Glycated haemoglobin (quantile) - Liver
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 | 627a4e1 | wesleycrouse | 2021-09-07 | adding heritability |
| 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 Glycated haemoglobin (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-30750_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.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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0119417027 0.0002107847 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
34.90073 20.38385 #report sample size
print(sample_size)[1] 344182#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01320015 0.10857332 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06926647 2.53478335Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
10849 DDAH2 6_26 1.000 14538.53 4.2e-02 -15.03
3212 CCND2 12_4 1.000 459.08 1.3e-03 -21.39
6498 ITGAD 16_25 0.999 169.89 4.9e-04 -11.72
5544 CNIH4 1_114 0.997 54.30 1.6e-04 -7.16
9390 GAS6 13_62 0.996 201.46 5.8e-04 -15.47
8493 OXSR1 3_27 0.985 46.83 1.3e-04 6.86
2546 LTBR 12_7 0.975 29.63 8.4e-05 3.97
3959 MYO5C 15_21 0.967 77.24 2.2e-04 -9.14
1783 ABCC1 16_15 0.967 42.46 1.2e-04 6.58
7264 ARFIP1 4_98 0.961 33.03 9.2e-05 5.29
9787 TMPRSS6 22_14 0.925 67.52 1.8e-04 -1.10
2048 GCDH 19_10 0.923 54.91 1.5e-04 9.77
4367 SEC14L4 22_10 0.921 58.44 1.6e-04 -7.56
6978 TRIM58 1_131 0.917 40.60 1.1e-04 9.12
10212 IL27 16_23 0.914 102.41 2.7e-04 9.95
889 EXOSC5 19_28 0.912 23.69 6.3e-05 -5.15
8565 NUDT4 12_55 0.897 33.35 8.7e-05 5.36
8811 SMIM19 8_37 0.886 230.45 5.9e-04 15.34
6558 AP3S2 15_41 0.881 67.86 1.7e-04 8.04
5291 SS18 18_13 0.880 29.83 7.6e-05 -5.05
11790 CYP2A6 19_28 0.873 24.79 6.3e-05 4.54
9442 ZNF438 10_23 0.857 22.66 5.6e-05 4.31
1231 PABPC4 1_24 0.850 67.10 1.7e-04 -8.67
3478 KLHL7 7_20 0.847 30.27 7.4e-05 -3.21
481 ITIH4 3_36 0.839 50.83 1.2e-04 -6.35
4736 HLX 1_112 0.823 22.69 5.4e-05 4.12
3883 PNKD 2_129 0.816 50.79 1.2e-04 7.18
9363 VMO1 17_4 0.814 20.89 4.9e-05 3.93
1723 KPNA3 13_21 0.808 23.76 5.6e-05 4.46Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
10602 RNF5 6_26 0 59524.96 0.0e+00 -11.02
12683 HCP5B 6_24 0 55846.74 0.0e+00 7.15
11007 PPT2 6_26 0 51384.93 0.0e+00 9.85
10848 CLIC1 6_26 0 45048.81 0.0e+00 -12.36
11541 C4A 6_26 0 37312.58 0.0e+00 -13.10
10663 TRIM31 6_24 0 29354.79 0.0e+00 -7.56
10601 AGER 6_26 0 27971.73 0.0e+00 0.83
4833 FLOT1 6_24 0 27955.62 0.0e+00 8.58
10599 NOTCH4 6_26 0 27576.27 3.0e-08 -14.22
10626 MPIG6B 6_26 0 15225.12 0.0e+00 5.33
10849 DDAH2 6_26 1 14538.53 4.2e-02 -15.03
10625 MSH5 6_26 0 13658.53 0.0e+00 -7.89
10603 AGPAT1 6_26 0 13636.99 0.0e+00 10.19
10651 ABCF1 6_24 0 12864.86 0.0e+00 8.19
10137 HLA-DQA1 6_26 0 12796.82 0.0e+00 -8.32
11441 EGFL8 6_26 0 11537.38 0.0e+00 7.19
10616 EHMT2 6_26 0 11503.65 0.0e+00 -3.26
10606 FKBPL 6_26 0 11417.54 0.0e+00 5.23
5766 PPP1R18 6_24 0 11178.74 0.0e+00 7.58
11231 HLA-DQB2 6_26 0 9652.09 0.0e+00 7.55Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
10849 DDAH2 6_26 1.000 14538.53 0.04200 -15.03
3212 CCND2 12_4 1.000 459.08 0.00130 -21.39
8811 SMIM19 8_37 0.886 230.45 0.00059 15.34
9390 GAS6 13_62 0.996 201.46 0.00058 -15.47
6498 ITGAD 16_25 0.999 169.89 0.00049 -11.72
4990 CIR1 2_105 0.496 308.79 0.00045 15.74
5598 SCRN3 2_105 0.496 308.79 0.00045 -15.74
12661 LINC01126 2_27 0.675 162.38 0.00032 -16.23
10212 IL27 16_23 0.914 102.41 0.00027 9.95
3959 MYO5C 15_21 0.967 77.24 0.00022 -9.14
8506 FAM222B 17_17 0.491 132.01 0.00019 -12.06
9787 TMPRSS6 22_14 0.925 67.52 0.00018 -1.10
1231 PABPC4 1_24 0.850 67.10 0.00017 -8.67
6558 AP3S2 15_41 0.881 67.86 0.00017 8.04
4077 ARPC1B 7_61 0.782 69.96 0.00016 9.45
5544 CNIH4 1_114 0.997 54.30 0.00016 -7.16
4367 SEC14L4 22_10 0.921 58.44 0.00016 -7.56
2048 GCDH 19_10 0.923 54.91 0.00015 9.77
8493 OXSR1 3_27 0.985 46.83 0.00013 6.86
2810 PRKAR2A 3_35 0.711 56.88 0.00012 -8.61Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5322 FN3KRP 17_47 0.010 371.70 1.1e-05 -23.60
3212 CCND2 12_4 1.000 459.08 1.3e-03 -21.39
7755 FN3K 17_47 0.023 235.90 1.6e-05 -20.49
11681 RP11-673E1.1 4_94 0.001 253.57 6.5e-07 -20.38
10243 GYPE 4_94 0.001 252.78 5.1e-07 20.36
5321 TBCD 17_47 0.003 183.00 1.8e-06 -18.27
669 ATP11A 13_61 0.008 313.46 7.7e-06 18.01
8156 GYPA 4_94 0.000 140.06 1.8e-07 -17.17
12661 LINC01126 2_27 0.675 162.38 3.2e-04 -16.23
9646 SNAI3 16_53 0.001 292.88 1.2e-06 -15.96
4990 CIR1 2_105 0.496 308.79 4.5e-04 15.74
5598 SCRN3 2_105 0.496 308.79 4.5e-04 -15.74
9390 GAS6 13_62 0.996 201.46 5.8e-04 -15.47
8811 SMIM19 8_37 0.886 230.45 5.9e-04 15.34
10849 DDAH2 6_26 1.000 14538.53 4.2e-02 -15.03
10599 NOTCH4 6_26 0.000 27576.27 3.0e-08 -14.22
2661 HBS1L 6_89 0.000 218.04 1.2e-12 13.72
10667 HLA-G 6_24 0.000 6753.42 0.0e+00 13.58
12181 RP11-370I10.12 12_30 0.000 155.41 1.3e-10 -13.34
6177 SPC25 2_102 0.000 165.93 5.0e-09 -13.30Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03935419#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5322 FN3KRP 17_47 0.010 371.70 1.1e-05 -23.60
3212 CCND2 12_4 1.000 459.08 1.3e-03 -21.39
7755 FN3K 17_47 0.023 235.90 1.6e-05 -20.49
11681 RP11-673E1.1 4_94 0.001 253.57 6.5e-07 -20.38
10243 GYPE 4_94 0.001 252.78 5.1e-07 20.36
5321 TBCD 17_47 0.003 183.00 1.8e-06 -18.27
669 ATP11A 13_61 0.008 313.46 7.7e-06 18.01
8156 GYPA 4_94 0.000 140.06 1.8e-07 -17.17
12661 LINC01126 2_27 0.675 162.38 3.2e-04 -16.23
9646 SNAI3 16_53 0.001 292.88 1.2e-06 -15.96
4990 CIR1 2_105 0.496 308.79 4.5e-04 15.74
5598 SCRN3 2_105 0.496 308.79 4.5e-04 -15.74
9390 GAS6 13_62 0.996 201.46 5.8e-04 -15.47
8811 SMIM19 8_37 0.886 230.45 5.9e-04 15.34
10849 DDAH2 6_26 1.000 14538.53 4.2e-02 -15.03
10599 NOTCH4 6_26 0.000 27576.27 3.0e-08 -14.22
2661 HBS1L 6_89 0.000 218.04 1.2e-12 13.72
10667 HLA-G 6_24 0.000 6753.42 0.0e+00 13.58
12181 RP11-370I10.12 12_30 0.000 155.41 1.3e-10 -13.34
6177 SPC25 2_102 0.000 165.93 5.0e-09 -13.30Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 17_47"
genename region_tag susie_pip mu2 PVE z
8093 FASN 17_47 0.009 16.25 4.4e-07 1.44
8807 CCDC57 17_47 0.062 34.69 6.2e-06 -2.78
5317 SLC16A3 17_47 0.004 9.85 1.0e-07 -1.15
12077 LINC01970 17_47 0.023 25.85 1.8e-06 -2.66
11951 RP13-516M14.1 17_47 0.018 23.24 1.2e-06 2.50
8580 CD7 17_47 0.004 9.59 9.9e-08 1.14
11918 RP13-20L14.1 17_47 0.002 5.29 3.5e-08 0.78
8086 HEXDC 17_47 0.003 6.19 5.3e-08 0.54
9236 OGFOD3 17_47 0.004 7.78 8.8e-08 -0.11
5323 NARF 17_47 0.008 12.85 2.9e-07 0.39
5325 FOXK2 17_47 0.018 34.75 1.9e-06 -4.00
5329 WDR45B 17_47 0.106 96.65 3.0e-05 10.53
5318 RAB40B 17_47 0.003 38.36 3.4e-07 -8.46
5322 FN3KRP 17_47 0.010 371.70 1.1e-05 -23.60
12045 RP11-388C12.5 17_47 0.003 20.47 1.8e-07 6.12
7755 FN3K 17_47 0.023 235.90 1.6e-05 -20.49
5321 TBCD 17_47 0.003 183.00 1.8e-06 -18.27
12030 RP11-497H17.1 17_47 0.290 40.79 3.4e-05 3.31
8766 B3GNTL1 17_47 0.007 25.75 5.3e-07 0.03
12014 AC144831.1 17_47 0.003 10.85 9.2e-08 -0.89
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 12_4"
genename region_tag susie_pip mu2 PVE z
4041 CRACR2A 12_4 0 11.22 3.3e-09 1.11
2530 PARP11 12_4 0 5.78 9.2e-10 0.63
11823 RP11-320N7.2 12_4 0 14.52 5.4e-09 1.73
3212 CCND2 12_4 1 459.08 1.3e-03 -21.39
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 4_94"
genename region_tag susie_pip mu2 PVE z
2400 INPP4B 4_94 0.000 7.74 2.9e-09 0.82
10243 GYPE 4_94 0.001 252.78 5.1e-07 20.36
11681 RP11-673E1.1 4_94 0.001 253.57 6.5e-07 -20.38
8156 GYPA 4_94 0.000 140.06 1.8e-07 -17.17
7266 HHIP 4_94 0.007 16.07 3.2e-07 3.94
7267 ABCE1 4_94 0.001 10.23 1.7e-08 0.09
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 13_61"
genename region_tag susie_pip mu2 PVE z
3789 TUBGCP3 13_61 0.000 7.04 3.2e-09 0.62
12598 RP11-88E10.4 13_61 0.000 6.73 3.0e-09 0.55
669 ATP11A 13_61 0.008 313.46 7.7e-06 18.01
12510 RP11-88E10.5 13_61 0.001 26.61 4.7e-08 -4.15
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_27"
genename region_tag susie_pip mu2 PVE z
12661 LINC01126 2_27 0.675 162.38 3.2e-04 -16.23
2977 THADA 2_27 0.001 7.15 1.6e-08 -2.12
6208 PLEKHH2 2_27 0.001 5.40 9.8e-09 0.46
11022 C1GALT1C1L 2_27 0.002 25.76 1.4e-07 3.04
4930 DYNC2LI1 2_27 0.002 17.97 9.6e-08 -2.07
5563 ABCG8 2_27 0.001 6.03 1.3e-08 -0.55
4943 LRPPRC 2_27 0.001 7.72 1.7e-08 -0.15
| 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
981 rs2799182 1_3 1.000 80.20 2.3e-04 -9.51
11587 rs2786487 1_26 1.000 48.46 1.4e-04 7.03
31923 rs599134 1_69 1.000 47.12 1.4e-04 6.81
36767 rs2779116 1_78 1.000 683.12 2.0e-03 30.86
41368 rs9425587 1_84 1.000 43.23 1.3e-04 -6.79
53450 rs79687284 1_108 1.000 139.81 4.1e-04 13.92
70672 rs1042034 2_13 1.000 38.44 1.1e-04 -5.51
71531 rs565332541 2_14 1.000 101.20 2.9e-04 15.51
72408 rs780093 2_16 1.000 159.52 4.6e-04 11.09
78734 rs2121564 2_28 1.000 74.77 2.2e-04 8.61
113830 rs71397673 2_102 1.000 498.29 1.4e-03 28.67
113838 rs853789 2_102 1.000 1007.68 2.9e-03 38.94
140772 rs56395424 3_9 1.000 106.47 3.1e-04 -13.84
140830 rs10602803 3_9 1.000 61.31 1.8e-04 11.11
172874 rs72964564 3_76 1.000 288.27 8.4e-04 -18.72
192307 rs1027498 3_115 1.000 105.87 3.1e-04 6.97
225668 rs149027545 4_59 1.000 80.05 2.3e-04 8.05
243261 rs11727331 4_94 1.000 160.87 4.7e-04 -17.08
243455 rs34149094 4_94 1.000 67.95 2.0e-04 -7.15
259233 rs766378231 5_2 1.000 101.45 2.9e-04 -1.29
259243 rs60116306 5_2 1.000 110.06 3.2e-04 4.71
264499 rs529337207 5_12 1.000 74.39 2.2e-04 -8.65
307442 rs6885822 5_93 1.000 62.77 1.8e-04 7.67
317496 rs9378483 6_7 1.000 44.27 1.3e-04 5.38
317606 rs55792466 6_7 1.000 146.33 4.3e-04 -11.10
317642 rs75465676 6_7 1.000 56.34 1.6e-04 -5.10
322198 rs2206734 6_15 1.000 127.99 3.7e-04 15.04
324094 rs75080831 6_19 1.000 176.71 5.1e-04 -20.15
324246 rs34877685 6_20 1.000 163.96 4.8e-04 -9.72
324255 rs72834643 6_20 1.000 491.20 1.4e-03 -21.07
324276 rs115740542 6_20 1.000 837.28 2.4e-03 -28.80
324742 rs6908155 6_21 1.000 368.28 1.1e-03 8.45
324848 rs535096266 6_21 1.000 88.30 2.6e-04 6.25
325118 rs3130253 6_23 1.000 134.17 3.9e-04 13.88
325261 rs6935940 6_27 1.000 87.89 2.6e-04 3.82
329159 rs1005230 6_33 1.000 54.15 1.6e-04 7.06
350567 rs62420266 6_74 1.000 39.85 1.2e-04 -5.70
358106 rs199804242 6_89 1.000 8208.19 2.4e-02 2.81
366073 rs60425481 6_104 1.000 7230.70 2.1e-02 -6.69
386398 rs142235947 7_33 1.000 34.16 9.9e-05 -5.29
425361 rs1703982 8_11 1.000 614.50 1.8e-03 -6.43
425382 rs2428 8_11 1.000 693.09 2.0e-03 6.08
425387 rs758184196 8_11 1.000 753.23 2.2e-03 -0.53
437404 rs150722768 8_36 1.000 71.74 2.1e-04 -10.55
437568 rs76508735 8_36 1.000 137.94 4.0e-04 -5.99
437581 rs10099921 8_36 1.000 254.90 7.4e-04 -18.49
437588 rs12550646 8_36 1.000 235.97 6.9e-04 -16.78
437596 rs6989331 8_36 1.000 94.36 2.7e-04 -2.86
484823 rs10545172 9_37 1.000 70.02 2.0e-04 9.11
499143 rs57248636 9_62 1.000 36.58 1.1e-04 5.52
502370 rs117561717 9_70 1.000 42.24 1.2e-04 6.47
509091 rs61848333 10_10 1.000 108.48 3.2e-04 10.75
526504 rs111333451 10_45 1.000 63.84 1.9e-04 8.10
526819 rs4745982 10_46 1.000 1273.52 3.7e-03 -56.67
526820 rs6480402 10_46 1.000 8853.47 2.6e-02 -53.18
526829 rs73267631 10_46 1.000 2069.29 6.0e-03 6.15
532810 rs478839 10_56 1.000 58.31 1.7e-04 -7.51
539218 rs12244851 10_70 1.000 679.01 2.0e-03 24.38
547356 rs234856 11_2 1.000 128.82 3.7e-04 -8.70
550405 rs4910498 11_8 1.000 300.37 8.7e-04 -13.81
562519 rs2596407 11_29 1.000 60.79 1.8e-04 8.39
566962 rs12294913 11_36 1.000 59.28 1.7e-04 7.56
569337 rs4944832 11_41 1.000 65.16 1.9e-04 -8.05
575731 rs76838754 11_52 1.000 64.61 1.9e-04 -2.44
575734 rs10830962 11_52 1.000 316.07 9.2e-04 19.78
578410 rs73001144 11_57 1.000 34.79 1.0e-04 -5.71
606211 rs150158762 12_33 1.000 77.62 2.3e-04 -9.16
606917 rs7397189 12_36 1.000 43.82 1.3e-04 -6.60
617756 rs55692966 12_56 1.000 41.07 1.2e-04 -6.27
635274 rs576674 13_10 1.000 110.89 3.2e-04 -10.47
649265 rs1327315 13_40 1.000 60.25 1.8e-04 -7.81
670804 rs72681869 14_20 1.000 48.89 1.4e-04 -7.31
683108 rs35889227 14_45 1.000 85.36 2.5e-04 -9.34
693353 rs12912777 15_13 1.000 53.34 1.5e-04 6.19
701664 rs66461959 15_31 1.000 87.76 2.5e-04 3.57
701678 rs67453880 15_31 1.000 95.39 2.8e-04 3.50
738440 rs117100864 17_5 1.000 44.12 1.3e-04 -6.61
739430 rs72829444 17_7 1.000 101.42 2.9e-04 10.35
739592 rs10468482 17_7 1.000 78.46 2.3e-04 -10.14
751949 rs117348249 17_35 1.000 40.58 1.2e-04 5.04
757343 rs58711252 17_43 1.000 150.34 4.4e-04 14.36
757346 rs3813026 17_43 1.000 176.04 5.1e-04 10.84
757347 rs417780 17_43 1.000 397.07 1.2e-03 19.21
757350 rs61740060 17_43 1.000 148.80 4.3e-04 4.80
757458 rs11658216 17_44 1.000 39.46 1.1e-04 4.87
789282 rs59616136 19_14 1.000 210.50 6.1e-04 -18.27
814166 rs6066141 20_29 1.000 69.12 2.0e-04 -8.59
817659 rs6099616 20_33 1.000 79.38 2.3e-04 8.97
827397 rs2834259 21_14 1.000 61.18 1.8e-04 7.73
831372 rs8129767 21_22 1.000 36.26 1.1e-04 -4.62
831649 rs60426421 21_23 1.000 40.90 1.2e-04 -6.28
842848 rs72660919 1_18 1.000 125.75 3.7e-04 -9.76
917145 rs1611236 6_24 1.000 112347.05 3.3e-01 8.54
936761 rs201369106 6_25 1.000 6739.02 2.0e-02 1.55
939856 rs9279507 6_26 1.000 89287.17 2.6e-01 1.84
957622 rs138917529 7_32 1.000 111.47 3.2e-04 -12.14
995050 rs3217791 12_4 1.000 103.57 3.0e-04 -9.12
1049805 rs45625038 16_25 1.000 72.36 2.1e-04 6.08
1057134 rs61745086 16_53 1.000 370.14 1.1e-03 -18.28
1057579 rs551118 16_53 1.000 700.99 2.0e-03 23.96
1083757 rs5112 19_32 1.000 87.30 2.5e-04 -8.57
1090658 rs201074739 19_35 1.000 83.64 2.4e-04 -7.84
1107866 rs855791 22_14 1.000 543.50 1.6e-03 -26.89
113831 rs537183 2_102 0.999 974.91 2.8e-03 38.61
324107 rs2281074 6_19 0.999 154.85 4.5e-04 -19.39
356962 rs10457576 6_87 0.999 35.10 1.0e-04 5.73
547354 rs234852 11_2 0.999 67.97 2.0e-04 3.51
549865 rs3750952 11_6 0.999 37.09 1.1e-04 -5.95
595881 rs66720652 12_15 0.999 35.30 1.0e-04 -5.82
625676 rs80019595 12_74 0.999 91.58 2.7e-04 3.88
660695 rs9549304 13_61 0.999 43.64 1.3e-04 7.90
661889 rs17122779 14_3 0.999 34.58 1.0e-04 5.64
713177 rs11642004 16_4 0.999 33.99 9.9e-05 5.80
742847 rs59503666 17_15 0.999 83.41 2.4e-04 -13.24
995079 rs3217860 12_4 0.999 54.78 1.6e-04 9.32
324057 rs10498727 6_19 0.998 55.36 1.6e-04 1.65
325474 rs2856992 6_27 0.998 48.33 1.4e-04 -5.62
526479 rs117731828 10_45 0.998 32.97 9.6e-05 -6.82
606244 rs112538405 12_34 0.998 34.29 9.9e-05 -5.56
661938 rs34237552 14_3 0.998 37.42 1.1e-04 5.94
752960 rs62062484 17_37 0.998 31.28 9.1e-05 -5.14
757456 rs4371218 17_44 0.998 32.67 9.5e-05 -3.36
783928 rs351988 19_2 0.998 42.20 1.2e-04 -6.40
957592 rs3757840 7_32 0.998 244.00 7.1e-04 -27.52
203108 rs34927251 4_17 0.997 32.00 9.3e-05 -5.38
359204 rs540973884 6_92 0.997 58.18 1.7e-04 -8.58
550677 rs79057673 11_8 0.997 36.94 1.1e-04 6.04
742789 rs3816511 17_15 0.997 48.32 1.4e-04 -9.10
744242 rs9891654 17_18 0.997 46.61 1.3e-04 -6.36
113886 rs112308555 2_103 0.996 28.70 8.3e-05 4.91
287801 rs17462893 5_57 0.996 35.14 1.0e-04 6.77
578486 rs11224303 11_58 0.996 253.77 7.3e-04 -15.04
610958 rs2137537 12_44 0.996 33.78 9.8e-05 5.73
359196 rs590325 6_92 0.995 31.97 9.2e-05 6.70
547134 rs3842748 11_2 0.995 89.05 2.6e-04 -8.66
625668 rs112623431 12_74 0.995 86.49 2.5e-04 -3.50
839110 rs135101 22_18 0.995 32.89 9.5e-05 3.40
195375 rs9812813 3_120 0.994 49.16 1.4e-04 7.35
325008 rs3129685 6_23 0.994 69.97 2.0e-04 6.26
784986 rs10410896 19_4 0.994 38.76 1.1e-04 6.42
1032848 rs45617834 14_52 0.994 34.76 1.0e-04 -5.61
536468 rs6584362 10_64 0.993 29.33 8.5e-05 -4.40
562064 rs2863159 11_28 0.993 39.92 1.2e-04 6.42
1025 rs113120570 1_3 0.992 76.34 2.2e-04 -11.36
534213 rs1977833 10_59 0.992 128.51 3.7e-04 -11.86
552866 rs5215 11_12 0.992 84.38 2.4e-04 -9.02
624695 rs149837779 12_73 0.991 29.83 8.6e-05 5.96
675341 rs873642 14_30 0.991 42.28 1.2e-04 8.93
172891 rs6797915 3_76 0.990 44.02 1.3e-04 8.80
317409 rs201036 6_6 0.990 30.07 8.7e-05 -5.27
633584 rs60353775 13_7 0.990 104.97 3.0e-04 11.83
733107 rs2927324 16_45 0.988 38.59 1.1e-04 -6.33
999 rs140140100 1_3 0.987 29.91 8.6e-05 1.49
323157 rs191816 6_17 0.987 33.56 9.6e-05 5.41
376826 rs13235534 7_15 0.987 31.25 9.0e-05 5.35
538670 rs11195508 10_70 0.987 34.76 1.0e-04 -5.48
575738 rs271042 11_52 0.987 41.41 1.2e-04 -2.47
873617 rs3811444 1_131 0.987 59.43 1.7e-04 10.10
327681 rs10305514 6_30 0.986 31.90 9.1e-05 5.57
107114 rs1427297 2_86 0.985 30.58 8.8e-05 -5.27
786913 rs11880903 19_7 0.985 28.23 8.1e-05 5.05
469458 rs10758593 9_4 0.984 46.39 1.3e-04 6.79
759185 rs2635417 17_47 0.984 241.53 6.9e-04 22.39
72195 rs1554481 2_15 0.983 27.00 7.7e-05 4.60
569264 rs11603349 11_41 0.981 123.16 3.5e-04 -11.10
140815 rs709149 3_9 0.980 85.30 2.4e-04 -13.67
153187 rs71623875 3_39 0.980 30.17 8.6e-05 4.93
378082 rs7778372 7_17 0.979 36.04 1.0e-04 -5.76
634521 rs9508717 13_9 0.979 38.79 1.1e-04 -5.99
784962 rs11878545 19_4 0.979 33.17 9.4e-05 5.69
458346 rs138983405 8_78 0.978 71.74 2.0e-04 -9.06
568462 rs3781660 11_39 0.978 27.06 7.7e-05 -4.85
734945 rs11641197 16_49 0.976 32.22 9.1e-05 6.79
782433 rs531621 18_45 0.976 46.13 1.3e-04 6.73
324138 rs115902543 6_20 0.975 30.27 8.6e-05 -3.87
818205 rs6026545 20_34 0.975 37.88 1.1e-04 5.83
535371 rs35909109 10_62 0.974 26.42 7.5e-05 4.76
416862 rs10227304 7_93 0.973 31.59 8.9e-05 -4.20
743536 rs2946517 17_16 0.972 49.06 1.4e-04 -8.71
670887 rs2883893 14_20 0.971 30.72 8.7e-05 4.66
173519 rs7622489 3_78 0.964 46.48 1.3e-04 6.84
675356 rs17245565 14_30 0.964 48.54 1.4e-04 -8.58
739441 rs1641549 17_7 0.964 38.17 1.1e-04 8.54
751890 rs34221578 17_34 0.964 56.66 1.6e-04 7.42
35864 rs2990245 1_76 0.963 47.78 1.3e-04 7.69
151518 rs77833543 3_33 0.963 26.41 7.4e-05 5.03
454393 rs485453 8_69 0.961 27.53 7.7e-05 5.15
395373 rs374118515 7_48 0.960 30.72 8.6e-05 -5.38
503788 rs1886296 9_73 0.959 25.64 7.1e-05 -4.47
191888 rs9880677 3_114 0.958 35.01 9.7e-05 6.12
988927 rs374499153 11_1 0.958 78.45 2.2e-04 9.65
323896 rs34706906 6_19 0.955 54.51 1.5e-04 -11.13
355584 rs41285272 6_85 0.955 26.65 7.4e-05 4.76
480557 rs34280179 9_26 0.955 29.72 8.2e-05 5.01
609395 rs2884593 12_40 0.955 30.12 8.4e-05 6.48
355109 rs1744620 6_83 0.954 24.96 6.9e-05 -4.66
598401 rs7953190 12_19 0.953 79.59 2.2e-04 -8.99
618452 rs10777868 12_58 0.953 34.91 9.7e-05 -7.00
754346 rs8070232 17_39 0.953 29.88 8.3e-05 5.35
539248 rs66808559 10_70 0.951 31.16 8.6e-05 4.52
959003 rs41295942 7_62 0.951 29.81 8.2e-05 -5.02
173458 rs1260471 3_77 0.950 48.41 1.3e-04 -7.16
503830 rs28624681 9_73 0.950 140.05 3.9e-04 12.54
839126 rs13055886 22_18 0.950 87.90 2.4e-04 -9.14
319282 rs4357124 6_11 0.949 27.45 7.6e-05 5.26
675339 rs41307086 14_29 0.949 28.29 7.8e-05 4.70
486193 rs13285167 9_40 0.948 25.00 6.9e-05 4.69
97060 rs650588 2_66 0.946 50.52 1.4e-04 -6.73
1073214 rs11672387 19_10 0.946 47.45 1.3e-04 8.27
315896 rs318468 6_3 0.945 30.65 8.4e-05 5.40
600046 rs7302975 12_21 0.942 25.59 7.0e-05 -4.71
794040 rs58526561 19_23 0.942 88.85 2.4e-04 -10.83
842910 rs61777615 1_18 0.942 109.63 3.0e-04 1.06
130076 rs13029395 2_133 0.941 26.42 7.2e-05 3.90
282389 rs13174383 5_45 0.941 54.54 1.5e-04 7.15
794347 rs889140 19_23 0.941 28.80 7.9e-05 -5.00
562279 rs75065406 11_28 0.940 27.09 7.4e-05 -5.12
819593 rs3901528 20_36 0.940 45.25 1.2e-04 -6.60
8390 rs35495299 1_19 0.939 63.21 1.7e-04 -5.95
507491 rs3824667 10_8 0.937 29.61 8.1e-05 5.17
425403 rs13265731 8_11 0.932 696.03 1.9e-03 6.18
548417 rs72883124 11_4 0.932 32.16 8.7e-05 -5.58
132524 rs7584554 2_137 0.928 39.40 1.1e-04 6.90
282667 rs12189028 5_45 0.927 31.84 8.6e-05 -2.39
151504 rs147347968 3_33 0.926 25.35 6.8e-05 4.74
156510 rs17775391 3_45 0.924 31.71 8.5e-05 -5.12
53459 rs3754140 1_108 0.923 77.67 2.1e-04 -10.21
526420 rs10998007 10_45 0.922 25.01 6.7e-05 3.88
324748 rs7775817 6_21 0.921 282.60 7.6e-04 -2.43
244102 rs10305918 4_95 0.920 25.94 6.9e-05 4.71
647365 rs9530281 13_36 0.920 24.56 6.6e-05 -4.56
187151 rs10653660 3_104 0.916 161.38 4.3e-04 -16.44
181172 rs28663084 3_94 0.915 63.08 1.7e-04 -7.84
78216 rs138452194 2_27 0.914 44.51 1.2e-04 -3.13
31045 rs72987493 1_67 0.911 37.51 9.9e-05 5.95
84331 rs11886868 2_40 0.909 33.92 9.0e-05 -5.87
660963 rs1760940 14_1 0.909 63.35 1.7e-04 7.97
36770 rs138055271 1_78 0.907 29.96 7.9e-05 -6.73
441585 rs56386732 8_45 0.907 29.93 7.9e-05 5.21
502540 rs115478735 9_70 0.907 137.05 3.6e-04 17.60
550695 rs11042847 11_8 0.905 73.04 1.9e-04 9.79
757267 rs74784618 17_43 0.905 46.85 1.2e-04 5.46
53455 rs340835 1_108 0.904 88.41 2.3e-04 12.37
737738 rs12449600 17_3 0.902 37.55 9.8e-05 -5.75
580755 rs117719056 11_62 0.901 24.05 6.3e-05 -4.22
416937 rs3793342 7_93 0.898 27.21 7.1e-05 -5.20
167086 rs62258976 3_65 0.897 23.53 6.1e-05 4.36
416944 rs743506 7_93 0.897 25.64 6.7e-05 -3.97
630122 rs10781644 12_82 0.895 28.16 7.3e-05 -5.43
306029 rs74417235 5_91 0.887 30.61 7.9e-05 -5.44
488442 rs62550974 9_45 0.887 227.54 5.9e-04 -19.55
547312 rs231842 11_2 0.885 48.49 1.2e-04 6.45
570116 rs1215071 11_42 0.885 32.29 8.3e-05 5.65
539212 rs117764423 10_70 0.884 158.79 4.1e-04 -6.70
259159 rs4956970 5_1 0.883 27.53 7.1e-05 -5.09
915619 rs2394122 6_22 0.883 89.73 2.3e-04 -12.62
728960 rs72799826 16_38 0.881 25.79 6.6e-05 -5.00
534757 rs17109928 10_60 0.878 31.44 8.0e-05 5.60
637836 rs374017936 13_16 0.878 30.18 7.7e-05 5.35
325624 rs6934244 6_27 0.877 30.57 7.8e-05 5.55
448551 rs60855359 8_58 0.877 25.11 6.4e-05 -4.59
225625 rs6532039 4_59 0.876 34.00 8.6e-05 -4.50
746857 rs118132312 17_23 0.875 24.49 6.2e-05 4.43
93135 rs4435501 2_57 0.872 30.50 7.7e-05 5.48
556773 rs4923464 11_19 0.867 28.78 7.2e-05 -5.03
57 rs201014604 1_1 0.866 24.95 6.3e-05 4.54
582715 rs139117557 11_67 0.866 23.97 6.0e-05 -4.35
509202 rs12218957 10_10 0.864 28.08 7.0e-05 4.99
1107888 rs881144 22_14 0.864 89.16 2.2e-04 14.34
151523 rs73079289 3_33 0.862 28.82 7.2e-05 -5.14
132458 rs6722529 2_137 0.861 34.00 8.5e-05 -5.83
568157 rs72932183 11_38 0.859 25.13 6.3e-05 -4.66
939845 rs3130292 6_26 0.858 89458.44 2.2e-01 14.05
123635 rs231811 2_120 0.857 25.60 6.4e-05 4.53
736439 rs8044191 16_54 0.857 34.96 8.7e-05 -6.58
604810 rs55770587 12_31 0.856 50.79 1.3e-04 -7.93
693911 rs77839142 15_14 0.854 24.89 6.2e-05 4.38
734937 rs247834 16_49 0.854 31.88 7.9e-05 6.63
529664 rs58142007 10_51 0.853 24.00 6.0e-05 -4.02
187420 rs2141746 3_105 0.850 76.73 1.9e-04 -8.38
739696 rs116982102 17_8 0.850 23.88 5.9e-05 -4.28
295993 rs11064 5_72 0.849 24.36 6.0e-05 -4.40
810545 rs61734341 20_19 0.848 27.69 6.8e-05 -5.10
144605 rs2173058 3_17 0.847 34.89 8.6e-05 -5.21
618399 rs10860185 12_58 0.843 24.48 6.0e-05 -3.68
604513 rs11168408 12_30 0.842 594.75 1.5e-03 19.93
609362 rs189339 12_40 0.841 39.53 9.7e-05 -7.92
782332 rs72973445 18_45 0.841 24.09 5.9e-05 4.26
67334 rs10167277 2_7 0.840 26.34 6.4e-05 -4.69
78165 rs57381820 2_27 0.839 172.02 4.2e-04 -16.56
975349 rs12555274 9_16 0.836 101.51 2.5e-04 10.14
612903 rs310792 12_47 0.835 25.17 6.1e-05 -4.51
872087 rs56043070 1_131 0.835 33.66 8.2e-05 -6.21
583680 rs71466797 11_70 0.829 28.67 6.9e-05 -4.16
362646 rs6921399 6_98 0.827 25.45 6.1e-05 4.46
319034 rs12663475 6_10 0.825 26.28 6.3e-05 -4.74
187421 rs11924635 3_105 0.823 29.24 7.0e-05 1.37
458694 rs28529793 8_78 0.822 101.11 2.4e-04 -7.87
244784 rs59435073 4_97 0.820 24.45 5.8e-05 -4.32
746436 rs12938438 17_22 0.820 25.12 6.0e-05 4.20
327810 rs6904583 6_31 0.819 25.77 6.1e-05 4.72
721939 rs58200984 16_24 0.819 25.51 6.1e-05 4.80
256862 rs62336098 4_119 0.818 25.93 6.2e-05 -4.47
566335 rs174548 11_34 0.817 98.02 2.3e-04 -9.84
85910 rs369551671 2_43 0.813 24.03 5.7e-05 -4.30
659449 rs754205 13_59 0.813 27.15 6.4e-05 -4.59
151864 rs6446297 3_35 0.807 81.42 1.9e-04 9.01
84320 rs11884411 2_40 0.806 44.45 1.0e-04 -7.27
380914 rs4552808 7_23 0.803 27.42 6.4e-05 -4.66
1024449 rs7329468 13_62 0.803 92.21 2.2e-04 -13.49
111645 rs270920 2_96 0.802 30.18 7.0e-05 -5.47
957448 rs2971681 7_32 0.802 74.48 1.7e-04 11.01SNPs 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
917145 rs1611236 6_24 1.000 112347.1 3.3e-01 8.54
917127 rs111734624 6_24 0.278 112088.1 9.1e-02 8.55
917124 rs2508055 6_24 0.278 112088.1 9.1e-02 8.55
917172 rs1611252 6_24 0.234 112087.9 7.6e-02 8.55
917189 rs1611260 6_24 0.216 112087.5 7.0e-02 8.55
917168 rs1611248 6_24 0.197 112087.4 6.4e-02 8.55
917195 rs1611265 6_24 0.204 112087.3 6.6e-02 8.55
917063 rs1633033 6_24 0.171 112086.1 5.6e-02 8.56
917199 rs2394171 6_24 0.109 112085.6 3.5e-02 8.55
917197 rs1611267 6_24 0.080 112085.3 2.6e-02 8.55
917120 rs1737020 6_24 0.106 112085.3 3.5e-02 8.55
917121 rs1737019 6_24 0.106 112085.3 3.5e-02 8.55
917201 rs2893981 6_24 0.097 112085.3 3.2e-02 8.55
917131 rs1611228 6_24 0.084 112085.1 2.7e-02 8.55
917076 rs2844838 6_24 0.095 112084.8 3.1e-02 8.55
917080 rs1633032 6_24 0.294 112079.1 9.6e-02 8.57
917114 rs1633020 6_24 0.013 112071.1 4.1e-03 8.54
917118 rs1633018 6_24 0.010 112070.2 3.1e-03 8.54
917143 rs1611234 6_24 0.002 112062.6 5.9e-04 8.53
917003 rs1610726 6_24 0.201 112060.8 6.6e-02 8.58
917071 rs2844840 6_24 0.008 112046.8 2.6e-03 8.55
917398 rs3129185 6_24 0.000 112040.0 4.1e-05 8.53
917413 rs1736999 6_24 0.000 112034.0 2.0e-06 8.51
917166 rs1611246 6_24 0.000 112025.3 7.4e-05 8.53
917426 rs1633001 6_24 0.000 112024.8 1.5e-06 8.51
917602 rs1632980 6_24 0.000 112018.3 2.0e-06 8.51
917099 rs1614309 6_24 0.000 111993.0 2.0e-05 8.55
917098 rs1633030 6_24 0.000 111902.0 2.4e-08 8.54
917211 rs9258382 6_24 0.000 111788.0 2.2e-07 8.63
917208 rs9258379 6_24 0.000 111602.5 8.3e-16 8.60
917157 rs1611241 6_24 0.000 111471.4 8.3e-16 8.65
917102 rs1633028 6_24 0.000 111314.1 0.0e+00 8.55
917160 rs1611244 6_24 0.000 110894.7 0.0e+00 8.66
917115 rs2735042 6_24 0.000 110713.3 0.0e+00 8.36
917196 rs1611266 6_24 0.000 109890.7 0.0e+00 8.83
917169 rs1611249 6_24 0.000 109408.4 0.0e+00 8.81
917135 rs1611230 6_24 0.000 109141.0 0.0e+00 8.82
917184 rs145043018 6_24 0.000 109117.9 0.0e+00 8.82
917194 rs147376303 6_24 0.000 109117.3 0.0e+00 8.82
917205 rs9258376 6_24 0.000 109115.9 0.0e+00 8.82
917212 rs1633016 6_24 0.000 109114.5 0.0e+00 8.82
917057 rs1633035 6_24 0.000 109112.3 0.0e+00 8.81
917090 rs1618670 6_24 0.000 109105.4 0.0e+00 8.82
917265 rs1633014 6_24 0.000 109103.6 0.0e+00 8.81
917117 rs1633019 6_24 0.000 109096.6 0.0e+00 8.80
917379 rs1633010 6_24 0.000 109066.3 0.0e+00 8.79
917503 rs909722 6_24 0.000 109047.9 0.0e+00 8.77
917535 rs1610713 6_24 0.000 109046.6 0.0e+00 8.77
917460 rs1736991 6_24 0.000 109045.5 0.0e+00 8.76
917515 rs1610648 6_24 0.000 109039.0 0.0e+00 8.76SNPs 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
917145 rs1611236 6_24 1.000 112347.05 0.3300 8.54
939856 rs9279507 6_26 1.000 89287.17 0.2600 1.84
939845 rs3130292 6_26 0.858 89458.44 0.2200 14.05
939842 rs3130291 6_26 0.606 89457.62 0.1600 14.05
917080 rs1633032 6_24 0.294 112079.14 0.0960 8.57
917124 rs2508055 6_24 0.278 112088.10 0.0910 8.55
917127 rs111734624 6_24 0.278 112088.11 0.0910 8.55
917172 rs1611252 6_24 0.234 112087.88 0.0760 8.55
917189 rs1611260 6_24 0.216 112087.46 0.0700 8.55
917003 rs1610726 6_24 0.201 112060.80 0.0660 8.58
917195 rs1611265 6_24 0.204 112087.32 0.0660 8.55
917168 rs1611248 6_24 0.197 112087.44 0.0640 8.55
917063 rs1633033 6_24 0.171 112086.10 0.0560 8.56
917120 rs1737020 6_24 0.106 112085.29 0.0350 8.55
917121 rs1737019 6_24 0.106 112085.29 0.0350 8.55
917199 rs2394171 6_24 0.109 112085.59 0.0350 8.55
917201 rs2893981 6_24 0.097 112085.27 0.0320 8.55
917076 rs2844838 6_24 0.095 112084.84 0.0310 8.55
917131 rs1611228 6_24 0.084 112085.10 0.0270 8.55
526820 rs6480402 10_46 1.000 8853.47 0.0260 -53.18
917197 rs1611267 6_24 0.080 112085.33 0.0260 8.55
358106 rs199804242 6_89 1.000 8208.19 0.0240 2.81
366073 rs60425481 6_104 1.000 7230.70 0.0210 -6.69
936761 rs201369106 6_25 1.000 6739.02 0.0200 1.55
358122 rs6923513 6_89 0.624 8246.99 0.0150 2.89
366069 rs3106169 6_104 0.598 7192.50 0.0130 2.33
936771 rs34259803 6_25 0.623 6751.83 0.0120 6.25
366078 rs3106167 6_104 0.454 7192.39 0.0095 2.33
358105 rs2327654 6_89 0.376 8246.42 0.0090 2.89
526828 rs79086908 10_46 0.547 5256.77 0.0084 11.40
366070 rs3127598 6_104 0.367 7192.34 0.0077 2.34
526825 rs35233497 10_46 0.453 5256.31 0.0069 11.40
526829 rs73267631 10_46 1.000 2069.29 0.0060 6.15
366062 rs11755965 6_104 0.269 7190.46 0.0056 2.34
936759 rs3869131 6_25 0.251 6751.68 0.0049 6.23
917114 rs1633020 6_24 0.013 112071.13 0.0041 8.54
526819 rs4745982 10_46 1.000 1273.52 0.0037 -56.67
917118 rs1633018 6_24 0.010 112070.22 0.0031 8.54
113838 rs853789 2_102 1.000 1007.68 0.0029 38.94
113831 rs537183 2_102 0.999 974.91 0.0028 38.61
917071 rs2844840 6_24 0.008 112046.78 0.0026 8.55
324276 rs115740542 6_20 1.000 837.28 0.0024 -28.80
939824 rs3132935 6_26 0.009 89437.63 0.0024 14.03
425387 rs758184196 8_11 1.000 753.23 0.0022 -0.53
36767 rs2779116 1_78 1.000 683.12 0.0020 30.86
425382 rs2428 8_11 1.000 693.09 0.0020 6.08
539218 rs12244851 10_70 1.000 679.01 0.0020 24.38
1057579 rs551118 16_53 1.000 700.99 0.0020 23.96
425403 rs13265731 8_11 0.932 696.03 0.0019 6.18
425361 rs1703982 8_11 1.000 614.50 0.0018 -6.43SNPs 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
526819 rs4745982 10_46 1.000 1273.52 3.7e-03 -56.67
526820 rs6480402 10_46 1.000 8853.47 2.6e-02 -53.18
526816 rs6480398 10_46 0.000 852.79 0.0e+00 46.60
113838 rs853789 2_102 1.000 1007.68 2.9e-03 38.94
113831 rs537183 2_102 0.999 974.91 2.8e-03 38.61
113832 rs518598 2_102 0.001 957.10 4.1e-06 38.19
113834 rs485094 2_102 0.000 908.39 4.4e-10 37.34
957635 rs1004558 7_32 0.345 714.20 7.2e-04 35.99
957634 rs1985469 7_32 0.239 713.12 4.9e-04 35.98
957586 rs1799884 7_32 0.097 711.29 2.0e-04 35.94
957607 rs741037 7_32 0.068 709.95 1.4e-04 35.93
957576 rs2971670 7_32 0.057 709.70 1.2e-04 35.92
957568 rs2908289 7_32 0.046 709.41 9.4e-05 35.91
957567 rs730497 7_32 0.056 710.63 1.2e-04 35.89
957629 rs12056308 7_32 0.020 706.21 4.1e-05 35.89
957617 rs2908286 7_32 0.023 706.63 4.8e-05 35.88
957644 rs2971668 7_32 0.025 707.41 5.1e-05 35.88
957661 rs2908282 7_32 0.006 702.93 1.3e-05 35.83
957600 rs6975024 7_32 0.010 705.72 2.0e-05 35.82
957648 rs2971667 7_32 0.004 702.70 9.1e-06 35.82
957650 rs917793 7_32 0.004 702.31 8.6e-06 35.81
957621 rs4607517 7_32 0.001 696.76 1.2e-06 35.72
957699 rs732360 7_32 0.000 659.82 5.2e-13 35.03
957599 rs2971669 7_32 0.000 662.14 1.7e-11 34.38
957707 rs2075066 7_32 0.000 628.68 2.0e-13 34.24
957680 rs878521 7_32 0.000 556.99 3.4e-11 32.15
36767 rs2779116 1_78 1.000 683.12 2.0e-03 30.86
113836 rs2544360 2_102 0.000 778.86 5.6e-10 30.12
113837 rs853791 2_102 0.000 772.11 5.1e-10 29.94
526841 rs142196758 10_46 0.000 812.22 0.0e+00 -29.25
324276 rs115740542 6_20 1.000 837.28 2.4e-03 -28.80
36779 rs863327 1_78 0.003 584.76 5.1e-06 28.76
113830 rs71397673 2_102 1.000 498.29 1.4e-03 28.67
113840 rs853785 2_102 0.162 699.49 3.3e-04 28.45
113839 rs860510 2_102 0.397 687.42 7.9e-04 28.07
113833 rs579275 2_102 0.441 674.85 8.6e-04 27.85
957517 rs2908292 7_32 0.462 247.20 3.3e-04 27.62
957518 rs2971671 7_32 0.333 245.38 2.4e-04 27.58
36747 rs12042917 1_78 0.002 534.14 3.3e-06 27.53
957592 rs3757840 7_32 0.998 244.00 7.1e-04 -27.52
957550 rs10259649 7_32 0.040 237.51 2.7e-05 27.49
957515 rs2908293 7_32 0.126 240.77 8.8e-05 27.48
36739 rs12405509 1_78 0.002 530.53 3.2e-06 27.45
957549 rs2300584 7_32 0.032 235.42 2.2e-05 27.41
957500 rs2908294 7_32 0.008 226.93 5.4e-06 27.14
759179 rs2256833 17_47 0.762 505.50 1.1e-03 -27.01
36705 rs11264980 1_78 0.002 510.91 2.6e-06 26.99
759180 rs2459703 17_47 0.239 502.76 3.5e-04 -26.96
1107866 rs855791 22_14 1.000 543.50 1.6e-03 -26.89
957686 rs13229610 7_32 0.002 221.74 1.1e-06 -26.46Gene 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] 29if (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)SMIM19 gene(s) from the input list not found in DisGeNET CURATEDITGAD gene(s) from the input list not found in DisGeNET CURATEDARFIP1 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDTRIM58 gene(s) from the input list not found in DisGeNET CURATEDSEC14L4 gene(s) from the input list not found in DisGeNET CURATEDNUDT4 gene(s) from the input list not found in DisGeNET CURATEDHLX gene(s) from the input list not found in DisGeNET CURATED Description
44 Opisthorchiasis
86 Glutaric aciduria, type 1
88 Opisthorchis felineus Infection
89 Opisthorchis viverrini Infection
136 RETINITIS PIGMENTOSA 42
143 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 3
147 CRISPONI/COLD-INDUCED SWEATING SYNDROME 3
149 Paroxysmal Nonkinesigenic Dyskinesia 1
62 Iron-Refractory Iron Deficiency Anemia
75 Carcinoma, Large Cell
FDR Ratio BgRatio
44 0.04112554 1/21 1/9703
86 0.04112554 1/21 1/9703
88 0.04112554 1/21 1/9703
89 0.04112554 1/21 1/9703
136 0.04112554 1/21 1/9703
143 0.04112554 1/21 1/9703
147 0.04112554 1/21 1/9703
149 0.04112554 1/21 1/9703
62 0.04924897 1/21 3/9703
75 0.04924897 1/21 2/9703******************************************
* *
* 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