HDL cholesterol (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 HDL cholesterol (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-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 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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0173481546 0.0002037697 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
27.72493 21.83352 #report sample size
print(sample_size)[1] 315133#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01663781 0.12278784 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04883558 2.83910628Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
6391 TTC39B 9_13 1.000 225.23 7.1e-04 -15.12
12613 GPIHBP1 8_94 0.999 151.60 4.8e-04 -12.51
8531 TNKS 8_12 0.997 125.39 4.0e-04 17.88
11684 RP11-136O12.2 8_83 0.995 208.98 6.6e-04 -16.01
1647 ARFRP1 20_38 0.995 4575.95 1.4e-02 -4.69
7410 ABCA1 9_53 0.993 220.25 6.9e-04 22.57
1144 ASAP3 1_16 0.988 32.86 1.0e-04 6.50
6509 NTAN1 16_15 0.985 94.68 3.0e-04 -9.88
11699 RP11-10A14.4 8_11 0.984 33.39 1.0e-04 4.57
2204 AKNA 9_59 0.982 33.66 1.0e-04 -6.51
9006 BEND3 6_71 0.977 25.63 7.9e-05 -4.73
2148 PCOLCE 7_62 0.975 23.84 7.4e-05 3.77
11399 TNFSF12 17_7 0.971 50.69 1.6e-04 7.33
3137 RPA2 1_19 0.960 24.99 7.6e-05 4.95
10104 SULF2 20_29 0.959 85.34 2.6e-04 -8.20
3210 LDAH 2_12 0.958 38.74 1.2e-04 -5.78
6801 PTH1R 3_33 0.953 26.50 8.0e-05 5.15
6100 ALLC 2_2 0.951 57.35 1.7e-04 7.62
9404 PTTG1IP 21_23 0.948 68.55 2.1e-04 8.09
12229 RP11-346C20.3 16_39 0.942 25.15 7.5e-05 -4.72
7329 DAGLB 7_9 0.939 89.88 2.7e-04 9.66
4435 PSRC1 1_67 0.933 105.34 3.1e-04 11.24
3774 ZNF436 1_16 0.932 29.27 8.7e-05 -6.19
12340 RP11-54O7.17 1_1 0.926 41.29 1.2e-04 -6.30
10502 SREBF2 22_17 0.905 22.79 6.5e-05 4.56
3177 PLAGL1 6_94 0.888 22.02 6.2e-05 -4.31
906 UBE2K 4_32 0.877 35.70 9.9e-05 5.51
9528 ZFP1 16_40 0.872 29.63 8.2e-05 -5.12
9447 KLHL25 15_39 0.866 20.38 5.6e-05 3.84
2678 TFEB 6_32 0.864 26.36 7.2e-05 5.91
12687 RP4-781K5.7 1_121 0.860 64.12 1.8e-04 -7.59
156 IFFO1 12_7 0.854 41.32 1.1e-04 6.77
2831 ABTB1 3_79 0.839 37.98 1.0e-04 -5.97
7794 TMC4 19_37 0.839 21.28 5.7e-05 4.29
9693 CD300LF 17_42 0.837 22.92 6.1e-05 4.25
9140 TH 11_2 0.818 29.49 7.7e-05 4.90
3300 C10orf88 10_77 0.804 21.55 5.5e-05 -4.16Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
7241 MST1R 3_35 0 71143.77 0.0e+00 -9.45
35 RBM6 3_35 0 69299.49 0.0e+00 10.30
9460 TRAIP 3_35 0 56142.02 0.0e+00 -6.58
11255 GPX1 3_35 0 48881.70 0.0e+00 -1.61
656 RHOA 3_35 0 48859.72 0.0e+00 -1.60
7235 APEH 3_35 0 48029.90 0.0e+00 2.08
9902 SLC38A3 3_35 0 30318.08 0.0e+00 5.33
11109 BSN-AS2 3_35 0 27807.46 0.0e+00 3.24
5666 NICN1 3_35 0 25930.86 0.0e+00 -3.21
8555 GMPPB 3_35 0 24757.48 0.0e+00 -2.06
12683 HCP5B 6_24 0 13958.34 6.2e-09 -6.72
7234 BSN 3_35 0 9896.99 0.0e+00 4.23
4634 EGLN1 1_118 0 8770.30 0.0e+00 3.11
7843 CDK2AP2 11_37 0 7930.18 1.9e-11 3.07
10663 TRIM31 6_24 0 7337.40 1.5e-15 6.58
3058 EXOC8 1_118 0 7321.57 0.0e+00 -3.52
4833 FLOT1 6_24 0 7007.60 1.7e-07 -8.53
8762 RPS6KB2 11_37 0 6848.59 1.6e-09 -4.07
117 CACNA2D2 3_35 0 6394.84 0.0e+00 2.66
5240 NLRC5 16_31 0 5258.29 0.0e+00 -86.44Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1647 ARFRP1 20_38 0.995 4575.95 0.01400 -4.69
6391 TTC39B 9_13 1.000 225.23 0.00071 -15.12
7410 ABCA1 9_53 0.993 220.25 0.00069 22.57
11684 RP11-136O12.2 8_83 0.995 208.98 0.00066 -16.01
12613 GPIHBP1 8_94 0.999 151.60 0.00048 -12.51
8531 TNKS 8_12 0.997 125.39 0.00040 17.88
11776 HCAR3 12_75 0.729 138.39 0.00032 13.86
4435 PSRC1 1_67 0.933 105.34 0.00031 11.24
6509 NTAN1 16_15 0.985 94.68 0.00030 -9.88
7329 DAGLB 7_9 0.939 89.88 0.00027 9.66
10104 SULF2 20_29 0.959 85.34 0.00026 -8.20
2432 MTCH2 11_29 0.506 142.07 0.00023 -17.58
9404 PTTG1IP 21_23 0.948 68.55 0.00021 8.09
10848 CLIC1 6_26 0.792 75.04 0.00019 8.52
8409 C1QTNF4 11_29 0.423 141.53 0.00019 17.57
12687 RP4-781K5.7 1_121 0.860 64.12 0.00018 -7.59
6100 ALLC 2_2 0.951 57.35 0.00017 7.62
4485 DDB2 11_29 0.717 69.76 0.00016 1.05
11399 TNFSF12 17_7 0.971 50.69 0.00016 7.33
2998 RALGPS2 1_87 0.678 65.00 0.00014 -8.42Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5240 NLRC5 16_31 0.000 5258.29 0.0e+00 -86.44
1120 CETP 16_31 0.000 2675.98 0.0e+00 -70.46
7547 LIPC 15_26 0.000 893.76 2.2e-18 -35.69
5991 FADS1 11_34 0.004 345.91 4.2e-06 23.87
2465 APOA5 11_70 0.000 492.35 7.6e-16 22.92
7410 ABCA1 9_53 0.993 220.25 6.9e-04 22.57
8739 LPL 8_21 0.000 617.60 0.0e+00 21.57
4507 FADS2 11_34 0.002 312.23 2.3e-06 21.24
7955 FEN1 11_34 0.002 312.23 2.3e-06 21.24
1597 PLTP 20_28 0.000 221.39 1.5e-07 21.12
8531 TNKS 8_12 0.997 125.39 4.0e-04 17.88
2432 MTCH2 11_29 0.506 142.07 2.3e-04 -17.58
8409 C1QTNF4 11_29 0.423 141.53 1.9e-04 17.57
290 NR1H3 11_29 0.052 152.56 2.5e-05 16.37
2485 MADD 11_29 0.062 146.38 2.9e-05 -16.37
11738 RP11-115J16.2 8_12 0.011 240.95 8.1e-06 16.25
9360 DDX28 16_36 0.011 233.39 8.3e-06 16.18
11684 RP11-136O12.2 8_83 0.995 208.98 6.6e-04 -16.01
7523 SLC39A13 11_29 0.002 116.97 6.3e-07 -15.85
1231 PABPC4 1_24 0.077 227.86 5.6e-05 15.80Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.02926337#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5240 NLRC5 16_31 0.000 5258.29 0.0e+00 -86.44
1120 CETP 16_31 0.000 2675.98 0.0e+00 -70.46
7547 LIPC 15_26 0.000 893.76 2.2e-18 -35.69
5991 FADS1 11_34 0.004 345.91 4.2e-06 23.87
2465 APOA5 11_70 0.000 492.35 7.6e-16 22.92
7410 ABCA1 9_53 0.993 220.25 6.9e-04 22.57
8739 LPL 8_21 0.000 617.60 0.0e+00 21.57
4507 FADS2 11_34 0.002 312.23 2.3e-06 21.24
7955 FEN1 11_34 0.002 312.23 2.3e-06 21.24
1597 PLTP 20_28 0.000 221.39 1.5e-07 21.12
8531 TNKS 8_12 0.997 125.39 4.0e-04 17.88
2432 MTCH2 11_29 0.506 142.07 2.3e-04 -17.58
8409 C1QTNF4 11_29 0.423 141.53 1.9e-04 17.57
290 NR1H3 11_29 0.052 152.56 2.5e-05 16.37
2485 MADD 11_29 0.062 146.38 2.9e-05 -16.37
11738 RP11-115J16.2 8_12 0.011 240.95 8.1e-06 16.25
9360 DDX28 16_36 0.011 233.39 8.3e-06 16.18
11684 RP11-136O12.2 8_83 0.995 208.98 6.6e-04 -16.01
7523 SLC39A13 11_29 0.002 116.97 6.3e-07 -15.85
1231 PABPC4 1_24 0.077 227.86 5.6e-05 15.80Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 16_31"
genename region_tag susie_pip mu2 PVE z
6688 CES5A 16_31 0 29.47 0 4.75
1124 GNAO1 16_31 0 6.21 0 -1.58
11561 RP11-461O7.1 16_31 0 20.94 0 0.43
6695 AMFR 16_31 0 47.54 0 -4.89
7710 NUDT21 16_31 0 6.21 0 2.02
3681 BBS2 16_31 0 32.30 0 -0.17
1122 MT3 16_31 0 32.53 0 -7.18
8094 MT1E 16_31 0 23.14 0 -1.47
10727 MT1M 16_31 0 86.95 0 -8.87
10725 MT1A 16_31 0 52.79 0 -6.53
10386 MT1F 16_31 0 51.70 0 6.63
9805 MT1X 16_31 0 35.71 0 2.54
1740 NUP93 16_31 0 23.80 0 -6.91
438 HERPUD1 16_31 0 371.87 0 -12.67
1120 CETP 16_31 0 2675.98 0 -70.46
5240 NLRC5 16_31 0 5258.29 0 -86.44
5239 CPNE2 16_31 0 50.06 0 2.62
8472 FAM192A 16_31 0 28.85 0 3.33
6698 RSPRY1 16_31 0 24.58 0 5.62
1745 PLLP 16_31 0 13.07 0 6.94
81 CX3CL1 16_31 0 22.31 0 2.65
1747 CCL17 16_31 0 9.01 0 1.89
52 CIAPIN1 16_31 0 8.20 0 0.41
1154 COQ9 16_31 0 6.09 0 -0.15
3685 DOK4 16_31 0 56.56 0 -2.49
4628 CCDC102A 16_31 0 5.39 0 -0.63
10722 ADGRG1 16_31 0 25.44 0 2.30
9366 ADGRG3 16_31 0 6.77 0 -0.65
5241 KATNB1 16_31 0 23.52 0 2.00
5242 KIFC3 16_31 0 5.36 0 -0.34
1754 USB1 16_31 0 8.72 0 -0.91
7571 ZNF319 16_31 0 9.93 0 -1.05
1753 MMP15 16_31 0 5.03 0 0.21
729 CFAP20 16_31 0 21.97 0 -1.92
730 CSNK2A2 16_31 0 6.36 0 0.57
9278 GINS3 16_31 0 5.69 0 -0.43
1757 NDRG4 16_31 0 8.10 0 -0.84
3680 CNOT1 16_31 0 37.92 0 -2.67
1759 SLC38A7 16_31 0 8.11 0 0.84
3684 GOT2 16_31 0 41.87 0 -2.82
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 15_26"
genename region_tag susie_pip mu2 PVE z
7547 LIPC 15_26 0 893.76 2.2e-18 -35.69
4905 ADAM10 15_26 0 8.49 1.2e-20 -0.16
6536 RNF111 15_26 0 14.03 4.0e-20 0.66
4889 SLTM 15_26 0 33.99 1.8e-19 -3.79
8386 LDHAL6B 15_26 0 10.78 2.3e-20 0.59
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_34"
genename region_tag susie_pip mu2 PVE z
9982 FAM111B 11_34 0.002 6.23 3.7e-08 0.72
7662 FAM111A 11_34 0.056 35.91 6.4e-06 2.69
2444 DTX4 11_34 0.003 10.06 9.9e-08 1.20
10267 MPEG1 11_34 0.002 5.01 2.7e-08 -0.08
7684 PATL1 11_34 0.003 12.34 1.2e-07 1.68
7687 STX3 11_34 0.002 5.65 3.2e-08 -0.55
7688 MRPL16 11_34 0.002 5.19 2.8e-08 -0.50
5997 MS4A2 11_34 0.009 20.41 5.5e-07 2.65
2453 MS4A6A 11_34 0.014 24.46 1.1e-06 -2.83
10924 MS4A4E 11_34 0.002 4.88 2.6e-08 0.40
7698 MS4A14 11_34 0.088 26.87 7.5e-06 -2.71
7697 MS4A7 11_34 0.003 9.07 7.5e-08 -1.47
2455 CCDC86 11_34 0.002 4.89 2.6e-08 0.29
2456 PRPF19 11_34 0.003 9.97 9.8e-08 -0.88
2457 TMEM109 11_34 0.012 21.96 8.0e-07 -1.92
2480 SLC15A3 11_34 0.059 41.83 7.8e-06 3.58
2481 CD5 11_34 0.002 7.89 4.7e-08 1.76
7874 VPS37C 11_34 0.002 6.00 3.2e-08 1.34
7875 VWCE 11_34 0.002 5.06 2.7e-08 -0.30
5990 TMEM138 11_34 0.002 8.78 4.9e-08 -2.14
6902 CYB561A3 11_34 0.002 8.78 4.9e-08 -2.14
9789 TMEM216 11_34 0.005 18.39 3.0e-07 -2.66
11817 RP11-286N22.8 11_34 0.012 25.00 9.9e-07 -2.37
5996 CPSF7 11_34 0.004 11.56 1.5e-07 -1.33
6903 PPP1R32 11_34 0.018 22.75 1.3e-06 0.80
11812 RP11-794G24.1 11_34 0.009 16.89 4.6e-07 -0.01
4508 TMEM258 11_34 0.002 63.57 4.8e-07 -8.56
4507 FADS2 11_34 0.002 312.23 2.3e-06 21.24
7955 FEN1 11_34 0.002 312.23 2.3e-06 21.24
5991 FADS1 11_34 0.004 345.91 4.2e-06 23.87
1196 GANAB 11_34 0.002 173.35 1.2e-06 -12.82
11004 FADS3 11_34 0.002 16.68 8.7e-08 5.01
7876 BEST1 11_34 0.004 47.57 5.6e-07 -7.56
3676 DKFZP434K028 11_34 0.039 47.55 5.8e-06 4.70
5994 INCENP 11_34 0.015 36.06 1.7e-06 -4.28
6904 ASRGL1 11_34 0.008 23.61 5.7e-07 -2.87
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_70"
genename region_tag susie_pip mu2 PVE z
4868 BUD13 11_70 0 276.54 2.3e-17 -7.73
2465 APOA5 11_70 0 492.35 7.6e-16 22.92
3154 APOA1 11_70 0 69.82 5.5e-18 -11.64
7898 PAFAH1B2 11_70 0 157.57 1.4e-17 11.33
6005 SIDT2 11_70 0 151.63 5.3e-17 -13.10
6006 TAGLN 11_70 0 58.02 4.3e-18 9.26
6785 PCSK7 11_70 0 206.77 4.0e-11 8.64
7745 RNF214 11_70 0 21.77 2.5e-18 -3.40
2466 CEP164 11_70 0 27.23 1.3e-17 4.20
9720 BACE1 11_70 0 107.74 1.5e-16 9.59
4881 FXYD2 11_70 0 9.05 9.6e-19 0.74
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 9_53"
genename region_tag susie_pip mu2 PVE z
7410 ABCA1 9_53 0.993 220.25 0.00069 22.57
2193 FKTN 9_53 0.000 30.23 0.00000 -2.06
1314 TMEM38B 9_53 0.000 16.58 0.00000 -2.59
| 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
29916 rs11102041 1_69 1.000 77.93 2.5e-04 7.93
56281 rs2103827 1_117 1.000 232.23 7.4e-04 22.05
56282 rs11122453 1_117 1.000 460.13 1.5e-03 25.82
56763 rs766167074 1_118 1.000 9441.35 3.0e-02 3.28
69007 rs1042034 2_13 1.000 510.31 1.6e-03 -21.96
71044 rs569546056 2_17 1.000 617.95 2.0e-03 2.35
180076 rs9817452 3_97 1.000 62.15 2.0e-04 8.17
189140 rs35374654 3_114 1.000 38.92 1.2e-04 6.03
227402 rs35518360 4_67 1.000 262.96 8.3e-04 -17.51
227468 rs13140033 4_68 1.000 165.76 5.3e-04 -13.28
272019 rs62369502 5_28 1.000 40.27 1.3e-04 -6.13
293829 rs11064 5_72 1.000 42.09 1.3e-04 6.39
365379 rs191555775 6_104 1.000 158.95 5.0e-04 -15.06
415904 rs6977416 7_93 1.000 64.71 2.1e-04 -6.76
429135 rs1372339 8_21 1.000 1936.94 6.1e-03 17.85
429171 rs75835816 8_21 1.000 678.01 2.2e-03 -26.36
429207 rs11986461 8_21 1.000 759.42 2.4e-03 25.57
490311 rs2777798 9_52 1.000 224.56 7.1e-04 13.11
490317 rs2777802 9_52 1.000 395.68 1.3e-03 12.36
490319 rs2777804 9_52 1.000 322.62 1.0e-03 4.27
513800 rs71007692 10_28 1.000 10536.52 3.3e-02 -3.29
535500 rs17875416 10_71 1.000 113.32 3.6e-04 9.59
557136 rs7123635 11_28 1.000 154.48 4.9e-04 -9.78
557313 rs1631174 11_29 1.000 106.53 3.4e-04 -15.84
558418 rs12361987 11_30 1.000 68.59 2.2e-04 0.85
560712 rs12294913 11_36 1.000 55.85 1.8e-04 -8.27
576805 rs3135506 11_70 1.000 952.50 3.0e-03 -20.84
576834 rs11216162 11_70 1.000 707.56 2.2e-03 15.29
577020 rs147611518 11_70 1.000 115.58 3.7e-04 -11.15
579878 rs4937122 11_77 1.000 56.77 1.8e-04 -7.42
599774 rs6581124 12_35 1.000 45.69 1.4e-04 7.41
599793 rs7397189 12_36 1.000 94.58 3.0e-04 11.92
619307 rs3782287 12_76 1.000 93.95 3.0e-04 -12.81
619323 rs61941677 12_76 1.000 201.06 6.4e-04 -16.01
635158 rs7999449 13_25 1.000 19145.57 6.1e-02 -3.39
635160 rs775834524 13_25 1.000 19191.70 6.1e-02 -3.45
672358 rs13379043 14_34 1.000 74.98 2.4e-04 7.79
682685 rs4983559 14_55 1.000 78.83 2.5e-04 -8.68
693072 rs7168508 15_24 1.000 387.15 1.2e-03 0.10
693074 rs10629766 15_24 1.000 1805.55 5.7e-03 3.27
693075 rs4424863 15_24 1.000 1821.95 5.8e-03 3.14
721510 rs12925793 16_36 1.000 47.03 1.5e-04 7.79
721690 rs200561116 16_36 1.000 259.48 8.2e-04 17.28
721879 rs2276329 16_37 1.000 56.22 1.8e-04 -7.06
725568 rs12443634 16_45 1.000 128.15 4.1e-04 13.60
739921 rs4793062 17_26 1.000 89.99 2.9e-04 -7.46
739947 rs55764662 17_26 1.000 220.45 7.0e-04 -16.87
764869 rs11082766 18_27 1.000 196.29 6.2e-04 12.42
764889 rs6507938 18_27 1.000 508.01 1.6e-03 28.37
764890 rs118043171 18_27 1.000 528.40 1.7e-03 23.95
765109 rs74461650 18_28 1.000 76.79 2.4e-04 8.82
778363 rs111500536 19_8 1.000 80.83 2.6e-04 8.95
778366 rs116843064 19_8 1.000 546.57 1.7e-03 25.68
779352 rs1865063 19_10 1.000 83.65 2.7e-04 -11.95
779354 rs3745683 19_10 1.000 103.63 3.3e-04 -12.71
786383 rs889140 19_23 1.000 76.49 2.4e-04 8.86
804096 rs147591082 20_28 1.000 58.95 1.9e-04 -7.58
804542 rs4812975 20_28 1.000 223.85 7.1e-04 21.69
857342 rs140584594 1_67 1.000 124.81 4.0e-04 12.66
892926 rs142955295 3_35 1.000 114839.74 3.6e-01 -7.40
921047 rs1611236 6_24 1.000 28529.96 9.1e-02 -4.36
1001089 rs4149307 9_53 1.000 385.94 1.2e-03 19.86
1001323 rs11789603 9_53 1.000 302.93 9.6e-04 18.82
1001402 rs2740488 9_53 1.000 509.05 1.6e-03 -26.99
1009870 rs773844590 10_39 1.000 18018.22 5.7e-02 -3.88
1049439 rs146923372 11_37 1.000 10919.20 3.5e-02 2.69
1079213 rs261290 15_26 1.000 1461.43 4.6e-03 -44.15
1079311 rs12708454 15_26 1.000 568.82 1.8e-03 29.56
1101259 rs5883 16_31 1.000 1280.07 4.1e-03 25.59
1101277 rs117427818 16_31 1.000 1060.08 3.4e-03 -58.01
1130014 rs11556624 17_23 1.000 101.77 3.2e-04 6.49
1158671 rs429358 19_32 1.000 512.77 1.6e-03 -24.04
1158741 rs35136575 19_32 1.000 89.84 2.9e-04 9.41
1158772 rs5167 19_32 1.000 285.29 9.1e-04 17.02
1191739 rs202143810 20_38 1.000 5023.78 1.6e-02 4.04
1200570 rs780018294 22_10 1.000 1192.61 3.8e-03 -0.82
1200691 rs6006310 22_10 1.000 1085.34 3.4e-03 -6.59
55705 rs878811 1_116 0.999 33.76 1.1e-04 5.66
409912 rs6961342 7_80 0.999 90.98 2.9e-04 -13.21
429137 rs17091881 8_21 0.999 597.32 1.9e-03 -24.49
599816 rs140734681 12_36 0.999 35.25 1.1e-04 -2.42
603834 rs2137537 12_44 0.999 32.68 1.0e-04 -5.19
739903 rs117007812 17_26 0.999 63.90 2.0e-04 4.71
972767 rs118027010 8_80 0.999 39.35 1.2e-04 5.42
981815 rs72647336 8_83 0.999 58.71 1.9e-04 -7.75
1072291 rs532140742 12_75 0.999 118.32 3.8e-04 -11.44
1101167 rs12448528 16_31 0.999 1391.94 4.4e-03 66.11
56274 rs6678475 1_117 0.998 39.44 1.2e-04 -1.80
94993 rs3789066 2_66 0.998 32.01 1.0e-04 -5.15
223127 rs4425336 4_60 0.998 39.53 1.3e-04 7.21
383415 rs9490 7_28 0.998 39.96 1.3e-04 5.29
693819 rs72737411 15_25 0.998 31.79 1.0e-04 -5.09
907730 rs6762415 3_83 0.998 47.22 1.5e-04 -6.87
1073877 rs533328276 12_75 0.998 57.20 1.8e-04 1.46
1136292 rs117380643 17_25 0.998 102.17 3.2e-04 -10.28
786345 rs56287732 19_23 0.997 41.79 1.3e-04 -5.27
1049434 rs57808037 11_37 0.997 10917.87 3.5e-02 2.67
1079189 rs11071376 15_26 0.997 211.45 6.7e-04 4.90
32940 rs185073199 1_73 0.996 30.68 9.7e-05 5.33
282513 rs115912456 5_49 0.996 30.31 9.6e-05 5.30
428945 rs113231830 8_20 0.996 31.87 1.0e-04 -5.70
778368 rs62117512 19_8 0.996 87.45 2.8e-04 13.30
53627 rs2642420 1_112 0.995 39.86 1.3e-04 -7.39
464691 rs1016565 9_1 0.995 30.86 9.7e-05 -5.31
725575 rs11641142 16_45 0.994 65.06 2.1e-04 10.95
787919 rs11879413 19_28 0.994 29.37 9.3e-05 5.43
1078649 rs28690720 15_26 0.993 283.66 8.9e-04 -21.34
764909 rs8093206 18_27 0.992 72.36 2.3e-04 -7.76
563294 rs695110 11_42 0.991 115.77 3.6e-04 -11.10
133931 rs4675812 2_144 0.990 36.07 1.1e-04 6.34
589392 rs66720652 12_15 0.989 33.02 1.0e-04 5.45
274870 rs173964 5_33 0.988 153.67 4.8e-04 -10.81
699874 rs16972386 15_38 0.988 30.06 9.4e-05 -5.13
750490 rs72854483 17_46 0.987 27.42 8.6e-05 -4.96
538245 rs10901802 10_78 0.985 30.71 9.6e-05 5.51
424209 rs1402522 8_13 0.984 33.25 1.0e-04 6.21
695321 rs11071771 15_29 0.984 42.11 1.3e-04 -6.23
320513 rs4134975 6_15 0.983 31.48 9.8e-05 4.79
672214 rs177392 14_34 0.983 29.64 9.2e-05 -4.40
767765 rs41292412 18_31 0.982 38.26 1.2e-04 -6.21
896402 rs73082723 3_36 0.980 38.96 1.2e-04 6.29
401736 rs2734897 7_61 0.979 29.96 9.3e-05 -5.53
326571 rs181268076 6_27 0.978 48.17 1.5e-04 -6.52
394348 rs367867252 7_48 0.977 32.15 1.0e-04 -5.36
1101185 rs183130 16_31 0.977 6445.83 2.0e-02 97.19
1007150 rs1044531 9_59 0.975 33.37 1.0e-04 6.42
764905 rs62101781 18_27 0.973 218.74 6.8e-04 17.00
375433 rs38172 7_16 0.971 28.28 8.7e-05 5.01
619213 rs11057671 12_76 0.971 68.19 2.1e-04 8.60
883903 rs79800183 2_12 0.970 50.70 1.6e-04 6.70
982093 rs200974272 8_83 0.970 48.96 1.5e-04 8.22
90041 rs9248 2_54 0.968 39.86 1.2e-04 6.23
379347 rs2699814 7_23 0.968 44.65 1.4e-04 6.12
551461 rs12288512 11_19 0.966 62.49 1.9e-04 -7.87
197705 rs17468437 4_12 0.965 26.11 8.0e-05 4.81
519931 rs2393730 10_42 0.964 27.28 8.3e-05 5.11
369633 rs6462198 7_2 0.963 37.32 1.1e-04 -7.07
825530 rs12321 22_9 0.962 29.35 9.0e-05 4.92
1047605 rs4930352 11_37 0.962 370.33 1.1e-03 8.12
281185 rs3733890 5_46 0.961 33.04 1.0e-04 -5.71
603955 rs1707498 12_44 0.956 31.11 9.4e-05 5.19
559113 rs145487327 11_32 0.955 36.31 1.1e-04 4.94
557051 rs1317826 11_28 0.954 68.04 2.1e-04 -5.41
572421 rs72980276 11_59 0.954 26.42 8.0e-05 -4.87
616379 rs653178 12_67 0.954 140.10 4.2e-04 10.81
216643 rs7696472 4_48 0.952 31.15 9.4e-05 5.26
324773 rs1131159 6_25 0.950 44.54 1.3e-04 8.41
659110 rs1955512 14_8 0.948 34.37 1.0e-04 5.52
329701 rs115482652 6_34 0.946 25.10 7.5e-05 -4.88
347377 rs2388334 6_67 0.946 32.08 9.6e-05 5.48
473661 rs145804707 9_18 0.946 24.34 7.3e-05 -4.54
304260 rs4958365 5_90 0.944 32.10 9.6e-05 4.88
757354 rs57440424 18_12 0.944 55.93 1.7e-04 7.71
1079459 rs2070895 15_26 0.940 1887.99 5.6e-03 43.96
560718 rs671976 11_36 0.937 32.84 9.8e-05 -6.73
325169 rs3869145 6_26 0.936 39.38 1.2e-04 -7.37
589347 rs11045182 12_15 0.936 51.12 1.5e-04 7.13
536244 rs113097445 10_72 0.933 25.48 7.5e-05 -4.72
777277 rs67868323 19_4 0.933 53.58 1.6e-04 -6.94
497120 rs111472765 9_67 0.931 23.99 7.1e-05 4.47
558254 rs72484110 11_30 0.931 365.95 1.1e-03 12.61
792369 rs2316866 20_1 0.931 25.30 7.5e-05 -4.69
298227 rs4705986 5_80 0.930 28.50 8.4e-05 4.86
968644 rs142752118 8_11 0.930 31.62 9.3e-05 -4.33
1092309 rs12921195 16_4 0.929 34.99 1.0e-04 -6.11
498182 rs115478735 9_70 0.928 56.80 1.7e-04 7.54
557182 rs2167079 11_29 0.926 208.02 6.1e-04 18.01
578360 rs1219430 11_74 0.926 29.97 8.8e-05 -5.60
15743 rs12140153 1_39 0.924 27.46 8.1e-05 4.53
599806 rs3809113 12_36 0.923 102.33 3.0e-04 11.00
815725 rs546634737 21_11 0.923 25.84 7.6e-05 4.59
786381 rs56361048 19_23 0.921 32.87 9.6e-05 6.75
737718 rs2011614 17_18 0.919 33.92 9.9e-05 -6.00
654518 rs191951647 13_62 0.918 24.42 7.1e-05 4.57
633586 rs78212345 13_21 0.917 33.18 9.7e-05 5.75
699943 rs1509559 15_38 0.916 27.26 7.9e-05 4.63
342479 rs560253203 6_56 0.913 23.87 6.9e-05 4.33
129451 rs11900497 2_135 0.909 27.37 7.9e-05 -4.92
54791 rs12132342 1_115 0.907 24.48 7.0e-05 -4.47
114540 rs71410739 2_107 0.906 27.16 7.8e-05 -4.97
329702 rs9472126 6_34 0.906 24.51 7.1e-05 4.71
591574 rs11614652 12_18 0.904 29.42 8.4e-05 5.16
560916 rs6591179 11_36 0.899 42.84 1.2e-04 7.29
359207 rs151288714 6_92 0.896 50.51 1.4e-04 7.62
38511 rs35039375 1_84 0.895 28.42 8.1e-05 -5.18
400345 rs12534274 7_58 0.893 28.48 8.1e-05 5.15
415913 rs4725377 7_93 0.892 32.89 9.3e-05 1.96
557079 rs61337452 11_28 0.892 266.64 7.5e-04 14.68
203058 rs56147366 4_22 0.887 57.92 1.6e-04 -7.71
546861 rs7121538 11_11 0.882 45.06 1.3e-04 6.46
717729 rs62039688 16_27 0.882 25.44 7.1e-05 4.50
350883 rs2038014 6_74 0.881 26.15 7.3e-05 -4.75
490194 rs34849882 9_52 0.880 52.85 1.5e-04 3.81
1009867 rs12768525 10_39 0.879 18091.03 5.0e-02 -4.13
820329 rs8128478 21_21 0.872 25.96 7.2e-05 4.91
546785 rs547219635 11_11 0.871 27.36 7.6e-05 4.11
96499 rs2130980 2_68 0.867 28.49 7.8e-05 5.09
36082 rs4657041 1_79 0.865 26.66 7.3e-05 -4.76
576795 rs9326246 11_70 0.864 530.41 1.5e-03 22.70
825242 rs73166732 22_9 0.863 24.53 6.7e-05 -4.01
1196802 rs9980311 21_23 0.861 59.23 1.6e-04 -6.68
788532 rs7248167 19_30 0.860 32.92 9.0e-05 -5.71
808113 rs41310841 20_34 0.860 25.61 7.0e-05 -4.62
599590 rs34358051 12_35 0.859 33.95 9.3e-05 -5.67
288985 rs55815433 5_62 0.856 25.16 6.8e-05 4.49
240699 rs116329078 4_94 0.855 27.26 7.4e-05 5.04
789767 rs4802880 19_35 0.855 70.74 1.9e-04 -8.38
678682 rs1242889 14_47 0.851 26.29 7.1e-05 4.68
394121 rs13247874 7_47 0.850 157.30 4.2e-04 12.82
739870 rs4039062 17_26 0.847 38.99 1.0e-04 2.90
1009936 rs12775129 10_39 0.847 18093.67 4.9e-02 -4.10
376188 rs17138358 7_17 0.844 130.08 3.5e-04 -11.94
429148 rs2410620 8_21 0.844 3112.95 8.3e-03 46.36
453126 rs2570952 8_69 0.844 42.42 1.1e-04 6.36
466318 rs447124 9_5 0.842 26.36 7.0e-05 -4.72
131796 rs11900603 2_139 0.841 24.51 6.5e-05 -4.43
109995 rs187764768 2_97 0.838 24.20 6.4e-05 4.06
463598 rs11778265 8_92 0.837 27.25 7.2e-05 -4.87
78472 rs4566412 2_31 0.836 36.60 9.7e-05 -5.54
615678 rs34132586 12_66 0.834 24.13 6.4e-05 3.95
456873 rs10095930 8_78 0.833 57.90 1.5e-04 4.72
746092 rs7210770 17_39 0.833 28.40 7.5e-05 4.89
83853 rs62143990 2_43 0.831 27.14 7.2e-05 4.91
705081 rs11634241 15_48 0.831 24.58 6.5e-05 -4.55
773344 rs4519424 18_43 0.830 24.24 6.4e-05 -4.34
369623 rs10480060 7_1 0.829 24.52 6.5e-05 -4.29
170991 rs62262433 3_76 0.824 25.42 6.7e-05 4.72
534992 rs4285809 10_70 0.819 92.20 2.4e-04 -9.88
226232 rs6532770 4_66 0.813 35.02 9.0e-05 5.40
626731 rs9554263 13_7 0.812 29.82 7.7e-05 -5.23
629212 rs117280550 13_13 0.810 24.50 6.3e-05 -4.21
796044 rs73604325 20_8 0.809 25.14 6.5e-05 -4.33
246124 rs11100443 4_105 0.807 23.40 6.0e-05 -3.79
359340 rs377695739 6_93 0.807 29.02 7.4e-05 5.26
78191 rs12105520 2_30 0.806 25.97 6.6e-05 -3.43
232434 rs58125305 4_77 0.805 28.97 7.4e-05 5.07
1187583 rs2281279 20_29 0.804 53.18 1.4e-04 6.32SNPs 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
892926 rs142955295 3_35 1.000 114839.7 3.6e-01 -7.40
892892 rs9853458 3_35 0.514 114681.1 1.9e-01 7.39
892890 rs9876508 3_35 0.339 114680.9 1.2e-01 7.39
892891 rs9815766 3_35 0.109 114675.3 4.0e-02 7.39
892863 rs1049256 3_35 0.068 114673.9 2.5e-02 7.39
892860 rs7634902 3_35 0.049 114673.8 1.8e-02 7.39
892857 rs3811696 3_35 0.018 114668.2 6.6e-03 7.39
892858 rs3811695 3_35 0.007 114667.8 2.7e-03 7.38
892856 rs4855850 3_35 0.013 114665.6 4.9e-03 7.38
892893 rs7374277 3_35 0.184 114664.7 6.7e-02 7.40
892951 rs34451146 3_35 0.149 114663.5 5.4e-02 -7.41
892964 rs9814765 3_35 0.087 114663.3 3.2e-02 -7.41
892965 rs11130221 3_35 0.087 114663.3 3.2e-02 -7.41
892971 rs13063621 3_35 0.078 114663.2 2.8e-02 -7.40
892980 rs9871654 3_35 0.053 114663.1 1.9e-02 -7.40
892894 rs7374183 3_35 0.060 114659.1 2.2e-02 7.40
892919 rs7634886 3_35 0.059 114658.3 2.2e-02 -7.41
892952 rs57648519 3_35 0.046 114657.9 1.7e-02 -7.41
892942 rs6446295 3_35 0.002 114657.0 5.6e-04 -7.38
892842 rs3749240 3_35 0.095 114652.2 3.4e-02 7.41
892937 rs7431106 3_35 0.001 114651.6 1.9e-04 -7.38
892923 rs9865480 3_35 0.009 114651.3 3.3e-03 -7.39
892849 rs34614773 3_35 0.034 114649.3 1.2e-02 7.41
892928 rs6809431 3_35 0.003 114645.7 9.2e-04 -7.39
892924 rs60205400 3_35 0.002 114645.5 7.9e-04 -7.39
892931 rs9859153 3_35 0.002 114645.0 9.0e-04 -7.39
892946 rs6766836 3_35 0.002 114645.0 9.0e-04 -7.39
892921 rs9882639 3_35 0.002 114644.1 6.0e-04 -7.39
892848 rs11130219 3_35 0.005 114642.3 1.7e-03 7.40
892816 rs1491986 3_35 0.035 114641.6 1.3e-02 7.42
892847 rs11130218 3_35 0.002 114636.4 6.7e-04 7.40
892866 rs10632976 3_35 0.003 114635.0 1.1e-03 7.38
892833 rs12381242 3_35 0.164 114633.2 6.0e-02 7.43
892905 rs7372730 3_35 0.108 114633.1 3.9e-02 7.42
892909 rs9855505 3_35 0.102 114633.1 3.7e-02 7.42
892900 rs7429353 3_35 0.013 114627.1 4.8e-03 7.41
892904 rs7372725 3_35 0.012 114627.1 4.5e-03 7.41
892825 rs11709680 3_35 0.001 114619.4 4.6e-04 7.40
892824 rs11716575 3_35 0.001 114619.4 4.9e-04 7.40
892836 rs4855862 3_35 0.000 114618.2 1.8e-04 7.39
892811 rs6785549 3_35 0.050 114613.3 1.8e-02 7.44
892907 rs9872864 3_35 0.003 114612.8 9.1e-04 7.41
892823 rs3749241 3_35 0.001 114610.5 2.5e-04 7.40
892828 rs4855841 3_35 0.000 114599.5 1.3e-05 7.39
892917 rs12490656 3_35 0.196 114582.8 7.1e-02 -7.47
892912 rs35365539 3_35 0.000 114566.1 2.7e-07 7.35
892898 rs7372966 3_35 0.000 114557.2 4.6e-06 7.39
892901 rs7426497 3_35 0.000 114551.7 2.6e-07 7.37
892807 rs9873183 3_35 0.000 114548.0 1.5e-06 7.40
892830 rs4855867 3_35 0.000 114531.9 2.1e-11 7.32SNPs 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
892926 rs142955295 3_35 1.000 114839.74 0.3600 -7.40
892892 rs9853458 3_35 0.514 114681.13 0.1900 7.39
892890 rs9876508 3_35 0.339 114680.87 0.1200 7.39
921047 rs1611236 6_24 1.000 28529.96 0.0910 -4.36
892917 rs12490656 3_35 0.196 114582.76 0.0710 -7.47
892893 rs7374277 3_35 0.184 114664.66 0.0670 7.40
635158 rs7999449 13_25 1.000 19145.57 0.0610 -3.39
635160 rs775834524 13_25 1.000 19191.70 0.0610 -3.45
892833 rs12381242 3_35 0.164 114633.25 0.0600 7.43
1009870 rs773844590 10_39 1.000 18018.22 0.0570 -3.88
892951 rs34451146 3_35 0.149 114663.53 0.0540 -7.41
1009867 rs12768525 10_39 0.879 18091.03 0.0500 -4.13
1009936 rs12775129 10_39 0.847 18093.67 0.0490 -4.10
892891 rs9815766 3_35 0.109 114675.32 0.0400 7.39
892905 rs7372730 3_35 0.108 114633.10 0.0390 7.42
892909 rs9855505 3_35 0.102 114633.08 0.0370 7.42
1049434 rs57808037 11_37 0.997 10917.87 0.0350 2.67
1049439 rs146923372 11_37 1.000 10919.20 0.0350 2.69
892842 rs3749240 3_35 0.095 114652.25 0.0340 7.41
513800 rs71007692 10_28 1.000 10536.52 0.0330 -3.29
921016 rs1633020 6_24 0.367 28519.78 0.0330 -4.40
892964 rs9814765 3_35 0.087 114663.28 0.0320 -7.41
892965 rs11130221 3_35 0.087 114663.28 0.0320 -7.41
56763 rs766167074 1_118 1.000 9441.35 0.0300 3.28
892971 rs13063621 3_35 0.078 114663.23 0.0280 -7.40
892863 rs1049256 3_35 0.068 114673.90 0.0250 7.39
892894 rs7374183 3_35 0.060 114659.09 0.0220 7.40
892919 rs7634886 3_35 0.059 114658.31 0.0220 -7.41
920978 rs2844838 6_24 0.233 28521.78 0.0210 -4.38
1101185 rs183130 16_31 0.977 6445.83 0.0200 97.19
513799 rs2474565 10_28 0.557 10590.16 0.0190 -3.38
892980 rs9871654 3_35 0.053 114663.05 0.0190 -7.40
513809 rs11011452 10_28 0.533 10590.53 0.0180 -3.36
892811 rs6785549 3_35 0.050 114613.27 0.0180 7.44
892860 rs7634902 3_35 0.049 114673.75 0.0180 7.39
892952 rs57648519 3_35 0.046 114657.94 0.0170 -7.41
921020 rs1633018 6_24 0.183 28519.28 0.0170 -4.39
513806 rs2472183 10_28 0.475 10590.15 0.0160 -3.37
635151 rs9527399 13_25 0.269 19079.36 0.0160 3.48
1191739 rs202143810 20_38 1.000 5023.78 0.0160 4.04
920965 rs1633033 6_24 0.159 28521.81 0.0140 -4.38
892816 rs1491986 3_35 0.035 114641.64 0.0130 7.42
635154 rs9597193 13_25 0.190 19079.44 0.0120 3.47
892849 rs34614773 3_35 0.034 114649.32 0.0120 7.41
635153 rs9527401 13_25 0.189 19079.33 0.0110 3.47
56760 rs10489611 1_118 0.324 9501.90 0.0098 3.63
56762 rs971534 1_118 0.310 9501.87 0.0093 3.63
635155 rs9537143 13_25 0.140 19080.77 0.0085 3.46
921033 rs1611228 6_24 0.094 28521.42 0.0085 -4.37
429148 rs2410620 8_21 0.844 3112.95 0.0083 46.36SNPs 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
1101185 rs183130 16_31 0.977 6445.83 2.0e-02 97.19
1101197 rs3764261 16_31 0.001 6433.11 1.1e-05 97.09
1101199 rs821840 16_31 0.023 6440.76 4.7e-04 97.05
1101182 rs247617 16_31 0.000 6411.96 5.4e-09 97.00
1101207 rs17231506 16_31 0.000 6425.09 2.4e-07 96.97
1101206 rs36229491 16_31 0.000 6409.78 5.5e-10 96.92
1101179 rs247616 16_31 0.000 6409.33 2.2e-10 96.89
1101172 rs12446515 16_31 0.000 6359.70 0.0e+00 96.60
1101173 rs56156922 16_31 0.000 6363.95 0.0e+00 96.41
1101195 rs12149545 16_31 0.000 6246.31 0.0e+00 95.44
1101174 rs56228609 16_31 0.000 6217.43 0.0e+00 95.21
1101175 rs173539 16_31 0.000 6257.28 0.0e+00 94.83
1101213 rs1800775 16_31 0.000 5008.34 0.0e+00 89.02
1101215 rs3816117 16_31 0.000 4957.79 0.0e+00 88.89
1101216 rs711752 16_31 0.000 5374.88 0.0e+00 87.85
1101250 rs1532625 16_31 0.000 5269.89 0.0e+00 87.85
1101249 rs7205804 16_31 0.000 5247.85 0.0e+00 87.77
1101217 rs708272 16_31 0.000 5362.10 0.0e+00 87.76
1101251 rs1532624 16_31 0.000 5233.51 0.0e+00 87.56
1101218 rs34620476 16_31 0.000 5303.81 0.0e+00 87.48
1101227 rs11508026 16_31 0.000 5201.28 0.0e+00 86.68
1101225 rs12720926 16_31 0.000 5195.34 0.0e+00 86.67
1101233 rs4784741 16_31 0.000 5162.04 0.0e+00 86.45
1101166 rs72786786 16_31 0.000 5048.51 0.0e+00 86.44
1101236 rs12444012 16_31 0.000 5160.92 0.0e+00 86.44
1101230 rs8045855 16_31 0.000 2230.90 4.6e-07 -83.63
1101255 rs11076175 16_31 0.661 2174.83 4.6e-03 -83.59
1101231 rs12720922 16_31 0.000 2210.20 3.3e-08 -83.56
1101256 rs7499892 16_31 0.339 2174.04 2.3e-03 -83.50
1101219 rs1864163 16_31 0.000 2792.27 0.0e+00 -83.36
1101234 rs12720908 16_31 0.000 2206.65 9.1e-08 -83.27
1101220 rs5817082 16_31 0.000 2736.61 0.0e+00 -82.02
1101247 rs9939224 16_31 0.000 2188.65 0.0e+00 81.97
1101226 rs7203984 16_31 0.000 2101.35 3.4e-14 -79.55
1101257 rs289713 16_31 0.000 2028.00 1.1e-14 79.32
1101232 rs118146573 16_31 0.000 1122.57 0.0e+00 -72.70
1101181 rs12923459 16_31 0.000 2626.45 0.0e+00 -70.46
1101202 rs711751 16_31 0.000 2220.05 0.0e+00 69.77
1101260 rs11076176 16_31 0.000 2003.74 0.0e+00 -68.44
1101170 rs7203286 16_31 0.000 2444.68 0.0e+00 -67.89
1101246 rs9926440 16_31 0.000 2380.83 0.0e+00 67.68
1101164 rs9989419 16_31 0.000 2679.19 3.5e-11 66.43
1101167 rs12448528 16_31 0.999 1391.94 4.4e-03 66.11
1101224 rs9929488 16_31 0.000 2199.31 0.0e+00 -64.88
1101165 rs193695 16_31 0.001 2615.97 6.8e-06 64.87
1101261 rs289714 16_31 0.000 1873.62 0.0e+00 64.41
1101201 rs36229786 16_31 0.000 1119.76 0.0e+00 -62.95
1101189 rs12934632 16_31 0.000 971.22 0.0e+00 -62.20
1101196 rs12708967 16_31 0.000 968.84 0.0e+00 -62.18
1101188 rs28888131 16_31 0.000 962.45 0.0e+00 -62.11Gene 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] 37if (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
1 regulation of cholesterol storage (GO:0010885)
2 protein localization to chromosome (GO:0034502)
3 negative regulation of cholesterol storage (GO:0010887)
4 response to laminar fluid shear stress (GO:0034616)
5 cellular response to low-density lipoprotein particle stimulus (GO:0071404)
6 cholesterol homeostasis (GO:0042632)
7 sterol homeostasis (GO:0055092)
8 negative regulation of lipid storage (GO:0010888)
9 regulation of cholesterol metabolic process (GO:0090181)
Overlap Adjusted.P.value Genes
1 3/16 0.001290405 ABCA1;SREBF2;TTC39B
2 3/34 0.006738224 TNKS;RPA2;IFFO1
3 2/10 0.014920166 ABCA1;TTC39B
4 2/10 0.014920166 ABCA1;SREBF2
5 2/14 0.018281918 ABCA1;SREBF2
6 3/71 0.018281918 ABCA1;GPIHBP1;TTC39B
7 3/72 0.018281918 ABCA1;GPIHBP1;TTC39B
8 2/20 0.030551572 ABCA1;TTC39B
9 2/21 0.030551572 SREBF2;TTC39B
[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)RP4-781K5.7 gene(s) from the input list not found in DisGeNET CURATEDRP11-10A14.4 gene(s) from the input list not found in DisGeNET CURATEDTNKS gene(s) from the input list not found in DisGeNET CURATEDTTC39B gene(s) from the input list not found in DisGeNET CURATEDRP11-346C20.3 gene(s) from the input list not found in DisGeNET CURATEDCD300LF gene(s) from the input list not found in DisGeNET CURATEDTMC4 gene(s) from the input list not found in DisGeNET CURATEDKLHL25 gene(s) from the input list not found in DisGeNET CURATEDAKNA gene(s) from the input list not found in DisGeNET CURATEDRPA2 gene(s) from the input list not found in DisGeNET CURATEDRP11-54O7.17 gene(s) from the input list not found in DisGeNET CURATEDUBE2K gene(s) from the input list not found in DisGeNET CURATEDDAGLB gene(s) from the input list not found in DisGeNET CURATEDABTB1 gene(s) from the input list not found in DisGeNET CURATEDNTAN1 gene(s) from the input list not found in DisGeNET CURATEDALLC gene(s) from the input list not found in DisGeNET CURATEDRP11-136O12.2 gene(s) from the input list not found in DisGeNET CURATEDIFFO1 gene(s) from the input list not found in DisGeNET CURATEDZFP1 gene(s) from the input list not found in DisGeNET CURATEDPTTG1IP gene(s) from the input list not found in DisGeNET CURATEDBEND3 gene(s) from the input list not found in DisGeNET CURATEDC10orf88 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATED Description
21 Hypercholesterolemia
22 Hypercholesterolemia, Familial
42 Tangier Disease
52 Jansen type metaphyseal chondrodysplasia
58 Hypoalphalipoproteinemias
68 Tangier Disease Neuropathy
91 Eiken Skeletal Dysplasia
93 Failure of Tooth Eruption, Primary
94 Chondrodysplasia, blomstrand type
98 DYSTONIA, DOPA-RESPONSIVE, WITH OR WITHOUT HYPERPHENYLALANINEMIA, AUTOSOMAL RECESSIVE (disorder)
FDR Ratio BgRatio
21 0.01154401 2/14 39/9703
22 0.01154401 2/14 18/9703
42 0.01154401 1/14 1/9703
52 0.01154401 1/14 1/9703
58 0.01154401 1/14 1/9703
68 0.01154401 1/14 1/9703
91 0.01154401 1/14 1/9703
93 0.01154401 1/14 1/9703
94 0.01154401 1/14 1/9703
98 0.01154401 1/14 1/9703******************************************
* *
* 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 Dyslipidaemia 84 5 0.02472682 disease_GLAD4U
2 Arteriosclerosis 173 6 0.02472682 disease_GLAD4U
3 Arterial Occlusive Diseases 174 6 0.02472682 disease_GLAD4U
4 Hyperlipoproteinemia Type II 23 3 0.04147764 disease_GLAD4U
userId
1 PSRC1;GPIHBP1;TTC39B;ABCA1;SREBF2
2 PSRC1;LDAH;TTC39B;ABCA1;TNFSF12;SREBF2
3 PSRC1;LDAH;TTC39B;ABCA1;TNFSF12;SREBF2
4 GPIHBP1;ABCA1;SREBF2
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