Urea (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 Urea (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-30670_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0143107432 0.0001871761 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.615354 12.971000 #report sample size
print(sample_size)[1] 344052#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.003975928 0.061374183 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02276007 0.59631662Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
5012 TRIM29 11_72 0.974 27.75 7.9e-05 5.21
5440 ANAPC11 17_46 0.968 22.93 6.5e-05 -4.49
982 CDC14A 1_61 0.960 34.09 9.5e-05 -5.99
6598 NRG1 8_31 0.890 22.74 5.9e-05 -4.58
6278 ZNF547 19_39 0.890 21.07 5.4e-05 4.38
5337 IQGAP1 15_43 0.883 30.59 7.9e-05 5.54
9002 WDR25 14_52 0.798 20.87 4.8e-05 -4.15
1863 CPPED1 16_13 0.793 27.67 6.4e-05 5.34
10459 HOXA4 7_23 0.790 24.70 5.7e-05 5.13
4564 PSRC1 1_67 0.778 18.98 4.3e-05 3.81
10505 UGT2B17 4_48 0.761 21.03 4.7e-05 4.47
6756 STC1 8_24 0.758 32.72 7.2e-05 -3.24
6404 PITPNC1 17_39 0.751 21.48 4.7e-05 4.35
8175 RAB24 5_106 0.730 31.56 6.7e-05 -6.62
9518 LINC00334 21_23 0.730 21.53 4.6e-05 -3.92
12342 TBC1D8-AS1 2_58 0.712 20.62 4.3e-05 -4.17
642 PRKCQ 10_7 0.706 23.41 4.8e-05 4.03
1043 NFE2L1 17_28 0.700 26.37 5.4e-05 -4.69
3752 KCNK17 6_30 0.691 22.86 4.6e-05 -4.23Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
168 SPRTN 1_118 0.000 5593.15 1.6e-16 4.83
3138 EXOC8 1_118 0.000 4019.99 0.0e+00 3.84
3232 RCN2 15_36 0.000 2381.27 0.0e+00 0.49
4687 TMEM60 7_49 0.000 2379.61 0.0e+00 -7.28
4733 AHI1 6_89 0.000 1260.30 0.0e+00 -1.51
5304 ETFA 15_36 0.000 988.58 0.0e+00 0.45
3140 TSNAX 1_118 0.000 770.24 0.0e+00 0.32
10381 ZGPAT 20_38 0.000 630.83 8.7e-07 2.49
1699 ARFRP1 20_38 0.000 569.33 4.7e-08 -0.69
5303 PSTPIP1 15_36 0.000 482.86 3.0e-18 6.33
10436 STMN3 20_38 0.000 482.64 3.9e-07 2.60
7145 DISC1 1_118 0.000 464.73 0.0e+00 -0.90
11094 APTR 7_49 0.001 450.59 1.1e-06 -3.43
5818 RPL37 5_27 0.043 415.37 5.2e-05 22.88
8411 TRMT61B 2_19 0.019 349.02 1.9e-05 -3.68
11039 PPP1CB 2_19 0.010 340.12 1.0e-05 3.43
1694 GMEB2 20_38 0.001 289.36 6.0e-07 -2.92
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
2782 C7 5_27 0.022 225.67 1.5e-05 -16.92
8177 THBS3 1_77 0.177 221.81 1.1e-04 21.52Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
5657 ACP1 2_1 0.443 95.71 1.2e-04 -10.53
8177 THBS3 1_77 0.177 221.81 1.1e-04 21.52
982 CDC14A 1_61 0.960 34.09 9.5e-05 -5.99
5012 TRIM29 11_72 0.974 27.75 7.9e-05 5.21
5337 IQGAP1 15_43 0.883 30.59 7.9e-05 5.54
6756 STC1 8_24 0.758 32.72 7.2e-05 -3.24
11634 GSTA2 6_39 0.352 66.09 6.8e-05 -8.63
8175 RAB24 5_106 0.730 31.56 6.7e-05 -6.62
5440 ANAPC11 17_46 0.968 22.93 6.5e-05 -4.49
1863 CPPED1 16_13 0.793 27.67 6.4e-05 5.34
6598 NRG1 8_31 0.890 22.74 5.9e-05 -4.58
10459 HOXA4 7_23 0.790 24.70 5.7e-05 5.13
10905 PDE7A 8_50 0.488 39.80 5.6e-05 -5.60
1043 NFE2L1 17_28 0.700 26.37 5.4e-05 -4.69
6278 ZNF547 19_39 0.890 21.07 5.4e-05 4.38
5194 ZCRB1 12_27 0.482 37.92 5.3e-05 -6.39
3804 OPRL1 20_38 0.482 37.78 5.3e-05 4.51
5818 RPL37 5_27 0.043 415.37 5.2e-05 22.88
897 MCM6 2_80 0.502 34.89 5.1e-05 -5.66Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
5818 RPL37 5_27 0.043 415.37 5.2e-05 22.88
8177 THBS3 1_77 0.177 221.81 1.1e-04 21.52
2782 C7 5_27 0.022 225.67 1.5e-05 -16.92
8670 MTX1 1_77 0.002 120.79 6.9e-07 -15.53
9050 FBXO46 19_32 0.036 220.94 2.3e-05 -15.44
2097 RASIP1 19_33 0.023 109.51 7.3e-06 -12.77
9034 MAMSTR 19_33 0.009 84.70 2.3e-06 11.55
4375 PRKAA1 5_27 0.034 97.72 9.5e-06 -11.01
8813 MSL2 3_84 0.093 79.92 2.2e-05 -10.57
5657 ACP1 2_1 0.443 95.71 1.2e-04 -10.53
7865 FBXO22 15_35 0.150 42.97 1.9e-05 9.88
2369 GOSR2 17_27 0.007 87.20 1.7e-06 9.52
8242 NRG4 15_35 0.006 87.70 1.4e-06 -9.50
8173 LMAN2 5_106 0.015 38.08 1.6e-06 9.33
10224 SEMA4A 1_77 0.023 65.14 4.3e-06 -8.70
11634 GSTA2 6_39 0.352 66.09 6.8e-05 -8.63
834 PPP2R3A 3_84 0.081 56.46 1.3e-05 -8.44
11629 GSTA1 6_39 0.047 67.28 9.2e-06 -8.33
5740 CNOT9 2_129 0.102 53.49 1.6e-05 7.98Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.0126183#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
5818 RPL37 5_27 0.043 415.37 5.2e-05 22.88
8177 THBS3 1_77 0.177 221.81 1.1e-04 21.52
2782 C7 5_27 0.022 225.67 1.5e-05 -16.92
8670 MTX1 1_77 0.002 120.79 6.9e-07 -15.53
9050 FBXO46 19_32 0.036 220.94 2.3e-05 -15.44
2097 RASIP1 19_33 0.023 109.51 7.3e-06 -12.77
9034 MAMSTR 19_33 0.009 84.70 2.3e-06 11.55
4375 PRKAA1 5_27 0.034 97.72 9.5e-06 -11.01
8813 MSL2 3_84 0.093 79.92 2.2e-05 -10.57
5657 ACP1 2_1 0.443 95.71 1.2e-04 -10.53
7865 FBXO22 15_35 0.150 42.97 1.9e-05 9.88
2369 GOSR2 17_27 0.007 87.20 1.7e-06 9.52
8242 NRG4 15_35 0.006 87.70 1.4e-06 -9.50
8173 LMAN2 5_106 0.015 38.08 1.6e-06 9.33
10224 SEMA4A 1_77 0.023 65.14 4.3e-06 -8.70
11634 GSTA2 6_39 0.352 66.09 6.8e-05 -8.63
834 PPP2R3A 3_84 0.081 56.46 1.3e-05 -8.44
11629 GSTA1 6_39 0.047 67.28 9.2e-06 -8.33
5740 CNOT9 2_129 0.102 53.49 1.6e-05 7.98Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 1_77"
genename region_tag susie_pip mu2 PVE z
7201 PMVK 1_77 0.020 37.48 2.2e-06 -3.92
7202 PBXIP1 1_77 0.011 32.33 1.1e-06 -3.71
6909 SHC1 1_77 0.001 6.04 1.6e-08 2.05
8673 CKS1B 1_77 0.001 6.14 1.6e-08 -2.06
6907 ZBTB7B 1_77 0.002 10.35 6.4e-08 0.04
7204 DCST2 1_77 0.001 21.59 9.3e-08 -0.61
7205 DCST1 1_77 0.001 21.59 9.3e-08 0.61
5634 ADAM15 1_77 0.001 9.94 2.7e-08 2.78
5644 EFNA3 1_77 0.002 8.44 4.0e-08 0.34
8179 EFNA1 1_77 0.001 13.41 3.7e-08 1.08
8178 SLC50A1 1_77 0.002 20.57 1.3e-07 -3.57
8670 MTX1 1_77 0.002 120.79 6.9e-07 -15.53
9813 MUC1 1_77 1.000 282.14 8.2e-04 -23.03
8177 THBS3 1_77 0.177 221.81 1.1e-04 21.52
9103 GBA 1_77 0.001 7.23 2.4e-08 1.39
6918 FAM189B 1_77 0.001 7.97 2.1e-08 -3.99
5649 HCN3 1_77 0.002 25.96 1.4e-07 6.92
6917 FDPS 1_77 0.002 23.46 1.1e-07 6.50
4425 DAP3 1_77 0.001 5.99 1.8e-08 1.68
7207 YY1AP1 1_77 0.002 15.37 1.1e-07 4.33
4434 SYT11 1_77 0.001 29.77 7.7e-08 7.31
5648 RIT1 1_77 0.002 20.55 1.4e-07 3.17
4426 KIAA0907 1_77 0.007 17.38 3.6e-07 -0.28
3099 ARHGEF2 1_77 0.001 15.32 4.6e-08 5.04
7227 SSR2 1_77 0.001 7.74 2.6e-08 1.46
3100 LAMTOR2 1_77 0.001 5.44 1.6e-08 -2.18
4430 RAB25 1_77 0.001 21.16 8.3e-08 4.74
10224 SEMA4A 1_77 0.023 65.14 4.3e-06 -8.70
6920 PMF1 1_77 0.001 5.19 1.4e-08 0.31
11586 BGLAP 1_77 0.010 23.84 6.7e-07 -1.73
6919 PAQR6 1_77 0.001 9.07 3.8e-08 -0.77
7226 TMEM79 1_77 0.001 9.55 3.1e-08 -1.88
10714 SMG5 1_77 0.001 10.90 4.0e-08 -2.07
10641 GLMP 1_77 0.001 11.00 4.1e-08 -2.08
7224 TSACC 1_77 0.002 9.48 6.4e-08 0.45
7225 CCT3 1_77 0.001 5.27 1.6e-08 -1.13
3101 MEF2D 1_77 0.002 10.65 5.2e-08 -1.19
9652 IQGAP3 1_77 0.001 4.64 1.2e-08 -0.21
10047 TTC24 1_77 0.006 18.63 3.4e-07 -2.09
7210 NAXE 1_77 0.001 7.17 2.7e-08 0.90
4428 BCAN 1_77 0.004 15.40 1.6e-07 -1.76
5586 RRNAD1 1_77 0.001 5.37 1.6e-08 -0.53
5588 ISG20L2 1_77 0.001 5.37 1.6e-08 0.53
5590 HDGF 1_77 0.009 23.95 6.0e-07 2.24
10592 NTRK1 1_77 0.005 18.99 2.8e-07 -1.97
314 SH2D2A 1_77 0.001 6.84 2.4e-08 -0.80
10039 PEAR1 1_77 0.006 16.84 3.1e-07 -2.10
4429 ARHGEF11 1_77 0.001 4.54 1.2e-08 0.02
12235 RP11-85G21.3 1_77 0.001 4.64 1.2e-08 -0.21
5585 FCRL5 1_77 0.001 4.48 1.2e-08 -0.03
6928 FCRL3 1_77 0.001 4.72 1.3e-08 -0.24
4432 FCRL2 1_77 0.001 6.91 2.3e-08 0.76
7243 FCRL1 1_77 0.001 8.13 3.1e-08 0.94
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 5_27"
genename region_tag susie_pip mu2 PVE z
2832 TTC33 5_27 0.061 30.20 5.4e-06 5.06
4375 PRKAA1 5_27 0.034 97.72 9.5e-06 -11.01
5818 RPL37 5_27 0.043 415.37 5.2e-05 22.88
4376 CARD6 5_27 0.027 7.32 5.8e-07 1.19
2782 C7 5_27 0.022 225.67 1.5e-05 -16.92
1062 OXCT1 5_27 0.033 8.43 8.1e-07 1.17
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_32"
genename region_tag susie_pip mu2 PVE z
9085 GPR4 19_32 0.023 8.52 5.7e-07 -1.00
177 GIPR 19_32 0.017 5.96 3.0e-07 0.66
207 QPCTL 19_32 0.017 6.91 3.5e-07 -0.99
9050 FBXO46 19_32 0.036 220.94 2.3e-05 -15.44
1996 DMPK 19_32 0.019 8.91 4.9e-07 -2.52
9850 DMWD 19_32 0.015 5.21 2.3e-07 0.15
3832 SYMPK 19_32 0.043 46.58 5.9e-06 6.94
8356 IRF2BP1 19_32 0.017 8.95 4.5e-07 -1.60
8976 MYPOP 19_32 0.028 14.71 1.2e-06 -3.02
2011 CCDC61 19_32 0.017 9.16 4.4e-07 1.91
147 PGLYRP1 19_32 0.027 10.61 8.2e-07 -1.49
3715 HIF3A 19_32 0.182 25.42 1.3e-05 2.67
208 PPP5C 19_32 0.026 9.79 7.3e-07 -1.73
10871 PNMAL2 19_32 0.051 15.45 2.3e-06 2.14
6827 CALM3 19_32 0.028 9.85 8.1e-07 -0.96
6826 PTGIR 19_32 0.027 9.61 7.6e-07 -1.00
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_33"
genename region_tag susie_pip mu2 PVE z
2050 PRKD2 19_33 0.012 7.30 2.5e-07 0.98
1257 STRN4 19_33 0.023 12.91 8.5e-07 -1.50
9389 FKRP 19_33 0.012 7.86 2.8e-07 0.96
400 AP2S1 19_33 0.013 8.79 3.3e-07 1.21
6825 ARHGAP35 19_33 0.011 7.07 2.2e-07 0.96
5502 SAE1 19_33 0.009 5.02 1.3e-07 -0.20
2055 BBC3 19_33 0.008 4.49 1.1e-07 0.06
2053 CCDC9 19_33 0.084 24.84 6.1e-06 2.66
11894 INAFM1 19_33 0.010 6.19 1.8e-07 -0.47
4639 C5AR2 19_33 0.056 21.19 3.4e-06 -2.21
4635 DHX34 19_33 0.009 5.20 1.3e-07 0.73
2077 MEIS3 19_33 0.222 32.32 2.1e-05 3.05
2074 NAPA 19_33 0.009 4.99 1.3e-07 0.28
3238 ZNF541 19_33 0.015 9.54 4.2e-07 1.13
572 GLTSCR1 19_33 0.008 4.73 1.2e-07 -0.19
294 EHD2 19_33 0.019 12.00 6.5e-07 1.52
2066 GLTSCR2 19_33 0.008 4.63 1.1e-07 -0.14
2073 SULT2A1 19_33 0.008 4.64 1.1e-07 -0.35
2089 PLA2G4C 19_33 0.008 4.51 1.1e-07 0.21
2086 LIG1 19_33 0.008 4.68 1.1e-07 0.38
9808 C19orf68 19_33 0.009 5.96 1.6e-07 -0.81
2091 CABP5 19_33 0.008 4.60 1.1e-07 0.49
2085 CARD8 19_33 0.011 6.91 2.2e-07 -0.68
5501 EMP3 19_33 0.014 8.23 3.3e-07 -0.31
2084 CCDC114 19_33 0.010 5.82 1.7e-07 -0.24
2081 GRWD1 19_33 0.081 26.93 6.3e-06 3.41
2080 CYTH2 19_33 0.105 30.15 9.2e-06 -3.73
9493 KCNJ14 19_33 0.023 15.82 1.1e-06 -2.72
5504 LMTK3 19_33 0.021 15.98 9.9e-07 -2.52
1173 SULT2B1 19_33 0.009 4.96 1.3e-07 0.07
573 SPHK2 19_33 0.022 25.84 1.6e-06 4.77
574 CA11 19_33 0.016 24.69 1.2e-06 4.93
5503 NTN5 19_33 0.048 43.71 6.1e-06 6.41
9034 MAMSTR 19_33 0.009 84.70 2.3e-06 11.55
2097 RASIP1 19_33 0.023 109.51 7.3e-06 -12.77
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 3_84"
genename region_tag susie_pip mu2 PVE z
834 PPP2R3A 3_84 0.081 56.46 1.3e-05 -8.44
8813 MSL2 3_84 0.093 79.92 2.2e-05 -10.57
2863 PCCB 3_84 0.038 25.43 2.8e-06 5.32
3234 STAG1 3_84 0.037 6.24 6.6e-07 -1.26
6668 NCK1 3_84 0.073 16.75 3.5e-06 -3.19
| 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
58111 rs766167074 1_118 1.000 6468.79 1.9e-02 -4.11
73790 rs780093 2_16 1.000 87.35 2.5e-04 -10.49
74091 rs569546056 2_19 1.000 610.21 1.8e-03 -2.90
93466 rs4441469 2_54 1.000 35.89 1.0e-04 6.44
99000 rs4849177 2_67 1.000 100.40 2.9e-04 10.12
101070 rs141849010 2_69 1.000 35.79 1.0e-04 5.91
117266 rs847164 2_106 1.000 55.19 1.6e-04 9.07
126634 rs11887861 2_124 1.000 75.01 2.2e-04 7.60
146663 rs7619139 3_18 1.000 79.48 2.3e-04 9.30
159086 rs13066345 3_45 1.000 156.73 4.6e-04 12.30
195357 rs16862782 3_115 1.000 362.07 1.1e-03 -21.08
206177 rs62411644 4_17 1.000 35.79 1.0e-04 -5.87
213877 rs12639940 4_32 1.000 58.67 1.7e-04 8.08
214239 rs11729899 4_33 1.000 49.37 1.4e-04 7.45
275501 rs11740818 5_23 1.000 50.96 1.5e-04 -7.09
312134 rs17645325 5_93 1.000 36.48 1.1e-04 -6.45
366476 rs199804242 6_89 1.000 6095.57 1.8e-02 3.25
376593 rs300143 6_108 1.000 224.35 6.5e-04 -15.74
378905 rs78148157 7_3 1.000 230.25 6.7e-04 -12.26
378906 rs13241427 7_3 1.000 185.06 5.4e-04 11.86
405346 rs10277379 7_49 1.000 4352.72 1.3e-02 6.01
405349 rs761767938 7_49 1.000 5021.16 1.5e-02 3.82
405357 rs1544459 7_49 1.000 4933.79 1.4e-02 4.42
411928 rs10276555 7_63 1.000 68.32 2.0e-04 6.61
441588 rs1397799 8_24 1.000 122.62 3.6e-04 11.95
469682 rs9720968 8_83 1.000 111.17 3.2e-04 10.36
469687 rs10505503 8_83 1.000 48.48 1.4e-04 6.02
485393 rs476924 9_17 1.000 4694.66 1.4e-02 -3.29
485396 rs141465689 9_17 1.000 4677.19 1.4e-02 -3.26
567903 rs369062552 11_21 1.000 148.95 4.3e-04 14.22
567913 rs34830202 11_21 1.000 254.58 7.4e-04 -14.10
616915 rs2657880 12_35 1.000 122.30 3.6e-04 11.72
617070 rs7397189 12_36 1.000 134.26 3.9e-04 -12.27
712679 rs3803487 15_27 1.000 64.42 1.9e-04 8.25
716454 rs2472297 15_35 1.000 62.54 1.8e-04 -9.67
716696 rs145727191 15_35 1.000 90.03 2.6e-04 11.74
735990 rs12927956 16_27 1.000 52.76 1.5e-04 6.72
759690 rs1058166 17_22 1.000 75.51 2.2e-04 9.53
759719 rs4794765 17_22 1.000 61.49 1.8e-04 -8.54
762030 rs137906947 17_27 1.000 145.62 4.2e-04 -9.70
779645 rs162000 18_14 1.000 44.02 1.3e-04 6.80
784968 rs4890562 18_25 1.000 64.37 1.9e-04 9.64
784971 rs12458806 18_25 1.000 80.93 2.4e-04 0.67
784976 rs12964854 18_25 1.000 114.03 3.3e-04 9.21
785755 rs9953845 18_26 1.000 72.99 2.1e-04 9.08
811822 rs814573 19_31 1.000 39.63 1.2e-04 -6.35
812086 rs34783010 19_32 1.000 250.04 7.3e-04 -15.84
813074 rs12978750 19_33 1.000 178.00 5.2e-04 -16.37
832162 rs6123359 20_32 1.000 38.57 1.1e-04 6.89
832168 rs2585441 20_32 1.000 40.23 1.2e-04 6.39
833883 rs62205363 20_34 1.000 67.85 2.0e-04 6.34
843679 rs219783 21_16 1.000 133.63 3.9e-04 -11.90
877251 rs3010096 1_102 1.000 41.91 1.2e-04 5.66
923528 rs752726045 15_36 1.000 11248.42 3.3e-02 -4.84
957459 rs202143810 20_38 1.000 941.02 2.7e-03 -2.44
98992 rs567964928 2_67 0.999 32.31 9.4e-05 5.14
275391 rs4703440 5_23 0.999 64.02 1.9e-04 7.92
604355 rs11056397 12_13 0.999 33.03 9.6e-05 -5.38
833341 rs12481011 20_33 0.999 33.72 9.8e-05 4.92
318017 rs12654812 5_106 0.998 65.75 1.9e-04 10.89
318023 rs7447593 5_106 0.998 61.46 1.8e-04 10.76
197542 rs7642977 3_118 0.997 37.09 1.1e-04 6.09
280206 rs113088001 5_31 0.997 39.41 1.1e-04 -5.94
376625 rs1445288 6_108 0.997 32.05 9.3e-05 5.23
386126 rs542176135 7_17 0.997 45.00 1.3e-04 -6.93
615130 rs863226 12_31 0.997 31.68 9.2e-05 4.54
762029 rs60372268 17_27 0.997 54.94 1.6e-04 -8.11
784829 rs72902699 18_24 0.997 44.09 1.3e-04 -6.89
399614 rs2709273 7_39 0.996 30.28 8.8e-05 5.43
836306 rs926167 21_2 0.996 41.63 1.2e-04 5.45
659189 rs9543236 13_35 0.995 31.43 9.1e-05 -5.34
378895 rs4724786 7_3 0.994 60.05 1.7e-04 3.34
545611 rs1408345 10_64 0.994 29.27 8.5e-05 5.37
426344 rs10224210 7_94 0.992 330.88 9.5e-04 19.30
615137 rs1878234 12_31 0.991 36.45 1.0e-04 4.94
767419 rs11079697 17_39 0.990 29.28 8.4e-05 -5.44
227524 rs13124978 4_56 0.989 26.38 7.6e-05 -4.87
699644 rs10141666 14_53 0.988 35.54 1.0e-04 -5.98
328211 rs793705 6_18 0.987 34.49 9.9e-05 -6.01
322243 rs13193887 6_7 0.985 28.46 8.1e-05 4.93
568955 rs2476504 11_23 0.985 27.11 7.8e-05 -4.83
923583 rs2456070 15_36 0.984 11245.22 3.2e-02 -4.74
233674 rs35518360 4_67 0.983 29.25 8.4e-05 -5.20
276939 rs115634741 5_26 0.983 33.68 9.6e-05 -7.51
411932 rs6968978 7_63 0.983 28.49 8.1e-05 -3.55
741303 rs59156463 16_37 0.983 42.27 1.2e-04 -8.30
396289 rs700752 7_34 0.981 43.32 1.2e-04 6.48
760781 rs12948083 17_25 0.980 30.00 8.5e-05 5.32
722395 rs59646751 15_48 0.978 76.26 2.2e-04 9.08
557477 rs2239681 11_2 0.976 34.85 9.9e-05 -6.81
285456 rs4302565 5_43 0.975 26.79 7.6e-05 4.21
597613 rs148884160 11_80 0.968 25.72 7.2e-05 4.82
762872 rs890398 17_29 0.968 26.78 7.5e-05 5.08
708394 rs8030172 15_19 0.967 24.61 6.9e-05 4.70
810674 rs12982615 19_30 0.966 28.77 8.1e-05 -5.22
485394 rs34033213 9_17 0.963 4617.81 1.3e-02 -3.16
813093 rs495315 19_33 0.963 55.73 1.6e-04 10.55
60546 rs17520491 1_123 0.961 26.30 7.3e-05 4.97
101428 rs1975379 2_70 0.960 30.04 8.4e-05 -6.71
773795 rs4513192 18_3 0.956 34.49 9.6e-05 -5.15
867837 rs111552903 1_77 0.955 45.37 1.3e-04 -3.27
779604 rs527616 18_14 0.954 26.00 7.2e-05 -5.24
842825 rs2154568 21_15 0.951 43.77 1.2e-04 6.46
74094 rs4580350 2_19 0.942 609.98 1.7e-03 2.96
617051 rs6581124 12_35 0.941 26.54 7.3e-05 -4.95
833326 rs1884500 20_33 0.941 28.16 7.7e-05 2.53
326580 rs41271299 6_15 0.940 25.00 6.8e-05 4.68
671887 rs7987209 13_59 0.936 59.13 1.6e-04 7.64
741474 rs139861017 16_37 0.933 26.61 7.2e-05 4.78
372855 rs1449674 6_101 0.930 25.37 6.9e-05 -4.77
820452 rs6040069 20_8 0.927 24.32 6.6e-05 -4.67
833885 rs1407040 20_34 0.925 45.09 1.2e-04 -3.89
512847 rs113790047 10_3 0.923 39.75 1.1e-04 6.42
511106 rs115478735 9_70 0.921 47.47 1.3e-04 6.96
337444 rs7742789 6_33 0.916 45.98 1.2e-04 -7.14
22359 rs60824360 1_48 0.913 25.92 6.9e-05 -4.68
290833 rs61552236 5_53 0.911 46.54 1.2e-04 -6.85
762730 rs9895945 17_28 0.910 40.67 1.1e-04 6.52
578521 rs10796869 11_38 0.909 46.81 1.2e-04 7.75
568286 rs11031796 11_22 0.907 29.47 7.8e-05 5.12
523653 rs79545879 10_20 0.903 25.00 6.6e-05 4.24
748243 rs34341288 16_50 0.898 25.81 6.7e-05 -4.80
533278 rs10821950 10_42 0.894 39.17 1.0e-04 -6.19
741587 rs192776582 16_38 0.884 25.86 6.6e-05 5.14
489757 rs34223057 9_27 0.883 27.02 6.9e-05 4.97
195372 rs62278004 3_115 0.882 105.50 2.7e-04 15.80
529356 rs4935194 10_33 0.881 24.11 6.2e-05 4.40
320390 rs1272694 6_3 0.876 33.29 8.5e-05 -5.73
389785 rs67971665 7_23 0.872 29.86 7.6e-05 -5.52
347021 rs2815715 6_50 0.871 24.86 6.3e-05 4.71
10043 rs56307352 1_21 0.870 26.20 6.6e-05 -4.83
154139 rs62259692 3_36 0.869 27.18 6.9e-05 4.72
809550 rs117236730 19_25 0.865 24.08 6.1e-05 4.72
722437 rs45506098 15_48 0.863 23.93 6.0e-05 4.42
195355 rs2679508 3_115 0.858 65.75 1.6e-04 -3.66
284719 rs11743158 5_41 0.850 32.82 8.1e-05 5.38
50082 rs113608553 1_104 0.849 32.58 8.0e-05 -5.54
757568 rs112861323 17_18 0.849 26.80 6.6e-05 -4.71
285449 rs745063 5_43 0.846 141.48 3.5e-04 13.29
721491 rs4335732 15_46 0.842 25.96 6.3e-05 -4.79
158395 rs28599817 3_43 0.840 132.89 3.2e-04 12.33
263409 rs62331274 5_2 0.840 23.95 5.8e-05 4.36
813105 rs837647 19_34 0.839 31.74 7.7e-05 -5.52
328929 rs115740542 6_20 0.838 25.01 6.1e-05 4.42
3331 rs115560453 1_7 0.836 25.15 6.1e-05 4.67
332456 rs4509168 6_26 0.831 34.29 8.3e-05 5.69
693673 rs17796675 14_41 0.825 24.24 5.8e-05 4.45
824169 rs10854249 20_15 0.825 29.15 7.0e-05 -5.10
198304 rs13059257 3_120 0.821 42.96 1.0e-04 6.50
658793 rs5804585 13_35 0.811 103.74 2.4e-04 -10.42
762532 rs3809778 17_28 0.811 30.63 7.2e-05 -5.73
946338 rs117643180 17_6 0.808 50.28 1.2e-04 7.00
195373 rs12634556 3_115 0.807 314.87 7.4e-04 -22.53
229226 rs149027545 4_59 0.806 26.58 6.2e-05 -4.90
527612 rs56059584 10_29 0.802 24.11 5.6e-05 4.44
868166 rs6676150 1_77 0.802 224.00 5.2e-04 20.90
159053 rs11707171 3_45 0.801 35.80 8.3e-05 -3.27SNPs 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
923528 rs752726045 15_36 1.000 11248.42 3.3e-02 -4.84
923583 rs2456070 15_36 0.984 11245.22 3.2e-02 -4.74
923522 rs2456040 15_36 0.238 11233.82 7.8e-03 -4.69
923512 rs2959846 15_36 0.000 11102.10 2.7e-10 -4.73
923511 rs2456042 15_36 0.000 11101.78 1.7e-10 -4.72
923683 rs2456067 15_36 0.000 11099.46 2.2e-10 -4.75
923672 rs2469214 15_36 0.000 11055.22 2.8e-12 -4.78
923627 rs2456074 15_36 0.000 11015.84 1.1e-12 -4.89
923691 rs2456066 15_36 0.000 11010.72 4.4e-13 -4.89
923665 rs2469538 15_36 0.000 11009.88 1.7e-13 -4.87
923738 rs2460158 15_36 0.000 11004.12 4.1e-15 -4.78
923651 rs2469539 15_36 0.000 10997.94 1.4e-14 -4.84
923669 rs8042255 15_36 0.000 10971.66 7.8e-15 -4.92
923735 rs2469211 15_36 0.000 10956.03 7.1e-18 -4.80
923734 rs2956875 15_36 0.000 10956.00 1.4e-17 -4.81
923722 rs2456063 15_36 0.000 10947.45 1.1e-16 -4.89
923662 rs35360285 15_36 0.000 10935.09 2.3e-15 -4.97
923740 rs2469573 15_36 0.000 10930.68 3.5e-17 -4.91
923741 rs2469212 15_36 0.000 10929.90 2.1e-17 -4.89
923490 rs2469564 15_36 0.000 10910.30 0.0e+00 -4.74
923486 rs2460153 15_36 0.000 10910.13 0.0e+00 -4.74
923586 rs71140202 15_36 0.000 10901.71 0.0e+00 4.82
923475 rs2456047 15_36 0.000 10898.61 0.0e+00 -4.75
923776 rs2469216 15_36 0.000 10739.50 0.0e+00 -4.93
923452 rs2469568 15_36 0.000 10658.58 0.0e+00 -4.68
923773 rs2469215 15_36 0.000 10652.80 0.0e+00 -5.04
923778 rs2469552 15_36 0.000 10651.98 0.0e+00 -5.07
923780 rs2456033 15_36 0.000 10640.65 0.0e+00 -5.05
923774 rs6495200 15_36 0.000 10638.35 0.0e+00 5.07
923435 rs2456054 15_36 0.000 10602.38 0.0e+00 -4.66
923431 rs2469559 15_36 0.000 10591.56 0.0e+00 -4.64
923802 rs4886490 15_36 0.000 9988.17 0.0e+00 5.31
923813 rs874224 15_36 0.000 9985.01 0.0e+00 -5.36
923792 rs11858964 15_36 0.000 9971.92 0.0e+00 5.44
923817 rs1810348 15_36 0.000 9969.37 0.0e+00 5.33
923811 rs4489970 15_36 0.000 9962.21 0.0e+00 5.35
923812 rs874223 15_36 0.000 9959.80 0.0e+00 5.35
923818 rs7180257 15_36 0.000 9906.33 0.0e+00 5.22
923540 rs77815174 15_36 0.000 9033.28 0.0e+00 -1.81
923591 rs77633900 15_36 0.000 9029.54 0.0e+00 -1.81
923590 rs77387260 15_36 0.000 9027.81 0.0e+00 -1.82
923678 rs2291449 15_36 0.000 8871.29 0.0e+00 -1.81
923676 rs1801591 15_36 0.000 8867.19 0.0e+00 -1.82
923717 rs74805465 15_36 0.000 8756.52 0.0e+00 -1.92
923495 rs79495512 15_36 0.000 8696.06 0.0e+00 -1.88
923769 rs78185702 15_36 0.000 8536.37 0.0e+00 -2.03
923446 rs17456573 15_36 0.000 8392.80 0.0e+00 -1.78
923893 rs78181637 15_36 0.000 7812.12 2.1e-14 -7.13
923832 rs2469535 15_36 0.000 7722.11 2.6e-14 -7.15
923834 rs2460161 15_36 0.000 7720.71 2.5e-14 -7.15SNPs 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
923528 rs752726045 15_36 1.000 11248.42 0.03300 -4.84
923583 rs2456070 15_36 0.984 11245.22 0.03200 -4.74
58111 rs766167074 1_118 1.000 6468.79 0.01900 -4.11
366476 rs199804242 6_89 1.000 6095.57 0.01800 3.25
405349 rs761767938 7_49 1.000 5021.16 0.01500 3.82
366475 rs2327654 6_89 0.782 6116.70 0.01400 2.70
405357 rs1544459 7_49 1.000 4933.79 0.01400 4.42
485393 rs476924 9_17 1.000 4694.66 0.01400 -3.29
485396 rs141465689 9_17 1.000 4677.19 0.01400 -3.26
405346 rs10277379 7_49 1.000 4352.72 0.01300 6.01
485394 rs34033213 9_17 0.963 4617.81 0.01300 -3.16
366492 rs6923513 6_89 0.687 6116.49 0.01200 2.70
923522 rs2456040 15_36 0.238 11233.82 0.00780 -4.69
58098 rs1076804 1_118 0.283 6404.49 0.00530 -4.28
58118 rs2211176 1_118 0.277 6411.14 0.00520 -4.25
58119 rs2790882 1_118 0.277 6411.14 0.00520 -4.25
58102 rs2256908 1_118 0.255 6412.62 0.00470 -4.24
58108 rs10489611 1_118 0.252 6412.98 0.00470 -4.23
58110 rs971534 1_118 0.252 6413.02 0.00470 -4.23
58109 rs2486737 1_118 0.223 6412.92 0.00420 -4.23
58105 rs2790891 1_118 0.218 6412.48 0.00410 -4.23
58106 rs2491405 1_118 0.218 6412.48 0.00410 -4.23
58117 rs2248646 1_118 0.205 6410.63 0.00380 -4.24
58120 rs1416913 1_118 0.200 6403.73 0.00370 -4.26
957459 rs202143810 20_38 1.000 941.02 0.00270 -2.44
74091 rs569546056 2_19 1.000 610.21 0.00180 -2.90
74094 rs4580350 2_19 0.942 609.98 0.00170 2.96
195357 rs16862782 3_115 1.000 362.07 0.00110 -21.08
277366 rs11956741 5_27 0.715 533.48 0.00110 -24.85
426344 rs10224210 7_94 0.992 330.88 0.00095 19.30
366479 rs113527452 6_89 0.053 6087.00 0.00094 2.74
58099 rs910824 1_118 0.041 6387.93 0.00077 -4.26
195373 rs12634556 3_115 0.807 314.87 0.00074 -22.53
567913 rs34830202 11_21 1.000 254.58 0.00074 -14.10
812086 rs34783010 19_32 1.000 250.04 0.00073 -15.84
378905 rs78148157 7_3 1.000 230.25 0.00067 -12.26
376593 rs300143 6_108 1.000 224.35 0.00065 -15.74
378906 rs13241427 7_3 1.000 185.06 0.00054 11.86
813074 rs12978750 19_33 1.000 178.00 0.00052 -16.37
868166 rs6676150 1_77 0.802 224.00 0.00052 20.90
405332 rs17156706 7_49 0.353 498.79 0.00051 -3.20
58123 rs2790874 1_118 0.026 6401.13 0.00049 -4.19
957456 rs6089961 20_38 0.181 930.68 0.00049 -2.60
957458 rs2738758 20_38 0.181 930.68 0.00049 -2.60
159086 rs13066345 3_45 1.000 156.73 0.00046 12.30
277361 rs28856650 5_27 0.294 531.76 0.00046 -24.81
923396 rs77476788 15_36 0.750 202.09 0.00044 10.93
567903 rs369062552 11_21 1.000 148.95 0.00043 14.22
762030 rs137906947 17_27 1.000 145.62 0.00042 -9.70
74093 rs2169748 2_19 0.225 603.43 0.00039 -2.89SNPs 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
277366 rs11956741 5_27 0.715 533.48 1.1e-03 -24.85
277361 rs28856650 5_27 0.294 531.76 4.6e-04 -24.81
277338 rs11955175 5_27 0.012 521.16 1.8e-05 -24.60
195373 rs12634556 3_115 0.807 314.87 7.4e-04 -22.53
195375 rs11924549 3_115 0.193 310.13 1.7e-04 -22.45
868275 rs760077 1_77 0.008 237.98 5.7e-06 -21.52
868286 rs2990223 1_77 0.002 235.07 1.7e-06 -21.47
195357 rs16862782 3_115 1.000 362.07 1.1e-03 -21.08
868166 rs6676150 1_77 0.802 224.00 5.2e-04 20.90
195382 rs1027498 3_115 0.000 221.00 1.4e-10 -20.38
868299 rs139558368 1_77 0.000 209.13 3.1e-08 20.25
868119 rs9330264 1_77 0.010 200.74 5.6e-06 -19.37
426344 rs10224210 7_94 0.992 330.88 9.5e-04 19.30
426346 rs10224002 7_94 0.011 323.83 1.0e-05 19.03
868262 rs423144 1_77 0.000 184.99 5.8e-09 -18.28
868260 rs7366775 1_77 0.000 183.97 5.7e-09 -18.25
868245 rs4971101 1_77 0.000 179.07 5.3e-09 -18.10
868246 rs2070803 1_77 0.000 178.87 5.3e-09 -18.10
868269 rs2075571 1_77 0.000 177.32 5.1e-09 -18.09
868242 rs4971100 1_77 0.000 177.06 5.2e-09 -18.03
868233 rs9426886 1_77 0.000 174.61 5.1e-09 -17.94
868239 rs541049493 1_77 0.000 173.45 5.1e-09 -17.87
868232 rs11264341 1_77 0.000 172.24 5.0e-09 -17.85
195368 rs73188608 3_115 0.000 167.82 4.3e-15 -17.82
195369 rs73188616 3_115 0.000 167.72 4.3e-15 -17.82
195374 rs4686916 3_115 0.000 167.58 4.3e-15 -17.82
195376 rs73188638 3_115 0.000 166.63 4.2e-15 -17.79
868197 rs141625351 1_77 0.000 146.48 5.0e-09 17.71
868912 rs12134456 1_77 0.000 162.84 7.4e-09 17.63
868289 rs1057941 1_77 0.000 171.88 5.5e-09 -17.62
195371 rs12233463 3_115 0.000 155.46 2.2e-15 -17.60
868240 rs4971099 1_77 0.000 165.91 5.3e-09 -17.04
426342 rs66497154 7_94 0.002 247.34 1.3e-06 16.71
868254 rs4072037 1_77 0.000 127.43 4.1e-09 -16.46
868231 rs3814316 1_77 0.000 147.88 5.0e-09 -16.45
868259 rs2974937 1_77 0.000 126.38 4.2e-09 -16.43
813074 rs12978750 19_33 1.000 178.00 5.2e-04 -16.37
868252 rs12743084 1_77 0.000 126.28 4.0e-09 -16.34
868230 rs4971059 1_77 0.000 140.77 4.3e-09 -16.33
868265 rs2066981 1_77 0.000 120.71 5.1e-09 -16.21
868271 rs370545 1_77 0.000 120.63 5.1e-09 -16.20
868272 rs914615 1_77 0.000 120.59 5.1e-09 -16.20
868255 rs12411216 1_77 0.000 122.30 4.2e-09 16.17
868223 rs4971091 1_77 0.000 126.15 3.6e-09 -15.89
868225 rs4971093 1_77 0.000 125.27 3.5e-09 -15.86
812086 rs34783010 19_32 1.000 250.04 7.3e-04 -15.84
813082 rs838145 19_33 0.047 188.80 2.6e-05 15.82
195372 rs62278004 3_115 0.882 105.50 2.7e-04 15.80
716672 rs28607641 15_35 0.553 225.41 3.6e-04 15.78
716673 rs7177266 15_35 0.447 224.93 2.9e-04 15.76Gene 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] 7if (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 regulation of exit from mitosis (GO:0007096)
2 ERBB signaling pathway (GO:0038127)
3 regulation of mitotic cell cycle (GO:0007346)
4 cardiac endothelial cell differentiation (GO:0003348)
5 cardiac muscle cell myoblast differentiation (GO:0060379)
6 endocardial cell differentiation (GO:0060956)
7 regulation of mitotic cell cycle phase transition (GO:1901990)
8 activation of transmembrane receptor protein tyrosine kinase activity (GO:0007171)
9 positive regulation of histone H4 acetylation (GO:0090240)
10 regulation of striated muscle cell differentiation (GO:0051153)
11 glomerular epithelial cell development (GO:0072310)
12 glomerular visceral epithelial cell development (GO:0072015)
13 ventricular trabecula myocardium morphogenesis (GO:0003222)
14 regulation of histone H4 acetylation (GO:0090239)
15 regulation of DNA-templated transcription in response to stress (GO:0043620)
16 negative regulation of cell adhesion mediated by integrin (GO:0033629)
17 negative regulation of transcription by competitive promoter binding (GO:0010944)
18 DNA damage response, signal transduction by p53 class mediator resulting in transcription of p21 class mediator (GO:0006978)
19 DNA damage response, signal transduction resulting in transcription (GO:0042772)
20 cardiocyte differentiation (GO:0035051)
21 glomerular visceral epithelial cell differentiation (GO:0072112)
22 positive regulation of metaphase/anaphase transition of cell cycle (GO:1902101)
23 positive regulation of mitotic metaphase/anaphase transition (GO:0045842)
24 positive regulation of mitotic sister chromatid separation (GO:1901970)
25 regulation of cell cycle (GO:0051726)
26 negative regulation of intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator (GO:1902166)
27 negative regulation of secretion (GO:0051048)
28 regulation of intrinsic apoptotic signaling pathway in response to DNA damage by p53 class mediator (GO:1902165)
29 regulation of secretion (GO:0051046)
30 myoblast differentiation (GO:0045445)
31 negative regulation of intrinsic apoptotic signaling pathway by p53 class mediator (GO:1902254)
32 stem cell development (GO:0048864)
33 heart trabecula morphogenesis (GO:0061384)
34 striated muscle cell differentiation (GO:0051146)
35 neural crest cell differentiation (GO:0014033)
36 mesenchymal cell development (GO:0014031)
37 positive regulation of histone acetylation (GO:0035066)
38 positive regulation of transcription from RNA polymerase II promoter in response to stress (GO:0036003)
39 cardiac muscle cell differentiation (GO:0055007)
40 mammary gland development (GO:0030879)
41 regulation of mitotic metaphase/anaphase transition (GO:0030071)
42 negative regulation of intrinsic apoptotic signaling pathway in response to DNA damage (GO:1902230)
43 positive regulation of striated muscle cell differentiation (GO:0051155)
44 positive regulation of muscle cell differentiation (GO:0051149)
45 cellular response to epidermal growth factor stimulus (GO:0071364)
46 protein K11-linked ubiquitination (GO:0070979)
47 activation of protein kinase B activity (GO:0032148)
48 ERBB2 signaling pathway (GO:0038128)
49 neuron projection extension (GO:1990138)
50 regulation of cell adhesion mediated by integrin (GO:0033628)
51 ventricular cardiac muscle tissue morphogenesis (GO:0055010)
52 positive regulation of mitotic nuclear division (GO:0045840)
53 positive regulation of cytokinesis (GO:0032467)
54 positive regulation of intracellular signal transduction (GO:1902533)
55 positive regulation of cell division (GO:0051781)
56 neural crest cell development (GO:0014032)
57 epidermal growth factor receptor signaling pathway (GO:0007173)
58 regulation of cellular component movement (GO:0051270)
59 negative regulation of transport (GO:0051051)
60 cardiac muscle tissue development (GO:0048738)
61 DNA damage response, signal transduction by p53 class mediator resulting in cell cycle arrest (GO:0006977)
62 positive regulation of mitotic cell cycle phase transition (GO:1901992)
63 O-glycan processing (GO:0016266)
64 mitotic G1 DNA damage checkpoint signaling (GO:0031571)
65 cellular response to calcium ion (GO:0071277)
66 wound healing (GO:0042060)
67 gland development (GO:0048732)
68 negative regulation of cell adhesion (GO:0007162)
69 regulation of actin filament-based process (GO:0032970)
70 DNA damage response, signal transduction by p53 class mediator (GO:0030330)
Overlap Adjusted.P.value Genes
1 2/39 0.009975182 CDC14A;ANAPC11
2 2/66 0.014373002 NRG1;IQGAP1
3 2/178 0.022521751 IQGAP1;ANAPC11
4 1/5 0.022521751 NRG1
5 1/5 0.022521751 NRG1
6 1/5 0.022521751 NRG1
7 2/188 0.022521751 CDC14A;ANAPC11
8 1/6 0.022521751 NRG1
9 1/7 0.022521751 MUC1
10 1/7 0.022521751 NRG1
11 1/7 0.022521751 IQGAP1
12 1/7 0.022521751 IQGAP1
13 1/8 0.022521751 NRG1
14 1/9 0.022521751 MUC1
15 1/9 0.022521751 MUC1
16 1/10 0.022521751 MUC1
17 1/10 0.022521751 MUC1
18 1/10 0.022521751 MUC1
19 1/11 0.022521751 MUC1
20 1/12 0.022521751 NRG1
21 1/12 0.022521751 IQGAP1
22 1/12 0.022521751 ANAPC11
23 1/12 0.022521751 ANAPC11
24 1/12 0.022521751 ANAPC11
25 2/296 0.022521751 IQGAP1;CDC14A
26 1/13 0.022527343 MUC1
27 1/14 0.022527343 NRG1
28 1/14 0.022527343 MUC1
29 1/15 0.022527343 NRG1
30 1/15 0.022527343 NRG1
31 1/17 0.023764627 MUC1
32 1/17 0.023764627 NRG1
33 1/19 0.023764627 NRG1
34 1/19 0.023764627 NRG1
35 1/19 0.023764627 NRG1
36 1/19 0.023764627 NRG1
37 1/23 0.026996407 MUC1
38 1/24 0.026996407 MUC1
39 1/24 0.026996407 NRG1
40 1/24 0.026996407 NRG1
41 1/26 0.027597558 ANAPC11
42 1/26 0.027597558 MUC1
43 1/27 0.027597558 NRG1
44 1/27 0.027597558 NRG1
45 1/28 0.027979505 IQGAP1
46 1/29 0.028344553 ANAPC11
47 1/31 0.029645800 NRG1
48 1/32 0.029951107 NRG1
49 1/33 0.029951107 IQGAP1
50 1/34 0.029951107 MUC1
51 1/34 0.029951107 NRG1
52 1/36 0.031093755 ANAPC11
53 1/37 0.031349802 CDC14A
54 2/546 0.034072454 NRG1;IQGAP1
55 1/44 0.035887521 CDC14A
56 1/45 0.036042332 NRG1
57 1/47 0.036972709 IQGAP1
58 1/50 0.038637149 NRG1
59 1/51 0.038736124 NRG1
60 1/53 0.039572408 NRG1
61 1/56 0.041108427 MUC1
62 1/58 0.041877316 ANAPC11
63 1/61 0.043324807 MUC1
64 1/65 0.045417213 MUC1
65 1/69 0.047210138 IQGAP1
66 1/70 0.047210138 NRG1
67 1/71 0.047210138 NRG1
68 1/73 0.047210138 MUC1
69 1/73 0.047210138 IQGAP1
70 1/74 0.047210138 MUC1
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value Genes
1 filtration diaphragm (GO:0036056) 1/5 0.02623375 IQGAP1
2 slit diaphragm (GO:0036057) 1/5 0.02623375 IQGAP1
[1] "GO_Molecular_Function_2021"
Term
1 protein kinase activator activity (GO:0030295)
2 ErbB-3 class receptor binding (GO:0043125)
3 transmembrane receptor protein tyrosine kinase activator activity (GO:0030297)
4 ubiquitin-ubiquitin ligase activity (GO:0034450)
5 MAP-kinase scaffold activity (GO:0005078)
6 GTPase inhibitor activity (GO:0005095)
7 cadherin binding (GO:0045296)
8 cadherin binding involved in cell-cell adhesion (GO:0098641)
9 protein tyrosine kinase activator activity (GO:0030296)
10 chemorepellent activity (GO:0045499)
11 phosphatidylinositol-3,4,5-trisphosphate binding (GO:0005547)
12 protein serine/threonine kinase activator activity (GO:0043539)
13 cell-cell adhesion mediator activity (GO:0098632)
Overlap Adjusted.P.value Genes
1 2/63 0.007510719 NRG1;IQGAP1
2 1/5 0.027190168 NRG1
3 1/7 0.027190168 NRG1
4 1/11 0.027190168 ANAPC11
5 1/11 0.027190168 IQGAP1
6 1/13 0.027190168 IQGAP1
7 2/322 0.027190168 TRIM29;IQGAP1
8 1/18 0.027264844 TRIM29
9 1/19 0.027264844 NRG1
10 1/25 0.032258316 NRG1
11 1/35 0.039713799 IQGAP1
12 1/37 0.039713799 IQGAP1
13 1/42 0.041581635 TRIM29ANAPC11 gene(s) from the input list not found in DisGeNET CURATEDZNF547 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
74 DEAFNESS, AUTOSOMAL RECESSIVE 32 0.02241806 1/5 1/9703
75 Medullary cystic kidney disease 1 0.02241806 1/5 1/9703
4 Cannabis Dependence 0.04962313 1/5 17/9703
5 Neoplastic Cell Transformation 0.04962313 2/5 139/9703
7 Prelingual Deafness 0.04962313 1/5 20/9703
26 Oligospermia 0.04962313 1/5 12/9703
28 Peritoneal Neoplasms 0.04962313 1/5 10/9703
33 Gastric ulcer 0.04962313 1/5 18/9703
35 Aganglionosis, Colonic 0.04962313 1/5 15/9703
36 Hearing Loss, Extreme 0.04962313 1/5 20/9703******************************************
* *
* 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