Last updated: 2025-05-13
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Knit directory: prs/
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# Load the gene expression data
gene_expr_file <- "data/gene_reads_2017-06-05_v8_whole_blood.gct"
raw_count_df <- fread(gene_expr_file, header = TRUE)
raw_count_df <- raw_count_df[, -c(1:3)]
# modify GTEx sample names matching names used in PRS data
colnames(raw_count_df) <- sub("^(GTEX-[^-.]+).*", "\\1", colnames(raw_count_df))# load PRS data for blood trait
prs_file <- "analysis/prs_blood_cell.txt"
prs_blood <- fread(prs_file, header = T, stringsAsFactors = FALSE)
# obtain GTEx sample id
id <- prs_blood$sample_id
# Load the PRS data for immune trait
prs_file <- "analysis/prs_immune.txt"
prs_immune <- fread(prs_file, header = T, stringsAsFactors = FALSE)
# subset samples
matching_samples <- intersect(id, colnames(raw_count_df))
prs_blood <- prs_blood[prs_blood$sample_id %in% matching_samples, ]
prs_immune <- prs_immune[prs_immune$sample_id %in% matching_samples, ]cov_file <- "data/Whole_Blood.v8.covariates.txt"
cov <- fread(cov_file, header = T, stringsAsFactors = FALSE)
cov <- cov %>% filter(ID %in% c(paste0("PC", 1:5), "sex"))
col <- cov$ID
cov <- as.matrix(t(cov[, -1]))
colnames(cov) <- col# continuous traits
metadata <- cbind(cov, prs_blood[, 2:30], prs_immune[,2:9])
rownames(metadata) <- matching_samples
metadata$sex <- as.factor(metadata$sex)
head(metadata) PC1 PC2 PC3 PC4 PC5 sex Basophil count
<num> <num> <num> <num> <num> <fctr> <num>
1: 0.0154 -0.0093 0.0107 -0.0038 -0.0083 1 0.5410653
2: 0.0139 -0.0097 0.0067 -0.0144 0.0296 2 0.2568363
3: 0.0145 -0.0093 0.0327 0.0056 0.0510 2 0.4498869
4: -0.0728 -0.0077 -0.0044 -0.0146 0.0063 1 0.2381773
5: 0.0106 0.0037 -0.0116 -0.0239 -0.0036 2 0.0698766
6: 0.0139 -0.0056 -0.0096 -0.0022 0.0177 1 0.2181521
Basophil percentage of white cells Eosinophil count
<num> <num>
1: 1.0156237 -3.584264
2: 0.9157099 -4.195580
3: 0.9594237 -4.354380
4: 0.8845315 -3.847279
5: 0.6832743 -3.697957
6: 0.8695345 -3.709683
Eosinophil percentage of white cells Hematocrit Hemoglobin concentration
<num> <num> <num>
1: -3.380372 0.0788395 1.386430
2: -3.219698 0.4280533 2.024007
3: -3.727362 -0.3491181 1.248190
4: -3.262860 0.2038707 1.859773
5: -3.324072 0.3378556 2.139808
6: -3.616119 -0.0546889 1.734757
High light scatter reticulocyte count
<num>
1: 4.759111
2: 4.202698
3: 4.561965
4: 4.206057
5: 4.207088
6: 4.300133
High light scatter reticulocyte percentage of red cells
<num>
1: 4.850884
2: 4.515898
3: 4.653108
4: 4.560175
5: 4.453233
6: 4.315553
Immature fraction of reticulocytes Lymphocyte count
<num> <num>
1: 3.073905 -0.0895070
2: 3.180095 0.1487145
3: 3.066648 0.0183342
4: 2.724009 -0.0072076
5: 2.500608 -0.4254034
6: 2.662584 -0.1073850
Lymphocyte percentage of white cells Mean corpuscular hemoglobin
<num> <num>
1: 0.2374592 2.636155
2: 0.2513990 2.790015
3: -0.0272472 2.227260
4: -0.0555840 2.433216
5: 0.0748983 3.202261
6: 0.3191792 3.315746
Mean corpuscular hemoglobin concentration Mean corpuscular volume
<num> <num>
1: 1.632886 2.411277
2: 1.781026 2.681980
3: 1.870210 1.707190
4: 1.923650 1.997743
5: 2.100451 2.924678
6: 1.901161 3.245288
Monocyte count Monocyte percentage of white cells Mean platelet volume
<num> <num> <num>
1: 2.0512947 0.9079310 0.7683186
2: 1.2232565 0.0900392 1.0050662
3: 1.2055734 0.5576229 0.5335173
4: 0.9881165 -0.4985315 0.9883115
5: 1.1199602 0.2586746 0.3808877
6: 1.0788232 0.1757293 0.9693893
Mean reticulocyte volume Mean sphered corpuscular volume Neutrophil count
<num> <num> <num>
1: -0.6711704 -0.1915244 -0.7245853
2: -0.7286477 -0.0609871 -1.0541924
3: -0.9191123 0.0123445 -0.5928827
4: -1.0593541 -0.1854266 -0.7716597
5: -0.7623533 -0.1125280 -1.1219023
6: 0.1781564 0.7304979 -1.2248109
Neutrophil percentage of white cells Platelet crit
<num> <num>
1: -0.3556682 3.317813
2: -0.5039774 2.423712
3: 0.0069968 2.904152
4: -0.1767276 3.195600
5: -0.0572866 3.074190
6: -0.1422708 2.890912
Platelet distribution width Platelet count Red blood cell count
<num> <num> <num>
1: 1.995272 1.2545477 0.5340879
2: 2.592336 0.6764149 0.9435796
3: 1.301701 1.5763316 0.6182782
4: 2.720634 1.2843909 0.3390140
5: 2.202220 1.0326955 0.8687638
6: 1.622803 1.4565123 -0.1164535
Red cell distribution width Reticulocyte count
<num> <num>
1: 0.9369695 4.549213
2: 1.2345469 4.160999
3: 1.6481568 4.525393
4: 1.0718717 4.426697
5: 0.2488524 4.553244
6: 0.9978962 4.165813
Reticulocyte fraction of red cells White blood cell count
<num> <num>
1: 4.758601 -0.5033859
2: 4.367083 -0.8890277
3: 4.599732 -0.6487270
4: 4.391039 -0.4018935
5: 4.626704 -0.9232945
6: 4.265575 -0.9320550
Celiac_GCST90014442 Celiac_GCST90468120 IBD_GCST90013901 IBD_GCST90013951
<num> <num> <num> <num>
1: -0.26979202 -1.664964 -0.9357205 -1.031167
2: -0.29874466 -2.266667 -0.9111857 -1.023133
3: -0.30873975 -2.037649 -0.9511192 -1.084680
4: -0.08861998 -2.234620 -0.9120145 -1.027695
5: -0.19971851 -2.091880 -0.8808438 -1.034828
6: -0.02010055 -2.856789 -0.9944172 -1.112842
T1D_GCST90000529 T1D_GCST90014023 LUPUS_GCST003156 LUPUS_GCST011096
<num> <num> <num> <num>
1: -3.54534 -60.34438 5.0958297 5.11865538
2: -20.04924 -66.73086 -1.0720317 -1.10548637
3: -19.21859 -54.94239 -2.6333984 -2.91795672
4: -24.05473 -58.12421 0.1342057 0.04848093
5: -18.64035 -61.99394 6.5632418 7.13558573
6: -16.68449 -59.92546 1.0688929 1.10597115write.table(metadata, "analysis/metadata.txt", sep="\t", row.names = T,
col.names = T, quote = F)for (i in 2:ncol(prs_blood)) {
# Extract the PRS values for the current trait
prs_trait <- prs_blood[[i]]
# Calculate the 75th percentile for the trait (top 25% threshold)
p75 <- quantile(prs_trait, 0.75, na.rm = TRUE)
# Create a new column for the group classification based on the 75th percentile
group <- ifelse(prs_trait > p75, "Top 25%", "Remaining")
# Assign the group to the new column for this trait
prs_blood[[colnames(prs_blood)[i]]] <- factor(group,
levels = c("Remaining", "Top 25%"))
}for (i in 2:ncol(prs_immune)) {
# Extract the PRS values for the current trait
prs_trait <- prs_immune[[i]]
# Calculate the 75th percentile for the trait (top 25% threshold)
p75 <- quantile(prs_trait, 0.75, na.rm = TRUE)
# Create a new column for the group classification based on the 75th percentile
group <- ifelse(prs_trait > p75, "Top 25%", "Remaining")
# Assign the group to the new column for this trait
prs_immune[[colnames(prs_immune)[i]]] <- factor(group,
levels = c("Remaining", "Top 25%"))
}metadata <- cbind(cov, prs_blood[, 2:30], prs_immune[,2:9])
rownames(metadata) <- matching_samples
metadata$sex <- as.factor(metadata$sex)
head(metadata) PC1 PC2 PC3 PC4 PC5 sex Basophil count
<num> <num> <num> <num> <num> <fctr> <fctr>
1: 0.0154 -0.0093 0.0107 -0.0038 -0.0083 1 Top 25%
2: 0.0139 -0.0097 0.0067 -0.0144 0.0296 2 Remaining
3: 0.0145 -0.0093 0.0327 0.0056 0.0510 2 Top 25%
4: -0.0728 -0.0077 -0.0044 -0.0146 0.0063 1 Remaining
5: 0.0106 0.0037 -0.0116 -0.0239 -0.0036 2 Remaining
6: 0.0139 -0.0056 -0.0096 -0.0022 0.0177 1 Remaining
Basophil percentage of white cells Eosinophil count
<fctr> <fctr>
1: Top 25% Top 25%
2: Remaining Remaining
3: Remaining Remaining
4: Remaining Remaining
5: Remaining Top 25%
6: Remaining Top 25%
Eosinophil percentage of white cells Hematocrit Hemoglobin concentration
<fctr> <fctr> <fctr>
1: Remaining Remaining Remaining
2: Remaining Top 25% Top 25%
3: Remaining Remaining Remaining
4: Remaining Top 25% Top 25%
5: Remaining Top 25% Top 25%
6: Remaining Remaining Remaining
High light scatter reticulocyte count
<fctr>
1: Top 25%
2: Remaining
3: Top 25%
4: Remaining
5: Remaining
6: Remaining
High light scatter reticulocyte percentage of red cells
<fctr>
1: Top 25%
2: Remaining
3: Top 25%
4: Remaining
5: Remaining
6: Remaining
Immature fraction of reticulocytes Lymphocyte count
<fctr> <fctr>
1: Top 25% Remaining
2: Top 25% Top 25%
3: Top 25% Remaining
4: Remaining Remaining
5: Remaining Remaining
6: Remaining Remaining
Lymphocyte percentage of white cells Mean corpuscular hemoglobin
<fctr> <fctr>
1: Top 25% Remaining
2: Top 25% Remaining
3: Remaining Remaining
4: Remaining Remaining
5: Remaining Top 25%
6: Top 25% Top 25%
Mean corpuscular hemoglobin concentration Mean corpuscular volume
<fctr> <fctr>
1: Remaining Remaining
2: Remaining Remaining
3: Remaining Remaining
4: Remaining Remaining
5: Top 25% Top 25%
6: Remaining Top 25%
Monocyte count Monocyte percentage of white cells Mean platelet volume
<fctr> <fctr> <fctr>
1: Top 25% Top 25% Remaining
2: Remaining Remaining Remaining
3: Remaining Top 25% Remaining
4: Remaining Remaining Remaining
5: Remaining Remaining Remaining
6: Remaining Remaining Remaining
Mean reticulocyte volume Mean sphered corpuscular volume Neutrophil count
<fctr> <fctr> <fctr>
1: Remaining Remaining Remaining
2: Remaining Remaining Remaining
3: Remaining Remaining Top 25%
4: Remaining Remaining Remaining
5: Remaining Remaining Remaining
6: Top 25% Top 25% Remaining
Neutrophil percentage of white cells Platelet crit
<fctr> <fctr>
1: Remaining Top 25%
2: Remaining Remaining
3: Remaining Remaining
4: Remaining Top 25%
5: Remaining Top 25%
6: Remaining Remaining
Platelet distribution width Platelet count Red blood cell count
<fctr> <fctr> <fctr>
1: Remaining Top 25% Remaining
2: Top 25% Remaining Top 25%
3: Remaining Top 25% Remaining
4: Top 25% Top 25% Remaining
5: Remaining Remaining Top 25%
6: Remaining Top 25% Remaining
Red cell distribution width Reticulocyte count
<fctr> <fctr>
1: Remaining Top 25%
2: Top 25% Remaining
3: Top 25% Top 25%
4: Remaining Remaining
5: Remaining Top 25%
6: Remaining Remaining
Reticulocyte fraction of red cells White blood cell count
<fctr> <fctr>
1: Top 25% Remaining
2: Remaining Remaining
3: Remaining Remaining
4: Remaining Top 25%
5: Remaining Remaining
6: Remaining Remaining
Celiac_GCST90014442 Celiac_GCST90468120 IBD_GCST90013901 IBD_GCST90013951
<fctr> <fctr> <fctr> <fctr>
1: Remaining Top 25% Remaining Top 25%
2: Remaining Remaining Top 25% Top 25%
3: Remaining Remaining Remaining Remaining
4: Top 25% Remaining Top 25% Top 25%
5: Remaining Remaining Top 25% Top 25%
6: Top 25% Remaining Remaining Remaining
T1D_GCST90000529 T1D_GCST90014023 LUPUS_GCST003156 LUPUS_GCST011096
<fctr> <fctr> <fctr> <fctr>
1: Top 25% Remaining Top 25% Top 25%
2: Remaining Remaining Remaining Remaining
3: Remaining Top 25% Remaining Remaining
4: Remaining Remaining Remaining Remaining
5: Remaining Remaining Top 25% Top 25%
6: Remaining Remaining Remaining Remainingwrite.table(metadata, "analysis/metadata_quantile.txt", sep="\t", row.names = T,
col.names = T, quote = F)
sessionInfo()R version 4.2.2 (2022-10-31)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur ... 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/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_1.1.4 data.table_1.16.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] Rcpp_1.0.14 compiler_4.2.2 pillar_1.10.1 bslib_0.9.0
[5] later_1.4.1 git2r_0.33.0 jquerylib_0.1.4 tools_4.2.2
[9] getPass_0.2-4 digest_0.6.37 jsonlite_1.8.9 evaluate_1.0.3
[13] lifecycle_1.0.4 tibble_3.2.1 pkgconfig_2.0.3 rlang_1.1.5
[17] cli_3.6.3 rstudioapi_0.17.1 yaml_2.3.10 xfun_0.50
[21] fastmap_1.2.0 httr_1.4.7 stringr_1.5.1 knitr_1.49
[25] generics_0.1.3 fs_1.6.5 vctrs_0.6.5 sass_0.4.9
[29] tidyselect_1.2.1 rprojroot_2.0.4 glue_1.8.0 R6_2.5.1
[33] processx_3.8.5 rmarkdown_2.29 callr_3.7.6 magrittr_2.0.3
[37] whisker_0.4.1 ps_1.8.1 promises_1.3.2 htmltools_0.5.8.1
[41] httpuv_1.6.15 stringi_1.8.4 cachem_1.1.0