = region %>% arrange(desc(logp))
  region_r = c()
  for(i in 1:22){
    region.chr = region %>% filter(CHR == i) %>% arrange(start)
    if(nrow(region.chr) == 0){
      next
    }
    tmp = region.chr %>% group_by(g = cumsum(cummax(lag(end, default = first(end))) < start)) %>%
      summarise(start = first(start), end = max(end), .groups = 'drop') %>% 
      mutate(length = end - start) %>%
      mutate(CHR = i) %>% select(CHR, start, end, length)
    region_r = rbind(region_r, tmp)
  }
  trait_regions[[trait]] = region_r
}

Summary of region length for each phenotype:

lapply(trait_regions, function(x) summary(x$length))
$WBC_count
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 378854  500000  500000  610315  500000 2301820 

$RBC_count
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 500000  500000  500000  653221  755004 2940124 

$Haemoglobin
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 301703  500000  500000  611650  500000 3251867 

$MCV
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 500000  500000  500000  670089  772563 2920824 

$RDW
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 275738  500000  500000  650422  500000 4089364 

$Platelet_count
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 270883  500000  500000  678600  766174 3646000 

$Plateletcrit
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 270883  500000  500000  640939  754988 3593140 

$PDW
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 268602  500000  500000  650832  751519 4239970 

$Lymphocyte_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 378854  500000  500000  621719  753623 2239954 

$Monocyte_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 310142  500000  500000  659465  751945 3864804 

$Neutrophill_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 368127  500000  500000  612052  500000 2208916 

$Eosinophill_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 397874  500000  500000  656940  750663 3994646 

$Basophill_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 500000  500000  500000  561068  500000 1748896 

$Reticulocyte_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 303607  500000  500000  653925  753414 5547602 

$MSCV
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 272793  500000  500000  656841  754786 3254290 

$HLR_perc
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 381586  500000  500000  674729  768513 5973226 

For HLR_perc, the maximum region is at CHR 3 from 46234573 to 52207799, which includes 9572 SNPs.

gwas_HLR_perc = fread('~/Desktop/ukb-bloodcells/bloodcells_gwas_HLR_perc')
colnames(gwas_HLR_perc)[1] = 'CHR'
gwas_HLR_perc$P = as.numeric(gwas_HLR_perc$P)
gwas_HLR_perc = gwas_HLR_perc %>% select(CHR, POS, T_STAT, P) %>% mutate(logp = -log10(P))
gwas_HLR_perc.sub = gwas_HLR_perc %>% filter(CHR == 3, POS >= 46234573, POS <=52207799)
plot(gwas_HLR_perc.sub$POS, gwas_HLR_perc.sub$logp, xlab='CHR 3 POS', ylab='-log10(p)')

For Reticulocyte_perc, the maximum region is at CHR 3 from 48155661 to 53703263, which includes 8888 SNPs.

gwas_Reticulocyte_perc = fread('~/Desktop/ukb-bloodcells/bloodcells_gwas_Reticulocyte_perc')
colnames(gwas_Reticulocyte_perc)[1] = 'CHR'
gwas_Reticulocyte_perc$P = as.numeric(gwas_Reticulocyte_perc$P)
gwas_Reticulocyte_perc = gwas_Reticulocyte_perc %>% select(CHR, POS, T_STAT, P) %>% mutate(logp = -log10(P))
gwas_Reticulocyte_perc.sub = gwas_Reticulocyte_perc %>% filter(CHR == 3, POS >= 48155661, POS <=53703263)
plot(gwas_Reticulocyte_perc.sub$POS, gwas_Reticulocyte_perc.sub$logp, xlab='CHR 3 POS', ylab='-log10(p)')

For PDW, the maximum region is at CHR 8 from 7838230 to 12078200, which includes 16605 SNPs.

gwas_PDW = fread('~/Desktop/ukb-bloodcells/bloodcells_gwas_PDW')
colnames(gwas_PDW)[1] = 'CHR'
gwas_PDW$P = as.numeric(gwas_PDW$P)
gwas_PDW = gwas_PDW %>% select(CHR, POS, T_STAT, P) %>% mutate(logp = -log10(P))
gwas_PDW.sub = gwas_PDW %>% filter(CHR == 8, POS >= 7838230, POS <=12078200)
plot(gwas_PDW.sub$POS, gwas_PDW.sub$logp, xlab='CHR 3 POS', ylab='-log10(p)')

Select regions across phenotype:

tb = bind_rows(trait_regions, .id = "column_label")
res.final = c()
for(i in 1:22){
  tb.chr = tb %>% filter(CHR == i) %>% arrange(start)
  if(nrow(tb.chr) == 0){
    next
  }
  tmp = tb.chr %>% group_by(g = cumsum(cummax(lag(end, default = first(end))) < start)) %>%
    summarise(start = first(start), end = max(end), .groups = 'drop') %>% 
    mutate(length = end - start) %>%
    mutate(CHR = i) %>% select(CHR, start, end, length)
  res.final = rbind(res.final, tmp)
}
snpsnum = c()
for(i in 1:nrow(res.final)){
  snpsnum = c(snpsnum, gwas_PDW %>% filter(CHR == res.final$CHR[i],
                                           POS >= res.final$start[i], 
                                           POS <= res.final$end[i]) %>% nrow )
}
res.final$snpsnum = snpsnum

Summary of region length:

summary(res.final$length)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 307855  500000  658060  932086 1057863 8729501 

Summary of SNP number for each region:

summary(res.final$snpsnum)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
     21    1512    2015    2641    3146   21219 

There are 972 regoins in total, 68 regions with length greater than 2Mb, 84 regions contain greater than 5000 SNPs.

The region with maximum length and maximum number of SNPs:

par(mfrow=c(3,1))
gwas_HLR_perc.max = gwas_HLR_perc %>% filter(CHR == 17, POS >= 39754910, POS <=48484411)
plot(gwas_HLR_perc.max$POS, gwas_HLR_perc.max$logp, xlab='CHR 17 POS', ylab='-log10(p)', main='HLR_perc')
gwas_Reticulocyte_perc.max = gwas_Reticulocyte_perc %>% filter(CHR == 17, POS >= 39754910, POS <=48484411)
plot(gwas_Reticulocyte_perc.max$POS, gwas_Reticulocyte_perc.max$logp, xlab='CHR 17 POS', ylab='-log10(p)', main='Reticulocyte_perc')
gwas_PDW.max = gwas_PDW %>% filter(CHR == 17, POS >= 39754910, POS <=48484411)
plot(gwas_PDW.max$POS, gwas_PDW.max$logp, xlab='CHR 17 POS', ylab='-log10(p)', main='PDW')

gwas_Lymphocyte_perc = fread('~/Desktop/ukb-bloodcells/bloodcells_gwas_Lymphocyte_perc')
colnames(gwas_Lymphocyte_perc)[1] = 'CHR'
gwas_Lymphocyte_perc$P = as.numeric(gwas_Lymphocyte_perc$P)
gwas_Lymphocyte_perc = gwas_Lymphocyte_perc %>% select(CHR, POS, T_STAT, P) %>% mutate(logp = -log10(P))
gwas_Lymphocyte_perc.max = gwas_Lymphocyte_perc %>% filter(CHR == 17, POS >= 39754910, POS <=48484411)
plot(gwas_Lymphocyte_perc.max$POS, gwas_Lymphocyte_perc.max$logp, xlab='CHR 17 POS', ylab='-log10(p)', main='Lymphocyte_perc')


sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/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] dplyr_1.0.2       data.table_1.13.2 workflowr_1.6.2  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       rstudioapi_0.11  whisker_0.4      knitr_1.30      
 [5] magrittr_1.5     tidyselect_1.1.0 R6_2.5.0         rlang_0.4.8     
 [9] stringr_1.4.0    tools_3.6.3      xfun_0.19        git2r_0.27.1    
[13] htmltools_0.5.0  ellipsis_0.3.1   rprojroot_1.3-2  yaml_2.2.1      
[17] digest_0.6.27    tibble_3.0.4     lifecycle_0.2.0  crayon_1.3.4    
[21] purrr_0.3.4      later_1.1.0.1    vctrs_0.3.4      promises_1.1.1  
[25] fs_1.5.0         glue_1.4.2       evaluate_0.14    rmarkdown_2.5   
[29] stringi_1.5.3    compiler_3.6.3   pillar_1.4.6     generics_0.1.0  
[33] backports_1.2.0  httpuv_1.5.4     pkgconfig_2.0.3