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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] 4599[1] "Number of unique disease terms: 2372"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: 2375"[1] "Number of unique disease terms: 2166"[1] "Number of unique disease terms: 1967"Then, I map the harmonised disease trait labels to ICD-10 codes, by
the following step-wise procedure: 1. Where available, extract author
provided ICD-10 Codes in GWAS Catalog DISEASE/TRAIT metadata 2. Where
available, extract author provided PheCodes in GWAS Catalog
DISEASE/TRAIT metadata 3. Where author provided PheCodes are available,
map PheCode to ICD-10 codes using PheWAS map, and manually confirmed
PheCode to ICD-10cm mappings (from https://phenomics.va.ornl.gov/phecodemap/). 4. If author
provided ICD-10 or PheCodes are not available, match
DISEASE/TRAIT labels to ICD-10 and PheCode code
descriptions 4. Remap remaining unmapped traits are manually mapped with
the help of ICD-10 codes of studies of the same disease, and the WHO
ICD-10 2019 index (https://icd.who.int/browse10/2019).
The final map of disease traits to ICD-10 codes is produce
Allowing us to deal with overlapping samples.
Fixing & harmonising cohort labels
Mostly just correcting for typos and different ways of writing the same cohort name (e.g. Finland vs FINLAND)
Replicate observed ancestry biases figures in GWAS dataset
Replicating Martin et al. 2019 Figure
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 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] workflowr_1.7.1
loaded via a namespace (and not attached):
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[13] yaml_2.3.10 fastmap_1.2.0 here_1.0.1 R6_2.6.1
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[33] magrittr_2.0.4 ps_1.9.1 digest_0.6.37 processx_3.8.6
[37] rstudioapi_0.17.1 lifecycle_1.0.4 vctrs_0.6.5 evaluate_1.0.5
[41] glue_1.8.0 data.table_1.17.8 whisker_0.4.1 rmarkdown_2.30
[45] httr_1.4.7 tools_4.3.1 pkgconfig_2.0.3 htmltools_0.5.8.1