Last updated: 2025-08-06

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Knit directory: genomics_ancest_disease_dispar/

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

1 Overview / introduction:

I’m interested in identifying which studies use the same cohort/samples in the GWAS catalogue. Why? To produce ancestry proportion information that controls for the same samples (particularly big biobanks) being reused many times.

Within the same research article, this will likely be easier to achieve as: - Labelling of the cohort is likely to be consistent within the same research article (for e.g. one article will likely refer to UKBB samples as UKBB always, and not sometimes UK Biobank) - Many papers study the same cohort across diseases. Thus, this kind of cohort reuse can be identified by noticing that these papers have the same or similar sample sizes across diseases - Many papers use the same cohort/s, and then study particular subsamples and the overall cohort. This kind of sample reuse cohort can be identified when it involves, e.g. ancestry groups, as the overall study sample demographics are provided and can be matched to the study subsamples - (?) I believe the GWAS catalog somewhat reduces / identifys cohort overlap in the same study when producing ancestry metadata for the whole catalog

More recently, the GWAS catalog has allowed authors to supply cohort label information (e.g. UK Biobank). However, this doesn’t make it necessarily easier to compare across studies as: - Not all studies provide cohort information/label (especially studies before this policy was implemented) - Studies inconsistency label the same cohort (e.g. UK Biobank is listed as UKBB, UKB etc.) - Many studies use multiple cohorts

I believe I will be able to identify when the same samples are used across different research articles by matching sample sizes (e.g., does their sample description match the cohort of the UK Biobank), citation patterns (e.g., cite the UK Biobank dataset papers), etc. This will likely require considering publication date, as the sample sizes of biobanks/cohorts and the canonical dataset papers to cite will change over time.

This page aims to investigate how feasible this analysis will be.

Specifically, for sample re-use in the same study: - Can I easily categorise it into: subsample use / testing, & re-use across disease? - Are their papers which don’t match either of these categories?

For sample re-use across study: - How much missingness is there in cohort labelling? - Can I identify the UK Biobank dataset papers?

1.1 1. Load / pre-process GWAS Catalog data

# Load GWAS Catalog studies
gwas_study_info <- fread("data/gwas_catalog/gwas-catalog-v1.0.3.1-studies-r2025-07-21.tsv",
                         sep = "\t", quote = "")

# Standardize column names (remove spaces)
gwas_study_info <- gwas_study_info |>
  rename_all(~gsub(" ", "_", .x))

1.2 2. Cohort Summary

# Number of unique cohorts
length(unique(gwas_study_info$COHORT))
[1] 1239
# Studies per cohort
studies_per_cohort = gwas_study_info |>
  group_by(COHORT) |>
  summarise(n_studies = n()) |>
  arrange(desc(n_studies)) 

studies_per_cohort |>
  ggplot(aes(x=n_studies)) + 
  geom_histogram() + 
  theme_bw() + 
  labs(title = "Distribution of the number of studies per cohort label")


sessionInfo()
R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 15.5

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     dplyr_1.1.4       data.table_1.17.8 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      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        cachem_1.1.0       stringi_1.8.7      httpuv_1.6.16     
[29] xfun_0.52          getPass_0.2-4      fs_1.6.6           sass_0.4.10       
[33] cli_3.6.5          withr_3.0.2        magrittr_2.0.3     ps_1.9.1          
[37] grid_4.3.1         digest_0.6.37      processx_3.8.6     rstudioapi_0.17.1 
[41] lifecycle_1.0.4    vctrs_0.6.5        evaluate_1.0.4     glue_1.8.0        
[45] farver_2.1.2       whisker_0.4.1      rmarkdown_2.29     httr_1.4.7        
[49] tools_4.3.1        pkgconfig_2.0.3    htmltools_0.5.8.1