SHBG (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 SHBG (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-30830_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0120859107 0.0001862757 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
20.29972 29.53435 #report sample size
print(sample_size)[1] 312215#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.008718523 0.153255452 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02911682 2.52514533Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
5673 PSEN2 1_116 0.982 23.56 7.4e-05 -4.35
2002 AES 19_4 0.981 62.75 2.0e-04 -8.01
8523 ZNF217 20_31 0.977 27.20 8.5e-05 4.90
4972 PGBD1 6_22 0.973 31.53 9.8e-05 -5.30
5095 DNAJC13 3_82 0.968 24.47 7.6e-05 -2.39
10954 NYNRIN 14_3 0.965 44.49 1.4e-04 -5.21
9736 H1FX 3_80 0.964 25.11 7.8e-05 5.05
6609 TMED6 16_37 0.955 23.92 7.3e-05 -4.80
9284 SERTAD2 2_42 0.947 95.68 2.9e-04 13.85
8572 PDZD3 11_71 0.943 31.27 9.5e-05 -2.16
9079 MIEF2 17_15 0.938 34.57 1.0e-04 -7.07
5025 THBS1 15_13 0.928 21.87 6.5e-05 -4.34
3499 MAPK8IP1 11_28 0.925 26.28 7.8e-05 4.61
6590 NTAN1 16_15 0.921 64.85 1.9e-04 -8.81
7960 SERPINF2 17_2 0.893 186.85 5.3e-04 -11.58
4360 TRIM5 11_4 0.888 35.93 1.0e-04 -5.03
10772 TCEA3 1_16 0.869 44.33 1.2e-04 5.78
7810 TAC3 12_35 0.864 21.83 6.0e-05 -4.57
10194 SLC35E2B 1_1 0.853 24.24 6.6e-05 -4.39
2072 TYK2 19_9 0.829 24.18 6.4e-05 -4.56
4011 VPREB3 22_6 0.825 19.75 5.2e-05 -3.94
8876 ARHGAP1 11_28 0.801 23.49 6.0e-05 -4.50Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 0 121541.69 0.0e+00 -6.96
5520 RCN3 19_34 0 38818.61 0.0e+00 -8.47
4733 AHI1 6_89 0 9803.63 0.0e+00 2.05
8165 CPT1C 19_34 0 8482.37 0.0e+00 4.30
4687 TMEM60 7_49 0 6194.67 0.0e+00 -5.03
11526 TNFSF12 17_7 0 5397.77 0.0e+00 65.40
4093 ATP1B2 17_7 0 3268.79 0.0e+00 -72.82
571 SLC6A16 19_34 0 2154.73 0.0e+00 -0.24
10492 CTC-301O7.4 19_34 0 2048.60 0.0e+00 -1.13
8293 CHRNB1 17_7 0 1973.26 0.0e+00 1.39
9608 PSMG1 21_19 0 1953.69 0.0e+00 5.30
7008 TNFSF13 17_7 0 1414.85 0.0e+00 -1.92
11220 ADM5 19_34 0 1295.17 0.0e+00 0.17
5427 SAT2 17_7 0 1278.98 0.0e+00 -11.18
6980 ALDH16A1 19_34 0 1221.93 0.0e+00 0.44
846 TEAD2 19_34 0 1217.01 0.0e+00 -0.66
7255 EIF5A2 3_104 0 1043.12 3.2e-14 2.68
5425 WRAP53 17_7 0 1040.92 0.0e+00 -43.17
11094 APTR 7_49 0 992.40 0.0e+00 -0.43
9834 BRWD1 21_19 0 974.91 0.0e+00 0.32Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
7960 SERPINF2 17_2 0.893 186.85 5.3e-04 -11.58
9820 TLCD2 17_2 0.769 193.94 4.8e-04 -7.05
9284 SERTAD2 2_42 0.947 95.68 2.9e-04 13.85
2002 AES 19_4 0.981 62.75 2.0e-04 -8.01
6590 NTAN1 16_15 0.921 64.85 1.9e-04 -8.81
546 PIGV 1_18 0.354 120.15 1.4e-04 14.70
6089 FADS1 11_34 0.435 98.45 1.4e-04 -10.07
10954 NYNRIN 14_3 0.965 44.49 1.4e-04 -5.21
1267 PABPC4 1_24 0.524 78.41 1.3e-04 9.75
1145 ACHE 7_62 0.283 127.62 1.2e-04 11.93
1185 TGDS 13_47 0.575 63.69 1.2e-04 7.90
10772 TCEA3 1_16 0.869 44.33 1.2e-04 5.78
5074 EMILIN1 2_16 0.685 56.43 1.2e-04 -8.91
6943 PPP1R16A 8_94 0.730 49.07 1.1e-04 -8.11
5035 RMDN3 15_14 0.649 51.21 1.1e-04 -6.18
5318 USP3 15_29 0.564 62.00 1.1e-04 -8.84
4398 UNK 17_42 0.792 45.05 1.1e-04 -7.09
4360 TRIM5 11_4 0.888 35.93 1.0e-04 -5.03
9079 MIEF2 17_15 0.938 34.57 1.0e-04 -7.07
4972 PGBD1 6_22 0.973 31.53 9.8e-05 -5.30Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
4093 ATP1B2 17_7 0.000 3268.79 0.0e+00 -72.82
11526 TNFSF12 17_7 0.000 5397.77 0.0e+00 65.40
5425 WRAP53 17_7 0.000 1040.92 0.0e+00 -43.17
9555 NAA38 17_7 0.000 761.42 0.0e+00 34.38
4096 MPDU1 17_7 0.000 683.99 0.0e+00 -26.22
9403 POLR2A 17_7 0.000 288.41 0.0e+00 24.87
7009 SENP3 17_7 0.000 581.83 0.0e+00 23.93
8788 TNK1 17_6 0.000 308.57 0.0e+00 -20.05
4402 KDM6B 17_7 0.000 219.79 0.0e+00 -19.95
10765 ZDHHC18 1_18 0.002 265.14 1.5e-06 -19.64
5430 TP53 17_7 0.000 390.61 0.0e+00 18.69
7846 GNGT2 17_28 0.000 144.51 1.2e-11 -17.26
2953 NRBP1 2_16 0.033 295.74 3.1e-05 -17.20
811 ACAP1 17_6 0.000 255.39 0.0e+00 -15.54
2956 SNX17 2_16 0.015 261.15 1.2e-05 -15.28
7786 CATSPER2 15_16 0.045 227.75 3.3e-05 -14.91
546 PIGV 1_18 0.354 120.15 1.4e-04 14.70
9225 RMI1 9_41 0.025 189.10 1.5e-05 14.63
8532 ZNF554 19_4 0.000 183.62 4.5e-08 14.02
9284 SERTAD2 2_42 0.947 95.68 2.9e-04 13.85Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03073457#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
4093 ATP1B2 17_7 0.000 3268.79 0.0e+00 -72.82
11526 TNFSF12 17_7 0.000 5397.77 0.0e+00 65.40
5425 WRAP53 17_7 0.000 1040.92 0.0e+00 -43.17
9555 NAA38 17_7 0.000 761.42 0.0e+00 34.38
4096 MPDU1 17_7 0.000 683.99 0.0e+00 -26.22
9403 POLR2A 17_7 0.000 288.41 0.0e+00 24.87
7009 SENP3 17_7 0.000 581.83 0.0e+00 23.93
8788 TNK1 17_6 0.000 308.57 0.0e+00 -20.05
4402 KDM6B 17_7 0.000 219.79 0.0e+00 -19.95
10765 ZDHHC18 1_18 0.002 265.14 1.5e-06 -19.64
5430 TP53 17_7 0.000 390.61 0.0e+00 18.69
7846 GNGT2 17_28 0.000 144.51 1.2e-11 -17.26
2953 NRBP1 2_16 0.033 295.74 3.1e-05 -17.20
811 ACAP1 17_6 0.000 255.39 0.0e+00 -15.54
2956 SNX17 2_16 0.015 261.15 1.2e-05 -15.28
7786 CATSPER2 15_16 0.045 227.75 3.3e-05 -14.91
546 PIGV 1_18 0.354 120.15 1.4e-04 14.70
9225 RMI1 9_41 0.025 189.10 1.5e-05 14.63
8532 ZNF554 19_4 0.000 183.62 4.5e-08 14.02
9284 SERTAD2 2_42 0.947 95.68 2.9e-04 13.85Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 17_7"
genename region_tag susie_pip mu2 PVE z
7010 FGF11 17_7 0 107.56 0 -2.88
8293 CHRNB1 17_7 0 1973.26 0 1.39
9403 POLR2A 17_7 0 288.41 0 24.87
11526 TNFSF12 17_7 0 5397.77 0 65.40
7008 TNFSF13 17_7 0 1414.85 0 -1.92
7009 SENP3 17_7 0 581.83 0 23.93
4096 MPDU1 17_7 0 683.99 0 -26.22
5427 SAT2 17_7 0 1278.98 0 -11.18
4093 ATP1B2 17_7 0 3268.79 0 -72.82
5425 WRAP53 17_7 0 1040.92 0 -43.17
5430 TP53 17_7 0 390.61 0 18.69
4402 KDM6B 17_7 0 219.79 0 -19.95
7989 TMEM88 17_7 0 15.14 0 3.17
9555 NAA38 17_7 0 761.42 0 34.38
8272 CHD3 17_7 0 68.74 0 -4.90
9286 AC025335.1 17_7 0 32.75 0 5.02
8279 KCNAB3 17_7 0 198.05 0 -3.44
8277 CNTROB 17_7 0 201.16 0 -1.44
8278 TRAPPC1 17_7 0 37.83 0 5.89
11172 VAMP2 17_7 0 61.05 0 -3.43
9234 TMEM107 17_7 0 120.51 0 4.38
10292 BORCS6 17_7 0 9.75 0 -1.68
9228 LINC00324 17_7 0 14.14 0 -1.05
9218 PFAS 17_7 0 5.41 0 1.27
9226 CTC1 17_7 0 46.35 0 -3.01
3790 SLC25A35 17_7 0 57.78 0 5.94
9716 KRBA2 17_7 0 9.01 0 -2.31
7011 RPL26 17_7 0 32.90 0 3.26
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_6"
genename region_tag susie_pip mu2 PVE z
4089 FAM64A 17_6 0 31.15 0 -1.99
1296 PITPNM3 17_6 0 6.35 0 0.75
4092 TXNDC17 17_6 0 5.24 0 0.40
10704 KIAA0753 17_6 0 10.45 0 1.09
11876 C17orf100 17_6 0 5.29 0 -0.46
12045 CTC-281F24.1 17_6 0 5.15 0 -0.40
4405 XAF1 17_6 0 29.82 0 -1.66
2420 ALOX12 17_6 0 38.57 0 2.23
12048 RP11-589P10.5 17_6 0 5.89 0 0.50
11164 RNASEK 17_6 0 43.57 0 -2.48
11905 C17orf49 17_6 0 40.24 0 2.25
7006 BCL6B 17_6 0 37.39 0 -2.37
8792 SLC16A13 17_6 0 7.21 0 -1.20
4403 CLEC10A 17_6 0 6.46 0 -1.23
7007 ASGR2 17_6 0 14.82 0 -1.61
5428 ASGR1 17_6 0 18.23 0 2.51
4406 DLG4 17_6 0 21.93 0 1.76
50 DVL2 17_6 0 23.48 0 -0.42
386 PHF23 17_6 0 16.05 0 2.87
8313 GABARAP 17_6 0 326.46 0 -11.55
8311 ELP5 17_6 0 67.46 0 -6.31
10044 PLSCR3 17_6 0 325.58 0 11.55
9448 CLDN7 17_6 0 21.14 0 -2.60
8941 CTDNEP1 17_6 0 87.99 0 6.86
86 YBX2 17_6 0 6.77 0 -0.36
9446 SLC2A4 17_6 0 9.21 0 0.40
4401 EIF5A 17_6 0 18.11 0 -1.38
4404 GPS2 17_6 0 106.11 0 7.85
11127 NEURL4 17_6 0 49.11 0 6.68
811 ACAP1 17_6 0 255.39 0 -15.54
11056 KCTD11 17_6 0 12.25 0 0.65
8788 TNK1 17_6 0 308.57 0 -20.05
10927 TMEM256 17_6 0 45.29 0 -5.34
8787 ZBTB4 17_6 0 375.56 0 -10.97
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_18"
genename region_tag susie_pip mu2 PVE z
3213 SYF2 1_18 0.003 11.51 1.0e-07 2.03
3214 RSRP1 1_18 0.002 30.37 1.7e-07 -5.49
9637 TMEM50A 1_18 0.002 30.37 1.7e-07 -5.49
9978 RHD 1_18 0.002 30.37 1.7e-07 -5.49
10768 TMEM57 1_18 0.002 6.18 4.4e-08 0.98
10121 RHCE 1_18 0.129 52.36 2.2e-05 7.48
11243 RP11-70P17.1 1_18 0.002 9.18 7.2e-08 1.39
3217 MAN1C1 1_18 0.011 24.66 8.4e-07 -2.92
7057 SELENON 1_18 0.018 19.81 1.1e-06 -1.33
6659 PAFAH2 1_18 0.002 6.09 3.4e-08 0.90
6661 TRIM63 1_18 0.002 7.48 4.8e-08 1.81
8858 PDIK1L 1_18 0.003 7.69 6.2e-08 1.45
10401 FAM110D 1_18 0.002 5.65 4.0e-08 1.01
5531 CNKSR1 1_18 0.003 14.29 1.5e-07 2.42
4215 CEP85 1_18 0.003 9.02 7.8e-08 -0.97
6665 UBXN11 1_18 0.038 25.26 3.1e-06 -1.93
8205 CD52 1_18 0.007 24.01 5.7e-07 2.81
8964 AIM1L 1_18 0.002 9.63 5.8e-08 2.53
3219 DHDDS 1_18 0.458 50.18 7.4e-05 5.60
10674 HMGN2 1_18 0.028 29.41 2.6e-06 4.25
3222 ARID1A 1_18 0.002 7.08 4.8e-08 1.26
546 PIGV 1_18 0.354 120.15 1.4e-04 14.70
10765 ZDHHC18 1_18 0.002 265.14 1.5e-06 -19.64
5539 GPN2 1_18 0.003 12.59 1.3e-07 5.21
1254 NUDC 1_18 0.003 19.45 2.0e-07 -2.99
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_28"
genename region_tag susie_pip mu2 PVE z
84 OSBPL7 17_28 0.000 25.60 6.5e-12 3.19
5401 LRRC46 17_28 0.000 21.86 3.0e-12 2.99
6750 MRPL10 17_28 0.002 80.08 4.2e-07 4.99
5402 SCRN2 17_28 0.000 9.28 7.2e-13 -0.53
7861 SP2 17_28 0.000 5.48 4.1e-13 1.14
2370 PNPO 17_28 0.000 9.68 8.5e-13 -2.28
7862 PRR15L 17_28 0.000 39.41 4.7e-11 4.96
2373 CDK5RAP3 17_28 0.000 32.20 5.2e-11 -1.13
64 COPZ2 17_28 0.000 12.05 2.7e-12 1.35
1043 NFE2L1 17_28 0.000 23.65 5.2e-12 -4.27
12573 RP5-890E16.5 17_28 0.000 42.63 1.0e-10 4.91
2374 CBX1 17_28 0.000 42.79 7.1e-11 -5.18
21 SNX11 17_28 0.000 37.95 5.5e-11 -4.78
5400 SKAP1 17_28 0.000 11.95 1.2e-12 -1.31
3394 HOXB3 17_28 0.000 6.59 5.0e-13 -0.22
9531 HOXB4 17_28 0.000 6.52 4.9e-13 -0.27
8755 HOXB2 17_28 0.000 9.52 2.3e-12 0.20
8369 HOXB9 17_28 0.000 21.47 9.5e-12 -1.16
11962 HOXB7 17_28 0.000 41.66 1.7e-10 -2.30
11650 LINC02086 17_28 0.000 49.72 6.3e-10 -2.54
4852 CALCOCO2 17_28 0.000 24.37 1.3e-09 1.96
6759 ATP5G1 17_28 0.000 58.13 2.7e-09 4.79
6761 UBE2Z 17_28 0.000 74.35 1.8e-08 5.66
6762 SNF8 17_28 0.000 48.58 6.3e-11 -5.81
7846 GNGT2 17_28 0.000 144.51 1.2e-11 -17.26
2412 ABI3 17_28 0.000 37.64 7.6e-12 -5.68
8749 PHOSPHO1 17_28 0.000 31.66 3.8e-11 -0.01
11703 RP11-1079K10.3 17_28 0.000 113.14 1.9e-11 -1.31
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_16"
genename region_tag susie_pip mu2 PVE z
11045 SLC35F6 2_16 0.008 21.12 5.5e-07 5.23
3366 TMEM214 2_16 0.013 9.78 4.0e-07 -2.93
5074 EMILIN1 2_16 0.685 56.43 1.2e-04 -8.91
5061 KHK 2_16 0.011 9.00 3.1e-07 2.86
5059 CGREF1 2_16 0.012 8.75 3.3e-07 2.30
5070 PREB 2_16 0.013 10.41 4.3e-07 0.54
5076 ATRAID 2_16 0.007 107.14 2.3e-06 11.67
1090 CAD 2_16 0.444 32.22 4.6e-05 6.15
5071 SLC5A6 2_16 0.007 30.82 7.2e-07 -4.51
7303 UCN 2_16 0.010 31.28 1.0e-06 8.00
2952 GTF3C2 2_16 0.010 30.79 9.7e-07 -7.94
2956 SNX17 2_16 0.015 261.15 1.2e-05 -15.28
7304 ZNF513 2_16 0.015 149.14 7.2e-06 -11.19
2953 NRBP1 2_16 0.033 295.74 3.1e-05 -17.20
5057 IFT172 2_16 0.169 59.68 3.2e-05 9.79
1087 GCKR 2_16 0.135 58.43 2.5e-05 -9.76
10613 GPN1 2_16 0.007 35.10 7.5e-07 -5.02
9018 CCDC121 2_16 0.013 11.23 4.8e-07 1.20
6660 BRE 2_16 0.016 39.32 2.0e-06 -7.57
| 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
7831 rs79598313 1_18 1.000 349.82 1.1e-03 -21.18
30408 rs1730862 1_66 1.000 315.64 1.0e-03 -18.15
34854 rs9427104 1_75 1.000 111.52 3.6e-04 10.68
53160 rs1223802 1_108 1.000 62.32 2.0e-04 -10.01
55367 rs2642420 1_112 1.000 86.40 2.8e-04 -8.06
97373 rs3789066 2_66 1.000 46.41 1.5e-04 -6.67
145727 rs11719769 3_18 1.000 90.59 2.9e-04 -8.77
152750 rs113569731 3_33 1.000 45.17 1.4e-04 -7.57
153453 rs62259692 3_36 1.000 46.75 1.5e-04 -6.89
197693 rs114524202 4_4 1.000 67.80 2.2e-04 11.06
197709 rs3748034 4_4 1.000 92.52 3.0e-04 13.72
197710 rs3752442 4_4 1.000 100.50 3.2e-04 -15.97
197724 rs36205397 4_4 1.000 101.77 3.3e-04 17.79
222144 rs6811535 4_52 1.000 81.85 2.6e-04 9.84
225278 rs28529445 4_58 1.000 85.66 2.7e-04 -9.95
225465 rs71633359 4_59 1.000 195.17 6.3e-04 -16.88
232170 rs17039766 4_71 1.000 44.47 1.4e-04 6.65
277860 rs58477254 5_33 1.000 50.41 1.6e-04 -7.31
331783 rs34880700 6_32 1.000 96.62 3.1e-04 -9.47
339315 rs112354376 6_46 1.000 1543.47 4.9e-03 -3.29
339316 rs208453 6_46 1.000 1532.61 4.9e-03 -0.53
361259 rs199804242 6_89 1.000 45828.19 1.5e-01 -3.57
369226 rs60425481 6_104 1.000 12549.87 4.0e-02 10.63
400132 rs761767938 7_49 1.000 14034.20 4.5e-02 4.57
400140 rs1544459 7_49 1.000 14126.84 4.5e-02 4.45
401536 rs3839804 7_51 1.000 48.01 1.5e-04 -6.55
406175 rs4268041 7_60 1.000 338.89 1.1e-03 23.71
415287 rs125124 7_80 1.000 59.29 1.9e-04 7.98
441278 rs12543287 8_37 1.000 146.87 4.7e-04 8.71
445418 rs4738679 8_45 1.000 82.41 2.6e-04 9.38
452134 rs382796 8_57 1.000 93.18 3.0e-04 13.00
508428 rs1886296 9_73 1.000 66.83 2.1e-04 7.78
534027 rs2186235 10_51 1.000 117.14 3.8e-04 -11.10
571701 rs12804411 11_38 1.000 129.02 4.1e-04 12.03
599817 rs66720652 12_15 1.000 104.75 3.4e-04 9.08
610041 rs7397189 12_36 1.000 137.99 4.4e-04 12.07
620780 rs61935502 12_55 1.000 63.56 2.0e-04 -7.76
623193 rs375115050 12_59 1.000 109.24 3.5e-04 -11.08
626568 rs75622376 12_67 1.000 146.27 4.7e-04 12.42
643251 rs9533828 13_18 1.000 1057.97 3.4e-03 4.04
643252 rs58290986 13_18 1.000 154.05 4.9e-04 -0.42
643261 rs78750369 13_18 1.000 1135.42 3.6e-03 3.38
643264 rs9567428 13_18 1.000 927.02 3.0e-03 4.16
646014 rs566812111 13_25 1.000 5750.21 1.8e-02 -2.84
675201 rs72681869 14_20 1.000 88.17 2.8e-04 9.60
682538 rs13379043 14_34 1.000 64.86 2.1e-04 7.25
687445 rs2110705 14_45 1.000 37.79 1.2e-04 5.84
689119 rs11439803 14_49 1.000 497.91 1.6e-03 2.33
689126 rs1243165 14_49 1.000 555.00 1.8e-03 6.12
692857 rs2494743 14_55 1.000 52.91 1.7e-04 6.05
700958 rs4363819 15_21 1.000 50.18 1.6e-04 -3.50
700977 rs2414183 15_22 1.000 214.20 6.9e-04 -13.29
701219 rs72743115 15_22 1.000 134.15 4.3e-04 -11.63
714488 rs58217463 15_46 1.000 299.13 9.6e-04 18.25
714490 rs8028588 15_46 1.000 177.21 5.7e-04 12.63
714493 rs961229 15_46 1.000 103.00 3.3e-04 16.57
739171 rs889639 16_49 1.000 42.62 1.4e-04 6.61
739189 rs2255451 16_49 1.000 75.76 2.4e-04 8.90
744141 rs35985803 17_6 1.000 325.19 1.0e-03 -19.38
744156 rs7223885 17_6 1.000 402.08 1.3e-03 -22.29
744157 rs968580 17_6 1.000 243.60 7.8e-04 -13.61
744158 rs73233955 17_6 1.000 281.98 9.0e-04 -11.43
746137 rs62053897 17_12 1.000 52.22 1.7e-04 -7.13
752943 rs4794044 17_28 1.000 193.17 6.2e-04 10.12
756563 rs1801689 17_38 1.000 134.57 4.3e-04 -11.80
778236 rs1217565 18_30 1.000 43.58 1.4e-04 -7.39
788548 rs10401485 19_7 1.000 110.89 3.6e-04 -10.90
790436 rs141356897 19_14 1.000 261.87 8.4e-04 16.44
796004 rs4806075 19_24 1.000 142.05 4.5e-04 -4.36
796674 rs140965448 19_26 1.000 41.61 1.3e-04 -5.90
798684 rs58701309 19_32 1.000 178.20 5.7e-04 -1.86
798685 rs7259871 19_32 1.000 295.71 9.5e-04 10.96
815394 rs3212201 20_28 1.000 186.81 6.0e-04 14.22
859979 rs140584594 1_67 1.000 99.82 3.2e-04 -10.15
868976 rs1260326 2_16 1.000 796.08 2.5e-03 30.11
891788 rs200216446 3_104 1.000 4399.90 1.4e-02 -4.20
921421 rs17256042 7_94 1.000 57.48 1.8e-04 -2.68
941270 rs11601507 11_4 1.000 58.73 1.9e-04 7.02
974511 rs36179992 13_21 1.000 59.72 1.9e-04 7.08
982740 rs11621792 14_3 1.000 239.97 7.7e-04 -15.30
1008779 rs11078597 17_2 1.000 252.80 8.1e-04 13.37
1016658 rs3867595 17_7 1.000 1967.09 6.3e-03 -68.50
1017164 rs62059837 17_7 1.000 5775.19 1.8e-02 30.95
1017174 rs858519 17_7 1.000 8212.14 2.6e-02 90.66
1017180 rs1799941 17_7 1.000 4987.88 1.6e-02 88.95
1025537 rs56032910 17_19 1.000 2377.54 7.6e-03 -2.90
1030301 rs9897429 17_29 1.000 148.47 4.8e-04 -13.03
1030358 rs139260434 17_29 1.000 78.15 2.5e-04 10.23
1074037 rs61371437 19_34 1.000 125667.97 4.0e-01 6.86
1074046 rs113176985 19_34 1.000 125902.25 4.0e-01 7.00
1074049 rs374141296 19_34 1.000 126581.62 4.1e-01 6.36
1097354 rs34079499 21_19 1.000 6769.83 2.2e-02 4.12
132274 rs11682084 2_135 0.999 34.76 1.1e-04 -5.80
141611 rs10602803 3_9 0.999 49.06 1.6e-04 -5.00
331787 rs12664213 6_32 0.999 40.42 1.3e-04 -4.77
358549 rs58321169 6_84 0.999 39.45 1.3e-04 -6.49
452201 rs2400362 8_57 0.999 82.31 2.6e-04 11.26
556722 rs34623292 11_10 0.999 39.70 1.3e-04 -7.89
646018 rs12430288 13_25 0.999 5795.69 1.9e-02 -2.67
790727 rs11668601 19_14 0.999 92.99 3.0e-04 -9.58
795411 rs889140 19_23 0.999 50.93 1.6e-04 5.95
225261 rs116755775 4_58 0.998 34.05 1.1e-04 6.48
530055 rs4746440 10_43 0.998 31.61 1.0e-04 5.27
700951 rs8032322 15_21 0.998 51.09 1.6e-04 -4.17
1047593 rs60018147 19_4 0.998 41.23 1.3e-04 6.21
400136 rs11972122 7_49 0.997 12927.95 4.1e-02 3.99
406521 rs138124694 7_61 0.997 48.18 1.5e-04 7.46
505688 rs13289095 9_66 0.997 95.88 3.1e-04 -9.94
757505 rs8070232 17_39 0.997 58.50 1.9e-04 -1.02
474363 rs1616572 9_7 0.996 33.78 1.1e-04 -5.83
539691 rs2039616 10_62 0.996 43.57 1.4e-04 6.47
552855 rs2239681 11_2 0.996 48.12 1.5e-04 7.93
600024 rs56020380 12_16 0.996 75.06 2.4e-04 -8.11
653712 rs7323648 13_40 0.996 31.15 9.9e-05 5.28
623173 rs11837065 12_59 0.995 33.42 1.1e-04 -6.16
752856 rs57114236 17_28 0.995 48.56 1.5e-04 -3.39
4768 rs4336844 1_11 0.994 86.84 2.8e-04 9.45
452065 rs11994858 8_57 0.994 91.94 2.9e-04 10.84
570775 rs1047739 11_34 0.994 42.47 1.4e-04 6.18
752567 rs117974417 17_28 0.994 61.40 2.0e-04 -7.62
762965 rs117823974 18_3 0.994 30.09 9.6e-05 -5.10
472129 rs1016565 9_1 0.993 30.97 9.9e-05 -5.39
788804 rs11667165 19_7 0.993 38.46 1.2e-04 5.91
56925 rs3845509 1_115 0.992 33.00 1.0e-04 5.24
599892 rs10841577 12_15 0.992 32.06 1.0e-04 -4.82
600185 rs4149081 12_16 0.992 300.59 9.6e-04 -18.14
701213 rs8040040 15_22 0.992 64.77 2.1e-04 -7.71
332312 rs1005230 6_33 0.989 28.93 9.2e-05 -5.10
452119 rs445036 8_57 0.989 186.85 5.9e-04 14.59
621652 rs55692966 12_56 0.989 30.36 9.6e-05 5.25
222075 rs6838435 4_52 0.988 44.78 1.4e-04 -6.60
501785 rs2763193 9_59 0.988 51.08 1.6e-04 6.64
507565 rs34755157 9_71 0.988 30.00 9.5e-05 5.10
601439 rs78444263 12_18 0.988 139.12 4.4e-04 -11.99
406877 rs3177697 7_62 0.987 39.21 1.2e-04 6.94
47880 rs10801583 1_98 0.986 39.96 1.3e-04 -8.37
145732 rs6803476 3_18 0.986 30.70 9.7e-05 -3.70
744215 rs1465650 17_8 0.986 27.51 8.7e-05 -4.73
327000 rs9267088 6_26 0.985 45.57 1.4e-04 -7.54
790319 rs138466679 19_14 0.984 36.29 1.1e-04 5.73
721915 rs4780401 16_12 0.982 42.42 1.3e-04 5.37
188192 rs149368105 3_105 0.981 47.23 1.5e-04 -7.98
16338 rs79574044 1_38 0.979 26.96 8.5e-05 -5.13
801631 rs11084395 19_38 0.978 28.39 8.9e-05 4.96
390203 rs150560724 7_32 0.977 29.92 9.4e-05 -5.04
415296 rs12533527 7_80 0.976 27.07 8.5e-05 -5.03
796003 rs1688031 19_24 0.976 101.61 3.2e-04 11.19
290112 rs114964731 5_60 0.971 29.54 9.2e-05 -5.22
491491 rs796003 9_41 0.971 283.95 8.8e-04 17.80
676412 rs12881212 14_23 0.971 26.81 8.3e-05 -4.76
78663 rs34636718 2_26 0.970 53.97 1.7e-04 7.24
225409 rs13120301 4_59 0.968 81.22 2.5e-04 -14.39
493389 rs78648697 9_45 0.968 28.04 8.7e-05 -4.98
245701 rs72727873 4_98 0.965 30.94 9.6e-05 -5.19
243004 rs1579737 4_94 0.964 30.73 9.5e-05 5.36
761358 rs62076019 17_46 0.964 48.26 1.5e-04 -6.88
691384 rs35007880 14_52 0.963 65.40 2.0e-04 -8.22
236937 rs68018489 4_80 0.957 27.56 8.4e-05 -5.03
277714 rs173964 5_33 0.957 203.97 6.3e-04 -12.13
473554 rs10758593 9_4 0.954 27.29 8.3e-05 -4.98
86233 rs62143990 2_43 0.953 30.11 9.2e-05 5.32
1097512 rs55740356 21_19 0.953 5955.53 1.8e-02 4.53
244345 rs34690971 4_96 0.949 85.54 2.6e-04 -9.47
664173 rs750598 13_59 0.949 28.53 8.7e-05 5.12
97449 rs2166862 2_66 0.948 31.57 9.6e-05 -5.35
329664 rs41270056 6_28 0.947 27.53 8.4e-05 4.92
795405 rs16968072 19_23 0.947 29.33 8.9e-05 -3.03
1030765 rs184781483 17_29 0.947 35.56 1.1e-04 6.17
1017593 rs117387630 17_7 0.946 776.42 2.4e-03 -38.67
53113 rs340835 1_108 0.945 68.78 2.1e-04 -7.18
318102 rs55792466 6_7 0.942 38.93 1.2e-04 6.88
614082 rs2137537 12_44 0.942 24.24 7.3e-05 -4.42
787068 rs4807612 19_2 0.940 42.97 1.3e-04 6.30
152847 rs140341914 3_34 0.937 24.61 7.4e-05 -3.84
816118 rs6066141 20_29 0.936 31.67 9.5e-05 5.65
835499 rs78668392 22_9 0.935 24.66 7.4e-05 3.78
689146 rs72692809 14_49 0.934 50.40 1.5e-04 7.82
714359 rs11343871 15_46 0.933 40.74 1.2e-04 -7.20
626569 rs147598676 12_67 0.932 62.80 1.9e-04 7.93
594360 rs568620198 12_4 0.926 30.59 9.1e-05 5.67
735957 rs11649531 16_42 0.926 28.62 8.5e-05 -4.99
8314 rs7516039 1_20 0.922 26.50 7.8e-05 -4.86
594301 rs79988477 12_4 0.918 26.03 7.7e-05 4.88
205645 rs2946394 4_20 0.914 24.64 7.2e-05 4.27
361275 rs6923513 6_89 0.912 45862.36 1.3e-01 -3.26
299032 rs10057561 5_77 0.911 28.59 8.3e-05 -5.24
926640 rs76471228 8_58 0.911 31.31 9.1e-05 -5.64
10335 rs71642659 1_24 0.910 28.24 8.2e-05 6.02
624014 rs4764939 12_62 0.909 176.82 5.1e-04 -13.66
557452 rs201519335 11_12 0.907 31.98 9.3e-05 2.32
1069172 rs5112 19_31 0.901 30.57 8.8e-05 5.29
390188 rs149901303 7_32 0.900 24.28 7.0e-05 -4.28
84569 rs35510572 2_39 0.898 24.88 7.2e-05 4.09
751053 rs146909119 17_25 0.895 30.12 8.6e-05 4.38
422659 rs11761498 7_98 0.894 24.87 7.1e-05 -4.45
243542 rs11727676 4_94 0.892 24.62 7.0e-05 -4.45
276777 rs1694060 5_31 0.892 29.61 8.5e-05 -4.71
796542 rs149349299 19_25 0.890 46.37 1.3e-04 -6.44
1025538 rs56024867 17_19 0.889 2377.34 6.8e-03 -3.18
13352 rs112681075 1_33 0.886 26.05 7.4e-05 4.58
153135 rs112874936 3_35 0.884 34.36 9.7e-05 -7.25
25431 rs164899 1_55 0.879 28.01 7.9e-05 -5.42
429231 rs7012814 8_12 0.878 29.87 8.4e-05 6.06
682242 rs61986270 14_34 0.876 26.78 7.5e-05 4.17
80669 rs55761545 2_31 0.870 32.58 9.1e-05 -5.48
629576 rs2393775 12_74 0.870 166.45 4.6e-04 14.43
436997 rs117380715 8_27 0.867 24.45 6.8e-05 4.37
507626 rs12351482 9_71 0.867 97.63 2.7e-04 10.02
406257 rs117501142 7_60 0.865 24.36 6.7e-05 4.39
235311 rs138204164 4_77 0.863 29.12 8.1e-05 -5.05
112684 rs1460670 2_99 0.855 26.09 7.1e-05 4.61
741313 rs12935186 16_54 0.853 65.75 1.8e-04 -10.20
205636 rs112396442 4_20 0.851 24.69 6.7e-05 -4.25
353111 rs117864346 6_73 0.849 30.43 8.3e-05 5.16
754885 rs2632527 17_34 0.846 25.75 7.0e-05 -4.53
588424 rs10750224 11_75 0.838 25.38 6.8e-05 4.43
791083 rs55989964 19_15 0.834 25.48 6.8e-05 -4.35
415701 rs4731855 7_80 0.832 25.50 6.8e-05 -4.41
188213 rs234043 3_106 0.831 39.16 1.0e-04 -5.98
575876 rs11600848 11_46 0.831 29.00 7.7e-05 -5.02
416533 rs2551778 7_82 0.829 46.67 1.2e-04 -6.65
390878 rs7778803 7_34 0.827 29.37 7.8e-05 5.91
557024 rs7946907 11_11 0.822 27.73 7.3e-05 4.85
1031041 rs56371118 17_29 0.819 37.94 1.0e-04 -5.08
501731 rs2808798 9_58 0.815 24.50 6.4e-05 4.41
247535 rs17285611 4_102 0.814 41.15 1.1e-04 -2.34
133251 rs62192912 2_137 0.809 29.62 7.7e-05 4.42
452104 rs28435511 8_57 0.809 73.89 1.9e-04 -5.24
55389 rs11588625 1_112 0.808 27.44 7.1e-05 -3.46
112878 rs7607980 2_100 0.803 58.99 1.5e-04 9.78
120957 rs10202868 2_113 0.801 54.79 1.4e-04 -7.73SNPs 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
1074049 rs374141296 19_34 1 126581.6 4.1e-01 6.36
1074046 rs113176985 19_34 1 125902.2 4.0e-01 7.00
1074037 rs61371437 19_34 1 125668.0 4.0e-01 6.86
1074039 rs35295508 19_34 0 125547.4 1.6e-12 7.05
1074053 rs2946865 19_34 0 125190.2 0.0e+00 6.96
1074027 rs739349 19_34 0 125165.5 0.0e+00 6.83
1074028 rs756628 19_34 0 125165.4 0.0e+00 6.83
1074044 rs73056069 19_34 0 125113.4 0.0e+00 7.13
1074024 rs739347 19_34 0 124929.4 0.0e+00 6.80
1074041 rs2878354 19_34 0 124830.4 0.0e+00 7.15
1074025 rs2073614 19_34 0 124797.5 0.0e+00 6.76
1074030 rs2077300 19_34 0 124466.9 0.0e+00 6.92
1074020 rs4802613 19_34 0 124247.5 0.0e+00 6.77
1074034 rs73056059 19_34 0 124231.8 0.0e+00 6.97
1074054 rs60815603 19_34 0 123401.5 0.0e+00 7.20
1074057 rs1316885 19_34 0 122817.0 0.0e+00 7.11
1074059 rs60746284 19_34 0 122641.8 0.0e+00 7.33
1074062 rs2946863 19_34 0 122591.1 0.0e+00 7.04
1074018 rs10403394 19_34 0 122516.7 0.0e+00 6.80
1074055 rs35443645 19_34 0 122482.8 0.0e+00 7.08
1074019 rs17555056 19_34 0 122467.5 0.0e+00 6.75
1074035 rs73056062 19_34 0 121041.5 0.0e+00 6.99
1074065 rs553431297 19_34 0 119290.9 0.0e+00 6.78
1074048 rs112283514 19_34 0 118981.9 0.0e+00 6.51
1074050 rs11270139 19_34 0 118167.4 0.0e+00 7.17
1074015 rs10421294 19_34 0 110653.3 0.0e+00 6.07
1074014 rs8108175 19_34 0 110638.1 0.0e+00 6.07
1074007 rs59192944 19_34 0 110428.4 0.0e+00 6.07
1074013 rs1858742 19_34 0 110426.5 0.0e+00 6.04
1074004 rs55991145 19_34 0 110350.3 0.0e+00 6.08
1073999 rs3786567 19_34 0 110307.1 0.0e+00 6.08
1073998 rs4801801 19_34 0 110264.1 0.0e+00 6.05
1073995 rs2271952 19_34 0 110263.5 0.0e+00 6.08
1073994 rs2271953 19_34 0 110141.9 0.0e+00 6.04
1073996 rs2271951 19_34 0 110136.9 0.0e+00 6.05
1073985 rs60365978 19_34 0 110035.5 0.0e+00 6.02
1073991 rs4802612 19_34 0 109601.6 0.0e+00 6.16
1074001 rs2517977 19_34 0 109479.6 0.0e+00 5.83
1073988 rs55893003 19_34 0 109302.1 0.0e+00 6.16
1073980 rs55992104 19_34 0 106729.2 0.0e+00 6.04
1073974 rs60403475 19_34 0 106702.6 0.0e+00 6.03
1073977 rs4352151 19_34 0 106697.5 0.0e+00 6.01
1073971 rs11878448 19_34 0 106621.5 0.0e+00 6.01
1073965 rs9653100 19_34 0 106586.6 0.0e+00 6.04
1073961 rs4802611 19_34 0 106516.4 0.0e+00 6.03
1073953 rs7251338 19_34 0 106354.2 0.0e+00 6.02
1073952 rs59269605 19_34 0 106342.8 0.0e+00 6.05
1073973 rs1042120 19_34 0 106079.6 0.0e+00 6.13
1073969 rs113220577 19_34 0 105985.7 0.0e+00 6.12
1073963 rs9653118 19_34 0 105821.5 0.0e+00 6.16SNPs 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
1074049 rs374141296 19_34 1.000 126581.62 0.4100 6.36
1074037 rs61371437 19_34 1.000 125667.97 0.4000 6.86
1074046 rs113176985 19_34 1.000 125902.25 0.4000 7.00
361259 rs199804242 6_89 1.000 45828.19 0.1500 -3.57
361275 rs6923513 6_89 0.912 45862.36 0.1300 -3.26
361258 rs2327654 6_89 0.616 45859.55 0.0910 -3.25
400132 rs761767938 7_49 1.000 14034.20 0.0450 4.57
400140 rs1544459 7_49 1.000 14126.84 0.0450 4.45
400136 rs11972122 7_49 0.997 12927.95 0.0410 3.99
369226 rs60425481 6_104 1.000 12549.87 0.0400 10.63
1017174 rs858519 17_7 1.000 8212.14 0.0260 90.66
369223 rs3127598 6_104 0.548 12485.20 0.0220 -6.70
1097354 rs34079499 21_19 1.000 6769.83 0.0220 4.12
369231 rs3106167 6_104 0.473 12485.14 0.0190 -6.70
646018 rs12430288 13_25 0.999 5795.69 0.0190 -2.67
646014 rs566812111 13_25 1.000 5750.21 0.0180 -2.84
1017164 rs62059837 17_7 1.000 5775.19 0.0180 30.95
1097512 rs55740356 21_19 0.953 5955.53 0.0180 4.53
369222 rs3106169 6_104 0.412 12485.15 0.0160 -6.71
1017180 rs1799941 17_7 1.000 4987.88 0.0160 88.95
891788 rs200216446 3_104 1.000 4399.90 0.0140 -4.20
369215 rs11755965 6_104 0.299 12481.93 0.0120 -6.70
1097355 rs34578707 21_19 0.367 6695.85 0.0079 4.17
1097368 rs77090950 21_19 0.364 6697.05 0.0078 4.17
1025537 rs56032910 17_19 1.000 2377.54 0.0076 -2.90
1097318 rs2836974 21_19 0.351 6696.21 0.0075 4.17
1097372 rs35560196 21_19 0.347 6697.04 0.0074 4.17
1025538 rs56024867 17_19 0.889 2377.34 0.0068 -3.18
1016658 rs3867595 17_7 1.000 1967.09 0.0063 -68.50
339315 rs112354376 6_46 1.000 1543.47 0.0049 -3.29
339316 rs208453 6_46 1.000 1532.61 0.0049 -0.53
891831 rs12493271 3_104 0.348 4374.15 0.0049 -2.33
891770 rs61793869 3_104 0.293 4374.46 0.0041 -2.33
643261 rs78750369 13_18 1.000 1135.42 0.0036 3.38
1025540 rs7213689 17_19 0.468 2377.88 0.0036 -3.08
643251 rs9533828 13_18 1.000 1057.97 0.0034 4.04
891779 rs61793896 3_104 0.224 4374.29 0.0031 -2.32
643264 rs9567428 13_18 1.000 927.02 0.0030 4.16
529798 rs6479896 10_42 0.451 1989.90 0.0029 47.72
891824 rs61791061 3_104 0.201 4375.87 0.0028 -2.30
868976 rs1260326 2_16 1.000 796.08 0.0025 30.11
891776 rs61793871 3_104 0.181 4374.18 0.0025 -2.31
369243 rs624319 6_104 0.334 2201.52 0.0024 14.25
891864 rs12490982 3_104 0.175 4370.50 0.0024 -2.34
1017593 rs117387630 17_7 0.946 776.42 0.0024 -38.67
1097306 rs34672724 21_19 0.112 6685.40 0.0024 4.18
891823 rs74402546 3_104 0.167 4375.75 0.0023 -2.29
1097388 rs8128894 21_19 0.109 6690.49 0.0023 4.19
1097389 rs8129147 21_19 0.097 6694.36 0.0021 4.18
369242 rs637614 6_104 0.276 2197.35 0.0019 14.23SNPs 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
1017174 rs858519 17_7 1 8212.14 2.6e-02 90.66
1017190 rs727428 17_7 0 8186.08 4.1e-10 90.59
1017188 rs858516 17_7 0 8152.13 1.1e-15 90.49
1017180 rs1799941 17_7 1 4987.88 1.6e-02 88.95
1017177 rs62059839 17_7 0 4967.62 7.8e-06 88.82
1017146 rs12150660 17_7 0 4548.33 0.0e+00 85.02
1017156 rs62059835 17_7 0 4546.16 0.0e+00 85.01
1017153 rs62059834 17_7 0 4542.10 0.0e+00 84.96
1017122 rs149932962 17_7 0 4503.03 0.0e+00 84.44
1017178 rs858518 17_7 0 7337.09 0.0e+00 83.74
1016940 rs9902027 17_7 0 2325.92 0.0e+00 82.83
1016986 rs77294902 17_7 0 2363.24 0.0e+00 -82.81
1016938 rs8073177 17_7 0 2320.13 0.0e+00 82.80
1016930 rs9892862 17_7 0 2309.73 0.0e+00 82.78
1016969 rs11078694 17_7 0 2331.33 0.0e+00 -82.59
1016968 rs11651783 17_7 0 2329.42 0.0e+00 -82.58
1016963 rs9900162 17_7 0 2321.75 0.0e+00 -82.48
1017162 rs142675740 17_7 0 4512.91 0.0e+00 82.47
1017163 rs62059836 17_7 0 4511.47 0.0e+00 82.46
1017147 rs57828263 17_7 0 4527.09 0.0e+00 82.35
1016962 rs11656013 17_7 0 2294.94 0.0e+00 -82.32
1017116 rs12452603 17_7 0 4494.37 0.0e+00 81.96
1017107 rs73242239 17_7 0 4495.37 0.0e+00 81.95
1017141 rs62059833 17_7 0 4486.50 0.0e+00 81.83
1017091 rs4227 17_7 0 4389.24 0.0e+00 -80.79
1017103 rs3933469 17_7 0 4257.10 0.0e+00 79.91
1017270 rs1641523 17_7 0 6673.32 0.0e+00 79.14
1017297 rs1642762 17_7 0 6535.07 0.0e+00 78.88
1017294 rs1624085 17_7 0 6542.38 0.0e+00 78.86
1017022 rs78744936 17_7 0 3770.34 0.0e+00 76.93
1016990 rs62059804 17_7 0 3692.23 0.0e+00 75.86
1016989 rs9899183 17_7 0 3682.70 0.0e+00 -75.66
1017015 rs11078696 17_7 0 1460.72 0.0e+00 75.34
1017013 rs116600817 17_7 0 3663.62 0.0e+00 75.26
1016982 rs12945977 17_7 0 3622.43 0.0e+00 75.20
1016983 rs34790908 17_7 0 3622.69 0.0e+00 75.20
1016981 rs12945084 17_7 0 3620.67 0.0e+00 75.18
1016970 rs34951138 17_7 0 3619.01 0.0e+00 75.14
1016993 rs62059805 17_7 0 3638.91 0.0e+00 75.11
1016924 rs62059793 17_7 0 3557.78 0.0e+00 74.38
1016948 rs62059797 17_7 0 3547.06 0.0e+00 74.35
1016933 rs35049113 17_7 0 3542.69 0.0e+00 74.25
1017001 rs12602989 17_7 0 1839.48 0.0e+00 -74.19
1016988 rs80067372 17_7 0 3536.93 0.0e+00 74.07
1016991 rs12940684 17_7 0 3799.77 0.0e+00 -74.06
1016922 rs6503037 17_7 0 3629.87 0.0e+00 -73.79
1016971 rs12941509 17_7 0 3505.18 0.0e+00 73.74
1016975 rs12948869 17_7 0 3502.80 0.0e+00 73.72
1017302 rs1642764 17_7 0 5493.71 0.0e+00 73.61
1016952 rs4968222 17_7 0 3486.16 0.0e+00 -73.46Gene set enrichment for genes with PIP>0.8
#GO enrichment analysis
library(enrichR)Welcome to enrichR
Checking connection ... Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
#number of genes for gene set enrichment
length(genes)[1] 22if (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 negative regulation of cGMP-mediated signaling (GO:0010754)
2 negative regulation of plasminogen activation (GO:0010757)
3 regulation of cGMP-mediated signaling (GO:0010752)
4 negative regulation of fibrinolysis (GO:0051918)
5 positive regulation of transforming growth factor beta production (GO:0071636)
6 regulation of plasminogen activation (GO:0010755)
7 regulation of fibrinolysis (GO:0051917)
8 negative regulation of protein processing (GO:0010955)
9 positive regulation of blood coagulation (GO:0030194)
10 positive regulation of smooth muscle cell proliferation (GO:0048661)
11 regulation of smooth muscle cell proliferation (GO:0048660)
Overlap Adjusted.P.value Genes
1 2/5 0.001838521 PDZD3;THBS1
2 2/5 0.001838521 SERPINF2;THBS1
3 2/7 0.002570519 PDZD3;THBS1
4 2/9 0.003300566 SERPINF2;THBS1
5 2/11 0.004025987 SERPINF2;THBS1
6 2/12 0.004025987 SERPINF2;THBS1
7 2/13 0.004075561 SERPINF2;THBS1
8 2/15 0.004794157 SERPINF2;THBS1
9 2/17 0.005512280 SERPINF2;THBS1
10 2/46 0.037033337 SERPINF2;THBS1
11 2/49 0.038176870 SERPINF2;THBS1
[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)TCEA3 gene(s) from the input list not found in DisGeNET CURATEDSLC35E2B gene(s) from the input list not found in DisGeNET CURATEDVPREB3 gene(s) from the input list not found in DisGeNET CURATEDAES gene(s) from the input list not found in DisGeNET CURATEDPDZD3 gene(s) from the input list not found in DisGeNET CURATEDTMED6 gene(s) from the input list not found in DisGeNET CURATEDNYNRIN gene(s) from the input list not found in DisGeNET CURATEDMIEF2 gene(s) from the input list not found in DisGeNET CURATEDNTAN1 gene(s) from the input list not found in DisGeNET CURATEDSERTAD2 gene(s) from the input list not found in DisGeNET CURATEDH1FX gene(s) from the input list not found in DisGeNET CURATED Description FDR
65 Tyrosine Kinase 2 Deficiency 0.01859410
66 ALPHA-2-PLASMIN INHIBITOR DEFICIENCY 0.01859410
67 Cardiomyopathy, Dilated, 1V 0.01859410
70 Anti-plasmin deficiency, congenital 0.01859410
72 HYPOGONADOTROPIC HYPOGONADISM 10 WITH OR WITHOUT ANOSMIA 0.01859410
33 Lymphomatoid Papulosis 0.02323065
57 Primary Cutaneous Anaplastic Large Cell Lymphoma 0.02323065
63 ALZHEIMER DISEASE 4 0.02323065
3 Asphyxia Neonatorum 0.06182082
14 Diabetic Angiopathies 0.06709444
Ratio BgRatio
65 1/11 1/9703
66 1/11 1/9703
67 1/11 1/9703
70 1/11 1/9703
72 1/11 1/9703
33 1/11 2/9703
57 1/11 2/9703
63 1/11 2/9703
3 1/11 6/9703
14 1/11 16/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULL
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.0.0
[5] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.57.0
[3] fs_1.3.1 bit64_4.0.5
[5] doParallel_1.0.16 progress_1.2.2
[7] httr_1.4.1 rprojroot_2.0.2
[9] GenomeInfoDb_1.20.0 doRNG_1.8.2
[11] tools_3.6.1 utf8_1.2.1
[13] R6_2.5.0 DBI_1.1.1
[15] BiocGenerics_0.30.0 colorspace_1.4-1
[17] withr_2.4.1 tidyselect_1.1.0
[19] prettyunits_1.0.2 bit_4.0.4
[21] curl_3.3 compiler_3.6.1
[23] git2r_0.26.1 Biobase_2.44.0
[25] DelayedArray_0.10.0 rtracklayer_1.44.0
[27] labeling_0.3 scales_1.1.0
[29] readr_1.4.0 apcluster_1.4.8
[31] stringr_1.4.0 digest_0.6.20
[33] Rsamtools_2.0.0 svglite_1.2.2
[35] rmarkdown_1.13 XVector_0.24.0
[37] pkgconfig_2.0.3 htmltools_0.3.6
[39] fastmap_1.1.0 BSgenome_1.52.0
[41] rlang_0.4.11 RSQLite_2.2.7
[43] generics_0.0.2 farver_2.1.0
[45] jsonlite_1.6 BiocParallel_1.18.0
[47] dplyr_1.0.7 VariantAnnotation_1.30.1
[49] RCurl_1.98-1.1 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.1 Matrix_1.2-18
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.22.1 fansi_0.5.0
[57] gdtools_0.1.9 lifecycle_1.0.0
[59] stringi_1.4.3 whisker_0.3-2
[61] yaml_2.2.0 SummarizedExperiment_1.14.1
[63] zlibbioc_1.30.0 plyr_1.8.4
[65] grid_3.6.1 blob_1.2.1
[67] parallel_3.6.1 promises_1.0.1
[69] crayon_1.4.1 lattice_0.20-38
[71] Biostrings_2.52.0 GenomicFeatures_1.36.3
[73] hms_1.1.0 knitr_1.23
[75] pillar_1.6.1 igraph_1.2.4.1
[77] GenomicRanges_1.36.0 rjson_0.2.20
[79] rngtools_1.5 codetools_0.2-16
[81] reshape2_1.4.3 biomaRt_2.40.1
[83] stats4_3.6.1 XML_3.98-1.20
[85] glue_1.4.2 evaluate_0.14
[87] data.table_1.14.0 foreach_1.5.1
[89] vctrs_0.3.8 httpuv_1.5.1
[91] gtable_0.3.0 purrr_0.3.4
[93] assertthat_0.2.1 cachem_1.0.5
[95] xfun_0.8 later_0.8.0
[97] tibble_3.1.2 iterators_1.0.13
[99] GenomicAlignments_1.20.1 AnnotationDbi_1.46.0
[101] memoise_2.0.0 IRanges_2.18.1
[103] workflowr_1.6.2 ellipsis_0.3.2