Cystatin C (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 Cystatin C (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-30720_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0113419558 0.0002237702 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
16.02701 19.13712 #report sample size
print(sample_size)[1] 344264#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.00585836 0.10818652 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02292221 1.45616039Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
982 CDC14A 1_61 0.993 59.02 1.7e-04 -7.83
9813 MUC1 1_77 0.992 73.30 2.1e-04 -9.76
3338 DCAF4 14_34 0.992 58.38 1.7e-04 7.95
1445 MMP11 22_6 0.989 26.13 7.5e-05 4.82
6206 ZNF827 4_95 0.979 32.53 9.3e-05 -5.26
6729 CDA 1_14 0.973 24.23 6.8e-05 4.77
380 RAI14 5_23 0.973 25.61 7.2e-05 4.86
10816 MBD5 2_88 0.972 65.88 1.9e-04 8.51
6010 PTGES 9_67 0.969 25.73 7.2e-05 4.48
6446 CXXC1 18_28 0.955 24.04 6.7e-05 -4.62
6598 NRG1 8_31 0.954 44.15 1.2e-04 -5.09
8999 LPCAT4 15_10 0.953 23.50 6.5e-05 -4.77
3750 MEA1 6_33 0.941 25.36 6.9e-05 5.66
6986 LSM12 17_26 0.919 20.56 5.5e-05 -4.00
5012 TRIM29 11_72 0.893 25.19 6.5e-05 4.83
5558 ZNF697 1_73 0.885 26.03 6.7e-05 5.33
3979 VIL1 2_129 0.864 94.23 2.4e-04 9.95
11105 MEG3 14_52 0.836 33.60 8.2e-05 6.33
10110 C15orf52 15_14 0.811 22.43 5.3e-05 4.68
4915 TEX10 9_50 0.786 22.32 5.1e-05 4.22Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
4687 TMEM60 7_49 0.000 19035.32 0.0e+00 -11.57
1732 CST3 20_17 0.000 19019.98 0.0e+00 142.78
4733 AHI1 6_89 0.000 6890.72 0.0e+00 -3.41
168 SPRTN 1_118 0.000 6846.48 7.7e-06 3.61
3138 EXOC8 1_118 0.000 4922.91 1.2e-08 3.29
11094 APTR 7_49 0.000 3561.07 0.0e+00 -2.79
417 MAP4 3_34 0.000 2313.10 6.0e-07 2.66
98 PHTF2 7_49 0.000 2119.66 0.0e+00 -1.05
3140 TSNAX 1_118 0.000 960.31 0.0e+00 2.70
7365 ZNF589 3_34 0.001 843.67 3.4e-06 -5.58
2818 SLC12A7 5_2 0.000 733.57 2.0e-09 1.78
11334 SPINK8 3_34 0.003 718.47 5.3e-06 -5.74
7363 CDC25A 3_34 0.001 706.17 2.7e-06 -5.43
3838 GZF1 20_17 0.000 643.44 0.0e+00 -13.59
7145 DISC1 1_118 0.000 582.22 0.0e+00 -2.64
271 SLC7A9 19_23 0.000 359.86 7.8e-09 -9.90
3990 PTPN12 7_49 0.000 214.68 0.0e+00 -1.88
3839 NAPB 20_17 0.000 200.74 0.0e+00 -15.40
7366 PLXNB1 3_34 0.136 186.15 7.4e-05 6.40
7367 CCDC51 3_34 0.167 183.91 8.9e-05 6.44Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
3979 VIL1 2_129 0.864 94.23 2.4e-04 9.95
9813 MUC1 1_77 0.992 73.30 2.1e-04 -9.76
10816 MBD5 2_88 0.972 65.88 1.9e-04 8.51
982 CDC14A 1_61 0.993 59.02 1.7e-04 -7.83
3338 DCAF4 14_34 0.992 58.38 1.7e-04 7.95
6598 NRG1 8_31 0.954 44.15 1.2e-04 -5.09
7091 NEXN 1_48 0.767 47.62 1.1e-04 8.14
5657 ACP1 2_1 0.355 99.40 1.0e-04 -10.52
6206 ZNF827 4_95 0.979 32.53 9.3e-05 -5.26
7367 CCDC51 3_34 0.167 183.91 8.9e-05 6.44
11380 TMA7 3_34 0.167 183.91 8.9e-05 6.44
11105 MEG3 14_52 0.836 33.60 8.2e-05 6.33
10392 SND1 7_79 0.344 78.10 7.8e-05 9.19
2872 COL7A1 3_34 0.525 50.47 7.7e-05 -5.36
10739 METTL10 10_78 0.498 51.95 7.5e-05 -9.13
1445 MMP11 22_6 0.989 26.13 7.5e-05 4.82
7366 PLXNB1 3_34 0.136 186.15 7.4e-05 6.40
380 RAI14 5_23 0.973 25.61 7.2e-05 4.86
6010 PTGES 9_67 0.969 25.73 7.2e-05 4.48
4564 PSRC1 1_67 0.768 31.98 7.1e-05 5.71Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1732 CST3 20_17 0.000 19019.98 0.0e+00 142.78
3837 CD93 20_16 0.000 183.61 0.0e+00 20.22
8173 LMAN2 5_106 0.011 152.51 4.8e-06 16.22
3839 NAPB 20_17 0.000 200.74 0.0e+00 -15.40
3838 GZF1 20_17 0.000 643.44 0.0e+00 -13.59
5875 CRIP3 6_33 0.004 143.26 1.8e-06 -13.58
4046 IRF5 7_79 0.000 127.01 1.6e-09 13.27
7712 C2 6_26 0.000 118.10 1.4e-11 12.31
10808 NEU1 6_26 0.000 116.94 9.3e-12 -12.28
10825 APOM 6_26 0.000 114.94 1.0e-11 -12.25
11652 C4A 6_26 0.000 112.91 7.4e-13 -12.23
11047 CLIC1 6_26 0.000 113.20 4.7e-12 -12.18
11218 C4B 6_26 0.000 104.80 1.4e-13 11.79
4687 TMEM60 7_49 0.000 19035.32 0.0e+00 -11.57
11346 RP4-737E23.2 20_16 0.000 116.81 0.0e+00 -11.40
2611 ALDH2 12_67 0.020 126.81 7.5e-06 -11.33
11891 RP3-473L9.4 12_67 0.006 151.75 2.8e-06 -11.25
8691 MAP3K11 11_36 0.001 113.23 4.8e-07 11.00
1210 MAPKAPK5 12_67 0.017 100.02 5.0e-06 10.55
1227 ERP29 12_67 0.017 99.70 4.9e-06 10.54Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03262731#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1732 CST3 20_17 0.000 19019.98 0.0e+00 142.78
3837 CD93 20_16 0.000 183.61 0.0e+00 20.22
8173 LMAN2 5_106 0.011 152.51 4.8e-06 16.22
3839 NAPB 20_17 0.000 200.74 0.0e+00 -15.40
3838 GZF1 20_17 0.000 643.44 0.0e+00 -13.59
5875 CRIP3 6_33 0.004 143.26 1.8e-06 -13.58
4046 IRF5 7_79 0.000 127.01 1.6e-09 13.27
7712 C2 6_26 0.000 118.10 1.4e-11 12.31
10808 NEU1 6_26 0.000 116.94 9.3e-12 -12.28
10825 APOM 6_26 0.000 114.94 1.0e-11 -12.25
11652 C4A 6_26 0.000 112.91 7.4e-13 -12.23
11047 CLIC1 6_26 0.000 113.20 4.7e-12 -12.18
11218 C4B 6_26 0.000 104.80 1.4e-13 11.79
4687 TMEM60 7_49 0.000 19035.32 0.0e+00 -11.57
11346 RP4-737E23.2 20_16 0.000 116.81 0.0e+00 -11.40
2611 ALDH2 12_67 0.020 126.81 7.5e-06 -11.33
11891 RP3-473L9.4 12_67 0.006 151.75 2.8e-06 -11.25
8691 MAP3K11 11_36 0.001 113.23 4.8e-07 11.00
1210 MAPKAPK5 12_67 0.017 100.02 5.0e-06 10.55
1227 ERP29 12_67 0.017 99.70 4.9e-06 10.54Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 20_17"
genename region_tag susie_pip mu2 PVE z
4423 NXT1 20_17 0 25.62 0 -0.27
3838 GZF1 20_17 0 643.44 0 -13.59
3839 NAPB 20_17 0 200.74 0 -15.40
1732 CST3 20_17 0 19019.98 0 142.78
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 20_16"
genename region_tag susie_pip mu2 PVE z
3837 CD93 20_16 0 183.61 0 20.22
11346 RP4-737E23.2 20_16 0 116.81 0 -11.40
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 5_106"
genename region_tag susie_pip mu2 PVE z
3537 KIAA1191 5_106 0.006 6.77 1.1e-07 -0.84
8898 CLTB 5_106 0.005 4.72 6.6e-08 0.06
8282 SIMC1 5_106 0.007 7.72 1.5e-07 0.93
420 NOP16 5_106 0.005 5.83 9.0e-08 0.62
848 CDHR2 5_106 0.005 5.01 7.2e-08 0.19
5864 RNF44 5_106 0.014 14.62 6.2e-07 -1.53
8185 GPRIN1 5_106 0.006 6.47 1.1e-07 0.51
419 TSPAN17 5_106 0.007 7.62 1.5e-07 0.54
6932 HK3 5_106 0.005 5.84 9.1e-08 -0.37
1153 UIMC1 5_106 0.026 16.67 1.3e-06 -1.55
6929 FGFR4 5_106 0.008 23.98 5.3e-07 -4.88
7613 NSD1 5_106 0.085 20.07 5.0e-06 -4.09
8175 RAB24 5_106 0.036 25.55 2.7e-06 -5.38
8176 PRELID1 5_106 0.027 16.68 1.3e-06 3.09
11015 MXD3 5_106 0.025 13.90 1.0e-06 -1.43
8173 LMAN2 5_106 0.011 152.51 4.8e-06 16.22
4277 PRR7 5_106 0.005 7.80 1.1e-07 2.42
10351 PDLIM7 5_106 0.016 26.05 1.2e-06 -4.41
9581 DDX41 5_106 0.005 6.72 9.4e-08 1.83
5866 DOK3 5_106 0.009 8.86 2.2e-07 -0.22
5861 FAM193B 5_106 0.014 18.16 7.5e-07 2.67
9732 TMED9 5_106 0.005 5.70 8.9e-08 0.37
313 B4GALT7 5_106 0.006 7.59 1.4e-07 1.16
8280 FAM153A 5_106 0.005 5.02 7.0e-08 0.76
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_33"
genename region_tag susie_pip mu2 PVE z
12013 RP11-7K24.3 6_33 0.008 15.61 3.5e-07 -1.68
4979 TAF8 6_33 0.004 8.34 9.0e-08 -0.90
2754 GUCA1B 6_33 0.003 5.32 4.1e-08 0.53
425 MRPS10 6_33 0.010 16.08 4.7e-07 1.56
3722 TRERF1 6_33 0.003 7.79 7.9e-08 0.82
293 UBR2 6_33 0.003 5.29 4.0e-08 1.17
2755 PRPH2 6_33 0.242 41.14 2.9e-05 5.25
3743 TBCC 6_33 0.004 8.10 8.3e-08 0.74
2756 GLTSCR1L 6_33 0.004 10.34 1.3e-07 -2.05
11174 C6orf226 6_33 0.003 6.66 4.9e-08 1.69
4947 CNPY3 6_33 0.233 45.95 3.1e-05 -4.72
3732 PEX6 6_33 0.018 22.85 1.2e-06 -3.43
3748 GNMT 6_33 0.008 18.34 4.4e-07 3.43
2757 PPP2R5D 6_33 0.003 10.48 8.1e-08 2.56
3727 RRP36 6_33 0.003 6.15 5.4e-08 -0.08
3750 MEA1 6_33 0.941 25.36 6.9e-05 5.66
3747 KLHDC3 6_33 0.013 20.35 7.8e-07 1.75
406 CUL7 6_33 0.009 16.52 4.3e-07 1.42
2758 MRPL2 6_33 0.006 15.84 2.6e-07 2.72
2759 PTK7 6_33 0.012 19.56 6.7e-07 1.84
2760 CUL9 6_33 0.009 22.35 5.7e-07 -2.44
2761 DNPH1 6_33 0.003 16.24 1.5e-07 -2.04
5875 CRIP3 6_33 0.004 143.26 1.8e-06 -13.58
8462 ZNF318 6_33 0.047 31.15 4.2e-06 -2.52
3731 ABCC10 6_33 0.620 30.76 5.5e-05 -1.49
8460 DLK2 6_33 0.004 13.41 1.4e-07 3.39
4957 TJAP1 6_33 0.004 11.41 1.2e-07 -1.06
4953 YIPF3 6_33 0.012 21.50 7.4e-07 2.73
3730 XPO5 6_33 0.012 31.37 1.1e-06 5.19
8370 POLH 6_33 0.006 15.68 2.9e-07 -1.79
8581 GTPBP2 6_33 0.006 14.17 2.6e-07 0.78
3745 MAD2L1BP 6_33 0.003 7.87 7.0e-08 -1.41
8579 RSPH9 6_33 0.012 43.16 1.5e-06 6.28
1381 MRPS18A 6_33 0.024 22.86 1.6e-06 1.58
2768 VEGFA 6_33 0.005 11.88 1.6e-07 2.03
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 7_79"
genename region_tag susie_pip mu2 PVE z
32 ARF5 7_79 0.001 56.94 1.0e-07 -4.07
10392 SND1 7_79 0.344 78.10 7.8e-05 9.19
12576 SND1-IT1 7_79 0.000 36.73 4.4e-10 5.99
4042 LRRC4 7_79 0.000 38.28 2.3e-08 2.35
8822 LEP 7_79 0.000 5.16 6.8e-11 -0.79
2198 RBM28 7_79 0.000 15.62 5.0e-10 -2.11
11230 PRRT4 7_79 0.000 5.93 7.4e-11 1.04
2200 IMPDH1 7_79 0.000 5.98 8.7e-11 0.00
4689 HILPDA 7_79 0.001 55.92 1.5e-07 3.48
7538 METTL2B 7_79 0.002 64.10 3.1e-07 4.04
12375 RP11-212P7.2 7_79 0.000 10.11 2.5e-10 -0.52
12227 RP11-274B21.10 7_79 0.000 4.88 6.0e-11 0.83
10893 FAM71F2 7_79 0.000 4.83 5.8e-11 -0.69
12222 RP11-274B21.9 7_79 0.000 18.72 1.1e-09 1.39
4043 CALU 7_79 0.000 5.91 7.1e-11 1.60
4048 OPN1SW 7_79 0.000 34.02 7.6e-09 -2.88
4044 CCDC136 7_79 0.000 4.95 5.9e-11 0.07
4041 FLNC 7_79 0.000 9.55 2.1e-10 0.51
4036 ATP6V1F 7_79 0.000 11.59 2.3e-10 -1.98
12341 RP11-309L24.4 7_79 0.000 9.14 1.5e-10 1.65
4692 KCP 7_79 0.000 5.23 6.3e-11 0.05
4046 IRF5 7_79 0.000 127.01 1.6e-09 13.27
594 TNPO3 7_79 0.000 64.53 9.6e-09 -7.62
6693 TSPAN33 7_79 0.000 8.94 1.3e-10 2.42
6694 AHCYL2 7_79 0.000 8.38 1.5e-10 -1.08
11542 SMKR1 7_79 0.000 11.09 2.9e-10 1.31
2211 NRF1 7_79 0.000 13.04 6.5e-10 -2.19
12379 RP11-448A19.1 7_79 0.000 9.10 5.2e-10 -2.00
9944 UBE2H 7_79 0.048 23.45 3.3e-06 -4.42
1300 ZC3HC1 7_79 0.028 34.36 2.8e-06 5.04
4047 KLHDC10 7_79 0.005 59.93 9.5e-07 -4.84
2215 MEST 7_79 0.000 5.31 6.8e-11 -0.45
6703 CPA5 7_79 0.000 15.03 5.3e-10 -1.29
1299 CPA1 7_79 0.000 20.24 1.3e-09 -2.06
6710 COPG2 7_79 0.000 6.77 1.0e-10 -0.60
12104 KLF14 7_79 0.000 5.24 6.8e-11 0.55
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag susie_pip mu2 PVE z
21116 rs71658797 1_48 1.000 73.25 2.1e-04 9.85
30192 rs1730862 1_66 1.000 72.58 2.1e-04 -8.64
56568 rs766167074 1_118 1.000 7488.98 2.2e-02 -3.36
99527 rs141849010 2_69 1.000 111.94 3.3e-04 10.82
115440 rs863678 2_106 1.000 200.01 5.8e-04 12.03
122552 rs72926961 2_120 1.000 241.05 7.0e-04 15.67
193631 rs7642977 3_118 1.000 76.25 2.2e-04 8.90
221581 rs111470070 4_52 1.000 78.48 2.3e-04 7.90
258767 rs766378231 5_2 1.000 4893.44 1.4e-02 3.04
258769 rs544289197 5_2 1.000 4853.67 1.4e-02 2.95
275069 rs113088001 5_31 1.000 44.44 1.3e-04 -5.32
279582 rs11743158 5_41 1.000 87.46 2.5e-04 9.57
312886 rs7447593 5_106 1.000 182.82 5.3e-04 18.10
314983 rs7763581 6_2 1.000 40.40 1.2e-04 -5.67
317108 rs630258 6_7 1.000 103.32 3.0e-04 11.48
344019 rs194944 6_56 1.000 118.04 3.4e-04 10.92
360291 rs199804242 6_89 1.000 31695.80 9.2e-02 4.44
372720 rs78148157 7_3 1.000 116.74 3.4e-04 -7.23
372721 rs13241427 7_3 1.000 157.96 4.6e-04 11.36
390104 rs700752 7_34 1.000 137.65 4.0e-04 11.82
398507 rs10277379 7_49 1.000 33305.88 9.7e-02 10.56
398510 rs761767938 7_49 1.000 43248.80 1.3e-01 9.38
398515 rs11406602 7_49 1.000 39412.01 1.1e-01 9.38
398518 rs1544459 7_49 1.000 42689.71 1.2e-01 9.72
413107 rs3757387 7_79 1.000 211.31 6.1e-04 16.58
421500 rs11767572 7_97 1.000 47.90 1.4e-04 6.26
435132 rs4871905 8_24 1.000 278.40 8.1e-04 19.28
490431 rs1360200 9_45 1.000 47.89 1.4e-04 -7.01
500752 rs141350020 9_62 1.000 41.18 1.2e-04 -6.65
505570 rs113790047 10_3 1.000 120.71 3.5e-04 11.91
560626 rs369062552 11_21 1.000 298.79 8.7e-04 14.85
560636 rs34830202 11_21 1.000 318.88 9.3e-04 -15.76
596614 rs11616030 12_11 1.000 61.70 1.8e-04 -7.93
597639 rs11056397 12_13 1.000 78.53 2.3e-04 -8.54
610335 rs6581124 12_35 1.000 42.89 1.2e-04 -6.34
626208 rs7970581 12_68 1.000 40.80 1.2e-04 -6.35
628975 rs1169300 12_74 1.000 167.31 4.9e-04 13.19
644055 rs141110756 13_21 1.000 163.60 4.8e-04 -13.98
645954 rs7999449 13_25 1.000 18958.36 5.5e-02 3.82
645956 rs775834524 13_25 1.000 18906.05 5.5e-02 3.86
706562 rs145727191 15_35 1.000 48.28 1.4e-04 8.96
708109 rs7174325 15_38 1.000 76.60 2.2e-04 4.22
727663 rs12927956 16_27 1.000 127.98 3.7e-04 9.45
770855 rs162000 18_14 1.000 46.88 1.4e-04 6.99
795441 rs771303621 19_19 1.000 2897.49 8.4e-03 -2.34
800896 rs814573 19_31 1.000 68.30 2.0e-04 -9.01
800978 rs346738 19_31 1.000 37.80 1.1e-04 -6.49
813679 rs80346074 20_14 1.000 35.48 1.0e-04 -5.75
814043 rs6082726 20_15 1.000 78.15 2.3e-04 9.17
814194 rs6113933 20_16 1.000 279.22 8.1e-04 -22.57
814251 rs2050735 20_16 1.000 251.77 7.3e-04 -19.85
814269 rs6137887 20_16 1.000 782.70 2.3e-03 -33.20
814360 rs113822376 20_17 1.000 349.87 1.0e-03 26.33
814389 rs73102315 20_17 1.000 3362.59 9.8e-03 -43.87
814391 rs3827142 20_17 1.000 19616.61 5.7e-02 -143.60
814868 rs73101426 20_18 1.000 68.43 2.0e-04 -8.63
815023 rs13039195 20_18 1.000 80.19 2.3e-04 -8.00
815075 rs6076340 20_19 1.000 57.42 1.7e-04 1.18
815118 rs117932602 20_19 1.000 66.66 1.9e-04 7.57
815243 rs6084065 20_19 1.000 96.03 2.8e-04 -9.53
821793 rs209955 20_32 1.000 96.38 2.8e-04 11.08
821797 rs2585441 20_32 1.000 52.91 1.5e-04 7.34
832744 rs2834321 21_15 1.000 74.01 2.1e-04 10.08
833592 rs219783 21_16 1.000 67.36 2.0e-04 -8.10
902658 rs2307874 3_34 1.000 2587.89 7.5e-03 -2.74
1049809 rs71176182 19_23 1.000 2025.33 5.9e-03 3.69
27957 rs12407689 1_62 0.999 45.50 1.3e-04 6.66
38566 rs9425587 1_84 0.999 59.46 1.7e-04 7.69
72247 rs780093 2_16 0.999 54.85 1.6e-04 8.48
127040 rs2068218 2_128 0.999 32.10 9.3e-05 -5.41
346376 rs854922 6_61 0.999 34.95 1.0e-04 5.42
612005 rs61931197 12_40 0.999 34.78 1.0e-04 5.70
701411 rs57194033 15_25 0.999 47.68 1.4e-04 -6.65
725746 rs76597446 16_23 0.999 44.56 1.3e-04 6.80
727497 rs7205341 16_27 0.999 70.27 2.0e-04 8.38
750750 rs3760511 17_22 0.999 32.04 9.3e-05 5.67
750966 rs530253 17_23 0.999 47.90 1.4e-04 -7.08
934874 rs7742789 6_33 0.999 202.24 5.9e-04 -14.41
70261 rs3771257 2_12 0.998 33.73 9.8e-05 -5.51
79707 rs77981979 2_30 0.998 32.07 9.3e-05 5.54
323617 rs1980449 6_19 0.998 43.43 1.3e-04 -6.51
323792 rs115740542 6_20 0.998 34.67 1.0e-04 5.96
324277 rs187257713 6_21 0.998 33.94 9.8e-05 -6.33
505090 rs12380852 9_73 0.998 32.86 9.5e-05 6.56
538378 rs117081694 10_64 0.998 32.56 9.4e-05 -5.62
550400 rs186376420 11_2 0.998 43.95 1.3e-04 -6.85
664159 rs630943 13_59 0.998 31.03 9.0e-05 -5.30
701382 rs7162116 15_25 0.998 51.74 1.5e-04 8.38
714075 rs8037855 15_48 0.998 66.93 1.9e-04 11.40
716559 rs138922864 16_3 0.998 35.60 1.0e-04 5.77
740623 rs4843216 16_52 0.998 31.41 9.1e-05 4.09
814415 rs6515382 20_17 0.998 1436.85 4.2e-03 52.69
821820 rs6068816 20_32 0.998 36.41 1.1e-04 -5.82
837167 rs73907568 21_23 0.998 31.29 9.1e-05 5.42
99519 rs142743147 2_69 0.997 31.21 9.0e-05 5.33
237505 rs115336319 4_83 0.997 30.78 8.9e-05 -4.40
324596 rs138975185 6_22 0.997 32.64 9.5e-05 -5.94
435122 rs310311 8_24 0.997 122.25 3.5e-04 -14.62
706591 rs2955742 15_36 0.997 56.91 1.6e-04 7.22
813902 rs13044691 20_15 0.997 32.12 9.3e-05 -4.09
814174 rs112734453 20_16 0.997 157.44 4.6e-04 -16.69
349588 rs9496567 6_67 0.996 29.85 8.6e-05 5.30
460640 rs376277175 8_79 0.996 39.58 1.1e-04 -7.81
702443 rs340029 15_27 0.996 40.41 1.2e-04 6.22
743754 rs181752000 17_7 0.996 33.29 9.6e-05 4.75
213824 rs723585 4_40 0.994 75.04 2.2e-04 -8.70
317141 rs3799511 6_7 0.994 40.56 1.2e-04 -4.52
421484 rs288762 7_97 0.994 112.61 3.3e-04 12.52
483692 rs117451470 9_30 0.994 29.02 8.4e-05 -4.89
421751 rs12697965 7_98 0.993 36.92 1.1e-04 8.26
626096 rs35287743 12_66 0.993 33.04 9.5e-05 6.11
629928 rs1055941 12_75 0.993 38.09 1.1e-04 6.36
701428 rs11071331 15_25 0.993 41.59 1.2e-04 2.82
723530 rs7203451 16_19 0.993 251.46 7.3e-04 -12.43
30496 rs11102041 1_69 0.992 77.87 2.2e-04 -6.06
258835 rs62331274 5_2 0.992 58.59 1.7e-04 5.92
714064 rs11634241 15_48 0.992 164.71 4.7e-04 15.47
756486 rs139064373 17_36 0.992 27.76 8.0e-05 -3.98
379917 rs17644994 7_17 0.991 36.12 1.0e-04 6.34
505077 rs1886296 9_73 0.991 29.56 8.5e-05 6.18
113115 rs7594986 2_103 0.990 61.35 1.8e-04 7.46
593138 rs723672 12_2 0.990 28.76 8.3e-05 5.20
39168 rs34484492 1_85 0.989 32.28 9.3e-05 -5.95
97447 rs11123169 2_67 0.989 54.57 1.6e-04 -7.35
566278 rs11381239 11_29 0.988 37.24 1.1e-04 6.71
787402 rs8108787 19_2 0.988 30.93 8.9e-05 -5.38
1027144 rs72811597 16_54 0.988 60.18 1.7e-04 -7.10
99649 rs2311597 2_70 0.987 67.43 1.9e-04 -8.18
607844 rs11830037 12_30 0.987 29.50 8.5e-05 5.71
233974 rs10024666 4_75 0.986 27.17 7.8e-05 4.93
331448 rs9357429 6_34 0.985 28.98 8.3e-05 -5.21
714051 rs75422555 15_47 0.985 33.77 9.7e-05 -6.70
310546 rs1422755 5_102 0.984 34.75 9.9e-05 5.71
421803 rs11761498 7_98 0.984 57.79 1.7e-04 7.88
578777 rs57569860 11_52 0.984 26.53 7.6e-05 4.87
826144 rs78581838 21_2 0.984 38.12 1.1e-04 -6.35
982067 rs3184504 12_67 0.984 783.03 2.2e-03 -27.94
30494 rs201469841 1_69 0.983 45.87 1.3e-04 -0.98
321647 rs3763278 6_15 0.983 29.82 8.5e-05 4.39
950138 rs11557049 8_50 0.983 44.16 1.3e-04 6.79
78830 rs588206 2_28 0.982 39.10 1.1e-04 6.25
31942 rs149803516 1_71 0.981 30.88 8.8e-05 -5.35
351725 rs12196331 6_71 0.981 29.87 8.5e-05 5.73
637489 rs79490353 13_7 0.980 27.15 7.7e-05 4.58
313742 rs4701140 5_108 0.979 26.93 7.7e-05 4.90
285747 rs3952745 5_53 0.978 28.11 8.0e-05 -5.36
326475 rs1793893 6_26 0.978 84.77 2.4e-04 8.96
56606 rs1769794 1_118 0.977 4174.83 1.2e-02 5.93
548933 rs75184896 10_84 0.976 26.82 7.6e-05 4.92
324962 rs3130253 6_23 0.975 38.80 1.1e-04 -5.88
743725 rs9898876 17_7 0.975 49.26 1.4e-04 6.21
813699 rs6082285 20_15 0.974 34.02 9.6e-05 5.54
228593 rs6532770 4_66 0.972 36.10 1.0e-04 6.09
331361 rs10223666 6_34 0.972 228.23 6.4e-04 15.67
689806 rs55964922 14_53 0.972 27.55 7.8e-05 -5.26
328595 rs56144236 6_27 0.971 28.81 8.1e-05 -5.74
551193 rs1983100 11_3 0.971 36.11 1.0e-04 5.82
688126 rs72698888 14_49 0.970 26.67 7.5e-05 4.87
474891 rs16931379 9_12 0.966 28.97 8.1e-05 -5.17
813491 rs6112780 20_14 0.966 26.17 7.3e-05 4.36
815667 rs142348466 20_19 0.966 40.50 1.1e-04 -5.72
26398 rs9432440 1_58 0.965 33.21 9.3e-05 5.86
110180 rs1980154 2_96 0.965 33.49 9.4e-05 6.27
465109 rs10094480 8_87 0.961 31.97 8.9e-05 -5.39
659533 rs565714342 13_49 0.960 31.10 8.7e-05 5.46
732995 rs244423 16_37 0.960 78.33 2.2e-04 -10.67
2539 rs61772085 1_6 0.959 30.55 8.5e-05 5.72
390211 rs113473694 7_35 0.958 26.26 7.3e-05 -4.71
63057 rs4335411 1_131 0.957 25.28 7.0e-05 -4.73
346551 rs9359877 6_61 0.957 26.99 7.5e-05 5.68
275091 rs255749 5_31 0.956 32.70 9.1e-05 4.79
531028 rs1649987 10_50 0.956 26.06 7.2e-05 -4.86
139044 rs59302296 3_7 0.953 28.34 7.8e-05 5.13
3355 rs205474 1_9 0.951 29.53 8.2e-05 -5.34
329151 rs493871 6_28 0.951 31.63 8.7e-05 5.07
832738 rs2154568 21_15 0.951 37.05 1.0e-04 7.63
522079 rs4935194 10_33 0.945 31.27 8.6e-05 6.79
609609 rs113897279 12_33 0.944 26.62 7.3e-05 4.77
301858 rs12109255 5_84 0.943 26.87 7.4e-05 -4.96
455064 rs1786344 8_69 0.942 26.57 7.3e-05 4.66
703320 rs8041454 15_29 0.942 61.46 1.7e-04 -9.80
789404 rs146992497 19_6 0.941 23.55 6.4e-05 4.47
982129 rs150383897 12_67 0.941 92.18 2.5e-04 6.13
813449 rs61571241 20_14 0.940 25.21 6.9e-05 4.03
333883 rs76572975 6_39 0.937 37.64 1.0e-04 -6.96
258689 rs386057 5_1 0.936 45.40 1.2e-04 -6.21
3329 rs284317 1_7 0.935 25.29 6.9e-05 -4.00
11713 rs2484713 1_27 0.934 35.02 9.5e-05 5.68
516692 rs11007559 10_21 0.934 29.30 8.0e-05 5.17
844247 rs71195055 22_16 0.934 35.90 9.7e-05 6.17
77961 rs13428381 2_27 0.933 33.87 9.2e-05 -6.15
99866 rs35275076 2_70 0.933 88.84 2.4e-04 10.97
343550 rs2444819 6_55 0.933 47.42 1.3e-04 7.14
582200 rs117680242 11_59 0.933 25.55 6.9e-05 4.54
91965 rs11686739 2_54 0.929 27.55 7.4e-05 4.82
142942 rs711731 3_15 0.927 25.23 6.8e-05 4.67
331239 rs1015149 6_32 0.926 26.99 7.3e-05 -5.21
776080 rs12954053 18_24 0.926 30.52 8.2e-05 4.84
235047 rs17296659 4_78 0.925 33.41 9.0e-05 -5.66
701432 rs7166305 15_25 0.924 56.48 1.5e-04 -4.72
301104 rs156094 5_83 0.923 28.21 7.6e-05 -5.14
271775 rs3096211 5_26 0.921 30.49 8.2e-05 3.95
346378 rs1209058 6_61 0.921 33.04 8.8e-05 -7.35
421749 rs6967289 7_98 0.919 45.75 1.2e-04 7.69
813516 rs6046722 20_14 0.919 25.61 6.8e-05 -4.63
587923 rs73018243 11_75 0.916 24.03 6.4e-05 -4.45
727688 rs72803263 16_27 0.916 25.55 6.8e-05 3.06
274901 rs9716017 5_31 0.915 29.95 8.0e-05 -4.77
462427 rs4604455 8_83 0.912 26.11 6.9e-05 -5.41
32781 rs1975283 1_72 0.909 57.01 1.5e-04 -7.61
746135 rs1005395 17_13 0.909 23.96 6.3e-05 4.42
763141 rs28454947 17_46 0.908 26.70 7.0e-05 5.30
371476 rs9456260 6_110 0.907 25.64 6.8e-05 4.93
14937 rs2780869 1_35 0.906 31.23 8.2e-05 -5.45
522077 rs35182775 10_33 0.906 32.61 8.6e-05 -7.34
708709 rs62027546 15_38 0.906 26.31 6.9e-05 4.75
22836 rs6661091 1_50 0.902 55.20 1.4e-04 7.49
492288 rs141649706 9_48 0.898 25.98 6.8e-05 -5.14
837813 rs34526805 22_1 0.898 26.66 7.0e-05 4.94
707400 rs3128 15_37 0.897 25.86 6.7e-05 3.93
730931 rs79574106 16_33 0.897 24.94 6.5e-05 4.64
137038 rs4621315 3_4 0.896 25.58 6.7e-05 4.78
317074 rs10458103 6_7 0.896 52.00 1.4e-04 9.14
373143 rs62442558 7_4 0.896 25.94 6.8e-05 4.86
817645 rs6029393 20_24 0.896 40.11 1.0e-04 -6.18
550195 rs7115054 11_2 0.895 106.40 2.8e-04 9.67
708107 rs12442871 15_38 0.895 59.34 1.5e-04 -1.35
258757 rs10040050 5_2 0.893 4507.85 1.2e-02 3.65
644384 rs77871802 13_21 0.893 34.43 8.9e-05 -5.49
814888 rs147493439 20_18 0.890 28.35 7.3e-05 1.72
33182 rs148295181 1_74 0.886 23.07 5.9e-05 -4.25
156772 rs1866264 3_46 0.884 32.35 8.3e-05 -5.50
703122 rs4569205 15_28 0.883 31.82 8.2e-05 5.55
802188 rs116922356 19_34 0.883 26.24 6.7e-05 -4.61
571181 rs12420758 11_38 0.882 35.70 9.1e-05 -6.35
325460 rs2248162 6_26 0.881 116.96 3.0e-04 -10.90
437238 rs139800483 8_29 0.881 25.31 6.5e-05 -4.70
579536 rs7934169 11_54 0.880 23.71 6.1e-05 -4.37
46839 rs72739200 1_100 0.879 25.62 6.5e-05 4.65
79276 rs935375 2_29 0.879 25.35 6.5e-05 -4.77
793558 rs8113096 19_15 0.879 24.26 6.2e-05 -3.41
815951 rs138112660 20_20 0.878 25.11 6.4e-05 -4.35
372717 rs4487642 7_3 0.877 51.35 1.3e-04 -3.40
787869 rs1064543 19_2 0.874 26.25 6.7e-05 4.75
695431 rs12908082 15_11 0.870 24.70 6.2e-05 -4.47
113601 rs139389756 2_104 0.868 24.67 6.2e-05 4.55
299255 rs12153431 5_79 0.867 37.00 9.3e-05 5.44
569090 rs4938939 11_34 0.862 29.00 7.3e-05 5.09
630110 rs11057830 12_76 0.861 24.30 6.1e-05 4.39
111742 rs7607980 2_100 0.857 38.33 9.5e-05 6.04
873224 rs75246752 1_73 0.857 31.40 7.8e-05 5.17
12710 rs115398900 1_30 0.856 24.33 6.1e-05 -4.44
231507 rs56011514 4_71 0.855 26.34 6.5e-05 4.81
561044 rs10835944 11_22 0.855 26.39 6.6e-05 -4.50
548987 rs2767419 10_85 0.852 24.21 6.0e-05 -4.45
560977 rs6484575 11_22 0.852 27.34 6.8e-05 3.73
57789 rs113358743 1_122 0.851 26.15 6.5e-05 4.60
639880 rs57217617 13_13 0.849 24.74 6.1e-05 4.59
360290 rs2327654 6_89 0.847 31753.42 7.8e-02 4.57
697213 rs2016840 15_17 0.842 25.55 6.3e-05 -4.68
225537 rs9996470 4_60 0.840 27.06 6.6e-05 -4.86
816162 rs111791178 20_21 0.840 28.35 6.9e-05 5.59
736805 rs9928026 16_44 0.839 76.34 1.9e-04 -8.23
31666 rs6679677 1_70 0.838 24.90 6.1e-05 4.46
119904 rs10207044 2_113 0.837 26.57 6.5e-05 5.12
732235 rs11390603 16_36 0.837 26.31 6.4e-05 -4.57
804964 rs371808578 19_38 0.834 30.66 7.4e-05 5.34
256613 rs181147923 4_120 0.833 26.81 6.5e-05 4.66
570891 rs75592015 11_37 0.832 26.90 6.5e-05 4.90
1005444 rs56261560 14_52 0.830 32.81 7.9e-05 5.48
190987 rs822362 3_114 0.829 79.09 1.9e-04 9.06
36834 rs72691538 1_82 0.828 26.67 6.4e-05 -4.60
151026 rs146456061 3_35 0.824 27.82 6.7e-05 -4.57
221700 rs17253722 4_52 0.824 668.27 1.6e-03 29.86
793759 rs3794991 19_15 0.824 38.42 9.2e-05 5.98
282840 rs17263175 5_47 0.823 23.82 5.7e-05 4.31
825558 rs6062681 20_38 0.822 26.98 6.4e-05 -4.89
272178 rs149976817 5_27 0.819 23.50 5.6e-05 4.11
365320 rs4870114 6_99 0.816 28.45 6.7e-05 5.06
194393 rs13059257 3_120 0.815 31.90 7.6e-05 5.38
324930 rs1233385 6_23 0.815 74.60 1.8e-04 9.07
144505 rs11711833 3_18 0.814 67.08 1.6e-04 -8.28
708467 rs28587326 15_38 0.814 32.55 7.7e-05 5.45
791961 rs144089403 19_11 0.813 27.04 6.4e-05 -4.53
32514 rs3949262 1_72 0.812 29.24 6.9e-05 -5.07
608421 rs1878234 12_31 0.811 32.75 7.7e-05 -5.71
115500 rs72940807 2_106 0.810 30.96 7.3e-05 6.69
950124 rs77732976 8_50 0.806 31.22 7.3e-05 -5.46
258616 rs142220278 5_1 0.803 26.77 6.2e-05 3.95SNPs 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
398510 rs761767938 7_49 1.000 43248.80 1.3e-01 9.38
398518 rs1544459 7_49 1.000 42689.71 1.2e-01 9.72
398514 rs11972122 7_49 0.000 39427.03 2.1e-06 9.25
398515 rs11406602 7_49 1.000 39412.01 1.1e-01 9.38
398519 rs1544458 7_49 0.000 38833.39 0.0e+00 9.45
398509 rs6465794 7_49 0.000 38228.80 0.0e+00 8.90
398508 rs6465793 7_49 0.000 38228.22 0.0e+00 8.90
398539 rs10272350 7_49 0.000 38164.98 0.0e+00 9.12
398543 rs2463008 7_49 0.000 36310.83 0.0e+00 9.80
398549 rs10267180 7_49 0.000 36294.23 0.0e+00 9.74
398489 rs13240016 7_49 0.000 36167.87 0.0e+00 8.60
398498 rs7799383 7_49 0.000 35303.88 0.0e+00 8.13
398507 rs10277379 7_49 1.000 33305.88 9.7e-02 10.56
398501 rs7795371 7_49 0.000 32839.76 0.0e+00 10.68
360290 rs2327654 6_89 0.847 31753.42 7.8e-02 4.57
360307 rs6923513 6_89 0.630 31752.03 5.8e-02 4.56
360291 rs199804242 6_89 1.000 31695.80 9.2e-02 4.44
360294 rs113527452 6_89 0.000 31585.57 1.6e-12 4.54
360299 rs200796875 6_89 0.000 31396.72 0.0e+00 4.41
360312 rs7756915 6_89 0.000 31196.78 0.0e+00 4.41
360305 rs6570040 6_89 0.000 29946.62 0.0e+00 4.50
398563 rs848470 7_49 0.000 29931.63 0.0e+00 -7.24
360292 rs6570031 6_89 0.000 29876.06 0.0e+00 4.51
360293 rs9389323 6_89 0.000 29859.53 0.0e+00 4.46
360309 rs9321531 6_89 0.000 26220.45 0.0e+00 4.48
360282 rs9321528 6_89 0.000 25902.76 0.0e+00 5.03
398457 rs9640663 7_49 0.000 24891.63 0.0e+00 7.45
398453 rs2868787 7_49 0.000 24889.65 0.0e+00 7.42
360310 rs9494389 6_89 0.000 24614.08 0.0e+00 4.10
360314 rs5880262 6_89 0.000 24554.47 0.0e+00 3.92
398468 rs4727451 7_49 0.000 24523.03 0.0e+00 7.20
398487 rs58729654 7_49 0.000 24450.56 0.0e+00 8.05
398481 rs6465771 7_49 0.000 23923.30 0.0e+00 7.31
360288 rs2208574 6_89 0.000 23765.61 0.0e+00 4.35
360287 rs1033755 6_89 0.000 23755.83 0.0e+00 4.19
398573 rs34022094 7_49 0.000 23346.89 0.0e+00 -6.60
360285 rs2038551 6_89 0.000 23342.48 0.0e+00 5.03
398571 rs848458 7_49 0.000 23331.34 0.0e+00 -6.51
360296 rs9494377 6_89 0.000 23319.90 0.0e+00 4.07
360283 rs2038550 6_89 0.000 23278.70 0.0e+00 4.97
398447 rs1972568 7_49 0.000 22171.27 0.0e+00 7.28
398438 rs7788492 7_49 0.000 22164.41 0.0e+00 7.20
398440 rs67630171 7_49 0.000 22151.85 0.0e+00 7.17
398439 rs4729540 7_49 0.000 22139.74 0.0e+00 7.22
398445 rs7806750 7_49 0.000 22120.00 0.0e+00 7.25
398435 rs7357107 7_49 0.000 22117.77 0.0e+00 7.22
398527 rs4729772 7_49 0.000 20835.71 0.0e+00 8.90
398466 rs12705075 7_49 0.000 19747.39 0.0e+00 7.41
814391 rs3827142 20_17 1.000 19616.61 5.7e-02 -143.60
814392 rs5030707 20_17 0.000 19478.55 0.0e+00 -142.96SNPs 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
398510 rs761767938 7_49 1.000 43248.80 0.13000 9.38
398518 rs1544459 7_49 1.000 42689.71 0.12000 9.72
398515 rs11406602 7_49 1.000 39412.01 0.11000 9.38
398507 rs10277379 7_49 1.000 33305.88 0.09700 10.56
360291 rs199804242 6_89 1.000 31695.80 0.09200 4.44
360290 rs2327654 6_89 0.847 31753.42 0.07800 4.57
360307 rs6923513 6_89 0.630 31752.03 0.05800 4.56
814391 rs3827142 20_17 1.000 19616.61 0.05700 -143.60
645954 rs7999449 13_25 1.000 18958.36 0.05500 3.82
645956 rs775834524 13_25 1.000 18906.05 0.05500 3.86
56568 rs766167074 1_118 1.000 7488.98 0.02200 -3.36
258767 rs766378231 5_2 1.000 4893.44 0.01400 3.04
258769 rs544289197 5_2 1.000 4853.67 0.01400 2.95
56606 rs1769794 1_118 0.977 4174.83 0.01200 5.93
258757 rs10040050 5_2 0.893 4507.85 0.01200 3.65
814389 rs73102315 20_17 1.000 3362.59 0.00980 -43.87
795441 rs771303621 19_19 1.000 2897.49 0.00840 -2.34
902658 rs2307874 3_34 1.000 2587.89 0.00750 -2.74
56565 rs10489611 1_118 0.318 7474.24 0.00690 -3.63
56559 rs2256908 1_118 0.290 7473.83 0.00630 -3.64
1049809 rs71176182 19_23 1.000 2025.33 0.00590 3.69
56562 rs2790891 1_118 0.268 7473.78 0.00580 -3.63
56563 rs2491405 1_118 0.268 7473.78 0.00580 -3.63
56566 rs2486737 1_118 0.267 7474.13 0.00580 -3.63
795443 rs111064632 19_19 0.676 2893.03 0.00570 -2.24
56567 rs971534 1_118 0.235 7474.06 0.00510 -3.62
814415 rs6515382 20_17 0.998 1436.85 0.00420 52.69
795447 rs12151080 19_19 0.414 2885.73 0.00350 -2.30
795445 rs6511437 19_19 0.381 2890.96 0.00320 -2.28
1049771 rs10414879 19_23 0.399 2090.26 0.00240 3.88
814269 rs6137887 20_16 1.000 782.70 0.00230 -33.20
982067 rs3184504 12_67 0.984 783.03 0.00220 -27.94
902680 rs800762 3_34 0.281 2579.90 0.00210 -2.80
221700 rs17253722 4_52 0.824 668.27 0.00160 29.86
56555 rs1076804 1_118 0.063 7462.76 0.00140 -3.63
258756 rs10040611 5_2 0.107 4493.21 0.00140 3.87
56575 rs2211176 1_118 0.059 7469.69 0.00130 -3.59
56576 rs2790882 1_118 0.059 7469.69 0.00130 -3.59
1049769 rs28633567 19_23 0.221 2090.96 0.00130 3.84
902674 rs1684942 3_34 0.155 2571.89 0.00120 -2.88
795448 rs28410659 19_19 0.135 2888.05 0.00110 -2.26
1049765 rs8109247 19_23 0.176 2087.43 0.00110 3.89
814360 rs113822376 20_17 1.000 349.87 0.00100 26.33
560636 rs34830202 11_21 1.000 318.88 0.00093 -15.76
902661 rs55721964 3_34 0.124 2573.36 0.00093 -2.79
560626 rs369062552 11_21 1.000 298.79 0.00087 14.85
795449 rs8105841 19_19 0.104 2887.50 0.00087 -2.26
419891 rs10224210 7_94 0.746 395.75 0.00086 20.73
723534 rs9933330 16_19 0.680 418.30 0.00083 -19.23
435132 rs4871905 8_24 1.000 278.40 0.00081 19.28SNPs 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
814391 rs3827142 20_17 1.000 19616.61 5.7e-02 -143.60
814392 rs5030707 20_17 0.000 19478.55 0.0e+00 -142.96
814387 rs13039536 20_17 0.000 19449.48 0.0e+00 -142.76
814383 rs8121283 20_17 0.000 18959.33 0.0e+00 -140.90
814385 rs8121405 20_17 0.000 18937.23 0.0e+00 -140.84
814384 rs8115417 20_17 0.000 18929.82 0.0e+00 -140.80
814380 rs57549987 20_17 0.000 18929.36 0.0e+00 -140.79
814379 rs1555355 20_17 0.000 18926.33 0.0e+00 -140.78
814382 rs6036471 20_17 0.000 18909.42 0.0e+00 -140.73
814377 rs56077567 20_17 0.000 18852.63 0.0e+00 -140.52
814381 rs6036470 20_17 0.000 18807.64 0.0e+00 -140.48
814376 rs13043266 20_17 0.000 18744.50 0.0e+00 -140.24
814371 rs4815223 20_17 0.000 18308.48 0.0e+00 -138.69
814373 rs34792920 20_17 0.000 18226.74 0.0e+00 -138.52
814372 rs6048925 20_17 0.000 18344.63 0.0e+00 -138.45
814394 rs199651024 20_17 0.000 14005.18 0.0e+00 -128.69
814370 rs200582457 20_17 0.000 10315.16 0.0e+00 -99.72
814432 rs4629231 20_17 0.000 6442.47 0.0e+00 -90.22
814410 rs77770287 20_17 0.000 6290.29 0.0e+00 -89.45
814412 rs2226058 20_17 0.000 6252.34 0.0e+00 -89.20
814386 rs200585819 20_17 0.000 9333.59 0.0e+00 -84.58
814374 rs726217 20_17 0.000 9271.32 0.0e+00 84.19
814405 rs2983605 20_17 0.000 8374.73 0.0e+00 78.56
814415 rs6515382 20_17 0.998 1436.85 4.2e-03 52.69
814414 rs1538909 20_17 0.000 1991.95 0.0e+00 -52.55
814417 rs7263473 20_17 0.002 1419.60 9.7e-06 52.49
814431 rs75841856 20_17 0.000 1400.90 3.2e-12 52.15
814416 rs6036488 20_17 0.000 1949.33 0.0e+00 -52.06
814433 rs6083243 20_17 0.000 1393.68 0.0e+00 -45.68
814422 rs62208893 20_17 0.000 1388.45 0.0e+00 -45.65
814421 rs6132654 20_17 0.000 1388.22 0.0e+00 -45.64
814436 rs11087433 20_17 0.000 1386.72 0.0e+00 -45.64
814439 rs6049062 20_17 0.000 1384.99 0.0e+00 -45.59
814448 rs35488686 20_17 0.000 1291.33 0.0e+00 45.52
814389 rs73102315 20_17 1.000 3362.59 9.8e-03 -43.87
814378 rs62208864 20_17 0.000 839.80 0.0e+00 43.59
814429 rs35783127 20_17 0.000 1027.60 0.0e+00 41.94
814423 rs35627338 20_17 0.000 1019.36 0.0e+00 41.79
814435 rs4380313 20_17 0.000 1018.84 0.0e+00 41.78
814427 rs8121966 20_17 0.000 1017.33 0.0e+00 41.77
814437 rs60609640 20_17 0.000 1017.08 0.0e+00 41.74
814411 rs10854252 20_17 0.000 649.49 0.0e+00 40.06
814452 rs6114276 20_17 0.000 1384.62 0.0e+00 -39.78
814463 rs6114287 20_17 0.000 1382.70 0.0e+00 -39.66
814474 rs72490829 20_17 0.000 1387.19 0.0e+00 -39.66
814488 rs6106724 20_17 0.000 1382.54 0.0e+00 -39.62
814489 rs8115480 20_17 0.000 1382.72 0.0e+00 -39.62
814483 rs6106721 20_17 0.000 1380.43 0.0e+00 -39.61
814499 rs6114316 20_17 0.000 1382.99 0.0e+00 -39.61
814466 rs144538582 20_17 0.000 1088.01 0.0e+00 36.48Gene 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] 19if (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)LSM12 gene(s) from the input list not found in DisGeNET CURATEDC15orf52 gene(s) from the input list not found in DisGeNET CURATEDCXXC1 gene(s) from the input list not found in DisGeNET CURATEDMEA1 gene(s) from the input list not found in DisGeNET CURATEDRAI14 gene(s) from the input list not found in DisGeNET CURATEDZNF697 gene(s) from the input list not found in DisGeNET CURATEDZNF827 gene(s) from the input list not found in DisGeNET CURATEDDCAF4 gene(s) from the input list not found in DisGeNET CURATEDLPCAT4 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
76 DEAFNESS, AUTOSOMAL RECESSIVE 32 0.02447949 1/10 1/9703
78 Medullary cystic kidney disease 1 0.02447949 1/10 1/9703
81 Mental Retardation, Autosomal Dominant 1 0.02447949 1/10 1/9703
91 2q23.1 microdeletion syndrome 0.02447949 1/10 1/9703
77 Uniparental disomy, paternal, chromosome 14 0.05869628 1/10 3/9703
21 Hydronephrosis 0.06981173 1/10 5/9703
37 Polyuria 0.06981173 1/10 5/9703
6 Cannabis Dependence 0.08439931 1/10 17/9703
8 Neoplastic Cell Transformation 0.08439931 2/10 139/9703
11 Prelingual Deafness 0.08439931 1/10 20/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR database
1 Neoplasms, Squamous Cell 246 6 0.009657657 disease_GLAD4U
2 Adenocarcinoma 203 5 0.034821659 disease_GLAD4U
userId
1 MUC1;VIL1;PTGES;TRIM29;MEG3;MMP11
2 MUC1;VIL1;PTGES;MEG3;MMP11
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