Collapse / filtering traits

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Last updated: 2025-08-21

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Knit directory: genomics_ancest_disease_dispar/

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File	Version	Author	Date	Message
Rmd	d4ec61e	IJbeasley	2025-08-21	Adding investigation harmonizing mapped traits

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knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE)
library(data.table)
library(dplyr)
library(ggplot2)
library(stringr)

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

# 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 |>
  slice_head(n = 20) |>
  ggplot(aes(y = reorder(MAPPED_TRAIT, n_studies), x = n_studies)) + 
  geom_col() + 
  theme_bw() + 
  labs(y = "Trait", x = "Number of studies")

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

# this study tests 
# 6,790 proteins or protein complexes
gwas_study_info |>
  filter(PUBMED_ID == 35870639) |>
  nrow()

[1] 6790

# not all are unmapped 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == "35870639") |>
  nrow()# map to blood protein amount

[1] 3411

# serum proteome - i.e. blood proteome 

gwas_study_info  = 
gwas_study_info |>
  dplyr::rows_update(tibble(PUBMED_ID = 35870639, 
                            MAPPED_TRAIT = "blood protein amount",
                            MAPPED_TRAIT_URI = "http://purl.obolibrary.org/obo/OBA_VT0005416"),
                     unmatched = "ignore")

gwas_study_info |>
  filter(PUBMED_ID == 38412862) |>
  nrow()

[1] 2821

# 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 = 3841286, 
                            MAPPED_TRAIT = "blood protein amount",
                            MAPPED_TRAIT_URI = "http://purl.obolibrary.org/obo/OBA_VT0005416"),
                     unmatched = "ignore")

gwas_study_info |>
  filter(PUBMED_ID == 39789286) |>
  nrow()

[1] 3049

# not all are unmapped 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == 39789286) |>
  nrow()

[1] 612

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == 39789286) |>
  pull(MAPPED_TRAIT)

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

[1] 612

grepl("level of", not_well_mapped_traits) |> length()

[1] 612

# All unmapped traits are protein levels in blood 

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == 39789286) |>
  dplyr::filter(grepl("in blood", MAPPED_TRAIT)) |> 
  nrow()

[1] 612

gwas_study_info = gwas_study_info |>
  rowwise() |>
  mutate(MAPPED_TRAIT = ifelse(PUBMED_ID == 39789286 && MAPPED_TRAIT %in% unique_mapped_traits,
                               "blood protein amount",
                               MAPPED_TRAIT)
         ) |>
    mutate(MAPPED_TRAIT_URI = ifelse(PUBMED_ID == 39789286 && MAPPED_TRAIT %in% unique_mapped_traits,
                                       "http://purl.obolibrary.org/obo/OBA_VT0005416",
                                     MAPPED_TRAIT_URI)
           ) |>
  ungroup()

gwas_study_info |>
  filter(PUBMED_ID == 29875488) |>
  nrow()

[1] 3283

gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == 29875488) |>
  nrow()

[1] 388

not_well_mapped_traits = 
gwas_study_info |>
  dplyr::filter(MAPPED_TRAIT %in% unique_mapped_traits,
                PUBMED_ID == 29875488) |>
  pull(MAPPED_TRAIT)

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

[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 == 29875488 && MAPPED_TRAIT %in% unique_mapped_traits,
                               "blood protein amount",
                               MAPPED_TRAIT)
         ) |>
    mutate(MAPPED_TRAIT_URI = ifelse(PUBMED_ID == 29875488 && 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

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

# An atlas of genetic influences on human blood metabolites.

# 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: 1,550 × 4
# Groups:   PUBMED_ID, STUDY [1,513]
   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  24816252 An atlas of genetic influences on huma… "Twin…                  127
 5  36482414 Comprehensive characterization of puta… "othe…                  121
 6  37253714 Whole-Genome Sequencing Analysis of Hu… "ARIC…                   95
 7  37794183 Rare variant associations with plasma … "UKBB"                   89
 8  34737426 A generalized linear mixed model assoc… "UKBB"                   81
 9  26068415 Genome-wide association study identifi… "EGCU…                   76
10  35347128 Genome-wide association studies of met… "METS…                   55
# ℹ 1,540 more rows

Session information

sessionInfo()

R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.6

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