UKBiobank Blood Cell
Yuxin Zou
10/26/2020
Last updated: 2020-11-02
Checks: 7 0
Knit directory: finemap-uk-biobank/
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The UkB blood cell traits are
| PheID | Abbrev | Phenotype | Cell type | Determination |
|---|---|---|---|---|
| 30000 | WBCcount | White blood cell count | Compound white cell | Measured |
| 30010 | RBCcount | Red blood cell count | Mature red cell | Measured |
| 30020 | HGB | Haemoglobin concentration | Mature red cell | Measured |
| 30030 | HCT | Haematocrit percentage | Mature red cell | (RBCcount x MCV) / 10 |
| 30040 | MCV | Mean corpuscular volume | Mature red cell | Measured |
| 30050 | MCH | Mean corpuscular haemoglobin | Mature red cell | (hemoglobin/RBCcount) x 10 |
| 30060 | MCHC | Mean corpuscular haemoglobin concentration | Mature red cell | (haemoglobin/haematocrit) x 100 |
| 30070 | RDW | Red blood cell distribution width | Mature red cell | Measured |
| 30080 | PLTcount | Platelet count | Platelet | Measured |
| 30090 | PCT | Platelet crit | Platelet | Measured |
| 30100 | MPV | Mean platelet (thrombocyte) volume | Platelet | (PCT/PLTcount) x 10000 |
| 30110 | PDW | Platelet distribution width | Platelet | Measured |
| 30120 | LYMPHcount | Lymphocyte count | Lymphoid white cell | (LYMPHperc/100) x WBCcount |
| 30130 | MONOcount | Monocyte count | Myeloid white cell | (MONOperc/100) x WBCcount |
| 30140 | NEUTcount | Neutrophill count | Myeloid white cell | (NEUTperc/100) x WBCcount |
| 30150 | EOcount | Eosinophill count | Myeloid white cell | (EOperc/100) x WBCcount |
| 30160 | BASOcount | Basophill count | Myeloid white cell | (BASOperc/100) x WBCcount |
| 30180 | LYMPHperc | Lymphocyte percentage | Compound white cell | Measured |
| 30190 | MONOperc | Monocyte percentage | Compound white cell | Measured |
| 30200 | NEUTperc | Neutrophill percentage | Compound white cell | Measured |
| 30210 | EOperc | Eosinophill percentage | Compound white cell | Measured |
| 30220 | BASOperc | Basophill percentage | Compound white cell | Measured |
| 30240 | RETperc | Reticulocyte percentage | Immature red cell | Measured |
| 30250 | RETcount | Reticulocyte count | Immature red cell | (RETperc/100) × RBCcount |
| 30260 | MRV | Mean reticulocyte volume | Immature red cell | MCV x (RETperc/100) |
| 30270 | MSCV | Mean sphered cell volume | Mature red cell | Measured |
| 30280 | IRF | Immature reticulocyte fraction | Immature red cell | HLRcount/RETcount |
| 30290 | HLRperc | High light scatter reticulocyte percentage | Immature red cell | Measured |
| 30300 | HLRcount | High light scatter reticulocyte count | Immature red cell | (HLRperc/100) × RBCcount |
We filtered individuals with following criteria:
Remove samples that are not marked as being "White British".
Remove samples with missing values.
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_V1.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')
}
| Version | Author | Date |
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| b6f3f56 | zouyuxin | 2020-10-27 |
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| b6f3f56 | zouyuxin | 2020-10-27 |
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| b6f3f56 | zouyuxin | 2020-10-27 |
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| b6f3f56 | zouyuxin | 2020-10-27 |
| Version | Author | Date |
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| b6f3f56 | zouyuxin | 2020-10-27 |
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')
| Version | Author | Date |
|---|---|---|
| b6f3f56 | zouyuxin | 2020-10-27 |
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$HLR_perc > 2), ' inds > 2'), xlab='x')
| Version | Author | Date |
|---|---|---|
| b6f3f56 | zouyuxin | 2020-10-27 |
Basophill
Basophils count is the proportion of ( basophils / 100 ) x white blood cell count.
Basophils_count = dat$Basophill_perc * dat$WBC_count / 100
{plot(Basophils_count, dat$Basophill_count, ylab='UKB Basophill count')
abline(0,1)}
| Version | Author | Date |
|---|---|---|
| b6f3f56 | zouyuxin | 2020-10-27 |
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')
| Version | Author | Date |
|---|---|---|
| b6f3f56 | zouyuxin | 2020-10-27 |
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$Haematocrit)*100
dat$MPV = (dat$Plateletcrit/dat$Platelet_count) * 10000
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$Neutrophill_count = (dat$Neutrophill_perc * dat$WBC_count)/100
dat$Reticulocyte_count = (dat$Reticulocyte_perc * dat$RBC_count)/100
dat$HLR_count = (dat$HLR_perc * dat$RBC_count)/100
dat$IRF = dat$HLR_perc/dat$Reticulocyte_percInverse normalization for each trait:
dat_1 = dat
for(i in 16:44){
dat_1[,i] = qnorm((rank(dat_1[,i],na.last="keep")-0.5)/sum(!is.na(dat_1[,i])))
}par(mfrow=c(2,3))
for(i in 16:44){
hist(dat_1[,i], breaks = 50, main=colnames(dat_1)[i], xlab='x')
}
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| 9e0d1c3 | zouyuxin | 2020-11-02 |
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| 9e0d1c3 | zouyuxin | 2020-11-02 |
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| 9e0d1c3 | zouyuxin | 2020-11-02 |
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| 9e0d1c3 | zouyuxin | 2020-11-02 |
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| 9e0d1c3 | zouyuxin | 2020-11-02 |
Identify Outliers
Since we will jointly model these traits using multivariate normal distribution, we want to find outliers in multivariate normal, instead of univariate normal. There are derived phenotypes which depend on several measured phenotype. To simplify the problem, we discard directly calculated phenotypes. We use 16 phenotypes.
sub_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")
dat_1_sub = dat_1 %>% select(sub_names)Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(sub_names)` instead of `sub_names` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.Covariace matrix of traits
library(corrplot)corrplot 0.84 loadedcovy_sub = cov(dat_1_sub)
corrplot(covy_sub, method='color', type='upper', tl.col="black", tl.srt=45, is.corr = FALSE)
| Version | Author | Date |
|---|---|---|
| 9e0d1c3 | zouyuxin | 2020-11-02 |
D2_sub = stats::mahalanobis(dat_1_sub, center=0, cov=covy_sub)
dat_sub_select = dat_1[D2_sub < qchisq(0.01, df=16, lower.tail = F),]There are 248979 individuals.
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] corrplot_0.84 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 pillar_1.4.6 compiler_3.6.3 later_1.1.0.1
[5] git2r_0.27.1 tools_3.6.3 digest_0.6.27 evaluate_0.14
[9] lifecycle_0.2.0 tibble_3.0.4 pkgconfig_2.0.3 rlang_0.4.8
[13] cli_2.1.0 rstudioapi_0.11 yaml_2.2.1 xfun_0.19
[17] stringr_1.4.0 knitr_1.30 generics_0.1.0 fs_1.5.0
[21] vctrs_0.3.4 rprojroot_1.3-2 tidyselect_1.1.0 glue_1.4.2
[25] R6_2.5.0 fansi_0.4.1 rmarkdown_2.5 purrr_0.3.4
[29] magrittr_1.5 whisker_0.4 backports_1.1.10 promises_1.1.1
[33] ellipsis_0.3.1 htmltools_0.5.0 assertthat_0.2.1 httpuv_1.5.4
[37] stringi_1.5.3 crayon_1.3.4