IGF-1 (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 IGF-1 (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-30770_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.010966355 0.000211134 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
39.44803 25.06010 #report sample size
print(sample_size)[1] 342439#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01401625 0.13438277 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.2333435 2.3431922Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
10765 ZDHHC18 1_18 1.000 188.41 5.5e-04 -14.65
7372 RNF123 3_35 1.000 71676.96 2.1e-01 4.33
3804 OPRL1 20_38 0.989 127.46 3.7e-04 -10.80
8739 STAT5B 17_25 0.981 25.79 7.4e-05 3.88
4732 NHSL1 6_92 0.978 28.21 8.1e-05 4.95
8641 OXSR1 3_27 0.975 27.42 7.8e-05 -4.94
9181 BEND3 6_71 0.946 32.41 9.0e-05 -5.38
2565 GTF2H1 11_13 0.938 66.55 1.8e-04 -8.64
2252 TGFBR1 9_50 0.935 32.61 8.9e-05 5.79
2002 AES 19_4 0.914 25.85 6.9e-05 4.73
8201 NPR1 1_75 0.901 23.01 6.1e-05 -5.13
5012 TRIM29 11_72 0.899 26.27 6.9e-05 5.34
2919 ZBTB47 3_31 0.893 41.44 1.1e-04 -6.08
1533 TTLL12 22_18 0.884 21.15 5.5e-05 3.90
2073 SULT2A1 19_33 0.851 57.20 1.4e-04 -7.73
8036 VASN 16_4 0.849 35.33 8.8e-05 6.06
7786 CATSPER2 15_16 0.848 359.54 8.9e-04 -19.25
9247 FUCA1 1_17 0.839 26.92 6.6e-05 4.86
361 CUL3 2_132 0.831 40.49 9.8e-05 6.44
8177 THBS3 1_77 0.825 26.84 6.5e-05 -4.92
2936 ACTR1B 2_57 0.821 21.78 5.2e-05 -4.13Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
7372 RNF123 3_35 1 71676.96 0.21 4.33
38 RBM6 3_35 0 42297.95 0.00 -3.66
7375 MON1A 3_35 0 42297.54 0.00 -3.67
9642 TRAIP 3_35 0 34485.00 0.00 -5.51
7376 MST1R 3_35 0 27983.98 0.00 -2.07
8603 ZMAT3 3_110 0 19345.62 0.00 1.29
8697 DAG1 3_35 0 16230.39 0.00 -4.25
11405 GPX1 3_35 0 14734.77 0.00 1.16
417 MAP4 3_34 0 12860.13 0.00 4.52
8713 GMPPB 3_35 0 10916.60 0.00 -2.13
8416 KCNMB3 3_110 0 8750.25 0.00 -3.68
168 SPRTN 1_118 0 5752.13 0.00 -2.05
11610 NAT6 3_35 0 5590.77 0.00 -2.46
9957 HYAL3 3_35 0 5336.78 0.00 2.54
7371 APEH 3_35 0 4543.54 0.00 -3.11
7365 ZNF589 3_34 0 4421.46 0.00 -3.40
881 ZNF37A 10_28 0 4162.37 0.00 1.49
3138 EXOC8 1_118 0 4153.00 0.00 -2.95
9608 PSMG1 21_19 0 3991.36 0.00 -7.05
123 CACNA2D2 3_35 0 3964.63 0.00 3.58Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
7372 RNF123 3_35 1.000 71676.96 2.1e-01 4.33
7786 CATSPER2 15_16 0.848 359.54 8.9e-04 -19.25
10765 ZDHHC18 1_18 1.000 188.41 5.5e-04 -14.65
3804 OPRL1 20_38 0.989 127.46 3.7e-04 -10.80
9322 F2 11_28 0.743 111.26 2.4e-04 -10.26
4564 PSRC1 1_67 0.643 124.18 2.3e-04 10.44
6089 FADS1 11_34 0.745 87.84 1.9e-04 -8.53
2565 GTF2H1 11_13 0.938 66.55 1.8e-04 -8.64
5092 EXOC6 10_59 0.686 67.78 1.4e-04 7.43
2073 SULT2A1 19_33 0.851 57.20 1.4e-04 -7.73
6366 CMIP 16_46 0.733 59.08 1.3e-04 -8.28
12004 RP11-196G11.2 16_24 0.225 162.03 1.1e-04 -13.12
6766 CBR1 21_16 0.311 119.67 1.1e-04 -8.61
2919 ZBTB47 3_31 0.893 41.44 1.1e-04 -6.08
10021 ZKSCAN4 6_22 0.331 104.65 1.0e-04 -9.17
5464 PNMT 17_23 0.769 44.10 9.9e-05 -7.23
361 CUL3 2_132 0.831 40.49 9.8e-05 6.44
7130 PM20D1 1_104 0.787 41.60 9.6e-05 6.86
5299 BAHD1 15_14 0.659 48.43 9.3e-05 6.52
8875 CTDSP2 12_36 0.724 43.19 9.1e-05 -3.68Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
10733 CENPW 6_84 0.012 720.28 2.5e-05 -26.87
7786 CATSPER2 15_16 0.848 359.54 8.9e-04 -19.25
3448 WASHC3 12_61 0.000 327.28 3.9e-14 -16.33
7782 CASC4 15_17 0.023 252.81 1.7e-05 16.16
10765 ZDHHC18 1_18 1.000 188.41 5.5e-04 -14.65
2737 TRIM38 6_20 0.000 96.19 8.3e-08 14.49
6765 RUNX1 21_16 0.011 115.78 3.7e-06 14.24
2072 TYK2 19_9 0.000 132.65 2.7e-15 -14.09
5692 ASXL2 2_15 0.002 190.15 1.0e-06 13.95
6235 FBXO4 5_28 0.000 158.28 1.8e-10 13.27
12004 RP11-196G11.2 16_24 0.225 162.03 1.1e-04 -13.12
6064 PTPRJ 11_29 0.002 82.93 5.0e-07 12.76
7653 SLC39A13 11_29 0.003 101.48 1.0e-06 -12.75
1418 IGFALS 16_2 0.000 137.95 1.5e-08 12.31
2068 ICAM5 19_9 0.000 109.96 1.6e-12 -12.28
10508 ADH5 4_66 0.015 140.45 6.1e-06 -12.26
10486 EME2 16_2 0.000 131.73 1.4e-08 -12.26
913 ICAM3 19_9 0.008 271.94 6.4e-06 11.98
4610 ACP2 11_29 0.008 67.55 1.6e-06 -11.88
2953 NRBP1 2_16 0.010 224.18 6.5e-06 -11.84Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.04118973#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
10733 CENPW 6_84 0.012 720.28 2.5e-05 -26.87
7786 CATSPER2 15_16 0.848 359.54 8.9e-04 -19.25
3448 WASHC3 12_61 0.000 327.28 3.9e-14 -16.33
7782 CASC4 15_17 0.023 252.81 1.7e-05 16.16
10765 ZDHHC18 1_18 1.000 188.41 5.5e-04 -14.65
2737 TRIM38 6_20 0.000 96.19 8.3e-08 14.49
6765 RUNX1 21_16 0.011 115.78 3.7e-06 14.24
2072 TYK2 19_9 0.000 132.65 2.7e-15 -14.09
5692 ASXL2 2_15 0.002 190.15 1.0e-06 13.95
6235 FBXO4 5_28 0.000 158.28 1.8e-10 13.27
12004 RP11-196G11.2 16_24 0.225 162.03 1.1e-04 -13.12
6064 PTPRJ 11_29 0.002 82.93 5.0e-07 12.76
7653 SLC39A13 11_29 0.003 101.48 1.0e-06 -12.75
1418 IGFALS 16_2 0.000 137.95 1.5e-08 12.31
2068 ICAM5 19_9 0.000 109.96 1.6e-12 -12.28
10508 ADH5 4_66 0.015 140.45 6.1e-06 -12.26
10486 EME2 16_2 0.000 131.73 1.4e-08 -12.26
913 ICAM3 19_9 0.008 271.94 6.4e-06 11.98
4610 ACP2 11_29 0.008 67.55 1.6e-06 -11.88
2953 NRBP1 2_16 0.010 224.18 6.5e-06 -11.84Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 6_84"
genename region_tag susie_pip mu2 PVE z
2687 HDDC2 6_84 0.009 4.99 1.3e-07 -0.46
2689 NCOA7 6_84 0.011 12.26 4.0e-07 -2.71
2688 HINT3 6_84 0.020 11.85 7.0e-07 -0.67
10733 CENPW 6_84 0.012 720.28 2.5e-05 -26.87
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 15_16"
genename region_tag susie_pip mu2 PVE z
1325 SNAP23 15_16 0.015 14.10 6.2e-07 1.82
9382 LRRC57 15_16 0.015 13.88 5.9e-07 1.92
5030 HAUS2 15_16 0.007 13.17 2.6e-07 -3.01
6785 STARD9 15_16 0.062 29.65 5.4e-06 2.87
5300 CDAN1 15_16 0.010 8.35 2.5e-07 0.73
4064 TTBK2 15_16 0.103 39.58 1.2e-05 -5.26
7829 CCNDBP1 15_16 0.088 37.34 9.6e-06 5.50
1905 TGM5 15_16 0.019 23.02 1.3e-06 -1.44
8115 ADAL 15_16 0.007 61.95 1.2e-06 7.05
8116 LCMT2 15_16 0.007 61.95 1.2e-06 7.05
5034 TUBGCP4 15_16 0.011 9.66 3.0e-07 -1.65
5295 ZSCAN29 15_16 0.239 33.40 2.3e-05 -1.48
7831 MAP1A 15_16 0.037 27.36 2.9e-06 -1.18
7786 CATSPER2 15_16 0.848 359.54 8.9e-04 -19.25
7839 PDIA3 15_16 0.036 18.10 1.9e-06 -1.20
4065 ELL3 15_16 0.040 26.20 3.1e-06 -4.24
5294 SERF2 15_16 0.040 26.20 3.1e-06 -4.24
5291 MFAP1 15_16 0.008 74.88 1.8e-06 -7.79
1323 WDR76 15_16 0.181 34.23 1.8e-05 -3.08
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 12_61"
genename region_tag susie_pip mu2 PVE z
10207 MYBPC1 12_61 0.000 7.67 6.9e-17 -1.30
2647 CHPT1 12_61 0.000 6.96 5.5e-17 -1.01
2648 GNPTAB 12_61 0.000 6.53 5.2e-17 -1.30
4801 DRAM1 12_61 0.000 7.74 6.4e-17 1.51
3448 WASHC3 12_61 0.000 327.28 3.9e-14 -16.33
870 NUP37 12_61 0.001 217.41 9.1e-07 11.14
9811 PARPBP 12_61 0.000 67.37 1.2e-11 7.21
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 15_17"
genename region_tag susie_pip mu2 PVE z
7782 CASC4 15_17 0.023 252.81 1.7e-05 16.16
11335 PATL2 15_17 0.103 23.52 7.1e-06 1.83
7780 B2M 15_17 0.019 9.72 5.4e-07 -0.87
9861 TRIM69 15_17 0.019 10.77 5.8e-07 -1.46
5293 SORD 15_17 0.070 23.47 4.8e-06 -2.27
5042 DUOX1 15_17 0.011 5.04 1.6e-07 -0.17
8498 GATM 15_17 0.011 5.34 1.8e-07 -0.48
8497 SPATA5L1 15_17 0.013 7.00 2.7e-07 1.07
12481 RP11-96O20.5 15_17 0.016 8.50 3.9e-07 0.94
5023 SQRDL 15_17 0.011 5.25 1.7e-07 -0.03
12408 CTD-2306A12.1 15_17 0.011 5.01 1.6e-07 0.40
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_18"
genename region_tag susie_pip mu2 PVE z
3213 SYF2 1_18 0.007 6.09 1.2e-07 0.42
3214 RSRP1 1_18 0.028 20.27 1.7e-06 2.34
9637 TMEM50A 1_18 0.028 20.27 1.7e-06 2.34
9978 RHD 1_18 0.028 20.27 1.7e-06 2.34
10768 TMEM57 1_18 0.016 14.65 6.7e-07 -1.64
10121 RHCE 1_18 0.010 11.57 3.4e-07 -2.15
11243 RP11-70P17.1 1_18 0.007 6.78 1.4e-07 -1.00
3217 MAN1C1 1_18 0.007 6.21 1.2e-07 0.66
7057 SELENON 1_18 0.029 28.71 2.4e-06 -4.03
6659 PAFAH2 1_18 0.006 13.42 2.4e-07 -3.86
6661 TRIM63 1_18 0.021 68.15 4.2e-06 -9.57
8858 PDIK1L 1_18 0.401 75.65 8.9e-05 -9.99
10401 FAM110D 1_18 0.006 10.09 1.8e-07 -2.00
5531 CNKSR1 1_18 0.006 9.87 1.7e-07 1.69
4215 CEP85 1_18 0.006 6.81 1.3e-07 -1.92
6665 UBXN11 1_18 0.009 8.45 2.3e-07 0.39
8205 CD52 1_18 0.010 7.96 2.3e-07 2.18
8964 AIM1L 1_18 0.006 9.49 1.7e-07 -1.23
3219 DHDDS 1_18 0.365 29.71 3.2e-05 -7.49
10674 HMGN2 1_18 0.010 8.17 2.4e-07 -0.41
3222 ARID1A 1_18 0.016 12.48 5.7e-07 -0.73
546 PIGV 1_18 0.006 81.05 1.5e-06 9.82
10765 ZDHHC18 1_18 1.000 188.41 5.5e-04 -14.65
5539 GPN2 1_18 0.324 57.93 5.5e-05 6.54
1254 NUDC 1_18 0.023 36.66 2.4e-06 5.45
| 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
25176 rs164877 1_55 1.000 310.93 9.1e-04 14.93
35085 rs77369503 1_80 1.000 44.18 1.3e-04 -6.59
50792 rs7548045 1_108 1.000 55.79 1.6e-04 -4.14
55217 rs287613 1_116 1.000 472.02 1.4e-03 3.46
55223 rs71180790 1_116 1.000 468.09 1.4e-03 2.98
56183 rs766167074 1_118 1.000 5959.98 1.7e-02 2.75
57456 rs822928 1_122 1.000 52.27 1.5e-04 7.74
71862 rs780093 2_16 1.000 450.72 1.3e-03 24.69
71863 rs6744393 2_16 1.000 128.18 3.7e-04 16.13
86558 rs35641591 2_46 1.000 57.82 1.7e-04 -7.78
146614 rs9854123 3_24 1.000 40.76 1.2e-04 6.29
160874 rs56320121 3_58 1.000 792.88 2.3e-03 -3.10
160890 rs768688512 3_58 1.000 982.88 2.9e-03 -3.54
186021 rs519352 3_105 1.000 84.77 2.5e-04 12.46
186039 rs6445061 3_105 1.000 154.90 4.5e-04 -14.75
188281 rs146797780 3_110 1.000 95146.91 2.8e-01 -5.92
188282 rs7636471 3_110 1.000 95048.89 2.8e-01 -5.66
190098 rs6778003 3_114 1.000 43.22 1.3e-04 -6.08
190131 rs6773553 3_114 1.000 35.51 1.0e-04 4.88
195583 rs114524202 4_4 1.000 37.77 1.1e-04 -6.94
209237 rs12639940 4_32 1.000 83.09 2.4e-04 7.30
209326 rs58932203 4_32 1.000 138.08 4.0e-04 -10.74
211287 rs116419948 4_35 1.000 75.81 2.2e-04 5.68
218275 rs7696472 4_48 1.000 113.22 3.3e-04 -10.48
273263 rs55681913 5_28 1.000 246.47 7.2e-04 15.62
300038 rs329123 5_80 1.000 56.37 1.6e-04 7.99
323857 rs1980449 6_19 1.000 57.71 1.7e-04 8.94
324498 rs6908155 6_21 1.000 38.27 1.1e-04 1.41
350315 rs657536 6_67 1.000 43.24 1.3e-04 -6.95
352546 rs3800231 6_73 1.000 316.00 9.2e-04 18.75
367335 rs12208357 6_103 1.000 144.49 4.2e-04 9.39
367438 rs60425481 6_104 1.000 299.85 8.8e-04 -13.20
369536 rs2323036 6_108 1.000 164.32 4.8e-04 14.90
383999 rs11761979 7_24 1.000 53.00 1.5e-04 -7.18
388934 rs185529878 7_33 1.000 83.25 2.4e-04 7.39
388963 rs1542820 7_34 1.000 227.90 6.7e-04 -17.00
389188 rs2107787 7_34 1.000 238.94 7.0e-04 17.50
389284 rs700752 7_34 1.000 2231.42 6.5e-03 47.41
389382 rs79306382 7_35 1.000 37.20 1.1e-04 -6.34
412812 rs125124 7_80 1.000 474.82 1.4e-03 22.58
421124 rs78609178 7_98 1.000 35.62 1.0e-04 -4.52
432040 rs1495743 8_20 1.000 79.37 2.3e-04 -9.05
443709 rs4738679 8_45 1.000 84.20 2.5e-04 -9.59
472292 rs79531507 9_5 1.000 42.08 1.2e-04 -6.61
472312 rs12552790 9_5 1.000 67.64 2.0e-04 -8.04
472356 rs41303235 9_6 1.000 107.69 3.1e-04 9.73
520025 rs71007692 10_28 1.000 9761.30 2.9e-02 2.95
537043 rs35443777 10_60 1.000 62.75 1.8e-04 -6.24
538918 rs10883563 10_64 1.000 115.24 3.4e-04 10.79
550767 rs11042594 11_2 1.000 399.71 1.2e-03 17.70
550776 rs7481173 11_2 1.000 176.39 5.2e-04 -0.73
550777 rs17885785 11_2 1.000 361.01 1.1e-03 24.68
550778 rs2239681 11_2 1.000 233.48 6.8e-04 -25.38
550779 rs3842762 11_2 1.000 329.70 9.6e-04 -19.28
593936 rs2856322 12_11 1.000 103.37 3.0e-04 -10.04
600924 rs7302975 12_21 1.000 139.46 4.1e-04 12.91
600947 rs7967974 12_22 1.000 46.18 1.3e-04 -8.27
627326 rs80019595 12_74 1.000 302.01 8.8e-04 19.61
627540 rs140184587 12_75 1.000 48.40 1.4e-04 6.47
644315 rs7999449 13_25 1.000 38777.65 1.1e-01 -4.29
644317 rs775834524 13_25 1.000 38857.03 1.1e-01 -4.23
665704 rs2332328 14_3 1.000 49.93 1.5e-04 -6.82
681515 rs13379043 14_34 1.000 75.28 2.2e-04 -8.81
689670 rs12147987 14_52 1.000 66.01 1.9e-04 -4.57
689678 rs12885370 14_52 1.000 69.38 2.0e-04 -4.81
703358 rs4474658 15_28 1.000 67.03 2.0e-04 -11.20
706571 rs876383 15_35 1.000 59.08 1.7e-04 8.12
714429 rs72767924 15_47 1.000 74.13 2.2e-04 5.08
714431 rs9672558 15_47 1.000 79.51 2.3e-04 5.56
714512 rs3743250 15_48 1.000 55.60 1.6e-04 -6.91
716110 rs117544769 16_1 1.000 86.28 2.5e-04 -10.97
716121 rs11248852 16_1 1.000 142.59 4.2e-04 -16.99
716129 rs2076421 16_1 1.000 119.96 3.5e-04 15.41
716421 rs28469124 16_2 1.000 322.97 9.4e-04 19.72
716423 rs9923699 16_2 1.000 320.40 9.4e-04 19.60
736940 rs9931108 16_46 1.000 103.62 3.0e-04 5.65
738348 rs2255451 16_49 1.000 82.44 2.4e-04 -9.42
757332 rs1801689 17_38 1.000 130.48 3.8e-04 11.78
780295 rs77728352 18_32 1.000 41.56 1.2e-04 -6.23
786833 rs77169818 18_46 1.000 77.72 2.3e-04 -8.89
796733 rs73924758 19_22 1.000 46.33 1.4e-04 -5.31
800314 rs814573 19_31 1.000 36.09 1.1e-04 5.88
808117 rs200167482 20_8 1.000 35.29 1.0e-04 -5.77
811528 rs6112780 20_14 1.000 77.99 2.3e-04 -10.08
811604 rs10470054 20_14 1.000 55.99 1.6e-04 8.31
821540 rs79723704 20_34 1.000 42.04 1.2e-04 -6.38
823321 rs6122476 20_37 1.000 35.35 1.0e-04 -5.50
895735 rs4973409 2_136 1.000 1787.93 5.2e-03 -3.46
895736 rs142215640 2_136 1.000 1788.17 5.2e-03 -3.60
910825 rs2307874 3_34 1.000 13619.92 4.0e-02 -4.40
910925 rs56123512 3_34 1.000 13645.97 4.0e-02 -4.16
913570 rs142955295 3_35 1.000 69798.02 2.0e-01 4.29
918774 rs7728690 5_52 1.000 37346.18 1.1e-01 -9.41
918776 rs150854935 5_52 1.000 37784.37 1.1e-01 -9.28
999722 rs773484935 14_36 1.000 5386.07 1.6e-02 -3.04
1028517 rs5388 17_37 1.000 434.58 1.3e-03 22.63
1028943 rs76708468 17_37 1.000 91.88 2.7e-04 13.72
1054836 rs142998071 19_33 1.000 44.04 1.3e-04 6.85
1071298 rs34079499 21_19 1.000 13144.50 3.8e-02 -6.20
1071456 rs55740356 21_19 1.000 11564.95 3.4e-02 -6.65
7369 rs1042114 1_19 0.999 54.68 1.6e-04 -7.84
18208 rs11209239 1_43 0.999 34.85 1.0e-04 5.63
95987 rs3789066 2_66 0.999 35.08 1.0e-04 5.93
218220 rs146674238 4_48 0.999 38.71 1.1e-04 -7.67
275566 rs113088001 5_31 0.999 47.99 1.4e-04 7.38
300176 rs11242237 5_80 0.999 88.90 2.6e-04 -8.00
302190 rs853161 5_84 0.999 45.24 1.3e-04 -6.61
312047 rs2340010 5_104 0.999 33.66 9.8e-05 5.60
372227 rs4719415 7_4 0.999 60.69 1.8e-04 7.92
466154 rs12674961 8_88 0.999 50.89 1.5e-04 -8.89
529532 rs10823504 10_46 0.999 34.14 1.0e-04 5.62
621561 rs882409 12_61 0.999 120.90 3.5e-04 16.43
624322 rs75622376 12_67 0.999 61.61 1.8e-04 7.66
755117 rs11079157 17_32 0.999 40.45 1.2e-04 6.51
800957 rs55975925 19_34 0.999 39.43 1.2e-04 -6.16
1043806 rs12720356 19_9 0.999 147.12 4.3e-04 -14.75
60758 rs150491879 1_129 0.998 34.12 9.9e-05 5.60
175363 rs12489068 3_85 0.998 92.49 2.7e-04 -10.65
326379 rs2524082 6_26 0.998 50.24 1.5e-04 -6.71
371859 rs13226659 7_3 0.998 71.45 2.1e-04 8.62
377950 rs34124255 7_15 0.998 38.24 1.1e-04 -4.46
388955 rs9658238 7_33 0.998 66.32 1.9e-04 9.39
389415 rs7791050 7_35 0.998 36.94 1.1e-04 -6.88
511496 rs60100723 10_12 0.998 38.55 1.1e-04 6.26
564832 rs12797220 11_30 0.998 41.81 1.2e-04 4.67
621493 rs186877434 12_61 0.998 69.84 2.0e-04 -11.39
621618 rs1580715 12_62 0.998 113.80 3.3e-04 -9.65
844249 rs5765672 22_20 0.998 31.94 9.3e-05 -5.23
50840 rs1223802 1_108 0.997 55.90 1.6e-04 -6.76
53047 rs2642420 1_112 0.997 51.21 1.5e-04 9.83
364697 rs9479504 6_100 0.997 78.42 2.3e-04 9.02
1043788 rs34536443 19_9 0.997 95.90 2.8e-04 -8.42
186038 rs28507699 3_105 0.996 150.88 4.4e-04 -10.47
324107 rs9467715 6_20 0.996 46.36 1.3e-04 -2.60
420881 rs7810268 7_98 0.996 36.17 1.1e-04 5.54
800666 rs7249509 19_32 0.996 29.26 8.5e-05 -4.98
842249 rs138703 22_16 0.996 129.16 3.8e-04 -11.01
377953 rs6954572 7_15 0.995 76.63 2.2e-04 -7.97
559007 rs56133711 11_19 0.995 38.84 1.1e-04 -6.15
73491 rs72787520 2_20 0.994 37.76 1.1e-04 -5.31
302256 rs6894302 5_84 0.994 40.90 1.2e-04 5.82
310226 rs2974438 5_100 0.994 260.49 7.6e-04 -17.69
350380 rs7763983 6_67 0.994 33.36 9.7e-05 6.37
374318 rs186587982 7_9 0.994 148.00 4.3e-04 -13.53
762487 rs36000545 17_46 0.994 33.73 9.8e-05 -5.70
39344 rs10913276 1_86 0.993 118.99 3.5e-04 16.90
65142 rs13018091 2_4 0.993 42.94 1.2e-04 -6.64
149522 rs1605068 3_36 0.993 29.73 8.6e-05 5.00
367348 rs1443844 6_103 0.993 116.50 3.4e-04 -6.29
537044 rs3740365 10_60 0.993 56.69 1.6e-04 -5.74
472351 rs7032169 9_6 0.992 36.36 1.1e-04 3.67
529147 rs2305196 10_46 0.992 38.60 1.1e-04 -5.79
656015 rs57684439 13_45 0.992 30.41 8.8e-05 4.33
726971 rs17616063 16_27 0.992 29.75 8.6e-05 -5.05
83936 rs1621048 2_40 0.991 33.64 9.7e-05 -4.94
310234 rs6885027 5_100 0.991 45.52 1.3e-04 8.79
606318 rs117564283 12_33 0.991 33.49 9.7e-05 5.83
621768 rs4764939 12_62 0.991 40.44 1.2e-04 6.25
139356 rs139232179 3_9 0.990 36.51 1.1e-04 5.90
435131 rs11780047 8_26 0.990 35.94 1.0e-04 -5.84
367340 rs4646275 6_103 0.988 38.49 1.1e-04 -5.21
600851 rs113987763 12_21 0.987 158.28 4.6e-04 10.27
705028 rs143717852 15_31 0.987 82.73 2.4e-04 -8.48
811490 rs6136911 20_14 0.987 56.53 1.6e-04 -9.34
71492 rs62127724 2_15 0.985 286.94 8.2e-04 17.32
139977 rs2227998 3_10 0.985 43.54 1.3e-04 6.10
218222 rs34168560 4_48 0.985 122.15 3.5e-04 -13.59
974568 rs10838681 11_29 0.985 84.96 2.4e-04 12.59
559644 rs521371 11_21 0.982 31.86 9.1e-05 -3.50
567901 rs1203614 11_37 0.982 27.34 7.8e-05 4.20
529244 rs11597602 10_46 0.981 31.34 9.0e-05 -4.85
250062 rs17540470 4_109 0.979 33.75 9.7e-05 5.79
536835 rs12355020 10_59 0.979 30.74 8.8e-05 -6.10
409 rs10910028 1_2 0.978 37.79 1.1e-04 5.73
300148 rs35914524 5_80 0.977 32.91 9.4e-05 4.56
367277 rs554987322 6_103 0.977 35.38 1.0e-04 6.28
796732 rs7249790 19_22 0.976 30.68 8.7e-05 -2.65
358125 rs142620810 6_85 0.975 28.70 8.2e-05 5.13
737014 rs112290554 16_46 0.975 68.73 2.0e-04 -9.40
282413 rs77561962 5_45 0.974 33.12 9.4e-05 5.78
621632 rs1874872 12_62 0.974 46.98 1.3e-04 -1.20
415003 rs12155147 7_84 0.973 30.54 8.7e-05 5.40
811607 rs3827963 20_14 0.973 34.53 9.8e-05 -6.07
50797 rs3754140 1_108 0.972 66.31 1.9e-04 6.87
542475 rs12244851 10_70 0.972 33.53 9.5e-05 5.60
741558 rs558760274 17_1 0.972 25.51 7.2e-05 4.74
163623 rs4928057 3_64 0.970 32.05 9.1e-05 -7.36
430739 rs75886735 8_17 0.970 27.79 7.9e-05 4.94
910828 rs55721964 3_34 0.970 13639.42 3.9e-02 -4.25
714515 rs58060839 15_48 0.968 37.32 1.1e-04 -5.24
786959 rs62104512 18_46 0.967 48.55 1.4e-04 -6.88
450410 rs445036 8_57 0.966 56.41 1.6e-04 -7.48
468860 rs13253652 8_92 0.966 28.04 7.9e-05 2.53
576681 rs12795994 11_53 0.966 26.60 7.5e-05 -5.31
716420 rs80253441 16_2 0.964 170.62 4.8e-04 -12.35
555120 rs61885960 11_11 0.960 29.84 8.4e-05 5.10
324285 rs140967207 6_21 0.959 30.57 8.6e-05 5.10
367369 rs75885118 6_104 0.957 29.95 8.4e-05 3.02
369580 rs76523601 6_108 0.957 49.09 1.4e-04 -3.70
30170 rs1730862 1_66 0.956 25.53 7.1e-05 -4.69
377874 rs7802610 7_15 0.956 26.62 7.4e-05 5.19
408194 rs1868757 7_70 0.953 27.12 7.5e-05 5.35
816487 rs6103338 20_27 0.953 31.77 8.8e-05 5.45
195599 rs3748034 4_4 0.952 30.85 8.6e-05 -6.03
779920 rs9953884 18_31 0.952 55.67 1.5e-04 6.80
636740 rs7999704 13_9 0.951 29.55 8.2e-05 -5.10
831653 rs9974208 21_17 0.951 25.15 7.0e-05 4.29
755790 rs12947269 17_34 0.950 27.48 7.6e-05 -5.71
389535 rs13230267 7_35 0.946 31.06 8.6e-05 5.19
721126 rs34967165 16_12 0.946 32.40 8.9e-05 5.36
176062 rs58020426 3_87 0.945 24.82 6.8e-05 -4.30
817216 rs577036133 20_28 0.945 25.94 7.2e-05 4.55
460431 rs2737205 8_78 0.944 136.95 3.8e-04 -9.91
600979 rs11051788 12_22 0.944 32.76 9.0e-05 -6.27
674833 rs10136844 14_21 0.944 27.37 7.5e-05 -4.95
697187 rs3803361 15_13 0.943 25.67 7.1e-05 -4.74
50793 rs340835 1_108 0.942 42.98 1.2e-04 -6.14
703242 rs2414752 15_28 0.942 30.61 8.4e-05 -4.32
747041 rs28489441 17_15 0.942 25.59 7.0e-05 4.35
658859 rs892252 13_51 0.940 25.21 6.9e-05 4.66
428215 rs77304020 8_14 0.939 40.11 1.1e-04 -5.57
536864 rs78382982 10_59 0.939 26.37 7.2e-05 5.10
53039 rs72472375 1_112 0.938 31.85 8.7e-05 6.37
823934 rs2823025 21_2 0.936 25.01 6.8e-05 -4.70
122578 rs10622618 2_120 0.933 32.30 8.8e-05 -5.71
312766 rs62389092 5_105 0.932 24.48 6.7e-05 -4.55
397306 rs11762191 7_47 0.931 57.09 1.6e-04 8.71
514627 rs750689165 10_16 0.931 39.02 1.1e-04 -7.35
736966 rs12934751 16_46 0.931 133.59 3.6e-04 11.08
775573 rs991014 18_24 0.931 34.77 9.5e-05 5.69
895730 rs7592098 2_136 0.931 1736.24 4.7e-03 -3.71
699558 rs12050772 15_20 0.929 56.27 1.5e-04 -7.07
519768 rs2505692 10_27 0.928 24.87 6.7e-05 3.78
176010 rs4683606 3_86 0.927 195.05 5.3e-04 -13.36
999731 rs9989201 14_36 0.927 5388.42 1.5e-02 -3.43
755783 rs8074463 17_34 0.925 29.10 7.9e-05 -5.88
96063 rs2166862 2_66 0.924 30.02 8.1e-05 5.18
329065 rs72880536 6_28 0.923 26.78 7.2e-05 -4.75
576725 rs509723 11_54 0.920 31.80 8.5e-05 -5.29
679975 rs34489253 14_33 0.920 46.34 1.2e-04 -7.04
280781 rs10062008 5_43 0.918 25.58 6.9e-05 4.34
468851 rs56114972 8_92 0.918 24.18 6.5e-05 -3.81
751924 rs17614452 17_26 0.915 27.53 7.4e-05 5.04
815079 rs2246443 20_23 0.915 24.91 6.7e-05 4.15
190061 rs6782470 3_114 0.910 25.75 6.8e-05 4.51
626345 rs149837779 12_73 0.910 24.76 6.6e-05 -4.56
40041 rs4442334 1_89 0.907 44.13 1.2e-04 -6.82
53061 rs10863568 1_112 0.907 152.07 4.0e-04 -14.82
593916 rs12824533 12_11 0.906 26.35 7.0e-05 3.80
185586 rs10653660 3_104 0.903 58.64 1.5e-04 7.76
479557 rs10965488 9_17 0.903 28.80 7.6e-05 4.98
227877 rs1813867 4_66 0.902 32.19 8.5e-05 -6.79
367387 rs315996 6_104 0.902 29.23 7.7e-05 1.15
197315 rs12152650 4_8 0.901 263.30 6.9e-04 13.54
680026 rs3784139 14_33 0.901 31.30 8.2e-05 -6.39
708445 rs72734182 15_38 0.901 25.64 6.7e-05 4.39
796680 rs73019624 19_21 0.899 38.45 1.0e-04 -6.29
464260 rs2648832 8_84 0.898 24.53 6.4e-05 -4.50
769240 rs8093352 18_11 0.897 24.66 6.5e-05 4.28
185440 rs4955590 3_104 0.895 26.70 7.0e-05 -5.25
412591 rs4507692 7_79 0.893 34.98 9.1e-05 -5.67
1043416 rs8105174 19_9 0.893 229.01 6.0e-04 -15.80
352033 rs4515420 6_70 0.888 32.25 8.4e-05 5.30
815708 rs62209440 20_24 0.887 25.30 6.6e-05 -4.64
943179 rs35887778 7_61 0.887 38.46 1.0e-04 6.85
598006 rs74842514 12_18 0.886 32.58 8.4e-05 -5.42
632802 rs4294650 13_2 0.886 54.16 1.4e-04 -7.29
78188 rs2121564 2_28 0.885 26.51 6.9e-05 -4.80
442853 rs71519448 8_44 0.885 50.20 1.3e-04 2.30
231753 rs77893550 4_72 0.881 24.55 6.3e-05 -4.49
167665 rs148695018 3_70 0.880 25.57 6.6e-05 4.53
329272 rs1187117 6_28 0.880 59.74 1.5e-04 7.74
529961 rs780662 10_48 0.880 26.13 6.7e-05 4.65
713605 rs1464445 15_46 0.880 49.51 1.3e-04 -6.81
14621 rs55869368 1_35 0.879 25.07 6.4e-05 -4.48
421803 rs12698259 7_99 0.879 26.20 6.7e-05 3.95
368643 rs777679051 6_106 0.877 30.16 7.7e-05 -5.19
803220 rs11556769 19_37 0.875 27.80 7.1e-05 -4.95
25175 rs146501986 1_55 0.874 259.03 6.6e-04 16.90
723098 rs6497339 16_18 0.872 34.42 8.8e-05 -5.53
7068 rs138012132 1_19 0.871 51.00 1.3e-04 -6.21
96552 rs4849177 2_67 0.870 57.28 1.5e-04 7.63
418746 rs5888418 7_94 0.870 28.59 7.3e-05 5.00
722112 rs35512524 16_15 0.870 27.11 6.9e-05 5.24
704704 rs36120854 15_30 0.868 24.54 6.2e-05 4.28
714567 rs35477848 15_48 0.868 25.49 6.5e-05 -4.01
115565 rs141607132 2_107 0.863 24.77 6.2e-05 4.41
317286 rs2765359 6_7 0.862 36.19 9.1e-05 -4.63
582900 rs75794878 11_67 0.861 33.74 8.5e-05 -5.55
732758 rs71403855 16_38 0.859 26.04 6.5e-05 4.98
369513 rs118014721 6_108 0.853 85.88 2.1e-04 4.80
761061 rs8065893 17_43 0.852 25.34 6.3e-05 4.44
277392 rs12656462 5_35 0.851 38.31 9.5e-05 -5.81
48932 rs12048709 1_106 0.849 27.99 6.9e-05 4.90
317335 rs545632 6_7 0.849 27.60 6.8e-05 -5.66
918780 rs9293511 5_52 0.849 37258.57 9.2e-02 -9.44
359701 rs765215967 6_89 0.848 24.87 6.2e-05 -4.40
139149 rs11128570 3_9 0.847 27.38 6.8e-05 5.33
488583 rs7847368 9_38 0.847 25.17 6.2e-05 -4.49
607640 rs2657880 12_35 0.847 36.41 9.0e-05 -5.97
98744 rs2311597 2_70 0.845 57.75 1.4e-04 7.64
109246 rs834837 2_93 0.845 25.54 6.3e-05 4.57
382511 rs2249325 7_23 0.845 25.64 6.3e-05 4.49
175349 rs940191 3_85 0.844 38.16 9.4e-05 -6.92
352452 rs9400205 6_73 0.844 32.40 8.0e-05 -6.22
585206 rs10892819 11_74 0.842 28.71 7.1e-05 -5.26
788812 rs10408455 19_5 0.842 46.30 1.1e-04 -6.30
67292 rs5829382 2_8 0.841 25.65 6.3e-05 4.62
843711 rs136908 22_20 0.840 28.57 7.0e-05 5.05
989988 rs117104648 11_36 0.840 51.01 1.3e-04 7.19
188411 rs6793063 3_111 0.839 28.19 6.9e-05 4.88
317359 rs9379083 6_7 0.838 50.06 1.2e-04 5.84
639789 rs61630147 13_15 0.838 159.42 3.9e-04 12.88
150673 rs79987842 3_38 0.837 31.28 7.6e-05 -4.74
677441 rs6573307 14_27 0.837 93.03 2.3e-04 10.00
143013 rs17400314 3_17 0.836 26.17 6.4e-05 5.14
438058 rs10087804 8_33 0.832 28.59 6.9e-05 4.98
389220 rs11773764 7_34 0.830 86.99 2.1e-04 12.06
627330 rs2393775 12_74 0.830 73.89 1.8e-04 -12.43
501278 rs569990989 9_63 0.829 24.37 5.9e-05 4.45
820517 rs6127693 20_33 0.829 30.13 7.3e-05 5.95
586666 rs10893498 11_77 0.827 34.07 8.2e-05 -5.75
838959 rs9608723 22_9 0.827 36.39 8.8e-05 -6.39
759471 rs7216472 17_41 0.822 35.63 8.6e-05 -5.77
802824 rs7256521 19_37 0.822 38.54 9.3e-05 -6.01
388915 rs10246245 7_33 0.821 88.11 2.1e-04 7.96
841355 rs5755943 22_14 0.819 56.90 1.4e-04 7.67
956734 rs78509281 9_54 0.818 53.89 1.3e-04 9.52
60692 rs61833239 1_128 0.817 25.71 6.1e-05 -2.13
314657 rs77507057 5_110 0.817 44.58 1.1e-04 6.48
491887 rs1360200 9_45 0.817 28.96 6.9e-05 -5.47
717500 rs76814483 16_6 0.816 88.05 2.1e-04 -9.51
140069 rs1038300 3_10 0.814 25.82 6.1e-05 -4.30
325905 rs1264357 6_26 0.814 80.19 1.9e-04 -8.91
697607 rs138570705 15_17 0.814 287.73 6.8e-04 -17.66
662276 rs1079971 13_59 0.812 25.36 6.0e-05 4.33
864412 rs140604451 1_67 0.812 62.98 1.5e-04 -6.89
466609 rs2315839 8_88 0.810 54.47 1.3e-04 7.50
478454 rs13284903 9_15 0.807 34.19 8.1e-05 5.52
1082434 rs779656515 22_24 0.807 34.85 8.2e-05 -5.70
357920 rs2184968 6_84 0.804 750.12 1.8e-03 27.80
760509 rs2665984 17_42 0.804 24.96 5.9e-05 -4.35
663898 rs143614549 13_62 0.801 36.86 8.6e-05 6.02SNPs 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
188281 rs146797780 3_110 1 95146.91 0.28 -5.92
188282 rs7636471 3_110 1 95048.89 0.28 -5.66
188284 rs6769162 3_110 0 92312.24 0.00 -5.56
188265 rs6807293 3_110 0 84673.08 0.00 -5.50
188253 rs6794252 3_110 0 84575.78 0.00 -5.52
188285 rs9838117 3_110 0 73292.14 0.00 -4.83
913570 rs142955295 3_35 1 69798.02 0.20 4.29
913536 rs9853458 3_35 0 69697.62 0.00 -4.31
913534 rs9876508 3_35 0 69697.49 0.00 -4.31
913535 rs9815766 3_35 0 69696.80 0.00 -4.32
913504 rs7634902 3_35 0 69695.78 0.00 -4.30
913507 rs1049256 3_35 0 69695.71 0.00 -4.30
913501 rs3811696 3_35 0 69695.06 0.00 -4.32
913502 rs3811695 3_35 0 69694.80 0.00 -4.31
913500 rs4855850 3_35 0 69693.41 0.00 -4.32
913486 rs3749240 3_35 0 69692.72 0.00 -4.31
913493 rs34614773 3_35 0 69692.26 0.00 -4.32
913460 rs1491986 3_35 0 69690.88 0.00 -4.32
913492 rs11130219 3_35 0 69688.60 0.00 -4.33
913624 rs9871654 3_35 0 69687.16 0.00 4.30
913615 rs13063621 3_35 0 69687.09 0.00 4.29
913608 rs9814765 3_35 0 69687.08 0.00 4.29
913609 rs11130221 3_35 0 69687.08 0.00 4.29
913595 rs34451146 3_35 0 69687.04 0.00 4.29
913563 rs7634886 3_35 0 69686.37 0.00 4.30
913596 rs57648519 3_35 0 69686.34 0.00 4.31
913537 rs7374277 3_35 0 69686.05 0.00 -4.31
913538 rs7374183 3_35 0 69685.33 0.00 -4.33
913455 rs6785549 3_35 0 69684.72 0.00 -4.33
913586 rs6446295 3_35 0 69684.39 0.00 4.30
913491 rs11130218 3_35 0 69684.29 0.00 -4.31
913581 rs7431106 3_35 0 69683.70 0.00 4.31
913567 rs9865480 3_35 0 69679.73 0.00 4.30
913477 rs12381242 3_35 0 69679.00 0.00 -4.31
913568 rs60205400 3_35 0 69678.69 0.00 4.29
913572 rs6809431 3_35 0 69678.51 0.00 4.29
913575 rs9859153 3_35 0 69678.38 0.00 4.30
913590 rs6766836 3_35 0 69678.37 0.00 4.30
913565 rs9882639 3_35 0 69677.84 0.00 4.29
913468 rs11716575 3_35 0 69675.68 0.00 -4.32
913469 rs11709680 3_35 0 69675.67 0.00 -4.32
913480 rs4855862 3_35 0 69675.16 0.00 -4.32
913510 rs10632976 3_35 0 69672.98 0.00 -4.34
913467 rs3749241 3_35 0 69667.91 0.00 -4.32
913553 rs9855505 3_35 0 69665.05 0.00 -4.31
913549 rs7372730 3_35 0 69664.86 0.00 -4.31
913548 rs7372725 3_35 0 69664.16 0.00 -4.32
913544 rs7429353 3_35 0 69663.98 0.00 -4.32
913472 rs4855841 3_35 0 69663.59 0.00 -4.32
913551 rs9872864 3_35 0 69655.08 0.00 -4.32SNPs 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
188281 rs146797780 3_110 1.000 95146.91 0.2800 -5.92
188282 rs7636471 3_110 1.000 95048.89 0.2800 -5.66
913570 rs142955295 3_35 1.000 69798.02 0.2000 4.29
644315 rs7999449 13_25 1.000 38777.65 0.1100 -4.29
644317 rs775834524 13_25 1.000 38857.03 0.1100 -4.23
918774 rs7728690 5_52 1.000 37346.18 0.1100 -9.41
918776 rs150854935 5_52 1.000 37784.37 0.1100 -9.28
918780 rs9293511 5_52 0.849 37258.57 0.0920 -9.44
918762 rs13167261 5_52 0.489 37248.08 0.0530 -9.50
918763 rs13167262 5_52 0.489 37248.08 0.0530 -9.50
910825 rs2307874 3_34 1.000 13619.92 0.0400 -4.40
910925 rs56123512 3_34 1.000 13645.97 0.0400 -4.16
910828 rs55721964 3_34 0.970 13639.42 0.0390 -4.25
1071298 rs34079499 21_19 1.000 13144.50 0.0380 -6.20
1071456 rs55740356 21_19 1.000 11564.95 0.0340 -6.65
520025 rs71007692 10_28 1.000 9761.30 0.0290 2.95
1071262 rs2836974 21_19 0.742 12992.25 0.0280 -6.12
56183 rs766167074 1_118 1.000 5959.98 0.0170 2.75
999722 rs773484935 14_36 1.000 5386.07 0.0160 -3.04
999731 rs9989201 14_36 0.927 5388.42 0.0150 -3.43
520022 rs9299760 10_28 0.504 9741.00 0.0140 2.94
520031 rs2472183 10_28 0.500 9745.42 0.0140 2.91
644312 rs9537143 13_25 0.126 38625.81 0.0140 4.38
1071316 rs35560196 21_19 0.372 12993.02 0.0140 -6.10
520034 rs11011452 10_28 0.395 9745.76 0.0110 2.89
1071299 rs34578707 21_19 0.271 12989.70 0.0100 -6.09
520024 rs2474565 10_28 0.342 9745.18 0.0097 2.90
1071312 rs77090950 21_19 0.226 12992.71 0.0086 -6.09
389284 rs700752 7_34 1.000 2231.42 0.0065 47.41
56181 rs2486737 1_118 0.298 5992.99 0.0052 2.27
895735 rs4973409 2_136 1.000 1787.93 0.0052 -3.46
895736 rs142215640 2_136 1.000 1788.17 0.0052 -3.60
999739 rs12589638 14_36 0.324 5371.85 0.0051 -3.48
56182 rs971534 1_118 0.282 5992.96 0.0049 2.27
895730 rs7592098 2_136 0.931 1736.24 0.0047 -3.71
644308 rs9527399 13_25 0.036 38621.74 0.0041 4.38
56189 rs2248646 1_118 0.208 5990.93 0.0036 2.28
644307 rs7337153 13_25 0.032 38741.77 0.0036 -4.27
644310 rs9527401 13_25 0.029 38621.92 0.0032 4.38
644311 rs9597193 13_25 0.029 38622.15 0.0032 4.38
56190 rs2211176 1_118 0.169 5991.06 0.0030 2.27
56191 rs2790882 1_118 0.169 5991.06 0.0030 2.27
56177 rs2790891 1_118 0.167 5992.38 0.0029 2.26
56178 rs2491405 1_118 0.167 5992.38 0.0029 2.26
160890 rs768688512 3_58 1.000 982.88 0.0029 -3.54
999720 rs61990327 14_36 0.178 5379.55 0.0028 -3.34
56180 rs10489611 1_118 0.141 5992.65 0.0025 2.26
160874 rs56320121 3_58 1.000 792.88 0.0023 -3.10
644303 rs9537123 13_25 0.021 38592.69 0.0023 4.39
56174 rs2256908 1_118 0.123 5992.25 0.0022 2.26SNPs 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
389284 rs700752 7_34 1.000 2231.42 6.5e-03 47.41
389283 rs1917609 7_34 0.000 1617.86 1.6e-17 -39.95
389273 rs7801650 7_34 0.000 1541.61 1.2e-17 -39.07
389276 rs7782135 7_34 0.000 1540.42 1.2e-17 -39.06
389274 rs7788438 7_34 0.000 1538.45 1.2e-17 -39.03
389267 rs35692095 7_34 0.000 1518.73 9.8e-18 -38.79
389269 rs4724488 7_34 0.000 1519.47 1.0e-17 -38.78
621545 rs5742678 12_61 0.531 536.29 8.3e-04 -29.02
621537 rs1520222 12_61 0.469 535.73 7.3e-04 -28.95
357920 rs2184968 6_84 0.804 750.12 1.8e-03 27.80
357918 rs4897179 6_84 0.190 747.93 4.1e-04 27.76
357921 rs1361109 6_84 0.009 739.85 1.9e-05 27.64
357923 rs4895808 6_84 0.007 738.77 1.4e-05 27.62
357924 rs1844594 6_84 0.006 737.69 1.2e-05 27.60
357928 rs9398810 6_84 0.005 736.20 9.9e-06 27.57
357929 rs9401885 6_84 0.005 736.86 1.1e-05 27.57
357926 rs9372839 6_84 0.004 732.19 8.0e-06 27.50
357912 rs2326387 6_84 0.003 715.00 6.6e-06 27.14
357915 rs1361262 6_84 0.003 715.18 6.7e-06 27.14
357911 rs9375435 6_84 0.003 706.14 6.5e-06 26.97
357934 rs6921183 6_84 0.011 636.04 2.1e-05 25.91
357935 rs9401890 6_84 0.011 634.84 2.0e-05 25.89
357936 rs9375448 6_84 0.011 632.88 2.0e-05 25.85
357942 rs9491653 6_84 0.008 615.94 1.4e-05 25.55
357941 rs4629707 6_84 0.007 614.13 1.2e-05 25.53
357940 rs7738836 6_84 0.007 613.90 1.2e-05 25.52
357944 rs9375449 6_84 0.007 614.30 1.3e-05 25.52
357946 rs4895813 6_84 0.007 613.83 1.3e-05 25.51
357949 rs11154367 6_84 0.007 614.07 1.3e-05 25.51
357950 rs853987 6_84 0.007 609.77 1.2e-05 -25.44
550778 rs2239681 11_2 1.000 233.48 6.8e-04 -25.38
621539 rs6539035 12_61 0.000 472.52 4.2e-15 -25.18
621546 rs6539036 12_61 0.000 471.52 3.5e-15 -25.16
621543 rs4764696 12_61 0.000 471.02 3.2e-15 -25.14
357932 rs6925689 6_84 0.007 591.70 1.2e-05 25.05
389285 rs856541 7_34 0.000 717.33 1.7e-12 24.79
71862 rs780093 2_16 1.000 450.72 1.3e-03 24.69
550777 rs17885785 11_2 1.000 361.01 1.1e-03 24.68
357951 rs1101563 6_84 0.006 571.38 9.2e-06 -24.62
357954 rs979197 6_84 0.005 569.64 8.9e-06 -24.58
357955 rs1015446 6_84 0.005 566.14 8.6e-06 -24.51
389278 rs856586 7_34 0.000 652.44 3.4e-15 24.07
1028517 rs5388 17_37 1.000 434.58 1.3e-03 22.63
412812 rs125124 7_80 1.000 474.82 1.4e-03 22.58
389287 rs2204413 7_34 0.000 536.23 3.5e-13 -21.53
389292 rs1357901 7_34 0.000 536.20 3.6e-13 -21.53
39271 rs1506779 1_86 0.331 247.95 2.4e-04 21.26
39269 rs10913207 1_86 0.387 248.31 2.8e-04 21.25
39265 rs6671048 1_86 0.174 246.07 1.3e-04 21.20
39263 rs11806613 1_86 0.108 244.82 7.8e-05 21.14Gene 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] 21if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
Term Overlap
1 transforming growth factor beta binding (GO:0050431) 2/24
Adjusted.P.value Genes
1 0.01371817 TGFBR1;VASNACTR1B gene(s) from the input list not found in DisGeNET CURATEDZBTB47 gene(s) from the input list not found in DisGeNET CURATEDBEND3 gene(s) from the input list not found in DisGeNET CURATEDTTLL12 gene(s) from the input list not found in DisGeNET CURATEDGTF2H1 gene(s) from the input list not found in DisGeNET CURATEDZDHHC18 gene(s) from the input list not found in DisGeNET CURATEDNHSL1 gene(s) from the input list not found in DisGeNET CURATEDAES gene(s) from the input list not found in DisGeNET CURATED Description FDR
14 Fucosidase Deficiency Disease 0.01030715
24 Leukemia, T-Cell, Chronic 0.01030715
52 Fucosidosis Type I 0.01030715
53 Fucosidosis Type II 0.01030715
56 Multiple self-healing epithelioma of Ferguson-Smith 0.01030715
60 Enteropathy-Associated T-Cell Lymphoma 0.01030715
64 Multiple self-healing squamous epithelioma 0.01030715
73 Leukemia, Large Granular Lymphocytic 0.01030715
77 Laron syndrome type 2 0.01030715
78 Leukemia, Natural Killer Cell Large Granular Lymphocytic 0.01030715
Ratio BgRatio
14 1/13 1/9703
24 1/13 1/9703
52 1/13 1/9703
53 1/13 1/9703
56 1/13 1/9703
60 1/13 1/9703
64 1/13 1/9703
73 1/13 1/9703
77 1/13 1/9703
78 1/13 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULL
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.0.0
[5] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.57.0
[3] fs_1.3.1 bit64_4.0.5
[5] doParallel_1.0.16 progress_1.2.2
[7] httr_1.4.1 rprojroot_2.0.2
[9] GenomeInfoDb_1.20.0 doRNG_1.8.2
[11] tools_3.6.1 utf8_1.2.1
[13] R6_2.5.0 DBI_1.1.1
[15] BiocGenerics_0.30.0 colorspace_1.4-1
[17] withr_2.4.1 tidyselect_1.1.0
[19] prettyunits_1.0.2 bit_4.0.4
[21] curl_3.3 compiler_3.6.1
[23] git2r_0.26.1 Biobase_2.44.0
[25] DelayedArray_0.10.0 rtracklayer_1.44.0
[27] labeling_0.3 scales_1.1.0
[29] readr_1.4.0 apcluster_1.4.8
[31] stringr_1.4.0 digest_0.6.20
[33] Rsamtools_2.0.0 svglite_1.2.2
[35] rmarkdown_1.13 XVector_0.24.0
[37] pkgconfig_2.0.3 htmltools_0.3.6
[39] fastmap_1.1.0 BSgenome_1.52.0
[41] rlang_0.4.11 RSQLite_2.2.7
[43] generics_0.0.2 farver_2.1.0
[45] jsonlite_1.6 BiocParallel_1.18.0
[47] dplyr_1.0.7 VariantAnnotation_1.30.1
[49] RCurl_1.98-1.1 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.1 Matrix_1.2-18
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.22.1 fansi_0.5.0
[57] gdtools_0.1.9 lifecycle_1.0.0
[59] stringi_1.4.3 whisker_0.3-2
[61] yaml_2.2.0 SummarizedExperiment_1.14.1
[63] zlibbioc_1.30.0 plyr_1.8.4
[65] grid_3.6.1 blob_1.2.1
[67] parallel_3.6.1 promises_1.0.1
[69] crayon_1.4.1 lattice_0.20-38
[71] Biostrings_2.52.0 GenomicFeatures_1.36.3
[73] hms_1.1.0 knitr_1.23
[75] pillar_1.6.1 igraph_1.2.4.1
[77] GenomicRanges_1.36.0 rjson_0.2.20
[79] rngtools_1.5 codetools_0.2-16
[81] reshape2_1.4.3 biomaRt_2.40.1
[83] stats4_3.6.1 XML_3.98-1.20
[85] glue_1.4.2 evaluate_0.14
[87] data.table_1.14.0 foreach_1.5.1
[89] vctrs_0.3.8 httpuv_1.5.1
[91] gtable_0.3.0 purrr_0.3.4
[93] assertthat_0.2.1 cachem_1.0.5
[95] xfun_0.8 later_0.8.0
[97] tibble_3.1.2 iterators_1.0.13
[99] GenomicAlignments_1.20.1 AnnotationDbi_1.46.0
[101] memoise_2.0.0 IRanges_2.18.1
[103] workflowr_1.6.2 ellipsis_0.3.2