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### Filtering variants ### # Reading HapMap3+ variants info <- readRDS("/home/chronos/trp/map_hm3_plus.rds") # Reading pathway info bim_path <- "/home/chronos/tdora_uj/sirt1_data/new/comorbid_pathway.bim" pathway_info <- fread(bim_path, data.table = FALSE) # reading bim file pathway_info <- setNames(pathway_info[c(1, 4)], c("chr", "pos")) # Merging the pathway to HapMap3+ variants info_plus <- full_join( info[c("chr", "pos")], pathway_info, by = c("chr", "pos") ) # Filtering by position filtered_sumstats <- inner_join(sumstats, info_plus, by = c("chr", "pos")) ### Matching discovery and target genetic data ### info_snp <- snp_match( filtered_sumstats, map, join_by_pos = TRUE, return_flip_and_rev = TRUE ) # Creating a subset for the pathway of interest pathway_snp <- inner_join(info_snp, pathway_info, by = c("chr", "pos")) pathway_indices <- which(df_beta[["_NUM_ID_"]] %in% pathway_snp[["_NUM_ID_"]]) ### Run LDpred2-auto as usual ### ### Pathway-based polygenic risk ### pathway_auto <- big_prodVec( G, beta_auto[pathway_indices], ind.col = pathway_snp[["_NUM_ID_"]] )