Blood cells regions

Last updated: 2020-11-22

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Knit directory: finemap-uk-biobank/

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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

Find the most significantly associated SNP.
Choose region +- 250kb around the SNP.
Find the next most significantly associated SNP ouside the selected regions.
Choose region +- 250kb around the SNP.
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)
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){
  # region = 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), logp = max(logp),.groups = 'drop') %>% 
      mutate(length = end - start) %>%
      mutate(CHR = i) %>% select(CHR, start, end, length, logp)
    region_r = rbind(region_r, tmp)
  }
  trait_regions[[trait]] = region_r
}

Summary of region length for each phenotype before selecting regions across traits:

library(kableExtra)


Attaching package: 'kableExtra'

The following object is masked from 'package:dplyr':

    group_rows

for(trait in pheno_names){
  tb = rbind(summary(trait_regions[[trait]]$length), summary(trait_regions[[trait]]$logp))
  rownames(tb) = c('region_length', 'region_max_log10p')
  round(tb,3) %>% kbl(caption = paste0(trait, ': ', nrow(trait_regions[[trait]]), ' regions')) %>% kable_styling() %>% print
  cat("\n")
}

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>WBC_count: 280 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 378854.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 610315.389 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 2301820.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.327 </td>
   <td style="text-align:right;"> 8.531e+00 </td>
   <td style="text-align:right;"> 10.807 </td>
   <td style="text-align:right;"> 15.286 </td>
   <td style="text-align:right;"> 14.601 </td>
   <td style="text-align:right;"> 213.266 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>RBC_count: 310 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 653220.6 </td>
   <td style="text-align:right;"> 755004.000 </td>
   <td style="text-align:right;"> 2940124 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.307e+00 </td>
   <td style="text-align:right;"> 8.779e+00 </td>
   <td style="text-align:right;"> 11.363 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 18.728 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Haemoglobin: 250 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 301703.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 611650.000 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 3251867.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.302 </td>
   <td style="text-align:right;"> 8.249e+00 </td>
   <td style="text-align:right;"> 10.408 </td>
   <td style="text-align:right;"> 17.123 </td>
   <td style="text-align:right;"> 16.425 </td>
   <td style="text-align:right;"> 261.853 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>MCV: 326 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 670089.4 </td>
   <td style="text-align:right;"> 772562.750 </td>
   <td style="text-align:right;"> 2920824 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.312e+00 </td>
   <td style="text-align:right;"> 9.576e+00 </td>
   <td style="text-align:right;"> 13.151 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 21.175 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>RDW: 275 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 275738.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 650422 </td>
   <td style="text-align:right;"> 500000.00 </td>
   <td style="text-align:right;"> 4089364 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.309 </td>
   <td style="text-align:right;"> 9.108e+00 </td>
   <td style="text-align:right;"> 12.833 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 24.55 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Platelet_count: 397 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 270883.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 678599.6 </td>
   <td style="text-align:right;"> 766174.000 </td>
   <td style="text-align:right;"> 3646000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.324 </td>
   <td style="text-align:right;"> 9.018e+00 </td>
   <td style="text-align:right;"> 12.161 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 21.916 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Plateletcrit: 382 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 270883.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 640938.8 </td>
   <td style="text-align:right;"> 754987.500 </td>
   <td style="text-align:right;"> 3593140 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.317 </td>
   <td style="text-align:right;"> 8.826e+00 </td>
   <td style="text-align:right;"> 11.431 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 19.293 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>PDW: 299 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 268602.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 650832 </td>
   <td style="text-align:right;"> 751519.000 </td>
   <td style="text-align:right;"> 4239970 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.318 </td>
   <td style="text-align:right;"> 8.975e+00 </td>
   <td style="text-align:right;"> 12.738 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 21.975 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Lymphocyte_perc: 232 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 378854.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 621718.961 </td>
   <td style="text-align:right;"> 753623.00 </td>
   <td style="text-align:right;"> 2239954.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.325 </td>
   <td style="text-align:right;"> 9.048e+00 </td>
   <td style="text-align:right;"> 11.351 </td>
   <td style="text-align:right;"> 16.049 </td>
   <td style="text-align:right;"> 16.54 </td>
   <td style="text-align:right;"> 152.602 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Monocyte_perc: 262 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 310142.000 </td>
   <td style="text-align:right;"> 5.00e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 659465.2 </td>
   <td style="text-align:right;"> 751945.000 </td>
   <td style="text-align:right;"> 3864804 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.361 </td>
   <td style="text-align:right;"> 8.85e+00 </td>
   <td style="text-align:right;"> 12.112 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 21.705 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Neutrophill_perc: 218 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 368127.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 612052.165 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 2208916.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.303 </td>
   <td style="text-align:right;"> 8.403e+00 </td>
   <td style="text-align:right;"> 10.917 </td>
   <td style="text-align:right;"> 15.978 </td>
   <td style="text-align:right;"> 16.034 </td>
   <td style="text-align:right;"> 193.111 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Eosinophill_perc: 277 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 397874.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 656940.123 </td>
   <td style="text-align:right;"> 750663.000 </td>
   <td style="text-align:right;"> 3994646.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.328 </td>
   <td style="text-align:right;"> 9.148e+00 </td>
   <td style="text-align:right;"> 13.378 </td>
   <td style="text-align:right;"> 20.676 </td>
   <td style="text-align:right;"> 20.883 </td>
   <td style="text-align:right;"> 212.968 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Basophill_perc: 85 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 561068.212 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 1748896.000 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.367e+00 </td>
   <td style="text-align:right;"> 8.567e+00 </td>
   <td style="text-align:right;"> 11.392 </td>
   <td style="text-align:right;"> 17.352 </td>
   <td style="text-align:right;"> 16.739 </td>
   <td style="text-align:right;"> 134.511 </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>Reticulocyte_perc: 237 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 303607.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 653925.3 </td>
   <td style="text-align:right;"> 753414.000 </td>
   <td style="text-align:right;"> 5547602 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.302 </td>
   <td style="text-align:right;"> 8.534e+00 </td>
   <td style="text-align:right;"> 12.249 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 20.093 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>MSCV: 285 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 272793.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 656840.8 </td>
   <td style="text-align:right;"> 754786.000 </td>
   <td style="text-align:right;"> 3254290 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.328 </td>
   <td style="text-align:right;"> 9.262e+00 </td>
   <td style="text-align:right;"> 12.434 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 21.282 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>
<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>HLR_perc: 246 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 381586.000 </td>
   <td style="text-align:right;"> 5.000e+05 </td>
   <td style="text-align:right;"> 500000.000 </td>
   <td style="text-align:right;"> 674728.8 </td>
   <td style="text-align:right;"> 768513.250 </td>
   <td style="text-align:right;"> 5973226 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.304 </td>
   <td style="text-align:right;"> 8.853e+00 </td>
   <td style="text-align:right;"> 12.103 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 19.311 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>

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

# gwas_HLR_perc = fread('/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/gwas/bloodcells_gwas_HLR_perc')
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)', main='HLR_perc')

Version	Author	Date
50a6e41	zouyuxin	2020-11-20
717d6b1	zouyuxin	2020-11-20

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

# gwas_Reticulocyte_perc = fread('/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/gwas/bloodcells_gwas_Reticulocyte_perc')
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)', main='Reticulocyte_perc')

Version	Author	Date
50a6e41	zouyuxin	2020-11-20
717d6b1	zouyuxin	2020-11-20

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

# gwas_PDW = fread('/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/gwas/bloodcells_gwas_PDW')
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)', main='PDW')

Version	Author	Date
50a6e41	zouyuxin	2020-11-20
717d6b1	zouyuxin	2020-11-20

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), logp = max(logp), .groups = 'drop') %>% 
    mutate(length = end - start) %>%
    mutate(CHR = i) %>% select(CHR, start, end, length, logp)
  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 regions:

tb = rbind(summary(res.final$length), summary(res.final$snpsnum),  summary(res.final$logp))
rownames(tb) = c('region_length', 'region_num_snps', 'region_max_log10p')
round(tb,3) %>% kbl(caption = paste0(nrow(res.final), ' regions')) %>% kable_styling() %>% print

<table class="table" style="margin-left: auto; margin-right: auto;">
<caption>972 regions</caption>
 <thead>
  <tr>
   <th style="text-align:left;">   </th>
   <th style="text-align:right;"> Min. </th>
   <th style="text-align:right;"> 1st Qu. </th>
   <th style="text-align:right;"> Median </th>
   <th style="text-align:right;"> Mean </th>
   <th style="text-align:right;"> 3rd Qu. </th>
   <th style="text-align:right;"> Max. </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> region_length </td>
   <td style="text-align:right;"> 307855.000 </td>
   <td style="text-align:right;"> 500000.00 </td>
   <td style="text-align:right;"> 658060.500 </td>
   <td style="text-align:right;"> 932085.792 </td>
   <td style="text-align:right;"> 1057863.250 </td>
   <td style="text-align:right;"> 8729501 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_num_snps </td>
   <td style="text-align:right;"> 21.000 </td>
   <td style="text-align:right;"> 1512.50 </td>
   <td style="text-align:right;"> 2015.000 </td>
   <td style="text-align:right;"> 2641.288 </td>
   <td style="text-align:right;"> 3146.250 </td>
   <td style="text-align:right;"> 21219 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> region_max_log10p </td>
   <td style="text-align:right;"> 7.328 </td>
   <td style="text-align:right;"> 9.94 </td>
   <td style="text-align:right;"> 15.255 </td>
   <td style="text-align:right;"> Inf </td>
   <td style="text-align:right;"> 31.907 </td>
   <td style="text-align:right;"> Inf </td>
  </tr>
</tbody>
</table>

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:

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')

Version	Author	Date
9ab9598	zouyuxin	2020-11-22

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')

Version	Author	Date
9ab9598	zouyuxin	2020-11-22

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')

Version	Author	Date
9ab9598	zouyuxin	2020-11-22

# gwas_Lymphocyte_perc = fread('/gpfs/data/stephens-lab/finemap-uk-biobank/data/raw/BloodCells/gwas/bloodcells_gwas_Lymphocyte_perc')
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')

Version	Author	Date
9ab9598	zouyuxin	2020-11-22

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] kableExtra_1.3.1  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        highr_0.8         pillar_1.4.6      compiler_3.6.3   
 [5] later_1.1.0.1     git2r_0.27.1      tools_3.6.3       digest_0.6.27    
 [9] viridisLite_0.3.0 evaluate_0.14     lifecycle_0.2.0   tibble_3.0.4     
[13] pkgconfig_2.0.3   rlang_0.4.8       rstudioapi_0.11   yaml_2.2.1       
[17] xfun_0.19         xml2_1.3.2        stringr_1.4.0     httr_1.4.2       
[21] knitr_1.30        generics_0.1.0    fs_1.5.0          vctrs_0.3.4      
[25] webshot_0.5.2     rprojroot_1.3-2   tidyselect_1.1.0  glue_1.4.2       
[29] R6_2.5.0          rmarkdown_2.5     purrr_0.3.4       magrittr_1.5     
[33] whisker_0.4       scales_1.1.1      backports_1.2.0   promises_1.1.1   
[37] ellipsis_0.3.1    htmltools_0.5.0   rvest_0.3.6       colorspace_1.4-1 
[41] httpuv_1.5.4      stringi_1.5.3     munsell_0.5.0     crayon_1.3.4

Blood cells regions

Yuxin Zou

11/19/2020