A curated subset of GWAS catalog summary statistics prepared for pleiotropy analysis.
Format
A data.table with 1000-5000 rows and 5 columns:
- SNPS
Character. SNP identifiers (rs numbers)
- MAPPED_TRAIT
Character. Standardized trait or phenotype name
- PVALUE_MLOG
Numeric. -log10 transformed p-values for significance
- CHR_ID
Character. Chromosome identifier (1-22, X, Y)
- CHR_POS
Numeric. Chromosomal position in base pairs
Source
GWAS Catalog associations file (2024 release), downloaded from: https://www.ebi.ac.uk/gwas/api/search/downloads/alternative
The subset was created by: 1. Loading the complete GWAS catalog associations file 2. Filtering for genome-wide significant associations (P < 1e-5) 3. Selecting top traits by number of associations 4. Creating a balanced subset of ~500 associations 5. Ensuring representation across multiple chromosomes
Details
This dataset contains genome-wide association study (GWAS) summary statistics from the GWAS Catalog (https://www.ebi.ac.uk/gwas/), focusing on diverse traits to demonstrate pleiotropy detection and visualization.
This dataset includes associations with diverse traits such as:
Body mass index
Alzheimer's disease
Type 2 diabetes
Coronary artery disease
Blood pressure traits
Lipid levels (LDL, HDL cholesterol)
The data is suitable for:
Demonstrating pleiotropy detection (SNPs associated with multiple traits)
Testing visualization functions
Teaching GWAS analysis workflows
Benchmarking analysis methods
References
GWAS Catalog Team. (2024). GWAS Catalog: a knowledgebase for genome-wide association studies. *Nucleic Acids Research*, 51(D1), D944-D949. https://doi.org/10.1093/nar/gkab1009
Examples
# Load the data
data(gwas_subset)
head(gwas_subset)
#> DISEASE/TRAIT CHR_ID CHR_POS REPORTED GENE(S) SNPS P-VALUE
#> <char> <char> <char> <char> <char> <char>
#> 1: Body mass index 1 201815159 NR rs2820292 8E-11
#> 2: Body mass index 2 630323 NR rs6725549 1E-74
#> 3: Body mass index 2 24927427 NR rs10182181 2E-30
#> 4: Body mass index 2 26732753 NR rs3739081 2E-9
#> 5: Body mass index 2 58630284 NR rs13011109 1E-14
#> 6: Body mass index 2 59078490 NR rs1016287 4E-13
#> PVALUE_MLOG P-VALUE (TEXT) MAPPED_TRAIT
#> <num> <char> <char>
#> 1: 10.09691 <NA> body mass index
#> 2: 74.00000 <NA> body mass index
#> 3: 29.69897 <NA> body mass index
#> 4: 8.69897 <NA> body mass index
#> 5: 14.00000 <NA> body mass index
#> 6: 12.39794 <NA> body mass index
# Basic statistics
n_unique_snps <- length(unique(gwas_subset$SNPS))
n_unique_traits <- length(unique(gwas_subset$MAPPED_TRAIT))
cat("Unique SNPs:", n_unique_snps, "\n")
#> Unique SNPs: 452164
cat("Unique traits:", n_unique_traits, "\n")
#> Unique traits: 16452
# Preprocess and detect pleiotropy
gwas_clean <- preprocess_gwas(gwas_subset, pvalue_threshold = 1e-5)
pleio_results <- detect_pleiotropy(gwas_clean)
head(pleio_results)
#> # A tibble: 6 × 7
#> SNPS N_TRAITS TRAITS MAPPED_TRAIT PVALUE_MLOG CHR_ID CHR_POS
#> <chr> <int> <chr> <chr> <dbl> <chr> <chr>
#> 1 esv3585367 2 rheumatoid arthri… rheumatoid … 27 1 173496…
#> 2 esv3585367 2 rheumatoid arthri… rheumatoid … 32.4 1 173496…
#> 3 esv3585367 2 rheumatoid arthri… rheumatoid … 29.7 1 173496…
#> 4 esv3585367 2 rheumatoid arthri… rheumatoid … 24.7 1 173496…
#> 5 esv3585367 2 rheumatoid arthri… rheumatoid … 10.7 1 173496…
#> 6 esv3585367 2 rheumatoid arthri… rheumatoid … 8 1 173496…