LDL direct (quantile) - Whole_Blood
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-30500_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30600_irnt_Liver.Rmd
Modified: analysis/ukb-d-30600_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30610_irnt_Liver.Rmd
Modified: analysis/ukb-d-30610_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30620_irnt_Liver.Rmd
Modified: analysis/ukb-d-30620_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30630_irnt_Liver.Rmd
Modified: analysis/ukb-d-30630_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30640_irnt_Liver.Rmd
Modified: analysis/ukb-d-30640_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30650_irnt_Liver.Rmd
Modified: analysis/ukb-d-30650_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30660_irnt_Liver.Rmd
Modified: analysis/ukb-d-30660_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30670_irnt_Liver.Rmd
Modified: analysis/ukb-d-30670_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30680_irnt_Liver.Rmd
Modified: analysis/ukb-d-30690_irnt_Liver.Rmd
Modified: analysis/ukb-d-30690_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30700_irnt_Liver.Rmd
Modified: analysis/ukb-d-30700_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30710_irnt_Liver.Rmd
Modified: analysis/ukb-d-30710_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30720_irnt_Liver.Rmd
Modified: analysis/ukb-d-30720_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30730_irnt_Liver.Rmd
Modified: analysis/ukb-d-30740_irnt_Liver.Rmd
Modified: analysis/ukb-d-30740_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30750_irnt_Liver.Rmd
Modified: analysis/ukb-d-30750_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30760_irnt_Liver.Rmd
Modified: analysis/ukb-d-30760_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30770_irnt_Liver.Rmd
Modified: analysis/ukb-d-30770_irnt_Whole_Blood.Rmd
Modified: analysis/ukb-d-30780_irnt_Liver.Rmd
Modified: analysis/ukb-d-30780_irnt_Whole_Blood.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
Modified: analysis/ukb-d-30870_irnt_Liver.Rmd
Modified: analysis/ukb-d-30880_irnt_Liver.Rmd
Modified: analysis/ukb-d-30890_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-30780_irnt_Whole_Blood.Rmd) and HTML (docs/ukb-d-30780_irnt_Whole_Blood.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 | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
| html | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
| Rmd | 4068e9b | wesleycrouse | 2021-07-29 | finalizing automation |
| Rmd | 0e62fa9 | wesleycrouse | 2021-07-29 | Automating reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait LDL direct (quantile) using Whole_Blood 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-30780_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 Whole_Blood 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] 11095#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1129 747 624 400 479 621 560 383 404 430 682 652 192 362 331
16 17 18 19 20 21 22
551 725 159 911 313 130 310 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.762776Load 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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0022909820 0.0001025144 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
188.10618 26.40187 #report sample size
print(sample_size)[1] 343621#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01391465 0.06850552 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.01390607 0.42873726Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
8166 PCSK9 1_34 1.000 165.35 4.8e-04 21.99
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
4151 LDLR 19_9 1.000 653.66 1.9e-03 -24.71
5665 CNIH4 1_114 0.999 50.71 1.5e-04 6.79
6892 PKN3 9_66 0.976 53.27 1.5e-04 -6.97
5839 TIMD4 5_92 0.960 189.84 5.3e-04 13.88
7128 ACP6 1_73 0.924 27.03 7.3e-05 4.67
7089 USP1 1_39 0.862 265.34 6.7e-04 16.26
4096 MPDU1 17_7 0.796 28.39 6.6e-05 4.63
7462 DAGLB 7_9 0.792 32.03 7.4e-05 5.20
12535 PKD1L3 16_38 0.782 144.07 3.3e-04 -3.78
6089 FADS1 11_34 0.739 160.92 3.5e-04 -12.59
10343 ZFP28 19_38 0.733 31.66 6.7e-05 -5.16
9109 CD163L1 12_7 0.636 26.92 5.0e-05 -4.67
11362 AC011747.3 2_6 0.583 26.76 4.5e-05 4.56
12392 HIST1H4K 6_21 0.544 34.81 5.5e-05 3.59
1975 SARS2 19_26 0.523 26.48 4.0e-05 4.48
9198 GRINA 8_94 0.499 49.27 7.2e-05 -6.68
405 ADRB1 10_71 0.490 42.70 6.1e-05 -6.18
3979 VIL1 2_129 0.464 30.46 4.1e-05 4.73Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
4691 SRPK2 7_65 0.000 3464.46 0.0e+00 -1.72
73 KMT2E 7_65 0.000 2120.21 0.0e+00 -0.10
11489 RP11-325F22.2 7_65 0.000 2089.28 0.0e+00 0.70
11441 APOC2 19_31 0.000 1857.93 9.6e-09 45.63
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
4151 LDLR 19_9 1.000 653.66 1.9e-03 -24.71
4137 MAU2 19_15 0.004 358.06 4.1e-06 18.78
331 SARS 1_67 0.002 346.85 1.6e-06 -18.23
7089 USP1 1_39 0.862 265.34 6.7e-04 16.26
2131 ATP13A1 19_15 0.012 265.32 9.0e-06 -16.07
4159 NECTIN2 19_31 0.000 264.46 1.6e-09 16.37
3102 DOCK7 1_39 0.001 225.94 6.0e-07 14.99
5562 CELSR2 1_67 0.001 196.05 5.7e-07 13.74
7053 BSND 1_34 0.000 190.03 1.3e-10 21.19
5839 TIMD4 5_92 0.960 189.84 5.3e-04 13.88
8166 PCSK9 1_34 1.000 165.35 4.8e-04 21.99
6089 FADS1 11_34 0.739 160.92 3.5e-04 -12.59
5511 TIMM29 19_9 0.000 158.82 0.0e+00 -10.55
2496 ZPR1 11_70 0.017 157.03 7.9e-06 -11.85
12254 CTC-366B18.4 5_44 0.001 155.22 2.3e-07 -15.55Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
4151 LDLR 19_9 1.000 653.66 1.9e-03 -24.71
7089 USP1 1_39 0.862 265.34 6.7e-04 16.26
5839 TIMD4 5_92 0.960 189.84 5.3e-04 13.88
8166 PCSK9 1_34 1.000 165.35 4.8e-04 21.99
6089 FADS1 11_34 0.739 160.92 3.5e-04 -12.59
12535 PKD1L3 16_38 0.782 144.07 3.3e-04 -3.78
5665 CNIH4 1_114 0.999 50.71 1.5e-04 6.79
6892 PKN3 9_66 0.976 53.27 1.5e-04 -6.97
7462 DAGLB 7_9 0.792 32.03 7.4e-05 5.20
7128 ACP6 1_73 0.924 27.03 7.3e-05 4.67
9198 GRINA 8_94 0.499 49.27 7.2e-05 -6.68
10343 ZFP28 19_38 0.733 31.66 6.7e-05 -5.16
4096 MPDU1 17_7 0.796 28.39 6.6e-05 4.63
405 ADRB1 10_71 0.490 42.70 6.1e-05 -6.18
12392 HIST1H4K 6_21 0.544 34.81 5.5e-05 3.59
9109 CD163L1 12_7 0.636 26.92 5.0e-05 -4.67
11362 AC011747.3 2_6 0.583 26.76 4.5e-05 4.56
3979 VIL1 2_129 0.464 30.46 4.1e-05 4.73
1975 SARS2 19_26 0.523 26.48 4.0e-05 4.48Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.000 1857.93 9.6e-09 45.63
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
4151 LDLR 19_9 1.000 653.66 1.9e-03 -24.71
8166 PCSK9 1_34 1.000 165.35 4.8e-04 21.99
7053 BSND 1_34 0.000 190.03 1.3e-10 21.19
4137 MAU2 19_15 0.004 358.06 4.1e-06 18.78
331 SARS 1_67 0.002 346.85 1.6e-06 -18.23
4159 NECTIN2 19_31 0.000 264.46 1.6e-09 16.37
7089 USP1 1_39 0.862 265.34 6.7e-04 16.26
2131 ATP13A1 19_15 0.012 265.32 9.0e-06 -16.07
12254 CTC-366B18.4 5_44 0.001 155.22 2.3e-07 -15.55
3102 DOCK7 1_39 0.001 225.94 6.0e-07 14.99
2793 COL4A3BP 5_44 0.000 143.00 2.0e-07 14.79
5839 TIMD4 5_92 0.960 189.84 5.3e-04 13.88
5562 CELSR2 1_67 0.001 196.05 5.7e-07 13.74
6089 FADS1 11_34 0.739 160.92 3.5e-04 -12.59
5512 CARM1 19_9 0.000 150.89 0.0e+00 -12.26
2496 ZPR1 11_70 0.017 157.03 7.9e-06 -11.85
5511 TIMM29 19_9 0.000 158.82 0.0e+00 -10.55
10121 RHCE 1_18 0.025 105.96 7.6e-06 10.26Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.01883731#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.000 1857.93 9.6e-09 45.63
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
4151 LDLR 19_9 1.000 653.66 1.9e-03 -24.71
8166 PCSK9 1_34 1.000 165.35 4.8e-04 21.99
7053 BSND 1_34 0.000 190.03 1.3e-10 21.19
4137 MAU2 19_15 0.004 358.06 4.1e-06 18.78
331 SARS 1_67 0.002 346.85 1.6e-06 -18.23
4159 NECTIN2 19_31 0.000 264.46 1.6e-09 16.37
7089 USP1 1_39 0.862 265.34 6.7e-04 16.26
2131 ATP13A1 19_15 0.012 265.32 9.0e-06 -16.07
12254 CTC-366B18.4 5_44 0.001 155.22 2.3e-07 -15.55
3102 DOCK7 1_39 0.001 225.94 6.0e-07 14.99
2793 COL4A3BP 5_44 0.000 143.00 2.0e-07 14.79
5839 TIMD4 5_92 0.960 189.84 5.3e-04 13.88
5562 CELSR2 1_67 0.001 196.05 5.7e-07 13.74
6089 FADS1 11_34 0.739 160.92 3.5e-04 -12.59
5512 CARM1 19_9 0.000 150.89 0.0e+00 -12.26
2496 ZPR1 11_70 0.017 157.03 7.9e-06 -11.85
5511 TIMM29 19_9 0.000 158.82 0.0e+00 -10.55
10121 RHCE 1_18 0.025 105.96 7.6e-06 10.26Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 19_31"
genename region_tag susie_pip mu2 PVE z
6822 ZNF235 19_31 0 53.10 2.5e-10 -6.60
12136 ZNF285 19_31 0 12.18 9.5e-11 -0.66
7892 ZNF180 19_31 0 36.77 1.6e-09 2.29
820 PVR 19_31 0 45.86 2.2e-10 -9.55
11152 IGSF23 19_31 0 25.71 1.3e-10 -2.76
9941 CEACAM19 19_31 0 56.03 7.8e-10 8.62
4159 NECTIN2 19_31 0 264.46 1.6e-09 16.37
4161 TOMM40 19_31 0 40.68 2.0e-10 -1.40
12134 APOC4 19_31 0 108.73 2.0e-09 8.73
11441 APOC2 19_31 0 1857.93 9.6e-09 45.63
1977 CLPTM1 19_31 0 19.41 9.1e-11 -3.28
8368 ZNF296 19_31 0 103.95 7.7e-09 -7.47
5505 GEMIN7 19_31 0 31.92 7.0e-10 2.65
1979 PPP1R37 19_31 0 80.94 7.7e-09 -2.08
10171 BLOC1S3 19_31 0 10.52 4.8e-11 2.97
116 TRAPPC6A 19_31 0 30.00 2.2e-10 1.92
12615 EXOC3L2 19_31 0 8.14 5.0e-11 -1.34
111 MARK4 19_31 0 13.05 1.4e-10 -2.10
1988 KLC3 19_31 0 32.78 5.0e-09 -3.64
1982 PPP1R13L 19_31 0 18.34 1.3e-10 -3.08
3230 CD3EAP 19_31 0 18.34 1.3e-10 -3.08
213 ERCC1 19_31 0 10.01 4.6e-11 -2.30
11059 PPM1N 19_31 0 7.30 3.4e-11 -2.94
3830 RTN2 19_31 0 54.63 3.6e-10 5.73
3831 VASP 19_31 0 28.55 7.2e-10 4.57
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_67"
genename region_tag susie_pip mu2 PVE z
11280 RP11-356N1.2 1_67 0.001 11.90 4.6e-08 -1.96
1102 SLC25A24 1_67 0.001 10.97 4.6e-08 1.59
7095 FAM102B 1_67 0.002 27.95 1.6e-07 -4.14
7096 HENMT1 1_67 0.003 18.47 1.8e-07 -1.85
3080 STXBP3 1_67 0.002 19.79 1.2e-07 2.99
3522 GPSM2 1_67 0.001 8.10 3.0e-08 0.59
3521 CLCC1 1_67 0.001 16.94 5.1e-08 -3.37
10487 TAF13 1_67 0.001 61.31 2.5e-07 -7.05
11143 TMEM167B 1_67 0.001 9.21 3.2e-08 1.57
9291 C1orf194 1_67 0.001 10.46 3.1e-08 -1.03
1099 WDR47 1_67 0.001 11.56 3.5e-08 -1.30
3084 KIAA1324 1_67 0.001 38.00 1.3e-07 5.29
331 SARS 1_67 0.002 346.85 1.6e-06 -18.23
5562 CELSR2 1_67 0.001 196.05 5.7e-07 13.74
4564 PSRC1 1_67 1.000 1741.33 5.1e-03 -41.79
7099 ATXN7L2 1_67 0.001 12.15 3.4e-08 2.57
8776 CYB561D1 1_67 0.007 42.02 8.4e-07 4.60
9435 AMIGO1 1_67 0.003 38.97 3.0e-07 -4.99
617 GNAI3 1_67 0.001 41.56 1.7e-07 5.91
11016 GSTM2 1_67 0.002 12.41 7.0e-08 1.28
8107 GSTM4 1_67 0.001 36.61 1.0e-07 -5.50
4559 GSTM1 1_67 0.020 63.21 3.7e-06 5.90
4561 GSTM5 1_67 0.001 11.02 2.9e-08 2.40
4562 GSTM3 1_67 0.001 21.44 6.5e-08 -3.83
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_9"
genename region_tag susie_pip mu2 PVE z
4240 ZNF317 19_9 0 6.37 0.0000 -0.86
10208 ZNF699 19_9 0 37.05 0.0000 -2.16
10092 ZNF559 19_9 0 6.26 0.0000 0.80
8818 ZNF266 19_9 0 9.51 0.0000 -0.97
4245 ZNF426 19_9 0 18.05 0.0000 -1.11
12567 CTC-543D15.8 19_9 0 32.49 0.0000 2.01
10522 ZNF121 19_9 0 18.05 0.0000 -1.12
8463 ZNF561 19_9 0 6.54 0.0000 -0.81
8461 ZNF562 19_9 0 18.82 0.0000 -1.30
12539 CTD-3116E22.8 19_9 0 6.20 0.0000 -0.06
10303 ZNF846 19_9 0 6.39 0.0000 0.03
3954 FBXL12 19_9 0 12.10 0.0000 0.73
10572 UBL5 19_9 0 17.98 0.0000 -1.28
1004 COL5A3 19_9 0 11.02 0.0000 0.78
4243 ANGPTL6 19_9 0 9.67 0.0000 -0.78
11635 P2RY11 19_9 0 6.80 0.0000 -0.54
4241 PPAN 19_9 0 23.35 0.0000 -2.35
4244 C19orf66 19_9 0 11.60 0.0000 2.02
4242 EIF3G 19_9 0 8.47 0.0000 1.54
2062 MRPL4 19_9 0 14.18 0.0000 0.51
1256 ICAM1 19_9 0 25.15 0.0000 -1.15
2068 ICAM5 19_9 0 11.27 0.0000 -1.21
11171 ZGLP1 19_9 0 8.36 0.0000 -1.02
12143 FDX2 19_9 0 52.67 0.0000 -5.44
6996 RAVER1 19_9 0 12.30 0.0000 1.58
913 ICAM3 19_9 0 25.71 0.0000 -0.60
2072 TYK2 19_9 0 76.00 0.0000 2.45
650 PDE4A 19_9 0 30.30 0.0000 0.25
9357 S1PR5 19_9 0 23.06 0.0000 1.85
4228 ATG4D 19_9 0 54.84 0.0000 -5.45
4101 KRI1 19_9 0 13.32 0.0000 0.70
4104 CDKN2D 19_9 0 59.95 0.0000 3.63
4103 AP1M2 19_9 0 110.95 0.0000 -5.56
4102 SLC44A2 19_9 0 126.92 0.0000 -3.86
12119 ILF3-AS1 19_9 0 54.00 0.0000 -1.02
1398 TMED1 19_9 0 23.72 0.0000 -2.07
11089 C19orf38 19_9 0 23.72 0.0000 -2.07
5512 CARM1 19_9 0 150.89 0.0000 -12.26
5511 TIMM29 19_9 0 158.82 0.0000 -10.55
4227 YIPF2 19_9 0 11.06 0.0000 -3.10
3972 SMARCA4 19_9 0 12.03 0.0000 3.84
4151 LDLR 19_9 1 653.66 0.0019 -24.71
6998 SPC24 19_9 0 78.12 0.0000 8.96
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_34"
genename region_tag susie_pip mu2 PVE z
528 NDC1 1_34 0 34.97 6.8e-20 -2.46
527 YIPF1 1_34 0 19.81 6.4e-21 -1.50
10976 DIO1 1_34 0 32.15 5.2e-20 -2.28
1028 HSPB11 1_34 0 5.95 0.0e+00 -0.23
3074 LRRC42 1_34 0 5.95 0.0e+00 0.23
3072 TCEANC2 1_34 0 5.09 0.0e+00 0.01
3073 TMEM59 1_34 0 5.10 0.0e+00 -0.23
11148 CYB5RL 1_34 0 16.54 5.3e-21 1.16
3076 MRPL37 1_34 0 7.80 0.0e+00 0.45
6603 SSBP3 1_34 0 5.31 0.0e+00 -0.60
9687 MROH7 1_34 0 6.36 0.0e+00 0.86
11620 TTC4 1_34 0 5.15 0.0e+00 1.01
7051 PARS2 1_34 0 17.69 5.7e-21 -1.46
97 TTC22 1_34 0 5.66 0.0e+00 0.15
7052 LEXM 1_34 0 21.49 1.4e-20 2.10
3062 DHCR24 1_34 0 24.65 2.4e-20 -1.93
7053 BSND 1_34 0 190.03 1.3e-10 21.19
8166 PCSK9 1_34 1 165.35 4.8e-04 21.99
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_15"
genename region_tag susie_pip mu2 PVE z
4199 LSM4 19_15 0.001 9.82 2.4e-08 -0.73
4197 PGPEP1 19_15 0.001 6.55 1.1e-08 0.44
8907 LRRC25 19_15 0.001 7.78 1.3e-08 -1.18
4196 SSBP4 19_15 0.001 11.28 2.9e-08 -1.37
2112 ISYNA1 19_15 0.001 5.80 8.9e-09 0.43
2113 ELL 19_15 0.001 8.42 1.6e-08 1.21
2123 KXD1 19_15 0.001 5.46 8.1e-09 0.25
11192 UBA52 19_15 0.000 5.00 7.1e-09 -0.04
7904 KLHL26 19_15 0.001 14.29 4.4e-08 1.71
52 UPF1 19_15 0.001 15.88 5.3e-08 -2.03
2115 COPE 19_15 0.001 12.54 4.4e-08 -0.70
2116 DDX49 19_15 0.001 9.30 2.1e-08 -0.27
2118 ARMC6 19_15 0.001 7.07 1.2e-08 -1.09
599 SUGP2 19_15 0.001 8.84 1.8e-08 -0.87
596 TMEM161A 19_15 0.002 19.78 1.1e-07 -2.06
11075 MEF2B 19_15 0.001 27.48 8.2e-08 4.30
11817 BORCS8 19_15 0.004 64.00 7.6e-07 6.33
595 RFXANK 19_15 0.001 6.04 9.0e-09 0.53
4137 MAU2 19_15 0.004 358.06 4.1e-06 18.78
7905 GATAD2A 19_15 0.001 97.39 1.7e-07 -9.03
9879 NDUFA13 19_15 0.001 97.08 1.6e-07 -9.01
9152 TSSK6 19_15 0.001 14.18 2.7e-08 1.60
11726 YJEFN3 19_15 0.001 85.05 1.5e-07 -8.02
6840 CILP2 19_15 0.001 13.95 2.5e-08 -1.70
2128 PBX4 19_15 0.000 6.79 9.8e-09 -0.66
597 LPAR2 19_15 0.001 25.70 4.7e-08 -4.50
1235 GMIP 19_15 0.001 23.47 6.1e-08 -4.16
2131 ATP13A1 19_15 0.012 265.32 9.0e-06 -16.07
9450 ZNF101 19_15 0.003 18.28 1.3e-07 -0.06
2126 ZNF14 19_15 0.001 21.47 4.8e-08 4.25
| 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
54358 rs2807848 1_112 1.000 61.73 1.8e-04 -7.88
57978 rs6663780 1_122 1.000 89.52 2.6e-04 7.90
57983 rs822928 1_122 1.000 125.00 3.6e-04 12.37
70604 rs11679386 2_12 1.000 163.31 4.8e-04 11.91
70653 rs1042034 2_13 1.000 260.23 7.6e-04 16.57
70659 rs934197 2_13 1.000 413.20 1.2e-03 33.06
70662 rs548145 2_13 1.000 710.09 2.1e-03 33.09
70739 rs1848922 2_13 1.000 241.50 7.0e-04 25.41
72389 rs780093 2_16 1.000 190.40 5.5e-04 -14.14
78454 rs72800939 2_28 1.000 60.65 1.8e-04 -7.85
164505 rs768688512 3_58 1.000 669.21 1.9e-03 2.62
323219 rs11376017 6_13 1.000 71.46 2.1e-04 -8.51
326927 rs72834643 6_20 1.000 50.03 1.5e-04 -6.05
326948 rs115740542 6_20 1.000 173.55 5.1e-04 -12.53
327681 rs454182 6_22 1.000 149.15 4.3e-04 4.78
353792 rs9496567 6_67 1.000 42.05 1.2e-04 -6.34
372359 rs12208357 6_103 1.000 284.06 8.3e-04 12.28
372507 rs117733303 6_104 1.000 98.31 2.9e-04 10.10
372543 rs56393506 6_104 1.000 134.29 3.9e-04 14.09
393542 rs217396 7_32 1.000 84.57 2.5e-04 -9.43
411387 rs763798411 7_65 1.000 25578.54 7.4e-02 -3.27
433233 rs7012814 8_12 1.000 100.27 2.9e-04 10.91
448024 rs140753685 8_42 1.000 60.75 1.8e-04 7.80
449420 rs4738679 8_45 1.000 118.42 3.4e-04 -11.70
469083 rs13252684 8_83 1.000 291.25 8.5e-04 11.96
502680 rs2437818 9_53 1.000 80.87 2.4e-04 6.33
510897 rs115478735 9_70 1.000 337.53 9.8e-04 19.01
595822 rs4937122 11_77 1.000 81.31 2.4e-04 12.15
711375 rs2070895 15_27 1.000 63.74 1.9e-04 7.73
743069 rs57186116 16_38 1.000 75.20 2.2e-04 7.71
767670 rs1801689 17_38 1.000 87.38 2.5e-04 9.40
768586 rs113408695 17_39 1.000 161.76 4.7e-04 12.77
768612 rs8070232 17_39 1.000 194.46 5.7e-04 -8.09
801833 rs4804149 19_11 1.000 50.02 1.5e-04 6.52
801886 rs322144 19_11 1.000 69.16 2.0e-04 3.95
804604 rs3794991 19_15 1.000 501.38 1.5e-03 -21.49
804635 rs113619686 19_15 1.000 73.79 2.1e-04 0.59
811975 rs73036721 19_30 1.000 64.26 1.9e-04 -7.79
812020 rs62115478 19_30 1.000 200.19 5.8e-04 -14.33
812173 rs150262789 19_32 1.000 79.57 2.3e-04 -10.90
822914 rs6075251 20_13 1.000 69.01 2.0e-04 -2.33
822915 rs34507316 20_13 1.000 96.37 2.8e-04 -6.81
864220 rs11591147 1_34 1.000 1332.31 3.9e-03 -39.16
864283 rs499883 1_34 1.000 121.17 3.5e-04 16.11
946065 rs10422256 19_9 1.000 254.15 7.4e-04 12.77
948906 rs429358 19_31 1.000 2262.01 6.6e-03 59.27
948909 rs1065853 19_31 1.000 11025.86 3.2e-02 -110.92
948959 rs5112 19_31 1.000 408.32 1.2e-03 11.30
948976 rs35136575 19_31 1.000 435.82 1.3e-03 -6.36
57933 rs6586405 1_122 0.999 49.84 1.4e-04 8.96
78318 rs139029940 2_27 0.999 40.79 1.2e-04 6.81
284670 rs7701166 5_44 0.999 39.33 1.1e-04 -2.48
411398 rs4997569 7_65 0.999 25615.84 7.4e-02 -2.98
437751 rs1495743 8_20 0.999 44.25 1.3e-04 -6.52
502653 rs2297400 9_53 0.999 43.82 1.3e-04 6.61
742802 rs4396539 16_37 0.999 41.85 1.2e-04 -5.23
804244 rs2302209 19_14 0.999 46.12 1.3e-04 6.64
385740 rs56130071 7_19 0.998 105.41 3.1e-04 10.98
615811 rs7397189 12_36 0.998 36.72 1.1e-04 -5.77
328903 rs28780090 6_26 0.997 62.42 1.8e-04 6.87
592754 rs75542613 11_70 0.997 38.30 1.1e-04 -6.53
748521 rs2255451 16_49 0.997 42.09 1.2e-04 -6.36
409921 rs3197597 7_61 0.996 35.99 1.0e-04 -5.05
827868 rs76981217 20_24 0.996 36.71 1.1e-04 7.69
632397 rs653178 12_67 0.995 109.91 3.2e-04 11.05
30346 rs1730862 1_66 0.993 30.94 8.9e-05 -5.28
469072 rs79658059 8_83 0.993 337.24 9.8e-04 -16.02
671912 rs3934835 13_62 0.993 62.55 1.8e-04 7.94
812157 rs58701309 19_32 0.993 60.50 1.7e-04 1.22
141257 rs709149 3_9 0.990 38.69 1.1e-04 -6.78
620177 rs148481241 12_44 0.987 29.27 8.4e-05 5.10
284611 rs10062361 5_44 0.986 227.06 6.5e-04 20.32
827819 rs6029132 20_24 0.986 42.18 1.2e-04 -6.76
148267 rs9834932 3_24 0.985 71.75 2.1e-04 -8.48
592749 rs3135506 11_70 0.980 160.02 4.6e-04 12.37
610898 rs2638250 12_25 0.979 28.65 8.2e-05 -5.04
328118 rs3130253 6_23 0.976 29.71 8.4e-05 5.64
57980 rs4920269 1_122 0.974 71.91 2.0e-04 2.04
328926 rs62407548 6_26 0.974 82.00 2.3e-04 8.26
248024 rs114756490 4_100 0.973 27.60 7.8e-05 4.99
78331 rs13430143 2_27 0.971 98.94 2.8e-04 -3.34
636487 rs11057830 12_76 0.970 27.67 7.8e-05 4.93
328089 rs28986304 6_23 0.968 47.00 1.3e-04 7.38
225294 rs1458038 4_54 0.966 56.36 1.6e-04 -7.42
485002 rs1556516 9_16 0.966 79.68 2.2e-04 -8.99
8327 rs79598313 1_18 0.965 50.49 1.4e-04 7.02
78334 rs4076834 2_27 0.963 478.39 1.3e-03 -20.11
827872 rs73124945 20_24 0.963 32.99 9.2e-05 -7.78
635352 rs1169300 12_74 0.962 72.78 2.0e-04 8.69
326766 rs75080831 6_19 0.961 62.45 1.7e-04 -7.91
393592 rs141379002 7_33 0.959 27.52 7.7e-05 4.90
771745 rs4969183 17_44 0.957 52.71 1.5e-04 7.17
477136 rs7024888 9_3 0.954 27.44 7.6e-05 -5.06
836509 rs62219001 21_2 0.954 27.89 7.7e-05 -4.95
449388 rs56386732 8_45 0.951 36.27 1.0e-04 -7.01
576375 rs6591179 11_36 0.948 27.35 7.5e-05 4.89
630490 rs1196760 12_63 0.942 27.48 7.5e-05 -4.87
743067 rs9652628 16_38 0.935 137.80 3.8e-04 11.95
816370 rs34003091 19_39 0.935 110.82 3.0e-04 -10.42
812073 rs377297589 19_32 0.934 54.07 1.5e-04 -6.79
563392 rs7943121 11_13 0.927 33.16 8.9e-05 5.56
645374 rs1012130 13_10 0.925 47.90 1.3e-04 -2.78
356528 rs12199109 6_73 0.911 26.89 7.1e-05 4.86
515847 rs10905277 10_8 0.910 29.68 7.9e-05 5.13
548965 rs12244851 10_70 0.909 40.03 1.1e-04 -4.88
738910 rs821840 16_31 0.907 179.39 4.7e-04 -13.48
198705 rs36205397 4_4 0.904 42.89 1.1e-04 6.16
173261 rs189174 3_74 0.903 47.49 1.2e-04 6.77
70656 rs78610189 2_13 0.902 63.59 1.7e-04 -8.39
801874 rs322125 19_11 0.901 118.70 3.1e-04 -7.47
502673 rs2777788 9_53 0.893 66.98 1.7e-04 -5.74
711374 rs139823028 15_27 0.890 25.53 6.6e-05 3.99
589018 rs201912654 11_59 0.889 42.87 1.1e-04 -6.31
832007 rs10641149 20_32 0.886 29.15 7.5e-05 5.08
278218 rs1499279 5_31 0.882 68.00 1.7e-04 -8.37
362733 rs9321207 6_86 0.874 32.58 8.3e-05 5.40
196918 rs5855544 3_120 0.873 26.27 6.7e-05 -4.59
99022 rs138192199 2_69 0.871 26.16 6.6e-05 4.67
492989 rs11144506 9_35 0.871 28.66 7.3e-05 5.04
843752 rs2835302 21_16 0.870 27.14 6.9e-05 -4.65
123354 rs7569317 2_120 0.866 49.31 1.2e-04 7.90
822895 rs78348000 20_13 0.861 32.04 8.0e-05 5.22
284634 rs3843482 5_44 0.859 441.67 1.1e-03 25.03
39085 rs1795240 1_84 0.844 27.64 6.8e-05 -4.85
70456 rs6531234 2_12 0.843 43.93 1.1e-04 -7.17
595825 rs74612335 11_77 0.843 78.50 1.9e-04 11.90
768597 rs9303012 17_39 0.842 193.16 4.7e-04 2.26
827837 rs6102034 20_24 0.840 103.55 2.5e-04 -11.19
763257 rs4793601 17_28 0.838 32.48 7.9e-05 -6.21
645366 rs1799955 13_10 0.833 81.36 2.0e-04 -6.69
200930 rs2002574 4_10 0.831 27.08 6.5e-05 -4.56
743007 rs12708919 16_38 0.831 162.79 3.9e-04 11.30
818560 rs74273659 20_5 0.831 27.38 6.6e-05 4.65
826613 rs11167269 20_21 0.831 62.58 1.5e-04 -7.80
801843 rs58495388 19_11 0.825 36.28 8.7e-05 5.53
543144 rs10882161 10_59 0.819 31.42 7.5e-05 -5.48
844889 rs149577713 21_19 0.816 33.68 8.0e-05 3.32
730366 rs35782593 16_12 0.815 25.52 6.1e-05 -4.32
505251 rs2762469 9_57 0.813 26.43 6.3e-05 -4.53
828013 rs11086801 20_25 0.811 117.02 2.8e-04 10.98
237190 rs138204164 4_77 0.809 28.06 6.6e-05 -4.85
433202 rs117037226 8_11 0.808 26.51 6.2e-05 4.19
433244 rs13265179 8_12 0.808 38.83 9.1e-05 -7.41SNPs 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
411398 rs4997569 7_65 0.999 25615.84 7.4e-02 -2.98
411387 rs763798411 7_65 1.000 25578.54 7.4e-02 -3.27
411390 rs10274607 7_65 0.057 25551.86 4.2e-03 -2.87
411405 rs6952534 7_65 0.000 25524.97 3.7e-11 -2.89
411393 rs13230660 7_65 0.006 25512.94 4.4e-04 -2.95
411404 rs4730069 7_65 0.000 25502.68 1.6e-13 -2.87
411397 rs10242713 7_65 0.000 25406.49 0.0e+00 -2.81
411400 rs10249965 7_65 0.000 25205.04 0.0e+00 -2.85
411412 rs1013016 7_65 0.000 24157.93 0.0e+00 2.40
411437 rs8180737 7_65 0.000 22943.03 0.0e+00 -2.83
411430 rs17778396 7_65 0.000 22936.62 0.0e+00 -2.80
411431 rs2237621 7_65 0.000 22926.47 0.0e+00 -2.80
411402 rs71562637 7_65 0.000 22909.09 0.0e+00 -2.66
411464 rs10224564 7_65 0.000 22884.54 0.0e+00 -2.79
411449 rs10255779 7_65 0.000 22873.21 0.0e+00 -2.81
411466 rs78132606 7_65 0.000 22763.39 0.0e+00 -2.77
411469 rs4610671 7_65 0.000 22733.74 0.0e+00 -2.72
411471 rs12669532 7_65 0.000 21790.31 0.0e+00 -2.77
411428 rs2237618 7_65 0.000 21432.07 0.0e+00 -2.47
411473 rs118089279 7_65 0.000 21221.07 0.0e+00 -2.67
411460 rs73188303 7_65 0.000 21203.08 0.0e+00 -2.42
411470 rs560364150 7_65 0.000 16814.94 0.0e+00 -1.87
411456 rs10261738 7_65 0.000 13751.38 0.0e+00 -2.67
948909 rs1065853 19_31 1.000 11025.86 3.2e-02 -110.92
948907 rs7412 19_31 0.004 10998.74 1.1e-04 -110.75
411411 rs368909701 7_65 0.000 10532.03 0.0e+00 -0.78
411410 rs2299297 7_65 0.000 8303.06 0.0e+00 0.80
948913 rs72654473 19_31 0.000 7760.24 8.3e-07 -83.44
948924 rs390082 19_31 0.000 7712.56 9.5e-07 -83.03
948916 rs445925 19_31 0.000 7711.77 8.8e-07 -83.03
411396 rs6961668 7_65 0.000 7634.42 0.0e+00 -3.23
948947 rs190712692 19_31 0.005 7100.87 9.3e-05 -86.50
948950 rs141622900 19_31 0.000 6858.67 4.9e-06 -85.06
411454 rs56384866 7_65 0.000 6845.23 0.0e+00 -1.88
411478 rs147367948 7_65 0.000 5746.37 0.0e+00 0.14
948800 rs41290120 19_31 0.000 5527.16 4.7e-09 -79.87
411382 rs145194740 7_65 0.000 5503.63 0.0e+00 0.27
411378 rs11762333 7_65 0.000 5402.79 0.0e+00 0.05
411459 rs34356406 7_65 0.000 4921.85 0.0e+00 -2.11
948503 rs118147862 19_31 0.000 4606.05 5.2e-09 -72.73
948911 rs75627662 19_31 0.000 4155.62 2.9e-09 -30.77
948920 rs483082 19_31 0.000 3942.48 2.6e-09 -19.81
948923 rs438811 19_31 0.000 3940.96 2.6e-09 -19.85
948928 rs5117 19_31 0.000 3924.81 2.6e-09 -19.87
948879 rs111784051 19_31 0.000 3778.97 2.3e-06 -63.81
948874 rs61679753 19_31 0.000 3778.35 1.9e-06 -63.80
411452 rs143717474 7_65 0.000 3778.17 0.0e+00 1.56
411455 rs2385557 7_65 0.000 3759.07 0.0e+00 1.59
948857 rs1160983 19_31 0.000 3753.96 1.6e-06 -63.59
411379 rs12333765 7_65 0.000 3717.99 0.0e+00 -2.65SNPs 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
411387 rs763798411 7_65 1.000 25578.54 0.07400 -3.27
411398 rs4997569 7_65 0.999 25615.84 0.07400 -2.98
948909 rs1065853 19_31 1.000 11025.86 0.03200 -110.92
948906 rs429358 19_31 1.000 2262.01 0.00660 59.27
411390 rs10274607 7_65 0.057 25551.86 0.00420 -2.87
864220 rs11591147 1_34 1.000 1332.31 0.00390 -39.16
946025 rs12151108 19_9 0.481 2633.57 0.00370 -48.96
946026 rs73015024 19_9 0.321 2632.74 0.00250 -48.96
70662 rs548145 2_13 1.000 710.09 0.00210 33.09
164505 rs768688512 3_58 1.000 669.21 0.00190 2.62
804604 rs3794991 19_15 1.000 501.38 0.00150 -21.49
78334 rs4076834 2_27 0.963 478.39 0.00130 -20.11
948976 rs35136575 19_31 1.000 435.82 0.00130 -6.36
70659 rs934197 2_13 1.000 413.20 0.00120 33.06
948959 rs5112 19_31 1.000 408.32 0.00120 11.30
284634 rs3843482 5_44 0.859 441.67 0.00110 25.03
469072 rs79658059 8_83 0.993 337.24 0.00098 -16.02
510897 rs115478735 9_70 1.000 337.53 0.00098 19.01
469083 rs13252684 8_83 1.000 291.25 0.00085 11.96
372359 rs12208357 6_103 1.000 284.06 0.00083 12.28
946027 rs147985405 19_9 0.103 2630.73 0.00079 -48.94
70653 rs1042034 2_13 1.000 260.23 0.00076 16.57
946065 rs10422256 19_9 1.000 254.15 0.00074 12.77
164501 rs73141241 3_58 0.346 702.07 0.00071 2.80
946198 rs2738464 19_9 0.590 411.92 0.00071 6.87
70739 rs1848922 2_13 1.000 241.50 0.00070 25.41
284611 rs10062361 5_44 0.986 227.06 0.00065 20.32
812020 rs62115478 19_30 1.000 200.19 0.00058 -14.33
768612 rs8070232 17_39 1.000 194.46 0.00057 -8.09
72389 rs780093 2_16 1.000 190.40 0.00055 -14.14
372373 rs3818678 6_103 0.791 232.15 0.00053 -9.95
326948 rs115740542 6_20 1.000 173.55 0.00051 -12.53
164500 rs138503435 3_58 0.239 703.78 0.00049 2.73
70604 rs11679386 2_12 1.000 163.31 0.00048 11.91
738910 rs821840 16_31 0.907 179.39 0.00047 -13.48
768586 rs113408695 17_39 1.000 161.76 0.00047 12.77
768597 rs9303012 17_39 0.842 193.16 0.00047 2.26
592749 rs3135506 11_70 0.980 160.02 0.00046 12.37
946029 rs17248727 19_9 0.060 2629.43 0.00046 -48.92
411393 rs13230660 7_65 0.006 25512.94 0.00044 -2.95
327681 rs454182 6_22 1.000 149.15 0.00043 4.78
372543 rs56393506 6_104 1.000 134.29 0.00039 14.09
743007 rs12708919 16_38 0.831 162.79 0.00039 11.30
743067 rs9652628 16_38 0.935 137.80 0.00038 11.95
57983 rs822928 1_122 1.000 125.00 0.00036 12.37
864283 rs499883 1_34 1.000 121.17 0.00035 16.11
946057 rs28493980 19_9 0.508 239.63 0.00035 9.30
449420 rs4738679 8_45 1.000 118.42 0.00034 -11.70
946056 rs3745677 19_9 0.491 239.31 0.00034 9.34
946204 rs2915966 19_9 0.287 411.00 0.00034 6.85SNPs 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
948909 rs1065853 19_31 1.000 11025.86 3.2e-02 -110.92
948907 rs7412 19_31 0.004 10998.74 1.1e-04 -110.75
948947 rs190712692 19_31 0.005 7100.87 9.3e-05 -86.50
948950 rs141622900 19_31 0.000 6858.67 4.9e-06 -85.06
948913 rs72654473 19_31 0.000 7760.24 8.3e-07 -83.44
948916 rs445925 19_31 0.000 7711.77 8.8e-07 -83.03
948924 rs390082 19_31 0.000 7712.56 9.5e-07 -83.03
948800 rs41290120 19_31 0.000 5527.16 4.7e-09 -79.87
948503 rs118147862 19_31 0.000 4606.05 5.2e-09 -72.73
948879 rs111784051 19_31 0.000 3778.97 2.3e-06 -63.81
948874 rs61679753 19_31 0.000 3778.35 1.9e-06 -63.80
948857 rs1160983 19_31 0.000 3753.96 1.6e-06 -63.59
948836 rs7254892 19_31 0.000 3542.52 5.8e-07 -61.87
948906 rs429358 19_31 1.000 2262.01 6.6e-03 59.27
948209 rs62117160 19_31 0.000 2877.39 1.6e-09 -57.31
948937 rs12721051 19_31 0.000 1775.54 2.3e-09 56.30
948939 rs56131196 19_31 0.000 1762.20 2.1e-09 56.10
948942 rs144311893 19_31 0.000 3048.00 2.0e-08 -56.10
948940 rs4420638 19_31 0.000 1753.41 2.1e-09 56.01
948943 rs814573 19_31 0.000 1755.08 1.6e-09 55.54
948944 rs157592 19_31 0.000 1647.74 1.5e-09 54.36
948828 rs283809 19_31 0.000 2721.15 1.2e-06 -53.96
948827 rs283808 19_31 0.000 2718.63 1.1e-06 -53.94
948838 rs6857 19_31 0.000 1651.48 1.2e-09 52.66
948904 rs769449 19_31 0.000 1786.53 3.3e-09 51.90
948917 rs10414043 19_31 0.000 1768.12 3.0e-09 51.68
948918 rs7256200 19_31 0.000 1765.75 2.9e-09 51.66
948446 rs148933445 19_31 0.000 2122.75 1.3e-09 -51.20
948899 rs449647 19_31 0.000 1658.72 3.7e-09 -50.93
948690 rs365653 19_31 0.000 1857.56 1.3e-09 -50.92
948951 rs111789331 19_31 0.000 1372.77 1.1e-09 49.44
948933 rs12721046 19_31 0.000 1362.87 1.0e-09 49.38
948955 rs66626994 19_31 0.000 1362.06 1.1e-09 49.26
948863 rs11668327 19_31 0.000 1432.16 1.6e-09 -49.25
948735 rs112422902 19_31 0.000 2155.65 4.9e-08 -48.98
946025 rs12151108 19_9 0.481 2633.57 3.7e-03 -48.96
946026 rs73015024 19_9 0.321 2632.74 2.5e-03 -48.96
946036 rs6511720 19_9 0.031 2629.84 2.4e-04 -48.95
946027 rs147985405 19_9 0.103 2630.73 7.9e-04 -48.94
946029 rs17248727 19_9 0.060 2629.43 4.6e-04 -48.92
946035 rs57217136 19_9 0.003 2624.39 2.4e-05 -48.89
946028 rs17248720 19_9 0.000 2609.41 4.4e-09 -48.81
946011 rs73015020 19_9 0.000 2614.28 1.9e-07 -48.80
945991 rs138175288 19_9 0.000 2613.18 1.1e-07 -48.78
946002 rs73015013 19_9 0.000 2613.12 1.1e-07 -48.78
945990 rs114821903 19_9 0.000 2612.47 7.9e-08 -48.77
945992 rs112107114 19_9 0.000 2612.35 7.5e-08 -48.77
945993 rs115594766 19_9 0.000 2612.63 8.6e-08 -48.77
946009 rs61194703 19_9 0.000 2610.06 2.5e-08 -48.76
946008 rs138294113 19_9 0.000 2609.98 2.5e-08 -48.75Gene 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] 8if (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"
Term
1 cholesterol homeostasis (GO:0042632)
2 sterol homeostasis (GO:0055092)
3 alditol phosphate metabolic process (GO:0052646)
4 positive regulation of protein catabolic process in the vacuole (GO:1904352)
5 regulation of astrocyte activation (GO:0061888)
6 regulation of low-density lipoprotein particle receptor catabolic process (GO:0032803)
7 negative regulation of astrocyte differentiation (GO:0048712)
8 negative regulation of lipoprotein particle clearance (GO:0010985)
9 sterol import (GO:0035376)
10 monoubiquitinated protein deubiquitination (GO:0035520)
11 cholesterol import (GO:0070508)
12 negative regulation of macromolecule metabolic process (GO:0010605)
13 negative regulation of low-density lipoprotein receptor activity (GO:1905598)
14 positive regulation of receptor catabolic process (GO:2000646)
15 chylomicron remnant clearance (GO:0034382)
16 regulation of lysosomal protein catabolic process (GO:1905165)
17 negative regulation of microglial cell activation (GO:1903979)
18 regulation of nitrogen compound metabolic process (GO:0051171)
19 negative regulation of nitrogen compound metabolic process (GO:0051172)
20 intestinal cholesterol absorption (GO:0030299)
21 negative regulation of sodium ion transmembrane transport (GO:1902306)
22 negative regulation of sodium ion transmembrane transporter activity (GO:2000650)
23 low-density lipoprotein particle receptor catabolic process (GO:0032802)
24 low-density lipoprotein receptor particle metabolic process (GO:0032799)
25 regulation of low-density lipoprotein particle clearance (GO:0010988)
26 negative regulation of receptor binding (GO:1900121)
27 positive regulation of triglyceride biosynthetic process (GO:0010867)
28 intestinal lipid absorption (GO:0098856)
29 negative regulation of amyloid fibril formation (GO:1905907)
30 cellular response to low-density lipoprotein particle stimulus (GO:0071404)
31 negative regulation of cell activation (GO:0050866)
32 negative regulation of neuroinflammatory response (GO:0150079)
33 regulation of amyloid fibril formation (GO:1905906)
34 regulation of triglyceride biosynthetic process (GO:0010866)
35 intracellular cholesterol transport (GO:0032367)
36 regulation of microglial cell activation (GO:1903978)
37 negative regulation of macrophage activation (GO:0043031)
38 regulation of receptor recycling (GO:0001919)
39 protein autoprocessing (GO:0016540)
40 positive regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045737)
41 regulation of spindle organization (GO:0090224)
42 positive regulation of triglyceride metabolic process (GO:0090208)
43 long-term memory (GO:0007616)
44 positive regulation of cellular protein catabolic process (GO:1903364)
45 hepaticobiliary system development (GO:0061008)
46 positive regulation of cyclin-dependent protein kinase activity (GO:1904031)
47 negative regulation of ion transmembrane transporter activity (GO:0032413)
48 sterol transport (GO:0015918)
49 positive regulation of receptor internalization (GO:0002092)
50 positive regulation of neuron apoptotic process (GO:0043525)
51 negative regulation of signaling (GO:0023057)
52 organophosphate ester transport (GO:0015748)
53 receptor catabolic process (GO:0032801)
54 positive regulation of microtubule polymerization or depolymerization (GO:0031112)
55 negative regulation of receptor-mediated endocytosis (GO:0048261)
56 positive regulation of microtubule polymerization (GO:0031116)
57 liver development (GO:0001889)
58 regulation of mitotic spindle organization (GO:0060236)
59 positive regulation of lipid biosynthetic process (GO:0046889)
60 regulation of receptor internalization (GO:0002090)
61 negative regulation of cellular metabolic process (GO:0031324)
62 regulation of microtubule polymerization (GO:0031113)
63 regulation of sodium ion transmembrane transporter activity (GO:2000649)
64 epithelial cell migration (GO:0010631)
65 regulation of response to DNA damage stimulus (GO:2001020)
66 neurogenesis (GO:0022008)
67 positive regulation of receptor-mediated endocytosis (GO:0048260)
68 microtubule bundle formation (GO:0001578)
69 regulation of DNA repair (GO:0006282)
70 regulation of DNA metabolic process (GO:0051052)
71 positive regulation of neuron death (GO:1901216)
72 phosphatidic acid biosynthetic process (GO:0006654)
73 regulation of protein metabolic process (GO:0051246)
74 phosphatidic acid metabolic process (GO:0046473)
75 mitotic metaphase plate congression (GO:0007080)
76 cholesterol transport (GO:0030301)
77 ameboidal-type cell migration (GO:0001667)
78 negative regulation of protein metabolic process (GO:0051248)
79 interstrand cross-link repair (GO:0036297)
80 renal system development (GO:0072001)
81 phospholipid transport (GO:0015914)
82 cellular protein catabolic process (GO:0044257)
83 response to light stimulus (GO:0009416)
84 cellular response to nutrient levels (GO:0031669)
85 positive regulation of cell cycle (GO:0045787)
86 positive regulation of protein polymerization (GO:0032273)
87 kidney development (GO:0001822)
88 gland development (GO:0048732)
89 negative regulation of supramolecular fiber organization (GO:1902904)
90 phospholipid metabolic process (GO:0006644)
91 glycerophospholipid metabolic process (GO:0006650)
92 regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0000079)
93 response to insulin (GO:0032868)
94 regulation of neuron death (GO:1901214)
Overlap Adjusted.P.value Genes
1 2/71 0.02104344 PCSK9;LDLR
2 2/72 0.02104344 PCSK9;LDLR
3 1/5 0.02104344 ACP6
4 1/5 0.02104344 LDLR
5 1/5 0.02104344 LDLR
6 1/5 0.02104344 PCSK9
7 1/6 0.02104344 LDLR
8 1/6 0.02104344 PCSK9
9 1/6 0.02104344 LDLR
10 1/6 0.02104344 USP1
11 1/6 0.02104344 LDLR
12 2/194 0.02104344 PCSK9;LDLR
13 1/7 0.02104344 PCSK9
14 1/7 0.02104344 PCSK9
15 1/7 0.02104344 LDLR
16 1/7 0.02104344 LDLR
17 1/8 0.02104344 LDLR
18 1/8 0.02104344 LDLR
19 1/8 0.02104344 LDLR
20 1/9 0.02104344 LDLR
21 1/10 0.02104344 PCSK9
22 1/10 0.02104344 PCSK9
23 1/10 0.02104344 PCSK9
24 1/10 0.02104344 PCSK9
25 1/10 0.02104344 PCSK9
26 1/10 0.02104344 PCSK9
27 1/11 0.02149062 LDLR
28 1/11 0.02149062 LDLR
29 1/12 0.02263194 LDLR
30 1/14 0.02277717 LDLR
31 1/14 0.02277717 LDLR
32 1/14 0.02277717 LDLR
33 1/15 0.02277717 LDLR
34 1/15 0.02277717 LDLR
35 1/15 0.02277717 LDLR
36 1/15 0.02277717 LDLR
37 1/16 0.02322460 LDLR
38 1/17 0.02322460 PCSK9
39 1/17 0.02322460 PCSK9
40 1/17 0.02322460 PSRC1
41 1/18 0.02358895 PSRC1
42 1/19 0.02358895 LDLR
43 1/19 0.02358895 LDLR
44 1/19 0.02358895 PCSK9
45 1/20 0.02374674 PCSK9
46 1/20 0.02374674 PSRC1
47 1/21 0.02389098 PCSK9
48 1/21 0.02389098 LDLR
49 1/23 0.02562335 PCSK9
50 1/24 0.02574047 PCSK9
51 1/25 0.02574047 PCSK9
52 1/25 0.02574047 LDLR
53 1/25 0.02574047 PCSK9
54 1/26 0.02579212 PSRC1
55 1/26 0.02579212 PCSK9
56 1/28 0.02727060 PSRC1
57 1/32 0.03059822 PCSK9
58 1/35 0.03231539 PSRC1
59 1/35 0.03231539 LDLR
60 1/36 0.03267899 PCSK9
61 1/39 0.03432483 PCSK9
62 1/40 0.03432483 PSRC1
63 1/40 0.03432483 PCSK9
64 1/41 0.03432483 PKN3
65 1/41 0.03432483 USP1
66 1/44 0.03546482 PCSK9
67 1/44 0.03546482 PCSK9
68 1/45 0.03546482 PSRC1
69 1/45 0.03546482 USP1
70 1/46 0.03572879 USP1
71 1/47 0.03573757 PCSK9
72 1/48 0.03573757 ACP6
73 1/48 0.03573757 LDLR
74 1/50 0.03620861 ACP6
75 1/51 0.03620861 PSRC1
76 1/51 0.03620861 LDLR
77 1/52 0.03620861 PKN3
78 1/52 0.03620861 LDLR
79 1/55 0.03779298 USP1
80 1/57 0.03866416 PCSK9
81 1/59 0.03951290 LDLR
82 1/60 0.03968565 PCSK9
83 1/61 0.03985400 USP1
84 1/66 0.04206936 PCSK9
85 1/66 0.04206936 PSRC1
86 1/69 0.04344741 PSRC1
87 1/70 0.04356284 PCSK9
88 1/71 0.04367543 PCSK9
89 1/72 0.04378528 LDLR
90 1/76 0.04567235 ACP6
91 1/80 0.04751464 ACP6
92 1/82 0.04815631 PSRC1
93 1/84 0.04878338 PCSK9
94 1/86 0.04939630 PCSK9
[1] "GO_Cellular_Component_2021"
Term
1 endolysosome membrane (GO:0036020)
2 endolysosome (GO:0036019)
3 late endosome (GO:0005770)
4 extrinsic component of external side of plasma membrane (GO:0031232)
5 lytic vacuole (GO:0000323)
6 early endosome (GO:0005769)
7 endosome membrane (GO:0010008)
8 lysosomal membrane (GO:0005765)
Overlap Adjusted.P.value Genes
1 2/17 0.0006074504 PCSK9;LDLR
2 2/25 0.0006689158 PCSK9;LDLR
3 2/189 0.0204793401 PCSK9;LDLR
4 1/8 0.0204793401 PCSK9
5 2/219 0.0204793401 PCSK9;LDLR
6 2/266 0.0249613074 PCSK9;LDLR
7 2/325 0.0284663503 PCSK9;LDLR
8 2/330 0.0284663503 PCSK9;LDLR
[1] "GO_Molecular_Function_2021"
Term Overlap
1 low-density lipoprotein particle binding (GO:0030169) 2/17
2 lipoprotein particle binding (GO:0071813) 2/24
3 CCR5 chemokine receptor binding (GO:0031730) 1/5
4 apolipoprotein receptor binding (GO:0034190) 1/6
5 sodium channel inhibitor activity (GO:0019871) 1/8
6 clathrin heavy chain binding (GO:0032050) 1/9
7 endopeptidase activity (GO:0004175) 2/315
8 low-density lipoprotein particle receptor binding (GO:0050750) 1/23
9 lipoprotein particle receptor binding (GO:0070325) 1/28
10 ion channel inhibitor activity (GO:0008200) 1/37
11 sodium channel regulator activity (GO:0017080) 1/37
12 CCR chemokine receptor binding (GO:0048020) 1/42
13 phosphoric ester hydrolase activity (GO:0042578) 1/53
Adjusted.P.value Genes
1 0.0005125363 PCSK9;LDLR
2 0.0005193493 PCSK9;LDLR
3 0.0161770890 CNIH4
4 0.0161770890 PCSK9
5 0.0161770890 PCSK9
6 0.0161770890 LDLR
7 0.0250827929 USP1;PCSK9
8 0.0309303723 PCSK9
9 0.0334413153 PCSK9
10 0.0360988841 PCSK9
11 0.0360988841 PCSK9
12 0.0375295445 CNIH4
13 0.0436317835 ACP6USP1 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDPKN3 gene(s) from the input list not found in DisGeNET CURATEDACP6 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
7 Hypercholesterolemia, Familial 0.0003020913 2/3 18/9703
6 Hypercholesterolemia 0.0007304778 2/3 39/9703
2 Coronary Arteriosclerosis 0.0010233951 2/3 65/9703
26 Coronary Artery Disease 0.0010233951 2/3 65/9703
25 HYPERCHOLESTEROLEMIA, AUTOSOMAL DOMINANT, 3 0.0019171305 1/3 1/9703
12 Q Fever 0.0053231669 1/3 5/9703
16 Acute Q fever 0.0053231669 1/3 5/9703
19 Chronic Q Fever 0.0053231669 1/3 5/9703
29 Coxiella burnetii Infection 0.0053231669 1/3 5/9703
21 Hyperlipoproteinemia Type IIb 0.0124459395 1/3 13/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR
1 Dyslipidaemia 81 4 0.0002923723
2 Coronary Disease 185 4 0.0040657497
3 Hyperlipidemias 62 3 0.0060093303
4 Hypo-beta-lipoproteinemia 10 2 0.0088596345
5 Hypobetalipoproteinemias 10 2 0.0088596345
6 Hyperlipidemia, Familial Combined 16 2 0.0196494267
7 Hyperlipoproteinemia Type II 18 2 0.0209796969
8 Hyperlipoproteinemias 19 2 0.0209796969
9 Myocardial Infarction 139 3 0.0213248137
10 Infarction 141 3 0.0213248137
11 Coronary Artery Disease 169 3 0.0332386412
12 Cholesterol metabolism 31 2 0.0336133098
13 Arterial Occlusive Diseases 182 3 0.0336133098
14 Arteriosclerosis 184 3 0.0336133098
15 Myocardial Ischemia 195 3 0.0372616478
16 familial hypercholesterolemia 41 2 0.0499407514
database userId
1 disease_GLAD4U PCSK9;PSRC1;TIMD4;LDLR
2 disease_GLAD4U PCSK9;PSRC1;TIMD4;LDLR
3 disease_GLAD4U PCSK9;TIMD4;LDLR
4 disease_GLAD4U PCSK9;LDLR
5 disease_GLAD4U PCSK9;LDLR
6 disease_GLAD4U PCSK9;LDLR
7 disease_GLAD4U PCSK9;LDLR
8 disease_GLAD4U PCSK9;LDLR
9 disease_GLAD4U PCSK9;PSRC1;LDLR
10 disease_GLAD4U PCSK9;PSRC1;LDLR
11 disease_GLAD4U PCSK9;PSRC1;LDLR
12 pathway_KEGG PCSK9;LDLR
13 disease_GLAD4U PCSK9;PSRC1;LDLR
14 disease_GLAD4U PCSK9;PSRC1;LDLR
15 disease_GLAD4U PCSK9;PSRC1;LDLR
16 disease_GLAD4U PCSK9;LDLR
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