HDL cholesterol (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 | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
| html | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait HDL cholesterol (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-30760_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0217570808 0.0001937125 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
25.05420 18.46942 #report sample size
print(sample_size)[1] 315133#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01919175 0.09874228 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04357772 0.99304822Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
8482 SPSB1 1_6 1.000 135.03 4.3e-04 5.65
10765 ZDHHC18 1_18 1.000 128.92 4.1e-04 -12.13
4610 ACP2 11_29 1.000 200.54 6.4e-04 -19.05
11023 SIPA1 11_36 0.998 45.32 1.4e-04 -8.55
9863 LAMP1 13_62 0.992 37.01 1.2e-04 6.08
5397 VPS53 17_1 0.991 35.08 1.1e-04 5.63
4564 PSRC1 1_67 0.986 104.31 3.3e-04 11.36
2626 C12orf49 12_72 0.985 24.67 7.7e-05 4.53
6439 SLFN13 17_21 0.985 24.72 7.7e-05 4.69
6089 FADS1 11_34 0.984 381.82 1.2e-03 -24.03
6590 NTAN1 16_15 0.982 91.49 2.9e-04 -9.78
2195 PCOLCE 7_62 0.981 23.57 7.3e-05 3.77
3224 RPA2 1_19 0.977 25.54 7.9e-05 4.96
5389 CTRL 16_36 0.977 303.47 9.4e-04 17.12
3378 GPAM 10_70 0.976 98.29 3.0e-04 10.19
12304 RP11-54O7.17 1_1 0.963 41.97 1.3e-04 -6.37
1386 ITPR3 6_28 0.960 33.99 1.0e-04 -5.18
5665 CNIH4 1_114 0.952 30.08 9.1e-05 5.00
5834 TNFAIP8 5_72 0.946 41.91 1.3e-04 -6.40
6370 CEBPG 19_23 0.945 24.23 7.3e-05 -5.41
9777 RAB11B 19_8 0.939 83.58 2.5e-04 -13.29
6404 PITPNC1 17_39 0.938 28.32 8.4e-05 -4.89
6923 NBEAL2 3_33 0.936 30.78 9.1e-05 6.02
172 STARD3NL 7_28 0.932 21.08 6.2e-05 -3.88
9322 F2 11_28 0.917 95.88 2.8e-04 -14.63
8554 DPAGT1 11_71 0.904 40.61 1.2e-04 -4.59
10417 MRPL21 11_38 0.903 54.54 1.6e-04 7.78
11415 RPS28 19_8 0.900 32.76 9.4e-05 -7.77
5095 DNAJC13 3_82 0.889 21.81 6.2e-05 -4.27
2898 ABTB1 3_79 0.878 37.44 1.0e-04 -5.97
8552 C1QTNF4 11_29 0.866 295.90 8.1e-04 17.57
10505 UGT2B17 4_48 0.857 46.23 1.3e-04 7.18
11680 MIR210HG 11_1 0.854 25.01 6.8e-05 4.82
5464 PNMT 17_23 0.849 150.93 4.1e-04 -12.29
7513 FOXK1 7_6 0.844 20.98 5.6e-05 -4.24
697 HDAC4 2_143 0.841 21.54 5.7e-05 -4.11
1769 STK24 13_50 0.836 25.64 6.8e-05 4.69
2333 PITRM1 10_4 0.831 19.18 5.1e-05 -3.72
1145 ACHE 7_62 0.829 29.78 7.8e-05 3.99
5657 ACP1 2_1 0.827 38.76 1.0e-04 -5.62
2388 BLMH 17_18 0.815 30.66 7.9e-05 4.90
478 PHPT1 9_74 0.813 20.63 5.3e-05 3.97
11727 DNAH10OS 12_75 0.802 158.77 4.0e-04 14.26Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
168 SPRTN 1_118 0 7235.82 0.0e+00 -3.56
1818 ESRP2 16_36 0 5948.57 0.0e+00 -1.67
10901 PSMB10 16_36 0 5657.79 0.0e+00 -1.76
1794 NUTF2 16_36 0 5653.01 0.0e+00 1.89
6809 C16orf86 16_36 0 5586.02 0.0e+00 -1.81
1805 ACD 16_36 0 5538.76 0.0e+00 -1.81
3138 EXOC8 1_118 0 5212.66 0.0e+00 -3.70
1804 CTCF 16_36 0 4768.14 0.0e+00 1.69
374 EDC4 16_36 0 4734.07 0.0e+00 0.83
1796 CENPT 16_36 0 4686.14 0.0e+00 0.95
806 NFATC3 16_36 0 4636.91 0.0e+00 -3.01
6806 ATP6V0D1 16_36 0 4430.24 0.0e+00 -1.60
6805 ZDHHC1 16_36 0 4418.30 0.0e+00 1.66
7875 DUS2 16_36 0 4380.70 0.0e+00 6.71
6804 TPPP3 16_36 0 4296.26 0.0e+00 0.50
10904 E2F4 16_36 0 4223.83 0.0e+00 1.99
8926 RPS6KB2 11_37 0 4030.93 5.2e-12 -2.81
7978 NDUFV1 11_37 0 3734.04 1.8e-14 0.89
881 ZNF37A 10_28 0 3512.32 0.0e+00 -1.56
10381 ZGPAT 20_38 0 3306.62 0.0e+00 -2.81Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
6089 FADS1 11_34 0.984 381.82 0.00120 -24.03
5389 CTRL 16_36 0.977 303.47 0.00094 17.12
8552 C1QTNF4 11_29 0.866 295.90 0.00081 17.57
4610 ACP2 11_29 1.000 200.54 0.00064 -19.05
1267 PABPC4 1_24 0.708 227.03 0.00051 15.99
2496 ZPR1 11_70 0.273 570.70 0.00049 20.08
8482 SPSB1 1_6 1.000 135.03 0.00043 5.65
10765 ZDHHC18 1_18 1.000 128.92 0.00041 -12.13
5464 PNMT 17_23 0.849 150.93 0.00041 -12.29
11727 DNAH10OS 12_75 0.802 158.77 0.00040 14.26
4564 PSRC1 1_67 0.986 104.31 0.00033 11.36
3378 GPAM 10_70 0.976 98.29 0.00030 10.19
6590 NTAN1 16_15 0.982 91.49 0.00029 -9.78
9322 F2 11_28 0.917 95.88 0.00028 -14.63
9777 RAB11B 19_8 0.939 83.58 0.00025 -13.29
405 ADRB1 10_71 0.638 109.71 0.00022 9.27
616 UHRF1BP1 6_28 0.505 139.10 0.00022 -10.36
7462 DAGLB 7_9 0.781 88.79 0.00022 9.37
7786 CATSPER2 15_16 0.363 174.11 0.00020 -12.24
6959 CCDC116 22_4 0.478 111.20 0.00017 10.67Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8899 LPL 8_21 0.000 1474.89 0.0e+00 42.46
6089 FADS1 11_34 0.984 381.82 1.2e-03 -24.03
2496 ZPR1 11_70 0.273 570.70 4.9e-04 20.08
4610 ACP2 11_29 1.000 200.54 6.4e-04 -19.05
7654 PSMC3 11_29 0.000 130.08 7.5e-11 -18.85
8552 C1QTNF4 11_29 0.866 295.90 8.1e-04 17.57
4636 FADS2 11_34 0.003 250.26 2.0e-06 -17.42
5389 CTRL 16_36 0.977 303.47 9.4e-04 17.12
11020 LCAT 16_36 0.000 291.57 2.0e-10 16.34
1267 PABPC4 1_24 0.708 227.03 5.1e-04 15.99
5390 DPEP3 16_36 0.000 207.41 3.5e-16 -15.78
7653 SLC39A13 11_29 0.000 116.13 2.3e-11 -15.48
6813 PSKH1 16_36 0.000 1358.59 2.1e-11 -15.23
6808 CARMIL2 16_36 0.000 234.53 1.2e-18 -14.79
1807 PARD6A 16_36 0.000 207.90 6.6e-19 -14.70
9322 F2 11_28 0.917 95.88 2.8e-04 -14.63
1652 PCIF1 20_28 0.000 131.42 1.1e-07 -14.62
7874 DPEP2 16_36 0.000 227.13 0.0e+00 14.32
11727 DNAH10OS 12_75 0.802 158.77 4.0e-04 14.26
7655 RAPSN 11_29 0.000 126.87 5.9e-12 -14.15Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03388914#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8899 LPL 8_21 0.000 1474.89 0.0e+00 42.46
6089 FADS1 11_34 0.984 381.82 1.2e-03 -24.03
2496 ZPR1 11_70 0.273 570.70 4.9e-04 20.08
4610 ACP2 11_29 1.000 200.54 6.4e-04 -19.05
7654 PSMC3 11_29 0.000 130.08 7.5e-11 -18.85
8552 C1QTNF4 11_29 0.866 295.90 8.1e-04 17.57
4636 FADS2 11_34 0.003 250.26 2.0e-06 -17.42
5389 CTRL 16_36 0.977 303.47 9.4e-04 17.12
11020 LCAT 16_36 0.000 291.57 2.0e-10 16.34
1267 PABPC4 1_24 0.708 227.03 5.1e-04 15.99
5390 DPEP3 16_36 0.000 207.41 3.5e-16 -15.78
7653 SLC39A13 11_29 0.000 116.13 2.3e-11 -15.48
6813 PSKH1 16_36 0.000 1358.59 2.1e-11 -15.23
6808 CARMIL2 16_36 0.000 234.53 1.2e-18 -14.79
1807 PARD6A 16_36 0.000 207.90 6.6e-19 -14.70
9322 F2 11_28 0.917 95.88 2.8e-04 -14.63
1652 PCIF1 20_28 0.000 131.42 1.1e-07 -14.62
7874 DPEP2 16_36 0.000 227.13 0.0e+00 14.32
11727 DNAH10OS 12_75 0.802 158.77 4.0e-04 14.26
7655 RAPSN 11_29 0.000 126.87 5.9e-12 -14.15Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 8_21"
genename region_tag susie_pip mu2 PVE z
5936 CSGALNACT1 8_21 0 129.92 0 9.76
1947 INTS10 8_21 0 308.37 0 10.74
8899 LPL 8_21 0 1474.89 0 42.46
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_34"
genename region_tag susie_pip mu2 PVE z
10165 FAM111B 11_34 0.003 6.20 5.5e-08 0.72
7794 FAM111A 11_34 0.078 35.04 8.7e-06 2.69
2506 DTX4 11_34 0.002 4.88 3.9e-08 0.19
10468 MPEG1 11_34 0.003 5.99 5.3e-08 0.24
2515 MS4A6A 11_34 0.003 7.55 7.9e-08 -1.42
7815 PATL1 11_34 0.004 11.59 1.6e-07 1.62
7817 STX3 11_34 0.002 5.01 4.0e-08 0.24
7818 MRPL16 11_34 0.003 5.59 4.7e-08 -0.55
4634 GIF 11_34 0.005 12.97 2.0e-07 -1.80
4638 TCN1 11_34 0.002 4.88 3.8e-08 -0.39
6096 MS4A2 11_34 0.038 29.51 3.6e-06 3.30
11819 AP001257.1 11_34 0.003 7.48 7.8e-08 -0.53
11116 MS4A4E 11_34 0.071 37.30 8.4e-06 -3.63
2516 MS4A4A 11_34 0.004 8.23 9.7e-08 -1.17
7825 MS4A6E 11_34 0.004 10.57 1.4e-07 1.79
7826 MS4A7 11_34 0.003 5.02 4.1e-08 -0.08
7827 MS4A14 11_34 0.004 8.78 1.1e-07 1.51
2519 CCDC86 11_34 0.006 12.46 2.2e-07 -0.91
9570 PTGDR2 11_34 0.004 8.32 9.5e-08 0.75
6093 ZP1 11_34 0.002 5.02 3.9e-08 0.40
2520 PRPF19 11_34 0.004 8.75 1.1e-07 -0.73
2521 TMEM109 11_34 0.018 22.34 1.3e-06 -1.92
2546 SLC15A3 11_34 0.017 27.37 1.4e-06 3.14
2547 CD5 11_34 0.003 12.13 1.3e-07 -2.51
8008 VPS37C 11_34 0.003 6.00 4.8e-08 1.34
11874 PGA5 11_34 0.004 8.72 1.2e-07 -0.55
11340 PGA3 11_34 0.004 8.37 1.1e-07 -0.58
8009 VWCE 11_34 0.007 20.35 4.3e-07 -2.90
6088 TMEM138 11_34 0.003 8.64 7.1e-08 -2.14
7030 CYB561A3 11_34 0.003 8.64 7.1e-08 -2.14
9981 TMEM216 11_34 0.008 18.62 4.7e-07 -2.66
11871 RP11-286N22.8 11_34 0.026 28.13 2.3e-06 -2.59
4631 DAGLA 11_34 0.002 24.99 1.9e-07 5.33
3765 MYRF 11_34 0.003 32.46 2.7e-07 -6.73
4636 FADS2 11_34 0.003 250.26 2.0e-06 -17.42
4637 TMEM258 11_34 0.003 80.81 8.9e-07 -9.92
6089 FADS1 11_34 0.984 381.82 1.2e-03 -24.03
11190 FADS3 11_34 0.002 16.67 1.3e-07 5.01
8011 BEST1 11_34 0.005 47.40 8.2e-07 -7.56
6092 INCENP 11_34 0.021 35.49 2.4e-06 -4.29
7032 ASRGL1 11_34 0.003 7.50 7.6e-08 -0.86
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_70"
genename region_tag susie_pip mu2 PVE z
5007 BUD13 11_70 0.000 139.81 0.0e+00 -9.03
2496 ZPR1 11_70 0.273 570.70 4.9e-04 20.08
3237 APOA1 11_70 0.000 84.69 0.0e+00 -11.62
6898 SIK3 11_70 0.000 7.85 0.0e+00 -0.42
8030 PAFAH1B2 11_70 0.000 111.96 0.0e+00 -2.24
6104 TAGLN 11_70 0.000 21.18 0.0e+00 -1.61
6902 PCSK7 11_70 0.000 195.53 6.8e-17 8.60
7873 RNF214 11_70 0.000 10.91 0.0e+00 0.68
9915 BACE1 11_70 0.000 90.55 0.0e+00 8.30
2530 CEP164 11_70 0.000 90.85 0.0e+00 5.06
5018 FXYD2 11_70 0.000 12.32 0.0e+00 1.10
5017 FXYD6 11_70 0.000 6.11 0.0e+00 0.75
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_29"
genename region_tag susie_pip mu2 PVE z
6066 ARFGAP2 11_29 0.000 15.84 6.0e-14 2.22
300 NR1H3 11_29 0.000 94.83 3.4e-13 -13.98
4610 ACP2 11_29 1.000 200.54 6.4e-04 -19.05
2550 MADD 11_29 0.000 24.91 1.2e-14 3.98
4609 MYBPC3 11_29 0.000 346.28 9.4e-13 9.79
7654 PSMC3 11_29 0.000 130.08 7.5e-11 -18.85
7653 SLC39A13 11_29 0.000 116.13 2.3e-11 -15.48
7655 RAPSN 11_29 0.000 126.87 5.9e-12 -14.15
2551 PTPMT1 11_29 0.000 393.04 2.8e-13 -0.81
3631 KBTBD4 11_29 0.000 65.67 7.4e-14 1.05
8552 C1QTNF4 11_29 0.866 295.90 8.1e-04 17.57
7656 AGBL2 11_29 0.000 27.92 3.3e-14 6.68
2497 FNBP4 11_29 0.000 130.70 9.8e-14 -1.92
324 NUP160 11_29 0.000 42.61 2.5e-11 -11.86
6064 PTPRJ 11_29 0.007 112.82 2.6e-06 13.93
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 16_36"
genename region_tag susie_pip mu2 PVE z
4752 DYNC1LI2 16_36 0.000 17.95 0.0e+00 0.66
9282 CDH5 16_36 0.000 25.99 0.0e+00 -2.63
11678 LINC00920 16_36 0.000 99.62 0.0e+00 -2.33
7763 BEAN1 16_36 0.000 20.54 0.0e+00 2.65
7764 TK2 16_36 0.000 16.73 0.0e+00 2.37
11156 CKLF 16_36 0.000 38.99 0.0e+00 1.36
1233 CMTM1 16_36 0.000 20.01 0.0e+00 2.55
5365 CMTM3 16_36 0.000 96.26 0.0e+00 2.40
9636 CMTM4 16_36 0.000 13.17 0.0e+00 2.46
6794 NAE1 16_36 0.000 22.09 0.0e+00 -1.59
8627 PDP2 16_36 0.000 23.09 0.0e+00 -0.91
8626 CES2 16_36 0.000 202.34 0.0e+00 -2.31
8624 CES3 16_36 0.000 27.02 0.0e+00 2.95
695 CBFB 16_36 0.000 67.16 0.0e+00 -6.53
3773 C16orf70 16_36 0.000 65.39 0.0e+00 -6.49
11479 B3GNT9 16_36 0.000 181.23 0.0e+00 2.36
5366 NOL3 16_36 0.000 65.89 0.0e+00 5.42
1793 ELMO3 16_36 0.000 80.95 0.0e+00 7.47
10210 KIAA0895L 16_36 0.000 11.62 0.0e+00 0.41
9235 EXOC3L1 16_36 0.000 72.36 0.0e+00 7.20
10904 E2F4 16_36 0.000 4223.83 0.0e+00 1.99
4756 SLC9A5 16_36 0.000 80.14 0.0e+00 7.04
3769 LRRC29 16_36 0.000 83.02 0.0e+00 -7.54
4754 FHOD1 16_36 0.000 23.84 0.0e+00 0.54
10218 PLEKHG4 16_36 0.000 2344.96 0.0e+00 5.81
6804 TPPP3 16_36 0.000 4296.26 0.0e+00 0.50
6805 ZDHHC1 16_36 0.000 4418.30 0.0e+00 1.66
6806 ATP6V0D1 16_36 0.000 4430.24 0.0e+00 -1.60
1804 CTCF 16_36 0.000 4768.14 0.0e+00 1.69
12029 CTD-2012K14.6 16_36 0.000 59.80 0.0e+00 -0.55
6808 CARMIL2 16_36 0.000 234.53 1.2e-18 -14.79
1807 PARD6A 16_36 0.000 207.90 6.6e-19 -14.70
1805 ACD 16_36 0.000 5538.76 0.0e+00 -1.81
3665 ENKD1 16_36 0.000 43.07 0.0e+00 -1.55
6809 C16orf86 16_36 0.000 5586.02 0.0e+00 -1.81
5391 GFOD2 16_36 0.000 24.20 0.0e+00 1.15
1797 TSNAXIP1 16_36 0.000 24.75 0.0e+00 1.55
1794 NUTF2 16_36 0.000 5653.01 0.0e+00 1.89
1796 CENPT 16_36 0.000 4686.14 0.0e+00 0.95
374 EDC4 16_36 0.000 4734.07 0.0e+00 0.83
10064 NRN1L 16_36 0.000 81.29 0.0e+00 -4.04
6813 PSKH1 16_36 0.000 1358.59 2.1e-11 -15.23
5389 CTRL 16_36 0.977 303.47 9.4e-04 17.12
10901 PSMB10 16_36 0.000 5657.79 0.0e+00 -1.76
5390 DPEP3 16_36 0.000 207.41 3.5e-16 -15.78
11020 LCAT 16_36 0.000 291.57 2.0e-10 16.34
7875 DUS2 16_36 0.000 4380.70 0.0e+00 6.71
7874 DPEP2 16_36 0.000 227.13 0.0e+00 14.32
806 NFATC3 16_36 0.000 4636.91 0.0e+00 -3.01
1818 ESRP2 16_36 0.000 5948.57 0.0e+00 -1.67
1816 SLC7A6 16_36 0.000 203.07 0.0e+00 -13.41
1817 PLA2G15 16_36 0.000 141.85 0.0e+00 -11.07
4414 PRMT7 16_36 0.000 79.03 0.0e+00 9.08
9744 ZFP90 16_36 0.000 22.54 0.0e+00 1.13
| 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
54829 rs2103827 1_117 1.000 229.80 7.3e-04 22.05
54830 rs11122453 1_117 1.000 454.63 1.4e-03 25.82
55311 rs766167074 1_118 1.000 7488.50 2.4e-02 3.28
61484 rs10183939 2_2 1.000 37.21 1.2e-04 -6.06
67509 rs1042034 2_13 1.000 496.41 1.6e-03 -21.96
178236 rs9817452 3_97 1.000 62.12 2.0e-04 8.17
187300 rs35374654 3_114 1.000 38.70 1.2e-04 6.03
224442 rs35518360 4_67 1.000 258.77 8.2e-04 -17.51
224508 rs13140033 4_68 1.000 163.47 5.2e-04 -13.28
268561 rs62369502 5_28 1.000 39.46 1.3e-04 -6.13
326229 rs142449754 6_32 1.000 58.36 1.9e-04 -7.92
363653 rs191555775 6_104 1.000 158.77 5.0e-04 -15.06
413398 rs6977416 7_94 1.000 63.06 2.0e-04 -6.76
422149 rs7012814 8_12 1.000 176.51 5.6e-04 17.03
422160 rs13265179 8_12 1.000 235.09 7.5e-04 -17.38
427378 rs1372339 8_21 1.000 1811.87 5.7e-03 17.85
427414 rs75835816 8_21 1.000 668.52 2.1e-03 -26.36
427450 rs11986461 8_21 1.000 738.54 2.3e-03 25.57
458040 rs10956254 8_83 1.000 60.58 1.9e-04 -9.41
464318 rs7832515 8_94 1.000 142.64 4.5e-04 12.51
471422 rs677622 9_13 1.000 189.59 6.0e-04 14.52
491549 rs2777798 9_52 1.000 214.76 6.8e-04 13.11
491555 rs2777802 9_52 1.000 369.51 1.2e-03 12.36
491557 rs2777804 9_52 1.000 295.80 9.4e-04 4.27
491563 rs7024300 9_53 1.000 193.01 6.1e-04 14.80
491569 rs2297400 9_53 1.000 191.43 6.1e-04 13.31
491580 rs62568181 9_53 1.000 257.79 8.2e-04 -21.81
491593 rs2254819 9_53 1.000 211.41 6.7e-04 -20.60
515288 rs71007692 10_28 1.000 8198.27 2.6e-02 -3.29
559402 rs12361987 11_30 1.000 120.13 3.8e-04 0.85
576582 rs11216162 11_70 1.000 709.72 2.3e-03 15.29
576768 rs147611518 11_70 1.000 114.73 3.6e-04 -11.15
579233 rs4937122 11_77 1.000 54.63 1.7e-04 -7.42
600348 rs6581124 12_35 1.000 46.53 1.5e-04 7.41
600367 rs7397189 12_36 1.000 74.76 2.4e-04 11.92
619881 rs3782287 12_76 1.000 93.31 3.0e-04 -12.81
619897 rs61941677 12_76 1.000 198.94 6.3e-04 -16.01
635734 rs775834524 13_25 1.000 13957.84 4.4e-02 -3.45
671770 rs13379043 14_34 1.000 73.36 2.3e-04 7.79
691493 rs7168508 15_24 1.000 330.61 1.0e-03 0.10
691495 rs10629766 15_24 1.000 1556.78 4.9e-03 3.27
691496 rs4424863 15_24 1.000 1570.76 5.0e-03 3.14
692350 rs58038553 15_27 1.000 248.66 7.9e-04 -21.17
692352 rs1711037 15_27 1.000 114.95 3.6e-04 14.00
692410 rs28594460 15_27 1.000 240.14 7.6e-04 17.86
692426 rs62000868 15_27 1.000 656.19 2.1e-03 27.17
692432 rs2070895 15_27 1.000 1719.32 5.5e-03 43.96
719258 rs8064102 16_31 1.000 527.09 1.7e-03 8.00
719280 rs190575415 16_31 1.000 580.76 1.8e-03 20.50
719290 rs821840 16_31 1.000 7834.45 2.5e-02 97.05
719291 rs12720926 16_31 1.000 4824.04 1.5e-02 86.67
719295 rs66495554 16_31 1.000 1692.33 5.4e-03 -8.74
722114 rs2276329 16_37 1.000 55.28 1.8e-04 -7.06
726844 rs12443634 16_46 1.000 126.26 4.0e-04 13.60
763673 rs11082766 18_27 1.000 192.84 6.1e-04 12.42
763693 rs6507938 18_27 1.000 504.60 1.6e-03 28.37
763694 rs118043171 18_27 1.000 521.62 1.7e-03 23.95
763913 rs74461650 18_28 1.000 75.31 2.4e-04 8.82
777838 rs1865063 19_11 1.000 82.35 2.6e-04 -11.95
777840 rs3745683 19_11 1.000 102.44 3.3e-04 -12.71
787722 rs405509 19_31 1.000 69.31 2.2e-04 11.23
787726 rs814573 19_31 1.000 420.39 1.3e-03 -21.83
787732 rs4803775 19_31 1.000 321.84 1.0e-03 16.28
787738 rs4803784 19_31 1.000 121.84 3.9e-04 2.47
803032 rs147591082 20_28 1.000 58.19 1.8e-04 -7.58
803478 rs4812975 20_28 1.000 222.10 7.0e-04 21.69
803977 rs6063139 20_29 1.000 63.55 2.0e-04 3.37
862295 rs140584594 1_67 1.000 128.88 4.1e-04 12.66
930246 rs35733538 3_95 1.000 2030.02 6.4e-03 -4.87
1021867 rs3072639 11_29 1.000 2409.61 7.6e-03 2.02
1047118 rs146923372 11_37 1.000 7826.06 2.5e-02 2.69
1103689 rs4986970 16_36 1.000 148.77 4.7e-04 -12.75
1104520 rs56090907 16_36 1.000 8212.19 2.6e-02 4.70
1129532 rs11556624 17_23 1.000 97.28 3.1e-04 6.49
1136242 rs202007993 17_26 1.000 2138.65 6.8e-03 2.72
1136272 rs7209751 17_26 1.000 2146.39 6.8e-03 -7.24
1136274 rs72836561 17_26 1.000 672.70 2.1e-03 -26.31
1159982 rs116843064 19_8 1.000 532.72 1.7e-03 25.68
1194769 rs202143810 20_38 1.000 4394.65 1.4e-02 4.04
29519 rs11102041 1_69 0.999 75.94 2.4e-04 7.93
52948 rs2642420 1_112 0.999 36.77 1.2e-04 -7.39
54253 rs878811 1_116 0.999 33.32 1.1e-04 5.66
407406 rs6961342 7_80 0.999 90.46 2.9e-04 -13.21
427380 rs17091881 8_21 0.999 588.38 1.9e-03 -24.49
600390 rs140734681 12_36 0.999 34.56 1.1e-04 -2.42
604408 rs2137537 12_44 0.999 32.22 1.0e-04 -5.19
635732 rs7999449 13_25 0.999 13916.66 4.4e-02 -3.39
54822 rs6678475 1_117 0.998 38.57 1.2e-04 -1.80
220167 rs4425336 4_60 0.998 39.20 1.2e-04 7.21
692240 rs72737411 15_25 0.998 31.32 9.9e-05 -5.09
842792 rs4989532 1_6 0.998 538.26 1.7e-03 2.86
842793 rs2072735 1_6 0.998 516.58 1.6e-03 3.39
1065877 rs532140742 12_75 0.998 122.53 3.9e-04 -11.44
1135810 rs117380643 17_25 0.998 103.48 3.3e-04 -10.28
93890 rs3789066 2_66 0.997 31.34 9.9e-05 -5.15
279055 rs115912456 5_49 0.996 29.98 9.5e-05 5.30
427188 rs113231830 8_20 0.996 31.54 1.0e-04 -5.70
491596 rs2437818 9_53 0.996 103.81 3.3e-04 14.54
32543 rs185073199 1_73 0.995 30.29 9.6e-05 5.33
726851 rs11641142 16_46 0.995 65.75 2.1e-04 10.95
465260 rs1016565 9_1 0.994 30.45 9.6e-05 -5.31
563042 rs695110 11_42 0.993 111.92 3.5e-04 -11.10
803769 rs6066141 20_29 0.993 34.16 1.1e-04 5.54
804015 rs78492788 20_29 0.993 71.39 2.2e-04 8.17
1047113 rs57808037 11_37 0.992 7824.84 2.5e-02 2.67
763713 rs8093206 18_27 0.991 71.16 2.2e-04 -7.76
321931 rs1131159 6_26 0.990 51.45 1.6e-04 8.41
576553 rs3135506 11_70 0.990 572.46 1.8e-03 -20.84
786625 rs11879413 19_30 0.989 30.08 9.4e-05 5.43
271412 rs173964 5_33 0.988 150.34 4.7e-04 -10.81
589331 rs66720652 12_15 0.988 32.65 1.0e-04 5.45
131895 rs4675812 2_144 0.987 35.37 1.1e-04 6.34
698713 rs16972386 15_38 0.987 29.74 9.3e-05 -5.13
749294 rs72854483 17_46 0.985 27.16 8.5e-05 -4.96
540067 rs10901802 10_78 0.984 30.43 9.5e-05 5.51
1067463 rs533328276 12_75 0.984 54.51 1.7e-04 1.46
422452 rs1402522 8_13 0.983 32.92 1.0e-04 6.21
316691 rs4134975 6_15 0.981 30.89 9.6e-05 4.79
671626 rs177392 14_34 0.981 29.19 9.1e-05 -4.40
766569 rs41292412 18_31 0.981 37.80 1.2e-04 -6.21
323729 rs181268076 6_27 0.976 47.65 1.5e-04 -6.52
391842 rs367867252 7_48 0.976 31.78 9.8e-05 -5.36
521053 rs11510917 10_39 0.976 27.00 8.4e-05 4.72
367934 rs11971790 7_3 0.973 57.59 1.8e-04 -6.64
619787 rs11057671 12_76 0.972 67.42 2.1e-04 8.60
763709 rs62101781 18_27 0.972 216.45 6.7e-04 17.00
553875 rs12288512 11_19 0.970 62.04 1.9e-04 -7.87
373526 rs38172 7_16 0.969 27.94 8.6e-05 5.01
456564 rs9297630 8_80 0.969 47.91 1.5e-04 -6.67
1018929 rs7123635 11_28 0.969 91.43 2.8e-04 -9.78
377440 rs2699814 7_23 0.967 44.18 1.4e-04 6.12
694160 rs11071771 15_29 0.967 38.29 1.2e-04 -6.23
604529 rs1707498 12_44 0.964 30.83 9.4e-05 5.19
195243 rs17468437 4_12 0.963 25.89 7.9e-05 4.81
1011982 rs140201358 11_1 0.963 30.91 9.4e-05 -5.35
1045284 rs4930352 11_37 0.962 283.83 8.7e-04 8.12
521848 rs2393730 10_42 0.961 27.07 8.3e-05 5.11
825169 rs12321 22_9 0.957 28.95 8.8e-05 4.92
1166492 rs73024215 19_23 0.956 62.75 1.9e-04 -8.87
616953 rs653178 12_67 0.954 138.56 4.2e-04 10.81
422257 rs7016636 8_12 0.949 70.95 2.1e-04 -2.41
572169 rs72980276 11_59 0.947 26.16 7.9e-05 -4.87
658522 rs1955512 14_8 0.946 33.91 1.0e-04 5.52
842794 rs115843159 1_6 0.946 45.70 1.4e-04 -0.36
277727 rs3733890 5_46 0.943 32.30 9.7e-05 -5.71
344359 rs2388334 6_67 0.943 31.71 9.5e-05 5.48
300438 rs4958365 5_90 0.942 31.67 9.5e-05 4.88
326683 rs115482652 6_34 0.942 24.98 7.5e-05 -4.88
399230 rs2734897 7_61 0.942 28.75 8.6e-05 -5.53
756158 rs57440424 18_12 0.941 56.05 1.7e-04 7.71
537343 rs113097445 10_72 0.939 25.54 7.6e-05 -4.72
397839 rs12534274 7_58 0.938 28.29 8.4e-05 5.15
474899 rs145804707 9_18 0.936 24.23 7.2e-05 -4.54
589286 rs11045182 12_15 0.936 50.55 1.5e-04 7.13
205632 rs58932203 4_32 0.932 32.28 9.6e-05 5.40
776081 rs67868323 19_4 0.930 53.96 1.6e-04 -6.94
790723 rs2316866 20_1 0.928 25.12 7.4e-05 -4.69
500311 rs115478735 9_70 0.927 56.41 1.7e-04 7.54
294405 rs4705986 5_80 0.925 27.97 8.2e-05 4.86
577715 rs1219430 11_74 0.924 29.75 8.7e-05 -5.60
584667 rs10849492 12_7 0.924 41.96 1.2e-04 -6.60
15346 rs12140153 1_39 0.917 27.20 7.9e-05 4.53
499249 rs111472765 9_67 0.916 23.74 6.9e-05 4.47
560097 rs145487327 11_32 0.912 34.70 1.0e-04 4.94
698782 rs1509559 15_38 0.912 27.05 7.8e-05 4.63
276795 rs4496694 5_44 0.911 28.84 8.3e-05 4.80
53339 rs12132342 1_115 0.908 24.40 7.0e-05 -4.47
634160 rs78212345 13_21 0.908 32.73 9.4e-05 5.75
786776 rs6508974 19_30 0.906 30.47 8.8e-05 5.36
128348 rs11900497 2_135 0.904 27.19 7.8e-05 -4.92
113437 rs71410739 2_107 0.901 27.03 7.7e-05 -4.97
339461 rs560253203 6_56 0.901 23.80 6.8e-05 4.33
678094 rs1242889 14_47 0.901 26.17 7.5e-05 4.68
326684 rs9472126 6_34 0.897 24.52 7.0e-05 4.71
39058 rs35039375 1_84 0.895 28.40 8.1e-05 -5.18
591513 rs11614652 12_18 0.890 29.01 8.2e-05 5.16
1058353 rs10507274 12_72 0.889 29.34 8.3e-05 4.63
356723 rs151288714 6_92 0.887 50.08 1.4e-04 7.62
200596 rs56147366 4_22 0.885 57.16 1.6e-04 -7.71
549275 rs7121538 11_11 0.885 44.88 1.3e-04 6.46
815364 rs546634737 21_11 0.882 25.68 7.2e-05 4.59
491432 rs34849882 9_52 0.876 51.29 1.4e-04 3.81
717442 rs62039688 16_27 0.875 25.30 7.0e-05 4.50
576543 rs9326246 11_70 0.873 546.08 1.5e-03 22.70
348399 rs2038014 6_74 0.869 26.05 7.2e-05 -4.75
819968 rs8128478 21_21 0.868 25.97 7.2e-05 4.91
788952 rs4802880 19_35 0.864 65.30 1.8e-04 -8.38
95396 rs2130980 2_68 0.861 28.20 7.7e-05 5.09
413407 rs4725377 7_94 0.860 31.72 8.7e-05 1.96
559238 rs72484110 11_30 0.855 171.75 4.7e-04 12.61
1199832 rs9980311 21_23 0.855 58.43 1.6e-04 -6.68
549199 rs547219635 11_11 0.853 26.77 7.2e-05 4.11
824881 rs73166732 22_9 0.852 24.46 6.6e-05 -4.01
616252 rs34132586 12_66 0.851 23.79 6.4e-05 3.95
237739 rs116329078 4_94 0.850 27.13 7.3e-05 5.04
1051208 rs2229738 11_38 0.845 35.50 9.5e-05 -6.32
391615 rs13247874 7_47 0.844 154.25 4.1e-04 12.82
285527 rs55815433 5_62 0.843 25.26 6.8e-05 4.49
374281 rs17138358 7_17 0.841 127.84 3.4e-04 -11.94
427391 rs2410620 8_21 0.839 2988.31 8.0e-03 46.36
148423 rs75987913 3_35 0.836 28.72 7.6e-05 5.70
463659 rs11778265 8_92 0.834 26.99 7.1e-05 -4.87
455116 rs10095930 8_78 0.833 56.26 1.5e-04 4.72
827519 rs9610329 22_14 0.832 28.53 7.5e-05 -5.09
131933 rs59389004 2_144 0.830 26.23 6.9e-05 5.13
76974 rs4566412 2_31 0.829 36.21 9.5e-05 -5.54
466887 rs447124 9_5 0.825 26.45 6.9e-05 -4.72
704687 rs11634241 15_48 0.825 24.54 6.4e-05 -4.55
82750 rs62143990 2_43 0.824 26.82 7.0e-05 4.91
807752 rs41310841 20_34 0.824 26.05 6.8e-05 -4.62
1018554 rs71474191 11_28 0.824 42.99 1.1e-04 -6.70
1189555 rs12975366 19_37 0.824 44.81 1.2e-04 -8.30
130693 rs11900603 2_139 0.822 24.85 6.5e-05 -4.43
627305 rs9554263 13_7 0.810 29.52 7.6e-05 -5.23
772148 rs4519424 18_43 0.810 24.48 6.3e-05 -4.34
108892 rs187764768 2_97 0.809 24.58 6.3e-05 4.06
169883 rs62262433 3_76 0.806 25.77 6.6e-05 4.72
356856 rs377695739 6_93 0.804 28.84 7.4e-05 5.26SNPs 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
635734 rs775834524 13_25 1.000 13957.84 4.4e-02 -3.45
635732 rs7999449 13_25 0.999 13916.66 4.4e-02 -3.39
635724 rs7337153 13_25 0.071 13903.22 3.2e-03 -3.34
635729 rs9537143 13_25 0.151 13870.42 6.7e-03 3.46
635728 rs9597193 13_25 0.209 13869.54 9.2e-03 3.47
635725 rs9527399 13_25 0.282 13869.52 1.2e-02 3.48
635727 rs9527401 13_25 0.208 13869.46 9.2e-03 3.47
635723 rs9537125 13_25 0.010 13858.58 4.2e-04 3.41
635722 rs9527398 13_25 0.010 13858.54 4.2e-04 3.41
635720 rs9537123 13_25 0.006 13857.32 2.6e-04 3.40
635713 rs3013347 13_25 0.000 13601.26 3.8e-13 -3.33
635714 rs2937326 13_25 0.000 13601.20 3.4e-13 -3.33
635715 rs9597179 13_25 0.000 13564.03 1.3e-13 3.41
635739 rs9537159 13_25 0.000 13316.46 0.0e+00 -3.29
635716 rs9537116 13_25 0.000 13309.27 0.0e+00 3.49
635745 rs539380 13_25 0.000 13303.05 0.0e+00 -3.31
635738 rs35800055 13_25 0.000 13274.33 0.0e+00 3.32
635735 rs4536353 13_25 0.000 13274.20 0.0e+00 3.36
635737 rs67100646 13_25 0.000 13274.13 0.0e+00 3.37
635736 rs4296148 13_25 0.000 13273.39 0.0e+00 3.36
635742 rs7994036 13_25 0.000 13270.69 0.0e+00 3.37
635740 rs9597201 13_25 0.000 13270.16 0.0e+00 3.37
635744 rs9537174 13_25 0.000 13269.57 0.0e+00 3.36
635711 rs3105089 13_25 0.000 12629.48 0.0e+00 -3.78
635710 rs3124374 13_25 0.000 12554.92 0.0e+00 -3.88
635709 rs2315886 13_25 0.000 12551.28 0.0e+00 -3.89
635708 rs2315887 13_25 0.000 12551.24 0.0e+00 -3.89
635700 rs2315898 13_25 0.000 12537.42 0.0e+00 -3.90
635702 rs3105045 13_25 0.000 12534.42 0.0e+00 -3.90
635703 rs2315895 13_25 0.000 12534.42 0.0e+00 -3.89
635704 rs3124405 13_25 0.000 12534.13 0.0e+00 -3.89
635698 rs7317475 13_25 0.000 12520.62 0.0e+00 -3.86
635706 rs3124402 13_25 0.000 12516.22 0.0e+00 -3.91
635692 rs616312 13_25 0.000 12490.83 0.0e+00 -3.83
635695 rs520268 13_25 0.000 12490.81 0.0e+00 -3.83
635690 rs4635225 13_25 0.000 12490.60 0.0e+00 -3.82
635687 rs1960704 13_25 0.000 12490.35 0.0e+00 -3.84
635751 rs9569325 13_25 0.000 12368.69 0.0e+00 -3.27
635756 rs2095219 13_25 0.000 12321.66 0.0e+00 -3.31
635748 rs480215 13_25 0.000 12315.09 0.0e+00 -3.27
635755 rs4885924 13_25 0.000 12298.34 0.0e+00 -3.30
635754 rs4885918 13_25 0.000 12264.48 0.0e+00 -3.28
635760 rs9537216 13_25 0.000 12238.49 0.0e+00 -3.28
635762 rs475059 13_25 0.000 12235.82 0.0e+00 -3.29
635763 rs2780471 13_25 0.000 12233.23 0.0e+00 -3.29
635764 rs2780470 13_25 0.000 12233.20 0.0e+00 -3.29
635747 rs640805 13_25 0.000 12166.51 0.0e+00 -3.27
635753 rs9537189 13_25 0.000 12133.72 0.0e+00 -3.01
635773 rs668506 13_25 0.000 12122.62 0.0e+00 -3.27
635770 rs2991029 13_25 0.000 12098.89 0.0e+00 -3.15SNPs with highest PVE
#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
635732 rs7999449 13_25 0.999 13916.66 0.0440 -3.39
635734 rs775834524 13_25 1.000 13957.84 0.0440 -3.45
515288 rs71007692 10_28 1.000 8198.27 0.0260 -3.29
1104520 rs56090907 16_36 1.000 8212.19 0.0260 4.70
719290 rs821840 16_31 1.000 7834.45 0.0250 97.05
1047113 rs57808037 11_37 0.992 7824.84 0.0250 2.67
1047118 rs146923372 11_37 1.000 7826.06 0.0250 2.69
55311 rs766167074 1_118 1.000 7488.50 0.0240 3.28
719291 rs12720926 16_31 1.000 4824.04 0.0150 86.67
515287 rs2474565 10_28 0.552 8245.03 0.0140 -3.38
1194769 rs202143810 20_38 1.000 4394.65 0.0140 4.04
515294 rs2472183 10_28 0.478 8244.99 0.0130 -3.37
515297 rs11011452 10_28 0.506 8245.24 0.0130 -3.36
635725 rs9527399 13_25 0.282 13869.52 0.0120 3.48
1104490 rs71395853 16_36 0.407 8240.49 0.0110 1.69
635727 rs9527401 13_25 0.208 13869.46 0.0092 3.47
635728 rs9597193 13_25 0.209 13869.54 0.0092 3.47
427391 rs2410620 8_21 0.839 2988.31 0.0080 46.36
1021867 rs3072639 11_29 1.000 2409.61 0.0076 2.02
55308 rs10489611 1_118 0.301 7541.46 0.0072 3.63
55310 rs971534 1_118 0.287 7541.42 0.0069 3.63
1136242 rs202007993 17_26 1.000 2138.65 0.0068 2.72
1136272 rs7209751 17_26 1.000 2146.39 0.0068 -7.24
635729 rs9537143 13_25 0.151 13870.42 0.0067 3.46
930246 rs35733538 3_95 1.000 2030.02 0.0064 -4.87
1104522 rs71395854 16_36 0.243 8241.74 0.0063 1.66
55309 rs2486737 1_118 0.256 7541.35 0.0061 3.63
1194744 rs2315009 20_38 0.423 4453.09 0.0060 3.82
55302 rs2256908 1_118 0.241 7541.01 0.0058 3.63
427378 rs1372339 8_21 1.000 1811.87 0.0057 17.85
692432 rs2070895 15_27 1.000 1719.32 0.0055 43.96
719295 rs66495554 16_31 1.000 1692.33 0.0054 -8.74
1194748 rs67468102 20_38 0.357 4453.38 0.0051 3.81
691496 rs4424863 15_24 1.000 1570.76 0.0050 3.14
691495 rs10629766 15_24 1.000 1556.78 0.0049 3.27
515285 rs9299760 10_28 0.181 8240.12 0.0047 -3.37
1194747 rs35201382 20_38 0.313 4453.34 0.0044 3.79
55305 rs2790891 1_118 0.180 7540.85 0.0043 3.62
55306 rs2491405 1_118 0.180 7540.85 0.0043 3.62
1194749 rs2750483 20_38 0.283 4453.46 0.0040 3.79
55298 rs1076804 1_118 0.158 7531.54 0.0038 3.67
635724 rs7337153 13_25 0.071 13903.22 0.0032 -3.34
1047128 rs6591245 11_37 0.116 7812.85 0.0029 2.61
427450 rs11986461 8_21 1.000 738.54 0.0023 25.57
576582 rs11216162 11_70 1.000 709.72 0.0023 15.29
1104492 rs34530665 16_36 0.085 8240.49 0.0022 1.65
1104533 rs35189054 16_36 0.083 8239.89 0.0022 1.65
1194746 rs35046559 20_38 0.154 4437.14 0.0022 3.91
427414 rs75835816 8_21 1.000 668.52 0.0021 -26.36
692426 rs62000868 15_27 1.000 656.19 0.0021 27.17SNPs 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
719290 rs821840 16_31 1.000 7834.45 2.5e-02 97.05
719288 rs12446515 16_31 0.000 7773.72 1.3e-16 96.60
719291 rs12720926 16_31 1.000 4824.04 1.5e-02 86.67
719287 rs193695 16_31 0.000 3349.09 0.0e+00 64.87
427391 rs2410620 8_21 0.839 2988.31 8.0e-03 46.36
427398 rs1441762 8_21 0.161 2985.27 1.5e-03 46.33
427401 rs4126104 8_21 0.000 2952.53 3.6e-10 46.23
427397 rs35878331 8_21 0.000 2969.58 4.9e-07 46.15
427402 rs35369244 8_21 0.000 2899.53 0.0e+00 45.75
427396 rs6999813 8_21 0.000 1461.23 0.0e+00 44.52
427375 rs10645926 8_21 0.000 1507.65 0.0e+00 44.49
427382 rs78963197 8_21 0.000 1480.25 0.0e+00 44.28
692432 rs2070895 15_27 1.000 1719.32 5.5e-03 43.96
427387 rs17489226 8_21 0.000 1406.86 0.0e+00 43.81
427413 rs7816447 8_21 0.000 1407.45 0.0e+00 43.76
427411 rs11989309 8_21 0.000 1400.82 0.0e+00 43.75
427408 rs28675909 8_21 0.000 1398.17 0.0e+00 43.73
427415 rs11984698 8_21 0.000 1397.28 0.0e+00 43.71
427409 rs79198716 8_21 0.000 1389.50 0.0e+00 43.67
427410 rs7004149 8_21 0.000 1388.24 0.0e+00 43.66
427371 rs149553676 8_21 0.000 2367.28 0.0e+00 43.48
427370 rs287 8_21 0.000 2292.07 0.0e+00 43.36
427377 rs1569209 8_21 0.000 1352.81 0.0e+00 42.99
427379 rs80073370 8_21 0.000 1321.39 0.0e+00 42.50
427400 rs11986942 8_21 0.000 2712.79 0.0e+00 41.89
427440 rs80026582 8_21 0.000 1349.47 0.0e+00 41.88
719293 rs4784744 16_31 0.000 2061.82 0.0e+00 -37.95
719292 rs289717 16_31 0.000 2055.81 0.0e+00 -37.93
719294 rs4784745 16_31 0.000 2145.71 0.0e+00 -37.86
427393 rs4083261 8_21 0.000 2544.36 0.0e+00 37.40
427392 rs12541912 8_21 0.000 2584.97 0.0e+00 36.34
692434 rs8034802 15_27 0.000 1109.87 0.0e+00 34.81
692445 rs686958 15_27 0.000 1082.01 0.0e+00 -34.78
692435 rs633695 15_27 0.000 1048.29 0.0e+00 33.81
692440 rs488490 15_27 0.000 1065.40 0.0e+00 -33.44
692438 rs261341 15_27 0.000 999.93 0.0e+00 -32.91
427405 rs6586886 8_21 0.000 943.03 0.0e+00 32.48
692444 rs485671 15_27 0.000 1007.16 0.0e+00 -32.42
719231 rs79984435 16_31 0.000 889.77 0.0e+00 -30.34
719235 rs16962399 16_31 0.000 888.05 0.0e+00 -30.31
719240 rs3764266 16_31 0.000 790.05 0.0e+00 -30.08
719239 rs147569850 16_31 0.000 790.46 0.0e+00 -30.06
719245 rs1968493 16_31 0.000 487.49 0.0e+00 29.95
427353 rs73597690 8_21 0.000 839.40 0.0e+00 28.50
427403 rs2898495 8_21 0.000 987.73 0.0e+00 28.47
763693 rs6507938 18_27 1.000 504.60 1.6e-03 28.37
427439 rs117501405 8_21 0.000 629.44 0.0e+00 28.21
427394 rs4389957 8_21 0.000 1031.58 0.0e+00 28.13
763690 rs7241918 18_27 0.000 487.12 4.5e-07 27.97
719289 rs201825234 16_31 0.000 590.87 0.0e+00 -27.64Gene 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] 43if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
Term Overlap Adjusted.P.value
1 phospholipid biosynthetic process (GO:0008654) 3/37 0.03089371
Genes
1 DPAGT1;GPAM;FADS1
[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)PNMT gene(s) from the input list not found in DisGeNET CURATEDC12orf49 gene(s) from the input list not found in DisGeNET CURATEDC1QTNF4 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATEDSPSB1 gene(s) from the input list not found in DisGeNET CURATEDSTK24 gene(s) from the input list not found in DisGeNET CURATEDMRPL21 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDCEBPG gene(s) from the input list not found in DisGeNET CURATEDBLMH gene(s) from the input list not found in DisGeNET CURATEDZDHHC18 gene(s) from the input list not found in DisGeNET CURATEDPHPT1 gene(s) from the input list not found in DisGeNET CURATEDRP11-54O7.17 gene(s) from the input list not found in DisGeNET CURATEDLAMP1 gene(s) from the input list not found in DisGeNET CURATEDPITPNC1 gene(s) from the input list not found in DisGeNET CURATEDSLFN13 gene(s) from the input list not found in DisGeNET CURATEDFOXK1 gene(s) from the input list not found in DisGeNET CURATEDRPA2 gene(s) from the input list not found in DisGeNET CURATEDSTARD3NL gene(s) from the input list not found in DisGeNET CURATEDNTAN1 gene(s) from the input list not found in DisGeNET CURATEDABTB1 gene(s) from the input list not found in DisGeNET CURATEDMIR210HG gene(s) from the input list not found in DisGeNET CURATEDDNAH10OS gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
34 Inherited Factor II deficiency 0.03661177 1/20 1/9703
57 Sinus Thrombosis, Intracranial 0.03661177 1/20 2/9703
77 Skin Diseases, Vascular 0.03661177 1/20 1/9703
114 Mesenteric Venous Thrombosis 0.03661177 1/20 2/9703
115 Acid Phosphatase Deficiency 0.03661177 1/20 1/9703
119 Gray Platelet Syndrome 0.03661177 1/20 2/9703
120 Hereditary factor II deficiency disease 0.03661177 1/20 1/9703
124 Tubular aggregates 0.03661177 1/20 2/9703
165 Petrous Sinus Thrombophlebitis 0.03661177 1/20 2/9703
166 Intracranial Sinus Thrombophlebitis 0.03661177 1/20 2/9703******************************************
* *
* 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 Therapeutic abortion 12 3 0.03108406 disease_GLAD4U
userId
1 F2;ACP2;SIPA1
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