library(data.table) f_read = as.data.frame(fread("data2/kottgen_effect_filtered.txt", header=T)) ld_matrix = as.matrix(fread("data2/slc2a9_only.ld", header=F)) snpdat = as.data.frame(fread("data2/ABCG2.snpdat", header=T)) idx_in = which(snpdat$RSID %in% f_read$MarkerName) ld_abcg2 = ld_matrix[idx_in, idx_in] snpdat_abcg2 = snpdat[idx_in,] f_merge = merge(f_read, snpdat_abcg2, by.x="MarkerName", by.y="RSID") f_merge = f_merge[order(f_merge$POS),] f_merge$Effect = ifelse(toupper(f_merge$Allele1) == toupper(f_merge$A2), f_merge$Effect, -f_merge$Effect) colnames(f_merge)[1] = "SNP" library(coco) gg = prep_dataset_coco(data_set=f_merge, ld_matrix=ld_abcg2,hwe_variance = F,exact=T, var_y=1.6421) gg2 = prep_dataset_coco(data_set=f_merge, ld_matrix=ld_abcg2,hwe_variance = F,exact=F, var_y=1.61078) get_ld(c("rs2231142","rs3114020","rs2622629","rs2054576"),gg) diag(ld_abcg2) = diag(ld_abcg2) + 0.5 #f_merge$Freq1 = ifelse(f_merge$Freq1 > 0.5, 1 - f_merge$Freq1, f_merge$Freq1) #f_merge$FREQ1 = ifelse(f_merge$FREQ1 > 0.5, 1 - f_merge$FREQ1, f_merge$FREQ1) gg$exact = T #gg$neff = (gg$var_y - gg$var *gg$b^2)/(gg$var *gg$se^2) + 1 lel = stepwise_coco(gg, joint=F,p_value_threshold =9.186111e-06) get_ld(c("rs2231142","rs3114020","rs2622629","rs2728126"),gg)
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