Disease investigated by ancestry
Last updated: 2025-08-23
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 48dd80a | IJbeasley | 2025-08-23 | Update proportion ancestry investigated by disease |
| html | 42e854b | IJbeasley | 2025-08-21 | Build site. |
| Rmd | fa9a4da | IJbeasley | 2025-08-21 | Starting test of relationship between proportion european and total sample size |
| html | f5087d2 | IJBeasley | 2025-07-30 | Build site. |
| Rmd | 72172e3 | IJBeasley | 2025-07-30 | Split page into disease by ancest |
| html | 2fd5755 | Isobel Beasley | 2022-02-16 | Build site. |
| Rmd | 7347b5d | Isobel Beasley | 2022-02-16 | Add initial plotting using gwas cat stats |
1 Set up
library(dplyr)
library(data.table)
library(ggplot2)
source(here::here("code/custom_plotting.R"))1.1 Load data
# gwas_study_info = data.table::fread("data/gwas_catalog/gwas-catalog-v1.0.3-studies-r2022-02-02.tsv",
# sep = "\t",
# quote = "")
gwas_study_info <- fread(here::here("output/gwas_study_info_trait_corrected.csv"))
gwas_ancest_info <- fread(here::here("data/gwas_catalog/gwas-catalog-v1.0.3.1-ancestries-r2025-07-21.tsv"),
sep = "\t", quote = "")1.2 Basic data cleaning
# fixing the column names
gwas_study_info = gwas_study_info |>
dplyr::rename_with(~ gsub(" ", "_", .x))
gwas_ancest_info = gwas_ancest_info |>
dplyr::rename_with(~ gsub(" ", "_", .x))
# making sure arranged by DATE (oldest at the top)
gwas_ancest_info = gwas_ancest_info |>
dplyr::arrange(DATE)
gwas_study_info = gwas_study_info |>
dplyr::arrange(DATE)1.3 NA for number of individuals
# 44 studies / 44 rows
gwas_ancest_info |>
dplyr::filter(is.na(NUMBER_OF_INDIVIDUALS)) |>
nrow()[1] 44# from only 24 gwas papers
gwas_ancest_info |>
dplyr::filter(is.na(NUMBER_OF_INDIVIDUALS)) |>
select(PUBMED_ID) |>
distinct() |>
nrow()[1] 24gwas_ancest_info |>
dplyr::filter(PUBMED_ID == 28679651) |>
select(INITIAL_SAMPLE_DESCRIPTION,
REPLICATION_SAMPLE_DESCRIPTION,
BROAD_ANCESTRAL_CATEGORY) |>
distinct() INITIAL_SAMPLE_DESCRIPTION REPLICATION_SAMPLE_DESCRIPTION
<char> <char>
1: 404 cases, controls <NA>
2: 194 cases, controls <NA>
3: 426 cases, controls <NA>
4: 85 cases, controls <NA>
5: 535 cases, controls <NA>
6: 345 cases, controls <NA>
7: 835 cases, controls <NA>
8: 844 cases, controls <NA>
9: 447 cases, controls <NA>
BROAD_ANCESTRAL_CATEGORY
<char>
1: NR
2: NR
3: NR
4: NR
5: NR
6: NR
7: NR
8: NR
9: NR# 28679651 - problem seems to be that number of controls per disease not specifically listed
# see https://pubmed.ncbi.nlm.nih.gov/28679651/
# although paper they cite as where data comes from (https://www.nature.com/articles/leu2016387#Tab1)
# discloses: 1229 AL amyloidosis patients from Germany, UK and Italy, and 7526 healthy local controls1.3.1 Filter out NA number of individuals
gwas_ancest_info =
gwas_ancest_info |>
dplyr::filter(!is.na(NUMBER_OF_INDIVIDUALS))2 Top disease traits
The traits with the most number of pubmed ids are:
n_studies_trait = gwas_study_info |>
dplyr::select(MAPPED_TRAIT, MAPPED_TRAIT_URI, PUBMED_ID) |>
dplyr::distinct() |>
dplyr::group_by(MAPPED_TRAIT, MAPPED_TRAIT_URI) |>
dplyr::summarise(n_studies = dplyr::n()) |>
dplyr::arrange(desc(n_studies))`summarise()` has grouped output by 'MAPPED_TRAIT'. You can override using the
`.groups` argument.head(n_studies_trait)# A tibble: 6 × 3
# Groups: MAPPED_TRAIT [6]
MAPPED_TRAIT MAPPED_TRAIT_URI n_studies
<chr> <chr> <int>
1 high density lipoprotein cholesterol measurement http://www.ebi.ac.… 133
2 body mass index http://www.ebi.ac.… 132
3 triglyceride measurement http://www.ebi.ac.… 128
4 low density lipoprotein cholesterol measurement http://www.ebi.ac.… 118
5 type 2 diabetes mellitus http://purl.obolib… 117
6 total cholesterol measurement http://www.ebi.ac.… 1023 Make ancestry groups
Here we make the column ‘ancestry_group’ in the gwas_study_info datasets, ‘ancestry_group’ defines the broad ancestry group (like in Martin et al. 2019, European, Greater Middle Eastern etc.) that each group of individuals belongs to.
grouped_ancest = character()
for(study_ancest in unique(gwas_ancest_info$BROAD_ANCESTRAL_CATEGORY)){
grouped_ancest[study_ancest] = group_ancestry_fn(study_ancest)
}
grouped_ancest_map = tibble(
BROAD_ANCESTRAL_CATEGORY = names(grouped_ancest),
ancestry_group = unname(grouped_ancest)
)
print("See some example mapping between BROAD_ANCESTRAL_CATEGORY and ancestry_group")[1] "See some example mapping between BROAD_ANCESTRAL_CATEGORY and ancestry_group"print(dplyr::slice_sample(grouped_ancest_map, n = 5))# A tibble: 5 × 2
BROAD_ANCESTRAL_CATEGORY ancestry_group
<chr> <chr>
1 European, Central Asian Multiple
2 European, African American or Afro-Caribbean, NR, Asian unspec… Multiple
3 South Asian, European, East Asian, African American or Afro-Ca… Multiple
4 Hispanic or Latin American Hispanic/Lati…
5 European, African American or Afro-Caribbean, Hispanic or Lati… Multiple gwas_ancest_info = dplyr::left_join(
gwas_ancest_info,
grouped_ancest_map,
by = "BROAD_ANCESTRAL_CATEGORY")
gwas_ancest_info = gwas_ancest_info |>
dplyr::mutate(ancestry_group = factor(ancestry_group, levels = ancestry_levels))3.1 Check: How many individuals in each ancestry group?
Expecting highest to be in European
total_gwas_n =
gwas_ancest_info$NUMBER_OF_INDIVIDUALS |> sum(na.rm = T)
print("Total numbers (in millions) per ancestry group")[1] "Total numbers (in millions) per ancestry group"gwas_ancest_info |>
dplyr::group_by(ancestry_group) |>
dplyr::summarise(n = sum(NUMBER_OF_INDIVIDUALS, na.rm = TRUE)/10^6) |>
dplyr::mutate(prop = n* 10^6/total_gwas_n) |>
dplyr::arrange(desc(n)) # A tibble: 9 × 3
ancestry_group n prop
<fct> <dbl> <dbl>
1 European 9122. 0.880
2 African 408. 0.0393
3 Asian 313. 0.0302
4 Multiple 186. 0.0180
5 Hispanic/Latin American 179. 0.0172
6 Not reported 149. 0.0144
7 Middle Eastern 4.47 0.000431
8 Other 2.01 0.000194
9 Oceanic 0.134 0.00001303.2 Plot number of individuals per ancestry group over time
gwas_ancest_info |>
dplyr::group_by(ancestry_group) |>
dplyr::mutate(ancest_cumsum = cumsum(as.numeric(NUMBER_OF_INDIVIDUALS))) |>
add_final_totals() |>
# select(DATE, ancest_cumsum, ancestry_group, NUMBER_OF_INDIVIDUALS) |>
ggplot(aes(x=DATE,
y=ancest_cumsum/(10^6),
fill = ancestry_group
)
) +
geom_area(position = 'stack') +
scale_x_date(date_labels = '%Y',
date_breaks = "2 years"
) +
theme_classic() +
labs(x = "Year",
y = "Individuals in GWAS catalog (millions)") +
scale_fill_manual(values = ancestry_colors, name='Ancestry group') 
4 Plot number of individuals per ancestry group for a single trait
4.1 Set up - add trait information to ancestry information
gwas_ancest_info =
left_join(
gwas_ancest_info,
gwas_study_info |> select(STUDY_ACCESSION,
COHORT,
MAPPED_TRAIT),
by = "STUDY_ACCESSION"
)4.2 Select trait
gwas_ancest_info_plot =
gwas_ancest_info %>%
filter(!is.na(NUMBER_OF_INDIVIDUALS)) |>
filter(MAPPED_TRAIT == 'high density lipoprotein cholesterol measurement')
print("Total numbers (in millions) per ancestry group - for high density lipoprotein cholesterol measurement")[1] "Total numbers (in millions) per ancestry group - for high density lipoprotein cholesterol measurement"gwas_ancest_info_plot %>%
group_by(ancestry_group) %>%
summarise(n = sum(NUMBER_OF_INDIVIDUALS, na.rm = TRUE)/10^6)# A tibble: 9 × 2
ancestry_group n
<fct> <dbl>
1 European 21.0
2 Asian 3.55
3 African 1.45
4 Hispanic/Latin American 0.771
5 Middle Eastern 0.0172
6 Oceanic 0.00965
7 Other 0.00751
8 Multiple 5.21
9 Not reported 0.517 4.3 Plot
gwas_ancest_info_plot =
gwas_ancest_info_plot %>%
group_by(ancestry_group) %>%
mutate(ancest_cumsum = cumsum(as.numeric(NUMBER_OF_INDIVIDUALS)))
gwas_ancest_info_plot = add_final_totals(gwas_ancest_info_plot)
gwas_ancest_info_plot |>
ggplot(aes(x=DATE, y=ancest_cumsum/(10^6), fill = ancestry_group)) +
geom_area(position = 'stack') +
scale_x_date(date_labels = '%Y', date_breaks = "1 years") +
theme_classic() +
labs(x = "Year", y = "Individuals in GWAS catalog (millions)") +
scale_fill_manual(values = ancestry_colors, name='Ancestry group') 
5 Proportion European per trait
5.1 Proportion European overall
euro_n = gwas_ancest_info |>
filter(ancestry_group == "European") |>
pull(NUMBER_OF_INDIVIDUALS) |>
sum(na.rm = T)
total_n = gwas_ancest_info |>
pull(NUMBER_OF_INDIVIDUALS) |>
sum(na.rm = T)
100 * euro_n / total_n[1] 88.02662prop_euro = vector()
total_n_vec = vector()5.2 Proportion European per trait
for(trait in n_studies_trait$MAPPED_TRAIT[1:1000]){
euro_n = gwas_ancest_info |>
filter(ancestry_group == "European") |>
filter(MAPPED_TRAIT == trait) |>
pull(NUMBER_OF_INDIVIDUALS) |>
sum(na.rm = T)
total_n = gwas_ancest_info |>
filter(MAPPED_TRAIT == trait) |>
pull(NUMBER_OF_INDIVIDUALS) |>
sum(na.rm = T)
prop_euro[trait] = 100 * euro_n / total_n
total_n_vec[trait] = total_n
}
prop_euro_df = data.frame(prop_euro = prop_euro,
trait = names(prop_euro),
total_n = total_n_vec)
prop_euro_df = left_join(prop_euro_df,
n_studies_trait |> rename(trait = MAPPED_TRAIT),
by = "trait")
prop_euro_df$prop_euro |> summary() Min. 1st Qu. Median Mean 3rd Qu. Max.
12.07 83.33 91.64 89.13 98.52 100.00 prop_euro_df |> slice_min(prop_euro, n = 5) prop_euro trait total_n
1 12.06720 adolescent idiopathic scoliosis 386693
2 16.09211 response to antipsychotic drug 14678
3 17.40991 dilated cardiomyopathy 7229158
4 20.35347 axial length measurement 65861
5 20.70827 Cleft palate 45209
MAPPED_TRAIT_URI n_studies
1 http://www.ebi.ac.uk/efo/EFO_0005423 10
2 http://purl.obolibrary.org/obo/GO_0097332 12
3 http://www.ebi.ac.uk/efo/EFO_0000407 8
4 http://www.ebi.ac.uk/efo/EFO_0005318 7
5 http://purl.obolibrary.org/obo/HP_0000175 7prop_euro_df |> slice_max(prop_euro, n =5 ) prop_euro trait total_n
1 100.00000 testicular carcinoma 1796522
2 100.00000 self reported educational attainment 4908190
3 99.99760 health study participation 5873120
4 99.99201 hair color 1063503
5 99.99147 base metabolic rate measurement 3413130
MAPPED_TRAIT_URI n_studies
1 http://www.ebi.ac.uk/efo/EFO_0005088 11
2 http://www.ebi.ac.uk/efo/EFO_0004784 9
3 http://www.ebi.ac.uk/efo/EFO_0010130 8
4 http://www.ebi.ac.uk/efo/EFO_0003924 10
5 http://www.ebi.ac.uk/efo/EFO_0007777 8prop_euro_df |> slice_max(total_n, n = 5) prop_euro trait total_n
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491 http://www.ebi.ac.uk/efo/EFO_0803062 1
492 http://www.ebi.ac.uk/efo/EFO_0803070 1
493 http://www.ebi.ac.uk/efo/EFO_0803071 1
494 http://www.ebi.ac.uk/efo/EFO_0803072 1
495 http://www.ebi.ac.uk/efo/EFO_0803077 1
496 http://www.ebi.ac.uk/efo/EFO_0803078 1
497 http://www.ebi.ac.uk/efo/EFO_0803082 1
498 http://www.ebi.ac.uk/efo/EFO_0803083 1
499 http://www.ebi.ac.uk/efo/EFO_0803088 1
500 http://www.ebi.ac.uk/efo/EFO_0803091 1
501 http://www.ebi.ac.uk/efo/EFO_0803096 1
502 http://www.ebi.ac.uk/efo/EFO_0803097 1
503 http://www.ebi.ac.uk/efo/EFO_0803106 1
504 http://www.ebi.ac.uk/efo/EFO_0803107 1
505 http://www.ebi.ac.uk/efo/EFO_0803117 1
506 http://www.ebi.ac.uk/efo/EFO_0803119 1
507 http://www.ebi.ac.uk/efo/EFO_0803122 1
508 http://www.ebi.ac.uk/efo/EFO_0803124 1
509 http://www.ebi.ac.uk/efo/EFO_0803127 1
510 http://www.ebi.ac.uk/efo/EFO_0803128 1
511 http://www.ebi.ac.uk/efo/EFO_0803130 1
512 http://www.ebi.ac.uk/efo/EFO_0803136 1
513 http://www.ebi.ac.uk/efo/EFO_0803143 1
514 http://www.ebi.ac.uk/efo/EFO_0803153 1
515 http://www.ebi.ac.uk/efo/EFO_0803154 1
516 http://www.ebi.ac.uk/efo/EFO_0803162 1
517 http://www.ebi.ac.uk/efo/EFO_0803163 1
518 http://www.ebi.ac.uk/efo/EFO_0803169 1
519 http://www.ebi.ac.uk/efo/EFO_0803170 1
520 http://www.ebi.ac.uk/efo/EFO_0803176 1
521 http://www.ebi.ac.uk/efo/EFO_0803178 1
522 http://www.ebi.ac.uk/efo/EFO_0803179 1
523 http://www.ebi.ac.uk/efo/EFO_0803183 1
524 http://www.ebi.ac.uk/efo/EFO_0803184 1
525 http://www.ebi.ac.uk/efo/EFO_0803185 1
526 http://www.ebi.ac.uk/efo/EFO_0803186 1
527 http://www.ebi.ac.uk/efo/EFO_0803188 1
528 http://www.ebi.ac.uk/efo/EFO_0803189 1
529 http://www.ebi.ac.uk/efo/EFO_0803194 1
530 http://www.ebi.ac.uk/efo/EFO_0803195 1
531 http://www.ebi.ac.uk/efo/EFO_0803198 1
532 http://www.ebi.ac.uk/efo/EFO_0803204 1
533 http://www.ebi.ac.uk/efo/EFO_0803206 1
534 http://www.ebi.ac.uk/efo/EFO_0803212 1
535 http://www.ebi.ac.uk/efo/EFO_0803213 1
536 http://www.ebi.ac.uk/efo/EFO_0803218 1
537 http://www.ebi.ac.uk/efo/EFO_0803229 1
538 http://www.ebi.ac.uk/efo/EFO_0803231 1
539 http://www.ebi.ac.uk/efo/EFO_0803232 1
540 http://www.ebi.ac.uk/efo/EFO_0803237 1
541 http://www.ebi.ac.uk/efo/EFO_0803241 1
542 http://www.ebi.ac.uk/efo/EFO_0803243 1
543 http://www.ebi.ac.uk/efo/EFO_0803244 1
544 http://www.ebi.ac.uk/efo/EFO_0803249 1
545 http://www.ebi.ac.uk/efo/EFO_0803257 1
546 http://www.ebi.ac.uk/efo/EFO_0803265 1
547 http://www.ebi.ac.uk/efo/EFO_0803266 1
548 http://www.ebi.ac.uk/efo/EFO_0803267 1
549 http://www.ebi.ac.uk/efo/EFO_0803278 1
550 http://www.ebi.ac.uk/efo/EFO_0803279 1
551 http://www.ebi.ac.uk/efo/EFO_0803281 1
552 http://www.ebi.ac.uk/efo/EFO_0803285 1
553 http://www.ebi.ac.uk/efo/EFO_0803286 1
554 http://www.ebi.ac.uk/efo/EFO_0803289 1
555 http://www.ebi.ac.uk/efo/EFO_0803292 1
556 http://www.ebi.ac.uk/efo/EFO_0803296 1
557 http://www.ebi.ac.uk/efo/EFO_0803298 1
558 http://www.ebi.ac.uk/efo/EFO_0803303 1
559 http://www.ebi.ac.uk/efo/EFO_0803307 1
560 http://www.ebi.ac.uk/efo/EFO_0803310 1
561 http://www.ebi.ac.uk/efo/EFO_0803311 1
562 http://www.ebi.ac.uk/efo/EFO_0803315 1cor(prop_euro_df$prop_euro, prop_euro_df$total_n,
method = "spearman")[1] 0.6859372prop_euro_df |>
ggplot(aes(x = total_n, y = prop_euro)) +
geom_point() +
theme_bw() +
labs(x = "Total number of individuals (studied for this trait)",
y = "Proportion of European ancestry idividuals (studied for this trait)")
prop_euro_df |>
ggplot(aes(x = total_n, y = n_studies)) +
geom_point() +
theme_bw() +
labs(x = "Total number of individuals (studied for this trait)",
y = "Total number of unique PUBMED IDs for this trait")
prop_euro_df |>
ggplot(aes(x = n_studies, y = prop_euro)) +
geom_point() +
theme_bw() +
labs(x = "Total number of unique PUBMED IDs for this trait",
y = "Proportion of European ancestry idividuals (studied for this trait)")
6 Disease statistics CDC
# following steps from https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-019-0379-x/MediaObjects/41588_2019_379_MOESM1_ESM.pdf
# wonder cdc
# https://wonder.cdc.gov/wonder/help/QuickStart.html#
# https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=2A56E973A3DF13BC5DFDD9C43725
# https://cran.r-project.org/web/packages/whomds/index.html
# whomds: Calculate Results from WHO Model Disability Survey Data
cdc_stats = data.table::fread("data/cdc/Underlying Cause of Death, 1999-2020.txt",
drop = c("Notes", "Race Code", "Cause of death Code")) %>%
filter(!if_any(everything(), ~.x == ""))
cdc_stats %>%
group_by(Race) %>%
slice_max(Deaths,n=10)
# wonder cdc
# https://wonder.cdc.gov/wonder/help/QuickStart.html#
# https://wonder.cdc.gov/controller/datarequest/D76;jsessionid=2A56E973A3DF13BC5DFDD9C43725
# https://cran.r-project.org/web/packages/whomds/index.html
# whomds: Calculate Results from WHO Model Disability Survey Data
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] ggplot2_3.5.2 data.table_1.17.8 dplyr_1.1.4 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 stringr_1.5.1
[9] git2r_0.36.2 callr_3.7.6 later_1.4.2 jquerylib_0.1.4
[13] scales_1.4.0 yaml_2.3.10 fastmap_1.2.0 here_1.0.1
[17] R6_2.6.1 labeling_0.4.3 generics_0.1.4 knitr_1.50
[21] tibble_3.3.0 rprojroot_2.1.0 RColorBrewer_1.1-3 bslib_0.9.0
[25] pillar_1.11.0 rlang_1.1.6 utf8_1.2.6 cachem_1.1.0
[29] stringi_1.8.7 httpuv_1.6.16 xfun_0.52 getPass_0.2-4
[33] fs_1.6.6 sass_0.4.10 cli_3.6.5 withr_3.0.2
[37] magrittr_2.0.3 ps_1.9.1 grid_4.3.1 digest_0.6.37
[41] processx_3.8.6 rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5
[45] evaluate_1.0.4 glue_1.8.0 farver_2.1.2 whisker_0.4.1
[49] rmarkdown_2.29 httr_1.4.7 tools_4.3.1 pkgconfig_2.0.3
[53] htmltools_0.5.8.1