UKBiobank Blood Cell
Yuxin Zou
10/26/2020
Last updated: 2020-11-20
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 |
To prepare the phenotype data, we need to remove samples with abnormal observations. Since we will jointly model blood cell traits using multivariate normal distribution, we want to discard 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 derived phenotypes. We use 16 phenotypes.
trait_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")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
Identify outliers in multivariate normal.
Load UKBiobank Blood Cell traits (individuals are filtered using script before line 121).
library(data.table)
library(dplyr)
dat = fread('data/bloodcells1.csv')
class(dat) <- "data.frame"There are 257605 individuals.
Check distribution for each trait:
par(mfrow=c(2,3))
hist(dat$WBC_count[dat$WBC_count < 20], breaks = 100, main=paste0('WBC_count ', sum(dat$WBC_count > 20), ' inds > 20'), xlab='x')
hist(dat$RBC_count, main='RBC_count', xlab='x', breaks = 100)
hist(dat$Haemoglobin, main='Haemoglobin', xlab='x', breaks = 100)
hist(dat$MCV, main='MCV', xlab='x', breaks = 100)
hist(dat$RDW[dat$RDW < 20], main=paste0('RDW ', sum(dat$RDW > 20), ' inds > 20'), xlab='x', breaks = 100)
hist(dat$Platelet_count, main='Platelet_count', xlab='x', breaks = 100)
hist(dat$Plateletcrit, main='Plateletcrit', xlab='x', breaks = 100)
hist(dat$PDW, main='PDW', xlab='x', breaks = 50)
hist(dat$Lymphocyte_perc, main='Lymphocyte_perc', xlab='x', breaks = 100)
hist(dat$Monocyte_perc[dat$Monocyte_perc < 20], main=paste0('Monocyte_perc ', sum(dat$Monocyte_perc > 20), ' inds > 20'), xlab='x', breaks = 100)
hist(dat$Neutrophill_perc, main='Neutrophill_perc', xlab='x', breaks = 100)
hist(dat$Eosinophill_perc[dat$Eosinophill_perc < 10], main=paste0('Eosinophill_perc ', sum(dat$Eosinophill_perc > 10), ' inds > 10'), xlab='x', breaks = 100)
hist(dat$Basophill_perc[dat$Basophill_perc < 4], main=paste0('Basophill_perc ', sum(dat$Basophill_perc > 4), ' inds > 4'), xlab='x', breaks = 50)
hist(dat$Reticulocyte_perc[dat$Reticulocyte_perc < 4], main=paste0('Reticulocyte_perc ', sum(dat$Reticulocyte_perc > 4), ' inds > 4'), xlab='x', breaks = 50)
hist(dat$MSCV, main='MSCV', xlab='x', breaks = 100)
hist(dat$HLR_perc[dat$HLR_perc < 2], main=paste0('HLR_perc ', sum(dat$HLR_perc > 2), ' inds > 2'), xlab='x', breaks = 50)
| Version | Author | Date |
|---|---|---|
| 1dbfe6c | zouyuxin | 2020-11-18 |
Inverse normalization for each trait:
dat_1 = dat
for(i in 16:31){
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 16:31){
hist(dat_1[,i], breaks = 50, main=colnames(dat_1)[i], xlab='x')
}
| Version | Author | Date |
|---|---|---|
| 1dbfe6c | zouyuxin | 2020-11-18 |
| Version | Author | Date |
|---|---|---|
| 1dbfe6c | zouyuxin | 2020-11-18 |
Identify Outliers
trait_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_traits = dat_1 %>% select(all_of(trait_names))Covariace matrix of traits
library(corrplot)corrplot 0.84 loadedcovy = cov(dat_1_traits)
corrplot(covy, method='color', type='upper', tl.col="black", tl.srt=45, is.corr = FALSE)
Measure Mahalanobis distance
D2 = stats::mahalanobis(dat_1_traits, center=0, cov=covy) # squared Mahalanobis distance
{hist(D2[D2 < 60], breaks = 100, main=paste0('Mahalanobis distance: ', sum(D2 > 60), ' inds D2 > 60'), xlab='D2')
abline(v=qchisq(0.01, df=16, lower.tail = F))}
The Mahalanobis distance follows a Chi-Square distribution with df = 16. We compute the 99.9%-Quantile of the Chi-Square distribution with 16 degrees of freedomm and we remove samples with Mahalanobis distance greater than the distance.
dat_select = dat_1[D2 < qchisq(0.01, df=16, lower.tail = F),]There are 248980 individuals.
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] 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 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