Total protein (quantile) - Liver
wesleycrouse
2021-09-09
Last updated: 2021-09-09
Checks: 6 1
Knit directory: ctwas_applied/
This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20210726) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 59e5f4d. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Unstaged changes:
Modified: analysis/ukb-d-30500_irnt_Liver.Rmd
Modified: analysis/ukb-d-30600_irnt_Liver.Rmd
Modified: analysis/ukb-d-30610_irnt_Liver.Rmd
Modified: analysis/ukb-d-30620_irnt_Liver.Rmd
Modified: analysis/ukb-d-30630_irnt_Liver.Rmd
Modified: analysis/ukb-d-30640_irnt_Liver.Rmd
Modified: analysis/ukb-d-30650_irnt_Liver.Rmd
Modified: analysis/ukb-d-30660_irnt_Liver.Rmd
Modified: analysis/ukb-d-30670_irnt_Liver.Rmd
Modified: analysis/ukb-d-30680_irnt_Liver.Rmd
Modified: analysis/ukb-d-30690_irnt_Liver.Rmd
Modified: analysis/ukb-d-30700_irnt_Liver.Rmd
Modified: analysis/ukb-d-30710_irnt_Liver.Rmd
Modified: analysis/ukb-d-30720_irnt_Liver.Rmd
Modified: analysis/ukb-d-30730_irnt_Liver.Rmd
Modified: analysis/ukb-d-30740_irnt_Liver.Rmd
Modified: analysis/ukb-d-30750_irnt_Liver.Rmd
Modified: analysis/ukb-d-30760_irnt_Liver.Rmd
Modified: analysis/ukb-d-30770_irnt_Liver.Rmd
Modified: analysis/ukb-d-30780_irnt_Liver.Rmd
Modified: analysis/ukb-d-30790_irnt_Liver.Rmd
Modified: analysis/ukb-d-30800_irnt_Liver.Rmd
Modified: analysis/ukb-d-30810_irnt_Liver.Rmd
Modified: analysis/ukb-d-30820_irnt_Liver.Rmd
Modified: analysis/ukb-d-30830_irnt_Liver.Rmd
Modified: analysis/ukb-d-30840_irnt_Liver.Rmd
Modified: analysis/ukb-d-30850_irnt_Liver.Rmd
Modified: analysis/ukb-d-30860_irnt_Liver.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were made to the R Markdown (analysis/ukb-d-30860_irnt_Liver.Rmd) and HTML (docs/ukb-d-30860_irnt_Liver.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.
| 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 | 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 Total protein (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-30860_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0141893445 0.0002066997 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
24.96773 13.68896 #report sample size
print(sample_size)[1] 314921#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01226327 0.07814382 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.6111292 4.4067088Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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 43.34 1.4e-04 6.44
5389 RPS11 19_34 1.000 184046.32 5.8e-01 17.58
2173 TMEM176B 7_93 0.996 57.31 1.8e-04 -7.80
15 MAD1L1 7_4 0.995 1231.05 3.9e-03 -5.58
3212 CCND2 12_4 0.992 62.94 2.0e-04 -8.03
12467 RP11-219B17.3 15_27 0.982 62.91 2.0e-04 -7.94
4514 COL4A2 13_59 0.975 62.20 1.9e-04 -7.76
11582 BCKDHA 19_28 0.973 35.21 1.1e-04 -5.66
8865 FUT2 19_33 0.973 114.87 3.5e-04 -14.64
7656 CATSPER2 15_16 0.969 152.08 4.7e-04 12.68
2771 HMGXB3 5_88 0.958 30.75 9.4e-05 -5.61
4275 EIF5A 17_6 0.950 41.28 1.2e-04 -6.20
12704 EXOC3L2 19_32 0.948 63.83 1.9e-04 -7.88
12074 RP11-131K5.2 17_12 0.944 25.74 7.7e-05 -4.76
6100 ALLC 2_2 0.941 27.97 8.4e-05 5.04
1429 SH3BP1 22_15 0.914 21.53 6.2e-05 3.87
8765 ZNF77 19_3 0.900 20.29 5.8e-05 3.96
7915 GLYCTK 3_36 0.895 40.94 1.2e-04 -6.21
11296 NPIPB2 16_12 0.886 39.61 1.1e-04 4.52
7297 PRIMPOL 4_119 0.876 23.68 6.6e-05 -4.75
12151 AC008746.12 19_37 0.876 24.41 6.8e-05 -4.58
3714 MBOAT7 19_37 0.835 24.92 6.6e-05 -4.46
2128 NOD1 7_24 0.830 22.48 5.9e-05 4.12Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
5389 RPS11 19_34 1 184046.32 0.58 17.58
1227 FLT3LG 19_34 0 158985.39 0.00 -16.02
12683 HCP5B 6_24 0 73110.93 0.00 -13.94
5393 RCN3 19_34 0 59395.14 0.00 -13.40
1931 FCGRT 19_34 0 54193.89 0.00 -4.80
10663 TRIM31 6_24 0 38631.61 0.00 13.23
4833 FLOT1 6_24 0 37452.27 0.00 -18.00
10602 RNF5 6_26 0 28412.33 0.00 20.97
3804 PRRG2 19_34 0 26744.09 0.00 -21.45
11007 PPT2 6_26 0 24714.17 0.00 -21.52
10848 CLIC1 6_26 0 21672.92 0.00 19.82
3803 PRMT1 19_34 0 18081.45 0.00 -9.54
3805 SCAF1 19_34 0 17994.77 0.00 -10.70
11541 C4A 6_26 0 17896.28 0.00 18.62
3802 IRF3 19_34 0 17474.82 0.00 -10.31
10651 ABCF1 6_24 0 17010.90 0.00 -11.81
5766 PPP1R18 6_24 0 14686.05 0.00 -9.98
10601 AGER 6_26 0 13266.56 0.00 -11.31
10599 NOTCH4 6_26 0 13183.03 0.00 16.17
1940 SLC17A7 19_34 0 12770.37 0.00 -6.08Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
5389 RPS11 19_34 1.000 184046.32 0.58000 17.58
15 MAD1L1 7_4 0.995 1231.05 0.00390 -5.58
7656 CATSPER2 15_16 0.969 152.08 0.00047 12.68
8865 FUT2 19_33 0.973 114.87 0.00035 -14.64
4733 BLK 8_15 0.780 114.37 0.00028 -11.28
3212 CCND2 12_4 0.992 62.94 0.00020 -8.03
12467 RP11-219B17.3 15_27 0.982 62.91 0.00020 -7.94
10085 RFX8 2_59 0.705 84.76 0.00019 -9.46
4514 COL4A2 13_59 0.975 62.20 0.00019 -7.76
12704 EXOC3L2 19_32 0.948 63.83 0.00019 -7.88
2173 TMEM176B 7_93 0.996 57.31 0.00018 -7.80
5991 FADS1 11_34 0.304 175.67 0.00017 -12.98
5918 SEC16A 9_73 0.607 79.12 0.00015 8.94
5175 WDR20 14_53 0.770 60.13 0.00015 -7.68
1144 ASAP3 1_16 1.000 43.34 0.00014 6.44
5655 SRPRB 3_83 0.697 52.71 0.00012 -7.15
7915 GLYCTK 3_36 0.895 40.94 0.00012 -6.21
4275 EIF5A 17_6 0.950 41.28 0.00012 -6.20
11558 LINC01184 5_78 0.642 56.28 0.00011 7.42
2308 TUBD1 17_35 0.496 72.81 0.00011 -10.67Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5460 FCGR2A 1_79 0.000 518.06 1.5e-17 25.73
11007 PPT2 6_26 0.000 24714.17 0.0e+00 -21.52
3804 PRRG2 19_34 0.000 26744.09 0.0e+00 -21.45
10602 RNF5 6_26 0.000 28412.33 0.0e+00 20.97
10848 CLIC1 6_26 0.000 21672.92 0.0e+00 19.82
11541 C4A 6_26 0.000 17896.28 0.0e+00 18.62
4833 FLOT1 6_24 0.000 37452.27 0.0e+00 -18.00
10137 HLA-DQA1 6_26 0.000 6333.49 0.0e+00 17.65
5389 RPS11 19_34 1.000 184046.32 5.8e-01 17.58
4233 FCRLA 1_79 0.000 236.75 0.0e+00 -16.48
10599 NOTCH4 6_26 0.000 13183.03 0.0e+00 16.17
1227 FLT3LG 19_34 0.000 158985.39 0.0e+00 -16.02
4838 VARS2 6_25 0.000 198.81 6.6e-13 15.17
10591 HLA-DMA 6_27 0.000 220.04 3.6e-09 -15.05
11478 HLA-DMB 6_27 0.000 193.90 2.2e-09 -15.04
8865 FUT2 19_33 0.973 114.87 3.5e-04 -14.64
10603 AGPAT1 6_26 0.000 6592.97 0.0e+00 -14.32
12683 HCP5B 6_24 0.000 73110.93 0.0e+00 -13.94
10625 MSH5 6_26 0.000 6703.38 0.0e+00 13.60
5393 RCN3 19_34 0.000 59395.14 0.0e+00 -13.40Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.02953857#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5460 FCGR2A 1_79 0.000 518.06 1.5e-17 25.73
11007 PPT2 6_26 0.000 24714.17 0.0e+00 -21.52
3804 PRRG2 19_34 0.000 26744.09 0.0e+00 -21.45
10602 RNF5 6_26 0.000 28412.33 0.0e+00 20.97
10848 CLIC1 6_26 0.000 21672.92 0.0e+00 19.82
11541 C4A 6_26 0.000 17896.28 0.0e+00 18.62
4833 FLOT1 6_24 0.000 37452.27 0.0e+00 -18.00
10137 HLA-DQA1 6_26 0.000 6333.49 0.0e+00 17.65
5389 RPS11 19_34 1.000 184046.32 5.8e-01 17.58
4233 FCRLA 1_79 0.000 236.75 0.0e+00 -16.48
10599 NOTCH4 6_26 0.000 13183.03 0.0e+00 16.17
1227 FLT3LG 19_34 0.000 158985.39 0.0e+00 -16.02
4838 VARS2 6_25 0.000 198.81 6.6e-13 15.17
10591 HLA-DMA 6_27 0.000 220.04 3.6e-09 -15.05
11478 HLA-DMB 6_27 0.000 193.90 2.2e-09 -15.04
8865 FUT2 19_33 0.973 114.87 3.5e-04 -14.64
10603 AGPAT1 6_26 0.000 6592.97 0.0e+00 -14.32
12683 HCP5B 6_24 0.000 73110.93 0.0e+00 -13.94
10625 MSH5 6_26 0.000 6703.38 0.0e+00 13.60
5393 RCN3 19_34 0.000 59395.14 0.0e+00 -13.40Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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_79"
genename region_tag susie_pip mu2 PVE z
6979 SLAMF9 1_79 0 4.91 0.0e+00 0.05
6980 IGSF8 1_79 0 5.57 0.0e+00 -0.30
5469 PIGM 1_79 0 58.05 0.0e+00 -2.96
3451 COPA 1_79 0 5.17 0.0e+00 -0.02
6982 NCSTN 1_79 0 5.17 0.0e+00 0.02
6983 VANGL2 1_79 0 18.37 0.0e+00 1.71
621 CD84 1_79 0 9.73 0.0e+00 -1.37
3071 SLAMF1 1_79 0 6.24 0.0e+00 0.19
297 SLAMF7 1_79 0 107.41 0.0e+00 4.66
3452 CD244 1_79 0 18.02 0.0e+00 -0.16
9122 ITLN1 1_79 0 75.92 0.0e+00 3.01
6631 ITLN2 1_79 0 19.21 0.0e+00 2.06
6632 F11R 1_79 0 15.42 0.0e+00 -1.65
10959 TSTD1 1_79 0 5.43 0.0e+00 -0.44
6987 KLHDC9 1_79 0 27.42 0.0e+00 -1.96
5463 PFDN2 1_79 0 16.13 0.0e+00 1.07
6635 NIT1 1_79 0 22.34 0.0e+00 -2.24
5458 UFC1 1_79 0 21.87 0.0e+00 2.28
5459 PPOX 1_79 0 8.14 0.0e+00 0.78
6641 B4GALT3 1_79 0 15.83 0.0e+00 1.55
6643 ADAMTS4 1_79 0 6.26 0.0e+00 -0.63
6646 FCER1G 1_79 0 8.54 0.0e+00 1.26
6647 TOMM40L 1_79 0 23.67 0.0e+00 -4.86
11592 PCP4L1 1_79 0 12.10 0.0e+00 -1.03
12708 RP11-122G18.12 1_79 0 16.70 0.0e+00 -2.80
5460 FCGR2A 1_79 0 518.06 1.5e-17 25.73
4233 FCRLA 1_79 0 236.75 0.0e+00 -16.48
6985 FCRLB 1_79 0 328.41 5.8e-19 -11.97
988 DUSP12 1_79 0 343.20 0.0e+00 1.62
3163 ATF6 1_79 0 19.52 0.0e+00 -2.08
11406 C1orf226 1_79 0 76.21 0.0e+00 -4.17
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_26"
genename region_tag susie_pip mu2 PVE z
10632 BAG6 6_26 0 59.01 0 -0.62
10634 AIF1 6_26 0 46.76 0 -2.05
10633 PRRC2A 6_26 0 585.14 0 6.88
10631 APOM 6_26 0 160.38 0 3.49
10630 C6orf47 6_26 0 117.75 0 3.60
10629 CSNK2B 6_26 0 1635.34 0 10.64
11414 LY6G5B 6_26 0 1190.86 0 -8.43
10628 LY6G5C 6_26 0 503.43 0 -6.59
10627 ABHD16A 6_26 0 658.93 0 3.88
10626 MPIG6B 6_26 0 7188.56 0 -6.46
10849 DDAH2 6_26 0 6926.16 0 11.65
10625 MSH5 6_26 0 6703.38 0 13.60
10848 CLIC1 6_26 0 21672.92 0 19.82
10623 VWA7 6_26 0 113.25 0 -3.81
10622 LSM2 6_26 0 257.71 0 4.50
10621 HSPA1L 6_26 0 146.05 0 5.18
10619 C6orf48 6_26 0 91.95 0 -5.87
10618 SLC44A4 6_26 0 174.93 0 -3.19
10616 EHMT2 6_26 0 5390.73 0 4.69
10612 SKIV2L 6_26 0 612.95 0 1.57
10610 STK19 6_26 0 2993.98 0 5.09
10611 DXO 6_26 0 929.51 0 5.33
11541 C4A 6_26 0 17896.28 0 18.62
11216 CYP21A2 6_26 0 94.82 0 -6.34
11038 C4B 6_26 0 1243.58 0 -2.14
10844 ATF6B 6_26 0 2641.73 0 7.45
7949 TNXB 6_26 0 1876.44 0 -5.28
10606 FKBPL 6_26 0 5454.84 0 -9.76
11007 PPT2 6_26 0 24714.17 0 -21.52
10605 PRRT1 6_26 0 752.60 0 -4.92
11441 EGFL8 6_26 0 5383.85 0 -1.66
10603 AGPAT1 6_26 0 6592.97 0 -14.32
10601 AGER 6_26 0 13266.56 0 -11.31
10602 RNF5 6_26 0 28412.33 0 20.97
10600 PBX2 6_26 0 1163.31 0 6.00
10599 NOTCH4 6_26 0 13183.03 0 16.17
10597 HLA-DRA 6_26 0 3026.43 0 -12.09
10402 HLA-DRB5 6_26 0 2182.54 0 9.68
10023 HLA-DRB1 6_26 0 2067.64 0 9.58
10137 HLA-DQA1 6_26 0 6333.49 0 17.65
11366 HLA-DQA2 6_26 0 3977.91 0 -11.70
9089 HLA-DQB1 6_26 0 2782.28 0 9.89
11231 HLA-DQB2 6_26 0 4615.96 0 -10.43
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_34"
genename region_tag susie_pip mu2 PVE z
2042 BCAT2 19_34 0 135.92 0.00 5.34
1110 HSD17B14 19_34 0 10.24 0.00 0.17
2044 PLEKHA4 19_34 0 8.55 0.00 0.24
1921 NUCB1 19_34 0 7.57 0.00 0.37
1920 TULP2 19_34 0 60.90 0.00 2.04
1922 DHDH 19_34 0 19.70 0.00 0.93
1113 FTL 19_34 0 168.30 0.00 2.39
9401 RUVBL2 19_34 0 38.69 0.00 -1.17
1928 SNRNP70 19_34 0 295.44 0.00 0.25
1929 LIN7B 19_34 0 32.74 0.00 0.75
10994 C19orf73 19_34 0 157.04 0.00 -0.64
8899 PPFIA3 19_34 0 365.83 0.00 0.43
4086 TRPM4 19_34 0 91.67 0.00 5.41
545 SLC6A16 19_34 0 1648.04 0.00 -0.32
10291 CTC-301O7.4 19_34 0 4353.79 0.00 2.81
1940 SLC17A7 19_34 0 12770.37 0.00 -6.08
1932 PIH1D1 19_34 0 5657.97 0.00 5.73
6859 ALDH16A1 19_34 0 663.49 0.00 1.80
1227 FLT3LG 19_34 0 158985.39 0.00 -16.02
5390 RPL13A 19_34 0 1247.21 0.00 -9.14
5389 RPS11 19_34 1 184046.32 0.58 17.58
1931 FCGRT 19_34 0 54193.89 0.00 -4.80
5393 RCN3 19_34 0 59395.14 0.00 -13.40
3804 PRRG2 19_34 0 26744.09 0.00 -21.45
5392 NOSIP 19_34 0 717.18 0.00 -9.42
3805 SCAF1 19_34 0 17994.77 0.00 -10.70
3802 IRF3 19_34 0 17474.82 0.00 -10.31
3803 PRMT1 19_34 0 18081.45 0.00 -9.54
8030 CPT1C 19_34 0 2374.41 0.00 0.41
3807 TSKS 19_34 0 105.33 0.00 3.30
10164 AP2A1 19_34 0 28.06 0.00 0.17
162 FUZ 19_34 0 21.80 0.00 0.31
1958 MED25 19_34 0 20.04 0.00 2.49
365 PNKP 19_34 0 89.29 0.00 0.38
1951 TBC1D17 19_34 0 31.30 0.00 0.26
10797 NUP62 19_34 0 65.25 0.00 -0.93
8028 ATF5 19_34 0 333.91 0.00 -2.02
6860 SIGLEC11 19_34 0 185.17 0.00 0.59
5388 ZNF473 19_34 0 30.17 0.00 -2.39
1967 VRK3 19_34 0 85.34 0.00 -0.49
2009 MYH14 19_34 0 9.72 0.00 0.36
4176 NR1H2 19_34 0 14.40 0.00 1.48
4174 KCNC3 19_34 0 4.88 0.00 0.47
4175 NAPSA 19_34 0 41.41 0.00 3.02
543 POLD1 19_34 0 111.58 0.00 -1.46
12177 SPIB 19_34 0 149.93 0.00 -0.41
1108 MYBPC2 19_34 0 18.76 0.00 -1.14
10671 ASPDH 19_34 0 100.11 0.00 1.94
2030 CLEC11A 19_34 0 5.53 0.00 0.12
7829 C19orf48 19_34 0 11.97 0.00 -1.74
9147 LINC01869 19_34 0 11.59 0.00 -1.63
7830 KLK1 19_34 0 73.83 0.00 1.44
4005 KLK10 19_34 0 23.93 0.00 -1.86
7831 KLK11 19_34 0 22.88 0.00 -1.76
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_24"
genename region_tag susie_pip mu2 PVE z
10667 HLA-G 6_24 0 7739.34 0 -2.29
12683 HCP5B 6_24 0 73110.93 0 -13.94
10774 HLA-A 6_24 0 439.92 0 0.18
624 ZNRD1 6_24 0 5660.56 0 2.02
10664 RNF39 6_24 0 924.50 0 -3.53
10663 TRIM31 6_24 0 38631.61 0 13.23
10661 TRIM10 6_24 0 2434.43 0 3.39
11273 TRIM26 6_24 0 492.82 0 6.04
10657 TRIM39 6_24 0 132.43 0 1.68
10651 ABCF1 6_24 0 17010.90 0 -11.81
10649 MRPS18B 6_24 0 326.28 0 0.75
10648 C6orf136 6_24 0 1163.12 0 -5.23
10647 DHX16 6_24 0 39.08 0 -1.73
5766 PPP1R18 6_24 0 14686.05 0 -9.98
4836 NRM 6_24 0 8073.62 0 3.09
4833 FLOT1 6_24 0 37452.27 0 -18.00
11136 HCG20 6_24 0 1100.50 0 6.08
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_25"
genename region_tag susie_pip mu2 PVE z
10653 DDR1 6_25 0 30.87 1.6e-15 -0.51
4838 VARS2 6_25 0 198.81 6.6e-13 15.17
10854 GTF2H4 6_25 0 22.77 7.0e-16 -3.34
10044 SFTA2 6_25 0 75.02 1.2e-14 -10.43
10646 PSORS1C1 6_25 0 58.79 4.3e-15 0.32
10645 PSORS1C2 6_25 0 52.41 1.7e-15 -6.56
11297 HLA-B 6_25 0 65.19 5.4e-15 -7.06
4832 TCF19 6_25 0 39.29 3.2e-13 8.75
10644 CCHCR1 6_25 0 39.29 3.2e-13 8.75
10643 POU5F1 6_25 0 89.67 4.0e-15 -10.63
10771 HCG27 6_25 0 15.92 7.8e-16 -4.68
10642 HLA-C 6_25 0 58.07 3.8e-15 -6.82
12306 XXbac-BPG181B23.7 6_25 0 41.23 4.8e-08 -4.99
10640 MICA 6_25 0 100.99 1.3e-13 8.01
10639 MICB 6_25 0 105.52 3.4e-14 -10.05
10417 DDX39B 6_25 0 12.09 4.1e-16 2.61
10637 NFKBIL1 6_25 0 17.49 6.9e-16 -1.35
10852 ATP6V1G2 6_25 0 94.97 1.4e-11 -5.19
11110 LTA 6_25 0 20.20 2.2e-15 -3.94
11237 TNF 6_25 0 10.27 4.3e-16 0.93
10635 NCR3 6_25 0 17.04 7.3e-16 3.79
| 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
30776 rs61788682 1_69 1.000 38.64 1.2e-04 -5.49
36503 rs61804161 1_79 1.000 449.31 1.4e-03 13.50
36507 rs12145843 1_79 1.000 222.26 7.1e-04 25.22
36514 rs61804205 1_79 1.000 899.42 2.9e-03 -30.68
72933 rs780093 2_16 1.000 222.42 7.1e-04 -16.11
74915 rs17013001 2_21 1.000 34.89 1.1e-04 -5.94
97390 rs62161401 2_66 1.000 70.66 2.2e-04 8.25
182990 rs9817452 3_97 1.000 67.10 2.1e-04 8.33
192357 rs9863411 3_114 1.000 65.49 2.1e-04 -8.53
197584 rs3748034 4_4 1.000 80.00 2.5e-04 9.39
273474 rs2859493 5_26 1.000 38.99 1.2e-04 6.24
277590 rs577736887 5_33 1.000 55.78 1.8e-04 -7.92
279381 rs153429 5_37 1.000 77.07 2.4e-04 -4.54
279396 rs745863029 5_37 1.000 64.23 2.0e-04 -2.08
314899 rs58778501 6_1 1.000 86.01 2.7e-04 -5.80
324221 rs115740542 6_20 1.000 94.59 3.0e-04 -7.06
325359 rs1233385 6_23 1.000 126.41 4.0e-04 -14.94
326265 rs2256752 6_25 1.000 170.27 5.4e-04 -18.78
326363 rs2523581 6_25 1.000 126.57 4.0e-04 -7.25
326797 rs9276685 6_27 1.000 135.86 4.3e-04 -11.75
330095 rs7744080 6_32 1.000 42.56 1.4e-04 7.34
367469 rs60425481 6_104 1.000 777.49 2.5e-03 -5.51
371348 rs139588569 6_112 1.000 9143.36 2.9e-02 -4.62
371350 rs59421548 6_112 1.000 9208.95 2.9e-02 -4.32
388392 rs6583438 7_36 1.000 67.79 2.2e-04 7.15
396133 rs761767938 7_49 1.000 3605.80 1.1e-02 -3.74
396141 rs1544459 7_49 1.000 3541.28 1.1e-02 -3.11
404152 rs763798411 7_65 1.000 11193.57 3.6e-02 3.62
411288 rs125124 7_80 1.000 49.27 1.6e-04 -7.24
466777 rs56114972 8_92 1.000 48.94 1.6e-04 5.92
512358 rs17657502 10_14 1.000 62.65 2.0e-04 5.96
570139 rs12283874 11_36 1.000 59.49 1.9e-04 3.81
581979 rs117304134 11_59 1.000 47.09 1.5e-04 -6.51
585464 rs1176746 11_67 1.000 1349.01 4.3e-03 2.77
585466 rs2307599 11_67 1.000 1348.17 4.3e-03 2.96
638405 rs79490353 13_7 1.000 118.23 3.8e-04 9.78
690049 rs1998057 14_48 1.000 135.83 4.3e-04 0.40
690050 rs12893029 14_48 1.000 197.86 6.3e-04 -2.30
693110 rs12588969 14_54 1.000 112.72 3.6e-04 -13.24
706662 rs537559727 15_30 1.000 2077.72 6.6e-03 3.09
706671 rs762746560 15_30 1.000 2069.47 6.6e-03 3.19
743977 rs11078597 17_2 1.000 95.11 3.0e-04 11.98
743981 rs7502910 17_2 1.000 53.46 1.7e-04 9.94
745591 rs4968186 17_7 1.000 64.96 2.1e-04 -9.82
748422 rs11654694 17_15 1.000 56.20 1.8e-04 -7.82
754336 rs1808192 17_27 1.000 72.36 2.3e-04 9.21
759971 rs113408695 17_39 1.000 48.72 1.5e-04 -7.23
783267 rs150377214 18_35 1.000 74.59 2.4e-04 -8.33
801139 rs1688031 19_24 1.000 134.88 4.3e-04 13.97
801140 rs4806075 19_24 1.000 159.46 5.1e-04 -5.92
853195 rs11249215 1_17 1.000 58045.63 1.8e-01 -11.47
853201 rs753570588 1_17 1.000 60106.38 1.9e-01 -12.29
887804 rs142955295 3_35 1.000 13928.46 4.4e-02 4.24
920278 rs1611236 6_24 1.000 142011.88 4.5e-01 -14.25
931537 rs9279507 6_26 1.000 41081.14 1.3e-01 2.87
940482 rs9274442 6_26 1.000 13931.29 4.4e-02 -25.36
943683 rs139991383 7_4 1.000 1152.25 3.7e-03 -3.16
1043023 rs113176985 19_34 1.000 175535.93 5.6e-01 -17.85
1043026 rs374141296 19_34 1.000 176439.81 5.6e-01 -16.57
1057726 rs780018294 22_10 1.000 575.57 1.8e-03 2.17
293042 rs35552666 5_66 0.999 32.18 1.0e-04 -5.80
314892 rs4959611 6_1 0.999 60.60 1.9e-04 5.78
624593 rs2583223 12_62 0.999 35.41 1.1e-04 -5.70
627011 rs141105880 12_67 0.999 81.58 2.6e-04 -9.92
630931 rs12425627 12_76 0.999 36.69 1.2e-04 -6.14
790057 rs55748813 19_2 0.999 45.38 1.4e-04 -7.13
92587 rs10208803 2_54 0.998 73.48 2.3e-04 7.79
97280 rs12622400 2_66 0.998 42.33 1.3e-04 5.70
254613 rs7659414 4_114 0.998 47.09 1.5e-04 -7.21
484057 rs4745108 9_33 0.998 30.73 9.7e-05 -5.45
569097 rs79376486 11_34 0.998 43.29 1.4e-04 5.86
588162 rs1945396 11_75 0.998 34.83 1.1e-04 5.84
645467 rs7997446 13_21 0.998 33.96 1.1e-04 6.01
690067 rs2239651 14_48 0.997 70.88 2.2e-04 -7.09
742821 rs7194426 16_54 0.997 43.51 1.4e-04 -5.86
1069838 rs7287486 22_17 0.997 56.79 1.8e-04 -7.57
223809 rs12507099 4_53 0.996 29.84 9.4e-05 -5.41
325709 rs3095311 6_25 0.996 270.95 8.6e-04 -19.20
236084 rs138204164 4_77 0.995 60.14 1.9e-04 -7.95
333232 rs6458803 6_38 0.995 32.88 1.0e-04 5.71
534670 rs10887917 10_56 0.995 48.04 1.5e-04 7.04
587125 rs666741 11_71 0.995 64.63 2.0e-04 -8.52
630852 rs2229840 12_75 0.995 30.45 9.6e-05 -5.48
706669 rs11858985 15_30 0.995 2026.60 6.4e-03 2.96
50249 rs3813977 1_105 0.994 33.70 1.1e-04 5.48
84730 rs13012253 2_39 0.994 29.15 9.2e-05 -5.37
132206 rs1834748 2_135 0.994 36.64 1.2e-04 6.44
193236 rs79692229 3_116 0.992 40.01 1.3e-04 6.67
839697 rs12166267 22_7 0.992 32.37 1.0e-04 5.49
231153 rs144812644 4_68 0.991 29.36 9.2e-05 -6.53
753507 rs8072356 17_26 0.991 29.90 9.4e-05 5.11
796194 rs71332143 19_15 0.990 29.65 9.3e-05 -5.44
513924 rs148678804 10_16 0.989 27.88 8.8e-05 4.86
260044 rs56023411 5_2 0.988 37.69 1.2e-04 6.31
324200 rs72834643 6_20 0.988 33.10 1.0e-04 -3.68
694040 rs61310292 14_56 0.988 48.72 1.5e-04 -7.41
765145 rs9954032 18_1 0.988 30.55 9.6e-05 -5.43
745570 rs148093673 17_7 0.987 34.44 1.1e-04 5.42
671277 rs8011368 14_10 0.984 27.30 8.5e-05 5.02
782001 rs12960077 18_32 0.983 34.40 1.1e-04 -5.85
424680 rs11775663 8_10 0.982 27.93 8.7e-05 -5.28
1008090 rs148272371 17_6 0.982 31.19 9.7e-05 -5.05
431838 rs4871845 8_24 0.981 40.96 1.3e-04 6.44
462158 rs2720659 8_84 0.981 33.64 1.0e-04 -5.88
664307 rs73609086 13_57 0.981 26.41 8.2e-05 -4.91
544257 rs11199973 10_75 0.980 31.57 9.8e-05 -5.52
123931 rs231811 2_120 0.979 53.18 1.7e-04 7.75
304615 rs12189018 5_87 0.979 25.84 8.0e-05 4.88
325522 rs2246856 6_23 0.977 61.19 1.9e-04 -5.56
614391 rs2137537 12_44 0.977 29.99 9.3e-05 -5.45
326659 rs112357706 6_27 0.976 38.88 1.2e-04 5.80
397537 rs3839804 7_51 0.972 29.42 9.1e-05 -5.44
690158 rs2069987 14_48 0.972 33.09 1.0e-04 -5.58
676224 rs2883893 14_20 0.971 28.16 8.7e-05 -5.85
742800 rs11642017 16_53 0.967 26.09 8.0e-05 4.69
783307 rs4940573 18_35 0.967 119.48 3.7e-04 10.74
141311 rs17776482 3_9 0.965 27.05 8.3e-05 -5.33
570075 rs59286748 11_36 0.964 40.48 1.2e-04 -5.94
570080 rs11227230 11_36 0.964 52.76 1.6e-04 -5.29
197585 rs3752442 4_4 0.962 48.34 1.5e-04 -9.89
125921 rs62203749 2_124 0.958 25.97 7.9e-05 -4.49
770741 rs35796589 18_10 0.957 24.61 7.5e-05 -4.64
426120 rs7821812 8_14 0.955 94.62 2.9e-04 -11.91
727379 rs8061729 16_24 0.955 36.88 1.1e-04 5.12
424886 rs12543422 8_10 0.954 25.08 7.6e-05 4.59
696846 rs7497631 15_7 0.954 24.64 7.5e-05 -4.59
507717 rs1972409 10_7 0.952 34.19 1.0e-04 6.24
481644 rs11557154 9_26 0.949 36.06 1.1e-04 -5.67
790762 rs67868323 19_4 0.949 59.49 1.8e-04 8.04
74311 rs115472871 2_20 0.947 24.94 7.5e-05 -4.83
407385 rs38913 7_71 0.944 27.27 8.2e-05 5.22
590236 rs7932045 11_80 0.940 30.44 9.1e-05 7.01
314915 rs6942338 6_1 0.938 84.18 2.5e-04 10.28
324706 rs187257713 6_21 0.938 25.20 7.5e-05 -3.89
512373 rs2497836 10_14 0.938 40.49 1.2e-04 -3.55
497991 rs2812398 9_58 0.936 31.22 9.3e-05 5.52
326612 rs138924536 6_25 0.935 62.32 1.8e-04 -5.98
837222 rs12626883 21_24 0.934 24.66 7.3e-05 -4.68
107097 rs60882035 2_85 0.933 36.08 1.1e-04 -6.19
30774 rs56894897 1_69 0.932 26.08 7.7e-05 -3.72
141529 rs56395424 3_9 0.931 32.16 9.5e-05 -4.58
503614 rs495828 9_70 0.930 36.53 1.1e-04 5.32
318992 rs45449792 6_10 0.929 23.37 6.9e-05 4.49
638407 rs7989654 13_7 0.928 63.70 1.9e-04 5.76
931526 rs3130292 6_26 0.927 41374.56 1.2e-01 -19.06
610350 rs7397189 12_36 0.925 26.25 7.7e-05 -4.79
197589 rs1203107 4_4 0.924 70.80 2.1e-04 8.52
368699 rs766167 6_106 0.923 25.16 7.4e-05 -4.85
372238 rs79206451 7_3 0.923 24.13 7.1e-05 -4.55
380537 rs10228771 7_21 0.923 24.02 7.0e-05 -4.46
368596 rs9365555 6_106 0.922 24.25 7.1e-05 -4.61
667331 rs35477689 14_3 0.919 39.85 1.2e-04 -6.86
683375 rs61987084 14_34 0.919 28.80 8.4e-05 -5.11
689862 rs12588988 14_47 0.916 23.82 6.9e-05 4.62
377865 rs111683935 7_17 0.913 31.54 9.1e-05 -5.58
469954 rs10120959 9_4 0.913 23.79 6.9e-05 -4.51
74952 rs13388394 2_21 0.910 25.95 7.5e-05 -5.05
626936 rs653178 12_67 0.910 55.30 1.6e-04 -8.35
10235 rs2045791 1_23 0.909 23.55 6.8e-05 -4.39
1008456 rs149438782 17_6 0.908 30.76 8.9e-05 5.63
214768 rs768294452 4_39 0.905 23.54 6.8e-05 3.88
324180 rs140264349 6_20 0.904 31.95 9.2e-05 -4.78
594760 rs10734885 12_7 0.903 26.77 7.7e-05 -4.76
819522 rs74178731 20_29 0.902 28.91 8.3e-05 5.34
34880 rs1685606 1_75 0.897 42.59 1.2e-04 8.39
64584 rs4335411 1_131 0.897 23.86 6.8e-05 -4.41
404158 rs13230660 7_65 0.897 11122.87 3.2e-02 4.37
396137 rs11972122 7_49 0.895 3324.75 9.5e-03 -3.63
33797 rs12124727 1_73 0.892 25.54 7.2e-05 3.48
503992 rs7043538 9_71 0.891 24.99 7.1e-05 -4.64
748445 rs3751985 17_15 0.890 447.65 1.3e-03 25.61
425184 rs7833103 8_11 0.887 37.27 1.1e-04 7.32
92655 rs200937710 2_54 0.883 30.34 8.5e-05 5.02
348238 rs2388334 6_67 0.882 36.21 1.0e-04 -5.94
99671 rs2422391 2_69 0.879 28.43 7.9e-05 -5.00
590208 rs6590334 11_80 0.879 38.06 1.1e-04 7.28
8393 rs2491141 1_20 0.875 24.93 6.9e-05 4.71
884017 rs13063578 3_33 0.873 48.06 1.3e-04 -6.79
535711 rs11187129 10_59 0.871 30.77 8.5e-05 3.80
1026554 rs148933445 19_32 0.871 33.40 9.2e-05 -5.54
503946 rs56406717 9_70 0.870 25.50 7.0e-05 -4.87
148442 rs116823501 3_24 0.869 23.82 6.6e-05 3.20
231123 rs17032996 4_68 0.866 31.99 8.8e-05 6.69
282525 rs250722 5_45 0.861 30.74 8.4e-05 6.19
594860 rs4883268 12_7 0.859 28.95 7.9e-05 -4.98
226476 rs114646961 4_59 0.856 24.35 6.6e-05 4.50
495821 rs10733564 9_54 0.854 24.55 6.7e-05 4.45
189618 rs2141598 3_109 0.848 24.11 6.5e-05 4.45
553271 rs360130 11_8 0.846 44.49 1.2e-04 -5.27
47799 rs112840522 1_99 0.845 23.89 6.4e-05 -4.19
295686 rs71583081 5_71 0.845 23.27 6.2e-05 -4.32
511417 rs10906857 10_13 0.844 23.50 6.3e-05 -4.31
726987 rs153105 16_23 0.844 27.74 7.4e-05 5.78
754796 rs145023944 17_28 0.842 25.62 6.8e-05 -4.24
178044 rs9862179 3_86 0.839 24.47 6.5e-05 -4.46
756964 rs1040261 17_33 0.837 29.74 7.9e-05 -5.32
431896 rs4581062 8_24 0.833 26.39 7.0e-05 4.93
717217 rs10902585 15_49 0.833 24.79 6.6e-05 4.58
664326 rs17381234 13_57 0.832 25.81 6.8e-05 4.74
54968 rs61830291 1_112 0.831 47.65 1.3e-04 7.06
47413 rs74213209 1_98 0.827 34.91 9.2e-05 -5.83
276798 rs28499105 5_31 0.826 31.37 8.2e-05 5.33
364224 rs10872678 6_99 0.822 32.86 8.6e-05 5.60
95867 rs6711659 2_63 0.821 25.35 6.6e-05 4.59
206301 rs10007850 4_22 0.821 145.06 3.8e-04 2.08
815851 rs291675 20_20 0.819 35.50 9.2e-05 5.87
372297 rs12671734 7_5 0.818 26.08 6.8e-05 4.50
570328 rs574546203 11_37 0.818 25.64 6.7e-05 4.62
197567 rs115019205 4_4 0.816 26.28 6.8e-05 4.69
744114 rs3760230 17_3 0.813 137.14 3.5e-04 -12.48
487939 rs930340 9_41 0.810 60.34 1.6e-04 -8.21
572060 rs366066 11_40 0.808 24.10 6.2e-05 4.29
994585 rs455378 16_12 0.807 32.34 8.3e-05 4.98
328034 rs9348980 6_29 0.805 34.83 8.9e-05 5.33
667351 rs12885436 14_3 0.805 29.71 7.6e-05 -5.63
838097 rs62222326 22_4 0.804 29.87 7.6e-05 -5.05
821741 rs140571612 20_32 0.802 24.35 6.2e-05 -4.33SNPs 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
1043026 rs374141296 19_34 1 176439.8 0.56 -16.57
1043023 rs113176985 19_34 1 175535.9 0.56 -17.85
1043014 rs61371437 19_34 0 175528.8 0.00 -17.74
1043016 rs35295508 19_34 0 175179.0 0.00 -17.73
1043004 rs739349 19_34 0 175092.3 0.00 -17.67
1043005 rs756628 19_34 0 175090.6 0.00 -17.67
1043001 rs739347 19_34 0 174817.5 0.00 -17.75
1043002 rs2073614 19_34 0 174622.5 0.00 -17.75
1043030 rs2946865 19_34 0 174593.7 0.00 -17.84
1043021 rs73056069 19_34 0 174467.8 0.00 -17.77
1042997 rs4802613 19_34 0 174195.6 0.00 -17.66
1043018 rs2878354 19_34 0 174147.2 0.00 -17.74
1043007 rs2077300 19_34 0 174077.2 0.00 -17.69
1043011 rs73056059 19_34 0 173738.2 0.00 -17.73
1042995 rs10403394 19_34 0 172900.5 0.00 -17.58
1042996 rs17555056 19_34 0 172770.5 0.00 -17.63
1043031 rs60815603 19_34 0 172048.0 0.00 -17.84
1043034 rs1316885 19_34 0 171272.7 0.00 -17.95
1043036 rs60746284 19_34 0 170966.9 0.00 -17.92
1043039 rs2946863 19_34 0 170943.7 0.00 -18.01
1043032 rs35443645 19_34 0 170825.5 0.00 -18.08
1043012 rs73056062 19_34 0 169145.2 0.00 -17.00
1043042 rs553431297 19_34 0 166355.7 0.00 -17.07
1043025 rs112283514 19_34 0 165909.5 0.00 -16.37
1043027 rs11270139 19_34 0 164877.2 0.00 -16.89
1042992 rs10421294 19_34 0 156257.8 0.00 -16.76
1042991 rs8108175 19_34 0 156238.5 0.00 -16.77
1042984 rs59192944 19_34 0 155954.6 0.00 -16.75
1042990 rs1858742 19_34 0 155943.2 0.00 -16.77
1042981 rs55991145 19_34 0 155847.9 0.00 -16.79
1042976 rs3786567 19_34 0 155791.2 0.00 -16.78
1042972 rs2271952 19_34 0 155733.6 0.00 -16.79
1042975 rs4801801 19_34 0 155730.9 0.00 -16.79
1042971 rs2271953 19_34 0 155562.4 0.00 -16.82
1042973 rs2271951 19_34 0 155554.4 0.00 -16.82
1042962 rs60365978 19_34 0 155432.8 0.00 -16.84
1042968 rs4802612 19_34 0 154794.0 0.00 -16.81
1042978 rs2517977 19_34 0 154478.2 0.00 -16.80
1042965 rs55893003 19_34 0 154276.5 0.00 -16.85
1042957 rs55992104 19_34 0 150717.4 0.00 -15.98
1042951 rs60403475 19_34 0 150685.8 0.00 -15.96
1042954 rs4352151 19_34 0 150673.7 0.00 -15.99
1042948 rs11878448 19_34 0 150570.9 0.00 -15.98
1042942 rs9653100 19_34 0 150525.4 0.00 -15.97
1042938 rs4802611 19_34 0 150430.7 0.00 -15.98
1042930 rs7251338 19_34 0 150219.0 0.00 -15.99
1042929 rs59269605 19_34 0 150202.4 0.00 -16.00
1042950 rs1042120 19_34 0 149791.1 0.00 -16.00
1042946 rs113220577 19_34 0 149663.5 0.00 -16.00
1042940 rs9653118 19_34 0 149431.8 0.00 -15.98SNPs 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
1043023 rs113176985 19_34 1.000 175535.93 0.5600 -17.85
1043026 rs374141296 19_34 1.000 176439.81 0.5600 -16.57
920278 rs1611236 6_24 1.000 142011.88 0.4500 -14.25
853201 rs753570588 1_17 1.000 60106.38 0.1900 -12.29
920332 rs2394171 6_24 0.427 142619.12 0.1900 -14.32
920334 rs2893981 6_24 0.423 142618.89 0.1900 -14.32
853195 rs11249215 1_17 1.000 58045.63 0.1800 -11.47
853220 rs11249219 1_17 0.798 57900.77 0.1500 -13.55
931537 rs9279507 6_26 1.000 41081.14 0.1300 2.87
931526 rs3130292 6_26 0.927 41374.56 0.1200 -19.06
920264 rs1611228 6_24 0.168 142617.47 0.0760 -14.31
920330 rs1611267 6_24 0.162 142618.35 0.0740 -14.31
920253 rs1737020 6_24 0.134 142617.36 0.0610 -14.31
920254 rs1737019 6_24 0.134 142617.36 0.0610 -14.31
1043111 rs10419198 19_34 0.567 34148.09 0.0610 -25.81
931523 rs3130291 6_26 0.432 41373.39 0.0570 -19.05
853211 rs12407074 1_17 0.306 57923.53 0.0560 -13.56
1043136 rs36013629 19_34 0.433 33561.70 0.0460 -25.88
920301 rs1611248 6_24 0.099 142618.01 0.0450 -14.30
887804 rs142955295 3_35 1.000 13928.46 0.0440 4.24
940482 rs9274442 6_26 1.000 13931.29 0.0440 -25.36
920196 rs1633033 6_24 0.087 142617.59 0.0400 -14.31
853209 rs7555518 1_17 0.211 57923.23 0.0390 -13.55
404152 rs763798411 7_65 1.000 11193.57 0.0360 3.62
853217 rs7513156 1_17 0.182 57922.41 0.0330 -13.55
404158 rs13230660 7_65 0.897 11122.87 0.0320 4.37
371348 rs139588569 6_112 1.000 9143.36 0.0290 -4.62
371350 rs59421548 6_112 1.000 9208.95 0.0290 -4.32
404163 rs4997569 7_65 0.764 11147.81 0.0270 4.29
853218 rs10903121 1_17 0.129 57921.74 0.0240 -13.55
853214 rs7550635 1_17 0.123 57922.62 0.0230 -13.55
920209 rs2844838 6_24 0.052 142616.12 0.0230 -14.31
404170 rs6952534 7_65 0.570 11121.98 0.0200 4.44
853216 rs7542123 1_17 0.107 57922.12 0.0200 -13.55
853213 rs7550552 1_17 0.103 57922.27 0.0190 -13.55
404155 rs10274607 7_65 0.376 11138.26 0.0130 4.32
920322 rs1611260 6_24 0.026 142617.35 0.0120 -14.30
396133 rs761767938 7_49 1.000 3605.80 0.0110 -3.74
396141 rs1544459 7_49 1.000 3541.28 0.0110 -3.11
920247 rs1633020 6_24 0.025 142601.22 0.0110 -14.32
920251 rs1633018 6_24 0.025 142600.46 0.0110 -14.32
920305 rs1611252 6_24 0.024 142617.55 0.0110 -14.30
920328 rs1611265 6_24 0.025 142617.23 0.0110 -14.30
396137 rs11972122 7_49 0.895 3324.75 0.0095 -3.63
920257 rs2508055 6_24 0.017 142616.95 0.0077 -14.29
920260 rs111734624 6_24 0.017 142616.99 0.0077 -14.29
706662 rs537559727 15_30 1.000 2077.72 0.0066 3.09
706671 rs762746560 15_30 1.000 2069.47 0.0066 3.19
706669 rs11858985 15_30 0.995 2026.60 0.0064 2.96
887711 rs12381242 3_35 0.135 13974.69 0.0060 -4.27SNPs 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
36514 rs61804205 1_79 1.000 899.42 2.9e-03 -30.68
36490 rs189026820 1_79 0.000 766.82 0.0e+00 -27.18
36510 rs74816838 1_79 0.000 649.65 0.0e+00 -26.47
36469 rs7518087 1_79 0.000 499.48 8.8e-19 -25.90
1043136 rs36013629 19_34 0.433 33561.70 4.6e-02 -25.88
1043111 rs10419198 19_34 0.567 34148.09 6.1e-02 -25.81
748445 rs3751985 17_15 0.890 447.65 1.3e-03 25.61
748443 rs3794776 17_15 0.112 457.74 1.6e-04 25.37
940482 rs9274442 6_26 1.000 13931.29 4.4e-02 -25.36
36507 rs12145843 1_79 1.000 222.26 7.1e-04 25.22
748440 rs16961828 17_15 0.002 440.55 2.6e-06 25.04
940686 rs3852215 6_26 0.000 10930.86 1.7e-12 -24.97
940581 rs3891176 6_26 0.000 10325.40 2.6e-13 -24.90
940527 rs9274474 6_26 0.000 10949.42 2.6e-12 -24.81
940052 rs4993988 6_26 0.000 10449.13 3.5e-13 -24.79
940268 rs9274114 6_26 0.000 10472.33 2.3e-13 -24.78
940092 rs9273494 6_26 0.000 10348.28 1.3e-13 -24.76
940126 rs9273529 6_26 0.000 10357.42 1.4e-13 -24.76
940591 rs3891175 6_26 0.000 10353.23 1.2e-13 -24.76
940127 rs9273530 6_26 0.000 10351.68 9.8e-14 -24.75
940227 rs9273902 6_26 0.000 10347.11 1.0e-13 -24.75
940102 rs9273504 6_26 0.000 10351.96 8.0e-14 -24.74
940114 rs9273519 6_26 0.000 10353.28 8.2e-14 -24.74
940115 rs281875165 6_26 0.000 10353.28 8.2e-14 -24.74
940116 rs398122357 6_26 0.000 10353.28 8.2e-14 -24.74
940206 rs9273803 6_26 0.000 10343.20 6.3e-14 -24.73
940209 rs9273807 6_26 0.000 10342.06 6.6e-14 -24.73
940572 rs9274514 6_26 0.000 10344.75 5.2e-14 -24.72
940224 rs9273873 6_26 0.000 10392.90 2.7e-14 -24.70
940512 rs9274465 6_26 0.000 10387.05 2.2e-14 -24.68
940543 rs9274497 6_26 0.000 10342.07 1.6e-14 -24.67
940202 rs9273786 6_26 0.000 10289.32 5.9e-15 -24.64
940335 rs17613643 6_26 0.000 10532.04 1.7e-14 -24.64
940064 rs9273463 6_26 0.000 10001.05 5.4e-16 -24.57
940266 rs9274107 6_26 0.000 9745.73 2.7e-16 -24.57
940654 rs4988888 6_26 0.000 10311.80 1.2e-15 -24.57
940710 rs9274623 6_26 0.000 10411.84 8.6e-16 -24.56
940058 rs9273455 6_26 0.000 9501.98 1.7e-17 -24.49
940691 rs3844313 6_26 0.000 10117.20 0.0e+00 -24.31
940540 rs9274490 6_26 0.000 10021.40 0.0e+00 -24.23
940546 rs9274498 6_26 0.000 9871.21 0.0e+00 -24.19
940162 rs9273595 6_26 0.000 9797.87 0.0e+00 -23.97
940078 rs9273480 6_26 0.000 9913.81 0.0e+00 -23.93
940139 rs9273542 6_26 0.000 8852.83 0.0e+00 -23.91
940136 rs9273539 6_26 0.000 8852.48 0.0e+00 -23.87
1043194 rs111476047 19_34 0.000 31814.18 0.0e+00 -23.78
940314 rs17613599 6_26 0.000 8662.87 0.0e+00 -23.71
940317 rs17613606 6_26 0.000 8656.81 0.0e+00 -23.70
36485 rs61801830 1_79 0.000 218.87 2.1e-09 -23.65
940259 rs9274079 6_26 0.000 8195.94 0.0e+00 -23.59Gene 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] 23if (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)RP11-219B17.3 gene(s) from the input list not found in DisGeNET CURATEDRP11-131K5.2 gene(s) from the input list not found in DisGeNET CURATEDZNF77 gene(s) from the input list not found in DisGeNET CURATEDTMEM176B gene(s) from the input list not found in DisGeNET CURATEDALLC gene(s) from the input list not found in DisGeNET CURATEDAC008746.12 gene(s) from the input list not found in DisGeNET CURATEDHMGXB3 gene(s) from the input list not found in DisGeNET CURATEDNPIPB2 gene(s) from the input list not found in DisGeNET CURATEDRPS11 gene(s) from the input list not found in DisGeNET CURATEDSH3BP1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio
14 Hydrocephalus 0.006114723 2/13
36 Caliciviridae Infections 0.008625799 1/13
41 Infections, Calicivirus 0.008625799 1/13
44 Renal Cell Dysplasia 0.008625799 1/13
51 D-Glyceric aciduria 0.008625799 1/13
57 Anhydramnios 0.008625799 1/13
63 D-glycericacidemia 0.008625799 1/13
70 Maple Syrup Urine Disease, Type IA 0.008625799 1/13
78 VITAMIN B12 PLASMA LEVEL QUANTITATIVE TRAIT LOCUS 1 0.008625799 1/13
84 PORENCEPHALY 2 0.008625799 1/13
BgRatio
14 9/9703
36 1/9703
41 1/9703
44 1/9703
51 1/9703
57 1/9703
63 1/9703
70 1/9703
78 1/9703
84 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