Last updated: 2025-10-09
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genomics_ancest_disease_dispar/
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Ignored files:
Ignored: .DS_Store
Ignored: .Rproj.user/
Ignored: analysis/.DS_Store
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/manual_disease_icd10_mappings.xlsx
Ignored: data/icd/phecode_international_version_unrolled.csv
Ignored: data/icd/semiautomatic_ICD-pheno.txt
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/who/
Ignored: output/.DS_Store
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/icd_map/
Ignored: output/trait_ontology/
Ignored: renv/
Ignored: sup_table.xlsx
Unstaged changes:
Modified: analysis/disease_inves_by_ancest.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
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 8e94754 | IJbeasley | 2025-10-09 | Integrating gwas traits with ICD10 |
library(dplyr)
Attaching package: 'dplyr'The following objects are masked from 'package:stats':
filter, lagThe following objects are masked from 'package:base':
intersect, setdiff, setequal, unionlibrary(stringr)
library(purrr)
library(tidyr)
library(data.table)
Attaching package: 'data.table'The following object is masked from 'package:purrr':
transposeThe following objects are masked from 'package:dplyr':
between, first, lastgbd_2019 <- readxl::read_xlsx(here::here("data/icd/IHME_GBD_2019_NONFATAL_CAUSE_ICD_CODE_MAP_Y2020M10D15.XLSX"),
skip = 1)
gbd_2019 =
gbd_2019 |>
rename_with(~str_replace_all(., " ", "_")) |>
rename_with(~ str_replace_all(., "\\\\", "_")) |>
rename_with(~ tolower(.))
gbd_2019 =
gbd_2019 |>
filter(`...1` != 0)gbd_2021 <- readxl::read_xlsx(here::here("data/icd/IHME_GBD_2021_NONFATAL_CAUSE_ICD_CODE_MAP_Y2024M05D16.XLSX"),
skip = 1)
gbd_2021 =
gbd_2021 |>
rename_with(~str_replace_all(., " ", "_")) |>
rename_with(~ str_replace_all(., "\\\\", "_")) |>
rename_with(~ tolower(.))
cause_level =
gbd_2021 |>
select(cause_hierarchy_level, cause = cause_name)gbd_2019 =
left_join(
gbd_2019,
cause_level)
# check if any cause_hierarchy_level is NA
# meaning not filled in by GBD 2021
gbd_2019 |>
filter(is.na(cause_hierarchy_level))
# Pertussis == Wooping cough
# this is a level 3 cause
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Whooping cough",
3,
cause_hierarchy_level)
)
# Other direct maternal disorders appears (GBD 2021) to be equiv to other maternal disorders (GBD 2019)
# cause_hierarchy_level = 4
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Other maternal disorders",
4,
cause_hierarchy_level)
)
# Urinary tract infections and interstitial nephritis (GBD 2021) appears to be equiv to Urinary tract infections (GBD 2019)
# cause_hierarchy_level = 4
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Urinary tract infections",
4,
cause_hierarchy_level)
)
# Peripheral artery disease
# likely level = 3
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Peripheral artery disease",
3,
cause_hierarchy_level)
)
# Police conflict and executions (GBD 2021) equiv to Executions and police conflict (GBD 2019)
# level = 3
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Executions and police conflict",
3,
cause_hierarchy_level)
)
# Edentulism and severe tooth loss
# level = 3
gbd_2019 =
gbd_2019 |>
mutate(cause_hierarchy_level =
ifelse(cause == "Edentulism and severe tooth loss",
3,
cause_hierarchy_level)
)
# check that all cause levels filled in
gbd_2019 |>
filter(is.na(cause_hierarchy_level))gbd_2021 =
gbd_2021 |>
rename(icd10_code = icd10,
icd10_code_used_in_hosp = `icd10_used_in_hospital/claims_analyses`) |>
select(-1)
# expand multiple ICD codes into rows
gbd_2021 =
gbd_2021 |>
mutate(icd10_code = str_split(icd10_code, ",\\s*")) |>
tidyr::unnest(icd10_code)
# expand multiple ICD codes into rows
# gbd_2021 =
# gbd_2021 |>
# mutate(icd10_code_used_in_hosp = str_split(icd10_code_used_in_hosp, ",\\s*")) |>
# tidyr::unnest(icd10_code_used_in_hosp)
gbd_2021 =
gbd_2021 |>
tidyr::pivot_longer(c(#"icd10_code_used_in_hosp",
"icd10_code"),
names_to = "icd10_code_type",
values_to = "icd10_code")
gbd_2021 =
gbd_2021 |>
select(icd10_code,
cause_name,
cause_hierarchy_level) |>
distinct()
gbd_2021 =
gbd_2021 |>
tidyr::separate_wider_delim(col = "icd10_code",
delim = "-",
names = c("start_icd10_code", "end_icd10_code"),
too_few = "align_end"
) |>
mutate(start_icd10_code =
ifelse(is.na(start_icd10_code),
end_icd10_code,
start_icd10_code)
)
head(gbd_2021)# A tibble: 6 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 A50 A60.9 HIV/AIDS and sexually t… 2
2 A63 A64.0 HIV/AIDS and sexually t… 2
3 B20 B23.8 HIV/AIDS and sexually t… 2
4 B24 B24.0 HIV/AIDS and sexually t… 2
5 B63 B63 HIV/AIDS and sexually t… 2
6 B97.81 B97.81 HIV/AIDS and sexually t… 2gbd_2021 |>
filter(start_icd10_code == end_icd10_code &
!grepl(".", start_icd10_code, fixed = TRUE)
) |>
arrange(start_icd10_code) |>
head()# A tibble: 6 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 A09 A09 Enteric infections 2
2 A09 A09 Diarrheal diseases 3
3 A70 A70 Respiratory infections … 2
4 A70 A70 Lower respiratory infec… 3
5 A76 A76 Other infectious diseas… 2
6 A76 A76 Other unspecified infec… 3gbd_2021 =
gbd_2021 |>
mutate(end_icd10_code =
ifelse(
start_icd10_code == end_icd10_code &
!grepl(".", start_icd10_code, fixed = TRUE),
paste0(end_icd10_code, ".9"),
end_icd10_code
)
)gbd_2021 |>
filter(end_icd10_code == "N95.9")# A tibble: 3 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 N88 N95.9 Other non-communicable … 2
2 N88 N95.9 Gynecological diseases 3
3 N95.1 N95.9 Other gynecological dis… 4# check start_icd10_code and end_icd10_code start with the same letter
# if not, need to fix these rows
gbd_2021 |>
filter(str_extract(start_icd10_code, "^[A-Z]") !=
str_extract(end_icd10_code, "^[A-Z]")
)# A tibble: 6 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 O98.8 P22.9 Maternal and neonatal d… 2
2 C8 D24.9 Neoplasms 2
3 W49 X58.9 Unintentional injuries 2
4 T74.2 U03 Self-harm and interpers… 2
5 X66 Y08.9 Self-harm and interpers… 2
6 X85 Y08.9 Interpersonal violence 3gbd_2021 =
gbd_2021 |>
mutate(start_icd10_code =
ifelse(start_icd10_code == "W99" &
end_icd10_code == "X06.9",
"X00.0",
start_icd10_code)
)
gbd_2021 =
gbd_2021 |>
add_row(
start_icd10_code = "W99",
end_icd10_code = "W99",
cause_name = "Unintentional injuries",
cause_hierarchy_level = 2
)
gbd_2021 =
gbd_2021 |>
mutate(end_icd10_code =
ifelse(start_icd10_code == "O98.8" &
end_icd10_code == "P05.9",
"O99.9",
end_icd10_code)
)
gbd_2021 =
gbd_2021 |>
add_row(
start_icd10_code = "P00",
end_icd10_code = "P05.9",
cause_name = "Maternal and neonatal disorders",
cause_hierarchy_level = 2
)
gbd_2021 =
gbd_2021 |>
mutate(end_icd10_code =
ifelse(start_icd10_code == "T74.2" &
end_icd10_code == "U03",
"T98.3",
end_icd10_code)
)
gbd_2021 =
gbd_2021 |>
add_row(
start_icd10_code = "U00",
end_icd10_code = "U03",
cause_name = "Self-harm and interpersonal violence",
cause_hierarchy_level = 2
)
gbd_2021 =
gbd_2021 |>
mutate(end_icd10_code =
ifelse(start_icd10_code == "X66" &
end_icd10_code == "Y08.9",
"X99",
end_icd10_code)
)
gbd_2021 =
gbd_2021 |>
add_row(
start_icd10_code = "Y00",
end_icd10_code = "Y08.9",
cause_name = "Self-harm and interpersonal violence",
cause_hierarchy_level = 2
)
gbd_2021 =
gbd_2021 |>
mutate(end_icd10_code =
ifelse(start_icd10_code == "X85" &
end_icd10_code == "Y08.9",
"X99",
end_icd10_code)
)
gbd_2021 =
gbd_2021 |>
add_row(
start_icd10_code = "Y00",
end_icd10_code = "Y08.9",
cause_name = "Interpersonal violence",
cause_hierarchy_level = 2
)
gbd_2021 |>
filter(str_extract(start_icd10_code, "^[A-Z]") !=
str_extract(end_icd10_code, "^[A-Z]")
)# A tibble: 3 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 O98.8 P22.9 Maternal and neonatal d… 2
2 C8 D24.9 Neoplasms 2
3 W49 X58.9 Unintentional injuries 2 gbd_2021 |>
filter(
is.na(end_icd10_code)
| is.na(start_icd10_code)) |>
head()# A tibble: 6 × 4
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 <NA> <NA> HIV/AIDS - Multidrug-re… 4
2 <NA> <NA> HIV/AIDS - Extensively … 4
3 <NA> <NA> Latent tuberculosis inf… 4
4 <NA> <NA> Extensively drug-resist… 4
5 <NA> <NA> COVID-19 3
6 <NA> <NA> Liver cancer due to hep… 4 # some nas ...
# remove these rows at least for now
gbd_2021 =
gbd_2021 |>
filter(
!is.na(end_icd10_code)
& !is.na(start_icd10_code))gbd_2021 =
gbd_2021 |>
mutate(icd10_code_letter = str_extract(start_icd10_code, "^[A-Z]"))
gbd_2021 =
gbd_2021 |>
mutate(start_icd10_code_num = as.numeric(str_remove(start_icd10_code, "^[A-Z]")),
end_icd10_code_num = as.numeric(str_remove(end_icd10_code, "^[A-Z]"))
)Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `start_icd10_code_num = as.numeric(str_remove(start_icd10_code,
"^[A-Z]"))`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.gbd_2021 |>
filter(icd10_code_letter == "N" &
start_icd10_code_num <= 93.0 &
end_icd10_code_num >= 93.0)# A tibble: 3 × 7
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 N88 N95.9 Other non-communicable … 2
2 N88 N95.9 Gynecological diseases 3
3 N92 N93.9 Premenstrual syndrome 4
# ℹ 3 more variables: icd10_code_letter <chr>, start_icd10_code_num <dbl>,
# end_icd10_code_num <dbl>gbd_2021 |>
filter(end_icd10_code == "N95.9")# A tibble: 3 × 7
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 N88 N95.9 Other non-communicable … 2
2 N88 N95.9 Gynecological diseases 3
3 N95.1 N95.9 Other gynecological dis… 4
# ℹ 3 more variables: icd10_code_letter <chr>, start_icd10_code_num <dbl>,
# end_icd10_code_num <dbl>gbd_2021 |>
filter(start_icd10_code_num <= 93.0 &
end_icd10_code_num >= 93.0 &
icd10_code_letter == "N"
)# A tibble: 3 × 7
start_icd10_code end_icd10_code cause_name cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 N88 N95.9 Other non-communicable … 2
2 N88 N95.9 Gynecological diseases 3
3 N92 N93.9 Premenstrual syndrome 4
# ℹ 3 more variables: icd10_code_letter <chr>, start_icd10_code_num <dbl>,
# end_icd10_code_num <dbl>disease_mapping <- data.table::fread(
here::here("output/icd_map/gwas_disease_to_icd10_mapping.csv")
)
disease_mapping =
disease_mapping |>
mutate(icd10_code_letter = str_extract(icd10_code, "^[A-Z]")) |>
mutate(icd10_code_num = as.numeric(str_remove(icd10_code, "^[A-Z]")))Warning: There was 1 warning in `mutate()`.
ℹ In argument: `icd10_code_num = as.numeric(str_remove(icd10_code, "^[A-Z]"))`.
Caused by warning:
! NAs introduced by coerciondisease_mapping =
disease_mapping |>
mutate(icd10_code_num = as.numeric(icd10_code_num))disease_mapping_with_cause <- disease_mapping |>
rowwise() |>
mutate(
cause =
list(
gbd_2021$cause_name[
which(icd10_code_num >= gbd_2021$start_icd10_code_num &
icd10_code_num <= gbd_2021$end_icd10_code_num &
icd10_code_letter == gbd_2021$icd10_code_letter)
]),
cause_hierarchy_level =
list(
gbd_2021$cause_hierarchy_level[
which(icd10_code_num >= gbd_2021$start_icd10_code_num &
icd10_code_num <= gbd_2021$end_icd10_code_num &
icd10_code_letter == gbd_2021$icd10_code_letter)
])
) |>
ungroup()
disease_mapping_with_cause =
disease_mapping_with_cause |>
tidyr::unnest(c(cause, cause_hierarchy_level),
keep_empty = TRUE
)disease_mapping_with_cause =
disease_mapping_with_cause |>
filter(!is.na(cause))
# filtering na to deal with pivoting
disease_mapping_with_cause_grouped =
disease_mapping_with_cause |>
select(-icd10_code_num) |>
tidyr::pivot_wider(
id_cols = c("collected_all_disease_terms", "icd10_code", "icd10_description"),
names_from = cause_hierarchy_level,
names_glue = "l{.name}_{.value}",
values_from = cause,
values_fn = ~paste0(unique(.x), collapse = ", ")
)# check which l2 causes are NA but l3 causes are not NA
# can use l3 causes therefore to fill in l2 causes
disease_mapping_with_cause_grouped |>
rowwise() |>
filter(is.na(l2_cause) & !is.na(l3_cause)) |>
head()# A tibble: 6 × 6
# Rowwise:
collected_all_diseas…¹ icd10_code icd10_description l2_cause l3_cause l4_cause
<chr> <chr> <chr> <chr> <chr> <chr>
1 abnormal delivery P20 Intrauterine hyp… <NA> Neonata… Neonata…
2 abnormal delivery P20.9 Intrauterine hyp… <NA> Neonata… Neonata…
3 abnormal delivery P21 Birth asphyxia <NA> Neonata… Neonata…
4 abnormal delivery P21.0 Severe birth asp… <NA> Neonata… Neonata…
5 abnormal delivery P21.1 Mild and moderat… <NA> Neonata… Neonata…
6 abnormal delivery P21.9 Birth asphyxia, … <NA> Neonata… Neonata…
# ℹ abbreviated name: ¹collected_all_disease_termsneoplasms = c("Other neoplasms",
"Hodgkin lymphoma",
"Leukemia",
"Non-Hodgkin lymphoma",
"Multiple myeloma")
disease_mapping_with_cause_grouped =
disease_mapping_with_cause_grouped |>
rowwise() |>
mutate(l2_cause =
list(ifelse(is.na(l2_cause),
case_when(map_lgl(l3_cause, ~ .x %in% neoplasms) ~ "Neoplasms",
map_lgl(l3_cause, ~ .x %in% "Neonatal disorders") ~ "Maternal and neonatal disorders",
map_lgl(l3_cause, ~ .x %in% "Other cardiovascular and circulatory diseases") ~ "Cardiovascular diseases"
),
l2_cause
)
)) |>
ungroup()
# to_map_manually <-
# disease_mapping_with_cause_grouped |>
# rowwise() |>
# filter(is.null(l2_cause)) |>
# pull(collected_all_disease_terms) |>
# unique()
#
#
# update_unmapped_cause = function(df,
# unmapped_term,
# l2_cause_rep,
# l3_cause_rep){
#
# df =
# df |>
# mutate(l3_cause =
# ifelse(collected_all_disease_terms == unmapped_term,
# l3_cause_rep,
# l3_cause
# )) |>
# mutate(l2_cause =
# ifelse(collected_all_disease_terms == unmapped_term,
# l2_cause_rep,
# l2_cause
# ))
#
# return(df)
#
# }
#
# disease_mapping_with_cause_grouped =
# update_unmapped_cause(disease_mapping_with_cause_grouped,
# "bone cancer",
# "Neoplasms",
# "Malignant neoplasms of bone and articular cartilage")disease_mapping_orig <- data.table::fread(
here::here("output/icd_map/gwas_disease_to_icd10_mapping.csv")
)
disease_mapping_with_cause_grouped =
disease_mapping_with_cause_grouped |>
tidyr::unnest_longer(c("l2_cause", "l3_cause", "l4_cause"))
disease_mapping_final =
left_join(
disease_mapping_orig,
disease_mapping_with_cause_grouped,
by = c("collected_all_disease_terms",
"icd10_code",
"icd10_description")
)
gwas_study_info <- fread(here::here("output/gwas_cat/gwas_study_info_group_v2.csv"))
gwas_study_info =
gwas_study_info |>
select(`DISEASE/TRAIT`, PUBMED_ID, STUDY_ACCESSION, COHORT)
disease_mapping_final =
left_join(
disease_mapping_final,
gwas_study_info
)Joining with `by = join_by(`DISEASE/TRAIT`, PUBMED_ID, STUDY_ACCESSION)`data.table::fwrite(
disease_mapping_final,
here::here("output/icd_map/gwas_disease_to_icd10_mapping_gbd_cat.csv")
)gbd_data <- data.table::fread(here::here("data/gbd/ihme_gbd_2019_global_disease_burden_rate_all_ages.csv"))
gbd_data = gbd_data |>
dplyr::filter(metric == "Rate") |>
dplyr::filter(measure == "DALYs (Disability-Adjusted Life Years)") |>
dplyr::filter(year == 2019)
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] data.table_1.17.8 tidyr_1.3.1 purrr_1.1.0 stringr_1.5.1
[5] dplyr_1.1.4 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] jsonlite_2.0.0 compiler_4.3.1 renv_1.0.3 promises_1.3.3
[5] tidyselect_1.2.1 Rcpp_1.1.0 git2r_0.36.2 callr_3.7.6
[9] later_1.4.2 jquerylib_0.1.4 readxl_1.4.5 yaml_2.3.10
[13] fastmap_1.2.0 here_1.0.1 R6_2.6.1 generics_0.1.4
[17] knitr_1.50 tibble_3.3.0 rprojroot_2.1.0 bslib_0.9.0
[21] pillar_1.11.0 rlang_1.1.6 utf8_1.2.6 cachem_1.1.0
[25] stringi_1.8.7 httpuv_1.6.16 xfun_0.52 getPass_0.2-4
[29] fs_1.6.6 sass_0.4.10 cli_3.6.5 withr_3.0.2
[33] magrittr_2.0.3 ps_1.9.1 digest_0.6.37 processx_3.8.6
[37] rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.4
[41] glue_1.8.0 cellranger_1.1.0 whisker_0.4.1 rmarkdown_2.29
[45] httr_1.4.7 tools_4.3.1 pkgconfig_2.0.3 htmltools_0.5.8.1