Map SNPs (both coding and non-coding) onto genes using different methods.
map_snps2genes( sumstats_file, method = c("abc", "txdb"), dataset = "Nasser2020", abc = NULL, adjust_z = TRUE, drop_MHC = TRUE, model = NULL, log_vars = c("NSNPS", "NPARAM", "GENELEN"), formula = ZSTAT ~ NSNPS + logNSNPS + NPARAM + logNPARAM + GENELEN + logGENELEN, nCores = 1, save_dir = tempdir(), return_path = FALSE, verbose = TRUE )
| sumstats_file | GWAS summary statistics munged by format_sumstats. Can be a path to the saved file or data.table. |
|---|---|
| method | Method to use for mapping SNPs to genes. |
| dataset | Which ABC model to import. |
| abc | Use a previously downloaded ABC data.table. |
| adjust_z | Whether to adjust Z-statistic using
the |
| drop_MHC | Drop genes from the MHC (Major Histocompatibility Complex) region. |
| model | Statistical model to use. Defaults to lm. |
| log_vars | Variables to perform natural log transformation on first.
Only run on variables available in |
| formula | Formula to use in |
| nCores | Number of cores to parallelise across. |
| save_dir | Where to save results. |
| verbose | Print messages. |
gene_hits data.table.
H-MAGMA Really hard to use with no docs on how to create new annot files. It's literally just MAGMA (when you provide some extra args).
https://github.com/neurogenomics/orthogene Protein-coding variants only?
https://genetics.opentargets.org/ Could use colocalization results, but these are both GWAS- and QTL-specific. Could compute mean coloc score per gene for each SNP and use that. Other position-based functional data might still be useful.