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I find GWAS Catalog studies that of diseases, by looking for disease-related EFO ontology mapped terms in the ‘MAPPED_TRAIT’ column of the GWAS Catalog metadata. I categorize traits into disease, response, measurement etc. using the EFO ontology.
[1] "Number of pubmed ids studying at least one disease"[1] 4652[1] "Number of unique disease terms: 2509"Next, for GWAS studies of disease I harmonise disease trait labels to reduce redundancy due to typos, synonyms etc.
[1] "Number of unique disease terms: 2381"[1] "Number of unique disease terms: 2192"[1] "Number of unique disease terms: 1995"Then, I map the harmonised disease trait labels to ICD-10 codes, by the following step-wise procedure:
Where available, extract author provided ICD-10 Codes in GWAS Catalog DISEASE/TRAIT metadata
Where available, extract author provided PheCodes in GWAS Catalog DISEASE/TRAIT metadata. These PheCodes are converted to ICD-10 codes using the PheWAS R package international mapping file, and checked against PheCode to ICD-10cm mappings (from https://phenomics.va.ornl.gov/phecodemap/).
If author provided ICD-10 or PheCodes are not available, match
DISEASE/TRAIT labels to ICD-10 code descriptions
If not able to match DISEASE/TRAIT labels to ICD-10
code descriptions, try match DISEASE/TRAIT labels to
PheCode descriptions
If not able to match DISEASE/TRAIT labels to PheCode
descriptions, try match DISEASE/TRAIT labels to UMLS
terms
Use manually created excel mapping file (ICD-10 to
DISEASE/TRAIT terms, from WHO ICD-10 2019 index: https://icd.who.int/browse10/2019).
Repeat Steps 3-5 for but instead of trying to match terms to
DISEASE/TRAIT try to match terms to
collected_all_disease_terms labels (these are processed EFO
and other ontology terms)
Use author provide ICD-10 codes from studies of the same disease
(same collected_all_disease_terms )
The code creating the final map of disease traits to ICD-10 codes is below:
Use the above mapping of disease traits to ICD-10 codes to find GWAS Catalog studies of the Lancet Commission Priority Diseases. This is done by matching the ICD-10 codes of the diseases to the ICD-10 codes of the GWAS Catalog disease traits.
Allowing us to deal with overlapping samples.
Fixing & harmonising GWAS Catalog cohort metadata
Mostly just correcting for typos and different ways of writing the same cohort name (e.g. Finland vs FINLAND).
Missing cohort metadata in the GWAS Catalog
Investigation into missingness of GWAS Catalog cohort metadata, is it more common in older studies, or more recent studies?
Distribution of cohort information exploration
Explore the structure of GWAS Catalog metadata and how cohort labels are distributed across studies.
Pre-process article text for text mining:
Convert text to sentences: code/extract_text/spacy_obtain_sentences.pu
Replicate observed ancestry biases figures in GWAS dataset
Replicating Martin et al. 2019 Figure
Not included in analysis/study.
Global Burden of Disease Study data - DALYs, deaths etc.
Global Burden of Disease Study (GBD) global statistics on incidence, prevalence, DALYs (Disability-Adjusted Life Years) and PAF (population Attributable Fraction) for diseases and risk factors.
iCite - citation metrics for GWAS Catalog Papers
Replicating Reales & Wallace, 2023 - sharing gwas data results in more citations
Grouping cancer disease traits (removing disease subtypes etc)
Grouping non-cancer disease traits (removing disease subtypes etc)
Match GWAS studies to Global Burden of Disease Study Groupings
sessionInfo()R version 4.3.1 (2023-06-16)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS 26.3.1
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] workflowr_1.7.2
loaded via a namespace (and not attached):
[1] jsonlite_2.0.0 dplyr_1.1.4 compiler_4.3.1
[4] BiocManager_1.30.26 renv_1.1.8 promises_1.3.3
[7] tidyselect_1.2.1 Rcpp_1.1.0 stringr_1.6.0
[10] git2r_0.36.2 callr_3.7.6 later_1.4.4
[13] jquerylib_0.1.4 yaml_2.3.10 fastmap_1.2.0
[16] here_1.0.1 R6_2.6.1 generics_0.1.4
[19] knitr_1.50 tibble_3.3.0 rprojroot_2.1.0
[22] bslib_0.9.0 pillar_1.11.1 rlang_1.1.6
[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 data.table_1.17.8 evaluate_1.0.5
[43] glue_1.8.0 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