Cholesterol (quantile) - Whole_Blood
wesleycrouse
2021-09-09
Last updated: 2021-09-09
Checks: 6 1
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| html | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| Rmd | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| html | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| Rmd | 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 Cholesterol (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-30690_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0078757450 0.0001430904 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
31.01605 18.32981 #report sample size
print(sample_size)[1] 344278#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.007872202 0.066259029 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.0867693 0.5813093Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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 1.000 138.54 4.0e-04 20.24
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
5665 CNIH4 1_114 1.000 63.59 1.8e-04 7.94
23 M6PR 12_9 1.000 86.41 2.5e-04 7.82
4151 LDLR 19_9 1.000 506.62 1.5e-03 -22.23
1980 FCGRT 19_34 1.000 21028.31 6.1e-02 -4.14
7462 DAGLB 7_9 0.996 64.97 1.9e-04 8.15
5839 TIMD4 5_92 0.995 298.14 8.6e-04 15.97
6892 PKN3 9_66 0.982 51.91 1.5e-04 -7.05
6064 PTPRJ 11_29 0.981 68.23 1.9e-04 6.86
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
128 TEAD3 6_29 0.954 27.07 7.5e-05 -4.03
6089 FADS1 11_34 0.943 244.57 6.7e-04 -17.38
11023 SIPA1 11_36 0.942 30.40 8.3e-05 -5.99
9073 HIC1 17_3 0.939 28.36 7.7e-05 5.03
3378 GPAM 10_70 0.880 47.01 1.2e-04 6.02
10343 ZFP28 19_38 0.829 25.79 6.2e-05 -4.70
697 HDAC4 2_143 0.794 23.57 5.4e-05 -4.39
3979 VIL1 2_129 0.792 35.43 8.2e-05 5.71
7128 ACP6 1_73 0.786 23.22 5.3e-05 4.06Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 21028.31 6.1e-02 -4.14
5520 RCN3 19_34 0.000 6729.77 0.0e+00 -4.59
168 SPRTN 1_118 0.000 4252.95 1.1e-11 -3.04
3138 EXOC8 1_118 0.000 3058.18 0.0e+00 -2.76
8165 CPT1C 19_34 0.000 1484.76 0.0e+00 2.95
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
3140 TSNAX 1_118 0.000 588.46 0.0e+00 0.16
4151 LDLR 19_9 1.000 506.62 1.5e-03 -22.23
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
11441 APOC2 19_31 0.423 446.14 5.5e-04 36.37
4137 MAU2 19_15 0.009 420.84 1.1e-05 20.65
3102 DOCK7 1_39 0.012 407.96 1.5e-05 20.88
571 SLC6A16 19_34 0.000 380.78 0.0e+00 1.38
4161 TOMM40 19_31 0.000 372.63 0.0e+00 -1.44
7145 DISC1 1_118 0.000 359.72 0.0e+00 -0.79
10492 CTC-301O7.4 19_34 0.000 354.86 0.0e+00 0.54
4159 NECTIN2 19_31 0.000 353.83 0.0e+00 13.29
12134 APOC4 19_31 0.000 349.63 0.0e+00 11.29
2131 ATP13A1 19_15 0.190 313.84 1.7e-04 -17.67
5839 TIMD4 5_92 0.995 298.14 8.6e-04 15.97Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 21028.31 6.1e-02 -4.14
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
4151 LDLR 19_9 1.000 506.62 1.5e-03 -22.23
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
5839 TIMD4 5_92 0.995 298.14 8.6e-04 15.97
6089 FADS1 11_34 0.943 244.57 6.7e-04 -17.38
11441 APOC2 19_31 0.423 446.14 5.5e-04 36.37
8166 PCSK9 1_34 1.000 138.54 4.0e-04 20.24
23 M6PR 12_9 1.000 86.41 2.5e-04 7.82
7462 DAGLB 7_9 0.996 64.97 1.9e-04 8.15
6064 PTPRJ 11_29 0.981 68.23 1.9e-04 6.86
5665 CNIH4 1_114 1.000 63.59 1.8e-04 7.94
2131 ATP13A1 19_15 0.190 313.84 1.7e-04 -17.67
5355 DHX38 16_38 0.674 75.06 1.5e-04 7.91
6892 PKN3 9_66 0.982 51.91 1.5e-04 -7.05
1366 CWF19L1 10_64 0.778 63.07 1.4e-04 7.96
6395 UBASH3B 11_74 0.622 72.18 1.3e-04 -8.46
3378 GPAM 10_70 0.880 47.01 1.2e-04 6.02
10847 TRIM15 6_26 0.422 81.18 1.0e-04 8.40
5318 USP3 15_29 0.681 46.91 9.3e-05 6.34Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.423 446.14 5.5e-04 36.37
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
4151 LDLR 19_9 1.000 506.62 1.5e-03 -22.23
3102 DOCK7 1_39 0.012 407.96 1.5e-05 20.88
4137 MAU2 19_15 0.009 420.84 1.1e-05 20.65
8166 PCSK9 1_34 1.000 138.54 4.0e-04 20.24
7053 BSND 1_34 0.000 138.77 5.0e-11 19.00
2131 ATP13A1 19_15 0.190 313.84 1.7e-04 -17.67
6089 FADS1 11_34 0.943 244.57 6.7e-04 -17.38
5839 TIMD4 5_92 0.995 298.14 8.6e-04 15.97
331 SARS 1_67 0.038 246.30 2.7e-05 -15.58
12254 CTC-366B18.4 5_44 0.015 139.56 6.2e-06 -14.97
2793 COL4A3BP 5_44 0.019 134.61 7.2e-06 14.37
2496 ZPR1 11_70 0.098 161.61 4.6e-05 -13.84
4159 NECTIN2 19_31 0.000 353.83 0.0e+00 13.29
4636 FADS2 11_34 0.003 159.13 1.4e-06 -12.81
5562 CELSR2 1_67 0.010 142.87 4.3e-06 12.00
12134 APOC4 19_31 0.000 349.63 0.0e+00 11.29
5512 CARM1 19_9 0.000 114.65 0.0e+00 -10.98Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.02442542#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.423 446.14 5.5e-04 36.37
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
4151 LDLR 19_9 1.000 506.62 1.5e-03 -22.23
3102 DOCK7 1_39 0.012 407.96 1.5e-05 20.88
4137 MAU2 19_15 0.009 420.84 1.1e-05 20.65
8166 PCSK9 1_34 1.000 138.54 4.0e-04 20.24
7053 BSND 1_34 0.000 138.77 5.0e-11 19.00
2131 ATP13A1 19_15 0.190 313.84 1.7e-04 -17.67
6089 FADS1 11_34 0.943 244.57 6.7e-04 -17.38
5839 TIMD4 5_92 0.995 298.14 8.6e-04 15.97
331 SARS 1_67 0.038 246.30 2.7e-05 -15.58
12254 CTC-366B18.4 5_44 0.015 139.56 6.2e-06 -14.97
2793 COL4A3BP 5_44 0.019 134.61 7.2e-06 14.37
2496 ZPR1 11_70 0.098 161.61 4.6e-05 -13.84
4159 NECTIN2 19_31 0.000 353.83 0.0e+00 13.29
4636 FADS2 11_34 0.003 159.13 1.4e-06 -12.81
5562 CELSR2 1_67 0.010 142.87 4.3e-06 12.00
12134 APOC4 19_31 0.000 349.63 0.0e+00 11.29
5512 CARM1 19_9 0.000 114.65 0.0e+00 -10.98Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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 0.000 42.24 0.00000 -5.30
12136 ZNF285 19_31 0.000 13.46 0.00000 -0.09
7892 ZNF180 19_31 0.000 26.96 0.00000 1.26
820 PVR 19_31 0.000 165.29 0.00000 -8.31
11152 IGSF23 19_31 0.000 19.05 0.00000 -1.84
9941 CEACAM19 19_31 0.000 51.38 0.00000 6.75
4159 NECTIN2 19_31 0.000 353.83 0.00000 13.29
4161 TOMM40 19_31 0.000 372.63 0.00000 -1.44
12134 APOC4 19_31 0.000 349.63 0.00000 11.29
11441 APOC2 19_31 0.423 446.14 0.00055 36.37
1977 CLPTM1 19_31 0.000 26.78 0.00000 -3.78
8368 ZNF296 19_31 0.000 19.27 0.00000 -4.70
5505 GEMIN7 19_31 0.000 7.99 0.00000 3.43
1979 PPP1R37 19_31 0.000 146.88 0.00000 -3.47
10171 BLOC1S3 19_31 0.000 48.98 0.00000 3.08
116 TRAPPC6A 19_31 0.000 132.44 0.00000 2.54
12615 EXOC3L2 19_31 0.000 36.15 0.00000 -1.02
111 MARK4 19_31 0.000 10.21 0.00000 -2.01
1988 KLC3 19_31 0.000 10.63 0.00000 -3.03
1982 PPP1R13L 19_31 0.000 19.52 0.00000 -2.34
3230 CD3EAP 19_31 0.000 19.52 0.00000 -2.34
213 ERCC1 19_31 0.000 25.44 0.00000 -2.03
11059 PPM1N 19_31 0.000 20.29 0.00000 -2.54
3830 RTN2 19_31 0.000 12.46 0.00000 4.78
3831 VASP 19_31 0.000 5.97 0.00000 3.74
| 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 0.012 9.26 3.1e-07 -1.63
1102 SLC25A24 1_67 0.010 6.08 1.7e-07 0.87
7095 FAM102B 1_67 0.026 26.19 2.0e-06 -3.88
7096 HENMT1 1_67 0.052 21.70 3.3e-06 -2.46
3080 STXBP3 1_67 0.010 8.64 2.6e-07 1.92
3522 GPSM2 1_67 0.014 8.76 3.5e-07 0.75
3521 CLCC1 1_67 0.012 15.03 5.1e-07 -3.11
10487 TAF13 1_67 0.014 43.32 1.8e-06 -5.81
11143 TMEM167B 1_67 0.026 12.84 9.6e-07 0.36
9291 C1orf194 1_67 0.011 10.55 3.3e-07 -0.64
1099 WDR47 1_67 0.011 11.07 3.4e-07 -0.89
3084 KIAA1324 1_67 0.014 29.70 1.2e-06 4.46
331 SARS 1_67 0.038 246.30 2.7e-05 -15.58
5562 CELSR2 1_67 0.010 142.87 4.3e-06 12.00
4564 PSRC1 1_67 1.000 1137.34 3.3e-03 -34.67
7099 ATXN7L2 1_67 0.009 10.77 2.8e-07 2.44
8776 CYB561D1 1_67 0.086 38.57 9.6e-06 4.42
9435 AMIGO1 1_67 0.011 26.68 8.3e-07 -4.60
617 GNAI3 1_67 0.011 25.57 7.9e-07 4.62
11016 GSTM2 1_67 0.012 7.32 2.5e-07 -0.90
8107 GSTM4 1_67 0.009 27.81 7.3e-07 -4.95
4559 GSTM1 1_67 0.010 14.55 4.1e-07 3.14
4561 GSTM5 1_67 0.010 8.25 2.5e-07 1.44
4562 GSTM3 1_67 0.010 20.90 6.1e-07 -4.02
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_39"
genename region_tag susie_pip mu2 PVE z
7088 TM2D1 1_39 0.141 34.15 1.4e-05 3.25
4449 PATJ 1_39 0.009 5.33 1.4e-07 -0.59
7089 USP1 1_39 0.971 473.40 1.3e-03 22.52
3102 DOCK7 1_39 0.012 407.96 1.5e-05 20.88
3822 ATG4C 1_39 0.025 17.64 1.3e-06 -2.42
| 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 5.83 0.0e+00 -0.71
10208 ZNF699 19_9 0 29.95 0.0e+00 -2.00
10092 ZNF559 19_9 0 6.81 0.0e+00 0.92
8818 ZNF266 19_9 0 8.71 0.0e+00 -0.94
4245 ZNF426 19_9 0 15.67 0.0e+00 -1.09
12567 CTC-543D15.8 19_9 0 27.55 0.0e+00 1.92
10522 ZNF121 19_9 0 15.21 0.0e+00 -1.06
8463 ZNF561 19_9 0 5.16 0.0e+00 -0.40
8461 ZNF562 19_9 0 17.41 0.0e+00 -1.32
12539 CTD-3116E22.8 19_9 0 7.56 0.0e+00 0.34
10303 ZNF846 19_9 0 7.75 0.0e+00 -0.36
3954 FBXL12 19_9 0 13.59 0.0e+00 0.97
10572 UBL5 19_9 0 17.12 0.0e+00 -1.33
1004 COL5A3 19_9 0 9.51 0.0e+00 0.69
4243 ANGPTL6 19_9 0 12.93 0.0e+00 -1.17
11635 P2RY11 19_9 0 6.07 0.0e+00 -0.59
4241 PPAN 19_9 0 22.21 0.0e+00 -2.32
4244 C19orf66 19_9 0 8.93 0.0e+00 1.69
4242 EIF3G 19_9 0 7.29 0.0e+00 1.34
2062 MRPL4 19_9 0 19.46 0.0e+00 -0.15
1256 ICAM1 19_9 0 20.47 0.0e+00 -1.03
2068 ICAM5 19_9 0 9.26 0.0e+00 -1.29
11171 ZGLP1 19_9 0 7.51 0.0e+00 -0.79
12143 FDX2 19_9 0 40.23 0.0e+00 -4.64
6996 RAVER1 19_9 0 10.19 0.0e+00 1.46
913 ICAM3 19_9 0 25.81 0.0e+00 -0.82
2072 TYK2 19_9 0 96.91 1.8e-17 3.23
650 PDE4A 19_9 0 19.86 0.0e+00 -0.06
9357 S1PR5 19_9 0 20.48 0.0e+00 1.74
4228 ATG4D 19_9 0 42.20 0.0e+00 -4.81
4101 KRI1 19_9 0 11.40 0.0e+00 0.82
4104 CDKN2D 19_9 0 45.56 0.0e+00 3.26
4103 AP1M2 19_9 0 112.85 6.1e-17 -5.62
4102 SLC44A2 19_9 0 127.61 1.0e-13 -4.17
12119 ILF3-AS1 19_9 0 51.52 1.0e-19 -1.30
1398 TMED1 19_9 0 13.71 0.0e+00 -1.29
11089 C19orf38 19_9 0 13.71 0.0e+00 -1.29
5512 CARM1 19_9 0 114.65 0.0e+00 -10.98
5511 TIMM29 19_9 0 117.56 0.0e+00 -9.34
4227 YIPF2 19_9 0 7.68 0.0e+00 -2.10
3972 SMARCA4 19_9 0 10.63 0.0e+00 3.44
4151 LDLR 19_9 1 506.62 1.5e-03 -22.23
6998 SPC24 19_9 0 56.72 0.0e+00 7.84
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_15"
genename region_tag susie_pip mu2 PVE z
4199 LSM4 19_15 0.006 5.76 9.4e-08 -0.25
4197 PGPEP1 19_15 0.007 7.14 1.4e-07 0.40
8907 LRRC25 19_15 0.005 6.03 9.0e-08 -1.27
4196 SSBP4 19_15 0.015 15.64 6.6e-07 -2.02
2112 ISYNA1 19_15 0.006 6.49 1.1e-07 1.08
2113 ELL 19_15 0.008 10.19 2.3e-07 2.13
2123 KXD1 19_15 0.005 5.66 9.0e-08 0.26
11192 UBA52 19_15 0.005 5.74 9.1e-08 0.32
7904 KLHL26 19_15 0.008 9.99 2.2e-07 1.27
52 UPF1 19_15 0.030 22.82 2.0e-06 -2.02
2115 COPE 19_15 0.007 9.12 2.0e-07 -0.64
2116 DDX49 19_15 0.007 8.10 1.7e-07 -0.21
2118 ARMC6 19_15 0.005 5.45 8.2e-08 -1.18
599 SUGP2 19_15 0.024 17.81 1.3e-06 -1.18
596 TMEM161A 19_15 0.037 26.08 2.8e-06 -2.58
11075 MEF2B 19_15 0.005 20.29 3.0e-07 5.03
11817 BORCS8 19_15 0.005 35.35 5.3e-07 6.79
595 RFXANK 19_15 0.006 6.72 1.1e-07 1.16
4137 MAU2 19_15 0.009 420.84 1.1e-05 20.65
7905 GATAD2A 19_15 0.014 115.04 4.7e-06 -10.20
9879 NDUFA13 19_15 0.012 112.86 3.8e-06 -10.17
9152 TSSK6 19_15 0.015 15.01 6.3e-07 1.87
11726 YJEFN3 19_15 0.008 82.06 1.8e-06 -8.71
6840 CILP2 19_15 0.013 14.46 5.3e-07 -1.97
2128 PBX4 19_15 0.010 9.20 2.6e-07 -0.74
597 LPAR2 19_15 0.005 24.22 3.7e-07 -4.75
1235 GMIP 19_15 0.006 24.15 3.9e-07 -4.41
2131 ATP13A1 19_15 0.190 313.84 1.7e-04 -17.67
9450 ZNF101 19_15 0.133 26.96 1.0e-05 -0.16
2126 ZNF14 19_15 0.053 42.69 6.6e-06 4.55
| 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 57.12 1.7e-04 -7.85
56710 rs766167074 1_118 1.000 4324.39 1.3e-02 2.58
69585 rs11679386 2_12 1.000 113.04 3.3e-04 10.53
69634 rs1042034 2_13 1.000 121.49 3.5e-04 10.94
69640 rs934197 2_13 1.000 348.05 1.0e-03 29.22
69643 rs548145 2_13 1.000 578.96 1.7e-03 30.76
69720 rs1848922 2_13 1.000 196.52 5.7e-04 23.18
71370 rs780093 2_16 1.000 237.52 6.9e-04 -18.39
77299 rs139029940 2_27 1.000 42.45 1.2e-04 6.98
77435 rs72800939 2_28 1.000 48.51 1.4e-04 -7.11
162092 rs768688512 3_58 1.000 493.98 1.4e-03 2.57
196292 rs36205397 4_4 1.000 86.84 2.5e-04 7.28
228814 rs35518360 4_67 1.000 67.62 2.0e-04 -8.24
228880 rs13140033 4_68 1.000 53.20 1.5e-04 -7.51
319908 rs11376017 6_13 1.000 59.91 1.7e-04 -7.80
323637 rs115740542 6_20 1.000 154.72 4.5e-04 -11.58
349106 rs9496567 6_67 1.000 43.47 1.3e-04 -6.54
366881 rs191555775 6_104 1.000 69.89 2.0e-04 -7.78
387062 rs217396 7_32 1.000 62.87 1.8e-04 -8.00
426753 rs7012814 8_12 1.000 159.35 4.6e-04 14.05
431271 rs1495743 8_20 1.000 67.26 2.0e-04 -8.40
441544 rs140753685 8_42 1.000 62.35 1.8e-04 8.10
442940 rs4738679 8_45 1.000 129.55 3.8e-04 -12.10
462603 rs13252684 8_83 1.000 293.99 8.5e-04 12.43
496153 rs2777798 9_52 1.000 82.07 2.4e-04 8.30
496173 rs2297400 9_53 1.000 103.63 3.0e-04 10.41
496200 rs2437818 9_53 1.000 175.39 5.1e-04 11.08
504417 rs115478735 9_70 1.000 319.14 9.3e-04 18.70
529084 rs569165969 10_46 1.000 986.62 2.9e-03 3.04
529132 rs6480402 10_46 1.000 87.02 2.5e-04 -4.33
623241 rs653178 12_67 1.000 147.60 4.3e-04 12.87
627331 rs11057830 12_76 1.000 41.97 1.2e-04 4.54
701861 rs28594460 15_27 1.000 88.07 2.6e-04 9.89
701877 rs62000868 15_27 1.000 158.67 4.6e-04 12.87
701883 rs2070895 15_27 1.000 432.77 1.3e-03 21.94
729423 rs66495554 16_31 1.000 68.70 2.0e-04 -2.33
733597 rs9938506 16_38 1.000 139.67 4.1e-04 6.55
758375 rs1801689 17_38 1.000 47.97 1.4e-04 6.91
759291 rs113408695 17_39 1.000 114.09 3.3e-04 10.72
759317 rs8070232 17_39 1.000 148.08 4.3e-04 -6.91
800601 rs62115478 19_30 1.000 128.73 3.7e-04 -11.69
800884 rs116881820 19_31 1.000 1347.60 3.9e-03 21.90
800888 rs34878901 19_31 1.000 9484.32 2.8e-02 13.17
800889 rs405509 19_31 1.000 9438.02 2.7e-02 -24.81
800893 rs814573 19_31 1.000 2152.57 6.3e-03 47.81
801233 rs150262789 19_32 1.000 70.68 2.1e-04 -9.46
811093 rs34507316 20_13 1.000 68.90 2.0e-04 -5.79
851628 rs11591147 1_34 1.000 1072.63 3.1e-03 -35.66
1025523 rs10422256 19_9 1.000 182.34 5.3e-04 10.63
1025859 rs379309 19_11 1.000 69.33 2.0e-04 -8.17
1026265 rs148356565 19_11 1.000 83.82 2.4e-04 -9.42
1049475 rs374141296 19_34 1.000 20340.77 5.9e-02 3.89
323616 rs72834643 6_20 0.999 39.45 1.1e-04 -5.12
366863 rs117733303 6_104 0.999 80.12 2.3e-04 8.54
476026 rs677622 9_13 0.999 54.57 1.6e-04 7.68
496167 rs7024300 9_53 0.999 42.07 1.2e-04 6.43
627324 rs3782287 12_76 0.999 35.46 1.0e-04 -5.97
662756 rs3934835 13_62 0.999 70.48 2.0e-04 8.78
778097 rs118043171 18_27 0.999 98.08 2.8e-04 13.33
815280 rs73124945 20_24 0.999 38.80 1.1e-04 -8.30
851691 rs499883 1_34 0.999 104.16 3.0e-04 14.81
946730 rs662138 6_103 0.999 111.97 3.2e-04 10.69
496184 rs62568181 9_53 0.998 77.29 2.2e-04 -13.33
762450 rs4969183 17_44 0.998 83.94 2.4e-04 9.38
811092 rs6075251 20_13 0.998 49.64 1.4e-04 -2.26
7468 rs2742962 1_16 0.997 48.99 1.4e-04 6.94
733310 rs4396539 16_37 0.997 34.03 9.9e-05 -5.09
801181 rs111543904 19_32 0.997 51.21 1.5e-04 -7.22
462592 rs79658059 8_83 0.996 336.42 9.7e-04 -15.46
739029 rs2255451 16_49 0.996 35.99 1.0e-04 -5.89
801217 rs58701309 19_32 0.996 71.53 2.1e-04 1.99
281359 rs7701166 5_44 0.995 33.59 9.7e-05 -2.06
470656 rs7024888 9_3 0.995 30.20 8.7e-05 -5.31
634710 rs79490353 13_7 0.995 29.57 8.5e-05 -5.26
138844 rs709149 3_9 0.994 55.01 1.6e-04 -8.29
198517 rs2002574 4_10 0.994 29.76 8.6e-05 -5.24
800556 rs73036721 19_30 0.994 30.79 8.9e-05 -5.27
145854 rs9834932 3_24 0.993 63.01 1.8e-04 -8.06
583014 rs75542613 11_70 0.993 82.90 2.4e-04 -8.87
244713 rs114756490 4_100 0.992 28.63 8.3e-05 5.19
379260 rs56130071 7_19 0.992 86.58 2.5e-04 10.10
403441 rs3197597 7_61 0.992 36.95 1.1e-04 -4.86
946662 rs12208357 6_103 0.991 169.62 4.9e-04 11.52
815227 rs6029132 20_24 0.989 43.90 1.3e-04 -6.82
794005 rs2302209 19_14 0.988 37.55 1.1e-04 6.01
194505 rs5855544 3_120 0.987 27.67 7.9e-05 -5.02
426764 rs13265179 8_12 0.987 100.23 2.9e-04 -12.30
1016957 rs2908806 17_7 0.987 43.43 1.2e-04 -6.83
815276 rs76981217 20_24 0.981 35.20 1.0e-04 7.65
1049463 rs61371437 19_34 0.978 20154.50 5.7e-02 3.94
843248 rs145678077 22_17 0.977 27.02 7.7e-05 -5.39
77312 rs13430143 2_27 0.976 85.98 2.4e-04 -3.60
95924 rs1002015 2_66 0.974 28.86 8.2e-05 -4.75
778316 rs74461650 18_28 0.971 30.47 8.6e-05 5.38
478522 rs1556516 9_16 0.970 71.74 2.0e-04 -8.64
221983 rs1458038 4_54 0.969 51.20 1.4e-04 -7.15
495636 rs150108287 9_52 0.969 25.97 7.3e-05 4.62
611021 rs148481241 12_44 0.968 26.05 7.3e-05 4.78
387112 rs141379002 7_33 0.966 26.45 7.4e-05 4.89
641641 rs201796 13_21 0.966 29.13 8.2e-05 -5.29
752632 rs55764662 17_26 0.966 26.24 7.4e-05 -4.77
71371 rs6744393 2_16 0.964 62.62 1.8e-04 -10.58
621334 rs1196760 12_63 0.963 27.78 7.8e-05 -4.99
426722 rs117037226 8_11 0.961 35.14 9.8e-05 5.56
549493 rs10741735 11_2 0.961 28.39 7.9e-05 4.04
555371 rs7943121 11_13 0.959 39.38 1.1e-04 6.27
778112 rs62101781 18_27 0.958 80.57 2.2e-04 9.64
1049472 rs113176985 19_34 0.956 20209.48 5.6e-02 4.13
63609 rs10183939 2_2 0.949 25.66 7.1e-05 -4.81
496193 rs2777788 9_53 0.948 140.17 3.9e-04 -10.71
831160 rs2835302 21_16 0.948 27.94 7.7e-05 -4.92
801216 rs34942359 19_32 0.936 149.64 4.1e-04 -8.08
912360 rs9884390 4_48 0.936 113.71 3.1e-04 11.39
720874 rs35782593 16_12 0.934 27.31 7.4e-05 -4.89
733536 rs8046916 16_38 0.934 60.98 1.7e-04 -2.41
803694 rs397558 19_37 0.934 51.64 1.4e-04 7.13
366906 rs374071816 6_104 0.932 127.49 3.5e-04 14.36
77315 rs4076834 2_27 0.929 404.97 1.1e-03 -18.81
802153 rs838145 19_33 0.926 123.96 3.3e-04 -12.50
170848 rs189174 3_74 0.921 62.38 1.7e-04 8.04
323455 rs75080831 6_19 0.920 55.21 1.5e-04 -7.48
733575 rs9652628 16_38 0.918 152.75 4.1e-04 11.48
586085 rs74612335 11_77 0.913 92.94 2.5e-04 10.00
579278 rs201912654 11_59 0.907 55.87 1.5e-04 -7.45
627166 rs35480942 12_75 0.905 26.86 7.1e-05 -4.85
927888 rs376448220 6_23 0.903 47.04 1.2e-04 -6.87
636218 rs1012130 13_10 0.896 35.02 9.1e-05 -2.49
815246 rs6129631 20_24 0.895 93.63 2.4e-04 -10.50
583009 rs3135506 11_70 0.893 166.71 4.3e-04 14.25
804548 rs34003091 19_39 0.891 88.57 2.3e-04 -9.39
39085 rs1795240 1_84 0.884 29.75 7.6e-05 -5.20
814021 rs11167269 20_21 0.881 77.05 2.0e-04 -8.89
778093 rs7241918 18_27 0.876 133.51 3.4e-04 14.61
228096 rs148447389 4_67 0.874 24.90 6.3e-05 4.50
233879 rs138204164 4_77 0.874 32.33 8.2e-05 -5.41
549503 rs2519158 11_2 0.872 30.79 7.8e-05 -4.24
156666 rs6762369 3_47 0.869 33.49 8.5e-05 5.52
23616 rs11161548 1_52 0.863 26.36 6.6e-05 -4.87
281323 rs3843482 5_44 0.860 462.00 1.2e-03 24.18
299494 rs546280079 5_79 0.859 28.82 7.2e-05 -4.96
832297 rs149577713 21_19 0.858 36.85 9.2e-05 3.86
13613 rs138863615 1_29 0.848 25.77 6.4e-05 4.53
1037740 rs58542926 19_15 0.848 652.21 1.6e-03 -26.55
823917 rs62219001 21_2 0.847 24.70 6.1e-05 -4.40
924870 rs34723862 6_21 0.842 38.72 9.5e-05 -7.26
536664 rs10882161 10_59 0.840 28.95 7.1e-05 -5.27
729386 rs8064102 16_31 0.835 32.44 7.9e-05 3.63
8327 rs79598313 1_18 0.832 25.24 6.1e-05 4.53
403432 rs11761624 7_61 0.829 27.60 6.6e-05 -3.22
851622 rs17111503 1_34 0.829 90.21 2.2e-04 14.67
69637 rs78610189 2_13 0.826 55.37 1.3e-04 -8.16
915026 rs115725579 4_48 0.826 30.35 7.3e-05 -2.68
701882 rs139823028 15_27 0.818 42.51 1.0e-04 4.52
748833 rs117859452 17_18 0.815 26.95 6.4e-05 -4.15
946766 rs2297374 6_103 0.808 102.88 2.4e-04 -10.73
805970 rs796704474 20_5 0.803 28.82 6.7e-05 4.86
496036 rs34849882 9_52 0.802 28.86 6.7e-05 3.39SNPs 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
1049475 rs374141296 19_34 1.000 20340.77 5.9e-02 3.89
1049472 rs113176985 19_34 0.956 20209.48 5.6e-02 4.13
1049465 rs35295508 19_34 0.000 20162.81 1.7e-07 4.12
1049463 rs61371437 19_34 0.978 20154.50 5.7e-02 3.94
1049479 rs2946865 19_34 0.000 20147.90 6.3e-16 4.14
1049470 rs73056069 19_34 0.000 20097.82 2.8e-06 4.26
1049453 rs739349 19_34 0.010 20072.85 5.6e-04 3.93
1049454 rs756628 19_34 0.007 20072.67 3.9e-04 3.92
1049467 rs2878354 19_34 0.000 20046.35 1.7e-07 4.22
1049450 rs739347 19_34 0.000 20031.70 7.6e-07 3.86
1049451 rs2073614 19_34 0.000 20007.76 1.7e-08 3.84
1049456 rs2077300 19_34 0.006 19959.90 3.2e-04 4.04
1049460 rs73056059 19_34 0.000 19922.26 3.6e-06 4.01
1049446 rs4802613 19_34 0.000 19920.28 6.7e-10 3.88
1049480 rs60815603 19_34 0.000 19820.46 3.2e-17 4.05
1049483 rs1316885 19_34 0.000 19764.74 0.0e+00 4.10
1049488 rs2946863 19_34 0.000 19730.86 0.0e+00 4.14
1049485 rs60746284 19_34 0.000 19703.62 1.6e-14 4.24
1049481 rs35443645 19_34 0.000 19703.01 0.0e+00 3.99
1049444 rs10403394 19_34 0.000 19648.02 8.9e-16 3.91
1049445 rs17555056 19_34 0.000 19635.32 6.3e-18 3.86
1049461 rs73056062 19_34 0.000 19397.59 0.0e+00 3.52
1049491 rs553431297 19_34 0.000 19144.73 0.0e+00 3.71
1049474 rs112283514 19_34 0.000 19101.94 0.0e+00 4.15
1049476 rs11270139 19_34 0.000 18967.95 0.0e+00 3.88
1049441 rs10421294 19_34 0.000 17755.20 0.0e+00 3.91
1049440 rs8108175 19_34 0.000 17752.63 0.0e+00 3.91
1049433 rs59192944 19_34 0.000 17720.04 0.0e+00 3.95
1049439 rs1858742 19_34 0.000 17715.36 0.0e+00 3.93
1049430 rs55991145 19_34 0.000 17705.13 0.0e+00 3.93
1049425 rs3786567 19_34 0.000 17698.17 0.0e+00 3.93
1049421 rs2271952 19_34 0.000 17691.22 0.0e+00 3.93
1049424 rs4801801 19_34 0.000 17690.68 0.0e+00 3.96
1049420 rs2271953 19_34 0.000 17669.87 0.0e+00 3.92
1049422 rs2271951 19_34 0.000 17668.74 0.0e+00 3.90
1049411 rs60365978 19_34 0.000 17655.13 0.0e+00 3.93
1049417 rs4802612 19_34 0.000 17591.43 0.0e+00 4.15
1049427 rs2517977 19_34 0.000 17558.86 0.0e+00 3.77
1049414 rs55893003 19_34 0.000 17540.52 0.0e+00 4.03
1049406 rs55992104 19_34 0.000 17109.96 0.0e+00 3.29
1049400 rs60403475 19_34 0.000 17108.16 0.0e+00 3.34
1049403 rs4352151 19_34 0.000 17104.18 0.0e+00 3.33
1049397 rs11878448 19_34 0.000 17092.14 0.0e+00 3.34
1049391 rs9653100 19_34 0.000 17087.23 0.0e+00 3.31
1049387 rs4802611 19_34 0.000 17076.33 0.0e+00 3.32
1049379 rs7251338 19_34 0.000 17050.44 0.0e+00 3.33
1049378 rs59269605 19_34 0.000 17048.45 0.0e+00 3.33
1049399 rs1042120 19_34 0.000 17008.76 0.0e+00 3.51
1049395 rs113220577 19_34 0.000 16993.50 0.0e+00 3.50
1049389 rs9653118 19_34 0.000 16966.42 0.0e+00 3.48SNPs 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
1049475 rs374141296 19_34 1.000 20340.77 0.05900 3.89
1049463 rs61371437 19_34 0.978 20154.50 0.05700 3.94
1049472 rs113176985 19_34 0.956 20209.48 0.05600 4.13
800888 rs34878901 19_31 1.000 9484.32 0.02800 13.17
800889 rs405509 19_31 1.000 9438.02 0.02700 -24.81
56710 rs766167074 1_118 1.000 4324.39 0.01300 2.58
800893 rs814573 19_31 1.000 2152.57 0.00630 47.81
800884 rs116881820 19_31 1.000 1347.60 0.00390 21.90
56722 rs2790874 1_118 0.267 4349.11 0.00340 2.76
851628 rs11591147 1_34 1.000 1072.63 0.00310 -35.66
56697 rs1076804 1_118 0.231 4349.39 0.00290 2.75
529084 rs569165969 10_46 1.000 986.62 0.00290 3.04
56707 rs10489611 1_118 0.185 4354.42 0.00230 2.71
56719 rs1416913 1_118 0.180 4348.86 0.00230 2.74
1025483 rs12151108 19_9 0.368 2115.71 0.00230 -44.76
56701 rs2256908 1_118 0.170 4354.18 0.00220 2.71
529085 rs7909631 10_46 0.740 1028.02 0.00220 2.48
56709 rs971534 1_118 0.163 4354.36 0.00210 2.70
56708 rs2486737 1_118 0.150 4354.31 0.00190 2.70
56704 rs2790891 1_118 0.141 4354.08 0.00180 2.70
56705 rs2491405 1_118 0.141 4354.08 0.00180 2.70
1025484 rs73015024 19_9 0.294 2115.21 0.00180 -44.75
69643 rs548145 2_13 1.000 578.96 0.00170 30.76
1037740 rs58542926 19_15 0.848 652.21 0.00160 -26.55
162092 rs768688512 3_58 1.000 493.98 0.00140 2.57
701883 rs2070895 15_27 1.000 432.77 0.00130 21.94
1025494 rs6511720 19_9 0.204 2116.26 0.00130 -44.78
281323 rs3843482 5_44 0.860 462.00 0.00120 24.18
77315 rs4076834 2_27 0.929 404.97 0.00110 -18.81
56717 rs2211176 1_118 0.079 4352.83 0.00100 2.69
56718 rs2790882 1_118 0.079 4352.83 0.00100 2.69
69640 rs934197 2_13 1.000 348.05 0.00100 29.22
462592 rs79658059 8_83 0.996 336.42 0.00097 -15.46
504417 rs115478735 9_70 1.000 319.14 0.00093 18.70
462603 rs13252684 8_83 1.000 293.99 0.00085 12.43
56716 rs2248646 1_118 0.063 4352.55 0.00079 2.69
529083 rs7084697 10_46 0.254 1027.24 0.00076 2.43
800898 rs12721109 19_31 0.577 441.15 0.00074 -37.23
71370 rs780093 2_16 1.000 237.52 0.00069 -18.39
729418 rs821840 16_31 0.678 328.82 0.00065 16.71
1025485 rs147985405 19_9 0.095 2113.25 0.00059 -44.73
69720 rs1848922 2_13 1.000 196.52 0.00057 23.18
162088 rs73141241 3_58 0.381 508.07 0.00056 2.73
1049453 rs739349 19_34 0.010 20072.85 0.00056 3.93
1025523 rs10422256 19_9 1.000 182.34 0.00053 10.63
496200 rs2437818 9_53 1.000 175.39 0.00051 11.08
946662 rs12208357 6_103 0.991 169.62 0.00049 11.52
1025656 rs2738464 19_9 0.576 287.31 0.00048 5.62
426753 rs7012814 8_12 1.000 159.35 0.00046 14.05
701877 rs62000868 15_27 1.000 158.67 0.00046 12.87SNPs 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
800893 rs814573 19_31 1.000 2152.57 6.3e-03 47.81
1025494 rs6511720 19_9 0.204 2116.26 1.3e-03 -44.78
1025483 rs12151108 19_9 0.368 2115.71 2.3e-03 -44.76
1025484 rs73015024 19_9 0.294 2115.21 1.8e-03 -44.75
1025485 rs147985405 19_9 0.095 2113.25 5.9e-04 -44.73
1025487 rs17248727 19_9 0.034 2110.75 2.1e-04 -44.70
1025493 rs57217136 19_9 0.004 2107.23 2.4e-05 -44.67
1025469 rs73015020 19_9 0.000 2102.74 2.5e-06 -44.63
1025486 rs17248720 19_9 0.000 2098.22 4.1e-08 -44.61
1025467 rs61194703 19_9 0.000 2099.69 6.1e-07 -44.60
1025449 rs138175288 19_9 0.000 2098.03 2.8e-07 -44.58
1025450 rs112107114 19_9 0.000 2097.98 2.8e-07 -44.58
1025451 rs115594766 19_9 0.000 2097.97 2.7e-07 -44.58
1025460 rs73015013 19_9 0.000 2098.22 3.2e-07 -44.58
1025471 rs77140532 19_9 0.000 2096.70 1.2e-07 -44.58
1025473 rs73015021 19_9 0.000 2096.43 1.0e-07 -44.58
1025448 rs114821903 19_9 0.000 2097.47 2.1e-07 -44.57
1025466 rs138294113 19_9 0.000 2096.01 1.2e-07 -44.56
1025447 rs73015011 19_9 0.000 2094.99 6.8e-08 -44.55
1025458 rs142130958 19_9 0.000 2094.94 7.2e-08 -44.55
1025481 rs8106503 19_9 0.000 2091.58 1.9e-09 -44.55
1025465 rs10402112 19_9 0.000 2093.34 2.9e-08 -44.53
1025475 rs112552009 19_9 0.000 2095.56 1.2e-07 -44.53
1025476 rs10412048 19_9 0.000 2092.20 1.3e-08 -44.53
1025444 rs113722226 19_9 0.000 2089.06 3.8e-09 -44.48
1025443 rs148898583 19_9 0.000 2087.62 2.0e-09 -44.47
1025461 rs114846969 19_9 0.000 2082.69 8.3e-11 -44.46
1025463 rs73015016 19_9 0.000 2083.08 2.0e-10 -44.44
1025452 rs112032422 19_9 0.000 2084.57 4.9e-10 -44.43
1025442 rs112374545 19_9 0.000 2084.41 3.8e-10 -44.42
1025441 rs112898275 19_9 0.000 2082.87 1.9e-10 -44.41
1025439 rs56125973 19_9 0.000 2077.29 1.2e-11 -44.36
1025437 rs55997232 19_9 0.000 2076.27 8.1e-12 -44.35
1025438 rs55791371 19_9 0.000 2076.88 1.1e-11 -44.35
1025462 rs151113958 19_9 0.000 2070.15 1.2e-13 -44.34
1025433 rs143020224 19_9 0.000 2074.28 2.9e-12 -44.32
1025436 rs111989435 19_9 0.000 2074.46 3.1e-12 -44.32
1025440 rs56289821 19_9 0.000 2074.97 4.3e-12 -44.32
1025435 rs112736558 19_9 0.000 2072.89 1.5e-12 -44.31
1025434 rs144826254 19_9 0.000 2071.33 7.4e-13 -44.30
1025454 rs142158911 19_9 0.000 2067.78 4.5e-14 -44.30
1025472 rs375484700 19_9 0.000 2060.68 1.9e-15 -44.23
1025453 rs77265569 19_9 0.000 2036.81 0.0e+00 -43.85
1025457 rs139853365 19_9 0.000 1872.85 0.0e+00 -41.85
1025455 rs118068660 19_9 0.000 1863.97 0.0e+00 -41.77
1025456 rs145960625 19_9 0.000 1863.35 0.0e+00 -41.75
1025500 rs17242395 19_9 0.000 1564.98 0.0e+00 -37.59
1025499 rs17242381 19_9 0.000 1563.28 0.0e+00 -37.58
1025508 rs141820146 19_9 0.000 1565.77 0.0e+00 -37.58
1025503 rs117423069 19_9 0.000 1560.32 0.0e+00 -37.52Gene 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] 17if (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 positive regulation of triglyceride metabolic process (GO:0090208)
2 negative regulation of cell growth (GO:0030308)
3 negative regulation of growth (GO:0045926)
4 phospholipid biosynthetic process (GO:0008654)
5 organophosphate biosynthetic process (GO:0090407)
6 neurogenesis (GO:0022008)
7 regulation of cell growth (GO:0001558)
8 unsaturated fatty acid metabolic process (GO:0033559)
9 icosanoid metabolic process (GO:0006690)
10 cellular response to nutrient levels (GO:0031669)
11 cholesterol homeostasis (GO:0042632)
12 sterol homeostasis (GO:0055092)
13 phospholipid metabolic process (GO:0006644)
14 lipid biosynthetic process (GO:0008610)
15 long-chain fatty acid metabolic process (GO:0001676)
16 regulation of Fc-gamma receptor signaling pathway involved in phagocytosis (GO:1905449)
17 alditol phosphate metabolic process (GO:0052646)
18 positive regulation of protein catabolic process in the vacuole (GO:1904352)
19 regulation of astrocyte activation (GO:0061888)
20 regulation of low-density lipoprotein particle receptor catabolic process (GO:0032803)
21 cannabinoid signaling pathway (GO:0038171)
22 negative regulation of astrocyte differentiation (GO:0048712)
23 sterol import (GO:0035376)
24 cholesterol import (GO:0070508)
25 contact inhibition (GO:0060242)
26 glycerol-3-phosphate metabolic process (GO:0006072)
27 negative regulation of lipoprotein particle clearance (GO:0010985)
28 monoubiquitinated protein deubiquitination (GO:0035520)
29 regulation of primary metabolic process (GO:0080090)
30 regulation of cell adhesion (GO:0030155)
31 positive regulation of receptor catabolic process (GO:2000646)
32 chylomicron remnant clearance (GO:0034382)
33 regulation of lysosomal protein catabolic process (GO:1905165)
34 negative regulation of low-density lipoprotein receptor activity (GO:1905598)
35 receptor-mediated endocytosis (GO:0006898)
36 positive regulation of triglyceride catabolic process (GO:0010898)
37 diacylglycerol biosynthetic process (GO:0006651)
38 negative regulation of microglial cell activation (GO:1903979)
39 regulation of nitrogen compound metabolic process (GO:0051171)
40 negative regulation of nitrogen compound metabolic process (GO:0051172)
41 unsaturated fatty acid biosynthetic process (GO:0006636)
42 phosphatidylglycerol biosynthetic process (GO:0006655)
43 intestinal cholesterol absorption (GO:0030299)
44 cellular response to starvation (GO:0009267)
45 negative regulation of sodium ion transmembrane transport (GO:1902306)
46 negative regulation of sodium ion transmembrane transporter activity (GO:2000650)
47 low-density lipoprotein particle receptor catabolic process (GO:0032802)
48 low-density lipoprotein receptor particle metabolic process (GO:0032799)
49 regulation of low-density lipoprotein particle clearance (GO:0010988)
50 negative regulation of receptor binding (GO:1900121)
51 positive regulation of triglyceride biosynthetic process (GO:0010867)
52 negative regulation of platelet-derived growth factor receptor signaling pathway (GO:0010642)
53 intestinal lipid absorption (GO:0098856)
54 negative regulation of vascular permeability (GO:0043116)
55 regulation of triglyceride catabolic process (GO:0010896)
56 negative regulation of amyloid fibril formation (GO:1905907)
57 alpha-linolenic acid metabolic process (GO:0036109)
58 carboxylic acid biosynthetic process (GO:0046394)
59 regulation of platelet-derived growth factor receptor signaling pathway (GO:0010640)
60 negative regulation of cellular process (GO:0048523)
61 negative regulation of macromolecule metabolic process (GO:0010605)
62 cellular response to low-density lipoprotein particle stimulus (GO:0071404)
63 negative regulation of cell activation (GO:0050866)
64 neurotransmitter biosynthetic process (GO:0042136)
65 negative regulation of neuroinflammatory response (GO:0150079)
66 G protein-coupled glutamate receptor signaling pathway (GO:0007216)
67 regulation of amyloid fibril formation (GO:1905906)
68 positive regulation of DNA damage response, signal transduction by p53 class mediator (GO:0043517)
69 regulation of triglyceride biosynthetic process (GO:0010866)
70 regulation of microglial cell activation (GO:1903978)
71 prostanoid biosynthetic process (GO:0046457)
72 icosanoid biosynthetic process (GO:0046456)
73 intracellular cholesterol transport (GO:0032367)
74 negative regulation of macrophage activation (GO:0043031)
75 platelet-derived growth factor receptor signaling pathway (GO:0048008)
76 regulation of receptor recycling (GO:0001919)
77 positive regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045737)
78 protein autoprocessing (GO:0016540)
79 regulation of spindle organization (GO:0090224)
80 triglyceride biosynthetic process (GO:0019432)
81 positive chemotaxis (GO:0050918)
82 negative regulation of T cell receptor signaling pathway (GO:0050860)
83 long-term memory (GO:0007616)
84 positive regulation of cellular protein catabolic process (GO:1903364)
85 positive regulation of focal adhesion assembly (GO:0051894)
86 prostaglandin biosynthetic process (GO:0001516)
Overlap Adjusted.P.value Genes
1 2/19 0.01385147 DAGLB;LDLR
2 3/126 0.01385147 PSRC1;PTPRJ;SIPA1
3 3/126 0.01385147 PSRC1;PTPRJ;SIPA1
4 2/37 0.02641461 GPAM;FADS1
5 2/39 0.02641461 GPAM;FADS1
6 2/44 0.02803184 PCSK9;DAGLB
7 3/217 0.02920128 PSRC1;PTPRJ;SIPA1
8 2/54 0.03132443 DAGLB;FADS1
9 2/57 0.03132443 DAGLB;FADS1
10 2/66 0.03734396 PCSK9;FADS1
11 2/71 0.03734396 PCSK9;LDLR
12 2/72 0.03734396 PCSK9;LDLR
13 2/76 0.03835971 GPAM;FADS1
14 2/80 0.03941539 GPAM;FADS1
15 2/83 0.03955736 DAGLB;FADS1
16 1/5 0.04522642 PTPRJ
17 1/5 0.04522642 GPAM
18 1/5 0.04522642 LDLR
19 1/5 0.04522642 LDLR
20 1/5 0.04522642 PCSK9
21 1/5 0.04522642 DAGLB
22 1/6 0.04522642 LDLR
23 1/6 0.04522642 LDLR
24 1/6 0.04522642 LDLR
25 1/6 0.04522642 PTPRJ
26 1/6 0.04522642 GPAM
27 1/6 0.04522642 PCSK9
28 1/6 0.04522642 USP1
29 2/130 0.04522642 GPAM;LDLR
30 2/133 0.04522642 PTPRJ;SIPA1
31 1/7 0.04522642 PCSK9
32 1/7 0.04522642 LDLR
33 1/7 0.04522642 LDLR
34 1/7 0.04522642 PCSK9
35 2/143 0.04522642 M6PR;LDLR
36 1/8 0.04522642 DAGLB
37 1/8 0.04522642 GPAM
38 1/8 0.04522642 LDLR
39 1/8 0.04522642 LDLR
40 1/8 0.04522642 LDLR
41 1/9 0.04522642 FADS1
42 1/9 0.04522642 GPAM
43 1/9 0.04522642 LDLR
44 2/158 0.04522642 PCSK9;FADS1
45 1/10 0.04522642 PCSK9
46 1/10 0.04522642 PCSK9
47 1/10 0.04522642 PCSK9
48 1/10 0.04522642 PCSK9
49 1/10 0.04522642 PCSK9
50 1/10 0.04522642 PCSK9
51 1/11 0.04637282 LDLR
52 1/11 0.04637282 PTPRJ
53 1/11 0.04637282 LDLR
54 1/12 0.04637282 PTPRJ
55 1/12 0.04637282 DAGLB
56 1/12 0.04637282 LDLR
57 1/13 0.04637282 FADS1
58 1/13 0.04637282 FADS1
59 1/13 0.04637282 PTPRJ
60 3/566 0.04637282 PSRC1;PTPRJ;SIPA1
61 2/194 0.04637282 PCSK9;LDLR
62 1/14 0.04637282 LDLR
63 1/14 0.04637282 LDLR
64 1/14 0.04637282 DAGLB
65 1/14 0.04637282 LDLR
66 1/15 0.04637282 DAGLB
67 1/15 0.04637282 LDLR
68 1/15 0.04637282 HIC1
69 1/15 0.04637282 LDLR
70 1/15 0.04637282 LDLR
71 1/15 0.04637282 DAGLB
72 1/15 0.04637282 FADS1
73 1/15 0.04637282 LDLR
74 1/16 0.04812607 LDLR
75 1/16 0.04812607 PTPRJ
76 1/17 0.04914763 PCSK9
77 1/17 0.04914763 PSRC1
78 1/17 0.04914763 PCSK9
79 1/18 0.04978020 PSRC1
80 1/18 0.04978020 GPAM
81 1/18 0.04978020 PTPRJ
82 1/19 0.04978020 PTPRJ
83 1/19 0.04978020 LDLR
84 1/19 0.04978020 PCSK9
85 1/19 0.04978020 PTPRJ
86 1/19 0.04978020 DAGLB
[1] "GO_Cellular_Component_2021"
Term
1 endolysosome membrane (GO:0036020)
2 endolysosome (GO:0036019)
3 clathrin-coated endocytic vesicle membrane (GO:0030669)
4 clathrin-coated endocytic vesicle (GO:0045334)
5 lysosomal membrane (GO:0005765)
6 clathrin-coated vesicle membrane (GO:0030665)
7 extrinsic component of external side of plasma membrane (GO:0031232)
8 lysosome (GO:0005764)
9 endocytic vesicle membrane (GO:0030666)
10 late endosome (GO:0005770)
Overlap Adjusted.P.value Genes
1 2/17 0.003855166 PCSK9;LDLR
2 2/25 0.004235077 PCSK9;LDLR
3 2/69 0.018244377 M6PR;LDLR
4 2/85 0.018244377 M6PR;LDLR
5 3/330 0.018244377 PCSK9;DAGLB;LDLR
6 2/90 0.018244377 M6PR;LDLR
7 1/8 0.036411600 PCSK9
8 3/477 0.036411600 PCSK9;DAGLB;LDLR
9 2/158 0.036411600 M6PR;LDLR
10 2/189 0.046221302 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
Adjusted.P.value Genes
1 0.004773062 PCSK9;LDLR
2 0.004826363 PCSK9;LDLRDAGLB gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATEDZFP28 gene(s) from the input list not found in DisGeNET CURATEDUSP1 gene(s) from the input list not found in DisGeNET CURATEDTEAD3 gene(s) from the input list not found in DisGeNET CURATEDFCGRT gene(s) from the input list not found in DisGeNET CURATEDPKN3 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
19 Hypercholesterolemia, Familial 0.009758215 2/9 18/9703
18 Hypercholesterolemia 0.019480519 2/9 39/9703
28 polyps 0.019480519 1/9 1/9703
73 HYPERCHOLESTEROLEMIA, AUTOSOMAL DOMINANT, 3 0.019480519 1/9 1/9703
9 Coronary Arteriosclerosis 0.021615291 2/9 65/9703
74 Coronary Artery Disease 0.021615291 2/9 65/9703
16 Gastrointestinal Neoplasms 0.032414022 1/9 4/9703
30 Q Fever 0.032414022 1/9 5/9703
46 Acute Q fever 0.032414022 1/9 5/9703
49 Malignant neoplasm of gastrointestinal tract 0.032414022 1/9 4/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR database
1 Dyslipidaemia 81 5 0.0005195842 disease_GLAD4U
2 Coronary Disease 185 5 0.0155720736 disease_GLAD4U
userId
1 PCSK9;PSRC1;TIMD4;FADS1;LDLR
2 PCSK9;PSRC1;TIMD4;FADS1;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