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genomics_ancest_disease_dispar/
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Ignored: data/gbd/ihme_gbd_2021_global_disease_burden_rate_all_ages.csv
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 94b4ca3 | IJbeasley | 2026-01-12 | Update initial trait categorisation |
| html | bf2e581 | IJbeasley | 2026-01-05 | Build site. |
| Rmd | 11f714c | IJbeasley | 2026-01-05 | Update filtering of GWAS traits |
| html | 33cdcc6 | IJbeasley | 2026-01-03 | Build site. |
| Rmd | 6160eba | IJbeasley | 2026-01-03 | Update fixing of trait mapping |
| html | 9d0fd3f | IJbeasley | 2025-10-09 | Build site. |
| Rmd | 8e94754 | IJbeasley | 2025-10-09 | Integrating gwas traits with ICD10 |
library(dplyr)
library(stringr)
library(purrr)
library(tidyr)
library(data.table)gbd_2019 <- readxl::read_xlsx(here::here("data/icd/lancet_conditions_icd10.xlsx"))
gbd_2019 =
gbd_2019 |>
select(cause = mapped_gbd_term,
icd10 = icd_10_non_fatal,
cause_hierarchy_level = gbd_level)
gbd_2019 =
gbd_2019 |>
filter(!is.na(icd10))# expand multiple ICD codes into rows
gbd_2019 =
gbd_2019 |>
mutate(icd10 = str_split(icd10, ",\\s*")) |>
tidyr::unnest(icd10)
# separate ICD code ranges into start and end codes
gbd_2019 =
gbd_2019 |>
tidyr::separate_wider_delim(col = "icd10",
delim = "-",
names = c("start_icd10_code", "end_icd10_code"),
too_few = "align_end"
)
# if only one ICD code (i.e. no range), set start and end to be the same
gbd_2019 =
gbd_2019 |>
mutate(start_icd10_code =
ifelse(is.na(start_icd10_code),
end_icd10_code,
start_icd10_code)
)
gbd_2019 =
gbd_2019 |>
distinct()
head(gbd_2019)# A tibble: 6 × 4
cause start_icd10_code end_icd10_code cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 Ischemic heart disease I20 I21.6 3
2 Ischemic heart disease I21.9 I25.9 3
3 Ischemic heart disease Z82.4 Z82.49 3
4 Ischemic stroke G45 G46.8 4
5 Ischemic stroke I63 I63.9 4
6 Ischemic stroke I65 I66.9 4gbd_2019 |>
filter(start_icd10_code == end_icd10_code &
!grepl(".", start_icd10_code, fixed = TRUE)
) |>
arrange(start_icd10_code) |>
head()# A tibble: 5 × 4
cause start_icd10_code end_icd10_code cause_hierarchy_level
<chr> <chr> <chr> <dbl>
1 Diarrhoeal diseases A09 A09 3
2 Lower respiratory infec… A70 A70 3
3 Tracheal, bronchus, and… C33 C33 3
4 Maternal disorders N96 N96 3
5 HIV/AIDS Z21 Z21 3gbd_2019 =
gbd_2019 |>
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
)
)# replace O9A513 with O99.9
gbd_2019 =
gbd_2019 |>
mutate(end_icd10_code =
ifelse(end_icd10_code == "O9A513",
"O99.9",
end_icd10_code)
)
# replace Z3A49 with Z39.2
gbd_2019 =
gbd_2019 |>
mutate(end_icd10_code =
ifelse(end_icd10_code == "Z3A49",
"Z39.2",
end_icd10_code)
)
# check start_icd10_code and end_icd10_code start with the same letter
# if not, need to fix these rows
gbd_2019 |>
filter(str_extract(start_icd10_code, "^[A-Z]") !=
str_extract(end_icd10_code, "^[A-Z]")
)# A tibble: 0 × 4
# ℹ 4 variables: cause <chr>, start_icd10_code <chr>, end_icd10_code <chr>,
# cause_hierarchy_level <dbl>gbd_2019 =
gbd_2019 |>
mutate(icd10_code_letter = str_extract(start_icd10_code, "^[A-Z]"))
gbd_2019 =
gbd_2019 |>
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]")
)
)disease_mapping <- data.table::fread(
here::here("output/icd_map/gwas_disease_to_icd10_mapping.csv")
)
disease_mapping =
disease_mapping |>
mutate(icd10_code = sub("^([^-]+)-\\1$", "\\1", icd10_code))
disease_mapping =
disease_mapping |>
mutate(icd10_code_letter = str_extract(icd10_code, "^[A-Z]")) |>
mutate(icd10_code_num = as.numeric(str_remove(icd10_code, "^[A-Z]")))
disease_mapping |>
filter(is.na(icd10_code_num)) |>
nrow()[1] 133disease_mapping =
disease_mapping |>
filter(!is.na(icd10_code_num))
disease_mapping =
disease_mapping |>
mutate(icd10_code_num = as.numeric(icd10_code_num))disease_mapping_with_cause <- disease_mapping |>
rowwise() |>
mutate(
cause =
list(
gbd_2019$cause[
which(icd10_code_num >= gbd_2019$start_icd10_code_num &
icd10_code_num <= gbd_2019$end_icd10_code_num &
icd10_code_letter == gbd_2019$icd10_code_letter)
]),
cause_hierarchy_level =
list(
gbd_2019$cause_hierarchy_level[
which(icd10_code_num >= gbd_2019$start_icd10_code_num &
icd10_code_num <= gbd_2019$end_icd10_code_num &
icd10_code_letter == gbd_2019$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 = ~str_flatten(unique(.x), collapse = ", ", na.rm = TRUE)
# )# 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()
#
# neoplasms = 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("l3_cause", "l4_cause"))
disease_mapping_final = disease_mapping_with_cause
# left_join(
# disease_mapping_orig,
# disease_mapping_with_cause,
# 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
# )
# disease_mapping_final =
# disease_mapping_final |>
# group_by(STUDY_ACCESSION, collected_all_disease_terms) |>
# summarise(
# PUBMED_ID = paste0(unique(PUBMED_ID), collapse = "; "),
# COHORT = paste0(unique(COHORT), collapse = "; "),
# icd10_code = paste0(unique(icd10_code), collapse = "; "),
# icd10_description = paste0(unique(icd10_description), collapse = "; "),
# # l2_cause = str_flatten(unique(str_split(l2_cause, ", ")), # add separation step here ...
# # collapse = "; ", na.rm = TRUE),
# l3_cause = str_flatten(unique(str_split(l3_cause, ", ")),
# collapse = "; ", na.rm = TRUE),
# l4_cause = str_flatten(unique(str_split(l4_cause, ", ")),
# collapse = "; ", na.rm = TRUE)
# )
data.table::fwrite(
# disease_mapping_final,
disease_mapping_with_cause,
here::here("output/icd_map/gwas_study_gbd_causes.csv")
)
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.7.3
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_1.6.0
[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 BiocManager_1.30.26
[4] renv_1.0.3 promises_1.3.3 tidyselect_1.2.1
[7] Rcpp_1.1.0 git2r_0.36.2 callr_3.7.6
[10] later_1.4.4 jquerylib_0.1.4 readxl_1.4.5
[13] yaml_2.3.10 fastmap_1.2.0 here_1.0.1
[16] R6_2.6.1 generics_0.1.4 knitr_1.50
[19] tibble_3.3.0 rprojroot_2.1.0 bslib_0.9.0
[22] pillar_1.11.1 rlang_1.1.6 utf8_1.2.6
[25] cachem_1.1.0 stringi_1.8.7 httpuv_1.6.16
[28] xfun_0.55 getPass_0.2-4 fs_1.6.6
[31] sass_0.4.10 cli_3.6.5 withr_3.0.2
[34] magrittr_2.0.4 ps_1.9.1 digest_0.6.37
[37] processx_3.8.6 rstudioapi_0.17.1 lifecycle_1.0.4
[40] vctrs_0.6.5 evaluate_1.0.5 glue_1.8.0
[43] cellranger_1.1.0 whisker_0.4.1 rmarkdown_2.30
[46] httr_1.4.7 tools_4.3.1 pkgconfig_2.0.3
[49] htmltools_0.5.8.1