Gamma glutamyltransferase (quantile) - Liver
wesleycrouse
2021-09-09
Last updated: 2021-09-09
Checks: 6 1
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| html | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| Rmd | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| html | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| Rmd | 627a4e1 | wesleycrouse | 2021-09-07 | adding heritability |
| Rmd | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| html | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| Rmd | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| html | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| Rmd | 837dd01 | wesleycrouse | 2021-09-01 | adding additional fixedsigma report |
| Rmd | d0a5417 | wesleycrouse | 2021-08-30 | adding new reports to the index |
| Rmd | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 1c62980 | wesleycrouse | 2021-08-11 | Updating reports |
| Rmd | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| html | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| Rmd | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 03e541c | wesleycrouse | 2021-07-29 | Cleaning up report generation |
| Rmd | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
| html | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Gamma glutamyltransferase (quantile) using Liver gene weights.
The GWAS was conducted by the Neale Lab, and the biomarker traits we analyzed are discussed here. Summary statistics were obtained from IEU OpenGWAS using GWAS ID: ukb-d-30730_irnt. Results were obtained from from IEU rather than Neale Lab because they are in a standardard format (GWAS VCF). Note that 3 of the 34 biomarker traits were not available from IEU and were excluded from analysis.
The weights are mashr GTEx v8 models on Liver eQTL obtained from PredictDB. We performed a full harmonization of the variants, including recovering strand ambiguous variants. This procedure is discussed in a separate document. (TO-DO: add report that describes harmonization)
LD matrices were computed from a 10% subset of Neale lab subjects. Subjects were matched using the plate and well information from genotyping. We included only biallelic variants with MAF>0.01 in the original Neale Lab GWAS. (TO-DO: add more details [number of subjects, variants, etc])
Weight QC
TO-DO: add enhanced QC reporting (total number of weights, why each variant was missing for all genes)
qclist_all <- list()
qc_files <- paste0(results_dir, "/", list.files(results_dir, pattern="exprqc.Rd"))
for (i in 1:length(qc_files)){
load(qc_files[i])
chr <- unlist(strsplit(rev(unlist(strsplit(qc_files[i], "_")))[1], "[.]"))[1]
qclist_all[[chr]] <- cbind(do.call(rbind, lapply(qclist,unlist)), as.numeric(substring(chr,4)))
}
qclist_all <- data.frame(do.call(rbind, qclist_all))
colnames(qclist_all)[ncol(qclist_all)] <- "chr"
rm(qclist, wgtlist, z_gene_chr)
#number of imputed weights
nrow(qclist_all)[1] 10901#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1070 768 652 417 494 611 548 408 405 434 634 629 195 365 354
16 17 18 19 20 21 22
526 663 160 859 306 114 289 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8366205Load ctwas results
#load ctwas results
ctwas_res <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.susieIrss.txt"))
#make unique identifier for regions
ctwas_res$region_tag <- paste(ctwas_res$region_tag1, ctwas_res$region_tag2, sep="_")
#compute PVE for each gene/SNP
ctwas_res$PVE = ctwas_res$susie_pip*ctwas_res$mu2/sample_size #check PVE calculation
#separate gene and SNP results
ctwas_gene_res <- ctwas_res[ctwas_res$type == "gene", ]
ctwas_gene_res <- data.frame(ctwas_gene_res)
ctwas_snp_res <- ctwas_res[ctwas_res$type == "SNP", ]
ctwas_snp_res <- data.frame(ctwas_snp_res)
#add gene information to results
sqlite <- RSQLite::dbDriver("SQLite")
db = RSQLite::dbConnect(sqlite, paste0("/project2/compbio/predictdb/mashr_models/mashr_", weight, ".db"))
query <- function(...) RSQLite::dbGetQuery(db, ...)
gene_info <- query("select gene, genename from extra")
gene_info <- query("select gene, genename, gene_type from extra")
RSQLite::dbDisconnect(db)
ctwas_gene_res <- cbind(ctwas_gene_res, gene_info[sapply(ctwas_gene_res$id, match, gene_info$gene), c("genename", "gene_type")])
#add z score to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$id,]$z
#load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd")) #for new version, stored after harmonization
z_snp <- readRDS(paste0(results_dir, "/", trait_id, ".RDS")) #for old version, unharmonized
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,] #subset snps to those included in analysis, note some are duplicated, need to match which allele was used
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)] #for duplicated snps, this arbitrarily uses the first allele
ctwas_snp_res$z_flag <- as.numeric(ctwas_snp_res$id %in% z_snp$id[duplicated(z_snp$id)]) #mark the unclear z scores, flag=1
#formatting and rounding for tables
ctwas_gene_res$z <- round(ctwas_gene_res$z,2)
ctwas_snp_res$z <- round(ctwas_snp_res$z,2)
ctwas_gene_res$susie_pip <- round(ctwas_gene_res$susie_pip,3)
ctwas_snp_res$susie_pip <- round(ctwas_snp_res$susie_pip,3)
ctwas_gene_res$mu2 <- round(ctwas_gene_res$mu2,2)
ctwas_snp_res$mu2 <- round(ctwas_snp_res$mu2,2)
ctwas_gene_res$PVE <- signif(ctwas_gene_res$PVE, 2)
ctwas_snp_res$PVE <- signif(ctwas_snp_res$PVE, 2)
#merge gene and snp results with added information
ctwas_gene_res$z_flag=NA
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
#get number of SNPs from s1 results; adjust for thin
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
library(ggplot2)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************load(paste0(results_dir, "/", analysis_id, "_ctwas.s2.susieIrssres.Rd"))
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
value = c(group_prior_rec[1,], group_prior_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$group <- as.factor(df$group)
df$value[df$group=="SNP"] <- df$value[df$group=="SNP"]*thin #adjust parameter to account for thin argument
p_pi <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(pi)) +
ggtitle("Prior mean") +
theme_cowplot()
df <- data.frame(niter = rep(1:ncol(group_prior_var_rec), 2),
value = c(group_prior_var_rec[1,], group_prior_var_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_var_rec)))
df$group <- as.factor(df$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0212484510 0.0001986678 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
33.81137 16.70921 #report sample size
print(sample_size)[1] 344104#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02275971 0.08390341 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1009233 1.3435297Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z
1144 ASAP3 1_16 1.000 78.69 2.3e-04 10.00
3330 SEC16B 1_87 1.000 43.31 1.3e-04 7.15
12467 RP11-219B17.3 15_27 1.000 620.46 1.8e-03 -26.21
9482 ACTG1 17_46 1.000 536.51 1.6e-03 17.50
5389 RPS11 19_34 1.000 19117.39 5.6e-02 -4.87
6341 ENAH 1_115 0.999 71.69 2.1e-04 -7.24
3735 TRIP10 19_7 0.999 44.99 1.3e-04 -6.47
9942 PARVB 22_19 0.998 43.17 1.3e-04 6.22
6778 PKN3 9_66 0.996 192.98 5.6e-04 -14.01
10303 UGT2B17 4_48 0.994 126.59 3.7e-04 -9.36
4608 REPS1 6_92 0.993 56.16 1.6e-04 7.11
10432 TAT 16_38 0.993 31.84 9.2e-05 6.65
5769 MLIP 6_40 0.991 248.44 7.2e-04 -16.10
3212 CCND2 12_4 0.991 32.05 9.2e-05 -5.32
2546 LTBR 12_7 0.991 45.83 1.3e-04 5.57
9478 KMT5A 12_75 0.989 432.39 1.2e-03 -5.12
8128 ZNF747 16_24 0.989 118.20 3.4e-04 -11.14
4327 MYH10 17_8 0.987 30.03 8.6e-05 -5.15
1848 CD276 15_35 0.983 225.86 6.4e-04 15.12
6100 ALLC 2_2 0.982 92.12 2.6e-04 9.86
11478 HLA-DMB 6_27 0.981 48.57 1.4e-04 -9.33
11072 PTPRD-AS1 9_9 0.980 22.88 6.5e-05 -4.35
9855 PALM3 19_11 0.980 46.76 1.3e-04 -6.63
4078 FCHO1 19_14 0.979 109.20 3.1e-04 -10.53
8119 TM4SF4 3_92 0.976 37.24 1.1e-04 6.64
3501 CALD1 7_82 0.975 58.77 1.7e-04 -7.80
8531 TNKS 8_12 0.975 61.13 1.7e-04 10.57
4395 MICAL2 11_9 0.975 518.60 1.5e-03 14.49
4671 SCYL2 12_59 0.960 23.23 6.5e-05 4.66
2004 TGFB1 19_28 0.960 112.37 3.1e-04 10.45
6291 JAZF1 7_23 0.958 31.50 8.8e-05 -5.32
6171 ARL14EP 11_21 0.955 30.77 8.5e-05 -5.22
1925 NFKBIB 19_26 0.955 41.51 1.2e-04 6.24
8502 RELA 11_36 0.951 26.44 7.3e-05 -4.92
6936 RAVER2 1_41 0.950 21.57 6.0e-05 4.21
7656 CATSPER2 15_16 0.947 55.52 1.5e-04 -7.59
5748 TENM2 5_99 0.945 99.32 2.7e-04 -11.38
9985 LITAF 16_12 0.945 104.02 2.9e-04 -10.21
676 IFT80 3_99 0.940 117.20 3.2e-04 10.66
5521 HAX1 1_75 0.937 49.22 1.3e-04 -7.07
7040 INHBB 2_70 0.934 59.00 1.6e-04 8.39
11584 ADH1C 4_66 0.930 23.41 6.3e-05 -4.42
8148 SPDYE5 7_48 0.930 72.74 2.0e-04 8.51
3291 SLF2 10_64 0.926 49.89 1.3e-04 7.26
2373 SLC9A3R1 17_42 0.926 28.00 7.5e-05 -4.93
8007 TMEM129 4_3 0.925 54.30 1.5e-04 -6.97
9496 KCNJ12 17_16 0.919 30.36 8.1e-05 -5.14
8801 YES1 18_1 0.913 32.61 8.7e-05 -5.60
8803 DLEU1 13_21 0.910 26.11 6.9e-05 4.77
11564 RP11-7F17.5 14_36 0.903 21.89 5.7e-05 -4.21
8767 MLXIP 12_75 0.899 37.05 9.7e-05 -6.03
3562 ACVR1C 2_94 0.894 21.05 5.5e-05 4.07
11889 RP11-327J17.2 15_46 0.888 20.04 5.2e-05 -3.15
993 PHLPP1 18_35 0.886 20.40 5.3e-05 4.24
11698 TRNP1 1_18 0.885 26.04 6.7e-05 -3.67
2341 DDX5 17_37 0.881 19.87 5.1e-05 4.09
5362 IFITM3 11_1 0.874 25.35 6.4e-05 -4.30
12704 EXOC3L2 19_32 0.867 31.34 7.9e-05 5.47
12525 RP11-428O18.6 13_7 0.853 87.82 2.2e-04 9.59
2924 EFHD1 2_136 0.851 140.56 3.5e-04 11.92
10104 SULF2 20_29 0.851 32.05 7.9e-05 -4.92
10495 PRMT6 1_66 0.846 83.41 2.1e-04 -8.87
5221 FURIN 15_42 0.843 22.86 5.6e-05 4.14
6010 KIAA1755 20_22 0.843 19.85 4.9e-05 3.83
1268 TMEM101 17_26 0.820 26.42 6.3e-05 -4.86
5510 TP53BP2 1_114 0.817 64.12 1.5e-04 -7.95
2475 NECTIN1 11_72 0.803 19.86 4.6e-05 -3.83Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
998 STRADB 2_119 0 25678.94 0.0e+00 -5.31
5389 RPS11 19_34 1 19117.39 5.6e-02 -4.87
4556 TMEM60 7_49 0 17638.76 0.0e+00 7.05
1227 FLT3LG 19_34 0 16507.24 2.0e-15 4.10
6422 ALS2CR12 2_119 0 15509.36 0.0e+00 -6.10
8342 BPTF 17_39 0 9530.93 2.2e-08 -7.29
10186 ZGPAT 20_38 0 8186.57 2.3e-08 -5.82
3715 SLC2A4RG 20_38 0 8126.18 1.6e-14 -5.75
1647 ARFRP1 20_38 0 7565.31 0.0e+00 -3.71
1418 GGT1 22_7 0 6470.91 0.0e+00 76.46
5393 RCN3 19_34 0 6217.46 5.8e-14 4.48
1931 FCGRT 19_34 0 5671.48 4.8e-15 3.70
9756 C17orf58 17_39 0 5411.82 1.9e-12 -7.37
10889 ARL16 17_46 0 4385.14 0.0e+00 3.24
12649 RP11-147L13.11 17_39 0 4277.32 0.0e+00 -5.28
12611 RP11-147L13.13 17_39 0 4077.30 0.0e+00 4.94
12496 RP11-147L13.12 17_39 0 3514.38 0.0e+00 -5.11
10903 APTR 7_49 0 3434.13 0.0e+00 3.77
3804 PRRG2 19_34 0 2762.16 2.4e-14 3.07
1641 GMEB2 20_38 0 2711.84 0.0e+00 2.48Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
5389 RPS11 19_34 1.000 19117.39 0.05600 -4.87
12467 RP11-219B17.3 15_27 1.000 620.46 0.00180 -26.21
9482 ACTG1 17_46 1.000 536.51 0.00160 17.50
4395 MICAL2 11_9 0.975 518.60 0.00150 14.49
9478 KMT5A 12_75 0.989 432.39 0.00120 -5.12
5769 MLIP 6_40 0.991 248.44 0.00072 -16.10
1848 CD276 15_35 0.983 225.86 0.00064 15.12
6778 PKN3 9_66 0.996 192.98 0.00056 -14.01
3947 MYO1B 2_114 0.796 234.02 0.00054 -16.00
10303 UGT2B17 4_48 0.994 126.59 0.00037 -9.36
2924 EFHD1 2_136 0.851 140.56 0.00035 11.92
8128 ZNF747 16_24 0.989 118.20 0.00034 -11.14
2486 PTPMT1 11_29 0.430 267.27 0.00033 5.42
676 IFT80 3_99 0.940 117.20 0.00032 10.66
4078 FCHO1 19_14 0.979 109.20 0.00031 -10.53
2004 TGFB1 19_28 0.960 112.37 0.00031 10.45
9985 LITAF 16_12 0.945 104.02 0.00029 -10.21
10851 UGT2B11 4_48 0.556 176.27 0.00028 -5.47
5748 TENM2 5_99 0.945 99.32 0.00027 -11.38
6100 ALLC 2_2 0.982 92.12 0.00026 9.86Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1418 GGT1 22_7 0.000 6470.91 0.0e+00 76.46
4547 HNF1A 12_74 0.000 1561.75 4.2e-08 43.08
10731 EXOC3L4 14_54 0.013 1146.09 4.4e-05 42.97
8964 LRRC75B 22_7 0.000 1756.04 0.0e+00 -40.95
5400 EPHA2 1_11 0.017 964.65 4.9e-05 -32.40
12467 RP11-219B17.3 15_27 1.000 620.46 1.8e-03 -26.21
4319 RSG1 1_11 0.015 437.28 1.9e-05 -22.26
6086 DLG5 10_50 0.008 227.17 5.0e-06 20.44
8865 FUT2 19_33 0.002 178.11 1.0e-06 -18.56
1403 DDTL 22_7 0.040 333.09 3.9e-05 17.92
4364 GSTT2B 22_7 0.040 333.09 3.9e-05 17.92
11432 MIF 22_7 0.040 333.09 3.9e-05 -17.92
12376 KB-226F1.2 22_7 0.038 332.99 3.7e-05 17.92
9482 ACTG1 17_46 1.000 536.51 1.6e-03 17.50
9761 FSCN2 17_46 0.000 633.14 1.4e-10 16.41
7118 SLC2A2 3_104 0.032 253.20 2.4e-05 -16.31
2887 NRBP1 2_16 0.020 223.31 1.3e-05 16.14
1404 DDT 22_7 0.000 275.68 5.5e-18 16.11
5769 MLIP 6_40 0.991 248.44 7.2e-04 -16.10
3947 MYO1B 2_114 0.796 234.02 5.4e-04 -16.00Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.03485919#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1418 GGT1 22_7 0.000 6470.91 0.0e+00 76.46
4547 HNF1A 12_74 0.000 1561.75 4.2e-08 43.08
10731 EXOC3L4 14_54 0.013 1146.09 4.4e-05 42.97
8964 LRRC75B 22_7 0.000 1756.04 0.0e+00 -40.95
5400 EPHA2 1_11 0.017 964.65 4.9e-05 -32.40
12467 RP11-219B17.3 15_27 1.000 620.46 1.8e-03 -26.21
4319 RSG1 1_11 0.015 437.28 1.9e-05 -22.26
6086 DLG5 10_50 0.008 227.17 5.0e-06 20.44
8865 FUT2 19_33 0.002 178.11 1.0e-06 -18.56
1403 DDTL 22_7 0.040 333.09 3.9e-05 17.92
4364 GSTT2B 22_7 0.040 333.09 3.9e-05 17.92
11432 MIF 22_7 0.040 333.09 3.9e-05 -17.92
12376 KB-226F1.2 22_7 0.038 332.99 3.7e-05 17.92
9482 ACTG1 17_46 1.000 536.51 1.6e-03 17.50
9761 FSCN2 17_46 0.000 633.14 1.4e-10 16.41
7118 SLC2A2 3_104 0.032 253.20 2.4e-05 -16.31
2887 NRBP1 2_16 0.020 223.31 1.3e-05 16.14
1404 DDT 22_7 0.000 275.68 5.5e-18 16.11
5769 MLIP 6_40 0.991 248.44 7.2e-04 -16.10
3947 MYO1B 2_114 0.796 234.02 5.4e-04 -16.00Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 22_7"
genename region_tag susie_pip mu2 PVE z
12371 KB-208E9.1 22_7 0.000 33.60 0.0e+00 2.27
9999 DRICH1 22_7 0.000 24.88 0.0e+00 -1.41
6697 RGL4 22_7 0.000 19.01 0.0e+00 2.39
9845 ZNF70 22_7 0.000 55.33 0.0e+00 1.78
3906 VPREB3 22_7 0.000 83.40 0.0e+00 -2.73
1398 SMARCB1 22_7 0.000 39.17 0.0e+00 -3.99
1400 DERL3 22_7 0.000 53.35 0.0e+00 1.75
12425 AP000350.5 22_7 0.000 248.09 0.0e+00 -15.71
1403 DDTL 22_7 0.040 333.09 3.9e-05 17.92
4364 GSTT2B 22_7 0.040 333.09 3.9e-05 17.92
11432 MIF 22_7 0.040 333.09 3.9e-05 -17.92
1404 DDT 22_7 0.000 275.68 5.5e-18 16.11
12376 KB-226F1.2 22_7 0.038 332.99 3.7e-05 17.92
1405 CABIN1 22_7 0.000 232.91 0.0e+00 7.92
1407 SUSD2 22_7 0.000 389.61 0.0e+00 -9.97
1409 GGT5 22_7 0.000 293.21 0.0e+00 -5.23
1412 SPECC1L 22_7 0.000 11.99 0.0e+00 -1.49
3912 ADORA2A 22_7 0.000 146.37 0.0e+00 3.78
1414 UPB1 22_7 0.000 74.56 0.0e+00 -3.05
8964 LRRC75B 22_7 0.000 1756.04 0.0e+00 -40.95
5058 GUCD1 22_7 0.000 174.57 0.0e+00 5.40
1418 GGT1 22_7 0.000 6470.91 0.0e+00 76.46
7713 SGSM1 22_7 0.000 12.42 0.0e+00 2.85
11544 CRYBB2 22_7 0.000 6.62 0.0e+00 -0.10
1426 LRP5L 22_7 0.000 6.21 0.0e+00 -1.18
1428 GRK3 22_7 0.000 6.02 0.0e+00 -0.09
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 12_74"
genename region_tag susie_pip mu2 PVE z
11059 TMEM233 12_74 0.050 29.47 4.3e-06 6.51
11597 RP11-768F21.1 12_74 0.548 32.78 5.2e-05 6.12
2588 PRKAB1 12_74 0.000 13.61 1.4e-10 -1.36
3514 CIT 12_74 0.000 11.20 7.2e-11 0.51
2591 RAB35 12_74 0.000 65.94 1.5e-08 4.77
1184 GCN1 12_74 0.000 16.01 7.2e-11 0.63
1185 RPLP0 12_74 0.000 24.02 4.8e-10 -1.53
1186 PXN 12_74 0.000 69.57 5.6e-08 2.01
1187 SIRT4 12_74 0.000 31.96 1.3e-10 -2.45
4546 MSI1 12_74 0.000 6.39 1.5e-11 3.04
2593 COX6A1 12_74 0.000 42.99 7.0e-10 3.79
8244 TRIAP1 12_74 0.000 67.65 5.6e-08 5.29
11829 GATC 12_74 0.000 29.35 1.2e-09 1.45
1170 DYNLL1 12_74 0.000 22.91 4.2e-10 -2.14
2504 COQ5 12_74 0.000 74.97 5.5e-08 6.30
7747 POP5 12_74 0.000 14.73 4.8e-11 -3.13
2510 MLEC 12_74 0.000 43.89 1.3e-10 2.26
12607 RP11-173P15.9 12_74 0.000 28.94 1.4e-10 2.01
12570 RP11-173P15.10 12_74 0.000 187.15 4.8e-10 -3.90
3516 ACADS 12_74 0.000 29.18 7.1e-11 6.37
4547 HNF1A 12_74 0.000 1561.75 4.2e-08 43.08
4549 OASL 12_74 0.000 44.34 5.0e-08 -3.95
1176 P2RX7 12_74 0.175 75.43 3.8e-05 -5.78
12471 RP11-340F14.6 12_74 0.000 110.74 2.0e-08 3.27
4550 P2RX4 12_74 0.005 217.26 3.2e-06 -13.22
2512 CAMKK2 12_74 0.000 37.65 9.4e-11 -7.96
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 14_54"
genename region_tag susie_pip mu2 PVE z
1206 RCOR1 14_54 0.000 36.71 3.9e-08 2.29
7553 AMN 14_54 0.001 58.37 1.3e-07 -5.91
10452 CDC42BPB 14_54 0.000 20.29 3.2e-09 -6.71
10731 EXOC3L4 14_54 0.013 1146.09 4.4e-05 42.97
9589 TNFAIP2 14_54 0.000 343.00 1.4e-07 -2.84
11678 LINC00605 14_54 0.000 37.73 1.2e-08 1.18
840 MARK3 14_54 0.000 38.26 7.8e-09 -6.51
7563 CKB 14_54 0.000 30.34 7.2e-09 -4.60
7567 BAG5 14_54 0.000 15.27 2.3e-09 -4.24
3787 KLC1 14_54 0.000 35.55 4.6e-09 -7.12
11794 APOPT1 14_54 0.004 74.84 9.5e-07 -8.81
1562 ZFYVE21 14_54 0.000 62.20 8.5e-08 8.23
3788 XRCC3 14_54 0.000 22.26 3.8e-09 3.63
1157 PPP1R13B 14_54 0.000 6.38 8.3e-10 0.90
11859 CTD-2134A5.4 14_54 0.000 10.19 1.3e-09 -2.15
11876 CTD-2134A5.3 14_54 0.000 5.33 7.5e-10 -0.96
6470 TDRD9 14_54 0.000 20.77 4.1e-09 -4.05
6468 C14orf2 14_54 0.000 5.90 8.5e-10 -0.46
7570 ASPG 14_54 0.000 8.81 1.6e-09 -0.95
636 KIF26A 14_54 0.000 8.48 1.8e-09 -0.31
11875 RP11-260M19.2 14_54 0.000 7.93 1.5e-09 -0.30
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_11"
genename region_tag susie_pip mu2 PVE z
8350 TMEM51 1_11 0.010 5.68 1.7e-07 -0.12
5402 EFHD2 1_11 0.010 5.21 1.5e-07 -0.01
5398 CELA2A 1_11 0.010 5.92 1.7e-07 0.75
4320 CASP9 1_11 0.013 7.37 2.7e-07 -0.03
3043 AGMAT 1_11 0.012 6.96 2.4e-07 0.44
3047 PLEKHM2 1_11 0.018 11.41 6.0e-07 -1.18
11270 UQCRHL 1_11 0.091 39.29 1.0e-05 -5.24
599 SPEN 1_11 0.068 34.99 6.9e-06 -4.97
3050 ZBTB17 1_11 0.023 43.83 3.0e-06 -6.16
9739 CLCNKA 1_11 0.010 5.98 1.7e-07 0.40
8571 HSPB7 1_11 0.010 25.05 7.2e-07 -5.53
9630 FAM131C 1_11 0.023 34.92 2.3e-06 5.26
5400 EPHA2 1_11 0.017 964.65 4.9e-05 -32.40
5401 ARHGEF19 1_11 0.014 27.65 1.1e-06 -4.35
4319 RSG1 1_11 0.015 437.28 1.9e-05 -22.26
352 FBXO42 1_11 0.010 64.38 1.8e-06 8.09
9800 SPATA21 1_11 0.010 25.18 7.3e-07 -4.80
6519 NECAP2 1_11 0.016 11.47 5.5e-07 -1.72
11088 LINC01772 1_11 0.012 35.63 1.3e-06 -5.63
10977 NBPF1 1_11 0.053 17.91 2.8e-06 -0.70
11259 LINC01783 1_11 0.011 20.09 6.4e-07 -3.97
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 15_27"
genename region_tag susie_pip mu2 PVE z
5185 GCNT3 15_27 0.029 9.02 7.5e-07 0.93
5186 GTF2A2 15_27 0.021 6.41 3.8e-07 -0.62
3965 ICE2 15_27 0.020 49.28 2.8e-06 5.65
12467 RP11-219B17.3 15_27 1.000 620.46 1.8e-03 -26.21
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag susie_pip mu2 PVE z
25667 rs6680048 1_62 1.000 112.11 3.3e-04 10.97
31377 rs72692783 1_74 1.000 56.51 1.6e-04 7.33
33504 rs61804205 1_79 1.000 124.23 3.6e-04 11.85
68161 rs569546056 2_17 1.000 870.04 2.5e-03 3.10
73317 rs10604697 2_26 1.000 138.69 4.0e-04 -3.04
98402 rs3762466 2_79 1.000 94.79 2.8e-04 -9.99
108569 rs1862069 2_102 1.000 82.02 2.4e-04 10.89
181157 rs149368105 3_105 1.000 77.35 2.2e-04 9.83
203568 rs12639940 4_32 1.000 54.62 1.6e-04 -7.03
235485 rs4552481 4_95 1.000 1055.58 3.1e-03 33.83
280319 rs163895 5_63 1.000 145.39 4.2e-04 -12.32
359394 rs575426641 6_110 1.000 45.74 1.3e-04 -5.93
385663 rs13235543 7_47 1.000 391.94 1.1e-03 -20.17
386065 rs10277379 7_49 1.000 14666.34 4.3e-02 -7.47
386068 rs761767938 7_49 1.000 19050.63 5.5e-02 -6.77
386076 rs1544459 7_49 1.000 18759.16 5.5e-02 -6.92
415256 rs758184196 8_11 1.000 476.05 1.4e-03 -0.17
429404 rs140753685 8_42 1.000 162.57 4.7e-04 13.34
435881 rs2977929 8_55 1.000 54.58 1.6e-04 -5.71
491949 rs115478735 9_70 1.000 162.50 4.7e-04 13.37
506568 rs4934675 10_26 1.000 37.43 1.1e-04 5.41
510199 rs4935194 10_33 1.000 84.35 2.5e-04 6.38
510202 rs71508062 10_33 1.000 48.39 1.4e-04 2.83
510204 rs61856594 10_33 1.000 217.91 6.3e-04 -10.75
547661 rs286917 11_23 1.000 78.90 2.3e-04 1.17
570827 rs73018243 11_75 1.000 45.81 1.3e-04 6.76
579713 rs11056397 12_13 1.000 40.78 1.2e-04 -6.38
581392 rs66720652 12_15 1.000 49.24 1.4e-04 -6.92
603579 rs55692966 12_56 1.000 74.48 2.2e-04 -9.17
606343 rs10861545 12_63 1.000 63.26 1.8e-04 8.04
607903 rs75622376 12_67 1.000 84.76 2.5e-04 9.34
610907 rs80019595 12_74 1.000 263.42 7.7e-04 5.82
610908 rs1169286 12_74 1.000 783.86 2.3e-03 -39.32
610911 rs2393775 12_74 1.000 1923.35 5.6e-03 49.77
625736 rs566812111 13_25 1.000 2790.73 8.1e-03 2.60
625740 rs12430288 13_25 1.000 2818.31 8.2e-03 2.69
643858 rs151182529 13_59 1.000 73.93 2.1e-04 -7.82
655420 rs6572633 14_19 1.000 36.19 1.1e-04 4.05
655424 rs4900970 14_19 1.000 53.50 1.6e-04 6.28
705629 rs17616063 16_27 1.000 107.19 3.1e-04 10.67
713573 rs13334801 16_45 1.000 120.33 3.5e-04 9.06
713574 rs11645522 16_45 1.000 268.63 7.8e-04 15.59
725666 rs56032910 17_19 1.000 732.80 2.1e-03 -4.27
725667 rs3744618 17_19 1.000 764.10 2.2e-03 -2.97
735019 rs1477066 17_41 1.000 171.00 5.0e-04 12.06
736371 rs312827 17_43 1.000 86.89 2.5e-04 8.81
754151 rs62094231 18_31 1.000 99.00 2.9e-04 -9.99
754444 rs12373325 18_31 1.000 580.53 1.7e-03 -26.10
754475 rs77528544 18_31 1.000 60.38 1.8e-04 -10.10
762251 rs351988 19_2 1.000 180.86 5.3e-04 12.20
773581 rs601338 19_33 1.000 202.84 5.9e-04 20.14
773587 rs12978750 19_33 1.000 189.72 5.5e-04 19.76
807006 rs78946667 22_7 1.000 250.93 7.3e-04 -13.51
807116 rs73152503 22_7 1.000 175.56 5.1e-04 13.35
807141 rs5760492 22_7 1.000 7680.60 2.2e-02 87.08
893789 rs1260326 2_16 1.000 503.37 1.5e-03 -24.84
895696 rs11688682 2_70 1.000 51.70 1.5e-04 -8.07
904835 rs545223341 2_119 1.000 28498.07 8.3e-02 5.76
904836 rs147350044 2_119 1.000 28917.98 8.4e-02 5.48
904841 rs10931949 2_119 1.000 29086.60 8.5e-02 5.82
946696 rs201939100 4_48 1.000 474.31 1.4e-03 -2.15
954969 rs1229984 4_66 1.000 40.58 1.2e-04 6.11
963147 rs4074793 5_31 1.000 253.35 7.4e-04 16.63
982164 rs140852576 5_45 1.000 4037.20 1.2e-02 -4.33
1068712 rs10661403 11_9 1.000 5323.68 1.5e-02 -5.05
1078885 rs3072639 11_29 1.000 1341.08 3.9e-03 1.69
1134454 rs547584892 12_75 1.000 388.35 1.1e-03 -1.23
1158514 rs55975236 14_54 1.000 725.08 2.1e-03 28.50
1235742 rs764858365 17_39 1.000 14421.85 4.2e-02 -3.99
1250157 rs62080193 17_46 1.000 7483.92 2.2e-02 -3.12
1250165 rs113375436 17_46 1.000 7484.83 2.2e-02 -2.76
1303809 rs2387343 19_34 1.000 73.75 2.1e-04 8.66
1306435 rs113176985 19_34 1.000 18303.83 5.3e-02 4.87
1306438 rs374141296 19_34 1.000 18385.33 5.3e-02 4.29
1334825 rs202143810 20_38 1.000 7920.94 2.3e-02 5.34
1340734 rs957056 21_11 1.000 1790.50 5.2e-03 2.80
1340735 rs527413941 21_11 1.000 1780.49 5.2e-03 2.78
1354273 rs748492500 22_19 1.000 612.40 1.8e-03 3.37
140472 rs570964414 3_22 0.999 51.37 1.5e-04 6.10
249277 rs62336098 4_119 0.999 31.80 9.2e-05 -5.52
280295 rs25965 5_63 0.999 37.65 1.1e-04 -5.69
306518 rs6597256 6_7 0.999 37.23 1.1e-04 -6.62
430800 rs4738679 8_45 0.999 60.97 1.8e-04 -8.26
435877 rs2941459 8_55 0.999 38.85 1.1e-04 -4.00
531985 rs11199973 10_75 0.999 35.37 1.0e-04 -5.76
578549 rs12824533 12_11 0.999 32.24 9.4e-05 5.51
770918 rs889140 19_23 0.999 50.80 1.5e-04 -9.50
49339 rs1223802 1_108 0.998 38.31 1.1e-04 -5.62
608947 rs2287563 12_70 0.997 46.60 1.3e-04 6.83
1097835 rs12418845 11_36 0.997 37.71 1.1e-04 -5.98
1352919 rs6519133 22_15 0.997 134.95 3.9e-04 11.76
28245 rs325937 1_69 0.996 53.72 1.6e-04 -7.12
185859 rs237663 3_115 0.996 33.55 9.7e-05 5.87
764565 rs344576 19_6 0.996 33.04 9.6e-05 -4.98
397236 rs10435378 7_70 0.995 46.18 1.3e-04 9.15
762250 rs351992 19_2 0.995 51.77 1.5e-04 0.02
218173 rs1530923 4_60 0.994 40.10 1.2e-04 5.02
764223 rs778805 19_6 0.993 35.84 1.0e-04 6.40
803011 rs7281137 21_20 0.993 31.42 9.1e-05 -5.17
1256496 rs12454712 18_35 0.993 33.29 9.6e-05 -6.19
167054 rs67631613 3_77 0.992 46.41 1.3e-04 -8.43
371585 rs216748 7_24 0.992 31.69 9.1e-05 -5.45
1020392 rs7780562 7_23 0.991 201.60 5.8e-04 14.47
84132 rs77062045 2_49 0.990 32.66 9.4e-05 -5.53
221981 rs35518360 4_67 0.990 30.42 8.8e-05 -5.45
590907 rs1492237 12_33 0.989 54.47 1.6e-04 -7.43
359400 rs78380098 6_110 0.988 33.71 9.7e-05 -4.85
556854 rs10751299 11_44 0.988 85.29 2.4e-04 -8.66
713438 rs200735395 16_44 0.987 37.00 1.1e-04 -5.34
371679 rs60585163 7_24 0.986 30.40 8.7e-05 -6.05
805056 rs437773 22_2 0.986 28.42 8.1e-05 5.23
1114914 rs3782735 12_7 0.986 54.83 1.6e-04 -7.69
802789 rs28373070 21_20 0.985 34.45 9.9e-05 5.70
235277 rs11727676 4_94 0.981 29.32 8.4e-05 5.44
459149 rs10758593 9_4 0.981 49.54 1.4e-04 -6.27
591968 rs7397189 12_36 0.980 25.61 7.3e-05 4.71
720370 rs9904284 17_4 0.979 25.86 7.4e-05 4.81
311503 rs10946488 6_16 0.978 188.55 5.4e-04 -15.33
166833 rs9829784 3_77 0.976 38.46 1.1e-04 -5.55
188902 rs5855544 3_120 0.975 26.01 7.4e-05 -4.95
300664 rs2569215 5_103 0.975 37.86 1.1e-04 -6.09
131757 rs11920824 3_4 0.973 44.45 1.3e-04 8.63
73320 rs6728830 2_26 0.971 36.38 1.0e-04 -5.78
311504 rs9358470 6_16 0.970 37.18 1.0e-04 8.07
736365 rs12946105 17_43 0.970 27.36 7.7e-05 -5.33
631207 rs9592879 13_35 0.969 31.28 8.8e-05 -5.23
1315646 rs150622725 20_3 0.969 135.47 3.8e-04 11.22
419802 rs1495743 8_20 0.967 46.35 1.3e-04 -6.86
671866 rs142753671 14_53 0.967 27.69 7.8e-05 4.37
699198 rs12597581 16_11 0.967 26.24 7.4e-05 -4.43
189930 rs3748034 4_4 0.966 49.39 1.4e-04 7.87
52162 rs12405317 1_116 0.964 29.50 8.3e-05 5.28
257599 rs2624420 5_13 0.964 26.19 7.3e-05 4.90
519057 rs116260006 10_50 0.964 35.48 9.9e-05 4.27
1235747 rs11079703 17_39 0.962 14415.81 4.0e-02 -3.85
513401 rs3099367 10_39 0.961 28.15 7.9e-05 -5.10
189925 rs13116176 4_4 0.956 42.93 1.2e-04 -8.97
271830 rs853807 5_41 0.952 25.90 7.2e-05 4.85
489728 rs79964188 9_63 0.950 24.66 6.8e-05 -4.65
68164 rs4580350 2_17 0.949 869.51 2.4e-03 -3.20
393298 rs142762939 7_63 0.949 25.12 6.9e-05 4.70
360867 rs11768282 7_1 0.948 25.41 7.0e-05 4.61
75470 rs7575998 2_31 0.947 68.06 1.9e-04 8.45
465575 rs7868612 9_16 0.947 34.90 9.6e-05 -5.78
31785 rs12745423 1_77 0.946 25.03 6.9e-05 4.34
53886 rs12567597 1_119 0.946 30.37 8.4e-05 -4.96
72579 rs11124740 2_26 0.945 29.73 8.2e-05 -5.19
134632 rs709149 3_9 0.945 49.84 1.4e-04 -6.25
646875 rs77394539 14_3 0.945 25.64 7.0e-05 -4.65
140469 rs7624339 3_22 0.941 25.87 7.1e-05 -2.79
4149 rs371329832 1_12 0.940 27.46 7.5e-05 -5.40
68057 rs7606480 2_17 0.940 60.79 1.7e-04 -7.72
292253 rs2190787 5_85 0.939 24.33 6.6e-05 -4.50
448506 rs2432961 8_79 0.939 47.13 1.3e-04 6.56
318981 rs7757749 6_29 0.937 26.72 7.3e-05 4.81
532002 rs10886945 10_76 0.935 26.06 7.1e-05 -4.50
12128 rs56057935 1_33 0.934 25.18 6.8e-05 4.64
695077 rs78630004 16_2 0.934 37.85 1.0e-04 -5.79
167052 rs34151455 3_77 0.931 50.59 1.4e-04 -8.88
23346 rs80251022 1_56 0.930 23.65 6.4e-05 -4.16
773182 rs8182469 19_33 0.930 35.37 9.6e-05 6.02
524601 rs17109928 10_60 0.929 27.17 7.3e-05 5.69
771704 rs2251125 19_24 0.928 25.68 6.9e-05 4.22
606708 rs111260184 12_65 0.926 25.57 6.9e-05 4.51
121484 rs77451633 2_127 0.922 26.12 7.0e-05 -4.85
385664 rs12539160 7_47 0.922 25.42 6.8e-05 -2.63
46895 rs7522247 1_105 0.921 25.81 6.9e-05 4.80
296181 rs62383006 5_93 0.921 102.20 2.7e-04 -10.39
517624 rs7907410 10_47 0.920 24.48 6.5e-05 4.59
1113769 rs540208368 12_7 0.913 31.51 8.4e-05 3.85
166076 rs3732357 3_74 0.911 49.89 1.3e-04 -7.67
531992 rs2278202 10_76 0.911 31.10 8.2e-05 5.05
713541 rs140496642 16_45 0.910 23.76 6.3e-05 3.42
824598 rs1497406 1_11 0.909 1068.19 2.8e-03 35.65
766638 rs3794991 19_15 0.907 27.57 7.3e-05 5.00
999592 rs7383287 6_27 0.907 57.60 1.5e-04 -9.28
771526 rs12985670 19_24 0.901 27.01 7.1e-05 -4.93
721511 rs9891006 17_7 0.897 29.32 7.6e-05 -4.46
376799 rs758989 7_32 0.896 24.26 6.3e-05 4.06
682894 rs878958 15_25 0.896 27.64 7.2e-05 4.80
1104550 rs16761 11_38 0.896 32.19 8.4e-05 -5.68
175834 rs74965475 3_95 0.895 23.35 6.1e-05 4.33
581740 rs2291075 12_16 0.891 68.16 1.8e-04 11.57
671889 rs11626736 14_53 0.890 31.84 8.2e-05 -5.03
202858 rs62298204 4_31 0.888 34.70 9.0e-05 5.87
459044 rs6415788 9_4 0.888 26.24 6.8e-05 -2.77
982159 rs35628643 5_45 0.887 4022.42 1.0e-02 -4.15
386072 rs11972122 7_49 0.885 17450.77 4.5e-02 -7.51
721522 rs112315122 17_7 0.882 22.86 5.9e-05 -2.64
536496 rs2767419 10_85 0.876 23.34 5.9e-05 -4.21
556211 rs11236797 11_42 0.874 39.12 9.9e-05 -6.26
28728 rs77847499 1_69 0.870 27.15 6.9e-05 4.83
371678 rs73084217 7_24 0.868 28.84 7.3e-05 5.41
478650 rs1360200 9_45 0.868 30.61 7.7e-05 6.15
1243558 rs183491032 17_42 0.868 30.63 7.7e-05 -5.11
340821 rs9384679 6_73 0.865 25.74 6.5e-05 -4.78
493910 rs79308035 10_3 0.863 24.06 6.0e-05 -4.38
1068720 rs6485252 11_9 0.863 5304.92 1.3e-02 -5.22
262487 rs13172112 5_21 0.862 95.08 2.4e-04 13.63
669336 rs12432456 14_49 0.859 24.51 6.1e-05 -4.46
489608 rs10818810 9_63 0.858 24.92 6.2e-05 4.54
502464 rs141772897 10_18 0.857 24.04 6.0e-05 -4.39
108557 rs62171052 2_102 0.855 31.41 7.8e-05 -6.92
678141 rs149997567 15_14 0.854 24.54 6.1e-05 4.54
361106 rs577012471 7_3 0.853 35.00 8.7e-05 5.72
415272 rs13265731 8_11 0.852 446.77 1.1e-03 5.21
75618 rs77658297 2_31 0.850 24.52 6.1e-05 -4.27
523695 rs10648437 10_58 0.845 24.07 5.9e-05 -4.45
812227 rs61736524 22_17 0.843 25.21 6.2e-05 -4.48
443031 rs2844045 8_68 0.842 23.27 5.7e-05 -4.30
470481 rs11557154 9_26 0.842 23.88 5.8e-05 4.25
655489 rs72681869 14_20 0.841 27.07 6.6e-05 -4.95
719396 rs3760230 17_3 0.841 24.63 6.0e-05 -4.45
485263 rs77070310 9_55 0.838 35.37 8.6e-05 -6.80
599093 rs7305798 12_49 0.837 42.17 1.0e-04 6.39
622012 rs148480921 13_16 0.837 56.37 1.4e-04 -7.53
730478 rs8077316 17_29 0.836 26.16 6.4e-05 -4.65
44972 rs74490351 1_100 0.835 27.45 6.7e-05 -5.26
268251 rs173964 5_33 0.835 55.27 1.3e-04 5.52
705827 rs11642255 16_28 0.835 24.60 6.0e-05 4.45
793176 rs115012179 20_36 0.835 26.85 6.5e-05 4.83
176664 rs9817452 3_97 0.834 24.02 5.8e-05 4.38
181178 rs234043 3_106 0.834 24.67 6.0e-05 4.41
671738 rs149061976 14_53 0.832 26.11 6.3e-05 4.30
770789 rs17841839 19_23 0.831 67.71 1.6e-04 9.36
360889 rs13226702 7_2 0.830 83.82 2.0e-04 -10.06
633443 rs1327315 13_40 0.829 25.01 6.0e-05 -4.48
583015 rs17389465 12_18 0.824 45.99 1.1e-04 -6.78
764552 rs2642201 19_6 0.823 24.83 5.9e-05 4.13
787866 rs3212201 20_28 0.821 32.62 7.8e-05 5.46
150401 rs1482601 3_43 0.820 24.45 5.8e-05 -4.37
289065 rs72799445 5_80 0.815 34.14 8.1e-05 -6.86
316697 rs3128760 6_26 0.815 41.78 9.9e-05 6.52
260413 rs72745229 5_17 0.813 25.80 6.1e-05 -4.42
1027472 rs145743281 7_48 0.813 30.83 7.3e-05 -4.99
758258 rs17082441 18_40 0.808 24.04 5.6e-05 4.42
734978 rs79861549 17_41 0.807 31.55 7.4e-05 -3.07
751113 rs9953845 18_26 0.807 25.74 6.0e-05 4.66
725659 rs4794893 17_19 0.804 332.82 7.8e-04 -3.73
543126 rs12797612 11_14 0.802 31.01 7.2e-05 5.31
248319 rs13108469 4_118 0.801 36.36 8.5e-05 5.91
1068688 rs11022065 11_9 0.801 5272.93 1.2e-02 -5.22SNPs with largest effect sizes
#plot PIP vs effect size
plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
904841 rs10931949 2_119 1 29086.60 8.5e-02 5.82
904826 rs6717641 2_119 0 29027.92 6.0e-06 5.77
904817 rs4129011 2_119 0 29023.19 1.9e-07 5.74
904816 rs4129010 2_119 0 29022.94 1.2e-07 5.74
904809 rs7603584 2_119 0 28986.22 7.1e-10 5.75
904836 rs147350044 2_119 1 28917.98 8.4e-02 5.48
904858 rs7573536 2_119 0 28685.68 0.0e+00 5.74
904868 rs10460403 2_119 0 28678.39 0.0e+00 5.73
904793 rs6755428 2_119 0 28675.29 0.0e+00 5.65
904859 rs12991600 2_119 0 28573.39 0.0e+00 5.77
904835 rs545223341 2_119 1 28498.07 8.3e-02 5.76
904840 rs10931948 2_119 0 26999.88 0.0e+00 6.27
904832 rs2287054 2_119 0 26993.82 0.0e+00 6.26
904842 rs6435084 2_119 0 26987.67 0.0e+00 6.25
904831 rs2270315 2_119 0 26953.79 0.0e+00 6.20
904806 rs10497868 2_119 0 26950.77 0.0e+00 6.21
904860 rs1019299 2_119 0 26940.83 0.0e+00 6.29
904875 rs2540441 2_119 0 26836.45 0.0e+00 -6.15
904758 rs10804115 2_119 0 26231.38 0.0e+00 4.82
904747 rs11690546 2_119 0 26222.49 0.0e+00 4.85
904744 rs11691865 2_119 0 26214.40 0.0e+00 4.82
904740 rs34625194 2_119 0 26212.38 0.0e+00 4.82
904743 rs11691859 2_119 0 26212.29 0.0e+00 4.82
904735 rs11691118 2_119 0 26210.21 0.0e+00 4.81
904709 rs7575721 2_119 0 26209.55 0.0e+00 4.92
904706 rs887995 2_119 0 26196.16 0.0e+00 4.95
904729 rs6751543 2_119 0 26190.33 0.0e+00 4.83
904713 rs12468504 2_119 0 26187.29 0.0e+00 4.89
904726 rs6761777 2_119 0 26185.80 0.0e+00 4.83
904712 rs11680694 2_119 0 26184.10 0.0e+00 4.86
904711 rs3815515 2_119 0 26183.77 0.0e+00 4.87
904716 rs7597850 2_119 0 26181.19 0.0e+00 4.83
904715 rs13001194 2_119 0 26181.18 0.0e+00 4.83
904717 rs7571761 2_119 0 26180.85 0.0e+00 4.83
904721 rs10931944 2_119 0 26179.49 0.0e+00 4.83
904722 rs11681526 2_119 0 26179.47 0.0e+00 4.83
904725 rs6732993 2_119 0 26179.42 0.0e+00 4.83
904702 rs2349079 2_119 0 26166.66 0.0e+00 4.92
904724 rs13022344 2_119 0 26165.32 0.0e+00 4.82
904704 rs2349082 2_119 0 26156.92 0.0e+00 4.91
904699 rs3214366 2_119 0 26151.77 0.0e+00 4.89
904698 rs2241118 2_119 0 26146.33 0.0e+00 4.94
904682 rs13027669 2_119 0 26145.12 0.0e+00 4.92
904683 rs12623282 2_119 0 26140.38 0.0e+00 4.91
904703 rs2349080 2_119 0 26139.15 0.0e+00 4.93
904684 rs7579617 2_119 0 26138.04 0.0e+00 4.90
904685 rs7579853 2_119 0 26133.57 0.0e+00 4.89
904676 rs3795966 2_119 0 26133.25 0.0e+00 4.88
904679 rs34926038 2_119 0 26129.24 0.0e+00 4.90
904727 rs10693704 2_119 0 26084.22 0.0e+00 4.82SNPs with highest PVE
#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
904841 rs10931949 2_119 1.000 29086.60 0.0850 5.82
904836 rs147350044 2_119 1.000 28917.98 0.0840 5.48
904835 rs545223341 2_119 1.000 28498.07 0.0830 5.76
386068 rs761767938 7_49 1.000 19050.63 0.0550 -6.77
386076 rs1544459 7_49 1.000 18759.16 0.0550 -6.92
1306435 rs113176985 19_34 1.000 18303.83 0.0530 4.87
1306438 rs374141296 19_34 1.000 18385.33 0.0530 4.29
386072 rs11972122 7_49 0.885 17450.77 0.0450 -7.51
386065 rs10277379 7_49 1.000 14666.34 0.0430 -7.47
1235742 rs764858365 17_39 1.000 14421.85 0.0420 -3.99
1235747 rs11079703 17_39 0.962 14415.81 0.0400 -3.85
1334825 rs202143810 20_38 1.000 7920.94 0.0230 5.34
807141 rs5760492 22_7 1.000 7680.60 0.0220 87.08
1250157 rs62080193 17_46 1.000 7483.92 0.0220 -3.12
1250165 rs113375436 17_46 1.000 7484.83 0.0220 -2.76
1068712 rs10661403 11_9 1.000 5323.68 0.0150 -5.05
1068720 rs6485252 11_9 0.863 5304.92 0.0130 -5.22
982164 rs140852576 5_45 1.000 4037.20 0.0120 -4.33
1068688 rs11022065 11_9 0.801 5272.93 0.0120 -5.22
1334821 rs145835311 20_38 0.505 7981.86 0.0120 5.90
982159 rs35628643 5_45 0.887 4022.42 0.0100 -4.15
625740 rs12430288 13_25 1.000 2818.31 0.0082 2.69
625736 rs566812111 13_25 1.000 2790.73 0.0081 2.60
1334804 rs67468102 20_38 0.316 8032.72 0.0074 5.75
1334805 rs2750483 20_38 0.308 8033.33 0.0072 5.74
1334800 rs2315009 20_38 0.306 8031.78 0.0071 5.75
982155 rs246783 5_45 0.559 4019.77 0.0065 4.14
386073 rs11406602 7_49 0.115 17429.22 0.0058 -7.45
610911 rs2393775 12_74 1.000 1923.35 0.0056 49.77
1340734 rs957056 21_11 1.000 1790.50 0.0052 2.80
1340735 rs527413941 21_11 1.000 1780.49 0.0052 2.78
1334803 rs35201382 20_38 0.187 8032.34 0.0044 5.72
1078885 rs3072639 11_29 1.000 1341.08 0.0039 1.69
1235752 rs8079835 17_39 0.088 14405.98 0.0037 -3.81
1235730 rs12938098 17_39 0.081 14404.77 0.0034 -3.81
1334822 rs6089961 20_38 0.143 8033.05 0.0033 5.70
1334824 rs2738758 20_38 0.143 8033.05 0.0033 5.70
235485 rs4552481 4_95 1.000 1055.58 0.0031 33.83
1235732 rs11870061 17_39 0.075 14405.85 0.0031 -3.80
824598 rs1497406 1_11 0.909 1068.19 0.0028 35.65
1158463 rs10131298 14_54 0.557 1699.60 0.0027 45.28
68161 rs569546056 2_17 1.000 870.04 0.0025 3.10
68164 rs4580350 2_17 0.949 869.51 0.0024 -3.20
610908 rs1169286 12_74 1.000 783.86 0.0023 -39.32
725667 rs3744618 17_19 1.000 764.10 0.0022 -2.97
725666 rs56032910 17_19 1.000 732.80 0.0021 -4.27
1158514 rs55975236 14_54 1.000 725.08 0.0021 28.50
1354273 rs748492500 22_19 1.000 612.40 0.0018 3.37
754444 rs12373325 18_31 1.000 580.53 0.0017 -26.10
1235750 rs8075040 17_39 0.039 14406.86 0.0016 -3.81SNPs with largest z scores
#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
807141 rs5760492 22_7 1.000 7680.60 2.2e-02 87.08
807142 rs2017869 22_7 0.000 7371.66 0.0e+00 84.88
807144 rs5760499 22_7 0.000 4619.58 0.0e+00 65.47
807145 rs62231240 22_7 0.000 4182.68 0.0e+00 61.69
610911 rs2393775 12_74 1.000 1923.35 5.6e-03 49.77
610910 rs7979478 12_74 0.000 1897.18 2.0e-08 49.42
1158463 rs10131298 14_54 0.557 1699.60 2.7e-03 45.28
1158461 rs11624282 14_54 0.107 1695.99 5.3e-04 45.23
1158465 rs77071436 14_54 0.302 1698.45 1.5e-03 45.23
1158462 rs550999044 14_54 0.003 1690.27 1.6e-05 45.19
1158469 rs151184170 14_54 0.008 1690.63 3.9e-05 45.17
1158457 rs2297067 14_54 0.011 1690.82 5.2e-05 45.16
1158447 rs59643720 14_54 0.005 1689.52 2.5e-05 45.15
1158449 rs61462345 14_54 0.005 1689.37 2.4e-05 45.15
1158460 rs61418148 14_54 0.000 1674.56 6.7e-08 45.14
1158446 rs61007561 14_54 0.000 1685.18 2.3e-06 45.12
1158455 rs62006947 14_54 0.002 1687.03 1.0e-05 45.12
1158473 rs7150997 14_54 0.000 1683.15 1.2e-06 45.09
1158448 rs17101241 14_54 0.000 1670.54 4.3e-08 45.07
1158474 rs944002 14_54 0.000 1682.35 5.8e-07 45.07
1158468 rs72706640 14_54 0.000 1667.63 3.5e-08 45.04
1158451 rs56956502 14_54 0.000 1671.97 6.0e-08 44.81
1158452 rs36027406 14_54 0.000 1649.77 3.6e-08 44.69
1158458 rs2297066 14_54 0.000 1639.95 2.3e-08 44.56
1158456 rs7151779 14_54 0.000 1603.61 2.8e-08 44.24
1158470 rs147121761 14_54 0.000 1422.51 1.2e-08 43.58
610921 rs1169311 12_74 0.000 1491.12 5.5e-09 -43.34
610917 rs1169300 12_74 0.000 1172.48 1.4e-09 -43.29
610898 rs2701194 12_74 0.000 1133.28 2.8e-09 43.08
1158464 rs10142200 14_54 0.000 1081.69 4.6e-07 42.97
1158441 rs11628185 14_54 0.000 1072.29 4.2e-07 42.68
1158442 rs11624069 14_54 0.000 1061.95 3.0e-07 42.68
1158440 rs8017161 14_54 0.000 1031.50 5.8e-07 41.72
1158478 rs2274685 14_54 0.000 931.37 4.5e-08 41.14
1158480 rs113431001 14_54 0.000 1369.92 5.4e-09 41.07
1158481 rs149136706 14_54 0.000 1380.94 5.7e-09 41.06
1158471 rs9324058 14_54 0.000 1004.83 3.0e-07 40.78
610925 rs2258287 12_74 0.000 1055.19 2.9e-09 -40.69
1158476 rs10144543 14_54 0.000 952.52 5.5e-08 40.39
1158483 rs138371522 14_54 0.000 1284.25 3.5e-09 39.83
610908 rs1169286 12_74 1.000 783.86 2.3e-03 -39.32
1158432 rs57913635 14_54 0.000 1116.64 2.6e-09 38.36
1158482 rs145260258 14_54 0.000 1119.24 2.2e-09 37.69
824598 rs1497406 1_11 0.909 1068.19 2.8e-03 35.65
824617 rs36086195 1_11 0.092 1064.07 2.8e-04 35.58
1158431 rs34976218 14_54 0.000 686.26 4.6e-09 -35.49
1158427 rs12894515 14_54 0.000 678.35 4.6e-09 -35.25
824629 rs924204 1_11 0.001 1010.64 2.2e-06 34.30
235485 rs4552481 4_95 1.000 1055.58 3.1e-03 33.83
610923 rs2258043 12_74 0.000 913.95 4.9e-07 -33.58Gene set enrichment for genes with PIP>0.8
#GO enrichment analysis
library(enrichR)Welcome to enrichR
Checking connection ... Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
#number of genes for gene set enrichment
length(genes)[1] 67if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 focal adhesion (GO:0005925) 7/387 0.01339616
2 cell-substrate junction (GO:0030055) 7/394 0.01339616
Genes
1 ENAH;ARL14EP;YES1;ASAP3;PARVB;RPS11;ACTG1
2 ENAH;ARL14EP;YES1;ASAP3;PARVB;RPS11;ACTG1
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)KMT5A gene(s) from the input list not found in DisGeNET CURATEDPTPRD-AS1 gene(s) from the input list not found in DisGeNET CURATEDFCHO1 gene(s) from the input list not found in DisGeNET CURATEDTRNP1 gene(s) from the input list not found in DisGeNET CURATEDMLXIP gene(s) from the input list not found in DisGeNET CURATEDSLF2 gene(s) from the input list not found in DisGeNET CURATEDRP11-7F17.5 gene(s) from the input list not found in DisGeNET CURATEDTRIP10 gene(s) from the input list not found in DisGeNET CURATEDNFKBIB gene(s) from the input list not found in DisGeNET CURATEDSPDYE5 gene(s) from the input list not found in DisGeNET CURATEDMICAL2 gene(s) from the input list not found in DisGeNET CURATEDRP11-219B17.3 gene(s) from the input list not found in DisGeNET CURATEDTNKS gene(s) from the input list not found in DisGeNET CURATEDPKN3 gene(s) from the input list not found in DisGeNET CURATEDZNF747 gene(s) from the input list not found in DisGeNET CURATEDMLIP gene(s) from the input list not found in DisGeNET CURATEDDLEU1 gene(s) from the input list not found in DisGeNET CURATEDRP11-327J17.2 gene(s) from the input list not found in DisGeNET CURATEDTMEM129 gene(s) from the input list not found in DisGeNET CURATEDHLA-DMB gene(s) from the input list not found in DisGeNET CURATEDPALM3 gene(s) from the input list not found in DisGeNET CURATEDTMEM101 gene(s) from the input list not found in DisGeNET CURATEDKIAA1755 gene(s) from the input list not found in DisGeNET CURATEDPARVB gene(s) from the input list not found in DisGeNET CURATEDALLC gene(s) from the input list not found in DisGeNET CURATEDKCNJ12 gene(s) from the input list not found in DisGeNET CURATEDRP11-428O18.6 gene(s) from the input list not found in DisGeNET CURATEDRAVER2 gene(s) from the input list not found in DisGeNET CURATEDRPS11 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
11 Asphyxia 0.03005328 1/38 1/9703
31 Neoplastic Cell Transformation 0.03005328 4/38 139/9703
83 Hydrocephalus 0.03005328 2/38 9/9703
135 Peyronie Disease 0.03005328 1/38 1/9703
162 Ureteral obstruction 0.03005328 2/38 24/9703
201 Renal Cell Dysplasia 0.03005328 1/38 1/9703
210 Short upturned nose 0.03005328 1/38 1/9703
229 Charcot-Marie-Tooth disease, Type 1C 0.03005328 1/38 1/9703
258 Peritoneal Fibrosis 0.03005328 1/38 1/9703
268 Anhydramnios 0.03005328 1/38 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULL
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.0.0
[5] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.57.0
[3] fs_1.3.1 bit64_4.0.5
[5] doParallel_1.0.16 progress_1.2.2
[7] httr_1.4.1 rprojroot_2.0.2
[9] GenomeInfoDb_1.20.0 doRNG_1.8.2
[11] tools_3.6.1 utf8_1.2.1
[13] R6_2.5.0 DBI_1.1.1
[15] BiocGenerics_0.30.0 colorspace_1.4-1
[17] withr_2.4.1 tidyselect_1.1.0
[19] prettyunits_1.0.2 bit_4.0.4
[21] curl_3.3 compiler_3.6.1
[23] git2r_0.26.1 Biobase_2.44.0
[25] DelayedArray_0.10.0 rtracklayer_1.44.0
[27] labeling_0.3 scales_1.1.0
[29] readr_1.4.0 apcluster_1.4.8
[31] stringr_1.4.0 digest_0.6.20
[33] Rsamtools_2.0.0 svglite_1.2.2
[35] rmarkdown_1.13 XVector_0.24.0
[37] pkgconfig_2.0.3 htmltools_0.3.6
[39] fastmap_1.1.0 BSgenome_1.52.0
[41] rlang_0.4.11 RSQLite_2.2.7
[43] generics_0.0.2 farver_2.1.0
[45] jsonlite_1.6 BiocParallel_1.18.0
[47] dplyr_1.0.7 VariantAnnotation_1.30.1
[49] RCurl_1.98-1.1 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.1 Matrix_1.2-18
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.22.1 fansi_0.5.0
[57] gdtools_0.1.9 lifecycle_1.0.0
[59] stringi_1.4.3 whisker_0.3-2
[61] yaml_2.2.0 SummarizedExperiment_1.14.1
[63] zlibbioc_1.30.0 plyr_1.8.4
[65] grid_3.6.1 blob_1.2.1
[67] parallel_3.6.1 promises_1.0.1
[69] crayon_1.4.1 lattice_0.20-38
[71] Biostrings_2.52.0 GenomicFeatures_1.36.3
[73] hms_1.1.0 knitr_1.23
[75] pillar_1.6.1 igraph_1.2.4.1
[77] GenomicRanges_1.36.0 rjson_0.2.20
[79] rngtools_1.5 codetools_0.2-16
[81] reshape2_1.4.3 biomaRt_2.40.1
[83] stats4_3.6.1 XML_3.98-1.20
[85] glue_1.4.2 evaluate_0.14
[87] data.table_1.14.0 foreach_1.5.1
[89] vctrs_0.3.8 httpuv_1.5.1
[91] gtable_0.3.0 purrr_0.3.4
[93] assertthat_0.2.1 cachem_1.0.5
[95] xfun_0.8 later_0.8.0
[97] tibble_3.1.2 iterators_1.0.13
[99] GenomicAlignments_1.20.1 AnnotationDbi_1.46.0
[101] memoise_2.0.0 IRanges_2.18.1
[103] workflowr_1.6.2 ellipsis_0.3.2