Apoliprotein B (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 Apoliprotein B (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-30640_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
2.782596e-03 8.084458e-05 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
230.42406 42.76761 #report sample size
print(sample_size)[1] 342590#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02076494 0.08777628 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.2592707 1.4822193Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
4151 LDLR 19_9 1.000 647.73 1.9e-03 -24.64
1980 FCGRT 19_34 1.000 82373.60 2.4e-01 -3.37
8166 PCSK9 1_34 0.988 187.18 5.4e-04 21.41
6892 PKN3 9_66 0.987 41.39 1.2e-04 -6.04
5839 TIMD4 5_92 0.934 180.48 4.9e-04 13.61
12535 PKD1L3 16_38 0.917 144.04 3.9e-04 -1.79
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64
7838 CNPY4 7_61 0.833 28.48 6.9e-05 4.20
5380 DEF8 16_54 0.808 26.90 6.3e-05 -4.76
9109 CD163L1 12_7 0.791 28.60 6.6e-05 -4.90
11247 TRAM2-AS1 6_39 0.747 31.26 6.8e-05 5.30
7128 ACP6 1_73 0.707 28.19 5.8e-05 4.16
11025 SYNJ2BP 14_33 0.696 44.46 9.0e-05 7.38
1975 SARS2 19_26 0.650 25.97 4.9e-05 4.54
10343 ZFP28 19_38 0.645 37.66 7.1e-05 -5.68
33 SARM1 17_18 0.594 67.10 1.2e-04 8.11
9198 GRINA 8_94 0.534 59.67 9.3e-05 -7.45
5355 DHX38 16_38 0.506 38.23 5.6e-05 7.87
3384 C10orf88 10_77 0.491 38.57 5.5e-05 -5.64Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 82373.60 2.4e-01 -3.37
5520 RCN3 19_34 0.000 26124.28 0.0e+00 -3.85
4691 SRPK2 7_65 0.000 11841.99 0.0e+00 -1.34
73 KMT2E 7_65 0.000 7270.85 0.0e+00 -0.81
11489 RP11-325F22.2 7_65 0.000 7178.37 0.0e+00 1.71
8165 CPT1C 19_34 0.000 5679.82 0.0e+00 2.27
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
11441 APOC2 19_31 0.033 2550.21 2.5e-04 60.12
571 SLC6A16 19_34 0.000 1425.98 0.0e+00 1.30
10492 CTC-301O7.4 19_34 0.000 1350.10 0.0e+00 0.72
4159 NECTIN2 19_31 0.000 897.58 0.0e+00 21.05
11220 ADM5 19_34 0.000 847.62 0.0e+00 -0.50
6980 ALDH16A1 19_34 0.000 834.98 0.0e+00 -2.07
846 TEAD2 19_34 0.000 806.84 0.0e+00 0.09
4151 LDLR 19_9 1.000 647.73 1.9e-03 -24.64
11152 IGSF23 19_31 0.000 625.43 0.0e+00 -4.00
331 SARS 1_67 0.001 591.80 1.8e-06 -23.74
820 PVR 19_31 0.000 405.62 0.0e+00 -11.82
5562 CELSR2 1_67 0.001 344.47 5.3e-07 18.65
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 82373.60 2.4e-01 -3.37
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
4151 LDLR 19_9 1.000 647.73 1.9e-03 -24.64
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64
8166 PCSK9 1_34 0.988 187.18 5.4e-04 21.41
5839 TIMD4 5_92 0.934 180.48 4.9e-04 13.61
12535 PKD1L3 16_38 0.917 144.04 3.9e-04 -1.79
11441 APOC2 19_31 0.033 2550.21 2.5e-04 60.12
7089 USP1 1_39 0.455 165.12 2.2e-04 12.72
33 SARM1 17_18 0.594 67.10 1.2e-04 8.11
6892 PKN3 9_66 0.987 41.39 1.2e-04 -6.04
9198 GRINA 8_94 0.534 59.67 9.3e-05 -7.45
11025 SYNJ2BP 14_33 0.696 44.46 9.0e-05 7.38
10343 ZFP28 19_38 0.645 37.66 7.1e-05 -5.68
7838 CNPY4 7_61 0.833 28.48 6.9e-05 4.20
11247 TRAM2-AS1 6_39 0.747 31.26 6.8e-05 5.30
9109 CD163L1 12_7 0.791 28.60 6.6e-05 -4.90
5380 DEF8 16_54 0.808 26.90 6.3e-05 -4.76
7128 ACP6 1_73 0.707 28.19 5.8e-05 4.16
3645 ACVR1C 2_94 0.376 51.15 5.6e-05 -4.89Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.033 2550.21 2.5e-04 60.12
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
4151 LDLR 19_9 1.000 647.73 1.9e-03 -24.64
331 SARS 1_67 0.001 591.80 1.8e-06 -23.74
8166 PCSK9 1_34 0.988 187.18 5.4e-04 21.41
7053 BSND 1_34 0.012 283.91 1.0e-05 21.09
4159 NECTIN2 19_31 0.000 897.58 0.0e+00 21.05
5562 CELSR2 1_67 0.001 344.47 5.3e-07 18.65
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64
4137 MAU2 19_15 0.002 273.54 1.3e-06 16.48
2496 ZPR1 11_70 0.001 280.47 7.8e-07 -15.91
2131 ATP13A1 19_15 0.003 199.94 1.7e-06 -14.23
5839 TIMD4 5_92 0.934 180.48 4.9e-04 13.61
12254 CTC-366B18.4 5_44 0.001 115.61 2.4e-07 -13.48
4636 FADS2 11_34 0.003 187.11 1.5e-06 -13.25
2793 COL4A3BP 5_44 0.000 104.22 1.5e-07 12.75
7089 USP1 1_39 0.455 165.12 2.2e-04 12.72
5512 CARM1 19_9 0.000 147.19 0.0e+00 -12.52
820 PVR 19_31 0.000 405.62 0.0e+00 -11.82
1652 PCIF1 20_28 0.007 150.07 3.2e-06 11.69Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.02063993#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11441 APOC2 19_31 0.033 2550.21 2.5e-04 60.12
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
4151 LDLR 19_9 1.000 647.73 1.9e-03 -24.64
331 SARS 1_67 0.001 591.80 1.8e-06 -23.74
8166 PCSK9 1_34 0.988 187.18 5.4e-04 21.41
7053 BSND 1_34 0.012 283.91 1.0e-05 21.09
4159 NECTIN2 19_31 0.000 897.58 0.0e+00 21.05
5562 CELSR2 1_67 0.001 344.47 5.3e-07 18.65
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64
4137 MAU2 19_15 0.002 273.54 1.3e-06 16.48
2496 ZPR1 11_70 0.001 280.47 7.8e-07 -15.91
2131 ATP13A1 19_15 0.003 199.94 1.7e-06 -14.23
5839 TIMD4 5_92 0.934 180.48 4.9e-04 13.61
12254 CTC-366B18.4 5_44 0.001 115.61 2.4e-07 -13.48
4636 FADS2 11_34 0.003 187.11 1.5e-06 -13.25
2793 COL4A3BP 5_44 0.000 104.22 1.5e-07 12.75
7089 USP1 1_39 0.455 165.12 2.2e-04 12.72
5512 CARM1 19_9 0.000 147.19 0.0e+00 -12.52
820 PVR 19_31 0.000 405.62 0.0e+00 -11.82
1652 PCIF1 20_28 0.007 150.07 3.2e-06 11.69Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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 70.32 0.00000 -8.86
12136 ZNF285 19_31 0.000 9.63 0.00000 -1.42
7892 ZNF180 19_31 0.000 14.12 0.00000 3.44
820 PVR 19_31 0.000 405.62 0.00000 -11.82
11152 IGSF23 19_31 0.000 625.43 0.00000 -4.00
9941 CEACAM19 19_31 0.000 51.53 0.00000 11.14
4159 NECTIN2 19_31 0.000 897.58 0.00000 21.05
4161 TOMM40 19_31 0.000 39.26 0.00000 -1.33
12134 APOC4 19_31 0.000 69.85 0.00000 8.97
11441 APOC2 19_31 0.033 2550.21 0.00025 60.12
1977 CLPTM1 19_31 0.000 50.50 0.00000 -4.11
8368 ZNF296 19_31 0.000 82.96 0.00000 -9.89
5505 GEMIN7 19_31 0.000 90.05 0.00000 2.94
1979 PPP1R37 19_31 0.000 39.08 0.00000 -1.67
10171 BLOC1S3 19_31 0.000 31.54 0.00000 3.51
116 TRAPPC6A 19_31 0.000 28.19 0.00000 2.35
12615 EXOC3L2 19_31 0.000 53.89 0.00000 -2.14
111 MARK4 19_31 0.000 8.18 0.00000 -3.38
1988 KLC3 19_31 0.000 24.50 0.00000 -5.17
1982 PPP1R13L 19_31 0.000 33.77 0.00000 -3.78
3230 CD3EAP 19_31 0.000 33.77 0.00000 -3.78
213 ERCC1 19_31 0.000 23.42 0.00000 -2.75
11059 PPM1N 19_31 0.000 32.15 0.00000 -3.46
3830 RTN2 19_31 0.000 47.91 0.00000 7.44
3831 VASP 19_31 0.000 9.80 0.00000 6.21
| 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 19.55 6.5e-08 -2.73
1102 SLC25A24 1_67 0.001 10.19 2.1e-08 1.64
7095 FAM102B 1_67 0.001 40.28 1.6e-07 -5.32
7096 HENMT1 1_67 0.001 16.61 7.1e-08 -1.66
3080 STXBP3 1_67 0.001 24.92 9.0e-08 3.77
3522 GPSM2 1_67 0.001 7.62 1.4e-08 0.34
3521 CLCC1 1_67 0.001 22.44 3.4e-08 -4.12
10487 TAF13 1_67 0.001 90.04 1.6e-07 -8.70
11143 TMEM167B 1_67 0.000 14.49 2.1e-08 3.48
9291 C1orf194 1_67 0.005 41.42 5.9e-07 -2.23
1099 WDR47 1_67 0.003 39.75 3.1e-07 -2.50
3084 KIAA1324 1_67 0.002 75.84 3.9e-07 7.14
331 SARS 1_67 0.001 591.80 1.8e-06 -23.74
5562 CELSR2 1_67 0.001 344.47 5.3e-07 18.65
4564 PSRC1 1_67 1.000 3046.09 8.9e-03 -55.62
7099 ATXN7L2 1_67 0.001 17.32 3.2e-08 3.01
8776 CYB561D1 1_67 0.003 43.49 3.3e-07 4.59
9435 AMIGO1 1_67 0.003 64.52 4.9e-07 -6.71
617 GNAI3 1_67 0.003 85.92 6.5e-07 8.22
11016 GSTM2 1_67 0.001 19.71 4.4e-08 3.58
8107 GSTM4 1_67 0.001 60.67 9.7e-08 -7.20
4559 GSTM1 1_67 0.281 67.11 5.5e-05 9.74
4561 GSTM5 1_67 0.001 14.71 4.2e-08 3.54
4562 GSTM3 1_67 0.001 33.68 9.6e-08 -4.20
| 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 10.68 0.0000 -1.67
10208 ZNF699 19_9 0 41.28 0.0000 -2.31
10092 ZNF559 19_9 0 6.17 0.0000 0.78
8818 ZNF266 19_9 0 14.20 0.0000 -1.38
4245 ZNF426 19_9 0 19.63 0.0000 -1.20
12567 CTC-543D15.8 19_9 0 40.14 0.0000 2.30
10522 ZNF121 19_9 0 20.05 0.0000 -1.23
8463 ZNF561 19_9 0 11.75 0.0000 -1.44
8461 ZNF562 19_9 0 19.98 0.0000 -1.36
12539 CTD-3116E22.8 19_9 0 5.83 0.0000 -0.59
10303 ZNF846 19_9 0 5.67 0.0000 0.45
3954 FBXL12 19_9 0 9.97 0.0000 0.51
10572 UBL5 19_9 0 18.05 0.0000 -1.27
1004 COL5A3 19_9 0 15.22 0.0000 1.13
4243 ANGPTL6 19_9 0 7.61 0.0000 -0.50
11635 P2RY11 19_9 0 7.19 0.0000 -0.35
4241 PPAN 19_9 0 31.49 0.0000 -2.71
4244 C19orf66 19_9 0 27.46 0.0000 3.08
4242 EIF3G 19_9 0 17.41 0.0000 2.58
2062 MRPL4 19_9 0 10.70 0.0000 0.84
1256 ICAM1 19_9 0 23.43 0.0000 -1.26
2068 ICAM5 19_9 0 11.48 0.0000 -1.44
11171 ZGLP1 19_9 0 8.22 0.0000 -1.09
12143 FDX2 19_9 0 43.69 0.0000 -5.18
6996 RAVER1 19_9 0 11.88 0.0000 1.41
913 ICAM3 19_9 0 17.04 0.0000 -0.08
2072 TYK2 19_9 0 47.28 0.0000 1.58
650 PDE4A 19_9 0 48.55 0.0000 1.06
9357 S1PR5 19_9 0 9.45 0.0000 0.75
4228 ATG4D 19_9 0 54.86 0.0000 -5.55
4101 KRI1 19_9 0 13.89 0.0000 0.73
4104 CDKN2D 19_9 0 31.74 0.0000 2.54
4103 AP1M2 19_9 0 65.23 0.0000 -4.44
4102 SLC44A2 19_9 0 91.02 0.0000 -3.07
12119 ILF3-AS1 19_9 0 47.98 0.0000 -0.82
1398 TMED1 19_9 0 20.52 0.0000 -1.69
11089 C19orf38 19_9 0 20.52 0.0000 -1.69
5512 CARM1 19_9 0 147.19 0.0000 -12.52
5511 TIMM29 19_9 0 153.80 0.0000 -10.31
4227 YIPF2 19_9 0 24.85 0.0000 -4.25
3972 SMARCA4 19_9 0 15.17 0.0000 4.14
4151 LDLR 19_9 1 647.73 0.0019 -24.64
6998 SPC24 19_9 0 79.41 0.0000 9.02
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_34"
genename region_tag susie_pip mu2 PVE z
528 NDC1 1_34 0.000 32.89 2.2e-14 -2.44
527 YIPF1 1_34 0.000 16.07 2.1e-15 -1.26
10976 DIO1 1_34 0.000 28.19 1.1e-14 -2.16
1028 HSPB11 1_34 0.000 8.13 4.4e-16 -0.64
3074 LRRC42 1_34 0.000 8.13 4.4e-16 0.64
3072 TCEANC2 1_34 0.000 6.34 3.0e-16 -0.46
3073 TMEM59 1_34 0.000 5.09 2.1e-16 0.18
11148 CYB5RL 1_34 0.000 20.29 4.3e-15 1.35
3076 MRPL37 1_34 0.000 5.53 2.4e-16 0.08
6603 SSBP3 1_34 0.000 6.12 2.6e-16 -0.96
9687 MROH7 1_34 0.000 8.28 4.5e-16 1.19
11620 TTC4 1_34 0.000 5.53 2.3e-16 0.97
7051 PARS2 1_34 0.000 21.42 4.0e-15 -1.54
97 TTC22 1_34 0.000 6.91 3.3e-16 0.39
7052 LEXM 1_34 0.000 24.49 7.7e-15 2.27
3062 DHCR24 1_34 0.000 22.82 6.7e-15 -1.78
7053 BSND 1_34 0.012 283.91 1.0e-05 21.09
8166 PCSK9 1_34 0.988 187.18 5.4e-04 21.41
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_34"
genename region_tag susie_pip mu2 PVE z
10165 FAM111B 11_34 0.001 5.36 1.8e-08 -0.28
7794 FAM111A 11_34 0.001 5.17 1.7e-08 0.15
2506 DTX4 11_34 0.002 11.87 7.1e-08 1.27
10468 MPEG1 11_34 0.001 5.23 1.7e-08 -0.21
2515 MS4A6A 11_34 0.001 5.63 1.9e-08 0.91
7815 PATL1 11_34 0.017 31.07 1.5e-06 2.98
7817 STX3 11_34 0.001 5.87 2.0e-08 -0.62
7818 MRPL16 11_34 0.001 4.98 1.6e-08 0.19
4634 GIF 11_34 0.022 33.93 2.2e-06 -3.12
4638 TCN1 11_34 0.002 10.04 5.4e-08 0.88
6096 MS4A2 11_34 0.002 10.96 6.5e-08 -1.49
11819 AP001257.1 11_34 0.001 5.17 1.7e-08 0.07
11116 MS4A4E 11_34 0.009 21.00 5.4e-07 2.56
2516 MS4A4A 11_34 0.014 27.18 1.1e-06 3.15
7825 MS4A6E 11_34 0.061 28.46 5.1e-06 -3.25
7826 MS4A7 11_34 0.006 20.78 3.4e-07 1.99
7827 MS4A14 11_34 0.001 6.73 2.5e-08 0.63
2519 CCDC86 11_34 0.002 9.82 5.4e-08 -0.77
9570 PTGDR2 11_34 0.001 7.85 3.3e-08 -0.85
6093 ZP1 11_34 0.008 23.35 5.5e-07 -1.62
2520 PRPF19 11_34 0.004 18.62 2.2e-07 1.88
2521 TMEM109 11_34 0.004 17.34 1.9e-07 1.70
2546 SLC15A3 11_34 0.001 6.46 2.1e-08 1.08
2547 CD5 11_34 0.001 5.89 1.9e-08 -0.76
8008 VPS37C 11_34 0.001 5.36 1.7e-08 0.62
11874 PGA5 11_34 0.002 11.44 8.1e-08 0.18
11340 PGA3 11_34 0.004 14.50 1.5e-07 0.40
8009 VWCE 11_34 0.001 6.32 2.0e-08 -1.06
6088 TMEM138 11_34 0.001 10.44 3.9e-08 -2.01
7030 CYB561A3 11_34 0.001 10.44 3.9e-08 -2.01
9981 TMEM216 11_34 0.002 8.84 4.4e-08 0.46
11871 RP11-286N22.8 11_34 0.002 10.26 5.8e-08 0.90
4631 DAGLA 11_34 0.001 20.96 6.8e-08 4.02
3765 MYRF 11_34 0.001 31.25 1.0e-07 -5.19
4636 FADS2 11_34 0.003 187.11 1.5e-06 -13.25
4637 TMEM258 11_34 0.001 102.10 4.3e-07 -9.84
6089 FADS1 11_34 0.883 304.46 7.8e-04 -17.64
11190 FADS3 11_34 0.005 33.91 4.6e-07 4.31
8011 BEST1 11_34 0.002 35.74 2.1e-07 -5.28
6092 INCENP 11_34 0.001 6.20 2.0e-08 -1.11
7032 ASRGL1 11_34 0.001 5.07 1.6e-08 -0.29
| 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
60112 rs6586405 1_122 1.000 63.26 1.8e-04 9.55
60162 rs822928 1_122 1.000 120.10 3.5e-04 12.37
72783 rs11679386 2_12 1.000 272.77 8.0e-04 15.27
72832 rs1042034 2_13 1.000 787.16 2.3e-03 25.92
72838 rs934197 2_13 1.000 358.23 1.0e-03 36.28
72841 rs548145 2_13 1.000 1405.49 4.1e-03 43.00
72918 rs1848922 2_13 1.000 471.02 1.4e-03 33.23
74568 rs780093 2_16 1.000 287.72 8.4e-04 -19.84
80633 rs72800939 2_28 1.000 51.43 1.5e-04 -7.00
325398 rs11376017 6_13 1.000 72.14 2.1e-04 -8.44
329106 rs72834643 6_20 1.000 44.16 1.3e-04 -5.50
329127 rs115740542 6_20 1.000 153.67 4.5e-04 -11.47
329860 rs454182 6_22 1.000 142.86 4.2e-04 4.48
355971 rs9496567 6_67 1.000 52.44 1.5e-04 -7.07
373728 rs117733303 6_104 1.000 130.01 3.8e-04 10.35
373764 rs56393506 6_104 1.000 109.98 3.2e-04 14.79
394763 rs217396 7_32 1.000 57.84 1.7e-04 -7.51
412317 rs763798411 7_65 1.000 90272.52 2.6e-01 -3.84
412320 rs10274607 7_65 1.000 90043.23 2.6e-01 -3.41
412328 rs4997569 7_65 1.000 90152.37 2.6e-01 -3.54
434163 rs7012814 8_12 1.000 76.84 2.2e-04 8.60
439428 rs75835816 8_21 1.000 48.44 1.4e-04 6.88
448954 rs140753685 8_42 1.000 56.44 1.6e-04 7.39
450350 rs4738679 8_45 1.000 101.69 3.0e-04 -10.43
470013 rs13252684 8_83 1.000 374.62 1.1e-03 12.25
511827 rs115478735 9_70 1.000 174.10 5.1e-04 13.95
596752 rs4937122 11_77 1.000 83.46 2.4e-04 12.93
617302 rs7397189 12_36 1.000 57.85 1.7e-04 -7.60
637978 rs11057830 12_76 1.000 39.58 1.2e-04 6.05
675211 rs2332328 14_3 1.000 61.34 1.8e-04 7.75
712866 rs2070895 15_27 1.000 70.65 2.1e-04 8.36
740406 rs66495554 16_31 1.000 91.04 2.7e-04 0.58
749620 rs2255451 16_49 1.000 52.41 1.5e-04 -7.15
768690 rs1801689 17_38 1.000 100.88 2.9e-04 10.00
769606 rs113408695 17_39 1.000 140.76 4.1e-04 11.55
769632 rs8070232 17_39 1.000 194.75 5.7e-04 -7.51
805624 rs3794991 19_15 1.000 394.75 1.2e-03 -18.65
812330 rs62117204 19_31 1.000 1466.26 4.3e-03 -59.82
812348 rs111794050 19_31 1.000 1496.74 4.4e-03 -45.67
812381 rs814573 19_31 1.000 4395.67 1.3e-02 74.70
812383 rs113345881 19_31 1.000 1729.23 5.0e-03 -48.87
812721 rs150262789 19_32 1.000 105.99 3.1e-04 -12.81
823310 rs6075251 20_13 1.000 118.36 3.5e-04 -3.56
823311 rs34507316 20_13 1.000 146.95 4.3e-04 -7.80
864616 rs11591147 1_34 1.000 1329.31 3.9e-03 -38.51
936372 rs10422256 19_9 1.000 268.34 7.8e-04 13.21
945575 rs55840997 19_30 1.000 139.63 4.1e-04 -11.43
946003 rs62115559 19_30 1.000 379.29 1.1e-03 -19.47
948769 rs374141296 19_34 1.000 79241.62 2.3e-01 3.02
80497 rs139029940 2_27 0.999 39.31 1.1e-04 6.33
286849 rs7701166 5_44 0.999 39.06 1.1e-04 -2.82
331082 rs28780090 6_26 0.999 56.92 1.7e-04 6.45
593679 rs3135506 11_70 0.999 289.98 8.5e-04 16.52
593684 rs75542613 11_70 0.999 39.14 1.1e-04 -6.81
802872 rs2043302 19_11 0.999 56.23 1.6e-04 5.05
805655 rs113619686 19_15 0.999 62.06 1.8e-04 0.73
890942 rs12208357 6_103 0.999 269.47 7.9e-04 13.53
470002 rs79658059 8_83 0.998 538.10 1.6e-03 -20.19
740401 rs821840 16_31 0.998 491.75 1.4e-03 -19.39
72835 rs78610189 2_13 0.997 126.68 3.7e-04 -10.48
386961 rs56130071 7_19 0.997 116.29 3.4e-04 11.36
805264 rs2302209 19_14 0.997 37.60 1.1e-04 5.78
946086 rs185920692 19_30 0.997 96.49 2.8e-04 -9.35
802907 rs201868221 19_11 0.996 55.90 1.6e-04 7.69
948757 rs61371437 19_34 0.996 78777.09 2.3e-01 3.07
74569 rs6744393 2_16 0.995 68.71 2.0e-04 -10.90
331105 rs62407548 6_26 0.995 71.03 2.1e-04 7.51
31045 rs1109112 1_69 0.994 31.56 9.2e-05 -5.03
438681 rs1495743 8_20 0.994 37.56 1.1e-04 -5.88
691022 rs13379043 14_34 0.994 32.76 9.5e-05 -5.43
30794 rs1730862 1_66 0.992 31.79 9.2e-05 -5.29
31047 rs78221564 1_69 0.992 30.76 8.9e-05 -4.76
646865 rs1012130 13_10 0.992 97.01 2.8e-04 -3.82
621668 rs148481241 12_44 0.990 31.05 9.0e-05 5.22
818956 rs74273659 20_5 0.990 36.58 1.1e-04 6.07
559771 rs10838525 11_4 0.989 48.93 1.4e-04 -5.31
55764 rs2807848 1_112 0.988 33.19 9.6e-05 -6.39
828215 rs6029132 20_24 0.988 44.67 1.3e-04 -6.92
150446 rs9834932 3_24 0.987 72.72 2.1e-04 -8.42
754368 rs144129583 17_7 0.985 32.20 9.3e-05 -5.94
330268 rs28986304 6_23 0.983 44.39 1.3e-04 6.87
891010 rs662138 6_103 0.983 121.30 3.5e-04 11.25
864675 rs7552841 1_34 0.981 78.61 2.3e-04 8.74
802853 rs4804149 19_11 0.980 36.41 1.0e-04 7.46
280397 rs1499279 5_31 0.979 120.13 3.4e-04 -11.06
564322 rs7943121 11_13 0.978 52.78 1.5e-04 7.12
673403 rs3934835 13_62 0.978 64.74 1.8e-04 7.86
646870 rs206326 13_10 0.976 58.67 1.7e-04 -5.04
80513 rs4076834 2_27 0.974 395.41 1.1e-03 -18.14
805673 rs12984303 19_15 0.974 30.05 8.5e-05 5.24
744293 rs4396539 16_37 0.973 30.86 8.8e-05 -5.06
828268 rs73124945 20_24 0.973 33.43 9.5e-05 -7.68
633888 rs653178 12_67 0.972 69.67 2.0e-04 8.65
200869 rs3748034 4_4 0.969 61.29 1.7e-04 6.91
946008 rs143283769 19_30 0.969 37.95 1.1e-04 -5.27
483436 rs677622 9_13 0.967 32.04 9.0e-05 5.23
812386 rs12721109 19_31 0.967 2508.75 7.1e-03 -60.87
589948 rs201912654 11_59 0.966 38.59 1.1e-04 -5.86
549895 rs12244851 10_70 0.965 34.31 9.7e-05 -4.33
823291 rs78348000 20_13 0.962 37.65 1.1e-04 5.58
712860 rs62000868 15_27 0.960 28.78 8.1e-05 4.67
812704 rs34942359 19_32 0.957 55.73 1.6e-04 -5.80
828233 rs6102034 20_24 0.953 86.37 2.4e-04 -10.11
286790 rs10062361 5_44 0.948 185.78 5.1e-04 17.92
227473 rs1458038 4_54 0.947 42.80 1.2e-04 -6.28
8327 rs79598313 1_18 0.941 125.93 3.5e-04 11.30
388651 rs10268799 7_23 0.941 37.11 1.0e-04 5.33
812621 rs377297589 19_32 0.939 70.54 1.9e-04 -7.79
358707 rs12199109 6_73 0.936 28.41 7.8e-05 5.19
646857 rs1799955 13_10 0.928 160.54 4.3e-04 -9.26
816766 rs34003091 19_39 0.921 106.85 2.9e-04 -10.07
333831 rs112357706 6_27 0.917 27.86 7.5e-05 5.21
636843 rs1169300 12_74 0.906 61.49 1.6e-04 7.69
891046 rs2297374 6_103 0.901 144.99 3.8e-04 -12.07
612389 rs2638250 12_25 0.888 27.59 7.1e-05 -4.73
740391 rs190575415 16_31 0.888 46.38 1.2e-04 -1.36
596755 rs74612335 11_77 0.883 93.65 2.4e-04 12.89
278533 rs55681913 5_28 0.877 27.85 7.1e-05 -4.97
286813 rs3843482 5_44 0.877 350.19 9.0e-04 21.88
751308 rs77277579 16_52 0.876 28.19 7.2e-05 -4.83
143365 rs307572 3_9 0.872 28.83 7.3e-05 -5.25
769617 rs9303012 17_39 0.868 197.18 5.0e-04 2.40
544074 rs10882161 10_59 0.867 44.19 1.1e-04 -6.50
871214 rs34287152 1_67 0.866 37.96 9.6e-05 -1.67
764195 rs7212325 17_28 0.862 42.75 1.1e-04 -7.37
812649 rs11083779 19_32 0.856 87.91 2.2e-04 -11.80
8443 rs138012132 1_19 0.852 29.97 7.4e-05 4.96
893938 rs542985909 7_61 0.842 33.77 8.3e-05 -5.15
828264 rs76981217 20_24 0.837 33.24 8.1e-05 6.60
596739 rs1614592 11_77 0.836 31.21 7.6e-05 -6.08
659526 rs9564985 13_36 0.835 28.56 7.0e-05 -4.75
5089 rs4336844 1_11 0.834 32.20 7.8e-05 5.32
58408 rs11122453 1_117 0.830 78.43 1.9e-04 -8.77
101201 rs138192199 2_69 0.819 27.60 6.6e-05 4.70
367772 rs540973884 6_92 0.817 30.83 7.3e-05 -5.16
593669 rs9326246 11_70 0.809 204.82 4.8e-04 -13.23
101970 rs2311597 2_70 0.806 73.55 1.7e-04 8.47SNPs 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
412317 rs763798411 7_65 1.000 90272.52 2.6e-01 -3.84
412328 rs4997569 7_65 1.000 90152.37 2.6e-01 -3.54
412320 rs10274607 7_65 1.000 90043.23 2.6e-01 -3.41
412335 rs6952534 7_65 0.000 89879.82 0.0e+00 -3.46
412323 rs13230660 7_65 0.000 89848.86 4.0e-05 -3.48
412334 rs4730069 7_65 0.000 89802.89 0.0e+00 -3.44
412327 rs10242713 7_65 0.000 89487.65 0.0e+00 -3.36
412330 rs10249965 7_65 0.000 88752.31 0.0e+00 -3.33
412342 rs1013016 7_65 0.000 85111.68 0.0e+00 2.82
412367 rs8180737 7_65 0.000 80765.40 0.0e+00 -3.26
412360 rs17778396 7_65 0.000 80745.61 0.0e+00 -3.22
412361 rs2237621 7_65 0.000 80709.47 0.0e+00 -3.23
412332 rs71562637 7_65 0.000 80672.91 0.0e+00 -3.13
412394 rs10224564 7_65 0.000 80563.29 0.0e+00 -3.24
412379 rs10255779 7_65 0.000 80521.58 0.0e+00 -3.28
412396 rs78132606 7_65 0.000 80137.48 0.0e+00 -3.24
412399 rs4610671 7_65 0.000 80036.70 0.0e+00 -3.18
948773 rs2946865 19_34 0.000 79336.72 0.0e+00 3.37
948769 rs374141296 19_34 1.000 79241.62 2.3e-01 3.02
948766 rs113176985 19_34 0.000 79168.88 0.0e+00 3.42
948759 rs35295508 19_34 0.000 79051.86 0.0e+00 3.34
948764 rs73056069 19_34 0.000 78819.81 0.0e+00 3.61
948757 rs61371437 19_34 0.996 78777.09 2.3e-01 3.07
948761 rs2878354 19_34 0.000 78567.91 0.0e+00 3.52
948747 rs739349 19_34 0.000 78447.12 7.1e-06 3.09
948748 rs756628 19_34 0.000 78446.98 3.1e-06 3.08
948744 rs739347 19_34 0.000 78287.78 4.3e-11 3.05
948745 rs2073614 19_34 0.000 78190.32 1.4e-12 3.06
948750 rs2077300 19_34 0.004 77969.95 8.6e-04 3.30
948740 rs4802613 19_34 0.000 77841.87 0.0e+00 3.07
948754 rs73056059 19_34 0.000 77835.80 2.8e-08 3.31
948777 rs1316885 19_34 0.000 77814.43 0.0e+00 3.22
948774 rs60815603 19_34 0.000 77766.74 0.0e+00 3.27
948782 rs2946863 19_34 0.000 77676.17 0.0e+00 3.25
948775 rs35443645 19_34 0.000 77533.43 0.0e+00 3.19
948779 rs60746284 19_34 0.000 77271.13 0.0e+00 3.47
948738 rs10403394 19_34 0.000 76783.24 0.0e+00 3.01
948739 rs17555056 19_34 0.000 76729.61 0.0e+00 3.03
412401 rs12669532 7_65 0.000 76714.81 0.0e+00 -3.21
948755 rs73056062 19_34 0.000 75813.70 0.0e+00 2.81
412358 rs2237618 7_65 0.000 75460.30 0.0e+00 -2.88
948785 rs553431297 19_34 0.000 74898.63 0.0e+00 3.27
412403 rs118089279 7_65 0.000 74715.68 0.0e+00 -3.19
412390 rs73188303 7_65 0.000 74656.11 0.0e+00 -2.85
948768 rs112283514 19_34 0.000 74557.62 0.0e+00 3.31
948770 rs11270139 19_34 0.000 74182.82 0.0e+00 3.27
948735 rs10421294 19_34 0.000 69403.33 0.0e+00 3.05
948734 rs8108175 19_34 0.000 69393.56 0.0e+00 3.05
948727 rs59192944 19_34 0.000 69262.29 0.0e+00 3.10
948733 rs1858742 19_34 0.000 69238.40 0.0e+00 3.13SNPs 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
412317 rs763798411 7_65 1.000 90272.52 0.26000 -3.84
412320 rs10274607 7_65 1.000 90043.23 0.26000 -3.41
412328 rs4997569 7_65 1.000 90152.37 0.26000 -3.54
948757 rs61371437 19_34 0.996 78777.09 0.23000 3.07
948769 rs374141296 19_34 1.000 79241.62 0.23000 3.02
812381 rs814573 19_31 1.000 4395.67 0.01300 74.70
812386 rs12721109 19_31 0.967 2508.75 0.00710 -60.87
812383 rs113345881 19_31 1.000 1729.23 0.00500 -48.87
812348 rs111794050 19_31 1.000 1496.74 0.00440 -45.67
812330 rs62117204 19_31 1.000 1466.26 0.00430 -59.82
72841 rs548145 2_13 1.000 1405.49 0.00410 43.00
864616 rs11591147 1_34 1.000 1329.31 0.00390 -38.51
936332 rs12151108 19_9 0.430 2590.23 0.00320 -47.78
936333 rs73015024 19_9 0.355 2589.85 0.00270 -47.77
72832 rs1042034 2_13 1.000 787.16 0.00230 25.92
470002 rs79658059 8_83 0.998 538.10 0.00160 -20.19
72918 rs1848922 2_13 1.000 471.02 0.00140 33.23
740401 rs821840 16_31 0.998 491.75 0.00140 -19.39
936334 rs147985405 19_9 0.168 2588.43 0.00130 -47.76
805624 rs3794991 19_15 1.000 394.75 0.00120 -18.65
80513 rs4076834 2_27 0.974 395.41 0.00110 -18.14
470013 rs13252684 8_83 1.000 374.62 0.00110 12.25
946003 rs62115559 19_30 1.000 379.29 0.00110 -19.47
72838 rs934197 2_13 1.000 358.23 0.00100 36.28
286813 rs3843482 5_44 0.877 350.19 0.00090 21.88
948750 rs2077300 19_34 0.004 77969.95 0.00086 3.30
593679 rs3135506 11_70 0.999 289.98 0.00085 16.52
74568 rs780093 2_16 1.000 287.72 0.00084 -19.84
72783 rs11679386 2_12 1.000 272.77 0.00080 15.27
936505 rs2738464 19_9 0.615 443.55 0.00080 6.70
890942 rs12208357 6_103 0.999 269.47 0.00079 13.53
916621 rs3794695 16_38 0.619 432.43 0.00078 16.79
936372 rs10422256 19_9 1.000 268.34 0.00078 13.21
769632 rs8070232 17_39 1.000 194.75 0.00057 -7.51
286790 rs10062361 5_44 0.948 185.78 0.00051 17.92
511827 rs115478735 9_70 1.000 174.10 0.00051 13.95
769617 rs9303012 17_39 0.868 197.18 0.00050 2.40
593669 rs9326246 11_70 0.809 204.82 0.00048 -13.23
329127 rs115740542 6_20 1.000 153.67 0.00045 -11.47
646857 rs1799955 13_10 0.928 160.54 0.00043 -9.26
823311 rs34507316 20_13 1.000 146.95 0.00043 -7.80
936511 rs2915966 19_9 0.336 442.78 0.00043 6.69
329860 rs454182 6_22 1.000 142.86 0.00042 4.48
769606 rs113408695 17_39 1.000 140.76 0.00041 11.55
936364 rs28493980 19_9 0.517 270.49 0.00041 9.87
945575 rs55840997 19_30 1.000 139.63 0.00041 -11.43
72777 rs660069 2_12 0.591 218.61 0.00038 -9.40
373728 rs117733303 6_104 1.000 130.01 0.00038 10.35
891046 rs2297374 6_103 0.901 144.99 0.00038 -12.07
936363 rs3745677 19_9 0.482 270.05 0.00038 9.91SNPs 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
812381 rs814573 19_31 1.000 4395.67 1.3e-02 74.70
812386 rs12721109 19_31 0.967 2508.75 7.1e-03 -60.87
812330 rs62117204 19_31 1.000 1466.26 4.3e-03 -59.82
812317 rs1551891 19_31 0.000 790.71 0.0e+00 -56.10
871927 rs12740374 1_67 0.000 2933.87 2.8e-07 -55.62
871934 rs646776 1_67 0.000 2922.70 2.8e-07 55.51
871933 rs629301 1_67 0.000 2919.40 2.8e-07 55.48
871923 rs7528419 1_67 0.000 2908.61 2.8e-07 -55.38
871945 rs583104 1_67 0.000 2824.26 2.7e-07 54.58
871948 rs4970836 1_67 0.000 2823.08 2.7e-07 54.55
871950 rs1277930 1_67 0.000 2809.76 2.7e-07 54.42
871951 rs599839 1_67 0.000 2804.16 2.7e-07 54.37
871931 rs3832016 1_67 0.000 2754.88 2.6e-07 53.86
871928 rs660240 1_67 0.000 2740.69 2.6e-07 53.72
871946 rs602633 1_67 0.000 2684.03 2.5e-07 53.17
812377 rs405509 19_31 0.000 2307.20 0.0e+00 -49.85
812383 rs113345881 19_31 1.000 1729.23 5.0e-03 -48.87
936332 rs12151108 19_9 0.430 2590.23 3.2e-03 -47.78
936333 rs73015024 19_9 0.355 2589.85 2.7e-03 -47.77
936334 rs147985405 19_9 0.168 2588.43 1.3e-03 -47.76
936336 rs17248727 19_9 0.045 2585.79 3.4e-04 -47.73
936343 rs6511720 19_9 0.002 2579.69 1.3e-05 -47.71
936342 rs57217136 19_9 0.000 2576.65 3.1e-06 -47.66
936298 rs138175288 19_9 0.000 2568.30 4.5e-08 -47.58
936318 rs73015020 19_9 0.000 2568.25 4.5e-08 -47.58
936297 rs114821903 19_9 0.000 2568.03 4.0e-08 -47.57
936299 rs112107114 19_9 0.000 2567.37 2.8e-08 -47.57
936300 rs115594766 19_9 0.000 2567.24 2.7e-08 -47.57
936309 rs73015013 19_9 0.000 2568.14 4.2e-08 -47.57
936335 rs17248720 19_9 0.000 2559.03 3.3e-10 -47.57
936316 rs61194703 19_9 0.000 2565.25 9.8e-09 -47.56
936296 rs73015011 19_9 0.000 2565.18 9.3e-09 -47.55
936315 rs138294113 19_9 0.000 2565.44 1.1e-08 -47.55
936307 rs142130958 19_9 0.000 2564.27 5.8e-09 -47.54
936324 rs112552009 19_9 0.000 2565.34 9.9e-09 -47.52
936330 rs8106503 19_9 0.000 2553.06 1.7e-11 -47.52
936320 rs77140532 19_9 0.000 2559.18 4.5e-10 -47.51
936322 rs73015021 19_9 0.000 2558.92 4.0e-10 -47.51
936314 rs10402112 19_9 0.000 2561.22 1.2e-09 -47.50
936293 rs113722226 19_9 0.000 2559.42 4.9e-10 -47.49
936325 rs10412048 19_9 0.000 2555.82 8.1e-11 -47.47
936292 rs148898583 19_9 0.000 2556.94 1.4e-10 -47.46
936310 rs114846969 19_9 0.000 2546.31 6.3e-13 -47.43
936290 rs112898275 19_9 0.000 2551.86 1.0e-11 -47.41
936291 rs112374545 19_9 0.000 2553.27 2.1e-11 -47.41
936301 rs112032422 19_9 0.000 2552.37 1.3e-11 -47.41
936312 rs73015016 19_9 0.000 2548.41 1.8e-12 -47.40
936288 rs56125973 19_9 0.000 2545.66 4.3e-13 -47.35
936286 rs55997232 19_9 0.000 2544.11 1.9e-13 -47.33
936287 rs55791371 19_9 0.000 2543.69 1.5e-13 -47.33Gene 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] 10if (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 cellular response to nutrient levels (GO:0031669)
2 cholesterol homeostasis (GO:0042632)
3 sterol homeostasis (GO:0055092)
4 sensory perception of sour taste (GO:0050915)
5 positive regulation of protein catabolic process in the vacuole (GO:1904352)
6 regulation of astrocyte activation (GO:0061888)
7 regulation of low-density lipoprotein particle receptor catabolic process (GO:0032803)
8 cellular response to starvation (GO:0009267)
9 negative regulation of astrocyte differentiation (GO:0048712)
10 negative regulation of lipoprotein particle clearance (GO:0010985)
11 sterol import (GO:0035376)
12 cholesterol import (GO:0070508)
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 negative regulation of macromolecule metabolic process (GO:0010605)
21 unsaturated fatty acid biosynthetic process (GO:0006636)
22 intestinal cholesterol absorption (GO:0030299)
23 negative regulation of sodium ion transmembrane transport (GO:1902306)
24 negative regulation of sodium ion transmembrane transporter activity (GO:2000650)
25 low-density lipoprotein particle receptor catabolic process (GO:0032802)
26 low-density lipoprotein receptor particle metabolic process (GO:0032799)
27 regulation of low-density lipoprotein particle clearance (GO:0010988)
28 cellular response to acidic pH (GO:0071468)
29 negative regulation of receptor binding (GO:1900121)
30 positive regulation of triglyceride biosynthetic process (GO:0010867)
31 positive regulation of bone resorption (GO:0045780)
32 intestinal lipid absorption (GO:0098856)
33 sensory perception of taste (GO:0050909)
34 negative regulation of amyloid fibril formation (GO:1905907)
35 carboxylic acid biosynthetic process (GO:0046394)
36 alpha-linolenic acid metabolic process (GO:0036109)
37 positive regulation of ruffle assembly (GO:1900029)
38 cellular response to low-density lipoprotein particle stimulus (GO:0071404)
39 negative regulation of cell activation (GO:0050866)
40 negative regulation of neuroinflammatory response (GO:0150079)
41 response to acidic pH (GO:0010447)
42 icosanoid biosynthetic process (GO:0046456)
43 regulation of amyloid fibril formation (GO:1905906)
44 regulation of triglyceride biosynthetic process (GO:0010866)
45 intracellular cholesterol transport (GO:0032367)
46 regulation of microglial cell activation (GO:1903978)
47 negative regulation of macrophage activation (GO:0043031)
48 regulation of receptor recycling (GO:0001919)
49 protein autoprocessing (GO:0016540)
50 positive regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045737)
51 cellular response to pH (GO:0071467)
52 regulation of spindle organization (GO:0090224)
53 positive regulation of triglyceride metabolic process (GO:0090208)
54 detection of mechanical stimulus (GO:0050982)
55 long-term memory (GO:0007616)
56 positive regulation of cellular protein catabolic process (GO:1903364)
57 hepaticobiliary system development (GO:0061008)
58 positive regulation of cyclin-dependent protein kinase activity (GO:1904031)
59 negative regulation of ion transmembrane transporter activity (GO:0032413)
60 linoleic acid metabolic process (GO:0043651)
61 sterol transport (GO:0015918)
62 positive regulation of receptor internalization (GO:0002092)
63 positive regulation of neuron apoptotic process (GO:0043525)
64 regulation of ruffle assembly (GO:1900027)
65 negative regulation of signaling (GO:0023057)
66 organophosphate ester transport (GO:0015748)
67 receptor catabolic process (GO:0032801)
68 positive regulation of microtubule polymerization or depolymerization (GO:0031112)
69 negative regulation of receptor-mediated endocytosis (GO:0048261)
70 positive regulation of microtubule polymerization (GO:0031116)
71 long-chain fatty acid biosynthetic process (GO:0042759)
72 liver development (GO:0001889)
73 regulation of mitotic spindle organization (GO:0060236)
74 positive regulation of lipid biosynthetic process (GO:0046889)
75 regulation of receptor internalization (GO:0002090)
76 lysosome localization (GO:0032418)
77 phospholipid biosynthetic process (GO:0008654)
78 organophosphate biosynthetic process (GO:0090407)
79 negative regulation of cellular metabolic process (GO:0031324)
80 regulation of microtubule polymerization (GO:0031113)
81 regulation of sodium ion transmembrane transporter activity (GO:2000649)
82 epithelial cell migration (GO:0010631)
83 detection of chemical stimulus involved in sensory perception of taste (GO:0050912)
84 neurogenesis (GO:0022008)
85 positive regulation of receptor-mediated endocytosis (GO:0048260)
86 microtubule bundle formation (GO:0001578)
87 positive regulation of neuron death (GO:1901216)
88 regulation of protein metabolic process (GO:0051246)
89 mitotic metaphase plate congression (GO:0007080)
90 cholesterol transport (GO:0030301)
91 ameboidal-type cell migration (GO:0001667)
92 positive regulation of protein localization to cell periphery (GO:1904377)
93 positive regulation of protein localization to plasma membrane (GO:1903078)
94 negative regulation of protein metabolic process (GO:0051248)
95 unsaturated fatty acid metabolic process (GO:0033559)
96 renal system development (GO:0072001)
97 icosanoid metabolic process (GO:0006690)
98 phospholipid transport (GO:0015914)
99 cellular protein catabolic process (GO:0044257)
Overlap Adjusted.P.value Genes
1 2/66 0.02502945 PCSK9;FADS1
2 2/71 0.02502945 PCSK9;LDLR
3 2/72 0.02502945 PCSK9;LDLR
4 1/5 0.02502945 PKD1L3
5 1/5 0.02502945 LDLR
6 1/5 0.02502945 LDLR
7 1/5 0.02502945 PCSK9
8 2/158 0.02502945 PCSK9;FADS1
9 1/6 0.02502945 LDLR
10 1/6 0.02502945 PCSK9
11 1/6 0.02502945 LDLR
12 1/6 0.02502945 LDLR
13 1/7 0.02502945 PCSK9
14 1/7 0.02502945 PCSK9
15 1/7 0.02502945 LDLR
16 1/7 0.02502945 LDLR
17 1/8 0.02502945 LDLR
18 1/8 0.02502945 LDLR
19 1/8 0.02502945 LDLR
20 2/194 0.02502945 PCSK9;LDLR
21 1/9 0.02502945 FADS1
22 1/9 0.02502945 LDLR
23 1/10 0.02502945 PCSK9
24 1/10 0.02502945 PCSK9
25 1/10 0.02502945 PCSK9
26 1/10 0.02502945 PCSK9
27 1/10 0.02502945 PCSK9
28 1/10 0.02502945 PKD1L3
29 1/10 0.02502945 PCSK9
30 1/11 0.02502945 LDLR
31 1/11 0.02502945 DEF8
32 1/11 0.02502945 LDLR
33 1/12 0.02502945 PKD1L3
34 1/12 0.02502945 LDLR
35 1/13 0.02502945 FADS1
36 1/13 0.02502945 FADS1
37 1/13 0.02502945 DEF8
38 1/14 0.02502945 LDLR
39 1/14 0.02502945 LDLR
40 1/14 0.02502945 LDLR
41 1/15 0.02502945 PKD1L3
42 1/15 0.02502945 FADS1
43 1/15 0.02502945 LDLR
44 1/15 0.02502945 LDLR
45 1/15 0.02502945 LDLR
46 1/15 0.02502945 LDLR
47 1/16 0.02601915 LDLR
48 1/17 0.02601915 PCSK9
49 1/17 0.02601915 PCSK9
50 1/17 0.02601915 PSRC1
51 1/18 0.02601915 PKD1L3
52 1/18 0.02601915 PSRC1
53 1/19 0.02601915 LDLR
54 1/19 0.02601915 PKD1L3
55 1/19 0.02601915 LDLR
56 1/19 0.02601915 PCSK9
57 1/20 0.02638894 PCSK9
58 1/20 0.02638894 PSRC1
59 1/21 0.02638894 PCSK9
60 1/21 0.02638894 FADS1
61 1/21 0.02638894 LDLR
62 1/23 0.02842323 PCSK9
63 1/24 0.02857639 PCSK9
64 1/24 0.02857639 DEF8
65 1/25 0.02857639 PCSK9
66 1/25 0.02857639 LDLR
67 1/25 0.02857639 PCSK9
68 1/26 0.02885153 PSRC1
69 1/26 0.02885153 PCSK9
70 1/28 0.03061325 PSRC1
71 1/30 0.03232342 FADS1
72 1/32 0.03398418 PCSK9
73 1/35 0.03614123 PSRC1
74 1/35 0.03614123 LDLR
75 1/36 0.03666995 PCSK9
76 1/37 0.03670139 DEF8
77 1/37 0.03670139 FADS1
78 1/39 0.03768894 FADS1
79 1/39 0.03768894 PCSK9
80 1/40 0.03769241 PSRC1
81 1/40 0.03769241 PCSK9
82 1/41 0.03815499 PKN3
83 1/44 0.03947503 PKD1L3
84 1/44 0.03947503 PCSK9
85 1/44 0.03947503 PCSK9
86 1/45 0.03989379 PSRC1
87 1/47 0.04116943 PCSK9
88 1/48 0.04155825 LDLR
89 1/51 0.04210990 PSRC1
90 1/51 0.04210990 LDLR
91 1/52 0.04210990 PKN3
92 1/52 0.04210990 CNPY4
93 1/52 0.04210990 CNPY4
94 1/52 0.04210990 LDLR
95 1/54 0.04324977 FADS1
96 1/57 0.04468115 PCSK9
97 1/57 0.04468115 FADS1
98 1/59 0.04575644 LDLR
99 1/60 0.04605161 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 0.0009143328 PCSK9;LDLR
2 2/25 0.0010063130 PCSK9;LDLR
3 2/189 0.0299523470 PCSK9;LDLR
4 1/8 0.0299523470 PCSK9
5 2/219 0.0304139312 PCSK9;LDLR
6 2/266 0.0369547171 PCSK9;LDLR
7 2/325 0.0419655608 PCSK9;LDLR
8 2/330 0.0419655608 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 IgG binding (GO:0019864) 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 low-density lipoprotein particle receptor binding (GO:0050750) 1/23
8 lipoprotein particle receptor binding (GO:0070325) 1/28
9 taste receptor activity (GO:0008527) 1/31
10 ion channel inhibitor activity (GO:0008200) 1/37
11 sodium channel regulator activity (GO:0017080) 1/37
Adjusted.P.value Genes
1 0.0007924217 PCSK9;LDLR
2 0.0008025804 PCSK9;LDLR
3 0.0194646578 FCGRT
4 0.0194646578 PCSK9
5 0.0194646578 PCSK9
6 0.0194646578 LDLR
7 0.0425030170 PCSK9
8 0.0433743700 PCSK9
9 0.0433743700 PKD1L3
10 0.0433743700 PCSK9
11 0.0433743700 PCSK9DEF8 gene(s) from the input list not found in DisGeNET CURATEDPKN3 gene(s) from the input list not found in DisGeNET CURATEDFCGRT gene(s) from the input list not found in DisGeNET CURATEDPKD1L3 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
10 Hypercholesterolemia, Familial 0.001393696 2/5 18/9703
9 Hypercholesterolemia 0.003360327 2/5 39/9703
4 Coronary Arteriosclerosis 0.004432076 2/5 65/9703
34 HYPERCHOLESTEROLEMIA, AUTOSOMAL DOMINANT, 3 0.004432076 1/5 1/9703
35 Coronary Artery Disease 0.004432076 2/5 65/9703
17 Q Fever 0.012301172 1/5 5/9703
22 Acute Q fever 0.012301172 1/5 5/9703
27 Chronic Q Fever 0.012301172 1/5 5/9703
38 Coxiella burnetii Infection 0.012301172 1/5 5/9703
29 Hyperlipoproteinemia Type IIb 0.028737302 1/5 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 5 1.023299e-05
2 Coronary Disease 185 5 3.306265e-04
3 Coronary Artery Disease 169 4 7.133147e-03
4 Arterial Occlusive Diseases 182 4 7.133147e-03
5 Arteriosclerosis 184 4 7.133147e-03
6 Myocardial Ischemia 195 4 7.133147e-03
7 Hyperlipidemias 62 3 7.133147e-03
8 Hypo-beta-lipoproteinemia 10 2 9.175745e-03
9 Hypobetalipoproteinemias 10 2 9.175745e-03
10 Heart Diseases 234 4 9.252811e-03
11 Cardiovascular Diseases 276 4 1.607091e-02
12 Hyperlipidemia, Familial Combined 16 2 1.829748e-02
13 Hyperlipoproteinemia Type II 18 2 2.151358e-02
14 Hyperlipoproteinemias 19 2 2.231616e-02
15 Myocardial Infarction 139 3 3.637876e-02
16 Infarction 141 3 3.637876e-02
17 Cholesterol metabolism 31 2 4.968148e-02
database userId
1 disease_GLAD4U PCSK9;PSRC1;TIMD4;FADS1;LDLR
2 disease_GLAD4U PCSK9;PSRC1;TIMD4;FADS1;LDLR
3 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
4 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
5 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
6 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
7 disease_GLAD4U PCSK9;TIMD4;LDLR
8 disease_GLAD4U PCSK9;LDLR
9 disease_GLAD4U PCSK9;LDLR
10 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
11 disease_GLAD4U PCSK9;PSRC1;FADS1;LDLR
12 disease_GLAD4U PCSK9;LDLR
13 disease_GLAD4U PCSK9;LDLR
14 disease_GLAD4U PCSK9;LDLR
15 disease_GLAD4U PCSK9;PSRC1;LDLR
16 disease_GLAD4U PCSK9;PSRC1;LDLR
17 pathway_KEGG 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