Direct bilirubin (quantile) - Liver [fixed prior inclusion]
wesleycrouse
2021-08-18
Last updated: 2021-09-09
Checks: 7 0
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| html | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| html | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| Rmd | 47f58ac | wesleycrouse | 2021-09-06 | fixing thin argument for fixed pi results |
| html | 47f58ac | wesleycrouse | 2021-09-06 | fixing thin argument for fixed pi results |
| Rmd | 209346f | wesleycrouse | 2021-09-06 | updating additional analyses |
| html | 209346f | wesleycrouse | 2021-09-06 | updating additional analyses |
| html | b14741c | wesleycrouse | 2021-09-06 | switching from render to wflow_build |
| html | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| html | 837dd01 | wesleycrouse | 2021-09-01 | adding additional fixedsigma report |
| html | 2739f4f | wesleycrouse | 2021-08-30 | fixing typo |
| html | b1e6b7e | wesleycrouse | 2021-08-30 | fixing alignment on index |
| html | d7dfe76 | wesleycrouse | 2021-08-30 | Adding detailed reports for 30660 |
| Rmd | ea2e654 | wesleycrouse | 2021-08-30 | Exploring fixed priors and trimming large z scores |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Direct bilirubin (quantile) using Liver 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-30660_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 Liver 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] 10901#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1070 768 652 417 494 611 548 408 405 434 634 629 195 365 354
16 17 18 19 20 21 22
526 663 160 859 306 114 289 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8366205Load ctwas results
#load ctwas results
ctwas_res <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas_fixedpi.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$susie_pip*ctwas_res$mu/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_fixedpi.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
library(ggplot2)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************load(paste0(results_dir, "/", analysis_id, "_ctwas_fixedpi.s2.susieIrssres.Rd"))
#hardcode fixed pi, paramters not stored as part of the analysis
group_prior_rec[1,] <- 0.01
group_prior_rec[2,] <- 0.0001
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
value = c(group_prior_rec[1,], group_prior_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
#estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
1e-02 1e-04 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
7.54527 156.69298 #report sample size
print(sample_size)[1] 292933#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.002807843 0.465229439 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02861249 4.44575475Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
#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
3212 CCND2 12_4 0.988 27.78 9.4e-05 5.34
7040 INHBB 2_70 0.978 23.41 7.8e-05 4.81
3562 ACVR1C 2_94 0.949 22.27 7.2e-05 4.62
1320 CWF19L1 10_64 0.949 28.80 9.3e-05 -7.09
12467 RP11-219B17.3 15_27 0.946 46.41 1.5e-04 7.18
4269 ITGB4 17_42 0.935 20.92 6.7e-05 -4.91
2359 ABCC3 17_29 0.931 19.91 6.3e-05 4.38
11790 CYP2A6 19_28 0.927 22.10 7.0e-05 -4.73
5563 ABCG8 2_27 0.889 32.51 9.9e-05 5.88
1146 DNMT3B 20_19 0.873 18.11 5.4e-05 -3.98
1120 CETP 16_31 0.865 19.28 5.7e-05 -4.03
12687 RP4-781K5.7 1_121 0.846 19.58 5.7e-05 -4.17
1153 TGDS 13_47 0.813 17.85 5.0e-05 -4.00
10212 IL27 16_23 0.796 23.31 6.3e-05 -4.76
5978 ZC3H12C 11_65 0.785 19.55 5.2e-05 -4.19
10495 PRMT6 1_66 0.744 25.95 6.6e-05 5.14
6682 CYB5R1 1_102 0.742 19.33 4.9e-05 -3.95
1231 PABPC4 1_24 0.728 21.68 5.4e-05 4.52
1848 CD276 15_35 0.722 33.69 8.3e-05 6.13
666 COASY 17_25 0.720 20.08 4.9e-05 -3.97Genes 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 |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
12683 HCP5B 6_24 0 147799.60 9.5e-16 -3.52
10663 TRIM31 6_24 0 77351.52 5.9e-17 1.07
4833 FLOT1 6_24 0 73472.62 5.6e-17 -1.07
11533 UGT1A4 2_137 0 48265.96 0.0e+00 232.75
11447 UGT1A1 2_137 0 40648.97 0.0e+00 -230.41
10651 ABCF1 6_24 0 33573.32 1.4e-16 -3.76
11489 UGT1A3 2_137 0 32229.44 0.0e+00 213.80
5766 PPP1R18 6_24 0 29189.76 2.2e-16 -3.94
7732 UGT1A6 2_137 0 26862.67 0.0e+00 186.96
4836 NRM 6_24 0 16444.79 1.9e-17 -0.40
10667 HLA-G 6_24 0 15964.29 1.9e-11 -6.69
624 ZNRD1 6_24 0 11550.68 8.8e-18 0.19
11522 UGT1A7 2_137 0 5175.25 0.0e+00 -71.90
10661 TRIM10 6_24 0 4873.66 7.4e-18 -0.49
10648 C6orf136 6_24 0 2146.93 1.6e-18 0.11
11136 HCG20 6_24 0 1935.85 3.4e-17 -1.98
10664 RNF39 6_24 0 1772.07 2.0e-18 -1.02
1088 USP40 2_137 0 1610.92 0.0e+00 -46.64
6476 SUPV3L1 10_46 0 1310.26 0.0e+00 2.81
10774 HLA-A 6_24 0 898.51 6.8e-19 -0.83Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
12467 RP11-219B17.3 15_27 0.946 46.41 1.5e-04 7.18
5563 ABCG8 2_27 0.889 32.51 9.9e-05 5.88
3212 CCND2 12_4 0.988 27.78 9.4e-05 5.34
1320 CWF19L1 10_64 0.949 28.80 9.3e-05 -7.09
2924 EFHD1 2_136 0.715 37.43 9.1e-05 6.05
1848 CD276 15_35 0.722 33.69 8.3e-05 6.13
2004 TGFB1 19_28 0.702 33.94 8.1e-05 5.64
7040 INHBB 2_70 0.978 23.41 7.8e-05 4.81
3562 ACVR1C 2_94 0.949 22.27 7.2e-05 4.62
11790 CYP2A6 19_28 0.927 22.10 7.0e-05 -4.73
10000 ZKSCAN3 6_22 0.469 42.29 6.8e-05 3.82
11669 RP11-452H21.4 11_43 0.600 33.08 6.8e-05 5.78
4269 ITGB4 17_42 0.935 20.92 6.7e-05 -4.91
10495 PRMT6 1_66 0.744 25.95 6.6e-05 5.14
10212 IL27 16_23 0.796 23.31 6.3e-05 -4.76
2359 ABCC3 17_29 0.931 19.91 6.3e-05 4.38
8142 CNTROB 17_7 0.609 28.81 6.0e-05 -5.71
12687 RP4-781K5.7 1_121 0.846 19.58 5.7e-05 -4.17
1120 CETP 16_31 0.865 19.28 5.7e-05 -4.03
6291 JAZF1 7_23 0.704 23.28 5.6e-05 4.83Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11533 UGT1A4 2_137 0.000 48265.96 0.0e+00 232.75
11447 UGT1A1 2_137 0.000 40648.97 0.0e+00 -230.41
11489 UGT1A3 2_137 0.000 32229.44 0.0e+00 213.80
7732 UGT1A6 2_137 0.000 26862.67 0.0e+00 186.96
11522 UGT1A7 2_137 0.000 5175.25 0.0e+00 -71.90
1088 USP40 2_137 0.000 1610.92 0.0e+00 -46.64
10747 SLCO1B7 12_16 0.000 728.14 0.0e+00 12.26
3556 HJURP 2_137 0.000 154.82 0.0e+00 10.96
8651 MSL2 3_84 0.028 87.27 8.4e-06 10.28
2584 SLCO1B3 12_16 0.000 338.41 0.0e+00 9.93
2586 GOLT1B 12_16 0.000 65.15 0.0e+00 7.53
537 DGAT2 11_42 0.286 48.64 4.8e-05 -7.51
11290 MAPKAPK5-AS1 12_67 0.048 44.92 7.3e-06 -7.21
12467 RP11-219B17.3 15_27 0.946 46.41 1.5e-04 7.18
2541 ALDH2 12_67 0.039 42.68 5.7e-06 7.10
1320 CWF19L1 10_64 0.949 28.80 9.3e-05 -7.09
2536 SH2B3 12_67 0.018 36.13 2.3e-06 6.80
10667 HLA-G 6_24 0.000 15964.29 1.9e-11 -6.69
2170 AHR 7_17 0.021 30.55 2.1e-06 -6.58
4962 EXOC6 10_59 0.039 46.08 6.1e-06 -6.37Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.006696633#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11533 UGT1A4 2_137 0.000 48265.96 0.0e+00 232.75
11447 UGT1A1 2_137 0.000 40648.97 0.0e+00 -230.41
11489 UGT1A3 2_137 0.000 32229.44 0.0e+00 213.80
7732 UGT1A6 2_137 0.000 26862.67 0.0e+00 186.96
11522 UGT1A7 2_137 0.000 5175.25 0.0e+00 -71.90
1088 USP40 2_137 0.000 1610.92 0.0e+00 -46.64
10747 SLCO1B7 12_16 0.000 728.14 0.0e+00 12.26
3556 HJURP 2_137 0.000 154.82 0.0e+00 10.96
8651 MSL2 3_84 0.028 87.27 8.4e-06 10.28
2584 SLCO1B3 12_16 0.000 338.41 0.0e+00 9.93
2586 GOLT1B 12_16 0.000 65.15 0.0e+00 7.53
537 DGAT2 11_42 0.286 48.64 4.8e-05 -7.51
11290 MAPKAPK5-AS1 12_67 0.048 44.92 7.3e-06 -7.21
12467 RP11-219B17.3 15_27 0.946 46.41 1.5e-04 7.18
2541 ALDH2 12_67 0.039 42.68 5.7e-06 7.10
1320 CWF19L1 10_64 0.949 28.80 9.3e-05 -7.09
2536 SH2B3 12_67 0.018 36.13 2.3e-06 6.80
10667 HLA-G 6_24 0.000 15964.29 1.9e-11 -6.69
2170 AHR 7_17 0.021 30.55 2.1e-06 -6.58
4962 EXOC6 10_59 0.039 46.08 6.1e-06 -6.37Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 2_137"
genename region_tag susie_pip mu2 PVE z
10567 GIGYF2 2_137 0 28.08 0 -5.12
9340 C2orf82 2_137 0 5.92 0 0.45
620 NGEF 2_137 0 7.68 0 2.52
8006 INPP5D 2_137 0 14.31 0 4.05
879 DGKD 2_137 0 30.48 0 -0.07
1088 USP40 2_137 0 1610.92 0 -46.64
11522 UGT1A7 2_137 0 5175.25 0 -71.90
7732 UGT1A6 2_137 0 26862.67 0 186.96
11533 UGT1A4 2_137 0 48265.96 0 232.75
11489 UGT1A3 2_137 0 32229.44 0 213.80
11447 UGT1A1 2_137 0 40648.97 0 -230.41
3556 HJURP 2_137 0 154.82 0 10.96
11098 AC006037.2 2_137 0 5.20 0 1.50
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
[1] "Region: 12_16"
genename region_tag susie_pip mu2 PVE z
2584 SLCO1B3 12_16 0 338.41 0 9.93
10747 SLCO1B7 12_16 0 728.14 0 12.26
3400 IAPP 12_16 0 107.34 0 5.16
3399 PYROXD1 12_16 0 13.54 0 0.89
36 RECQL 12_16 0 149.90 0 3.81
2586 GOLT1B 12_16 0 65.15 0 7.53
4482 SPX 12_16 0 170.34 0 4.60
2587 LDHB 12_16 0 9.08 0 0.07
3401 KCNJ8 12_16 0 6.48 0 -0.33
689 ABCC9 12_16 0 7.74 0 2.11
2590 C2CD5 12_16 0 10.85 0 -1.39
5073 ETNK1 12_16 0 9.95 0 0.30
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
[1] "Region: 3_84"
genename region_tag susie_pip mu2 PVE z
796 PPP2R3A 3_84 0.059 15.36 3.1e-06 -2.46
8651 MSL2 3_84 0.028 87.27 8.4e-06 10.28
2795 PCCB 3_84 0.024 5.77 4.7e-07 1.22
3148 STAG1 3_84 0.024 4.76 4.0e-07 -0.03
6584 NCK1 3_84 0.024 8.49 7.0e-07 -2.19
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
[1] "Region: 11_42"
genename region_tag susie_pip mu2 PVE z
7611 XRRA1 11_42 0.051 8.05 1.4e-06 -1.00
3170 SPCS2 11_42 0.039 5.83 7.8e-07 0.73
6901 NEU3 11_42 0.034 4.61 5.4e-07 0.39
4848 SLCO2B1 11_42 0.035 4.62 5.5e-07 -0.22
12001 TPBGL 11_42 0.047 7.13 1.1e-06 0.68
6617 GDPD5 11_42 0.049 9.88 1.6e-06 1.95
8328 MAP6 11_42 0.043 6.32 9.3e-07 0.24
7603 MOGAT2 11_42 0.148 15.34 7.8e-06 0.85
537 DGAT2 11_42 0.286 48.64 4.8e-05 -7.51
10381 UVRAG 11_42 0.039 6.77 8.9e-07 1.62
1082 WNT11 11_42 0.049 7.88 1.3e-06 1.06
11773 RP11-619A14.3 11_42 0.040 5.79 7.8e-07 0.61
4849 THAP12 11_42 0.037 5.36 6.8e-07 -0.66
12265 RP11-111M22.5 11_42 0.041 6.24 8.7e-07 0.80
11766 RP11-111M22.3 11_42 0.035 4.64 5.5e-07 0.31
11751 RP11-672A2.4 11_42 0.038 5.35 6.9e-07 0.53
9350 TSKU 11_42 0.037 5.01 6.3e-07 0.25
905 ACER3 11_42 0.135 16.60 7.6e-06 -2.04
5976 CAPN5 11_42 0.092 13.49 4.2e-06 1.72
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
[1] "Region: 12_67"
genename region_tag susie_pip mu2 PVE z
5112 TCHP 12_67 0.296 16.14 1.6e-05 3.21
5111 GIT2 12_67 0.175 15.07 9.0e-06 3.36
8639 C12orf76 12_67 0.024 6.45 5.2e-07 -0.30
3515 IFT81 12_67 0.025 10.48 8.9e-07 2.50
10093 ANAPC7 12_67 0.027 9.24 8.4e-07 2.16
2531 ARPC3 12_67 0.029 7.80 7.7e-07 0.54
10684 FAM216A 12_67 0.021 8.80 6.4e-07 2.42
2532 GPN3 12_67 0.019 5.43 3.4e-07 1.40
2533 VPS29 12_67 0.019 5.49 3.5e-07 -1.41
10683 TCTN1 12_67 0.025 6.35 5.4e-07 0.02
3517 HVCN1 12_67 0.043 14.17 2.1e-06 2.67
9717 PPP1CC 12_67 0.042 14.11 2.0e-06 -2.65
10375 FAM109A 12_67 0.019 7.10 4.5e-07 -1.46
2536 SH2B3 12_67 0.018 36.13 2.3e-06 6.80
10680 ATXN2 12_67 0.018 17.51 1.1e-06 3.97
2541 ALDH2 12_67 0.039 42.68 5.7e-06 7.10
11290 MAPKAPK5-AS1 12_67 0.048 44.92 7.3e-06 -7.21
1191 ERP29 12_67 0.088 38.80 1.2e-05 6.25
10370 TMEM116 12_67 0.088 38.80 1.2e-05 -6.25
2544 NAA25 12_67 0.071 36.21 8.8e-06 -6.12
8505 HECTD4 12_67 0.095 41.00 1.3e-05 6.33
9084 PTPN11 12_67 0.026 7.36 6.4e-07 -0.78
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
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
133086 rs6731991 2_136 1.000 42.74 1.5e-04 -5.75
133395 rs2070959 2_137 1.000 46431.28 1.6e-01 238.14
133404 rs1875263 2_137 1.000 45491.47 1.6e-01 181.67
133423 rs76063448 2_137 1.000 6958.93 2.4e-02 70.66
133425 rs2885296 2_137 1.000 64774.79 2.2e-01 240.61
328252 rs72834643 6_20 1.000 129.78 4.4e-04 9.73
328273 rs115740542 6_20 1.000 194.26 6.6e-04 12.66
329443 rs3130253 6_23 1.000 43.01 1.5e-04 -6.63
372704 rs12208357 6_103 1.000 88.82 3.0e-04 -6.65
384490 rs542176135 7_17 1.000 174.83 6.0e-04 -8.38
536407 rs569165969 10_46 1.000 2515.63 8.6e-03 -1.12
536455 rs6480402 10_46 1.000 633.80 2.2e-03 8.90
607179 rs11045819 12_16 1.000 3088.45 1.1e-02 -14.34
607196 rs4363657 12_16 1.000 1660.58 5.7e-03 43.78
802732 rs59616136 19_14 1.000 52.66 1.8e-04 7.00
810878 rs113345881 19_32 1.000 40.74 1.4e-04 6.12
889972 rs1611236 6_24 1.000 391477.83 1.3e+00 -3.60
384512 rs4721597 7_17 0.999 106.35 3.6e-04 1.94
511740 rs115478735 9_70 0.999 68.71 2.3e-04 -8.02
810876 rs814573 19_32 0.998 38.91 1.3e-04 -6.68
557751 rs76153913 11_2 0.997 51.04 1.7e-04 6.70
617869 rs7397189 12_36 0.996 36.93 1.3e-04 5.69
634455 rs653178 12_67 0.996 170.95 5.8e-04 -13.12
822609 rs34507316 20_13 0.996 34.07 1.2e-04 5.60
800961 rs141645070 19_10 0.991 31.74 1.1e-04 -5.27
36470 rs2779116 1_78 0.983 73.09 2.5e-04 -8.25
133428 rs12052787 2_137 0.974 2286.99 7.6e-03 -11.25
810881 rs12721109 19_32 0.974 32.46 1.1e-04 6.44
749268 rs2608604 16_53 0.972 62.77 2.1e-04 -6.33
803517 rs3794991 19_15 0.957 141.37 4.6e-04 11.64
810812 rs1551891 19_32 0.950 35.15 1.1e-04 7.88
235153 rs17238095 4_72 0.949 30.51 9.9e-05 5.18
850915 rs34662558 22_10 0.941 32.14 1.0e-04 -5.19
606894 rs7962574 12_15 0.936 46.67 1.5e-04 -8.40
606899 rs73080739 12_15 0.930 32.72 1.0e-04 -7.24
440166 rs12549737 8_24 0.922 31.18 9.8e-05 5.15
497658 rs9410381 9_45 0.922 78.95 2.5e-04 8.62
512125 rs34755157 9_71 0.914 30.02 9.4e-05 -5.03
562204 rs34623292 11_10 0.912 30.92 9.6e-05 -5.06
814057 rs71185869 19_36 0.897 26.26 8.0e-05 4.56
328091 rs75080831 6_19 0.871 62.37 1.9e-04 7.96
296468 rs4566840 5_66 0.862 33.19 9.8e-05 -5.46
277953 rs79086423 5_29 0.861 26.45 7.8e-05 4.53
34263 rs12124727 1_73 0.857 26.40 7.7e-05 -4.54
606934 rs10770693 12_15 0.854 61.50 1.8e-04 8.86
853666 rs6000553 22_14 0.848 46.50 1.3e-04 6.47
560773 rs4910498 11_8 0.847 55.10 1.6e-04 6.71
588854 rs11224303 11_58 0.806 32.39 8.9e-05 5.20
395398 rs181292113 7_35 0.805 27.29 7.5e-05 4.44SNPs with largest effect sizes
#plot PIP vs effect size
plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| b14741c | wesleycrouse | 2021-09-06 |
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
889954 rs111734624 6_24 0.158 391480.4 2.1e-01 -3.62
889951 rs2508055 6_24 0.157 391480.4 2.1e-01 -3.62
889999 rs1611252 6_24 0.129 391480.2 1.7e-01 -3.62
889995 rs1611248 6_24 0.661 391479.6 8.8e-01 -3.63
890016 rs1611260 6_24 0.107 391478.9 1.4e-01 -3.62
890022 rs1611265 6_24 0.099 391478.5 1.3e-01 -3.62
889972 rs1611236 6_24 1.000 391477.8 1.3e+00 -3.60
889890 rs1633033 6_24 0.403 391473.2 5.4e-01 -3.63
890026 rs2394171 6_24 0.001 391472.0 1.1e-03 -3.61
890024 rs1611267 6_24 0.001 391471.7 1.9e-03 -3.62
890028 rs2893981 6_24 0.001 391471.1 1.0e-03 -3.61
889958 rs1611228 6_24 0.002 391470.3 2.7e-03 -3.62
889947 rs1737020 6_24 0.000 391470.0 3.1e-05 -3.60
889948 rs1737019 6_24 0.000 391470.0 3.1e-05 -3.60
889903 rs2844838 6_24 0.031 391469.3 4.1e-02 -3.63
889907 rs1633032 6_24 0.000 391444.0 2.5e-04 -3.62
889941 rs1633020 6_24 0.000 391422.6 1.3e-06 -3.61
889945 rs1633018 6_24 0.000 391419.3 1.7e-08 -3.60
889970 rs1611234 6_24 0.000 391395.6 2.2e-09 -3.60
889830 rs1610726 6_24 0.000 391374.7 1.7e-09 -3.61
889898 rs2844840 6_24 0.000 391335.2 1.2e-09 -3.62
890225 rs3129185 6_24 0.000 391320.5 2.9e-10 -3.63
890240 rs1736999 6_24 0.000 391304.0 4.1e-11 -3.63
890253 rs1633001 6_24 0.000 391271.4 3.1e-11 -3.64
889993 rs1611246 6_24 0.000 391266.0 1.5e-13 -3.62
890429 rs1632980 6_24 0.000 391247.4 1.6e-13 -3.63
889926 rs1614309 6_24 0.000 391149.8 0.0e+00 -3.63
889925 rs1633030 6_24 0.000 390829.9 0.0e+00 -3.61
890038 rs9258382 6_24 0.000 390416.6 0.0e+00 -3.69
890035 rs9258379 6_24 0.000 389781.2 0.0e+00 -3.62
889984 rs1611241 6_24 0.000 389310.5 0.0e+00 -3.84
889929 rs1633028 6_24 0.000 388775.2 0.0e+00 -3.72
889987 rs1611244 6_24 0.000 387298.8 0.0e+00 -3.64
889942 rs2735042 6_24 0.000 386693.5 0.0e+00 -3.61
890023 rs1611266 6_24 0.000 383755.4 0.0e+00 -3.97
889996 rs1611249 6_24 0.000 382076.2 0.0e+00 -4.10
889962 rs1611230 6_24 0.000 381139.6 0.0e+00 -4.08
890011 rs145043018 6_24 0.000 381060.6 0.0e+00 -4.09
890021 rs147376303 6_24 0.000 381058.6 0.0e+00 -4.09
890032 rs9258376 6_24 0.000 381053.6 0.0e+00 -4.09
890039 rs1633016 6_24 0.000 381049.0 0.0e+00 -4.09
889884 rs1633035 6_24 0.000 381040.1 0.0e+00 -4.09
890092 rs1633014 6_24 0.000 381012.6 0.0e+00 -4.09
889917 rs1618670 6_24 0.000 381012.0 0.0e+00 -4.09
889944 rs1633019 6_24 0.000 380987.5 0.0e+00 -4.06
890206 rs1633010 6_24 0.000 380888.1 0.0e+00 -4.09
890330 rs909722 6_24 0.000 380827.5 0.0e+00 -4.08
890362 rs1610713 6_24 0.000 380823.3 0.0e+00 -4.09
890287 rs1736991 6_24 0.000 380820.3 0.0e+00 -4.09
890342 rs1610648 6_24 0.000 380798.0 0.0e+00 -4.10SNPs 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
889972 rs1611236 6_24 1.000 391477.83 1.30000 -3.60
889995 rs1611248 6_24 0.661 391479.61 0.88000 -3.63
889890 rs1633033 6_24 0.403 391473.16 0.54000 -3.63
133425 rs2885296 2_137 1.000 64774.79 0.22000 240.61
889951 rs2508055 6_24 0.157 391480.36 0.21000 -3.62
889954 rs111734624 6_24 0.158 391480.42 0.21000 -3.62
889999 rs1611252 6_24 0.129 391480.17 0.17000 -3.62
133395 rs2070959 2_137 1.000 46431.28 0.16000 238.14
133404 rs1875263 2_137 1.000 45491.47 0.16000 181.67
890016 rs1611260 6_24 0.107 391478.89 0.14000 -3.62
890022 rs1611265 6_24 0.099 391478.50 0.13000 -3.62
889903 rs2844838 6_24 0.031 391469.34 0.04100 -3.63
133423 rs76063448 2_137 1.000 6958.93 0.02400 70.66
607179 rs11045819 12_16 1.000 3088.45 0.01100 -14.34
536407 rs569165969 10_46 1.000 2515.63 0.00860 -1.12
890107 rs372065521 6_24 0.191 12741.36 0.00830 0.27
890108 rs555157405 6_24 0.188 12694.57 0.00820 0.27
890109 rs572275478 6_24 0.188 12694.57 0.00820 0.27
133428 rs12052787 2_137 0.974 2286.99 0.00760 -11.25
607080 rs12366506 12_16 0.725 2947.01 0.00730 23.44
890106 rs376865941 6_24 0.162 12742.01 0.00710 0.25
536408 rs7909631 10_46 0.720 2514.18 0.00620 -3.53
607196 rs4363657 12_16 1.000 1660.58 0.00570 43.78
607086 rs11045612 12_16 0.528 2944.31 0.00530 23.44
889958 rs1611228 6_24 0.002 391470.33 0.00270 -3.62
536406 rs7084697 10_46 0.271 2513.72 0.00230 -3.50
536455 rs6480402 10_46 1.000 633.80 0.00220 8.90
607097 rs73062442 12_16 0.211 2945.43 0.00210 23.39
607089 rs11513221 12_16 0.192 2945.54 0.00190 23.38
890024 rs1611267 6_24 0.001 391471.66 0.00190 -3.62
890026 rs2394171 6_24 0.001 391472.03 0.00110 -3.61
890028 rs2893981 6_24 0.001 391471.12 0.00100 -3.61
895648 rs74419673 6_24 0.026 10214.20 0.00090 0.45
896731 rs115013107 6_24 0.029 8609.63 0.00086 0.10
536460 rs35233497 10_46 0.527 461.47 0.00083 -4.24
536463 rs79086908 10_46 0.472 461.42 0.00074 -4.24
328273 rs115740542 6_20 1.000 194.26 0.00066 12.66
895641 rs79828868 6_24 0.018 10274.69 0.00062 0.42
384490 rs542176135 7_17 1.000 174.83 0.00060 -8.38
634455 rs653178 12_67 0.996 170.95 0.00058 -13.12
606990 rs3060461 12_16 0.316 511.17 0.00055 -21.86
606994 rs35290079 12_16 0.294 508.74 0.00051 -21.76
895530 rs57471183 6_24 0.014 10266.69 0.00050 0.38
803517 rs3794991 19_15 0.957 141.37 0.00046 11.64
895660 rs78940852 6_24 0.013 10269.38 0.00045 0.39
328252 rs72834643 6_20 1.000 129.78 0.00044 9.73
536464 rs73267631 10_46 0.710 174.73 0.00042 -1.71
896058 rs78205117 6_24 0.012 9894.66 0.00041 0.47
178663 rs523118 3_84 0.532 212.96 0.00039 -14.52
384512 rs4721597 7_17 0.999 106.35 0.00036 1.94SNPs 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
133425 rs2885296 2_137 1 64774.79 2.2e-01 240.61
133421 rs17862875 2_137 0 64563.08 0.0e+00 239.53
133395 rs2070959 2_137 1 46431.28 1.6e-01 238.14
133394 rs1105880 2_137 0 44790.26 0.0e+00 232.16
133389 rs77070100 2_137 0 44515.47 0.0e+00 231.38
133403 rs6749496 2_137 0 43407.39 0.0e+00 208.13
133418 rs3821242 2_137 0 36396.23 0.0e+00 203.17
133416 rs2008584 2_137 0 36197.64 0.0e+00 202.59
133396 rs7583278 2_137 0 39812.28 0.0e+00 200.99
133414 rs57258852 2_137 0 39540.91 0.0e+00 198.41
133412 rs4663332 2_137 0 36835.35 0.0e+00 194.41
133413 rs200973045 2_137 0 36845.09 0.0e+00 194.10
133380 rs2741034 2_137 0 27363.03 0.0e+00 190.53
133372 rs2602363 2_137 0 27321.01 0.0e+00 190.40
133367 rs2741013 2_137 0 27245.27 0.0e+00 190.21
133402 rs2012734 2_137 0 31409.85 0.0e+00 187.89
133424 rs11888459 2_137 0 47816.22 4.4e-14 187.72
133390 rs6753320 2_137 0 34017.77 0.0e+00 186.96
133391 rs6736743 2_137 0 34017.77 0.0e+00 186.96
133407 rs13401281 2_137 0 47340.26 0.0e+00 186.34
133404 rs1875263 2_137 1 45491.47 1.6e-01 181.67
133439 rs2361502 2_137 0 27383.80 0.0e+00 157.91
133376 rs6431558 2_137 0 16382.41 0.0e+00 -144.34
133384 rs1113193 2_137 0 6259.59 0.0e+00 -97.79
133378 rs1823803 2_137 0 7465.59 0.0e+00 91.76
133436 rs10202032 2_137 0 6838.83 0.0e+00 -88.03
133437 rs6723936 2_137 0 8088.97 0.0e+00 78.31
133426 rs143373661 2_137 0 7078.53 0.0e+00 78.24
133374 rs13027376 2_137 0 5566.27 0.0e+00 -74.88
133371 rs4047192 2_137 0 5557.82 0.0e+00 -74.81
133393 rs12476197 2_137 0 6567.86 0.0e+00 -71.92
133387 rs4663871 2_137 0 6531.38 0.0e+00 -71.72
133392 rs765251456 2_137 0 6477.17 0.0e+00 -71.57
133423 rs76063448 2_137 1 6958.93 2.4e-02 70.66
133419 rs45510999 2_137 0 6873.94 0.0e+00 70.21
133411 rs183532563 2_137 0 6809.64 0.0e+00 69.70
133427 rs11568318 2_137 0 1337.57 0.0e+00 -65.81
133417 rs45507691 2_137 0 1314.17 0.0e+00 -65.58
133385 rs17863773 2_137 0 5148.42 0.0e+00 -65.44
133379 rs10929252 2_137 0 4367.07 0.0e+00 -63.60
133377 rs17863766 2_137 0 4269.00 0.0e+00 -63.58
133366 rs140719475 2_137 0 4351.33 0.0e+00 -63.55
133369 rs6713902 2_137 0 3692.41 0.0e+00 -60.77
133420 rs139257330 2_137 0 4374.67 0.0e+00 60.10
133368 rs7563478 2_137 0 983.67 0.0e+00 -59.73
133382 rs2602372 2_137 0 3949.45 0.0e+00 58.13
133429 rs2003569 2_137 0 3269.44 0.0e+00 -57.58
133352 rs62192764 2_137 0 2549.23 0.0e+00 -54.32
133344 rs62192761 2_137 0 2543.08 0.0e+00 -54.28
133354 rs4047189 2_137 0 3472.81 0.0e+00 53.85
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] cowplot_1.0.0 ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] Biobase_2.44.0 httr_1.4.1
[3] bit64_4.0.5 assertthat_0.2.1
[5] stats4_3.6.1 blob_1.2.1
[7] BSgenome_1.52.0 GenomeInfoDbData_1.2.1
[9] Rsamtools_2.0.0 yaml_2.2.0
[11] progress_1.2.2 pillar_1.6.1
[13] RSQLite_2.2.7 lattice_0.20-38
[15] glue_1.4.2 digest_0.6.20
[17] GenomicRanges_1.36.0 promises_1.0.1
[19] XVector_0.24.0 colorspace_1.4-1
[21] htmltools_0.3.6 httpuv_1.5.1
[23] Matrix_1.2-18 XML_3.98-1.20
[25] pkgconfig_2.0.3 biomaRt_2.40.1
[27] zlibbioc_1.30.0 purrr_0.3.4
[29] scales_1.1.0 whisker_0.3-2
[31] later_0.8.0 BiocParallel_1.18.0
[33] git2r_0.26.1 tibble_3.1.2
[35] farver_2.1.0 generics_0.0.2
[37] IRanges_2.18.1 ellipsis_0.3.2
[39] withr_2.4.1 cachem_1.0.5
[41] SummarizedExperiment_1.14.1 GenomicFeatures_1.36.3
[43] BiocGenerics_0.30.0 magrittr_2.0.1
[45] crayon_1.4.1 memoise_2.0.0
[47] evaluate_0.14 fs_1.3.1
[49] fansi_0.5.0 tools_3.6.1
[51] data.table_1.14.0 prettyunits_1.0.2
[53] hms_1.1.0 lifecycle_1.0.0
[55] matrixStats_0.57.0 stringr_1.4.0
[57] S4Vectors_0.22.1 munsell_0.5.0
[59] DelayedArray_0.10.0 AnnotationDbi_1.46.0
[61] Biostrings_2.52.0 compiler_3.6.1
[63] GenomeInfoDb_1.20.0 rlang_0.4.11
[65] grid_3.6.1 RCurl_1.98-1.1
[67] VariantAnnotation_1.30.1 labeling_0.3
[69] bitops_1.0-6 rmarkdown_1.13
[71] gtable_0.3.0 DBI_1.1.1
[73] R6_2.5.0 GenomicAlignments_1.20.1
[75] dplyr_1.0.7 knitr_1.23
[77] rtracklayer_1.44.0 utf8_1.2.1
[79] fastmap_1.1.0 bit_4.0.4
[81] workflowr_1.6.2 rprojroot_2.0.2
[83] stringi_1.4.3 parallel_3.6.1
[85] Rcpp_1.0.6 vctrs_0.3.8
[87] tidyselect_1.1.0 xfun_0.8