UKBiobank Blood Cell

Last updated: 2020-10-27

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Knit directory: finemap-uk-biobank/

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The UkB blood cell traits are

PheID	Abbrev	Phenotype	Cell type
30000	WBCcount	White blood cell count	Compound white cell
30010	RBCcount	Red blood cell count	Mature red cell
30020	HGB	Haemoglobin concentration	Mature red cell
30030	HCT	Haematocrit percentage	Mature red cell
30040	MCV	Mean corpuscular volume	Mature red cell
30050	MCH	Mean corpuscular haemoglobin	Mature red cell
30060	MCHC	Mean corpuscular haemoglobin concentration	Mature red cell
30070	RDW	Red blood cell distribution width	Mature red cell
30080	PLTcount	Platelet count	Platelet
30090	PCT	Platelet crit	Platelet
30100	MPV	Mean platelet (thrombocyte) volume	Platelet
30110	PDW	Platelet distribution width	Platelet
30120	LYMPHcount	Lymphocyte count	Lymphoid white cell
30130	MONOcount	Monocyte count	Myeloid white cell
30140	NEUTcount	Neutrophill count	Myeloid white cell
30150	EOcount	Eosinophill count	Myeloid white cell
30160	BASOcount	Basophill count	Myeloid white cell
30180	LYMPHperc	Lymphocyte percentage	Compound white cell
30190	MONOperc	Monocyte percentage	Compound white cell
30200	NEUTperc	Neutrophill percentage	Compound white cell
30210	EOperc	Eosinophill percentage	Compound white cell
30220	BASOperc	Basophill percentage	Compound white cell
30240	RETperc	Reticulocyte percentage	Immature red cell
30250	RETcount	Reticulocyte count	Immature red cell
30260	MRV	Mean reticulocyte volume	Immature red cell
30270	MSCV	Mean sphered cell volume	Mature red cell
30280	IRF	Immature reticulocyte fraction	Immature red cell
30290	HLRperc	High light scatter reticulocyte percentage	Immature red cell
30300	HLRcount	High light scatter reticulocyte count	Immature red cell

We filtered individuals with following criteria:

Remove samples with missing values.
Remove samples that are not marked as being “White British”.
Remove samples with mismatches between self-reported and genetic sex.
Remove outliers defined by UK Biobank.
Remove any individuals have at leat one relative based on the kinship calculations.
Remove any pregnant individuals.
Remove any individuals with following in hospital in-patient data:

leukemia, lymphoma, bone marrow transplant, chemotherapy, myelodysplastic syndrome, anemia, HIV, end-stage kidney disease, dialysis, cirrhosis, multiple myeloma, lymphocytic leukemia, myeloid leukemia, polycythaemia vera, haemochromatosis

Load UKBiobank Blood Cell traits (individuals are filtered using script)

library(data.table)
library(dplyr)
dat = fread('data/bloodcounts.csv')
class(dat) <- "data.frame"

There are 257604 individuals.

Check distribution for each trait:

par(mfrow=c(2,3))
for(i in 16:44){
  hist(dat[,i], breaks = 100, main=colnames(dat)[i], xlab='x')
}

Distributions for trait with small observartions:

par(mfrow=c(2,3))
hist(dat$WBC_count[dat$WBC_count < 20], breaks = 100, main=paste0('WBC_count ', sum(dat$WBC_count > 20), ' inds > 20'), xlab='x')
hist(dat$Lymphocyte_count[dat$Lymphocyte_count < 8], breaks = 100, main=paste0('Lymphocyte_count ', sum(dat$Lymphocyte_count > 8), ' inds > 8'), xlab='x')
hist(dat$Monocyte_count[dat$Monocyte_count < 2], breaks = 100, main=paste0('Monocyte_count ', sum(dat$Monocyte_count > 2), ' inds > 2'), xlab='x')
hist(dat$Eosinophill_count[dat$Eosinophill_count < 1], breaks = 100, main=paste0('Eosinophill_count ', sum(dat$Eosinophill_count > 1), ' inds > 1'), xlab='x')
hist(dat$Eosinophill_perc[dat$Eosinophill_perc < 10], breaks = 100, main=paste0('Eosinophill_perc ', sum(dat$Eosinophill_perc > 10), ' inds > 10'), xlab='x')
hist(dat$Basophill_count[dat$Basophill_count < 0.5], breaks = 100, main=paste0('Basophill_count ', sum(dat$Basophill_count > 0.5), ' inds > 0.5'), xlab='x')

hist(dat$Basophill_perc[dat$Basophill_perc < 2], breaks = 100, main=paste0('Basophill_perc ', sum(dat$Basophill_perc > 2), ' inds > 2'), xlab='x')
hist(dat$Reticulocyte_count[dat$Reticulocyte_count < 0.2], breaks = 100, main=paste0('Reticulocyte_count ', sum(dat$Reticulocyte_count > 0.2), ' inds > 0.2'), xlab='x')
hist(dat$Reticulocyte_perc[dat$Reticulocyte_perc < 4], breaks = 100, main=paste0('Reticulocyte_perc ', sum(dat$Reticulocyte_perc > 4), ' inds > 4'), xlab='x')
hist(dat$HLR_count[dat$HLR_count < 0.1], breaks = 100, main=paste0('HLR_count ', sum(dat$HLR_count > 0.1), ' inds > 0.1'), xlab='x')
hist(dat$HLR_perc[dat$HLR_perc < 2], breaks = 100, main=paste0('HLR_perc ', sum(dat$HLR_perc > 2), ' inds > 2'), xlab='x')

Basophill

Basophils count is the proportion of ( basophils / 100 ) x white blood cell count.

Basophils_count = dat$Basophill_perc * dat$WBC_count / 100
{plot(Basophils_count, dat$Basophill_count, ylab='UKB Basophill count')
abline(0,1)}

There are 835 0’s in computed Basophils_count, but there are 75056 0’s in UKB Basophils count.

hist(Basophils_count[Basophils_count < 0.5], breaks = 100, main=paste0('Derived Basophils_count ', sum(Basophils_count > 0.5), ' inds > 0.5'), xlab='x')

There are rounding errors in derived count traits.

We recompute all derived traits.

dat$Haematocrit = (dat$MCV * dat$RBC_count)/10
dat$MCH = (dat$Haemoglobin/dat$RBC_count) * 10
dat$MCHC = (dat$Haemoglobin/dat$Haematocrit)*100
dat$Lymphocyte_count = (dat$Lymphocyte_perc * dat$WBC_count)/100
dat$Monocyte_count = (dat$Monocyte_perc * dat$WBC_count)/100
dat$Eosinophill_count = (dat$Eosinophill_perc * dat$WBC_count)/100
dat$Basophill_count = (dat$Basophill_perc * dat$WBC_count)/100
dat$Reticulocyte_count = (dat$Reticulocyte_perc * dat$RBC_count)/100
dat$HLR_count = (dat$HLR_perc * dat$RBC_count)/100

par(mfrow=c(2,3))
hist(dat$WBC_count[dat$WBC_count < 20], breaks = 100, main=paste0('WBC_count ', sum(dat$WBC_count > 20), ' inds > 20'), xlab='x')
hist(dat$Lymphocyte_count[dat$Lymphocyte_count < 8], breaks = 100, main=paste0('Lymphocyte_count ', sum(dat$Lymphocyte_count > 8), ' inds > 8'), xlab='x')
hist(dat$Monocyte_count[dat$Monocyte_count < 2], breaks = 100, main=paste0('Monocyte_count ', sum(dat$Monocyte_count > 2), ' inds > 2'), xlab='x')
hist(dat$Eosinophill_count[dat$Eosinophill_count < 1], breaks = 100, main=paste0('Eosinophill_count ', sum(dat$Eosinophill_count > 1), ' inds > 1'), xlab='x')
hist(dat$Eosinophill_perc[dat$Eosinophill_perc < 10], breaks = 100, main=paste0('Eosinophill_perc ', sum(dat$Eosinophill_perc > 10), ' inds > 10'), xlab='x')
hist(dat$Basophill_count[dat$Basophill_count < 0.5], breaks = 100, main=paste0('Basophill_count ', sum(dat$Basophill_count > 0.5), ' inds > 0.5'), xlab='x')

hist(dat$Basophill_perc[dat$Basophill_perc < 2], breaks = 100, main=paste0('Basophill_perc ', sum(dat$Basophill_perc > 2), ' inds > 2'), xlab='x')
hist(dat$Reticulocyte_count[dat$Reticulocyte_count < 0.2], breaks = 100, main=paste0('Reticulocyte_count ', sum(dat$Reticulocyte_count > 0.2), ' inds > 0.2'), xlab='x')
hist(dat$Reticulocyte_perc[dat$Reticulocyte_perc < 4], breaks = 100, main=paste0('Reticulocyte_perc ', sum(dat$Reticulocyte_perc > 4), ' inds > 4'), xlab='x')
hist(dat$HLR_count[dat$HLR_count < 0.1], breaks = 100, main=paste0('HLR_count ', sum(dat$HLR_count > 0.1), ' inds > 0.1'), xlab='x')
hist(dat$HLR_perc[dat$HLR_perc < 2], breaks = 100, main=paste0('HLR_perc ', sum(dat$HLR_perc > 2), ' inds > 2'), xlab='x')

Remove extreme observatios

rem = c()
for(i in 16:44){
  rem = c(rem, which(dat[,i] < (median(dat[,i]) - 4*sqrt(mean((dat[,i] - median(dat[,i]))^2))) | dat[,i] > (median(dat[,i]) + 4*sqrt(mean((dat[,i] - median(dat[,i]))^2)))))
}
## remove 12758 individuals
dat_1 = dat[-unique(rem),]

par(mfrow=c(2,3))
for(i in 16:44){
  hist(dat_1[,i], breaks = 50, main=colnames(dat_1)[i], xlab='x')
}

Inverse normalization:

dat_2 = dat_1
for(i in 16:44){
  dat_2[,i] = qnorm((rank(dat_2[,i],na.last="keep")-0.5)/sum(!is.na(dat_2[,i])))
}

par(mfrow=c(2,3))
for(i in 16:44){
  hist(dat_2[,i], breaks = 50, main=colnames(dat_2)[i], xlab='x')
}

sessionInfo()

R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7

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_0.8.5       data.table_1.12.8 workflowr_1.6.1  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5       knitr_1.28       whisker_0.4      magrittr_1.5    
 [5] tidyselect_1.0.0 R6_2.4.1.9001    rlang_0.4.8      stringr_1.4.0   
 [9] tools_3.6.3      xfun_0.13        git2r_0.26.1     ellipsis_0.3.1  
[13] htmltools_0.4.0  yaml_2.2.1       digest_0.6.27    rprojroot_1.3-2 
[17] assertthat_0.2.1 lifecycle_0.2.0  tibble_3.0.4     crayon_1.3.4    
[21] purrr_0.3.4      later_1.0.0      vctrs_0.3.4      promises_1.1.0  
[25] fs_1.4.1         glue_1.4.2       evaluate_0.14    rmarkdown_2.1   
[29] stringi_1.5.3    pillar_1.4.6     compiler_3.6.3   backports_1.1.10
[33] httpuv_1.5.2     pkgconfig_2.0.3

UKBiobank Blood Cell

Yuxin Zou

10/26/2020

Basophill