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knitr::opts_chunk$set(echo = TRUE,
message = FALSE,
warning = FALSE
)
library(data.table)
library(dplyr)
library(ggplot2)
library(stringr)gwas_study_info <- fread(here::here("output/gwas_study_info_cohort_corrected.csv"))all_gwas_terms = unique(gwas_study_info$MAPPED_TRAIT)
all_gwas_terms = stringr::str_trim(tolower(all_gwas_terms))efo_descendants <- readLines(here::here("output/trait_ontology/efo_0000408_descendants.txt"))
mondo_descendants <- readLines(here::here("output/trait_ontology/mondo_0700096_descendants.txt"))
ncit_descendants <- readLines(here::here("output/trait_ontology/ncit_C2991_descendants.txt"))
orphanet_descendants <- readLines(here::here("output/trait_ontology/orphanet_557493_descendants.txt"))
age_of_onset_descendants <- readLines(here::here("output/trait_ontology/oba_2020000_descendants.txt"))
disease_measurement_terms <- readLines(here::here("output/trait_ontology/efo_0001444_disease_measurement_terms.txt"))
disease_typos = c("Alzheimer disease",
"late-onset Alzheimers disease",
"age of onset of Alzheimer disease",
"Chagas cardiomyopathy",
"Parkinson disease",
"Iron deficiency anemia",
"Churg-Strauss syndrome",
"Iridocyclitis",
"Phlebitis"
)
other <- c("Allergic disease",
"Lewy body dementia",
"Lewy body attribute",
"non-Hodgkins lymphoma",
"Ischemic Stroke",
"Lung disease",
"Respiratory System Disease",
"Iron deficiency anemia (disorder)",
"Alzheimer disease, APOE carrier status"
)
disease_terms = c(mondo_descendants,
efo_descendants,
ncit_descendants,
orphanet_descendants,
age_of_onset_descendants,
disease_measurement_terms,
disease_typos,
other) |>
unique()
disease_terms = stringr::str_trim(tolower(disease_terms))
print("Number of terms related to disease or disorder")[1] "Number of terms related to disease or disorder"length(disease_terms)[1] 53394# Find GWAS traits that fall within disease or disorder terms
simple_disease_terms = all_gwas_terms[all_gwas_terms %in% disease_terms]
# Also search for cases where there are multiple terms separated by commas
# and one of them is a disease term
not_simple_disease_terms = all_gwas_terms[!all_gwas_terms %in% disease_terms]
# sometimes there's multiple terms - check if any disease term is in these gwas terms
multiple_terms = grep(",", not_simple_disease_terms, value = T)
disease_chunks <- split(disease_terms, ceiling(seq_along(disease_terms) / 100))
disease_chunks <- lapply(disease_chunks, function(x) paste0(x, collapse = "|"))
mask <- Reduce(`|`, lapply(disease_chunks, function(x) grepl(x, multiple_terms, ignore.case = T)))
additional_disease_gwas <- multiple_terms[mask]
disease_gwas = c(all_gwas_terms[all_gwas_terms %in% disease_terms],
additional_disease_gwas)
not_disease_terms = not_simple_disease_terms[!not_simple_disease_terms %in% additional_disease_gwas]
print("Number of GWAS traits under disease or disorder terms")[1] "Number of GWAS traits under disease or disorder terms"length(all_gwas_terms) - length(not_disease_terms)[1] 3507print("Percentage of GWAS traits under disease or disorder terms")[1] "Percentage of GWAS traits under disease or disorder terms"round(100 * (length(all_gwas_terms) - length(not_disease_terms)) / length(all_gwas_terms),
digits = 1)[1] 15.3print("Percentage of GWAS traits not under disease or disorder terms")[1] "Percentage of GWAS traits not under disease or disorder terms"round(100 * length(not_disease_terms) / length(all_gwas_terms),
digits = 1)[1] 84.7not_accounted_for = not_disease_terms find_disease_terms <- function(MAPPED_TRAIT) {
# find all disease terms that appear in the trait
split_mapped_traits <- stringr::str_split(MAPPED_TRAIT, ", ") |>
unlist()
mapped_disease_terms <- split_mapped_traits[split_mapped_traits %in% disease_terms]
mapped_disease_terms = unique(mapped_disease_terms)
return(paste0(mapped_disease_terms, collapse = ", ")) # combine multiple matches
}
gwas_study_info <-
gwas_study_info |>
dplyr::rowwise() |>
dplyr::mutate(
disease_terms =
ifelse(tolower(MAPPED_TRAIT) %in% disease_gwas,
find_disease_terms(MAPPED_TRAIT),
NA)
)pheno_abnorm <- readLines(here::here("output/trait_ontology/hp_0000118_descendants.txt"))
pheno_abnorm = stringr::str_trim(tolower(pheno_abnorm))
# Find terms where all comma-split pieces are in measurement
pheno_abnorm_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts) # remove extra spaces
all(parts %in% pheno_abnorm)
})
]
additional_pheno_abnorm <- not_accounted_for[not_accounted_for %in% pheno_abnorm]
pheno_abnorm_gwas = c(pheno_abnorm_gwas, additional_pheno_abnorm) |> unique()
print("Percentage of GWAS traits under phenotype abnormality terms")[1] "Percentage of GWAS traits under phenotype abnormality terms"round(100 * length(pheno_abnorm_gwas) / length(all_gwas_terms),
digits = 1)[1] 1.5not_accounted_for = not_accounted_for[!not_accounted_for %in% pheno_abnorm_gwas]
print("Percentage of GWAS traits not accounted for so far")[1] "Percentage of GWAS traits not accounted for so far"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 83.1print("Number of GWAS traits not accounted for by so far")[1] "Number of GWAS traits not accounted for by so far"length(not_accounted_for)[1] 19023measurement <- readLines(here::here("output/trait_ontology/efo_0001444_descendants.txt"))
total_choles <- readLines(here::here("output/trait_ontology/efo_0004574_descendants.txt"))
measurement <- c(total_choles,
measurement)
measurement <- unique(measurement)
measurement <- c("cerebrospinal fluid composition attribute", measurement)
measurement = stringr::str_trim(tolower(measurement))# Find terms where all comma-split pieces are in measurement
measurement_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts)
all(parts %in% measurement)
})
]
additional_measurement <- not_accounted_for[not_accounted_for %in% measurement]
measurement_gwas = c(measurement_gwas, additional_measurement) |> unique()
print("Number of GWAS traits under measurement terms")[1] "Number of GWAS traits under measurement terms"length(measurement_gwas)[1] 18108print("Percentage of GWAS traits under measurement terms")[1] "Percentage of GWAS traits under measurement terms"round(100 * length(measurement_gwas) / length(all_gwas_terms),
digits = 1)[1] 79.2not_accounted_for = not_accounted_for[!not_accounted_for %in% measurement_gwas]
print("Percentage of GWAS traits not accounted for by disease, disorder or measurement terms")[1] "Percentage of GWAS traits not accounted for by disease, disorder or measurement terms"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 4print("Number of GWAS traits not accounted for by disease, disorder or measurement terms")[1] "Number of GWAS traits not accounted for by disease, disorder or measurement terms"length(not_accounted_for)[1] 914go_response = readLines(here::here("output/trait_ontology/go_0050896_descendants.txt"))
efo_response <- readLines(here::here("output/trait_ontology/efo_go_0050896_descendants.txt"))
response <- c(go_response,
efo_response,
"response to stimulus")
response <- unique(response)
response = stringr::str_trim(tolower(response))# Find terms where all comma-split pieces are in measurement
response_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts)
all(parts %in% response)
})
]
additional_response <- not_accounted_for[not_accounted_for %in% response]
measurement_gwas = c(measurement_gwas, additional_response) |> unique()
print("Percentage of GWAS traits under response terms")[1] "Percentage of GWAS traits under response terms"round(100 * length(response_gwas) / length(all_gwas_terms),
digits = 1)[1] 0.7not_accounted_for = not_accounted_for[!not_accounted_for %in% response_gwas]
print("Percentage of GWAS traits not accounted for by disease, measurement or response terms")[1] "Percentage of GWAS traits not accounted for by disease, measurement or response terms"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 3.3print("Number of GWAS traits not accounted for by disease, measurement or response terms")[1] "Number of GWAS traits not accounted for by disease, measurement or response terms"length(not_accounted_for)[1] 761mental <- readLines(here::here("output/trait_ontology/efo_0004323_descendants.txt"))
mental = stringr::str_trim(tolower(mental))
mental_gwas = not_accounted_for[not_accounted_for %in% mental]
print("Percentage of GWAS traits under mental process terms")[1] "Percentage of GWAS traits under mental process terms"round(100 * length(mental_gwas) / length(all_gwas_terms),
digits = 1)[1] 0.1not_accounted_for = not_accounted_for[!not_accounted_for %in% mental_gwas]
print("Percentage of GWAS traits not accounted for thus far")[1] "Percentage of GWAS traits not accounted for thus far"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 3.2print("Number of GWAS traits not accounted for thus far")[1] "Number of GWAS traits not accounted for thus far"length(not_accounted_for)[1] 743behavior <- readLines(here::here("output/trait_ontology/go_0007610_descendants.txt"))
behavior = stringr::str_trim(tolower(behavior))
behavior_gwas = not_accounted_for[not_accounted_for %in% behavior]
print("Percentage of GWAS traits under behavouir terms")[1] "Percentage of GWAS traits under behavouir terms"round(100 * length(behavior_gwas) / length(all_gwas_terms),
digits = 1)[1] 0.1not_accounted_for = not_accounted_for[!not_accounted_for %in% behavior_gwas]
print("Percentage of GWAS traits not accounted for so far")[1] "Percentage of GWAS traits not accounted for so far"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 3.1print("Number of GWAS traits not accounted for so far")[1] "Number of GWAS traits not accounted for so far"length(not_accounted_for)[1] 720injury <- readLines(here::here("output/trait_ontology/efo_0000546_descendants.txt"))
injury = stringr::str_trim(tolower(injury))
injury_gwas = not_accounted_for[not_accounted_for %in% injury]
print("Percentage of GWAS traits under injury terms")[1] "Percentage of GWAS traits under injury terms"round(100 * length(injury_gwas) / length(all_gwas_terms),
digits = 1)[1] 0.1not_accounted_for = not_accounted_for[!not_accounted_for %in% injury_gwas]
print("Percentage of GWAS traits not accounted for so far")[1] "Percentage of GWAS traits not accounted for so far"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 3.1print("Number of GWAS traits not accounted for so far")[1] "Number of GWAS traits not accounted for so far"length(not_accounted_for)[1] 701phenotype <- readLines(here::here("output/trait_ontology/efo_0000651_descendants.txt"))
phenotype = stringr::str_trim(tolower(phenotype))
phenotype_gwas = not_accounted_for[not_accounted_for %in% phenotype]
print("Percentage of GWAS traits under phenotype terms")[1] "Percentage of GWAS traits under phenotype terms"round(100 * length(phenotype_gwas) / length(all_gwas_terms),
digits = 1)[1] 0.2not_accounted_for = not_accounted_for[!not_accounted_for %in% phenotype_gwas]
print("Percentage of GWAS traits not accounted for so far")[1] "Percentage of GWAS traits not accounted for so far"round(100 * length(not_accounted_for) / length(all_gwas_terms),
digits = 1)[1] 2.9print("Number of GWAS traits not accounted for so far")[1] "Number of GWAS traits not accounted for so far"length(not_accounted_for)[1] 654gwas_study_info =
gwas_study_info |>
dplyr::mutate(MAPPED_TRAIT_CATEGORY = dplyr::case_when(is.na(MAPPED_TRAIT) ~ NA,
tolower(MAPPED_TRAIT) %in% disease_gwas ~ "Disease/Disorder",
tolower(MAPPED_TRAIT) %in% pheno_abnorm_gwas ~ "Phenotypic Abnormality",
tolower(MAPPED_TRAIT) %in% measurement_gwas ~ "Measurement",
tolower(MAPPED_TRAIT) %in% response_gwas ~ "Response",
tolower(MAPPED_TRAIT) %in% mental_gwas ~ "Mental Process",
tolower(MAPPED_TRAIT) %in% behavior_gwas ~ "Behavior",
tolower(MAPPED_TRAIT) %in% injury_gwas ~ "Injury",
tolower(MAPPED_TRAIT) %in% phenotype_gwas ~ "Phenotype",
TRUE ~ "Other"
)
)gwas_study_info$MAPPED_BACKGROUND_TRAIT |> unique() -> gwas_background
gwas_background = stringr::str_trim(tolower(gwas_background))
length(gwas_background)[1] 314multiple_terms = grep(",", gwas_background, value = T)
mask <- Reduce(`|`, lapply(disease_terms, function(x) grepl(x, multiple_terms)))
additional_disease_gwas <- multiple_terms[mask]
disease_gwas = c(gwas_background[gwas_background %in% disease_terms],
additional_disease_gwas)
print("Number of background GWAS traits under disease or disorder terms")[1] "Number of background GWAS traits under disease or disorder terms"length(disease_gwas)[1] 229print("Percentage of background GWAS traits under disease or disorder terms")[1] "Percentage of background GWAS traits under disease or disorder terms"round(100 * length(disease_gwas) / length(gwas_background),
digits = 1)[1] 72.9not_accounted_for = gwas_background[!gwas_background %in% disease_gwas]gwas_study_info <-
gwas_study_info |>
rowwise() |>
dplyr::mutate(
background_disease_terms =
ifelse(tolower(MAPPED_BACKGROUND_TRAIT) %in% disease_gwas,
find_disease_terms(MAPPED_BACKGROUND_TRAIT),
NA)
)pheno_abnorm <- readLines(here::here("output/trait_ontology/hp_0000118_descendants.txt"))
pheno_abnorm = stringr::str_trim(tolower(pheno_abnorm))
# Find terms where all comma-split pieces are in measurement
pheno_abnorm_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts) # remove extra spaces
all(parts %in% pheno_abnorm)
})
]
additional_pheno_abnorm <- not_accounted_for[not_accounted_for %in% pheno_abnorm]
pheno_abnorm_gwas = c(pheno_abnorm_gwas, additional_pheno_abnorm) |> unique()
print("Percentage of background GWAS traits under phenotype abnormality terms")[1] "Percentage of background GWAS traits under phenotype abnormality terms"round(100 * length(pheno_abnorm_gwas) / length(gwas_background),
digits = 1)[1] 2.2not_accounted_for = not_accounted_for[!not_accounted_for %in% pheno_abnorm_gwas]
print("Percentage of background GWAS traits not accounted for so far")[1] "Percentage of background GWAS traits not accounted for so far"round(100 * length(not_accounted_for) / length(gwas_background),
digits = 1)[1] 25.2print("Number of background GWAS traits not accounted for by so far")[1] "Number of background GWAS traits not accounted for by so far"length(not_accounted_for)[1] 79measurement_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts) # remove extra spaces
all(parts %in% measurement)
})
]
measurement_gwas = c(measurement_gwas, additional_measurement) |> unique()
additional_measurement <- not_accounted_for[not_accounted_for %in% measurement]
measurement_gwas = c(measurement_gwas, additional_measurement) |> unique()
not_accounted_for = not_accounted_for[!not_accounted_for %in% measurement_gwas]
print("Percentage of background GWAS traits not accounted for by disease, disorder or measurement terms")[1] "Percentage of background GWAS traits not accounted for by disease, disorder or measurement terms"round(100 * length(not_accounted_for) / length(gwas_background),
digits = 1)[1] 7.6print("Number of background GWAS traits not accounted for by disease, disorder or measurement terms")[1] "Number of background GWAS traits not accounted for by disease, disorder or measurement terms"length(not_accounted_for)[1] 24# Find terms where all comma-split pieces are in measurement
response_gwas <- not_accounted_for[
sapply(strsplit(not_accounted_for, ", "), function(parts) {
parts <- trimws(parts)
all(parts %in% response)
})
]
additional_response <- not_accounted_for[not_accounted_for %in% response]
response_gwas = c(response_gwas, additional_response) |> unique()
not_accounted_for = not_accounted_for[!not_accounted_for %in% response_gwas]
print("Percentage of background GWAS traits under response terms")[1] "Percentage of background GWAS traits under response terms"round(100 * length(response_gwas) / length(gwas_background),
digits = 1)[1] 1.6print("Number of background GWAS traits under response terms")[1] "Number of background GWAS traits under response terms"length(response_gwas)[1] 5print("Number of background GWAS traits not accounted for by disease, measurement or response terms")[1] "Number of background GWAS traits not accounted for by disease, measurement or response terms"length(not_accounted_for)[1] 19print("Percentage of background GWAS traits not accounted for by disease, measurement or response terms")[1] "Percentage of background GWAS traits not accounted for by disease, measurement or response terms"round(100 * length(not_accounted_for) / length(gwas_background),
digits = 1)[1] 6.1gwas_study_info =
gwas_study_info |>
dplyr::mutate(BACKGROUND_TRAIT_CATEGORY = dplyr::case_when(
MAPPED_BACKGROUND_TRAIT == "" ~ NA,
tolower(MAPPED_BACKGROUND_TRAIT) %in% disease_gwas ~ "Disease/Disorder",
tolower(MAPPED_BACKGROUND_TRAIT) %in% pheno_abnorm_gwas ~ "Phenotypic Abnormality",
tolower(MAPPED_BACKGROUND_TRAIT) %in% measurement_gwas ~ "Measurement",
tolower(MAPPED_BACKGROUND_TRAIT) %in% response_gwas ~ "Response",
TRUE ~ "Other"
))gwas_study_info |>
group_by(MAPPED_TRAIT_CATEGORY, BACKGROUND_TRAIT_CATEGORY) |>
summarise(n_studies = n()) |>
arrange(desc(n_studies))# A tibble: 33 × 3
# Groups: MAPPED_TRAIT_CATEGORY [9]
MAPPED_TRAIT_CATEGORY BACKGROUND_TRAIT_CATEGORY n_studies
<chr> <chr> <int>
1 Measurement <NA> 101886
2 Disease/Disorder <NA> 20296
3 Measurement Disease/Disorder 12223
4 Other <NA> 2692
5 Phenotypic Abnormality <NA> 2329
6 Measurement Measurement 969
7 Disease/Disorder Disease/Disorder 549
8 Injury <NA> 508
9 Phenotype <NA> 340
10 Other Disease/Disorder 335
# ℹ 23 more rowsgwas_study_info =
gwas_study_info |>
dplyr::rowwise() |>
dplyr::mutate(DISEASE_STUDY = ifelse(MAPPED_TRAIT_CATEGORY == "Disease/Disorder" | BACKGROUND_TRAIT_CATEGORY == "Disease/Disorder", T, F )) |>
dplyr::ungroup() combined_disease_terms = function(MAPPED_TRAIT_1, MAPPED_TRAIT_2){
MAPPED_TRAIT_1 = stringr::str_split(MAPPED_TRAIT_1, ", ") |> unlist()
MAPPED_TRAIT_2 = stringr::str_split(MAPPED_TRAIT_2, ", ") |> unlist()
all_mapped_disease_terms =
c(MAPPED_TRAIT_1, MAPPED_TRAIT_2) |>
unique()
combined_mapped_disease_terms = paste0(all_mapped_disease_terms,
collapse = ", ")
return(combined_mapped_disease_terms)
}
gwas_study_info <-
gwas_study_info |>
dplyr::rowwise() |>
dplyr::mutate(all_disease_terms =
case_when(is.na(background_disease_terms) & is.na(disease_terms) ~ NA,
is.na(background_disease_terms) & !is.na(disease_terms) ~ disease_terms,
!is.na(background_disease_terms) & is.na(disease_terms) ~ background_disease_terms,
!is.na(background_disease_terms) & !is.na(disease_terms) ~
combined_disease_terms(background_disease_terms,
disease_terms))
) |>
dplyr::ungroup()data.table::fwrite(gwas_study_info,
here::here("output/gwas_study_info_trait_ontology_info.csv"),
sep = ",")
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Los_Angeles
tzcode source: internal
attached base packages:
[1] stats graphics grDevices datasets utils methods base
other attached packages:
[1] stringr_1.5.1 ggplot2_3.5.2 dplyr_1.1.4 data.table_1.17.8
[5] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] gtable_0.3.6 jsonlite_2.0.0 compiler_4.3.1 renv_1.0.3
[5] promises_1.3.3 tidyselect_1.2.1 Rcpp_1.1.0 git2r_0.36.2
[9] callr_3.7.6 later_1.4.2 jquerylib_0.1.4 scales_1.4.0
[13] yaml_2.3.10 fastmap_1.2.0 here_1.0.1 R6_2.6.1
[17] generics_0.1.4 knitr_1.50 tibble_3.3.0 rprojroot_2.1.0
[21] RColorBrewer_1.1-3 bslib_0.9.0 pillar_1.11.0 rlang_1.1.6
[25] utf8_1.2.6 cachem_1.1.0 stringi_1.8.7 httpuv_1.6.16
[29] xfun_0.52 getPass_0.2-4 fs_1.6.6 sass_0.4.10
[33] cli_3.6.5 withr_3.0.2 magrittr_2.0.3 ps_1.9.1
[37] grid_4.3.1 digest_0.6.37 processx_3.8.6 rstudioapi_0.17.1
[41] lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.4 glue_1.8.0
[45] farver_2.1.2 whisker_0.4.1 rmarkdown_2.29 httr_1.4.7
[49] tools_4.3.1 pkgconfig_2.0.3 htmltools_0.5.8.1