Creatinine (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 Creatinine (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-30700_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0126890811 0.0002128067 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
22.2112 20.0238 #report sample size
print(sample_size)[1] 344104#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.009087404 0.107703036 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07180211 2.48330981Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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 90.14 2.6e-04 -7.02
6117 TBX6 16_24 1.000 47.19 1.4e-04 6.38
10765 ZDHHC18 1_18 0.999 75.48 2.2e-04 -8.72
1980 FCGRT 19_34 0.999 14001.37 4.1e-02 3.17
5095 DNAJC13 3_82 0.977 37.97 1.1e-04 -6.22
982 CDC14A 1_61 0.973 69.67 2.0e-04 -8.40
3750 MEA1 6_33 0.971 26.64 7.5e-05 4.51
6646 DRC1 2_15 0.958 22.87 6.4e-05 -4.41
9247 FUCA1 1_17 0.954 28.76 8.0e-05 5.36
10816 MBD5 2_88 0.950 114.28 3.2e-04 11.58
11271 RP11-274B18.4 9_30 0.948 37.19 1.0e-04 7.45
5939 BIN3 8_23 0.938 58.33 1.6e-04 -7.76
12181 EGLN2 19_30 0.936 24.61 6.7e-05 -4.86
6183 FAM177A1 14_9 0.935 26.82 7.3e-05 -5.26
2782 C7 5_27 0.926 23.13 6.2e-05 4.26
1693 PTK6 20_37 0.914 55.28 1.5e-04 -7.40
4564 PSRC1 1_67 0.907 116.07 3.1e-04 11.10
23 M6PR 12_9 0.900 21.95 5.7e-05 -4.10
1433 PALM 19_2 0.891 21.70 5.6e-05 3.99
9106 PNPLA2 11_1 0.886 21.13 5.4e-05 -4.55
9540 COL18A1 21_23 0.881 32.90 8.4e-05 -5.87
10557 MAN1A2 1_72 0.850 22.56 5.6e-05 4.37
8021 FADD 11_39 0.828 30.19 7.3e-05 -5.15
8866 ABO 9_70 0.826 28.63 6.9e-05 6.43Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
4687 TMEM60 7_49 0.000 25335.61 0.0000 -13.43
1980 FCGRT 19_34 0.999 14001.37 0.0410 3.17
168 SPRTN 1_118 0.000 13307.64 0.0000 4.87
10381 ZGPAT 20_38 0.000 10070.55 0.0000 5.41
4733 AHI1 6_89 0.000 9908.69 0.0000 -2.01
3138 EXOC8 1_118 0.000 9550.60 0.0000 3.37
1699 ARFRP1 20_38 0.000 9243.26 0.0000 -2.37
10436 STMN3 20_38 0.000 7605.90 0.0000 4.86
11094 APTR 7_49 0.000 4894.91 0.0000 -3.02
5520 RCN3 19_34 0.000 4469.32 0.0000 3.07
1694 GMEB2 20_38 0.000 4226.51 0.0000 -4.55
8332 MGMT 10_81 0.630 3239.13 0.0059 6.97
11906 RTEL1 20_38 0.000 2795.24 0.0000 1.50
8603 ZMAT3 3_110 0.000 2209.38 0.0000 -1.78
3140 TSNAX 1_118 0.000 1855.72 0.0000 2.75
98 PHTF2 7_49 0.000 1553.17 0.0000 -3.12
7145 DISC1 1_118 0.000 1127.17 0.0000 -2.86
8165 CPT1C 19_34 0.000 1011.26 0.0000 -3.22
8416 KCNMB3 3_110 0.000 988.51 0.0000 0.31
11612 TNFRSF6B 20_38 0.000 701.18 0.0000 1.76Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 0.999 14001.37 4.1e-02 3.17
8332 MGMT 10_81 0.630 3239.13 5.9e-03 6.97
8497 SPATA5L1 15_17 0.310 451.96 4.1e-04 25.90
10816 MBD5 2_88 0.950 114.28 3.2e-04 11.58
4564 PSRC1 1_67 0.907 116.07 3.1e-04 11.10
9813 MUC1 1_77 1.000 90.14 2.6e-04 -7.02
10765 ZDHHC18 1_18 0.999 75.48 2.2e-04 -8.72
982 CDC14A 1_61 0.973 69.67 2.0e-04 -8.40
10711 L3MBTL3 6_86 0.763 85.30 1.9e-04 9.36
5939 BIN3 8_23 0.938 58.33 1.6e-04 -7.76
1693 PTK6 20_37 0.914 55.28 1.5e-04 -7.40
6117 TBX6 16_24 1.000 47.19 1.4e-04 6.38
5657 ACP1 2_1 0.687 56.30 1.1e-04 -7.38
5095 DNAJC13 3_82 0.977 37.97 1.1e-04 -6.22
11271 RP11-274B18.4 9_30 0.948 37.19 1.0e-04 7.45
4947 CNPY3 6_33 0.637 48.51 9.0e-05 -4.51
9054 WDR73 15_39 0.661 46.96 9.0e-05 6.83
9540 COL18A1 21_23 0.881 32.90 8.4e-05 -5.87
7091 NEXN 1_48 0.502 56.33 8.2e-05 7.67
9247 FUCA1 1_17 0.954 28.76 8.0e-05 5.36Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8497 SPATA5L1 15_17 0.310 451.96 4.1e-04 25.90
8498 GATM 15_17 0.003 241.79 2.4e-06 -17.70
3821 MED1 17_23 0.038 174.93 1.9e-05 -15.68
3060 ALMS1 2_48 0.019 196.46 1.1e-05 -14.49
4687 TMEM60 7_49 0.000 25335.61 0.0e+00 -13.43
5464 PNMT 17_23 0.002 133.15 6.7e-07 -13.08
271 SLC7A9 19_23 0.002 101.08 6.2e-07 -12.59
12374 RP11-434P11.2 2_48 0.019 132.23 7.2e-06 -11.86
1740 PIGU 20_21 0.012 83.60 2.9e-06 11.67
10816 MBD5 2_88 0.950 114.28 3.2e-04 11.58
4564 PSRC1 1_67 0.907 116.07 3.1e-04 11.10
3467 TBX2 17_36 0.014 92.13 3.6e-06 -10.94
7865 FBXO22 15_35 0.207 63.83 3.8e-05 10.62
6970 PGAP3 17_23 0.000 72.27 9.1e-08 -10.23
2868 TFDP2 3_87 0.000 37.15 2.6e-08 10.06
6290 ZFP36L2 2_27 0.030 79.10 7.0e-06 -10.04
8173 LMAN2 5_106 0.037 56.80 6.1e-06 10.04
5463 MIEN1 17_23 0.000 68.80 8.3e-08 10.04
5312 MAN2C1 15_35 0.000 102.05 1.7e-08 9.89
8194 SNUPN 15_35 0.000 105.62 5.7e-09 9.66Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03325822#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8497 SPATA5L1 15_17 0.310 451.96 4.1e-04 25.90
8498 GATM 15_17 0.003 241.79 2.4e-06 -17.70
3821 MED1 17_23 0.038 174.93 1.9e-05 -15.68
3060 ALMS1 2_48 0.019 196.46 1.1e-05 -14.49
4687 TMEM60 7_49 0.000 25335.61 0.0e+00 -13.43
5464 PNMT 17_23 0.002 133.15 6.7e-07 -13.08
271 SLC7A9 19_23 0.002 101.08 6.2e-07 -12.59
12374 RP11-434P11.2 2_48 0.019 132.23 7.2e-06 -11.86
1740 PIGU 20_21 0.012 83.60 2.9e-06 11.67
10816 MBD5 2_88 0.950 114.28 3.2e-04 11.58
4564 PSRC1 1_67 0.907 116.07 3.1e-04 11.10
3467 TBX2 17_36 0.014 92.13 3.6e-06 -10.94
7865 FBXO22 15_35 0.207 63.83 3.8e-05 10.62
6970 PGAP3 17_23 0.000 72.27 9.1e-08 -10.23
2868 TFDP2 3_87 0.000 37.15 2.6e-08 10.06
6290 ZFP36L2 2_27 0.030 79.10 7.0e-06 -10.04
8173 LMAN2 5_106 0.037 56.80 6.1e-06 10.04
5463 MIEN1 17_23 0.000 68.80 8.3e-08 10.04
5312 MAN2C1 15_35 0.000 102.05 1.7e-08 9.89
8194 SNUPN 15_35 0.000 105.62 5.7e-09 9.66Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 15_17"
genename region_tag susie_pip mu2 PVE z
7782 CASC4 15_17 0.010 14.27 4.0e-07 2.28
11335 PATL2 15_17 0.005 12.63 1.7e-07 2.44
7780 B2M 15_17 0.003 5.11 5.0e-08 -0.44
9861 TRIM69 15_17 0.003 5.83 5.7e-08 1.11
5293 SORD 15_17 0.056 23.28 3.8e-06 -3.09
5042 DUOX1 15_17 0.006 17.61 3.2e-07 -5.32
8498 GATM 15_17 0.003 241.79 2.4e-06 -17.70
8497 SPATA5L1 15_17 0.310 451.96 4.1e-04 25.90
12481 RP11-96O20.5 15_17 0.004 38.41 4.2e-07 2.50
5023 SQRDL 15_17 0.004 5.22 5.4e-08 0.50
12408 CTD-2306A12.1 15_17 0.010 31.20 9.4e-07 5.28
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_23"
genename region_tag susie_pip mu2 PVE z
12395 EPOP 17_23 0.005 35.90 5.3e-07 -3.51
12545 CTB-58E17.1 17_23 0.005 34.74 4.5e-07 -3.48
12454 MLLT6 17_23 0.008 40.47 9.3e-07 -3.69
12546 CISD3 17_23 0.003 27.26 2.1e-07 2.77
12530 PCGF2 17_23 0.000 6.00 6.4e-09 -1.26
12543 PSMB3 17_23 0.000 4.98 5.3e-09 0.62
12506 PIP4K2B 17_23 0.000 7.66 1.1e-08 -0.81
12393 CWC25 17_23 0.000 5.87 6.5e-09 0.27
20 LASP1 17_23 0.000 6.74 7.9e-09 -1.52
12077 LINC00672 17_23 0.007 31.64 6.4e-07 -4.96
6969 PLXDC1 17_23 0.000 6.86 7.2e-09 -0.78
5465 STAC2 17_23 0.001 12.94 3.0e-08 -1.89
3821 MED1 17_23 0.038 174.93 1.9e-05 -15.68
12394 RP11-390P24.1 17_23 0.000 28.76 3.6e-08 5.92
4321 PPP1R1B 17_23 0.001 68.68 1.5e-07 -8.73
4319 STARD3 17_23 0.000 16.86 1.8e-08 3.95
8762 TCAP 17_23 0.001 14.12 2.8e-08 2.43
5464 PNMT 17_23 0.002 133.15 6.7e-07 -13.08
5461 ERBB2 17_23 0.000 51.46 5.4e-08 -8.54
6970 PGAP3 17_23 0.000 72.27 9.1e-08 -10.23
5463 MIEN1 17_23 0.000 68.80 8.3e-08 10.04
5462 GRB7 17_23 0.136 30.22 1.2e-05 -6.69
831 GSDMB 17_23 0.000 33.35 3.7e-08 -7.83
8537 ORMDL3 17_23 0.000 30.46 3.3e-08 -7.37
7994 GSDMA 17_23 0.000 6.81 8.7e-09 3.38
2359 PSMD3 17_23 0.001 20.30 7.9e-08 2.06
154 MED24 17_23 0.001 9.00 1.5e-08 -2.03
3899 THRA 17_23 0.000 13.13 1.4e-08 3.20
3900 NR1D1 17_23 0.003 25.99 2.3e-07 -1.84
2360 CASC3 17_23 0.468 29.43 4.0e-05 3.06
2361 RAPGEFL1 17_23 0.006 19.68 3.2e-07 -1.95
8464 WIPF2 17_23 0.000 5.94 6.6e-09 -1.34
1346 CDC6 17_23 0.000 5.56 7.0e-09 0.33
4320 RARA 17_23 0.005 30.69 4.3e-07 -2.11
5466 IGFBP4 17_23 0.007 19.77 3.8e-07 -2.96
4318 TNS4 17_23 0.009 27.00 6.8e-07 2.82
830 SMARCE1 17_23 0.001 13.94 4.4e-08 1.51
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_48"
genename region_tag susie_pip mu2 PVE z
4742 RAB11FIP5 2_48 0.008 11.19 2.5e-07 -2.41
4743 SMYD5 2_48 0.009 7.50 2.0e-07 -1.29
4739 PRADC1 2_48 0.012 22.12 7.8e-07 3.79
4741 CCT7 2_48 0.012 22.12 7.8e-07 3.79
7156 FBXO41 2_48 0.008 5.71 1.3e-07 -0.92
3060 ALMS1 2_48 0.019 196.46 1.1e-05 -14.49
5699 TPRKB 2_48 0.010 8.59 2.5e-07 -1.39
12374 RP11-434P11.2 2_48 0.019 132.23 7.2e-06 -11.86
3708 STAMBP 2_48 0.025 28.91 2.1e-06 4.11
7157 ACTG2 2_48 0.012 8.53 3.0e-07 0.34
2928 DGUOK 2_48 0.008 5.17 1.2e-07 -0.39
11157 RP11-287D1.4 2_48 0.078 28.29 6.4e-06 -2.99
2929 MOB1A 2_48 0.023 15.05 1.0e-06 -1.83
648 MTHFD2 2_48 0.008 5.34 1.3e-07 -0.56
10123 SLC4A5 2_48 0.011 7.56 2.4e-07 -0.49
10870 DCTN1 2_48 0.010 6.65 2.0e-07 0.04
70 WDR54 2_48 0.133 19.73 7.6e-06 -3.57
12621 C2orf81 2_48 0.133 19.73 7.6e-06 -3.57
2931 RTKN 2_48 0.009 9.79 2.6e-07 -2.18
2963 INO80B 2_48 0.029 15.36 1.3e-06 3.06
11530 WBP1 2_48 0.036 18.65 1.9e-06 3.19
2964 MOGS 2_48 0.008 5.29 1.2e-07 -1.00
2965 TTC31 2_48 0.145 19.51 8.2e-06 -3.62
4745 CCDC142 2_48 0.145 19.51 8.2e-06 3.62
9269 LBX2 2_48 0.302 21.70 1.9e-05 -3.81
2966 PCGF1 2_48 0.015 11.11 4.7e-07 1.27
5702 DQX1 2_48 0.016 12.02 5.6e-07 0.80
2972 HTRA2 2_48 0.008 7.00 1.6e-07 1.13
2973 LOXL3 2_48 0.012 8.61 3.0e-07 0.94
2974 DOK1 2_48 0.012 8.50 2.9e-07 0.88
6778 M1AP 2_48 0.012 8.04 2.7e-07 0.62
4740 SEMA4F 2_48 0.008 5.35 1.3e-07 -0.26
6781 HK2 2_48 0.008 4.83 1.1e-07 -0.13
2976 POLE4 2_48 0.021 14.67 8.9e-07 -1.78
10866 LINC01291 2_48 0.016 12.51 6.0e-07 -1.66
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 7_49"
genename region_tag susie_pip mu2 PVE z
4686 CCDC146 7_49 0 11.98 0 -1.47
3992 FGL2 7_49 0 118.44 0 -1.89
9888 GSAP 7_49 0 31.81 0 -0.02
3990 PTPN12 7_49 0 321.86 0 -2.99
11094 APTR 7_49 0 4894.91 0 -3.02
4687 TMEM60 7_49 0 25335.61 0 -13.43
98 PHTF2 7_49 0 1553.17 0 -3.12
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_23"
genename region_tag susie_pip mu2 PVE z
8119 ZNF507 19_23 0.001 8.33 3.4e-08 0.81
9215 DPY19L3 19_23 0.002 9.98 4.5e-08 1.36
2030 PDCD5 19_23 0.099 30.97 8.9e-06 -0.72
2031 ANKRD27 19_23 0.005 35.16 5.3e-07 5.40
11069 NUDT19 19_23 0.012 25.27 9.0e-07 -1.64
12135 CTD-2538C1.2 19_23 0.003 39.67 3.7e-07 -5.90
271 SLC7A9 19_23 0.002 101.08 6.2e-07 -12.59
912 GPATCH1 19_23 0.001 7.41 2.4e-08 1.51
4335 FAAP24 19_23 0.001 15.72 4.8e-08 5.31
3475 CEP89 19_23 0.001 16.20 5.1e-08 4.13
4333 RHPN2 19_23 0.004 17.42 1.8e-07 -1.31
4249 LRP3 19_23 0.001 8.73 3.0e-08 -0.70
7728 WDR88 19_23 0.002 14.17 8.5e-08 -1.25
4248 SLC7A10 19_23 0.001 7.98 3.1e-08 0.66
11668 CEBPA 19_23 0.001 5.70 1.8e-08 0.28
6370 CEBPG 19_23 0.002 10.60 6.9e-08 0.72
3706 PEPD 19_23 0.003 12.82 1.1e-07 -1.32
3707 CHST8 19_23 0.152 39.11 1.7e-05 3.17
| 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
30942 rs61818825 1_69 1.000 154.78 4.5e-04 12.64
38249 rs9425587 1_84 1.000 41.88 1.2e-04 6.25
41811 rs79091515 1_92 1.000 79.73 2.3e-04 9.80
54821 rs287613 1_116 1.000 765.18 2.2e-03 -3.26
54827 rs71180790 1_116 1.000 756.90 2.2e-03 -3.16
55787 rs766167074 1_118 1.000 14204.25 4.1e-02 -4.30
70934 rs780093 2_16 1.000 180.99 5.3e-04 17.73
70935 rs6744393 2_16 1.000 80.80 2.3e-04 12.12
77784 rs11689011 2_29 1.000 38.12 1.1e-04 -5.98
96134 rs11123169 2_67 1.000 73.72 2.1e-04 -8.05
98214 rs141849010 2_69 1.000 54.77 1.6e-04 7.31
111772 rs7565788 2_103 1.000 65.17 1.9e-04 9.30
114127 rs863678 2_106 1.000 167.24 4.9e-04 11.18
123499 rs11887861 2_124 1.000 304.88 8.9e-04 -11.03
188766 rs146797780 3_110 1.000 10967.96 3.2e-02 2.47
188767 rs7636471 3_110 1.000 10985.80 3.2e-02 2.53
193387 rs7642977 3_118 1.000 51.62 1.5e-04 7.37
212377 rs66998340 4_36 1.000 1259.35 3.7e-03 -3.18
259108 rs386057 5_1 1.000 58.66 1.7e-04 -7.24
259254 rs62331274 5_2 1.000 44.21 1.3e-04 6.71
280051 rs11743158 5_41 1.000 109.93 3.2e-04 10.79
313352 rs35716097 5_106 1.000 179.83 5.2e-04 18.22
313355 rs7447593 5_106 1.000 214.92 6.2e-04 19.58
317575 rs13193887 6_7 1.000 99.64 2.9e-04 10.02
324171 rs62394272 6_20 1.000 75.38 2.2e-04 -9.05
329064 rs56144236 6_27 1.000 47.57 1.4e-04 -4.54
331864 rs10223666 6_34 1.000 253.90 7.4e-04 16.43
360794 rs199804242 6_89 1.000 44599.12 1.3e-01 5.05
367762 rs3119311 6_104 1.000 47841.01 1.4e-01 16.16
367803 rs60425481 6_104 1.000 65229.65 1.9e-01 2.37
372265 rs78148157 7_3 1.000 214.43 6.2e-04 -11.39
372266 rs13241427 7_3 1.000 196.95 5.7e-04 12.32
388851 rs700752 7_34 1.000 126.18 3.7e-04 10.76
397908 rs10277379 7_49 1.000 43077.45 1.3e-01 12.60
397911 rs761767938 7_49 1.000 55686.95 1.6e-01 11.05
397919 rs1544459 7_49 1.000 55059.40 1.6e-01 11.28
427517 rs2428 8_11 1.000 753.56 2.2e-03 -8.02
427522 rs758184196 8_11 1.000 745.28 2.2e-03 2.64
427776 rs7012814 8_12 1.000 79.57 2.3e-04 -10.88
434136 rs4871905 8_24 1.000 229.84 6.7e-04 16.47
463421 rs6996786 8_84 1.000 3786.74 1.1e-02 1.60
463428 rs200311702 8_84 1.000 3697.65 1.1e-02 3.90
468898 rs72693377 8_94 1.000 50.94 1.5e-04 7.24
503275 rs1886296 9_73 1.000 45.88 1.3e-04 -3.91
503288 rs12380852 9_73 1.000 44.52 1.3e-04 4.33
503768 rs113790047 10_3 1.000 138.76 4.0e-04 12.47
520275 rs35182775 10_33 1.000 104.73 3.0e-04 -10.82
535865 rs1408345 10_64 1.000 37.09 1.1e-04 5.68
546578 rs231889 11_2 1.000 59.24 1.7e-04 -8.73
556802 rs369062552 11_21 1.000 317.92 9.2e-04 15.06
556812 rs34830202 11_21 1.000 353.93 1.0e-03 -16.29
567315 rs72917317 11_38 1.000 72.62 2.1e-04 -7.67
588638 rs3782860 12_1 1.000 196.99 5.7e-04 12.41
592217 rs11616030 12_11 1.000 58.10 1.7e-04 -7.64
593242 rs11056397 12_13 1.000 48.27 1.4e-04 -6.76
604943 rs2682323 12_33 1.000 56.69 1.6e-04 -7.02
605957 rs7397189 12_36 1.000 74.22 2.2e-04 -8.81
640719 rs7325692 13_21 1.000 356.93 1.0e-03 -4.38
640735 rs577954961 13_21 1.000 337.10 9.8e-04 -1.94
640869 rs1326122 13_21 1.000 68.36 2.0e-04 8.48
671262 rs72681869 14_20 1.000 63.64 1.8e-04 -8.12
704614 rs2472297 15_35 1.000 119.56 3.5e-04 -11.99
704856 rs145727191 15_35 1.000 79.42 2.3e-04 11.21
704885 rs2955742 15_36 1.000 63.21 1.8e-04 8.90
706403 rs7174325 15_38 1.000 37.27 1.1e-04 5.72
714086 rs138922864 16_3 1.000 42.53 1.2e-04 6.32
724857 rs12927956 16_27 1.000 117.34 3.4e-04 9.37
730882 rs7187317 16_40 1.000 45.91 1.3e-04 5.50
744611 rs139356332 17_16 1.000 52.09 1.5e-04 8.03
744623 rs7222869 17_16 1.000 43.38 1.3e-04 -8.09
748270 rs12453645 17_23 1.000 84.03 2.4e-04 13.02
753957 rs3032584 17_35 1.000 246.27 7.2e-04 16.69
754015 rs11650989 17_36 1.000 244.55 7.1e-04 -19.89
768352 rs162000 18_14 1.000 59.07 1.7e-04 7.75
774726 rs2878889 18_27 1.000 45.17 1.3e-04 -6.53
796338 rs11084684 19_23 1.000 86.62 2.5e-04 9.12
797391 rs1137844 19_24 1.000 64.12 1.9e-04 -8.09
798992 rs814573 19_31 1.000 71.98 2.1e-04 -8.73
819086 rs209955 20_32 1.000 63.71 1.9e-04 8.91
819090 rs2585441 20_32 1.000 82.94 2.4e-04 9.27
819113 rs6068816 20_32 1.000 43.21 1.3e-04 -6.75
820301 rs6025623 20_33 1.000 48.73 1.4e-04 7.26
837382 rs2103861 22_9 1.000 34.92 1.0e-04 -5.69
919036 rs57751786 6_32 1.000 2248.62 6.5e-03 2.80
935867 rs73025562 6_103 1.000 105.99 3.1e-04 -5.85
997442 rs201524046 10_81 1.000 17288.34 5.0e-02 -6.50
997461 rs568584257 10_81 1.000 17229.70 5.0e-02 -2.04
1068185 rs374141296 19_34 1.000 14014.15 4.1e-02 -3.31
1078141 rs202143810 20_38 1.000 13977.63 4.1e-02 -6.50
126296 rs112068790 2_129 0.999 37.66 1.1e-04 -7.53
198929 rs4533774 4_11 0.999 46.60 1.4e-04 6.65
221337 rs111470070 4_52 0.999 49.91 1.4e-04 5.66
230294 rs2903386 4_69 0.999 43.35 1.3e-04 -5.51
327674 rs116339629 6_26 0.999 51.11 1.5e-04 -5.78
427516 rs117209427 8_11 0.999 45.54 1.3e-04 1.60
830211 rs219783 21_16 0.999 53.89 1.6e-04 -7.26
1068182 rs113176985 19_34 0.999 13925.53 4.0e-02 -3.09
346879 rs854922 6_61 0.998 31.81 9.2e-05 4.64
503283 rs72773787 9_73 0.998 41.58 1.2e-04 4.15
730868 rs62053193 16_40 0.998 36.39 1.1e-04 4.57
197268 rs115976359 4_7 0.997 29.88 8.7e-05 -5.34
334386 rs76572975 6_39 0.997 37.23 1.1e-04 -7.13
546375 rs17885785 11_2 0.997 85.17 2.5e-04 8.76
546576 rs186376420 11_2 0.997 47.52 1.4e-04 -7.99
791998 rs35218652 19_15 0.997 53.77 1.6e-04 5.10
969980 rs142540788 9_50 0.997 35.19 1.0e-04 -5.60
15966 rs2474382 1_38 0.996 29.38 8.5e-05 -5.51
27507 rs12407689 1_62 0.996 33.06 9.6e-05 5.50
30939 rs78366259 1_69 0.996 31.55 9.1e-05 4.65
526457 rs72797524 10_46 0.996 30.86 8.9e-05 -5.41
271236 rs4703440 5_23 0.995 53.87 1.6e-04 7.02
482381 rs56030777 9_25 0.995 31.30 9.1e-05 4.66
755927 rs8072180 17_39 0.995 60.54 1.8e-04 8.03
813325 rs34106705 20_20 0.995 41.78 1.2e-04 6.72
126305 rs6747041 2_129 0.994 96.73 2.8e-04 -11.00
420885 rs288762 7_97 0.994 115.37 3.3e-04 10.61
687095 rs75432828 14_52 0.993 55.80 1.6e-04 7.71
695820 rs74009639 15_17 0.993 177.64 5.1e-04 11.23
322116 rs3763278 6_15 0.992 34.68 1.0e-04 5.03
191198 rs13069721 3_115 0.991 42.07 1.2e-04 -6.50
428015 rs10093915 8_13 0.990 62.99 1.8e-04 9.30
748148 rs530253 17_23 0.990 35.04 1.0e-04 -6.36
948634 rs11557049 8_50 0.990 63.33 1.8e-04 7.79
360153 rs9373056 6_88 0.989 54.71 1.6e-04 -8.43
272748 rs17395128 5_26 0.988 33.79 9.7e-05 -5.73
369985 rs1445288 6_108 0.988 31.05 8.9e-05 5.37
427330 rs2976846 8_11 0.988 247.27 7.1e-04 -8.89
739946 rs4790812 17_2 0.988 28.48 8.2e-05 5.01
194149 rs13059257 3_120 0.987 70.44 2.0e-04 7.67
210730 rs11732881 4_34 0.987 39.69 1.1e-04 -5.92
739032 rs34404057 16_54 0.987 90.83 2.6e-04 8.82
102959 rs7602029 2_81 0.985 42.35 1.2e-04 6.84
271346 rs11740818 5_23 0.985 32.43 9.3e-05 -5.28
275538 rs113088001 5_31 0.985 56.40 1.6e-04 -9.88
820243 rs6099616 20_33 0.985 29.78 8.5e-05 5.82
242660 rs115900720 4_94 0.984 28.03 8.0e-05 -4.91
436874 rs12544558 8_31 0.984 58.58 1.7e-04 -6.14
566474 rs4601790 11_36 0.983 58.52 1.7e-04 2.39
744637 rs56700256 17_16 0.983 26.63 7.6e-05 4.85
166732 rs7640740 3_66 0.982 33.90 9.7e-05 -5.61
750262 rs137906947 17_27 0.982 29.69 8.5e-05 5.17
406117 rs543883 7_65 0.980 26.98 7.7e-05 4.94
699701 rs11855136 15_25 0.978 27.41 7.8e-05 4.96
589633 rs12370932 12_3 0.977 33.32 9.5e-05 -4.53
721061 rs9933330 16_19 0.976 542.73 1.5e-03 -24.08
80905 rs3106204 2_36 0.975 57.19 1.6e-04 7.63
148130 rs811970 3_28 0.975 29.63 8.4e-05 -5.18
52660 rs61830291 1_112 0.974 74.25 2.1e-04 -8.72
676420 rs1997896 14_33 0.974 39.18 1.1e-04 -5.79
482677 rs11557154 9_27 0.972 35.69 1.0e-04 -6.02
110237 rs4667700 2_99 0.970 26.19 7.4e-05 -4.74
612436 rs1848968 12_48 0.970 39.48 1.1e-04 -6.17
843546 rs28477160 22_20 0.970 27.02 7.6e-05 4.16
470051 rs1538532 9_3 0.969 25.54 7.2e-05 4.74
838106 rs740219 22_10 0.967 35.78 1.0e-04 -3.87
412508 rs3757387 7_79 0.966 51.23 1.4e-04 8.53
490258 rs1360200 9_45 0.966 29.89 8.4e-05 -5.32
275537 rs1694060 5_31 0.965 49.91 1.4e-04 -7.45
779328 rs532969215 18_35 0.964 25.75 7.2e-05 -4.81
420946 rs6459970 7_97 0.963 31.24 8.7e-05 5.84
684369 rs8013584 14_47 0.962 27.96 7.8e-05 5.85
14356 rs1331858 1_35 0.961 58.26 1.6e-04 -7.81
454393 rs28628213 8_67 0.961 26.15 7.3e-05 4.73
800242 rs281380 19_33 0.961 50.26 1.4e-04 -6.97
660746 rs750598 13_59 0.960 60.12 1.7e-04 7.90
111802 rs7594986 2_103 0.959 46.98 1.3e-04 8.14
362706 rs12216122 6_94 0.959 28.16 7.8e-05 4.96
150989 rs146456061 3_35 0.958 29.91 8.3e-05 -4.46
498516 rs10817912 9_60 0.958 66.14 1.8e-04 -7.47
749763 rs2074292 17_27 0.958 32.01 8.9e-05 -5.39
387213 rs11761217 7_30 0.957 25.62 7.1e-05 4.63
842070 rs13055886 22_18 0.957 58.55 1.6e-04 6.98
76648 rs13428381 2_27 0.956 63.39 1.8e-04 -8.30
116068 rs11690832 2_110 0.955 35.03 9.7e-05 6.62
514890 rs11007559 10_21 0.954 34.63 9.6e-05 5.82
540294 rs1932558 10_73 0.954 26.03 7.2e-05 4.86
22386 rs6661091 1_50 0.949 78.81 2.2e-04 8.98
1028420 rs117139138 15_39 0.947 31.44 8.7e-05 4.96
382347 rs67971665 7_23 0.946 43.63 1.2e-04 -6.48
576675 rs10892860 11_57 0.946 26.81 7.4e-05 4.92
447742 rs2941452 8_55 0.940 38.60 1.1e-04 -5.34
798113 rs2228068 19_26 0.940 30.23 8.3e-05 -3.70
317481 rs9378483 6_7 0.938 25.11 6.8e-05 -3.71
273000 rs4957118 5_26 0.937 40.08 1.1e-04 7.31
841940 rs12484310 22_18 0.936 26.41 7.2e-05 4.72
561600 rs10219383 11_28 0.935 25.95 7.1e-05 -4.87
997445 rs74160216 10_81 0.935 17223.42 4.7e-02 -2.10
125727 rs2068218 2_128 0.928 24.41 6.6e-05 -4.13
52382 rs884127 1_112 0.927 27.49 7.4e-05 -4.85
360810 rs6923513 6_89 0.926 44673.08 1.2e-01 5.13
715474 rs148361522 16_6 0.926 24.13 6.5e-05 -4.50
807324 rs7264882 20_8 0.925 28.91 7.8e-05 5.18
259111 rs185228153 5_1 0.924 27.28 7.3e-05 3.20
284117 rs115912456 5_49 0.922 23.45 6.3e-05 4.43
150664 rs116643069 3_35 0.921 29.76 8.0e-05 3.37
447716 rs2672853 8_55 0.921 27.21 7.3e-05 -4.11
590057 rs78470967 12_5 0.919 25.38 6.8e-05 -5.22
110208 rs75483173 2_98 0.917 25.42 6.8e-05 -4.66
75661 rs13418726 2_26 0.916 34.00 9.1e-05 -5.70
304083 rs1800888 5_87 0.916 23.70 6.3e-05 -4.09
537928 rs2050996 10_69 0.916 35.77 9.5e-05 5.81
467810 rs56114972 8_92 0.911 24.26 6.4e-05 -4.15
704886 rs143214734 15_36 0.910 25.17 6.7e-05 4.16
421206 rs2530736 7_98 0.909 37.49 9.9e-05 5.99
885901 rs3806357 1_102 0.909 43.29 1.1e-04 5.28
586128 rs3935795 11_80 0.908 26.71 7.0e-05 5.10
604024 rs1878234 12_31 0.908 26.73 7.1e-05 -4.48
186148 rs6770214 3_105 0.907 24.69 6.5e-05 -4.59
359581 rs7753497 6_87 0.907 37.65 9.9e-05 7.44
738917 rs117652610 16_54 0.907 36.65 9.7e-05 -5.48
706802 rs28587782 15_38 0.906 45.82 1.2e-04 7.38
589697 rs79997404 12_3 0.905 110.48 2.9e-04 10.75
210069 rs278933 4_33 0.903 25.87 6.8e-05 4.71
352691 rs9285397 6_73 0.903 77.76 2.0e-04 -9.26
26002 rs138475481 1_58 0.901 37.95 9.9e-05 6.48
286216 rs3952745 5_53 0.901 25.55 6.7e-05 -5.13
790305 rs10854127 19_11 0.901 45.41 1.2e-04 6.60
744635 rs7224838 17_16 0.900 39.14 1.0e-04 6.57
231858 rs6533522 4_72 0.899 24.73 6.5e-05 -4.53
694439 rs62006522 15_13 0.898 24.68 6.4e-05 -3.68
721054 rs9923532 16_19 0.898 197.49 5.2e-04 10.53
317620 rs2842369 6_7 0.897 28.45 7.4e-05 5.18
421147 rs118063067 7_98 0.897 63.14 1.6e-04 -7.44
243619 rs10013413 4_96 0.896 32.40 8.4e-05 -5.40
557630 rs7938708 11_22 0.891 24.53 6.4e-05 4.40
365724 rs6939382 6_99 0.890 24.26 6.3e-05 4.35
586094 rs6590328 11_80 0.886 36.02 9.3e-05 -5.90
80810 rs10182366 2_36 0.885 57.31 1.5e-04 -7.54
724691 rs7205341 16_27 0.885 39.03 1.0e-04 5.97
434126 rs310311 8_24 0.877 88.97 2.3e-04 -11.91
792235 rs3794991 19_15 0.876 49.26 1.3e-04 7.01
397345 rs17685 7_48 0.875 85.96 2.2e-04 -9.47
636527 rs1539547 13_13 0.873 23.76 6.0e-05 -4.39
60782 rs1148917 1_130 0.872 25.42 6.4e-05 4.68
150364 rs73083115 3_33 0.872 25.60 6.5e-05 2.94
745665 rs117859452 17_18 0.872 26.23 6.6e-05 4.54
351301 rs6571142 6_70 0.870 24.01 6.1e-05 -4.35
114187 rs72940807 2_106 0.869 39.91 1.0e-04 8.03
711595 rs59646751 15_48 0.867 67.44 1.7e-04 8.23
128408 rs13029395 2_133 0.865 30.38 7.6e-05 -5.65
301424 rs62383025 5_82 0.864 29.82 7.5e-05 5.45
203955 rs61359609 4_20 0.863 39.72 1.0e-04 -6.41
491447 rs2185973 9_47 0.860 27.74 6.9e-05 -4.82
577259 rs625505 11_58 0.860 24.49 6.1e-05 -4.42
142780 rs697025 3_17 0.856 25.26 6.3e-05 -4.67
471010 rs10974435 9_4 0.854 33.52 8.3e-05 -6.63
7412 rs641452 1_16 0.852 46.23 1.1e-04 -7.05
360260 rs2327426 6_88 0.852 47.82 1.2e-04 6.61
96069 rs3811056 2_66 0.851 28.30 7.0e-05 4.73
694814 rs75855252 15_14 0.851 23.99 5.9e-05 -4.04
230283 rs57251748 4_69 0.850 25.89 6.4e-05 -2.91
46765 rs145759918 1_101 0.849 25.83 6.4e-05 -4.94
111834 rs6433115 2_103 0.848 29.09 7.2e-05 -5.77
31023 rs142669954 1_70 0.845 27.92 6.9e-05 5.24
625488 rs80019595 12_74 0.843 53.36 1.3e-04 7.36
613280 rs7137360 12_50 0.840 44.66 1.1e-04 6.48
47757 rs12024377 1_104 0.835 32.76 8.0e-05 -5.36
694989 rs4419033 15_15 0.835 117.17 2.8e-04 10.88
114860 rs12464787 2_108 0.834 30.20 7.3e-05 6.39
77558 rs74449116 2_28 0.830 26.02 6.3e-05 4.55
935871 rs662138 6_103 0.828 158.37 3.8e-04 -9.59
11263 rs2484713 1_27 0.827 57.37 1.4e-04 7.58
236752 rs10031936 4_81 0.825 24.23 5.8e-05 -4.32
611597 rs1690139 12_46 0.825 25.39 6.1e-05 -4.26
959298 rs4744712 9_30 0.825 191.72 4.6e-04 -15.18
48107 rs2075864 1_105 0.824 65.74 1.6e-04 8.32
304057 rs6885118 5_87 0.824 25.35 6.1e-05 4.24
359052 rs2326970 6_86 0.823 24.18 5.8e-05 -4.29
693531 rs12908082 15_11 0.822 25.34 6.1e-05 -4.42
98300 rs4241160 2_69 0.820 26.80 6.4e-05 4.31
829363 rs2834321 21_15 0.818 89.05 2.1e-04 9.57
763244 rs940131 18_4 0.816 72.26 1.7e-04 8.61
60212 rs2171975 1_128 0.815 74.60 1.8e-04 -8.72
91382 rs146133332 2_55 0.812 24.68 5.8e-05 -4.30
465011 rs57286830 8_87 0.812 24.90 5.9e-05 -4.38
546371 rs7115054 11_2 0.812 90.10 2.1e-04 8.84
728884 rs7204242 16_35 0.812 26.01 6.1e-05 4.75
736677 rs56404731 16_49 0.812 33.15 7.8e-05 -5.59
330791 rs10947659 6_29 0.811 25.95 6.1e-05 4.50
346875 rs7744005 6_61 0.810 48.05 1.1e-04 6.50
803806 rs535376 20_2 0.810 26.94 6.3e-05 4.72
592078 rs12824533 12_11 0.807 25.59 6.0e-05 -4.56
405058 rs438635 7_63 0.806 59.29 1.4e-04 7.36
152779 rs35364740 3_39 0.805 28.10 6.6e-05 5.00
436242 rs139800483 8_29 0.803 25.68 6.0e-05 -4.62
186467 rs59976239 3_105 0.802 25.00 5.8e-05 -4.41SNPs with largest effect sizes
#plot PIP vs effect size
plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
367799 rs3106169 6_104 0.727 65299.07 1.4e-01 10.90
367808 rs3106167 6_104 0.707 65298.74 1.3e-01 10.90
367800 rs3127598 6_104 0.124 65297.69 2.4e-02 10.89
367792 rs11755965 6_104 0.000 65277.12 9.3e-05 10.88
367803 rs60425481 6_104 1.000 65229.65 1.9e-01 2.37
367783 rs12194962 6_104 0.000 65138.10 0.0e+00 10.83
367801 rs3127597 6_104 0.000 65090.06 0.0e+00 10.77
397911 rs761767938 7_49 1.000 55686.95 1.6e-01 11.05
397919 rs1544459 7_49 1.000 55059.40 1.6e-01 11.28
397915 rs11972122 7_49 0.000 50191.90 3.4e-10 10.18
397916 rs11406602 7_49 0.000 50151.29 1.3e-06 10.20
397920 rs1544458 7_49 0.000 49300.08 0.0e+00 10.31
397910 rs6465794 7_49 0.000 48649.90 1.1e-16 10.03
397909 rs6465793 7_49 0.000 48649.24 1.3e-16 10.03
397940 rs10272350 7_49 0.000 48547.02 0.0e+00 9.89
367762 rs3119311 6_104 1.000 47841.01 1.4e-01 16.16
397944 rs2463008 7_49 0.000 46270.92 0.0e+00 10.84
397950 rs10267180 7_49 0.000 46256.79 0.0e+00 10.79
397890 rs13240016 7_49 0.000 46016.51 0.0e+00 9.61
397899 rs7799383 7_49 0.000 44900.07 0.0e+00 9.62
360810 rs6923513 6_89 0.926 44673.08 1.2e-01 5.13
360793 rs2327654 6_89 0.497 44669.12 6.5e-02 5.11
360794 rs199804242 6_89 1.000 44599.12 1.3e-01 5.05
360797 rs113527452 6_89 0.000 44435.59 8.6e-17 5.09
360802 rs200796875 6_89 0.000 44172.54 0.0e+00 4.95
360815 rs7756915 6_89 0.000 43901.26 0.0e+00 5.15
397908 rs10277379 7_49 1.000 43077.45 1.3e-01 12.60
397902 rs7795371 7_49 0.000 42373.83 1.8e-13 12.47
360808 rs6570040 6_89 0.000 42122.91 0.0e+00 4.88
360795 rs6570031 6_89 0.000 42016.05 0.0e+00 4.82
360796 rs9389323 6_89 0.000 41996.95 0.0e+00 4.80
397964 rs848470 7_49 0.000 38010.54 0.0e+00 -8.16
360812 rs9321531 6_89 0.000 36862.30 0.0e+00 4.53
360785 rs9321528 6_89 0.000 36409.70 0.0e+00 5.35
367756 rs3127579 6_104 0.000 35020.27 0.0e+00 17.91
360813 rs9494389 6_89 0.000 34614.86 0.0e+00 4.31
360817 rs5880262 6_89 0.000 34557.75 0.0e+00 4.66
360791 rs2208574 6_89 0.000 33410.58 0.0e+00 4.50
360790 rs1033755 6_89 0.000 33393.74 0.0e+00 4.27
360788 rs2038551 6_89 0.000 32802.19 0.0e+00 5.25
360799 rs9494377 6_89 0.000 32794.31 0.0e+00 4.32
360786 rs2038550 6_89 0.000 32714.39 0.0e+00 5.22
397858 rs9640663 7_49 0.000 32157.23 0.0e+00 8.74
397854 rs2868787 7_49 0.000 32156.75 0.0e+00 8.72
397888 rs58729654 7_49 0.000 31636.52 0.0e+00 10.11
397869 rs4727451 7_49 0.000 31610.83 0.0e+00 8.48
367750 rs10945658 6_104 0.000 30669.92 0.0e+00 18.00
367749 rs3119308 6_104 0.000 30597.65 0.0e+00 18.00
367745 rs3103352 6_104 0.000 30569.41 0.0e+00 17.77
367741 rs3101821 6_104 0.000 30461.44 0.0e+00 17.74SNPs 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
367803 rs60425481 6_104 1.000 65229.65 0.1900 2.37
397911 rs761767938 7_49 1.000 55686.95 0.1600 11.05
397919 rs1544459 7_49 1.000 55059.40 0.1600 11.28
367762 rs3119311 6_104 1.000 47841.01 0.1400 16.16
367799 rs3106169 6_104 0.727 65299.07 0.1400 10.90
360794 rs199804242 6_89 1.000 44599.12 0.1300 5.05
367808 rs3106167 6_104 0.707 65298.74 0.1300 10.90
397908 rs10277379 7_49 1.000 43077.45 0.1300 12.60
360810 rs6923513 6_89 0.926 44673.08 0.1200 5.13
360793 rs2327654 6_89 0.497 44669.12 0.0650 5.11
997442 rs201524046 10_81 1.000 17288.34 0.0500 -6.50
997461 rs568584257 10_81 1.000 17229.70 0.0500 -2.04
997445 rs74160216 10_81 0.935 17223.42 0.0470 -2.10
55787 rs766167074 1_118 1.000 14204.25 0.0410 -4.30
1068185 rs374141296 19_34 1.000 14014.15 0.0410 -3.31
1078141 rs202143810 20_38 1.000 13977.63 0.0410 -6.50
1068182 rs113176985 19_34 0.999 13925.53 0.0400 -3.09
188766 rs146797780 3_110 1.000 10967.96 0.0320 2.47
188767 rs7636471 3_110 1.000 10985.80 0.0320 2.53
1078138 rs6089961 20_38 0.666 13797.88 0.0270 -6.78
1078140 rs2738758 20_38 0.666 13797.88 0.0270 -6.78
367800 rs3127598 6_104 0.124 65297.69 0.0240 10.89
1068173 rs61371437 19_34 0.573 13897.49 0.0230 -3.04
55784 rs10489611 1_118 0.465 14114.83 0.0190 -4.67
55778 rs2256908 1_118 0.366 14113.84 0.0150 -4.67
1078121 rs2750483 20_38 0.356 13793.46 0.0140 -6.79
55786 rs971534 1_118 0.288 14114.60 0.0120 -4.65
55785 rs2486737 1_118 0.267 14114.55 0.0110 -4.65
463421 rs6996786 8_84 1.000 3786.74 0.0110 1.60
463428 rs200311702 8_84 1.000 3697.65 0.0110 3.90
55781 rs2790891 1_118 0.250 14113.61 0.0100 -4.66
55782 rs2491405 1_118 0.250 14113.61 0.0100 -4.66
1078119 rs35201382 20_38 0.243 13793.57 0.0097 -6.77
1078120 rs67468102 20_38 0.242 13791.57 0.0097 -6.78
55794 rs2211176 1_118 0.231 14108.98 0.0095 -4.66
55795 rs2790882 1_118 0.231 14108.98 0.0095 -4.66
1068163 rs739349 19_34 0.201 13847.58 0.0081 -3.09
1078116 rs2315009 20_38 0.186 13789.24 0.0075 -6.79
919036 rs57751786 6_32 1.000 2248.62 0.0065 2.80
1068164 rs756628 19_34 0.158 13847.47 0.0064 -3.09
55793 rs2248646 1_118 0.154 14108.16 0.0063 -4.65
55774 rs1076804 1_118 0.122 14093.39 0.0050 -4.68
919038 rs9369250 6_32 0.629 2250.40 0.0041 3.22
212377 rs66998340 4_36 1.000 1259.35 0.0037 -3.18
919025 rs714050 6_32 0.533 2250.13 0.0035 3.21
212380 rs728294 4_36 0.771 1291.25 0.0029 -3.21
212378 rs4359873 4_36 0.728 1291.23 0.0027 -3.21
919033 rs9369249 6_32 0.407 2249.54 0.0027 3.21
54821 rs287613 1_116 1.000 765.18 0.0022 -3.26
54827 rs71180790 1_116 1.000 756.90 0.0022 -3.16SNPs 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
695810 rs1145077 15_17 0.372 450.25 4.9e-04 27.01
695807 rs1153855 15_17 0.308 449.62 4.0e-04 27.00
695812 rs1346267 15_17 0.238 448.90 3.1e-04 26.98
695806 rs35410548 15_17 0.084 444.59 1.1e-04 26.89
221456 rs17253722 4_52 0.698 493.38 1.0e-03 26.25
221455 rs60529470 4_52 0.302 491.49 4.3e-04 26.19
695814 rs1145074 15_17 0.373 456.94 5.0e-04 26.09
695809 rs2114501 15_17 0.111 452.61 1.5e-04 26.00
695802 rs4775909 15_17 0.066 451.19 8.6e-05 25.98
695804 rs4625670 15_17 0.056 450.38 7.4e-05 25.96
695803 rs77940260 15_17 0.040 449.26 5.2e-05 25.91
695805 rs3047503 15_17 0.040 449.18 5.2e-05 25.91
695800 rs143910737 15_17 0.013 446.11 1.6e-05 25.78
695811 rs1153852 15_17 0.001 412.73 1.5e-06 25.47
695798 rs35715322 15_17 0.001 392.97 6.9e-07 25.39
695817 rs2433616 15_17 0.001 392.53 1.0e-06 24.72
695799 rs1613559 15_17 0.001 410.04 7.5e-07 24.70
695797 rs12593370 15_17 0.001 403.98 7.2e-07 24.56
221469 rs13146163 4_52 0.001 417.67 7.2e-07 24.33
695796 rs66893308 15_17 0.001 365.84 8.4e-07 24.15
721061 rs9933330 16_19 0.976 542.73 1.5e-03 -24.08
721059 rs28544423 16_19 0.023 535.25 3.6e-05 -23.84
721055 rs35830321 16_19 0.000 521.47 6.1e-10 -23.73
419292 rs10224210 7_94 0.667 520.88 1.0e-03 23.69
419294 rs10224002 7_94 0.335 521.32 5.1e-04 23.66
721056 rs12934320 16_19 0.000 526.28 5.4e-07 -23.62
721058 rs28640218 16_19 0.000 521.39 1.1e-08 -23.54
695795 rs2015295 15_17 0.001 308.19 7.1e-07 22.29
695793 rs11636114 15_17 0.001 301.86 7.7e-07 -22.11
695790 rs77342224 15_17 0.001 298.39 8.0e-07 -21.99
695787 rs12909625 15_17 0.001 276.96 1.0e-06 -21.16
695788 rs12909883 15_17 0.001 277.01 1.0e-06 -21.16
695789 rs8041874 15_17 0.001 276.58 1.0e-06 -21.15
695783 rs11854325 15_17 0.001 270.43 9.7e-07 -20.90
695784 rs11632778 15_17 0.001 270.15 9.9e-07 -20.89
221431 rs72657813 4_52 0.000 285.68 1.1e-07 20.55
221472 rs2068062 4_52 0.000 257.98 4.6e-08 20.55
221473 rs13106227 4_52 0.000 256.92 4.4e-08 20.53
221474 rs11730486 4_52 0.000 256.43 4.4e-08 20.52
221424 rs3839121 4_52 0.000 283.38 9.6e-08 20.51
221475 rs4859683 4_52 0.000 255.55 4.3e-08 20.50
695786 rs12910143 15_17 0.001 291.71 8.2e-07 -20.47
419290 rs66497154 7_94 0.001 381.75 8.3e-07 20.35
221440 rs59795151 4_52 0.000 265.90 6.2e-08 20.13
221476 rs4493564 4_52 0.000 240.48 3.2e-08 20.12
754015 rs11650989 17_36 1.000 244.55 7.1e-04 -19.89
721060 rs7193058 16_19 0.000 425.06 2.8e-10 19.85
272708 rs700231 5_26 0.586 203.85 3.5e-04 19.81
272710 rs700237 5_26 0.404 202.71 2.4e-04 19.79
313355 rs7447593 5_106 1.000 214.92 6.2e-04 19.58Gene 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] 24if (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"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)MAN1A2 gene(s) from the input list not found in DisGeNET CURATEDFAM177A1 gene(s) from the input list not found in DisGeNET CURATEDRP11-274B18.4 gene(s) from the input list not found in DisGeNET CURATEDFCGRT gene(s) from the input list not found in DisGeNET CURATEDPTK6 gene(s) from the input list not found in DisGeNET CURATEDBIN3 gene(s) from the input list not found in DisGeNET CURATEDZDHHC18 gene(s) from the input list not found in DisGeNET CURATEDPALM gene(s) from the input list not found in DisGeNET CURATEDMEA1 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
15 Fucosidase Deficiency Disease 0.008008658 1/14 1/9703
48 Fucosidosis Type I 0.008008658 1/14 1/9703
49 Fucosidosis Type II 0.008008658 1/14 1/9703
50 Severe myopia 0.008008658 1/14 1/9703
54 Vitreoretinal degeneration 0.008008658 1/14 1/9703
79 DEAFNESS, AUTOSOMAL RECESSIVE 32 0.008008658 1/14 1/9703
80 Knobloch syndrome 0.008008658 1/14 1/9703
81 Neutral Lipid Storage Disease with Myopathy 0.008008658 1/14 1/9703
83 Complement Component 7 Deficiency 0.008008658 1/14 1/9703
84 Medullary cystic kidney disease 1 0.008008658 1/14 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