Last updated: 2025-09-28
Checks: 7 0
Knit directory:
genomics_ancest_disease_dispar/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20220216) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 97d340d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rproj.user/
Ignored: data/.DS_Store
Ignored: data/gbd/.DS_Store
Ignored: data/gbd/IHME-GBD_2021_DATA-d8cf695e-1.csv
Ignored: data/gbd/ihme_gbd_2019_global_disease_burden_rate_all_ages.csv
Ignored: data/gbd/ihme_gbd_2019_global_paf_rate_percent_all_ages.csv
Ignored: data/gbd/ihme_gbd_2021_global_disease_burden_rate_all_ages.csv
Ignored: data/gbd/ihme_gbd_2021_global_paf_rate_percent_all_ages.csv
Ignored: data/gwas_catalog/
Ignored: data/icd/.DS_Store
Ignored: data/icd/IHME_GBD_2019_COD_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX
Ignored: data/icd/IHME_GBD_2019_NONFATAL_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX
Ignored: data/icd/IHME_GBD_2021_COD_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/icd/IHME_GBD_2021_NONFATAL_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/icd/UK_Biobank_master_file.tsv
Ignored: data/icd/cdc_valid_icd10_Sep_23_2025.xlsx
Ignored: data/icd/cdc_valid_icd9_Sep_23_2025.xlsx
Ignored: data/icd/phecode_international_version_unrolled.csv
Ignored: data/icd/semiautomatic_ICD-pheno.txt
Ignored: data/icd/~$IHME_GBD_2021_COD_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/icd/~$IHME_GBD_2021_NONFATAL_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX
Ignored: data/who/
Ignored: diseases.txt
Ignored: not_found_diseases.txt
Ignored: orig_phecode_map.csv
Ignored: original_phecodes_pheinfo.csv
Ignored: output/gwas_cat/
Ignored: output/gwas_study_info_cohort_corrected.csv
Ignored: output/gwas_study_info_trait_corrected.csv
Ignored: output/gwas_study_info_trait_ontology_info.csv
Ignored: output/gwas_study_info_trait_ontology_info_l1.csv
Ignored: output/gwas_study_info_trait_ontology_info_l2.csv
Ignored: output/trait_ontology/
Ignored: renv/
Ignored: sup_table.xlsx
Ignored: zooma.tsv
Ignored: zooma_res.tsv
Untracked files:
Untracked: analysis/garbage_icd_codes.Rmd
Untracked: disease_mapping.R
Unstaged changes:
Modified: analysis/disease_inves_by_ancest.Rmd
Modified: analysis/exclude_infectious_diseases.Rmd
Modified: analysis/gbd_data_plots.Rmd
Modified: analysis/index.Rmd
Modified: analysis/level_1_disease_group_non_cancer.Rmd
Modified: analysis/level_2_disease_group.Rmd
Modified: analysis/trait_ontology_categorization.Rmd
Modified: data/icd/README.md
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/map_trait_to_icd10.Rmd)
and HTML (docs/map_trait_to_icd10.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 97d340d | IJbeasley | 2025-09-28 | workflowr::wflow_publish("analysis/map_trait_to_icd10.Rmd") |
library(dplyr)
library(stringr)
library(data.table)source(here::here("code/get_term_descendants.R"))gwas_study_info <- fread(here::here("output/gwas_cat/gwas_study_info_group.csv"))disease_mapping <- gwas_study_info |>
filter(DISEASE_STUDY == T) |>
filter(!grepl(",", collected_all_disease_terms)) |>
filter(collected_all_disease_terms != " ") |>
filter(collected_all_disease_terms != "") |>
select(`DISEASE/TRAIT`, collected_all_disease_terms) |>
distinct()
diseases <- stringr::str_split(pattern = ", ",
gwas_study_info$collected_all_disease_terms[gwas_study_info$collected_all_disease_terms != ""]) |>
unlist() |>
stringr::str_trim()
diseases <- unique(diseases)
print(length(diseases))[1] 1697disease_mapping <- disease_mapping |>
mutate(
phecode = str_extract(`DISEASE/TRAIT`, "(?<=PheCode )[^)]+")
) |>
mutate(phecode = as.numeric(phecode))# phecode to ICD10 mapping from https://wei-lab.app.vumc.org/phecode-data/phecode_international_version
phecodes <- fread(here::here("data/icd/phecode_international_version_unrolled.csv"))
phecode_icd_map =
phecodes |>
select(icd10_code = ICD10,
phecode = PheCode
)
# if more than one ICD10 code per phecode, collapse into a single row
phecode_icd_map =
phecode_icd_map |>
group_by(phecode) |>
summarise(icd10_code =
str_flatten(unique(icd10_code), collapse = ", ", na.rm = T),
.groups = "drop")
disease_mapping =
left_join(disease_mapping,
phecode_icd_map,
by = "phecode",
relationship = "many-to-one",
na_matches = "never")disease_mapping =
disease_mapping |>
filter(icd10_code != "")
not_found_diseases <- diseases[!diseases %in% disease_mapping$collected_all_disease_terms]
not_found_diseases <- not_found_diseases[not_found_diseases != ""]
print(length(not_found_diseases))[1] 507phenotype_icd_map =
phecodes |>
group_by(Phenotype) |>
summarise(icd10_code =
str_flatten(ICD10, collapse = ", ", na.rm = T),
.groups = "drop")
matched_phenotypes =
phenotype_icd_map |>
filter(tolower(Phenotype) %in% not_found_diseases)
matched_phenotypes =
matched_phenotypes |>
mutate(collected_all_disease_terms = tolower(Phenotype)) |>
select(collected_all_disease_terms, icd10_code)
disease_mapping =
bind_rows(disease_mapping, matched_phenotypes) |>
distinct()disease_mapping =
disease_mapping |>
filter(icd10_code != "")
not_found_diseases <- diseases[!diseases %in% disease_mapping$collected_all_disease_terms]
not_found_diseases <- not_found_diseases[not_found_diseases != ""]
print(length(not_found_diseases))[1] 455to_add =
phecodes |>
filter(tolower(iconv(ICD_DESCRIPTION, to = "UTF-8")) %in% not_found_diseases) |>
mutate(collected_all_disease_terms = tolower(iconv(ICD_DESCRIPTION, to = "UTF-8"))) |>
select(collected_all_disease_terms,
icd10_code = ICD10)
phecodes |>
filter(tolower(iconv(ICD_DESCRIPTION, to = "UTF-8")) %in% "androgenetic alopecia") |>
mutate(collected_all_disease_terms = tolower(iconv(ICD_DESCRIPTION, to = "UTF-8"))) |>
select(collected_all_disease_terms,
icd10_code = ICD10) Empty data.table (0 rows and 2 cols): collected_all_disease_terms,icd10_codedisease_mapping =
bind_rows(disease_mapping, to_add) |>
distinct()disease_mapping =
disease_mapping |>
filter(icd10_code != "")
not_found_diseases <- diseases[!diseases %in% disease_mapping$collected_all_disease_terms]
not_found_diseases <- not_found_diseases[not_found_diseases != ""]
print(length(not_found_diseases))[1] 367collected_all_disease_terms = c("alcoholic liver cirrhosis",
"alcoholic pancreatitis",
"ischemic cardiomyopathy",
"systemic juvenile idiopathic arthritis",
"juvenile idiopathic arthritis",
"oligoarticular juvenile idiopathic arthritis",
"sapho syndrome",
"synovial plica syndrome",
"urgency urinary incontinence",
"abdominal distention",
"early-onset alzheimers disease",
"late-onset alzheimers disease",
"renal overload-type gout",
"vomiting of pregnancy",
"kleine-levin syndrome",
"autoimmune pancreatitis type 1",
"allergic contact dermatitis of eyelid",
"guillain-barre syndrome",
"idiopathic pulmonary fibrosis",
"behcets syndrome",
"kashin-beck disease",
"chronic thromboembolic pulmonary hypertension",
"pulmonary hypertension",
"pulmonary arterial hypertension",
"pulmonary coin lesion",
"pulmonary infarction",
"neuromyelitis optica",
"buruli ulcer disease",
"churg-strauss syndrome",
"graft versus host disease",
"takayasu arteritis",
"enuresis",
"cannabis dependence",
"orofacial cleft",
"eczema",
"drug dependence",
"cocaine-related disorders",
"pharynx cancer",
"pseudotumor cerebri",
"altitude sickness",
"high altitude pulmonary edema",
"intrahepatic cholestasis of pregnancy",
"brain injury",
"radiation-induced brain injury",
"abdominal infections code",
"secondary hyperparathyroidism of renal origin",
"gastroparesis",
"neuroblastoma",
"peripartum cardiomyopathy",
"retroperitoneal cancer",
"asphyxia neonatorum",
"postherpetic neuralgia",
"manic or hypomanic episode",
"allergic conjunctivitis",
"thiazide-induced hyponatremia",
"alpha 1-antitrypsin deficiency",
"autoimmune thyroid disease",
"hashimotos thyroiditis",
"charcot-marie-tooth disease type 1a",
"amyotrophic lateral sclerosis",
"fuchs endothelial corneal dystrophy",
"duchenne muscular dystrophy",
"familial apolipoprotein b hypobetalipoproteinemia",
"gastric metaplasia",
"inborn carbohydrate metabolic disorder",
"petaloid toenail",
"thyrotoxic periodic paralysis",
"schizoaffective disorder",
"rhegmatogenous retinal detachment",
"restless legs syndrome",
"preterm premature rupture of the membranes",
"porphyrin metabolism disease",
"peritoneal cancer",
"methamphetamine use disorders",
"familial sick sinus syndrome",
"drug misuse",
"abnormal ecg",
"adenoiditis",
"bacterial endocarditis",
"biliary atresia",
"bronchopulmonary dysplasia",
"cervical ectropion",
"chronic primary adrenal insufficiency",
"ciliopathy",
"collagenous colitis",
"colonic diverticula",
"craniofacial microsomia",
"cryptorchidism",
"plantar fasciitis",
"plantar fibromatosis",
"lewy body dementia",
"x-linked dystonia-parkinsonism",
"hippocampal sclerosis of aging",
"testicular dysgenesis syndrome",
"internet addiction disorder",
"food addiction",
"malignant lymphoid tumor",
"compartment syndrome",
"elevated lactate dehydrogenase",
"loss of consciousness",
"nephrosclerosis",
"periprosthetic osteolysis",
"polypoidal choroidal vasculopathy",
"pulmonary alveolar proteinosis",
"chorioamnionitis",
"hoarding disorder",
"unilateral renal agenesis",
"muscle spasm",
"oral ulcer",
"ileocolitis",
"microscopic colitis",
"lymphocytic colitis",
"drug-induced dyskinesia",
"plasma protein metabolism disease",
"oral lichen planus",
"epididymitis",
"orchitis",
"ectropion",
"entropion",
"cervical dystonia",
"clonal hematopoiesis",
"diffuse idiopathic skeletal hyperostosis",
"endocervicitis",
"eosinophilic esophagitis",
"focal segmental glomerulosclerosis",
"hypercalcemia",
"hypertriglyceridemia",
"hypocalcemia",
"lymphangioleiomyomatosis",
"mononucleosis",
"necrotizing enterocolitis",
"occupation-related stress disorder",
"ototoxicity",
"plantar warts",
"podoconiosis",
"posterior cortical atrophy",
"pigment dispersion syndrome",
"takotsubo cardiomyopathy",
"testicular germ cell tumor",
"normal pressure hydrocephalus",
"anti-nmda receptor encephalitis"
)
icd10_code = c("K70.3",
"K85.2, K85.20, K85.21, K85.22",
"I25.5",
"M08.20",
"M08.9",
"MO8.4",
"M86.3",
"M67.8",
"N39.4",
"R14",
"F00.0, G30.0",
"F00.1, G30.1",
"M10.3",
"O21, O21.9",
"G47.1",
"K86.1",
"H01.1",
"G61.0",
"J84.1",
"M35.2",
"M12.1",
"I27.8",
"I27.9",
"I27.9",
"R91",
"I26.9",
"G36.0",
"A31.1",
"M30.1",
"D89.8",
"M31.4",
"R32",
"F12.2",
"Q36, Q36.0, Q36.9, Q35, Q35.1, Q35.3, Q35.5, Q35.7, Q35.9",
"L30.9",
"F19.2",
"F14.1",
"C14.0",
"G93.2",
"T70.2",
"T70.2",
"O26.6",
"S06.9",
"S06.9",
"D73.3, K35-37, K57, K61, K63.0, K65, K75.0, K81, K83.0",
"N25.8",
"K31.8",
"C74.9",
"O90.3",
"C48.0",
"P24.8, P24.9",
"B02.2",
"F30.9",
"H10.1",
"E87.1",
"E88.0",
"E06.3",
"E06.3",
"G60.0",
"G12.2",
"H18.5",
"G71.0",
"E78.6",
"K31",
"E74.9",
"L60",
"G72.3",
"F25",
"H33.0",
"G25.8",
"O42",
"E80.2",
"C48.2",
"F15.1, F15.2",
"I49.5",
"F19.1",
"R94.3",
"J35",
"I33.0",
"Q44.2",
"P27.1",
"H02.1",
"E27.1",
"Q34.8",
"K52.8",
"K57.3",
"Q67.4",
"Q53",
"M72.2",
"M72.2",
"G31.8",
"G24.1",
"G93.8",
"E29",
"F63",
"F50.8, F50.9",
"C96.9",
"T79",
"R74",
"R40",
"I12",
"T84",
"H35",
"J84",
"O41.1",
"F42.3",
"Q60.0",
"M62.8",
"K12",
"K50.0",
"K52.8",
"K52.8",
"G25.8",
"E88",
"L43",
"N45",
"N45",
"H02.1",
"H02.0, H02.1",
"G24",
"D47",
"M48.1",
"N72",
"K20",
"N04.1",
"E83.5",
"E78.1",
"E83.5",
"J84.8",
"B27",
"P77",
"F43",
"H91.0",
"B07",
"I89.0",
"G31.1",
"H21.2",
"I51.8",
"D41",
"G91.2",
"A85"
)
to_add = data.frame(collected_all_disease_terms, icd10_code)
disease_mapping =
bind_rows(disease_mapping, to_add) |>
distinct()disease_mapping =
disease_mapping |>
filter(icd10_code != "")
not_found_diseases <- diseases[!diseases %in% disease_mapping$collected_all_disease_terms]
not_found_diseases <- not_found_diseases[not_found_diseases != ""]
print(length(not_found_diseases))[1] 229top_match_string =
function(string1, string2, method){
lcs_matrix = stringdistmatrix(string1, string2, method = method)
colnames(lcs_matrix) = string2
rownames(lcs_matrix) = string1
# only return rows where min distance is < 10
#lcs_matrix = lcs_matrix[apply(lcs_matrix, 1, min) < 5, ]
# then only return the colname for the min distance for each row
original_string1 = rownames(lcs_matrix)
top_match_string2 = vector()
distance = vector()
for(i in 1:nrow(lcs_matrix)){
col_n = which(min(lcs_matrix[i, ]) == lcs_matrix[i, ])
top_match_string2[i] = colnames(lcs_matrix)[col_n[1]]
distance[i] = min(lcs_matrix[i, ])
}
top_match_pairs = data.frame(original_string1,
top_match_string2,
distance)
return(top_match_pairs)
}
top_match_string(not_found_diseases,
unique(tolower(phecodes$Phenotype)),
method = "lv") -> fuzzy_matches
top_match_string(not_found_diseases,
unique(tolower(iconv(phecodes$ICD_DESCRIPTION, to = "UTF-8"))),
method = "lv") -> fuzzy_matcheswriteLines(not_found_diseases, con = here::here("not_found_diseases.txt"))
data.table::fread(here::here("zooma_res.tsv"), skip = 6) -> zooma_res
zooma_res =
zooma_res |>
rename_with(~str_replace_all(., " ", "_"))
multiple_mapping <-
zooma_res |>
group_by(PROPERTY_VALUE) |>
summarise(n = n()) |>
filter(n > 1)
zooma_res_to_check =
zooma_res |>
filter(PROPERTY_VALUE %in% multiple_mapping$PROPERTY_VALUE)
zooma_res =
zooma_res |>
rowwise() |>
filter(
(PROPERTY_VALUE == tolower(`ONTOLOGY_TERM_LABEL(S)`) &
PROPERTY_VALUE %in% multiple_mapping$PROPERTY_VALUE) |
!PROPERTY_VALUE %in% multiple_mapping$PROPERTY_VALUE
) |>
ungroup()
zooma_res =
zooma_res |>
group_by(PROPERTY_VALUE) |>
slice_sample(n= 1)
zooma_res =
zooma_res |>
select(uri = `ONTOLOGY_TERM(S)`,
collected_all_disease_terms = PROPERTY_VALUE)uk_efo_icd <- data.table::fread(here::here("data/icd/UK_Biobank_master_file.tsv"))
uk_efo_icd =
uk_efo_icd |>
tidyr::separate_longer_delim(MAPPED_TERM_URI, delim = ", ")
uk_efo_icd =
uk_efo_icd |>
rename_with(~str_replace_all(., " ", "_")) |>
rename_with(~str_replace_all(., "/", "_"))
uk_efo_icd =
uk_efo_icd |>
filter(grepl("^[A-Z]",
ICD10_CODE_SELF_REPORTED_TRAIT_FIELD_CODE)
)
uk_efo_icd =
uk_efo_icd |>
filter(MAPPED_TERM_URI %in% zooma_res$uri) |>
select(uri = MAPPED_TERM_URI,
icd10_code = ICD10_CODE_SELF_REPORTED_TRAIT_FIELD_CODE)
uk_efo_icd =
uk_efo_icd |>
group_by(uri) |>
summarise(icd10_code =
str_flatten(icd10_code, collapse = ", ", na.rm = T),
.groups = "drop")
to_add =
left_join(zooma_res,
uk_efo_icd,
by = c("uri"),
na_matches = "never") |>
select(collected_all_disease_terms, icd10_code) |>
filter(icd10_code != "") |>
distinct()
disease_mapping =
bind_rows(disease_mapping, to_add) readxl::read_xlsx(here::here("sup_table.xlsx"), sheet = 1) -> sup_table
sup_table <- sup_table |> mutate(Mapped_trait_URI = str_remove_all(pattern = "http://www.ebi.ac.uk/efo/|http://purl.obolibrary.org/obo/|http://www.orpha.net/ORDO/", Mapped_trait_URI))
sup_table |>
filter(Mapped_trait_URI %in% zooma_res$uri)
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] jsonlite_2.0.0 httr_1.4.7 data.table_1.17.8 stringr_1.5.1
[5] dplyr_1.1.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] compiler_4.3.1 renv_1.0.3 promises_1.3.3 tidyselect_1.2.1
[5] Rcpp_1.1.0 git2r_0.36.2 callr_3.7.6 later_1.4.2
[9] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0 here_1.0.1
[13] R6_2.6.1 generics_0.1.4 knitr_1.50 tibble_3.3.0
[17] rprojroot_2.1.0 bslib_0.9.0 pillar_1.11.0 rlang_1.1.6
[21] cachem_1.1.0 stringi_1.8.7 httpuv_1.6.16 xfun_0.52
[25] getPass_0.2-4 fs_1.6.6 sass_0.4.10 cli_3.6.5
[29] withr_3.0.2 magrittr_2.0.3 ps_1.9.1 digest_0.6.37
[33] processx_3.8.6 rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5
[37] evaluate_1.0.4 glue_1.8.0 whisker_0.4.1 rmarkdown_2.29
[41] tools_4.3.1 pkgconfig_2.0.3 htmltools_0.5.8.1