Alkaline phosphatase (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 Alkaline phosphatase (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-30610_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.019891227 0.000181867 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
27.05611 27.37420 #report sample size
print(sample_size)[1] 344292#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01703987 0.12576338 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.08040835 0.81837795Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
3330 SEC16B 1_87 1.000 9596.71 2.8e-02 8.39
1114 SRRT 7_62 1.000 84.30 2.4e-04 9.14
5219 MFGE8 15_41 1.000 210.56 6.1e-04 -7.25
5252 OSGIN1 16_48 1.000 68.97 2.0e-04 7.99
721 WIPI1 17_39 1.000 128.13 3.7e-04 -11.22
12135 S1PR2 19_9 1.000 76.74 2.2e-04 -8.65
3714 MBOAT7 19_37 1.000 289.40 8.4e-04 -20.38
5544 CNIH4 1_114 0.997 32.90 9.5e-05 -5.51
8531 TNKS 8_12 0.996 122.13 3.5e-04 -14.12
7040 INHBB 2_70 0.991 57.72 1.7e-04 -7.51
9017 ERN1 17_37 0.991 32.74 9.4e-05 -5.30
11790 CYP2A6 19_28 0.991 218.23 6.3e-04 -20.07
2209 RIC1 9_6 0.990 118.78 3.4e-04 -14.15
1488 MIEF1 22_16 0.988 186.44 5.3e-04 -8.95
1188 KIF16B 20_12 0.985 44.43 1.3e-04 6.58
6100 ALLC 2_2 0.979 56.43 1.6e-04 7.53
6566 PEX10 1_2 0.977 72.84 2.1e-04 6.54
2025 CNFN 19_29 0.977 26.43 7.5e-05 4.93
578 SBNO2 19_2 0.976 41.23 1.2e-04 4.45
3562 ACVR1C 2_94 0.973 47.63 1.3e-04 -6.73
10399 ANKRD35 1_73 0.971 24.74 7.0e-05 -4.57
7136 THOC7 3_43 0.971 23.28 6.6e-05 4.47
10303 UGT2B17 4_48 0.971 157.92 4.5e-04 -12.62
6849 PGAP3 17_23 0.966 175.11 4.9e-04 13.11
1074 MAP3K4 6_104 0.965 26.63 7.5e-05 4.93
8187 GPRC5C 17_41 0.965 53.56 1.5e-04 7.17
10312 ZNF311 6_23 0.960 23.85 6.7e-05 -4.91
1339 CDC5L 6_34 0.953 26.78 7.4e-05 -4.43
9390 GAS6 13_62 0.952 33.95 9.4e-05 -5.75
6494 PHKG2 16_24 0.951 63.33 1.8e-04 -7.22
1429 SH3BP1 22_15 0.948 27.67 7.6e-05 5.93
2718 NNT 5_28 0.946 21.44 5.9e-05 4.06
9273 ZNF329 19_39 0.945 27.39 7.5e-05 5.11
5742 TNIP1 5_88 0.942 22.47 6.1e-05 -4.29
5751 MYLK4 6_3 0.939 34.46 9.4e-05 -5.41
4239 TRIM5 11_4 0.939 134.21 3.7e-04 -10.36
5769 MLIP 6_40 0.931 64.28 1.7e-04 -7.94
4035 DOHH 19_4 0.928 25.09 6.8e-05 4.70
8119 TM4SF4 3_92 0.925 25.03 6.7e-05 4.78
10709 ARID3C 9_26 0.922 41.47 1.1e-04 -5.54
1737 ELMO3 16_36 0.920 21.85 5.8e-05 -4.02
10637 NFKBIL1 6_25 0.918 25.64 6.8e-05 -4.87
6391 TTC39B 9_13 0.918 26.66 7.1e-05 -4.82
11330 ZBTB22 6_28 0.912 31.05 8.2e-05 3.94
6703 UROC1 3_79 0.908 24.09 6.3e-05 -4.46
8447 CTSW 11_36 0.908 27.97 7.4e-05 4.72
12467 RP11-219B17.3 15_27 0.906 24.36 6.4e-05 -4.58
6223 GPR180 13_47 0.904 198.57 5.2e-04 16.33
10731 EXOC3L4 14_54 0.903 54.34 1.4e-04 7.37
1290 EZR 6_103 0.901 24.35 6.4e-05 -4.53
6906 LBHD1 11_35 0.898 20.66 5.4e-05 4.06
12687 RP4-781K5.7 1_121 0.896 36.73 9.6e-05 -6.56
6569 SKI 1_2 0.890 47.45 1.2e-04 5.13
7353 CHMP4C 8_58 0.885 21.60 5.6e-05 4.41
6115 VTI1A 10_70 0.884 38.32 9.8e-05 4.84
11710 KB-1732A1.1 8_69 0.870 45.97 1.2e-04 6.70
7616 CDYL2 16_45 0.868 62.52 1.6e-04 8.13
9516 SS18L1 20_36 0.868 50.21 1.3e-04 7.02
9462 NPIPA5 16_15 0.867 27.40 6.9e-05 -5.02
10513 L3MBTL3 6_86 0.863 25.62 6.4e-05 -4.73
10860 UBD 6_23 0.850 24.31 6.0e-05 -4.80
2621 PPARD 6_28 0.845 55.81 1.4e-04 7.62
10582 BMPR2 2_120 0.840 30.60 7.5e-05 5.93
6490 ATAD2 8_80 0.827 23.42 5.6e-05 4.38
1848 CD276 15_35 0.824 22.49 5.4e-05 4.37
7656 CATSPER2 15_16 0.820 73.64 1.8e-04 -8.61
9445 ZNF530 19_39 0.816 21.92 5.2e-05 -4.40
12229 RP11-346C20.3 16_39 0.815 20.83 4.9e-05 4.16
10551 LIME1 20_38 0.810 22.14 5.2e-05 -4.45
9126 CRIPAK 4_2 0.802 32.97 7.7e-05 5.22Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
3330 SEC16B 1_87 1.000 9596.71 2.8e-02 8.39
7802 ZNF283 19_30 0.714 4786.31 9.9e-03 5.01
8812 ZNF404 19_30 0.014 2660.98 1.1e-04 5.72
12106 RP11-15A1.3 19_30 0.013 2659.39 9.7e-05 5.72
5418 NBPF3 1_15 0.000 2506.10 0.0e+00 47.78
6715 LYPD5 19_30 0.000 1973.37 8.0e-15 -3.31
8865 FUT2 19_33 0.000 1125.51 1.1e-06 47.24
837 RASAL2 1_87 0.000 811.13 0.0e+00 -3.36
164 PRSS3 9_26 0.003 749.83 6.6e-06 2.21
2228 UBE2R2 9_26 0.002 695.48 4.0e-06 2.00
11699 RP11-10A14.4 8_11 0.000 669.93 0.0e+00 7.45
2649 ALDH5A1 6_18 0.000 660.73 0.0e+00 -33.49
2041 FAM83E 19_33 0.001 601.15 9.4e-07 -33.72
6567 RER1 1_2 0.000 568.33 2.2e-08 2.16
8862 MAMSTR 19_33 0.001 565.51 2.1e-06 -32.25
11738 RP11-115J16.2 8_12 0.008 551.89 1.2e-05 -27.56
4798 UBAP2 9_26 0.002 447.26 2.6e-06 -1.27
11684 RP11-136O12.2 8_83 0.006 440.20 7.6e-06 15.50
10689 ZNF155 19_30 0.000 432.89 8.4e-17 3.73
11726 CLDN23 8_11 0.000 419.16 0.0e+00 5.74Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
3330 SEC16B 1_87 1.000 9596.71 0.02800 8.39
7802 ZNF283 19_30 0.714 4786.31 0.00990 5.01
3714 MBOAT7 19_37 1.000 289.40 0.00084 -20.38
11790 CYP2A6 19_28 0.991 218.23 0.00063 -20.07
5219 MFGE8 15_41 1.000 210.56 0.00061 -7.25
1488 MIEF1 22_16 0.988 186.44 0.00053 -8.95
6223 GPR180 13_47 0.904 198.57 0.00052 16.33
6849 PGAP3 17_23 0.966 175.11 0.00049 13.11
10303 UGT2B17 4_48 0.971 157.92 0.00045 -12.62
75 YBX2 17_6 0.500 282.68 0.00041 -16.48
9270 SLC2A4 17_6 0.500 282.68 0.00041 -16.48
4239 TRIM5 11_4 0.939 134.21 0.00037 -10.36
721 WIPI1 17_39 1.000 128.13 0.00037 -11.22
8531 TNKS 8_12 0.996 122.13 0.00035 -14.12
2209 RIC1 9_6 0.990 118.78 0.00034 -14.15
1114 SRRT 7_62 1.000 84.30 0.00024 9.14
12135 S1PR2 19_9 1.000 76.74 0.00022 -8.65
6566 PEX10 1_2 0.977 72.84 0.00021 6.54
2437 B3GAT1 11_84 0.783 90.61 0.00021 -9.38
5917 INPP5E 9_73 0.556 122.04 0.00020 11.45Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5418 NBPF3 1_15 0.000 2506.10 0.0e+00 47.78
8865 FUT2 19_33 0.000 1125.51 1.1e-06 47.24
2041 FAM83E 19_33 0.001 601.15 9.4e-07 -33.72
2649 ALDH5A1 6_18 0.000 660.73 0.0e+00 -33.49
8862 MAMSTR 19_33 0.001 565.51 2.1e-06 -32.25
6767 CACFD1 9_70 0.001 336.29 6.8e-07 -29.33
11738 RP11-115J16.2 8_12 0.008 551.89 1.2e-05 -27.56
4547 HNF1A 12_74 0.009 349.19 8.7e-06 -22.68
3714 MBOAT7 19_37 1.000 289.40 8.4e-04 -20.38
11790 CYP2A6 19_28 0.991 218.23 6.3e-04 -20.07
7794 TMC4 19_37 0.083 285.24 6.9e-05 20.02
5991 FADS1 11_34 0.026 379.42 2.8e-05 -19.39
4507 FADS2 11_34 0.009 341.06 8.5e-06 -18.28
7955 FEN1 11_34 0.009 341.06 8.5e-06 -18.28
7364 TNFRSF11B 8_79 0.001 131.85 3.2e-07 -17.38
11994 MAFTRR 16_44 0.050 269.73 3.9e-05 -16.97
75 YBX2 17_6 0.500 282.68 4.1e-04 -16.48
9270 SLC2A4 17_6 0.500 282.68 4.1e-04 -16.48
6223 GPR180 13_47 0.904 198.57 5.2e-04 16.33
11684 RP11-136O12.2 8_83 0.006 440.20 7.6e-06 15.50Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03917072#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5418 NBPF3 1_15 0.000 2506.10 0.0e+00 47.78
8865 FUT2 19_33 0.000 1125.51 1.1e-06 47.24
2041 FAM83E 19_33 0.001 601.15 9.4e-07 -33.72
2649 ALDH5A1 6_18 0.000 660.73 0.0e+00 -33.49
8862 MAMSTR 19_33 0.001 565.51 2.1e-06 -32.25
6767 CACFD1 9_70 0.001 336.29 6.8e-07 -29.33
11738 RP11-115J16.2 8_12 0.008 551.89 1.2e-05 -27.56
4547 HNF1A 12_74 0.009 349.19 8.7e-06 -22.68
3714 MBOAT7 19_37 1.000 289.40 8.4e-04 -20.38
11790 CYP2A6 19_28 0.991 218.23 6.3e-04 -20.07
7794 TMC4 19_37 0.083 285.24 6.9e-05 20.02
5991 FADS1 11_34 0.026 379.42 2.8e-05 -19.39
4507 FADS2 11_34 0.009 341.06 8.5e-06 -18.28
7955 FEN1 11_34 0.009 341.06 8.5e-06 -18.28
7364 TNFRSF11B 8_79 0.001 131.85 3.2e-07 -17.38
11994 MAFTRR 16_44 0.050 269.73 3.9e-05 -16.97
75 YBX2 17_6 0.500 282.68 4.1e-04 -16.48
9270 SLC2A4 17_6 0.500 282.68 4.1e-04 -16.48
6223 GPR180 13_47 0.904 198.57 5.2e-04 16.33
11684 RP11-136O12.2 8_83 0.006 440.20 7.6e-06 15.50Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 1_15"
genename region_tag susie_pip mu2 PVE z
5418 NBPF3 1_15 0 2506.10 0 47.78
1235 USP48 1_15 0 63.74 0 -4.75
9856 LDLRAD2 1_15 0 197.65 0 -2.33
5419 HSPG2 1_15 0 46.06 0 -2.81
5417 CELA3A 1_15 0 9.97 0 0.49
10971 LINC00339 1_15 0 254.14 0 -10.52
735 CDC42 1_15 0 12.33 0 -4.33
6947 WNT4 1_15 0 42.75 0 -2.54
9541 ZBTB40 1_15 0 18.99 0 -2.56
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_33"
genename region_tag susie_pip mu2 PVE z
10231 DACT3 19_33 0.000 4.85 3.4e-09 -0.03
1999 PRKD2 19_33 0.000 6.45 5.2e-09 0.77
1219 STRN4 19_33 0.000 6.72 5.8e-09 -0.48
9210 FKRP 19_33 0.002 27.40 2.0e-07 2.17
1998 SLC1A5 19_33 0.000 5.33 3.9e-09 -0.49
6725 ARHGAP35 19_33 0.000 8.70 9.5e-09 0.88
4115 NPAS1 19_33 0.000 6.01 4.8e-09 -0.45
4114 ZC3H4 19_33 0.001 19.73 6.6e-08 -0.93
5375 SAE1 19_33 0.001 19.73 6.6e-08 -0.93
2002 CCDC9 19_33 0.000 8.37 8.5e-09 0.54
10232 C5AR1 19_33 0.000 6.34 5.6e-09 -1.04
11840 INAFM1 19_33 0.000 10.00 1.2e-08 1.82
4510 C5AR2 19_33 0.001 11.80 1.8e-08 -1.28
4505 DHX34 19_33 0.000 7.28 6.5e-09 -0.21
3155 ZNF541 19_33 0.000 5.94 4.3e-09 -0.71
546 GLTSCR1 19_33 0.000 5.81 4.8e-09 0.19
285 EHD2 19_33 0.000 10.31 1.2e-08 1.63
2021 SULT2A1 19_33 0.643 45.37 8.5e-05 -7.90
2035 PLA2G4C 19_33 0.000 6.26 4.4e-09 2.15
2033 LIG1 19_33 0.000 8.16 9.4e-09 0.02
9623 C19orf68 19_33 0.000 6.91 5.8e-09 -0.81
2032 CARD8 19_33 0.000 6.18 5.4e-09 -0.48
2031 CCDC114 19_33 0.000 5.17 3.6e-09 -0.70
5374 EMP3 19_33 0.000 10.99 8.0e-09 -3.79
2028 GRWD1 19_33 0.000 7.62 5.6e-09 -1.71
9317 KCNJ14 19_33 0.000 12.42 9.0e-09 -3.68
2027 CYTH2 19_33 0.000 6.12 5.0e-09 0.39
5376 LMTK3 19_33 0.000 6.75 4.8e-09 -2.47
1139 SULT2B1 19_33 0.000 6.40 4.5e-09 1.73
2041 FAM83E 19_33 0.001 601.15 9.4e-07 -33.72
547 SPHK2 19_33 0.000 109.30 7.6e-08 14.72
2037 DBP 19_33 0.002 36.85 2.2e-07 -4.38
548 CA11 19_33 0.000 49.24 5.2e-08 8.63
8865 FUT2 19_33 0.000 1125.51 1.1e-06 47.24
8862 MAMSTR 19_33 0.001 565.51 2.1e-06 -32.25
9314 IZUMO1 19_33 0.000 5.64 4.1e-09 -0.67
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_18"
genename region_tag susie_pip mu2 PVE z
2649 ALDH5A1 6_18 0 660.73 0.0e+00 -33.49
2648 GPLD1 6_18 0 382.97 2.3e-07 -12.58
2652 ACOT13 6_18 0 20.53 0.0e+00 -2.83
2598 TDP2 6_18 0 92.75 0.0e+00 11.43
2655 C6orf62 6_18 0 100.95 0.0e+00 11.27
2657 GMNN 6_18 0 20.36 0.0e+00 -4.28
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 9_70"
genename region_tag susie_pip mu2 PVE z
3712 DDX31 9_70 0.004 42.16 4.9e-07 -3.34
7490 SPACA9 9_70 0.005 26.55 3.7e-07 -2.64
7491 TSC1 9_70 0.001 9.87 2.1e-08 -1.64
5908 GTF3C5 9_70 0.004 21.01 2.3e-07 1.51
5904 SURF6 9_70 0.001 97.13 2.4e-07 -11.31
5905 MED22 9_70 0.003 75.87 5.7e-07 -12.29
5907 RPL7A 9_70 0.001 126.04 2.5e-07 14.19
5903 SURF2 9_70 0.001 76.08 1.6e-07 11.85
10488 STKLD1 9_70 0.001 120.52 2.4e-07 -14.38
5901 SURF4 9_70 0.001 43.30 1.5e-07 7.16
6766 ADAMTS13 9_70 0.001 51.66 2.2e-07 -5.23
5906 REXO4 9_70 0.002 55.96 3.9e-07 14.80
6767 CACFD1 9_70 0.001 336.29 6.8e-07 -29.33
5902 SURF1 9_70 0.006 63.72 1.1e-06 6.23
3553 DBH 9_70 0.001 10.88 3.4e-08 1.34
10168 FAM163B 9_70 0.001 6.86 1.6e-08 -1.19
3552 SARDH 9_70 0.001 16.78 4.5e-08 -3.92
11306 LINC00094 9_70 0.002 15.81 7.5e-08 -2.24
8124 BRD3 9_70 0.001 8.15 2.0e-08 1.49
10061 WDR5 9_70 0.001 9.86 3.2e-08 -1.15
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 8_12"
genename region_tag susie_pip mu2 PVE z
8531 TNKS 8_12 0.996 122.13 3.5e-04 -14.12
11738 RP11-115J16.2 8_12 0.008 551.89 1.2e-05 -27.56
| 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
6145 rs77025042 1_14 1.000 203.14 5.9e-04 -13.03
6161 rs148717955 1_14 1.000 527.41 1.5e-03 5.52
6167 rs72657133 1_14 1.000 1222.26 3.5e-03 -23.99
6192 rs12047493 1_15 1.000 2164.71 6.3e-03 -49.86
6211 rs76372215 1_15 1.000 1283.52 3.7e-03 -39.58
6250 rs34605986 1_15 1.000 907.31 2.6e-03 33.59
6260 rs148785605 1_15 1.000 1282.24 3.7e-03 -49.46
6279 rs16825755 1_15 1.000 881.94 2.6e-03 -18.84
32544 rs6679677 1_70 1.000 81.88 2.4e-04 -7.89
52092 rs1223802 1_108 1.000 112.90 3.3e-04 -10.30
62234 rs12239046 1_131 1.000 89.31 2.6e-04 9.72
101067 rs2277882 2_79 1.000 80.81 2.3e-04 -7.09
101120 rs1257220 2_79 1.000 133.34 3.9e-04 -10.61
110771 rs1862069 2_102 1.000 137.21 4.0e-04 -16.41
119287 rs2041080 2_117 1.000 50.75 1.5e-04 10.17
240484 rs72727873 4_98 1.000 39.31 1.1e-04 -4.44
267469 rs1428967 5_25 1.000 114.10 3.3e-04 11.11
316466 rs151189505 6_17 1.000 130.68 3.8e-04 10.73
316703 rs9393530 6_18 1.000 210.91 6.1e-04 0.12
316815 rs10946700 6_18 1.000 2030.55 5.9e-03 44.85
316996 rs114584234 6_19 1.000 149.96 4.4e-04 13.26
317000 rs7738816 6_19 1.000 58.67 1.7e-04 9.22
317007 rs9461081 6_19 1.000 135.48 3.9e-04 -13.60
417071 rs2428 8_11 1.000 2989.50 8.7e-03 15.16
417076 rs758184196 8_11 1.000 3117.01 9.1e-03 -3.89
417318 rs2048656 8_13 1.000 163.79 4.8e-04 13.82
418004 rs10105588 8_14 1.000 184.19 5.3e-04 -4.87
418012 rs10092177 8_14 1.000 271.13 7.9e-04 -12.10
418014 rs779417490 8_14 1.000 261.94 7.6e-04 -4.17
449446 rs10505348 8_79 1.000 202.31 5.9e-04 19.99
450445 rs13252684 8_83 1.000 388.25 1.1e-03 16.92
450446 rs6987702 8_83 1.000 362.13 1.1e-03 15.28
481145 rs2183745 9_50 1.000 367.64 1.1e-03 -21.03
481162 rs146562086 9_50 1.000 77.04 2.2e-04 -8.01
481176 rs35381859 9_50 1.000 178.85 5.2e-04 7.49
481233 rs10448294 9_50 1.000 119.66 3.5e-04 -0.68
491310 rs115478735 9_70 1.000 3936.95 1.1e-02 -108.55
526254 rs78362087 10_66 1.000 86.12 2.5e-04 -11.13
536102 rs72636980 11_1 1.000 143.10 4.2e-04 13.97
536142 rs55642248 11_1 1.000 208.14 6.0e-04 -13.11
554249 rs174553 11_34 1.000 400.56 1.2e-03 19.87
554441 rs17157266 11_34 1.000 83.55 2.4e-04 -7.15
573637 rs116891075 11_77 1.000 46.13 1.3e-04 -8.07
573685 rs240536 11_77 1.000 89.88 2.6e-04 -14.18
573693 rs10893498 11_77 1.000 320.69 9.3e-04 -18.84
573702 rs10790802 11_77 1.000 396.65 1.2e-03 25.30
573705 rs112282958 11_77 1.000 81.21 2.4e-04 -11.11
576585 rs2191159 12_1 1.000 240.91 7.0e-04 15.88
576586 rs6489532 12_1 1.000 56.85 1.7e-04 5.60
577935 rs61909253 12_5 1.000 45.18 1.3e-04 -5.67
606967 rs117615171 12_59 1.000 36.09 1.0e-04 5.58
672136 rs11439803 14_48 1.000 219.62 6.4e-04 0.83
672143 rs1243165 14_48 1.000 236.36 6.9e-04 4.28
722895 rs185342176 17_6 1.000 202.13 5.9e-04 13.69
723012 rs371440902 17_6 1.000 336.86 9.8e-04 14.78
723023 rs4796403 17_6 1.000 255.01 7.4e-04 13.71
723092 rs144129583 17_7 1.000 181.73 5.3e-04 13.93
769381 rs3794991 19_15 1.000 264.14 7.7e-04 -18.28
776080 rs71339519 19_30 1.000 4754.91 1.4e-02 -4.93
776081 rs769162207 19_30 1.000 4869.04 1.4e-02 -0.37
776428 rs814573 19_32 1.000 133.96 3.9e-04 -12.40
776429 rs117664574 19_32 1.000 47.94 1.4e-04 7.98
789938 rs2902942 20_24 1.000 99.91 2.9e-04 -10.36
805938 rs2836882 21_18 1.000 47.74 1.4e-04 -6.71
815529 rs16996442 22_14 1.000 45.11 1.3e-04 7.43
821604 rs199779538 1_2 1.000 2727.10 7.9e-03 -3.23
844953 rs58288190 1_87 1.000 25956.96 7.5e-02 1.61
868827 rs1260326 2_16 1.000 468.83 1.4e-03 -22.20
924841 rs201939100 4_48 1.000 64.83 1.9e-04 -2.32
1062621 rs60158239 9_26 1.000 4604.71 1.3e-02 3.58
1087530 rs11601507 11_4 1.000 280.88 8.2e-04 16.49
1128070 rs11621792 14_3 1.000 195.00 5.7e-04 -13.84
1154892 rs766871218 15_41 1.000 276.70 8.0e-04 -7.03
1178897 rs9302635 16_38 1.000 354.35 1.0e-03 17.64
1202316 rs11078597 17_2 1.000 100.50 2.9e-04 -12.61
1205039 rs201963278 17_23 1.000 487.60 1.4e-03 3.44
1274807 rs2387343 19_34 1.000 136.29 4.0e-04 -14.60
1274903 rs4801776 19_34 1.000 96.33 2.8e-04 -13.79
1316504 rs78645897 22_16 1.000 936.60 2.7e-03 3.83
1316505 rs62228479 22_16 1.000 923.05 2.7e-03 3.48
31143 rs507482 1_67 0.999 68.31 2.0e-04 -8.07
188128 rs56328339 3_115 0.999 35.63 1.0e-04 -5.73
313586 rs10456776 6_13 0.999 55.10 1.6e-04 -7.65
355227 rs6557156 6_99 0.999 33.27 9.7e-05 6.08
370779 rs11983782 7_20 0.999 41.51 1.2e-04 -6.32
402062 rs3757387 7_78 0.999 42.64 1.2e-04 6.42
469461 rs2812357 9_27 0.999 41.20 1.2e-04 6.36
608825 rs1215606 12_64 0.999 34.28 9.9e-05 5.68
767918 rs10405035 19_12 0.999 35.17 1.0e-04 -5.70
791573 rs4812975 20_28 0.999 64.82 1.9e-04 8.02
821611 rs7519807 1_2 0.999 2721.40 7.9e-03 -3.16
6104 rs3026894 1_14 0.998 229.94 6.7e-04 4.48
119299 rs7595923 2_118 0.998 34.79 1.0e-04 6.90
216927 rs6811535 4_52 0.998 50.27 1.5e-04 7.67
294045 rs4705986 5_80 0.998 38.47 1.1e-04 -6.01
393439 rs1207731 7_59 0.998 32.08 9.3e-05 -5.32
409072 rs7807051 7_94 0.998 31.59 9.2e-05 5.34
417270 rs2929451 8_11 0.998 1863.76 5.4e-03 -16.29
492558 rs1886296 9_73 0.998 33.16 9.6e-05 4.68
525676 rs7069475 10_64 0.998 47.75 1.4e-04 -8.16
736009 rs1801689 17_38 0.998 31.41 9.1e-05 -4.95
758057 rs62098355 18_34 0.998 43.18 1.3e-04 8.78
785036 rs34507316 20_13 0.998 37.04 1.1e-04 -2.87
137409 rs56395424 3_9 0.997 42.34 1.2e-04 -6.28
376037 rs6974574 7_28 0.997 33.42 9.7e-05 -4.93
592909 rs930900 12_33 0.997 89.20 2.6e-04 11.32
593829 rs7397189 12_36 0.997 41.53 1.2e-04 -6.46
753721 rs11872765 18_27 0.997 31.17 9.0e-05 -5.52
758062 rs56051253 18_34 0.997 62.25 1.8e-04 -8.96
768954 rs35576020 19_14 0.997 33.98 9.8e-05 6.23
671564 rs11624512 14_46 0.996 63.02 1.8e-04 -7.98
36257 rs61804205 1_79 0.995 44.93 1.3e-04 7.48
301115 rs13167291 5_93 0.995 60.54 1.7e-04 7.57
721237 rs2240731 17_3 0.995 38.44 1.1e-04 -6.17
753544 rs2878889 18_27 0.995 34.56 1.0e-04 -6.11
804217 rs12482821 21_15 0.995 30.21 8.7e-05 -4.85
597378 rs113479946 12_42 0.994 36.24 1.0e-04 -5.71
54030 rs884127 1_112 0.993 42.74 1.2e-04 6.44
167811 rs189174 3_74 0.993 61.78 1.8e-04 7.69
220002 rs13134099 4_58 0.993 29.51 8.5e-05 4.99
322894 rs78470916 6_32 0.993 32.88 9.5e-05 4.84
709533 rs17616063 16_27 0.993 30.88 8.9e-05 5.32
576587 rs7137297 12_1 0.991 81.84 2.4e-04 -9.53
92228 rs13014084 2_60 0.990 29.60 8.5e-05 4.64
842331 rs12083537 1_75 0.990 50.16 1.4e-04 -8.38
1277481 rs117080418 19_34 0.990 44.95 1.3e-04 6.39
6716 rs34957055 1_16 0.989 31.57 9.1e-05 -5.42
593765 rs1874888 12_35 0.989 29.95 8.6e-05 5.24
613364 rs2393775 12_74 0.989 400.52 1.2e-03 -24.49
619343 rs9552620 13_3 0.989 27.02 7.8e-05 4.84
790052 rs6029393 20_24 0.989 41.68 1.2e-04 -6.47
72350 rs17820747 2_20 0.988 39.00 1.1e-04 -5.66
1225097 rs77542162 17_39 0.986 43.94 1.3e-04 6.53
1118712 rs9604045 13_62 0.985 32.68 9.3e-05 -5.67
417589 rs11777976 8_13 0.984 170.78 4.9e-04 -15.73
132142 rs12619647 2_144 0.983 36.55 1.0e-04 -6.85
320857 rs78945013 6_29 0.982 27.98 8.0e-05 -5.07
316571 rs34350323 6_17 0.981 46.65 1.3e-04 5.16
401605 rs17864212 7_78 0.981 30.90 8.8e-05 4.75
818070 rs135577 22_21 0.981 32.22 9.2e-05 4.48
288247 rs12521324 5_69 0.980 29.84 8.5e-05 5.03
1197410 rs72791573 16_48 0.980 69.80 2.0e-04 8.98
557104 rs695110 11_42 0.979 52.02 1.5e-04 -6.76
776431 rs77719426 19_32 0.979 39.66 1.1e-04 6.57
1154881 rs546764840 15_41 0.978 307.03 8.7e-04 -7.25
576595 rs11513717 12_1 0.977 45.20 1.3e-04 1.23
418307 rs4841659 8_15 0.976 102.79 2.9e-04 15.90
431895 rs140753685 8_42 0.975 28.95 8.2e-05 4.94
39848 rs1063412 1_84 0.974 28.50 8.1e-05 -4.82
59662 rs12044944 1_126 0.974 26.51 7.5e-05 -4.78
416512 rs2928619 8_10 0.974 44.61 1.3e-04 6.51
422405 rs11986461 8_21 0.974 31.43 8.9e-05 -5.93
317304 rs75080831 6_19 0.973 53.72 1.5e-04 8.29
464027 rs776756 9_14 0.973 27.53 7.8e-05 -4.45
781704 rs6140010 20_5 0.973 41.17 1.2e-04 -6.12
721871 rs140384878 17_4 0.971 26.03 7.3e-05 4.77
239697 rs59435073 4_97 0.969 51.09 1.4e-04 -7.43
427977 rs11997272 8_34 0.968 25.98 7.3e-05 -4.47
94960 rs10170168 2_66 0.967 40.91 1.1e-04 -3.38
490480 rs8181197 9_68 0.966 64.61 1.8e-04 8.09
623412 rs11424749 13_10 0.966 31.33 8.8e-05 5.35
719793 rs7206699 16_53 0.966 41.98 1.2e-04 6.27
758070 rs2957132 18_34 0.966 28.68 8.0e-05 -5.10
942534 rs4074793 5_31 0.964 41.09 1.2e-04 6.24
43024 rs146203975 1_92 0.963 45.92 1.3e-04 -6.84
369650 rs7796210 7_18 0.961 33.18 9.3e-05 5.51
492706 rs914738 9_74 0.961 26.36 7.4e-05 4.74
526208 rs11594179 10_66 0.961 31.84 8.9e-05 -0.73
513925 rs9414798 10_42 0.960 102.43 2.9e-04 -14.18
310812 rs6597256 6_7 0.959 40.81 1.1e-04 -5.57
778488 rs146279443 19_36 0.959 26.34 7.3e-05 4.62
316540 rs554542699 6_17 0.958 33.86 9.4e-05 4.54
773532 rs17841839 19_23 0.957 72.87 2.0e-04 10.03
276683 rs4133339 5_45 0.956 46.16 1.3e-04 6.71
317037 rs34888581 6_19 0.955 35.11 9.7e-05 -5.03
626510 rs116944862 13_17 0.955 31.09 8.6e-05 -2.20
738079 rs11658216 17_44 0.955 26.37 7.3e-05 4.75
51242 rs74704885 1_107 0.951 42.33 1.2e-04 -5.25
756611 rs12373325 18_31 0.951 71.44 2.0e-04 -9.66
776500 rs77332277 19_32 0.951 45.90 1.3e-04 7.13
32 rs112905931 1_1 0.950 39.98 1.1e-04 6.18
351049 rs62432712 6_91 0.947 26.00 7.2e-05 4.68
482662 rs10991458 9_53 0.947 51.74 1.4e-04 4.42
693503 rs12915099 15_42 0.946 29.98 8.2e-05 3.33
758314 rs7242402 18_35 0.945 25.24 6.9e-05 4.60
132120 rs61747382 2_144 0.943 34.78 9.5e-05 6.60
70962 rs7606480 2_17 0.942 43.97 1.2e-04 -6.65
736346 rs189323 17_40 0.942 25.26 6.9e-05 3.87
319613 rs9270527 6_26 0.941 125.99 3.4e-04 -8.84
316843 rs78808915 6_18 0.940 1594.26 4.4e-03 -35.32
472623 rs11144105 9_35 0.940 25.05 6.8e-05 4.53
482636 rs2900388 9_53 0.940 42.17 1.2e-04 -2.86
776085 rs239943 19_30 0.940 3091.04 8.4e-03 -5.30
829619 rs75460349 1_18 0.940 64.04 1.7e-04 -7.78
555146 rs72917317 11_38 0.937 29.18 7.9e-05 5.31
671541 rs67868394 14_46 0.937 28.51 7.8e-05 5.32
844940 rs139385919 1_87 0.934 25967.26 7.0e-02 4.54
580451 rs2417261 12_12 0.933 26.77 7.3e-05 -4.81
417092 rs13265731 8_11 0.927 2850.98 7.7e-03 13.94
449500 rs7017788 8_79 0.927 44.01 1.2e-04 8.60
310740 rs2765359 6_7 0.926 36.17 9.7e-05 4.79
320980 rs9470183 6_29 0.926 25.34 6.8e-05 4.10
984505 rs33959228 6_28 0.926 48.25 1.3e-04 -7.13
585150 rs146970907 12_18 0.925 29.59 8.0e-05 5.27
703510 rs4780401 16_12 0.921 46.57 1.2e-04 7.45
1204998 rs16532 17_23 0.921 372.34 1.0e-03 6.07
72347 rs564066844 2_20 0.920 25.15 6.7e-05 -4.40
168860 rs72964564 3_76 0.920 32.96 8.8e-05 5.40
524510 rs10786262 10_61 0.919 31.73 8.5e-05 5.22
571309 rs7104819 11_71 0.916 29.26 7.8e-05 3.32
715031 rs557791532 16_41 0.916 25.29 6.7e-05 4.51
804656 rs928287 21_17 0.915 46.22 1.2e-04 -6.52
258569 rs112622661 5_9 0.913 24.03 6.4e-05 -4.43
524517 rs2039616 10_62 0.912 29.33 7.8e-05 5.09
538642 rs7102759 11_8 0.908 27.25 7.2e-05 -4.83
794204 rs2585441 20_32 0.908 25.11 6.6e-05 -4.63
1012466 rs146203232 6_103 0.907 75.97 2.0e-04 6.83
194503 rs113840252 4_9 0.906 26.31 6.9e-05 -4.82
146052 rs2844400 3_27 0.903 23.79 6.2e-05 -4.23
756220 rs1217565 18_30 0.900 34.96 9.1e-05 -5.56
805011 rs219783 21_17 0.898 42.58 1.1e-04 -6.43
70576 rs368027631 2_15 0.897 30.79 8.0e-05 -5.39
377219 rs12155027 7_30 0.894 24.87 6.5e-05 -4.59
273525 rs253232 5_40 0.893 25.21 6.5e-05 -4.54
584690 rs10842642 12_18 0.892 26.39 6.8e-05 -4.69
553089 rs4926 11_32 0.883 28.71 7.4e-05 -4.75
79938 rs75536720 2_34 0.877 24.81 6.3e-05 -4.46
213399 rs186589299 4_45 0.877 24.23 6.2e-05 -4.35
417539 rs2975676 8_13 0.876 40.46 1.0e-04 -1.41
148969 rs116643069 3_35 0.874 29.29 7.4e-05 -4.67
316901 rs9358773 6_18 0.873 183.11 4.6e-04 18.18
580903 rs4764086 12_12 0.873 46.98 1.2e-04 6.80
421583 rs2015440 8_20 0.871 27.04 6.8e-05 -4.81
488749 rs72759301 9_64 0.870 28.72 7.3e-05 -4.91
366409 rs115412782 7_13 0.864 23.97 6.0e-05 4.15
636302 rs9592980 13_36 0.864 63.53 1.6e-04 7.94
51188 rs1962918 1_107 0.859 29.24 7.3e-05 -5.64
776417 rs3729640 19_32 0.859 44.61 1.1e-04 -6.69
718200 rs60239983 16_50 0.858 25.94 6.5e-05 -4.64
523951 rs10509670 10_60 0.856 39.75 9.9e-05 -6.08
361351 rs78894484 7_2 0.855 29.74 7.4e-05 6.04
197752 rs10034719 4_16 0.854 26.80 6.6e-05 4.72
490999 rs71481395 9_69 0.854 26.54 6.6e-05 4.70
752380 rs73425984 18_24 0.852 27.44 6.8e-05 4.84
464011 rs71506880 9_14 0.847 33.06 8.1e-05 5.00
716502 rs17689455 16_44 0.846 331.34 8.1e-04 18.92
464366 rs556587401 9_15 0.845 29.41 7.2e-05 5.02
518422 rs1248889 10_50 0.845 56.50 1.4e-04 9.01
293926 rs6894249 5_79 0.844 27.55 6.8e-05 -4.87
25631 rs34303579 1_55 0.841 25.10 6.1e-05 -3.66
626492 rs34001253 13_16 0.841 48.11 1.2e-04 -8.90
8866 rs75339626 1_21 0.840 24.30 5.9e-05 4.30
526128 rs77041839 10_65 0.836 242.45 5.9e-04 -16.11
134677 rs12497013 3_4 0.821 28.06 6.7e-05 -4.78
54690 rs12132342 1_115 0.820 25.61 6.1e-05 4.78
735412 rs12452590 17_36 0.820 24.59 5.9e-05 -4.28
696970 rs28693883 15_48 0.815 24.12 5.7e-05 4.00
11157 rs368949592 1_25 0.810 25.77 6.1e-05 -4.05
95294 rs12467534 2_67 0.809 27.19 6.4e-05 -5.12
205500 rs17578029 4_31 0.809 26.64 6.3e-05 5.10
46673 rs2994256 1_98 0.806 28.86 6.8e-05 -4.93
491305 rs34357864 9_70 0.806 4461.77 1.0e-02 -102.31
360119 rs2880362 6_110 0.801 26.13 6.1e-05 4.58SNPs 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
844940 rs139385919 1_87 0.934 25967.26 7.0e-02 4.54
844956 rs181563306 1_87 0.472 25964.43 3.6e-02 4.54
844958 rs190347640 1_87 0.472 25964.43 3.6e-02 4.54
844953 rs58288190 1_87 1.000 25956.96 7.5e-02 1.61
844943 rs111775313 1_87 0.000 25796.16 2.8e-12 4.47
844938 rs61393240 1_87 0.000 25796.10 2.8e-12 4.47
844970 rs111314699 1_87 0.000 25774.60 2.4e-12 4.46
844950 rs139675328 1_87 0.000 25629.69 3.3e-12 4.43
844951 rs147098930 1_87 0.000 25503.31 1.4e-12 4.40
844952 rs147709014 1_87 0.000 25503.31 1.4e-12 4.40
844925 rs17360628 1_87 0.000 25502.76 1.4e-12 4.40
844945 rs141788986 1_87 0.000 25492.51 1.7e-12 4.42
844890 rs17275780 1_87 0.000 25486.25 1.2e-12 4.38
844907 rs80123481 1_87 0.000 25477.41 1.1e-12 4.37
844901 rs76640045 1_87 0.000 25476.58 1.3e-12 4.40
844895 rs111671843 1_87 0.000 25475.65 1.3e-12 4.40
844888 rs77291888 1_87 0.000 25474.24 1.3e-12 4.39
844920 rs111880540 1_87 0.000 25472.47 1.2e-12 4.38
844882 rs76579149 1_87 0.000 25472.00 1.3e-12 4.39
844879 rs79078214 1_87 0.000 25471.32 1.4e-12 4.40
844976 rs75082966 1_87 0.000 25200.57 1.4e-13 4.18
844979 rs79371453 1_87 0.000 24571.68 4.9e-13 4.39
845036 rs111467463 1_87 0.000 23295.85 6.7e-15 4.04
845059 rs111965288 1_87 0.000 23256.98 2.2e-15 3.91
844994 rs16852323 1_87 0.000 23212.67 4.0e-15 3.99
845072 rs12081230 1_87 0.000 23035.04 1.8e-15 3.91
845083 rs10913496 1_87 0.000 22971.13 9.6e-16 3.85
845090 rs7541136 1_87 0.000 22963.20 1.3e-15 3.88
845015 rs144639089 1_87 0.000 16001.57 0.0e+00 3.26
844927 rs11376467 1_87 0.000 10835.07 0.0e+00 4.18
844926 rs7542067 1_87 0.000 10793.37 0.0e+00 4.19
844941 rs10158257 1_87 0.000 10789.53 0.0e+00 4.18
844942 rs10158263 1_87 0.000 10789.51 0.0e+00 4.18
844947 rs6684563 1_87 0.000 10789.36 0.0e+00 4.18
844935 rs7550982 1_87 0.000 10789.33 0.0e+00 4.18
844929 rs6682663 1_87 0.000 10788.65 0.0e+00 4.18
844928 rs6425460 1_87 0.000 10787.78 0.0e+00 4.17
844933 rs6425461 1_87 0.000 10784.13 0.0e+00 4.16
844937 rs7536711 1_87 0.000 10783.62 0.0e+00 4.16
844939 rs10157654 1_87 0.000 10783.22 0.0e+00 4.16
844930 rs12061823 1_87 0.000 10782.85 0.0e+00 4.16
844924 rs1556976 1_87 0.000 10775.08 0.0e+00 4.21
844934 rs6425462 1_87 0.000 10770.78 0.0e+00 4.20
844919 rs6425459 1_87 0.000 10762.21 0.0e+00 4.19
844897 rs12093558 1_87 0.000 10540.95 0.0e+00 4.13
844857 rs113069470 1_87 0.000 8222.57 0.0e+00 2.24
845102 rs137959039 1_87 0.000 7630.30 0.0e+00 1.95
844906 rs12095164 1_87 0.000 7520.58 0.0e+00 3.77
845004 rs78143410 1_87 0.000 6996.90 0.0e+00 2.13
845113 rs946818 1_87 0.000 6933.97 0.0e+00 4.79SNPs 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
844953 rs58288190 1_87 1.000 25956.96 0.0750 1.61
844940 rs139385919 1_87 0.934 25967.26 0.0700 4.54
844956 rs181563306 1_87 0.472 25964.43 0.0360 4.54
844958 rs190347640 1_87 0.472 25964.43 0.0360 4.54
776080 rs71339519 19_30 1.000 4754.91 0.0140 -4.93
776081 rs769162207 19_30 1.000 4869.04 0.0140 -0.37
1062621 rs60158239 9_26 1.000 4604.71 0.0130 3.58
491310 rs115478735 9_70 1.000 3936.95 0.0110 -108.55
491305 rs34357864 9_70 0.806 4461.77 0.0100 -102.31
417076 rs758184196 8_11 1.000 3117.01 0.0091 -3.89
417071 rs2428 8_11 1.000 2989.50 0.0087 15.16
776085 rs239943 19_30 0.940 3091.04 0.0084 -5.30
821604 rs199779538 1_2 1.000 2727.10 0.0079 -3.23
821611 rs7519807 1_2 0.999 2721.40 0.0079 -3.16
417092 rs13265731 8_11 0.927 2850.98 0.0077 13.94
491306 rs677355 9_70 0.502 4463.81 0.0065 -102.39
6192 rs12047493 1_15 1.000 2164.71 0.0063 -49.86
316815 rs10946700 6_18 1.000 2030.55 0.0059 44.85
417270 rs2929451 8_11 0.998 1863.76 0.0054 -16.29
316843 rs78808915 6_18 0.940 1594.26 0.0044 -35.32
6211 rs76372215 1_15 1.000 1283.52 0.0037 -39.58
6260 rs148785605 1_15 1.000 1282.24 0.0037 -49.46
6167 rs72657133 1_14 1.000 1222.26 0.0035 -23.99
491284 rs10793962 9_70 0.658 1506.33 0.0029 9.89
1316504 rs78645897 22_16 1.000 936.60 0.0027 3.83
1316505 rs62228479 22_16 1.000 923.05 0.0027 3.48
6250 rs34605986 1_15 1.000 907.31 0.0026 33.59
6279 rs16825755 1_15 1.000 881.94 0.0026 -18.84
1062670 rs139424801 9_26 0.182 4590.82 0.0024 0.35
491309 rs674302 9_70 0.171 4459.09 0.0022 -102.40
1062668 rs117622511 9_26 0.163 4589.33 0.0022 0.37
491307 rs676457 9_70 0.160 4458.93 0.0021 -102.40
776076 rs2883946 19_30 0.150 4841.12 0.0021 4.77
1062666 rs17341977 9_26 0.142 4588.50 0.0019 0.37
1062669 rs150804130 9_26 0.134 4588.88 0.0018 0.36
1316489 rs62652622 22_16 0.673 890.34 0.0017 3.99
6161 rs148717955 1_14 1.000 527.41 0.0015 5.52
491286 rs8176759 9_70 0.342 1506.11 0.0015 9.85
513938 rs10640079 10_42 0.421 1227.15 0.0015 37.62
868827 rs1260326 2_16 1.000 468.83 0.0014 -22.20
1205039 rs201963278 17_23 1.000 487.60 0.0014 3.44
1274522 rs492602 19_33 0.364 1320.27 0.0014 -50.97
450444 rs2980858 8_83 0.730 603.69 0.0013 -17.16
722992 rs56244095 17_6 0.512 865.82 0.0013 33.93
554249 rs174553 11_34 1.000 400.56 0.0012 19.87
573702 rs10790802 11_77 1.000 396.65 0.0012 25.30
613364 rs2393775 12_74 0.989 400.52 0.0012 -24.49
722991 rs56115403 17_6 0.488 865.69 0.0012 33.92
450445 rs13252684 8_83 1.000 388.25 0.0011 16.92
450446 rs6987702 8_83 1.000 362.13 0.0011 15.28SNPs 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
491310 rs115478735 9_70 1.000 3936.95 1.1e-02 -108.55
491307 rs676457 9_70 0.160 4458.93 2.1e-03 -102.40
491309 rs674302 9_70 0.171 4459.09 2.2e-03 -102.40
491306 rs677355 9_70 0.502 4463.81 6.5e-03 -102.39
491305 rs34357864 9_70 0.806 4461.77 1.0e-02 -102.31
491308 rs782455289 9_70 0.003 4428.63 3.8e-05 -101.92
491314 rs495828 9_70 0.000 3321.04 1.7e-06 -99.78
491356 rs3758348 9_70 0.000 1454.46 1.4e-06 -69.56
491365 rs17474001 9_70 0.000 1361.73 1.2e-06 -67.32
491311 rs559723 9_70 0.000 1883.88 3.6e-07 -66.38
491295 rs2073828 9_70 0.001 1551.43 2.7e-06 62.33
491304 rs7036642 9_70 0.000 1363.11 9.8e-07 59.55
1274522 rs492602 19_33 0.364 1320.27 1.4e-03 -50.97
1274525 rs601338 19_33 0.237 1319.45 9.1e-04 -50.96
1274523 rs681343 19_33 0.279 1319.47 1.1e-03 -50.95
1274519 rs679574 19_33 0.047 1313.49 1.8e-04 -50.90
1274520 rs516316 19_33 0.038 1312.83 1.5e-04 -50.89
1274521 rs516246 19_33 0.034 1312.40 1.3e-04 -50.89
1274536 rs507855 19_33 0.109 1243.39 3.9e-04 -50.16
1274537 rs507766 19_33 0.125 1243.84 4.5e-04 -50.16
1274538 rs507711 19_33 0.101 1243.02 3.6e-04 -50.16
1274542 rs503279 19_33 0.075 1242.50 2.7e-04 -50.16
1274531 rs571689 19_33 0.053 1241.17 1.9e-04 -50.15
1274532 rs570794 19_33 0.055 1241.38 2.0e-04 -50.15
1274533 rs569970 19_33 0.051 1240.95 1.8e-04 -50.15
1274539 rs506897 19_33 0.054 1241.07 1.9e-04 -50.15
1274534 rs2251034 19_33 0.010 1235.90 3.5e-05 -50.11
1274540 rs504963 19_33 0.036 1238.86 1.3e-04 -50.11
1274543 rs633372 19_33 0.009 1235.12 3.3e-05 -50.10
1274548 rs1688264 19_33 0.182 1244.14 6.6e-04 -50.10
1274547 rs692854 19_33 0.109 1239.28 3.9e-04 -50.05
1274541 rs632111 19_33 0.004 1230.81 1.4e-05 -50.04
1274546 rs2548459 19_33 0.003 1230.03 1.2e-05 -50.04
1274549 rs1704773 19_33 0.017 1233.02 6.0e-05 -50.00
1274527 rs602662 19_33 0.001 1222.95 1.9e-06 -49.90
1274544 rs2638280 19_33 0.004 1229.59 1.5e-05 -49.87
6192 rs12047493 1_15 1.000 2164.71 6.3e-03 -49.86
1274545 rs2548458 19_33 0.001 1221.73 2.9e-06 -49.62
1274528 rs485186 19_33 0.000 1199.45 2.8e-08 -49.58
1274530 rs603985 19_33 0.000 1203.08 6.1e-08 -49.58
1274529 rs485073 19_33 0.000 1201.43 4.5e-08 -49.57
1274550 rs646327 19_33 0.002 1215.95 6.8e-06 -49.57
6260 rs148785605 1_15 1.000 1282.24 3.7e-03 -49.46
1274566 rs281379 19_33 0.000 1173.45 1.1e-08 -48.65
1274558 rs584768 19_33 0.000 1145.05 8.4e-09 -48.30
1274560 rs28894750 19_33 0.000 1150.12 9.0e-09 -48.29
1274559 rs2452170 19_33 0.000 1143.14 8.3e-09 -48.27
6205 rs3820292 1_15 0.000 1995.11 0.0e+00 -48.26
1274563 rs2638282 19_33 0.000 1142.57 8.3e-09 -48.26
1274556 rs676388 19_33 0.000 1140.40 8.3e-09 -48.25Gene 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] 70if (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"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)RP11-346C20.3 gene(s) from the input list not found in DisGeNET CURATEDTHOC7 gene(s) from the input list not found in DisGeNET CURATEDCTSW gene(s) from the input list not found in DisGeNET CURATEDSH3BP1 gene(s) from the input list not found in DisGeNET CURATEDCDYL2 gene(s) from the input list not found in DisGeNET CURATEDELMO3 gene(s) from the input list not found in DisGeNET CURATEDTTC39B gene(s) from the input list not found in DisGeNET CURATEDANKRD35 gene(s) from the input list not found in DisGeNET CURATEDKB-1732A1.1 gene(s) from the input list not found in DisGeNET CURATEDZNF311 gene(s) from the input list not found in DisGeNET CURATEDARID3C gene(s) from the input list not found in DisGeNET CURATEDMLIP gene(s) from the input list not found in DisGeNET CURATEDLIME1 gene(s) from the input list not found in DisGeNET CURATEDRIC1 gene(s) from the input list not found in DisGeNET CURATEDCNFN gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDCRIPAK gene(s) from the input list not found in DisGeNET CURATEDATAD2 gene(s) from the input list not found in DisGeNET CURATEDRP11-219B17.3 gene(s) from the input list not found in DisGeNET CURATEDNPIPA5 gene(s) from the input list not found in DisGeNET CURATEDDOHH gene(s) from the input list not found in DisGeNET CURATEDEXOC3L4 gene(s) from the input list not found in DisGeNET CURATEDZNF329 gene(s) from the input list not found in DisGeNET CURATEDZBTB22 gene(s) from the input list not found in DisGeNET CURATEDALLC gene(s) from the input list not found in DisGeNET CURATEDCDC5L gene(s) from the input list not found in DisGeNET CURATEDMIEF1 gene(s) from the input list not found in DisGeNET CURATEDLBHD1 gene(s) from the input list not found in DisGeNET CURATEDTNKS gene(s) from the input list not found in DisGeNET CURATEDRP4-781K5.7 gene(s) from the input list not found in DisGeNET CURATEDGPRC5C gene(s) from the input list not found in DisGeNET CURATED Description
65 Opisthorchiasis
110 Hyperandrogenism
125 Urocanase deficiency
126 Opisthorchis felineus Infection
127 Opisthorchis viverrini Infection
138 Pulmonary arterial hypertension induced by drug
169 Ovarian Serous Adenocarcinoma
178 DEAFNESS, AUTOSOMAL RECESSIVE 68
189 BONE MINERAL DENSITY QUANTITATIVE TRAIT LOCUS 12
199 GLUCOCORTICOID DEFICIENCY 4 WITH OR WITHOUT MINERALOCORTICOID DEFICIENCY
FDR Ratio BgRatio
65 0.05502087 1/39 1/9703
110 0.05502087 1/39 1/9703
125 0.05502087 1/39 1/9703
126 0.05502087 1/39 1/9703
127 0.05502087 1/39 1/9703
138 0.05502087 1/39 1/9703
169 0.05502087 2/39 23/9703
178 0.05502087 1/39 1/9703
189 0.05502087 1/39 1/9703
199 0.05502087 1/39 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