Collapse / filtering traits

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Last updated: 2025-08-23

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.

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Repository version: 9a9803b

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The results in this page were generated with repository version 9a9803b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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Ignored files:
    Ignored:    .Rproj.user/
    Ignored:    data/gwas_catalog/
    Ignored:    output/gwas_study_info_cohort_corrected.csv
    Ignored:    output/gwas_study_info_trait_corrected.csv

Untracked files:
    Untracked:  .DS_Store
    Untracked:  code/custom_plotting.R
    Untracked:  data/.DS_Store
    Untracked:  gwas_time.pdf
    Untracked:  renv/

Unstaged changes:
    Modified:   .Rprofile
    Modified:   analysis/disease_inves_by_ancest.Rmd
    Modified:   code/collapse_diseases.R

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.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/collapse_traits.Rmd) and HTML (docs/collapse_traits.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	9a9803b	IJbeasley	2025-08-23	Update correcting traits
html	605013e	IJbeasley	2025-08-21	Build site.
Rmd	d4ec61e	IJbeasley	2025-08-21	Adding investigation harmonizing mapped traits

---

1 Set up

knitr::opts_chunk$set(echo = TRUE, 
                      message = FALSE, 
                      warning = FALSE
                      )

library(data.table)
library(dplyr)
library(ggplot2)
library(stringr)

1.1 Load data

gwas_study_info <- fread(here::here("output/gwas_study_info_cohort_corrected.csv"))

2 Number of studies per mapped trait

# number of studies per mapped trait
n_studies_trait = gwas_study_info |>
  dplyr::group_by(MAPPED_TRAIT, MAPPED_TRAIT_URI) |>
  dplyr::summarise(n_studies = dplyr::n()) |>
  dplyr::arrange(desc(n_studies))

n_studies_trait |>
  head()

# A tibble: 6 × 3
# Groups:   MAPPED_TRAIT [6]
  MAPPED_TRAIT               MAPPED_TRAIT_URI                          n_studies
  <chr>                      <chr>                                         <int>
1 neuroimaging measurement   http://www.ebi.ac.uk/efo/EFO_0004346           9773
2 protein measurement        http://www.ebi.ac.uk/efo/EFO_0004747           8748
3 blood protein amount       http://purl.obolibrary.org/obo/OBA_VT000…      5336
4 metabolite measurement     http://www.ebi.ac.uk/efo/EFO_0004725           4559
5 blood metabolite level     http://purl.obolibrary.org/obo/OBA_20500…      2738
6 gut microbiome measurement http://www.ebi.ac.uk/efo/EFO_0007874           2363

n_studies_trait |>
  head() |>
  ggplot(aes(y = reorder(MAPPED_TRAIT, 
                         n_studies), 
             x = n_studies)
         ) + 
  geom_col() + 
  theme_bw() + 
  labs(y = "Trait", x = "Number of studies")

3 Unmapped / badly mapped traits:

# number of traits only observed once in the catalog 
# likely badly mapped traits: 

n_studies_trait |>
  dplyr::filter(n_studies == 1) |>
  nrow()

[1] 11592

unique_mapped_traits = n_studies_trait |>
  dplyr::filter(n_studies == 1) |>
  dplyr::pull(MAPPED_TRAIT)

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits) |>
  group_by(PUBMED_ID) |>
  summarise(n_unmapped_per_study = n()) |>
  arrange(desc(n_unmapped_per_study)) |>
  head(n = 10)

# A tibble: 10 × 2
   PUBMED_ID n_unmapped_per_study
       <int>                <int>
 1  35870639                 3411
 2  38412862                 2821
 3  39789286                  612
 4  29875488                  388
 5  39528826                  384
 6  33634981                  197
 7  24816252                  173
 8  36482414                  121
 9  26068415                   99
10  37253714                   95

3.1 PUBMED_ID 35870639

pubmed_id = 35870639
# this study tests 
# 6,790 proteins or protein complexes
print(paste0("How many studies for pubmed id: ", pubmed_id))

[1] "How many studies for pubmed id: 35870639"

gwas_study_info |>
  filter(PUBMED_ID == pubmed_id) |>
  nrow()

[1] 6790

# not all are unmapped 
print(paste0("How many studies for pubmed id: ", pubmed_id, 
        "have unmapped/not well mapped traits"
        )
        )

[1] "How many studies for pubmed id: 35870639have unmapped/not well mapped traits"

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  nrow()

[1] 3411

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  pull(MAPPED_TRAIT)

print("Of these unmapped studies, how many contain phrase in blood")

[1] "Of these unmapped studies, how many contain phrase in blood"

grepl("in blood serum", not_well_mapped_traits) |> sum()

[1] 3408

print("level of or amount of")

[1] "level of or amount of"

grepl("level of|amount of", not_well_mapped_traits) |> sum()

[1] 3408

# all unmapped 
# map to blood protein amount
# serum proteome - i.e. blood proteome 

gwas_study_info  = 
gwas_study_info |>
  dplyr::rows_update(tibble(PUBMED_ID = pubmed_id, 
                            MAPPED_TRAIT = "blood protein amount",
                            MAPPED_TRAIT_URI = "http://purl.obolibrary.org/obo/OBA_VT0005416"),
                     unmatched = "ignore")

3.2 PUBMED_ID: 39789286

pubmed_id = 39789286

print(paste0("How many studies for pubmed id: ", pubmed_id))

[1] "How many studies for pubmed id: 39789286"

gwas_study_info |>
  filter(PUBMED_ID == pubmed_id) |>
  nrow()

[1] 3049

# not all are unmapped 
print(paste0("How many studies for pubmed id: ", pubmed_id, "have unmapped/not well mapped traits"))

[1] "How many studies for pubmed id: 39789286have unmapped/not well mapped traits"

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  nrow()

[1] 612

# 2,821 ratios between protein levels
# i.e. each study is a association study between ratio between two proteins

# ? put under blood protein amount (even though it is a ratio between amounts not an amount...)
gwas_study_info  = 
gwas_study_info |>
  dplyr::rows_update(tibble(PUBMED_ID = pubmed_id, 
                            MAPPED_TRAIT = "blood protein amount",
                            MAPPED_TRAIT_URI = "http://purl.obolibrary.org/obo/OBA_VT0005416"),
                     unmatched = "ignore")

3.3 PUBMED_ID: 39789286

pubmed_id = 39789286

print(paste0("How many studies for pubmed id: ", pubmed_id))

[1] "How many studies for pubmed id: 39789286"

gwas_study_info |>
  filter(PUBMED_ID == pubmed_id) |>
  nrow()

[1] 3049

# not all are unmapped 
print(paste0("How many studies for pubmed id: ", pubmed_id, "have unmapped/not well mapped traits"))

[1] "How many studies for pubmed id: 39789286have unmapped/not well mapped traits"

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  nrow()

[1] 0

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  pull(MAPPED_TRAIT)

print("Of these unmapped studies, how many contain phrase in blood")

[1] "Of these unmapped studies, how many contain phrase in blood"

grepl("in blood", not_well_mapped_traits) |> sum()

[1] 0

print("level of or amount of")

[1] "level of or amount of"

grepl("level of|amount of", not_well_mapped_traits) |> sum()

[1] 0

print("All unmapped studies are measurements in blood")

[1] "All unmapped studies are measurements in blood"

gwas_study_info = gwas_study_info |>
  rowwise() |>
  mutate(MAPPED_TRAIT = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                               "blood protein amount",
                               MAPPED_TRAIT)
         ) |>
    mutate(MAPPED_TRAIT_URI = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                                       "http://purl.obolibrary.org/obo/OBA_VT0005416",
                                     MAPPED_TRAIT_URI)
           ) |>
  ungroup()

3.4 PUBMED_ID: 29875488

pubmed_id = 29875488

print(paste0("How many studies for pubmed id: ", pubmed_id))

[1] "How many studies for pubmed id: 29875488"

gwas_study_info |>
  filter(PUBMED_ID == pubmed_id) |>
  nrow()

[1] 3283

# not all are unmapped 
print(paste0("How many studies for pubmed id: ", pubmed_id, "have unmapped/not well mapped traits"))

[1] "How many studies for pubmed id: 29875488have unmapped/not well mapped traits"

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  nrow()

[1] 388

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  pull(MAPPED_TRAIT)

print("Of these unmapped studies, how many contain phrase in blood")

[1] "Of these unmapped studies, how many contain phrase in blood"

grepl("in blood", not_well_mapped_traits) |> sum()

[1] 0

print("level of or amount of")

[1] "level of or amount of"

grepl("level of|amount of|measurement", not_well_mapped_traits) |> sum()

[1] 388

grepl("measurement", not_well_mapped_traits) |> sum()

[1] 388

# study title is: Genomic atlas of the human plasma proteome.

# assume therefore, all these values are protein blood measurements

gwas_study_info = gwas_study_info |>
  rowwise() |>
  mutate(MAPPED_TRAIT = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                               "blood protein amount",
                               MAPPED_TRAIT)
         ) |>
    mutate(MAPPED_TRAIT_URI = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                               "http://purl.obolibrary.org/obo/OBA_VT0005416",
                               MAPPED_TRAIT_URI)
           ) |>
  ungroup()

# PUBMED_ID: 39528826

# suspect: 
# metabolite measurement    http://www.ebi.ac.uk/efo/EFO_0004725

# Title:
# Publication: Genetic architecture of cerebrospinal fluid and brain metabolite levels and the genetic colocalization of metabolites with human traits.

# PUBMED+ID: 33634981

# suspect: 
# metabolite measurement    http://www.ebi.ac.uk/efo/EFO_0004725

# Genome-wide association study of metabolites in patients with coronary artery disease identified novel metabolite quantitative trait loci.

# 24816252

pubmed_id = 24816252

# suspect: 
# metabolite measurement    http://www.ebi.ac.uk/efo/EFO_0004725

# An atlas of genetic influences on human blood metabolites.

print(paste0("How many studies for pubmed id: ", pubmed_id))

[1] "How many studies for pubmed id: 24816252"

gwas_study_info |>
  filter(PUBMED_ID == pubmed_id) |>
  nrow()

[1] 533

# not all are unmapped 
print(paste0("How many studies for pubmed id: ", pubmed_id, "have unmapped/not well mapped traits"))

[1] "How many studies for pubmed id: 24816252have unmapped/not well mapped traits"

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  nrow()

[1] 173

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == pubmed_id) |>
  pull(MAPPED_TRAIT)

print("Of these unmapped studies, how many contain phrase in blood")

[1] "Of these unmapped studies, how many contain phrase in blood"

grepl("in blood", not_well_mapped_traits) |> sum()

[1] 0

print("level of or amount of")

[1] "level of or amount of"

grepl("level of|amount of|measurement|ratio", not_well_mapped_traits) |> sum()

[1] 173

gwas_study_info = gwas_study_info |>
  rowwise() |>
  mutate(MAPPED_TRAIT = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                               "blood protein amount",
                               MAPPED_TRAIT)
         ) |>
    mutate(MAPPED_TRAIT_URI = ifelse(PUBMED_ID == pubmed_id && MAPPED_TRAIT %in% unique_mapped_traits,
                               "http://purl.obolibrary.org/obo/OBA_VT0005416",
                               MAPPED_TRAIT_URI)
           ) |>
  ungroup()

# 36482414
# Publication: Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort.

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits) |>
  group_by(PUBMED_ID, STUDY, COHORT) |>
  summarise(n_unmapped_per_study = n()) |>
  arrange(desc(n_unmapped_per_study))

# A tibble: 1,548 × 4
# Groups:   PUBMED_ID, STUDY [1,512]
   PUBMED_ID STUDY                                   COHORT n_unmapped_per_study
       <int> <chr>                                   <chr>                 <int>
 1  38412862 Genetic associations with ratios betwe… "UKBB"                 2821
 2  39528826 Genetic architecture of cerebrospinal … "Knig…                  381
 3  33634981 Genome-wide association study of metab… ""                      197
 4  36482414 Comprehensive characterization of puta… "othe…                  121
 5  37253714 Whole-Genome Sequencing Analysis of Hu… "ARIC…                   95
 6  37794183 Rare variant associations with plasma … "UKBB"                   89
 7  34737426 A generalized linear mixed model assoc… "UKBB"                   81
 8  26068415 Genome-wide association study identifi… "EGCU…                   76
 9  35347128 Genome-wide association studies of met… "METS…                   55
10  39024449 Diversity and scale: Genetic architect… "MVP"                    51
# ℹ 1,538 more rows

4 Saving:

data.table::fwrite(gwas_study_info,
                  here::here("output/gwas_study_info_trait_corrected.csv"), 
                  sep = ",")

Session information

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] labeling_0.4.3     generics_0.1.4     knitr_1.50         tibble_3.3.0      
[21] rprojroot_2.1.0    RColorBrewer_1.1-3 bslib_0.9.0        pillar_1.11.0     
[25] rlang_1.1.6        utf8_1.2.6         cachem_1.1.0       stringi_1.8.7     
[29] httpuv_1.6.16      xfun_0.52          getPass_0.2-4      fs_1.6.6          
[33] sass_0.4.10        cli_3.6.5          withr_3.0.2        magrittr_2.0.3    
[37] ps_1.9.1           grid_4.3.1         digest_0.6.37      processx_3.8.6    
[41] rstudioapi_0.17.1  lifecycle_1.0.4    vctrs_0.6.5        evaluate_1.0.4    
[45] glue_1.8.0         farver_2.1.2       whisker_0.4.1      rmarkdown_2.29    
[49] httr_1.4.7         tools_4.3.1        pkgconfig_2.0.3    htmltools_0.5.8.1