Creatinine (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 | 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 Creatinine (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-30700_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0116880953 0.0002238787 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
21.18549 19.03291 #report sample size
print(sample_size)[1] 344104#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.007844383 0.107699634 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07758519 2.67202283Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
5436 PSMA5 1_67 1.000 172.50 5.0e-04 13.25
10513 L3MBTL3 6_86 0.999 95.04 2.8e-04 9.94
11515 GSTA1 6_39 0.993 57.78 1.7e-04 7.62
2313 CDK5RAP3 17_28 0.989 34.52 9.9e-05 -4.64
938 CDC14A 1_61 0.988 71.18 2.0e-04 -8.51
11790 CYP2A6 19_28 0.978 29.34 8.3e-05 -4.98
8040 THBS3 1_76 0.975 80.62 2.3e-04 4.15
2718 NNT 5_28 0.975 29.01 8.2e-05 -5.17
8192 MGMT 10_81 0.975 1288.04 3.6e-03 8.06
939 RAPGEF3 12_30 0.972 33.51 9.5e-05 5.88
3774 ZNF436 1_16 0.970 42.59 1.2e-04 -7.04
8615 CYB561D1 1_67 0.968 25.75 7.2e-05 4.09
8803 DLEU1 13_21 0.967 66.38 1.9e-04 -8.56
5389 RPS11 19_34 0.966 11861.95 3.3e-02 3.43
10338 PRIM1 12_35 0.964 27.74 7.8e-05 4.95
6403 PPM1J 1_69 0.956 175.92 4.9e-04 -13.57
4838 VARS2 6_25 0.956 41.77 1.2e-04 -6.46
4811 CNPY3 6_33 0.956 41.28 1.1e-04 6.13
4287 NIP7 16_37 0.956 28.22 7.8e-05 3.83
8531 TNKS 8_12 0.954 48.98 1.4e-04 -10.55
11399 TNFSF12 17_7 0.943 23.13 6.3e-05 -4.52
3426 CCRL2 3_32 0.934 24.48 6.6e-05 -4.21
2173 TMEM176B 7_93 0.932 30.95 8.4e-05 7.05
3716 PPDPF 20_37 0.927 55.50 1.5e-04 -7.43
7040 INHBB 2_70 0.914 128.35 3.4e-04 10.97
5415 SYTL1 1_19 0.909 31.55 8.3e-05 5.79
5658 ALDH1L1 3_78 0.907 102.85 2.7e-04 7.66
3439 GTDC1 2_86 0.892 23.84 6.2e-05 -4.43
4818 SLC22A7 6_33 0.854 61.75 1.5e-04 -7.94
6997 KIF26B 1_129 0.851 24.72 6.1e-05 -4.48
5268 MYOCD 17_11 0.849 23.52 5.8e-05 -4.51
10131 AMZ2 17_39 0.824 27.01 6.5e-05 -5.37Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
4556 TMEM60 7_49 0.000 45632.09 0.000 -10.12
10186 ZGPAT 20_38 0.000 12729.24 0.000 6.48
3715 SLC2A4RG 20_38 0.000 12637.24 0.000 6.44
4634 EGLN1 1_118 0.000 12037.02 0.000 -4.02
5389 RPS11 19_34 0.966 11861.95 0.033 3.43
1647 ARFRP1 20_38 0.000 11818.42 0.000 4.88
10889 ARL16 17_46 0.458 10310.36 0.014 5.89
1227 FLT3LG 19_34 0.000 10242.78 0.000 -3.06
3058 EXOC8 1_118 0.000 10065.68 0.000 4.37
5799 SLC22A3 6_104 0.000 9457.08 0.000 2.61
10903 APTR 7_49 0.000 9075.51 0.000 -1.74
11199 LINC00271 6_89 0.000 8907.36 0.000 -2.01
3449 PLG 6_104 0.000 7724.17 0.000 -1.77
9811 RSBN1L 7_49 0.000 4897.21 0.000 -2.77
1641 GMEB2 20_38 0.000 4239.53 0.000 -4.41
11853 RTEL1 20_38 0.000 4208.54 0.000 0.68
5393 RCN3 19_34 0.000 3875.82 0.000 -3.15
1931 FCGRT 19_34 0.000 3551.91 0.000 -3.48
9342 TSPAN10 17_46 0.000 3354.33 0.000 -1.79
4604 AHI1 6_89 0.000 3075.19 0.000 -0.54Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
5389 RPS11 19_34 0.966 11861.95 0.03300 3.43
10889 ARL16 17_46 0.458 10310.36 0.01400 5.89
8192 MGMT 10_81 0.975 1288.04 0.00360 8.06
8353 SPATA5L1 15_17 0.705 457.32 0.00094 26.03
5436 PSMA5 1_67 1.000 172.50 0.00050 13.25
6403 PPM1J 1_69 0.956 175.92 0.00049 -13.57
7040 INHBB 2_70 0.914 128.35 0.00034 10.97
10513 L3MBTL3 6_86 0.999 95.04 0.00028 9.94
5658 ALDH1L1 3_78 0.907 102.85 0.00027 7.66
8040 THBS3 1_76 0.975 80.62 0.00023 4.15
938 CDC14A 1_61 0.988 71.18 0.00020 -8.51
8803 DLEU1 13_21 0.967 66.38 0.00019 -8.56
3641 SLC17A1 6_20 0.773 75.78 0.00017 -9.04
11515 GSTA1 6_39 0.993 57.78 0.00017 7.62
4818 SLC22A7 6_33 0.854 61.75 0.00015 -7.94
3716 PPDPF 20_37 0.927 55.50 0.00015 -7.43
8531 TNKS 8_12 0.954 48.98 0.00014 -10.55
7196 SENP2 3_114 0.540 82.29 0.00013 -9.96
721 WIPI1 17_39 0.712 60.77 0.00013 7.59
1058 GCKR 2_16 0.480 87.73 0.00012 12.35Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8353 SPATA5L1 15_17 0.705 457.32 9.4e-04 26.03
7163 CCDC158 4_52 0.086 155.34 3.9e-05 -18.00
5042 SHROOM3 4_52 0.018 178.58 9.6e-06 -17.46
3731 MED1 17_23 0.034 171.73 1.7e-05 -15.67
9992 FAM47E 4_52 0.005 135.03 2.0e-06 -14.18
3385 TBX2 17_36 0.003 103.07 9.1e-07 13.91
6403 PPM1J 1_69 0.956 175.92 4.9e-04 -13.57
5436 PSMA5 1_67 1.000 172.50 5.0e-04 13.25
1058 GCKR 2_16 0.480 87.73 1.2e-04 12.35
10987 C2orf16 2_16 0.480 87.73 1.2e-04 12.35
6970 ATXN7L2 1_67 0.015 129.93 5.5e-06 12.22
2297 FBXL20 17_23 0.001 93.05 1.4e-07 12.12
5580 DUSP11 2_48 0.013 122.83 4.7e-06 11.55
4435 PSRC1 1_67 0.017 144.00 7.0e-06 11.18
6849 PGAP3 17_23 0.001 87.70 1.8e-07 -11.15
8054 SNUPN 15_35 0.000 145.02 3.5e-08 11.14
3440 ACVR2A 2_88 0.098 78.19 2.2e-05 11.05
1684 MAP1LC3A 20_21 0.012 78.00 2.7e-06 10.97
7040 INHBB 2_70 0.914 128.35 3.4e-04 10.97
10624 MBD5 2_88 0.015 68.81 3.0e-06 10.74Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03311623#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8353 SPATA5L1 15_17 0.705 457.32 9.4e-04 26.03
7163 CCDC158 4_52 0.086 155.34 3.9e-05 -18.00
5042 SHROOM3 4_52 0.018 178.58 9.6e-06 -17.46
3731 MED1 17_23 0.034 171.73 1.7e-05 -15.67
9992 FAM47E 4_52 0.005 135.03 2.0e-06 -14.18
3385 TBX2 17_36 0.003 103.07 9.1e-07 13.91
6403 PPM1J 1_69 0.956 175.92 4.9e-04 -13.57
5436 PSMA5 1_67 1.000 172.50 5.0e-04 13.25
1058 GCKR 2_16 0.480 87.73 1.2e-04 12.35
10987 C2orf16 2_16 0.480 87.73 1.2e-04 12.35
6970 ATXN7L2 1_67 0.015 129.93 5.5e-06 12.22
2297 FBXL20 17_23 0.001 93.05 1.4e-07 12.12
5580 DUSP11 2_48 0.013 122.83 4.7e-06 11.55
4435 PSRC1 1_67 0.017 144.00 7.0e-06 11.18
6849 PGAP3 17_23 0.001 87.70 1.8e-07 -11.15
8054 SNUPN 15_35 0.000 145.02 3.5e-08 11.14
3440 ACVR2A 2_88 0.098 78.19 2.2e-05 11.05
1684 MAP1LC3A 20_21 0.012 78.00 2.7e-06 10.97
7040 INHBB 2_70 0.914 128.35 3.4e-04 10.97
10624 MBD5 2_88 0.015 68.81 3.0e-06 10.74Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 15_17"
genename region_tag susie_pip mu2 PVE z
7651 CASC4 15_17 0.004 10.12 1.2e-07 2.21
4888 CTDSPL2 15_17 0.003 5.00 4.1e-08 -0.30
9097 EIF3J-AS1 15_17 0.005 10.14 1.5e-07 -1.73
1861 SPG11 15_17 0.004 6.90 7.6e-08 0.81
11177 PATL2 15_17 0.003 4.91 4.1e-08 -0.20
9670 TRIM69 15_17 0.003 5.91 4.9e-08 1.14
12567 CTD-2008A1.3 15_17 0.015 19.92 9.0e-07 0.93
7711 TERB2 15_17 0.030 18.07 1.6e-06 0.32
4906 DUOX1 15_17 0.005 17.56 2.7e-07 -5.32
5177 DUOXA1 15_17 0.004 19.24 2.3e-07 -5.21
5006 SHF 15_17 0.003 45.21 4.4e-07 -4.08
4907 SLC28A2 15_17 0.065 33.79 6.4e-06 4.51
8353 SPATA5L1 15_17 0.705 457.32 9.4e-04 26.03
8354 GATM 15_17 0.005 27.02 4.2e-07 4.77
7692 C15orf48 15_17 0.003 50.85 4.8e-07 -3.23
4887 SQRDL 15_17 0.003 6.26 5.3e-08 1.57
12543 RP11-96O20.5 15_17 0.003 4.98 4.2e-08 0.64
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 4_52"
genename region_tag susie_pip mu2 PVE z
5038 SCARB2 4_52 0.015 16.36 7.2e-07 -0.23
9992 FAM47E 4_52 0.005 135.03 2.0e-06 -14.18
7163 CCDC158 4_52 0.086 155.34 3.9e-05 -18.00
5042 SHROOM3 4_52 0.018 178.58 9.6e-06 -17.46
5036 SEPT11 4_52 0.007 6.54 1.2e-07 0.07
9710 SOWAHB 4_52 0.005 5.00 7.3e-08 0.39
3202 CCNI 4_52 0.013 15.49 5.8e-07 2.03
5039 CCNG2 4_52 0.008 8.51 1.9e-07 -0.85
5040 CNOT6L 4_52 0.012 12.67 4.5e-07 2.44
8048 MRPL1 4_52 0.008 9.14 2.2e-07 1.02
5037 FRAS1 4_52 0.011 11.41 3.6e-07 1.29
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_23"
genename region_tag susie_pip mu2 PVE z
12452 EPOP 17_23 0.001 13.92 5.4e-08 -1.18
12620 PSMB3 17_23 0.001 5.07 7.6e-09 0.36
12575 PIP4K2B 17_23 0.001 14.06 5.4e-08 -1.33
12450 CWC25 17_23 0.001 5.39 8.1e-09 0.23
16 LASP1 17_23 0.001 6.75 1.1e-08 -1.52
12051 LINC00672 17_23 0.778 33.56 7.6e-05 6.23
6848 PLXDC1 17_23 0.000 5.26 7.5e-09 -0.44
2297 FBXL20 17_23 0.001 93.05 1.4e-07 12.12
3731 MED1 17_23 0.034 171.73 1.7e-05 -15.67
4202 STARD3 17_23 0.001 20.70 4.2e-08 4.32
8601 TCAP 17_23 0.001 13.39 3.4e-08 2.43
5343 PNMT 17_23 0.001 12.59 3.1e-08 -2.01
6849 PGAP3 17_23 0.001 87.70 1.8e-07 -11.15
5341 ERBB2 17_23 0.001 18.63 3.5e-08 -4.74
5342 GRB7 17_23 0.006 17.05 2.8e-07 0.16
6850 IKZF3 17_23 0.001 17.34 2.8e-08 4.71
8390 ORMDL3 17_23 0.008 17.92 4.4e-07 0.15
12065 RP11-387H17.4 17_23 0.001 31.94 1.3e-07 4.86
7860 GSDMA 17_23 0.017 25.64 1.3e-06 -1.64
2299 CSF3 17_23 0.007 27.82 5.9e-07 4.24
3800 NR1D1 17_23 0.019 27.55 1.5e-06 3.34
9964 MSL1 17_23 0.001 12.05 3.9e-08 -1.51
2300 RAPGEFL1 17_23 0.107 38.02 1.2e-05 3.83
8318 WIPF2 17_23 0.002 17.41 8.3e-08 -2.75
1306 CDC6 17_23 0.001 7.69 2.0e-08 0.96
5344 IGFBP4 17_23 0.002 15.96 8.5e-08 -1.35
4201 TNS4 17_23 0.013 28.91 1.1e-06 2.82
12085 RP5-1028K7.2 17_23 0.001 9.42 2.2e-08 0.37
3799 CCR7 17_23 0.001 13.39 5.4e-08 -0.83
793 SMARCE1 17_23 0.001 10.28 2.7e-08 0.73
10827 KRT222 17_23 0.002 15.36 7.5e-08 -1.20
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_36"
genename region_tag susie_pip mu2 PVE z
3385 TBX2 17_36 0.003 103.07 9.1e-07 13.91
11704 NACA2 17_36 0.003 5.53 4.3e-08 -0.77
4727 BRIP1 17_36 0.004 6.29 7.1e-08 0.31
2319 MED13 17_36 0.057 19.70 3.3e-06 -2.90
1138 METTL2A 17_36 0.017 22.70 1.1e-06 -2.40
5829 TLK2 17_36 0.016 14.06 6.7e-07 3.67
173 MRC2 17_36 0.003 5.39 4.6e-08 -0.82
8257 TANC2 17_36 0.003 4.80 3.6e-08 -0.06
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_69"
genename region_tag susie_pip mu2 PVE z
7972 AHCYL1 1_69 0.006 17.12 3.0e-07 -1.78
5435 STRIP1 1_69 0.002 5.60 3.0e-08 0.58
3012 KCNC4 1_69 0.002 5.12 2.6e-08 0.63
6991 RBM15 1_69 0.005 14.58 2.0e-07 1.63
4439 LAMTOR5 1_69 0.006 16.48 2.9e-07 2.01
11908 RP11-284N8.3 1_69 0.003 10.84 1.0e-07 1.36
5438 CD53 1_69 0.002 4.87 2.4e-08 0.17
3435 LRIF1 1_69 0.002 8.56 6.2e-08 -0.88
6452 DRAM2 1_69 0.103 40.36 1.2e-05 3.35
4442 CEPT1 1_69 0.026 28.33 2.2e-06 2.76
6992 DENND2D 1_69 0.003 8.54 6.4e-08 1.31
1072 OVGP1 1_69 0.002 7.06 4.4e-08 -0.79
5437 C1orf162 1_69 0.003 9.42 7.3e-08 1.06
12689 ADORA3 1_69 0.007 17.14 3.5e-07 -2.04
3436 TMIGD3 1_69 0.006 15.76 2.6e-07 1.16
10298 FAM212B 1_69 0.017 25.99 1.3e-06 -2.06
11207 LINC01750 1_69 0.003 8.66 6.9e-08 0.54
4437 WNT2B 1_69 0.002 5.43 2.7e-08 0.84
113 ST7L 1_69 0.002 9.87 5.3e-08 2.65
3017 CAPZA1 1_69 0.002 23.39 1.7e-07 4.72
6402 MOV10 1_69 0.018 28.61 1.5e-06 -2.99
6403 PPM1J 1_69 0.956 175.92 4.9e-04 -13.57
| 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
8152 rs79598313 1_18 1.000 55.79 1.6e-04 -7.91
39317 rs9425587 1_84 1.000 39.01 1.1e-04 6.25
42879 rs79091515 1_92 1.000 79.33 2.3e-04 9.80
56359 rs71180790 1_116 1.000 421.08 1.2e-03 -3.16
57319 rs766167074 1_118 1.000 13236.24 3.8e-02 -4.30
72537 rs780093 2_16 1.000 166.36 4.8e-04 17.73
72838 rs569546056 2_17 1.000 622.73 1.8e-03 -2.59
79387 rs11689011 2_29 1.000 37.87 1.1e-04 -5.98
97737 rs11123169 2_67 1.000 71.36 2.1e-04 -8.05
99817 rs141849010 2_69 1.000 54.45 1.6e-04 7.31
113117 rs7565788 2_103 1.000 64.99 1.9e-04 9.30
115472 rs863678 2_106 1.000 166.03 4.8e-04 11.18
124844 rs11887861 2_124 1.000 301.73 8.8e-04 -11.03
190069 rs146797780 3_110 1.000 9837.89 2.9e-02 2.47
190070 rs7636471 3_110 1.000 9855.72 2.9e-02 2.53
194690 rs7642977 3_119 1.000 51.97 1.5e-04 7.37
213680 rs66998340 4_36 1.000 1218.38 3.5e-03 -3.18
260411 rs386057 5_1 1.000 58.31 1.7e-04 -7.24
260557 rs62331274 5_2 1.000 44.10 1.3e-04 6.71
281256 rs11743158 5_41 1.000 109.74 3.2e-04 10.79
314557 rs35716097 5_106 1.000 177.80 5.2e-04 18.22
314560 rs7447593 5_106 1.000 213.02 6.2e-04 19.58
318263 rs13193887 6_7 1.000 98.98 2.9e-04 10.02
328253 rs56144236 6_27 1.000 47.21 1.4e-04 -4.54
331520 rs9471632 6_32 1.000 66.09 1.9e-04 -9.23
331636 rs10223666 6_34 1.000 253.25 7.4e-04 16.43
334158 rs76572975 6_38 1.000 40.23 1.2e-04 -7.13
358716 rs199804242 6_89 1.000 40055.20 1.2e-01 5.05
366568 rs629849 6_103 1.000 48.72 1.4e-04 -1.34
366589 rs1867350 6_103 1.000 48.77 1.4e-04 4.95
366642 rs3119311 6_104 1.000 43131.61 1.3e-01 16.16
366683 rs60425481 6_104 1.000 58747.63 1.7e-01 2.37
371145 rs78148157 7_2 1.000 212.20 6.2e-04 -11.39
371146 rs13241427 7_2 1.000 194.03 5.6e-04 12.32
387731 rs700752 7_34 1.000 125.50 3.6e-04 10.76
396788 rs10277379 7_49 1.000 40640.62 1.2e-01 12.60
396791 rs761767938 7_49 1.000 52520.77 1.5e-01 11.05
396799 rs1544459 7_49 1.000 51917.53 1.5e-01 11.28
410780 rs3757387 7_78 1.000 50.86 1.5e-04 8.53
431732 rs4871905 8_24 1.000 228.68 6.6e-04 16.47
443337 rs17397411 8_50 1.000 42.81 1.2e-04 6.28
461915 rs6996786 8_84 1.000 3441.00 1.0e-02 1.60
461922 rs200311702 8_84 1.000 3360.05 9.8e-03 3.90
467392 rs72693377 8_94 1.000 49.46 1.4e-04 7.24
504385 rs1886296 9_73 1.000 45.52 1.3e-04 -3.91
504398 rs12380852 9_73 1.000 44.02 1.3e-04 4.33
504878 rs113790047 10_2 1.000 138.23 4.0e-04 12.47
521385 rs35182775 10_33 1.000 104.24 3.0e-04 -10.82
537642 rs1408345 10_64 1.000 36.32 1.1e-04 5.68
549383 rs231889 11_2 1.000 59.11 1.7e-04 -8.73
559607 rs369062552 11_21 1.000 317.74 9.2e-04 15.06
559617 rs34830202 11_21 1.000 351.77 1.0e-03 -16.29
570120 rs72917317 11_38 1.000 70.82 2.1e-04 -7.67
595762 rs11616030 12_11 1.000 57.87 1.7e-04 -7.64
596787 rs11056397 12_13 1.000 47.71 1.4e-04 -6.76
608044 rs2682323 12_33 1.000 59.29 1.7e-04 -7.02
608692 rs7397189 12_36 1.000 74.36 2.2e-04 -8.81
673083 rs72681869 14_20 1.000 63.34 1.8e-04 -8.12
706200 rs2472297 15_35 1.000 119.10 3.5e-04 -11.99
706442 rs145727191 15_35 1.000 79.24 2.3e-04 11.21
706471 rs2955742 15_36 1.000 63.03 1.8e-04 8.90
707989 rs7174325 15_38 1.000 36.95 1.1e-04 5.72
726669 rs12927956 16_27 1.000 116.61 3.4e-04 9.37
732235 rs7187317 16_39 1.000 50.85 1.5e-04 5.50
745424 rs139356332 17_16 1.000 51.96 1.5e-04 8.03
745436 rs7222869 17_16 1.000 43.30 1.3e-04 -8.09
749083 rs12453645 17_23 1.000 84.15 2.4e-04 13.02
754278 rs3032584 17_35 1.000 245.54 7.1e-04 16.69
754336 rs11650989 17_36 1.000 243.42 7.1e-04 -19.89
767350 rs162000 18_14 1.000 58.81 1.7e-04 7.75
773724 rs2878889 18_27 1.000 44.92 1.3e-04 -6.53
794978 rs11084684 19_23 1.000 86.08 2.5e-04 9.12
796031 rs1137844 19_24 1.000 63.77 1.9e-04 -8.09
798234 rs814573 19_32 1.000 72.38 2.1e-04 -8.73
817239 rs209955 20_32 1.000 63.48 1.8e-04 8.91
817243 rs2585441 20_32 1.000 82.55 2.4e-04 9.27
817266 rs6068816 20_32 1.000 43.03 1.3e-04 -6.75
818454 rs6025623 20_33 1.000 48.59 1.4e-04 7.26
836030 rs2103861 22_9 1.000 34.80 1.0e-04 -5.69
893349 rs61114860 3_78 1.000 2188.49 6.4e-03 2.67
946073 rs667890 6_88 1.000 28879.64 8.4e-02 7.43
946075 rs148389913 6_88 1.000 29211.19 8.5e-02 7.02
946079 rs561826 6_88 1.000 28756.48 8.4e-02 7.06
967798 rs758184196 8_11 1.000 464.88 1.4e-03 2.64
974345 rs201524046 10_81 1.000 15840.10 4.6e-02 -6.50
974364 rs568584257 10_81 1.000 15784.68 4.6e-02 -2.04
990522 rs577954961 13_21 1.000 494.82 1.4e-03 -1.94
1017252 rs113956264 16_2 1.000 56.04 1.6e-04 -8.01
1061897 rs62080193 17_46 1.000 18469.38 5.4e-02 -4.07
1061905 rs113375436 17_46 1.000 18482.75 5.4e-02 -3.98
1078454 rs374141296 19_34 1.000 12084.60 3.5e-02 -3.31
1099298 rs202143810 20_38 1.000 13305.06 3.9e-02 -6.50
127641 rs112068790 2_129 0.999 37.63 1.1e-04 -7.53
200232 rs4533774 4_11 0.999 46.22 1.3e-04 6.65
222640 rs111470070 4_51 0.999 50.94 1.5e-04 5.66
231597 rs2903386 4_69 0.999 42.72 1.2e-04 -5.51
366580 rs12208357 6_103 0.999 49.68 1.4e-04 -2.51
798498 rs34783010 19_32 0.999 35.66 1.0e-04 5.84
828364 rs219783 21_17 0.999 54.50 1.6e-04 -7.26
924303 rs116339629 6_25 0.999 42.09 1.2e-04 -5.78
28190 rs12407689 1_62 0.998 33.23 9.6e-05 5.50
151354 rs6808104 3_35 0.998 56.89 1.6e-04 -3.74
504393 rs72773787 9_73 0.998 41.55 1.2e-04 4.15
527567 rs72797524 10_46 0.997 30.89 8.9e-05 -5.41
549180 rs17885785 11_2 0.997 86.03 2.5e-04 8.76
549381 rs186376420 11_2 0.997 47.28 1.4e-04 -7.99
990792 rs1885724 13_21 0.997 518.23 1.5e-03 -4.46
16649 rs2474382 1_38 0.996 29.28 8.5e-05 -5.51
569279 rs4601790 11_36 0.996 52.36 1.5e-04 2.39
836754 rs740219 22_10 0.996 35.84 1.0e-04 -3.87
1078451 rs113176985 19_34 0.996 12013.34 3.5e-02 -3.09
56353 rs287613 1_116 0.995 418.41 1.2e-03 -3.26
198571 rs115976359 4_8 0.995 29.69 8.6e-05 -5.34
272539 rs4703440 5_23 0.995 53.60 1.5e-04 7.02
732221 rs62053193 16_39 0.995 35.82 1.0e-04 4.57
750576 rs2074292 17_27 0.995 31.57 9.1e-05 -5.39
127650 rs6747041 2_129 0.994 96.36 2.8e-04 -11.00
418833 rs288762 7_97 0.994 114.88 3.3e-04 10.61
697406 rs74009639 15_17 0.994 177.26 5.1e-04 11.23
831939 rs73907568 21_23 0.994 28.97 8.4e-05 5.34
274051 rs17395128 5_26 0.992 34.97 1.0e-04 -5.73
322804 rs3763278 6_15 0.992 34.50 9.9e-05 5.03
688681 rs75432828 14_52 0.992 55.24 1.6e-04 7.71
812114 rs34106705 20_20 0.992 40.09 1.2e-04 6.72
192501 rs13069721 3_115 0.991 41.90 1.2e-04 -6.50
425214 rs10093915 8_13 0.989 61.88 1.8e-04 9.30
195452 rs13059257 3_120 0.988 72.20 2.1e-04 7.67
368865 rs1445288 6_108 0.988 30.94 8.9e-05 5.37
678006 rs1997896 14_32 0.988 39.52 1.1e-04 -5.79
790638 rs35218652 19_15 0.988 39.00 1.1e-04 5.10
212033 rs11732881 4_34 0.987 39.41 1.1e-04 -5.92
326934 rs2736429 6_26 0.987 57.70 1.7e-04 8.56
818396 rs6099616 20_33 0.987 29.81 8.5e-05 5.82
1017842 rs147350387 16_2 0.987 41.52 1.2e-04 -4.92
31795 rs3949262 1_72 0.986 36.68 1.1e-04 -5.91
503137 rs115478735 9_70 0.986 52.55 1.5e-04 -7.15
272649 rs11740818 5_23 0.985 32.30 9.2e-05 -5.28
276743 rs113088001 5_31 0.985 56.28 1.6e-04 -9.88
56370 rs1150916 1_116 0.984 253.33 7.2e-04 -4.05
104022 rs7602029 2_81 0.984 42.17 1.2e-04 6.84
243963 rs115900720 4_94 0.984 27.97 8.0e-05 -4.91
1068304 rs35601737 19_10 0.984 32.83 9.4e-05 5.87
167563 rs7640740 3_66 0.983 33.76 9.6e-05 -5.61
434470 rs12544558 8_31 0.983 58.05 1.7e-04 -6.14
745450 rs56700256 17_16 0.983 26.49 7.6e-05 4.85
799484 rs281380 19_33 0.981 50.06 1.4e-04 -6.97
404997 rs543883 7_65 0.980 26.89 7.7e-05 4.94
751075 rs137906947 17_27 0.980 29.39 8.4e-05 5.17
149475 rs811970 3_28 0.977 29.78 8.5e-05 -5.18
366593 rs1443844 6_103 0.977 165.77 4.7e-04 9.26
446210 rs2672853 8_55 0.976 29.35 8.3e-05 -4.11
592617 rs12370932 12_3 0.976 33.15 9.4e-05 -4.53
722540 rs9933330 16_19 0.976 538.20 1.5e-03 -24.08
740697 rs4790812 17_2 0.975 27.22 7.7e-05 5.01
54192 rs61830291 1_112 0.973 73.52 2.1e-04 -8.72
82508 rs3106204 2_36 0.973 57.03 1.6e-04 7.63
481171 rs11557154 9_26 0.973 34.77 9.8e-05 -6.02
701287 rs11855136 15_25 0.972 27.23 7.7e-05 4.96
842194 rs28477160 22_20 0.972 26.76 7.6e-05 4.16
489340 rs1360200 9_45 0.970 29.61 8.3e-05 -5.32
615171 rs1848968 12_48 0.969 39.33 1.1e-04 -6.17
411128 rs11764066 7_79 0.968 27.01 7.6e-05 5.31
674340 rs66913363 14_23 0.966 32.57 9.1e-05 5.22
276742 rs1694060 5_31 0.965 49.72 1.4e-04 -7.45
778326 rs532969215 18_35 0.965 25.66 7.2e-05 -4.81
452887 rs28628213 8_67 0.964 26.14 7.3e-05 4.73
418894 rs6459970 7_97 0.962 31.15 8.7e-05 5.84
685955 rs8013584 14_47 0.961 27.84 7.8e-05 5.85
15039 rs1331858 1_35 0.960 57.79 1.6e-04 -7.81
662482 rs750598 13_59 0.960 59.85 1.7e-04 7.90
542710 rs1932558 10_73 0.959 25.95 7.2e-05 4.86
113147 rs7594986 2_103 0.958 46.85 1.3e-04 8.14
117413 rs11690832 2_110 0.957 35.06 9.7e-05 6.62
360628 rs12216122 6_94 0.957 28.01 7.8e-05 4.96
498452 rs10817912 9_60 0.957 65.70 1.8e-04 -7.47
78251 rs13428381 2_27 0.956 63.03 1.8e-04 -8.30
386093 rs11761217 7_30 0.956 25.54 7.1e-05 4.63
840718 rs13055886 22_18 0.956 58.19 1.6e-04 6.98
516000 rs11007559 10_21 0.953 34.42 9.5e-05 5.82
796753 rs2228068 19_26 0.952 31.04 8.6e-05 -3.70
746478 rs117859452 17_17 0.950 25.02 6.9e-05 4.54
111582 rs4667700 2_99 0.949 26.14 7.2e-05 -4.74
331514 rs1015149 6_32 0.949 30.27 8.3e-05 -6.59
23069 rs6661091 1_50 0.948 78.46 2.2e-04 8.98
468545 rs1538532 9_3 0.948 25.78 7.1e-05 4.74
381227 rs67971665 7_23 0.945 43.51 1.2e-04 -6.48
579953 rs10892860 11_57 0.944 26.72 7.3e-05 4.92
274303 rs4957118 5_26 0.941 40.36 1.1e-04 7.31
318169 rs9378483 6_7 0.940 24.99 6.8e-05 -3.71
446236 rs2941452 8_55 0.939 38.46 1.0e-04 -5.34
564405 rs10219383 11_28 0.939 25.90 7.1e-05 -4.87
971908 rs34655427 8_12 0.937 31.36 8.5e-05 -3.01
127072 rs2068218 2_128 0.935 24.33 6.6e-05 -4.13
1018626 rs11546345 16_2 0.935 38.43 1.0e-04 6.18
840588 rs12484310 22_18 0.934 26.41 7.2e-05 4.72
274463 rs149976817 5_27 0.933 24.55 6.7e-05 4.32
260414 rs185228153 5_1 0.932 27.17 7.4e-05 3.20
806113 rs7264882 20_8 0.929 28.99 7.8e-05 5.18
790875 rs3794991 19_15 0.926 47.33 1.3e-04 7.01
53914 rs884127 1_112 0.925 27.40 7.4e-05 -4.85
490529 rs2185973 9_47 0.925 26.96 7.2e-05 -4.82
716953 rs148361522 16_6 0.924 24.09 6.5e-05 -4.50
285322 rs115912456 5_49 0.922 23.38 6.3e-05 4.43
358732 rs6923513 6_89 0.922 40129.02 1.1e-01 5.13
482682 rs2151421 9_30 0.922 192.08 5.1e-04 -14.63
974348 rs74160216 10_81 0.921 15779.22 4.2e-02 -2.10
111553 rs75483173 2_98 0.920 25.31 6.8e-05 -4.66
593041 rs78470967 12_5 0.920 25.25 6.8e-05 -5.22
305288 rs1800888 5_87 0.918 23.60 6.3e-05 -4.09
607125 rs1878234 12_31 0.918 27.68 7.4e-05 -4.48
77264 rs13418726 2_26 0.915 33.90 9.0e-05 -5.70
540344 rs2050996 10_69 0.915 35.56 9.5e-05 5.81
706472 rs143214734 15_36 0.912 25.09 6.7e-05 4.16
466304 rs56114972 8_92 0.911 24.18 6.4e-05 -4.15
187451 rs6770214 3_105 0.909 24.57 6.5e-05 -4.59
589406 rs3935795 11_80 0.909 26.65 7.0e-05 5.10
419154 rs2530736 7_98 0.908 37.36 9.9e-05 5.99
722533 rs9923532 16_19 0.907 194.37 5.1e-04 10.53
739783 rs34404057 16_54 0.907 86.75 2.3e-04 8.82
287421 rs3952745 5_53 0.905 25.31 6.7e-05 -5.13
357962 rs7753497 6_87 0.905 37.46 9.9e-05 7.44
739668 rs117652610 16_54 0.905 35.05 9.2e-05 -5.48
592681 rs79997404 12_3 0.904 110.03 2.9e-04 10.75
708388 rs28587782 15_38 0.904 45.72 1.2e-04 7.38
211372 rs278933 4_33 0.903 25.81 6.8e-05 4.71
351506 rs9285397 6_73 0.903 77.46 2.0e-04 -9.26
151195 rs73083115 3_33 0.899 25.47 6.7e-05 2.94
745448 rs7224838 17_16 0.898 38.74 1.0e-04 6.57
244922 rs10013413 4_96 0.896 32.37 8.4e-05 -5.40
318308 rs2842369 6_7 0.896 28.31 7.4e-05 5.18
419095 rs118063067 7_98 0.896 62.76 1.6e-04 -7.44
696025 rs62006522 15_13 0.895 24.61 6.4e-05 -3.68
363646 rs6939382 6_99 0.887 24.03 6.2e-05 4.35
589372 rs6590328 11_80 0.886 35.87 9.2e-05 -5.90
560435 rs7938708 11_22 0.882 24.56 6.3e-05 4.40
639262 rs1539547 13_13 0.880 23.54 6.0e-05 -4.39
82413 rs10182366 2_35 0.879 57.45 1.5e-04 -7.54
431722 rs310311 8_24 0.879 88.52 2.3e-04 -11.91
492688 rs1226592 9_50 0.875 27.05 6.9e-05 4.34
26685 rs138475481 1_58 0.874 37.25 9.5e-05 6.48
396225 rs17685 7_48 0.874 85.98 2.2e-04 -9.47
740835 rs3760230 17_3 0.873 33.73 8.6e-05 5.63
97672 rs3811056 2_66 0.872 28.07 7.1e-05 4.73
61853 rs1148917 1_130 0.869 25.44 6.4e-05 4.68
115532 rs72940807 2_106 0.869 39.87 1.0e-04 8.03
302629 rs62383025 5_82 0.869 29.97 7.6e-05 5.45
482601 rs117451470 9_30 0.869 25.68 6.5e-05 -4.67
480844 rs10971408 9_25 0.868 26.84 6.8e-05 -3.31
713948 rs59646751 15_48 0.868 67.29 1.7e-04 8.23
129753 rs13029395 2_133 0.867 30.27 7.6e-05 -5.65
350116 rs6571142 6_70 0.867 23.99 6.0e-05 -4.35
580537 rs625505 11_58 0.867 24.27 6.1e-05 -4.42
528343 rs11594851 10_47 0.866 24.65 6.2e-05 -4.49
870310 rs78366259 1_69 0.865 33.19 8.3e-05 4.65
205258 rs61359609 4_20 0.863 39.59 9.9e-05 -6.41
666658 rs2378813 14_7 0.857 24.77 6.2e-05 4.41
726503 rs7205341 16_27 0.856 39.06 9.7e-05 5.97
79161 rs74449116 2_28 0.855 25.57 6.4e-05 4.55
144125 rs697025 3_17 0.854 25.21 6.3e-05 -4.67
469504 rs10974435 9_4 0.854 33.44 8.3e-05 -6.63
696400 rs75855252 15_14 0.852 23.82 5.9e-05 -4.04
113179 rs6433115 2_103 0.848 29.05 7.2e-05 -5.77
964871 rs3918226 7_93 0.846 30.05 7.4e-05 5.57
30754 rs142669954 1_70 0.845 27.79 6.8e-05 5.24
72841 rs4580350 2_17 0.844 621.18 1.5e-03 2.57
785262 rs34188292 19_2 0.844 31.63 7.8e-05 6.17
49289 rs12024377 1_104 0.843 33.36 8.2e-05 -5.36
628223 rs80019595 12_74 0.843 53.20 1.3e-04 7.36
151820 rs146456061 3_35 0.842 30.85 7.5e-05 -4.46
172659 rs1501899 3_75 0.842 32.18 7.9e-05 -4.62
47833 rs145759918 1_101 0.839 25.96 6.3e-05 -4.94
616015 rs7137360 12_50 0.839 44.49 1.1e-04 6.48
238055 rs10031936 4_81 0.837 23.84 5.8e-05 -4.32
305262 rs6885118 5_87 0.825 25.26 6.1e-05 4.24
433838 rs139800483 8_29 0.825 25.63 6.1e-05 -4.62
329980 rs10947659 6_29 0.824 25.57 6.1e-05 4.50
614332 rs1690139 12_46 0.822 25.37 6.1e-05 -4.26
92985 rs146133332 2_55 0.821 24.39 5.8e-05 -4.30
99903 rs4241160 2_69 0.821 26.72 6.4e-05 4.31
695117 rs12908082 15_11 0.820 25.35 6.0e-05 -4.42
696575 rs4419033 15_15 0.820 118.03 2.8e-04 10.88
827516 rs2834321 21_15 0.817 88.68 2.1e-04 9.57
762242 rs940131 18_4 0.815 71.97 1.7e-04 8.61
61744 rs2171975 1_128 0.814 74.46 1.8e-04 -8.72
233161 rs6533522 4_72 0.813 26.41 6.2e-05 -4.53
463505 rs57286830 8_87 0.812 24.75 5.8e-05 -4.38
730696 rs7204242 16_35 0.810 25.94 6.1e-05 4.75
595623 rs12824533 12_11 0.809 25.46 6.0e-05 -4.56
187770 rs59976239 3_105 0.806 24.78 5.8e-05 -4.41
549176 rs7115054 11_2 0.805 88.42 2.1e-04 8.84
153610 rs35364740 3_39 0.801 27.93 6.5e-05 5.00SNPs 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
366679 rs3106169 6_104 0.722 58815.42 1.2e-01 10.90
366688 rs3106167 6_104 0.705 58815.13 1.2e-01 10.90
366680 rs3127598 6_104 0.135 58814.10 2.3e-02 10.89
366672 rs11755965 6_104 0.001 58795.53 1.4e-04 10.88
366683 rs60425481 6_104 1.000 58747.63 1.7e-01 2.37
366663 rs12194962 6_104 0.000 58670.08 0.0e+00 10.83
366681 rs3127597 6_104 0.000 58626.50 1.1e-16 10.77
396791 rs761767938 7_49 1.000 52520.77 1.5e-01 11.05
396799 rs1544459 7_49 1.000 51917.53 1.5e-01 11.28
396795 rs11972122 7_49 0.000 47342.11 1.4e-10 10.18
396796 rs11406602 7_49 0.000 47304.19 3.7e-07 10.20
396800 rs1544458 7_49 0.000 46487.06 0.0e+00 10.31
396790 rs6465794 7_49 0.000 45887.31 4.4e-17 10.03
396789 rs6465793 7_49 0.000 45886.69 5.9e-17 10.03
396820 rs10272350 7_49 0.000 45788.53 0.0e+00 9.89
396824 rs2463008 7_49 0.000 43648.16 0.0e+00 10.84
396830 rs10267180 7_49 0.000 43634.83 0.0e+00 10.79
396770 rs13240016 7_49 0.000 43402.80 0.0e+00 9.61
366642 rs3119311 6_104 1.000 43131.61 1.3e-01 16.16
396779 rs7799383 7_49 0.000 42348.78 0.0e+00 9.62
396788 rs10277379 7_49 1.000 40640.62 1.2e-01 12.60
358732 rs6923513 6_89 0.922 40129.02 1.1e-01 5.13
358715 rs2327654 6_89 0.485 40125.40 5.7e-02 5.11
358716 rs199804242 6_89 1.000 40055.20 1.2e-01 5.05
396782 rs7795371 7_49 0.000 39976.74 3.8e-13 12.47
358719 rs113527452 6_89 0.000 39915.74 1.5e-15 5.09
358724 rs200796875 6_89 0.000 39678.77 0.0e+00 4.95
358737 rs7756915 6_89 0.000 39436.09 0.0e+00 5.15
358730 rs6570040 6_89 0.000 37837.76 0.0e+00 4.88
358717 rs6570031 6_89 0.000 37741.52 0.0e+00 4.82
358718 rs9389323 6_89 0.000 37724.42 0.0e+00 4.80
396844 rs848470 7_49 0.000 35841.55 0.0e+00 -8.16
358734 rs9321531 6_89 0.000 33112.24 0.0e+00 4.53
358707 rs9321528 6_89 0.000 32710.24 0.0e+00 5.35
366636 rs3127579 6_104 0.000 31596.01 0.0e+00 17.91
358735 rs9494389 6_89 0.000 31093.10 0.0e+00 4.31
358739 rs5880262 6_89 0.000 31043.45 0.0e+00 4.66
396738 rs9640663 7_49 0.000 30333.36 0.0e+00 8.74
396734 rs2868787 7_49 0.000 30332.82 0.0e+00 8.72
358713 rs2208574 6_89 0.000 30012.60 0.0e+00 4.50
358712 rs1033755 6_89 0.000 29996.48 0.0e+00 4.27
396768 rs58729654 7_49 0.000 29854.37 0.0e+00 10.11
396749 rs4727451 7_49 0.000 29817.15 0.0e+00 8.48
358710 rs2038551 6_89 0.000 29470.18 0.0e+00 5.25
358721 rs9494377 6_89 0.000 29458.44 0.0e+00 4.32
358708 rs2038550 6_89 0.000 29391.21 0.0e+00 5.22
946075 rs148389913 6_88 1.000 29211.19 8.5e-02 7.02
946073 rs667890 6_88 1.000 28879.64 8.4e-02 7.43
946079 rs561826 6_88 1.000 28756.48 8.4e-02 7.06
396762 rs6465771 7_49 0.000 28679.65 0.0e+00 7.64SNPs 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
366683 rs60425481 6_104 1.000 58747.63 0.1700 2.37
396791 rs761767938 7_49 1.000 52520.77 0.1500 11.05
396799 rs1544459 7_49 1.000 51917.53 0.1500 11.28
366642 rs3119311 6_104 1.000 43131.61 0.1300 16.16
358716 rs199804242 6_89 1.000 40055.20 0.1200 5.05
366679 rs3106169 6_104 0.722 58815.42 0.1200 10.90
366688 rs3106167 6_104 0.705 58815.13 0.1200 10.90
396788 rs10277379 7_49 1.000 40640.62 0.1200 12.60
358732 rs6923513 6_89 0.922 40129.02 0.1100 5.13
946075 rs148389913 6_88 1.000 29211.19 0.0850 7.02
946073 rs667890 6_88 1.000 28879.64 0.0840 7.43
946079 rs561826 6_88 1.000 28756.48 0.0840 7.06
358715 rs2327654 6_89 0.485 40125.40 0.0570 5.11
1061897 rs62080193 17_46 1.000 18469.38 0.0540 -4.07
1061905 rs113375436 17_46 1.000 18482.75 0.0540 -3.98
974345 rs201524046 10_81 1.000 15840.10 0.0460 -6.50
974364 rs568584257 10_81 1.000 15784.68 0.0460 -2.04
974348 rs74160216 10_81 0.921 15779.22 0.0420 -2.10
1099298 rs202143810 20_38 1.000 13305.06 0.0390 -6.50
57319 rs766167074 1_118 1.000 13236.24 0.0380 -4.30
1078451 rs113176985 19_34 0.996 12013.34 0.0350 -3.09
1078454 rs374141296 19_34 1.000 12084.60 0.0350 -3.31
190069 rs146797780 3_110 1.000 9837.89 0.0290 2.47
190070 rs7636471 3_110 1.000 9855.72 0.0290 2.53
1099295 rs6089961 20_38 0.659 13131.44 0.0250 -6.78
1099297 rs2738758 20_38 0.659 13131.44 0.0250 -6.78
366680 rs3127598 6_104 0.135 58814.10 0.0230 10.89
57316 rs10489611 1_118 0.456 13150.75 0.0170 -4.67
1061986 rs57707013 17_46 0.503 11137.05 0.0160 -5.84
57310 rs2256908 1_118 0.363 13149.83 0.0140 -4.67
1099278 rs2750483 20_38 0.360 13127.25 0.0140 -6.79
57318 rs971534 1_118 0.285 13150.51 0.0110 -4.65
57317 rs2486737 1_118 0.266 13150.46 0.0100 -4.65
461915 rs6996786 8_84 1.000 3441.00 0.0100 1.60
461922 rs200311702 8_84 1.000 3360.05 0.0098 3.90
57313 rs2790891 1_118 0.251 13149.59 0.0096 -4.66
57314 rs2491405 1_118 0.251 13149.59 0.0096 -4.66
1099276 rs35201382 20_38 0.247 13127.33 0.0094 -6.77
1099277 rs67468102 20_38 0.248 13125.46 0.0094 -6.78
57326 rs2211176 1_118 0.231 13145.30 0.0088 -4.66
57327 rs2790882 1_118 0.231 13145.30 0.0088 -4.66
1099273 rs2315009 20_38 0.192 13123.23 0.0073 -6.79
893349 rs61114860 3_78 1.000 2188.49 0.0064 2.67
1078458 rs2946865 19_34 0.178 11949.98 0.0062 -3.17
57325 rs2248646 1_118 0.157 13144.52 0.0060 -4.65
57306 rs1076804 1_118 0.130 13130.83 0.0050 -4.68
1078449 rs73056069 19_34 0.108 11942.27 0.0037 -3.20
213680 rs66998340 4_36 1.000 1218.38 0.0035 -3.18
1078444 rs35295508 19_34 0.092 11982.46 0.0032 -3.15
213683 rs728294 4_36 0.771 1249.53 0.0028 -3.21SNPs 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
697396 rs1145077 15_17 0.373 447.75 4.9e-04 27.01
697393 rs1153855 15_17 0.313 447.14 4.1e-04 27.00
697398 rs1346267 15_17 0.229 446.31 3.0e-04 26.98
697392 rs35410548 15_17 0.088 442.15 1.1e-04 26.89
222759 rs17253722 4_52 0.697 504.83 1.0e-03 26.25
222758 rs60529470 4_52 0.305 502.99 4.5e-04 26.19
697400 rs1145074 15_17 0.161 455.96 2.1e-04 26.09
697395 rs2114501 15_17 0.048 452.16 6.3e-05 26.00
697388 rs4775909 15_17 0.028 450.52 3.7e-05 25.98
697390 rs4625670 15_17 0.024 449.96 3.2e-05 25.96
697389 rs77940260 15_17 0.017 448.86 2.2e-05 25.91
697391 rs3047503 15_17 0.017 448.78 2.2e-05 25.91
697386 rs143910737 15_17 0.006 445.43 7.2e-06 25.78
697397 rs1153852 15_17 0.001 409.81 1.5e-06 25.47
697384 rs35715322 15_17 0.001 390.61 6.7e-07 25.39
697403 rs2433616 15_17 0.001 390.28 1.0e-06 24.72
697385 rs1613559 15_17 0.001 409.68 7.2e-07 24.70
697383 rs12593370 15_17 0.001 403.84 6.9e-07 24.56
222772 rs13146163 4_52 0.008 401.16 9.8e-06 24.33
697382 rs66893308 15_17 0.001 363.34 8.2e-07 24.15
722540 rs9933330 16_19 0.976 538.20 1.5e-03 -24.08
722538 rs28544423 16_19 0.024 530.71 3.6e-05 -23.84
722534 rs35830321 16_19 0.000 517.55 6.9e-10 -23.73
417240 rs10224210 7_94 0.659 520.03 1.0e-03 23.69
417242 rs10224002 7_94 0.346 520.49 5.2e-04 23.66
722535 rs12934320 16_19 0.000 521.78 5.5e-07 -23.62
722537 rs28640218 16_19 0.000 517.23 1.2e-08 -23.54
697381 rs2015295 15_17 0.001 306.13 6.5e-07 22.29
697379 rs11636114 15_17 0.001 299.80 7.0e-07 -22.11
697376 rs77342224 15_17 0.001 296.32 7.3e-07 -21.99
697373 rs12909625 15_17 0.001 275.29 8.9e-07 -21.16
697374 rs12909883 15_17 0.001 275.35 8.9e-07 -21.16
697375 rs8041874 15_17 0.001 274.91 9.0e-07 -21.15
697369 rs11854325 15_17 0.001 268.54 8.9e-07 -20.90
697370 rs11632778 15_17 0.001 268.26 9.0e-07 -20.89
222734 rs72657813 4_52 0.003 278.92 2.4e-06 20.55
222775 rs2068062 4_52 0.004 276.45 2.9e-06 20.55
222776 rs13106227 4_52 0.003 275.29 2.8e-06 20.53
222777 rs11730486 4_52 0.003 274.78 2.7e-06 20.52
222727 rs3839121 4_52 0.003 276.67 2.2e-06 20.51
222778 rs4859683 4_52 0.003 273.86 2.7e-06 20.50
697372 rs12910143 15_17 0.001 291.75 8.0e-07 -20.47
417238 rs66497154 7_94 0.002 380.62 2.2e-06 20.35
222743 rs59795151 4_52 0.002 259.80 1.5e-06 20.13
222779 rs4493564 4_52 0.002 258.32 1.8e-06 20.12
754336 rs11650989 17_36 1.000 243.42 7.1e-04 -19.89
722539 rs7193058 16_19 0.000 421.77 3.1e-10 19.85
274011 rs700231 5_26 0.587 206.74 3.5e-04 19.81
274013 rs700237 5_26 0.405 205.61 2.4e-04 19.79
314560 rs7447593 5_106 1.000 213.02 6.2e-04 19.58Gene 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] 32if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)SLC22A7 gene(s) from the input list not found in DisGeNET CURATEDCYB561D1 gene(s) from the input list not found in DisGeNET CURATEDSYTL1 gene(s) from the input list not found in DisGeNET CURATEDRPS11 gene(s) from the input list not found in DisGeNET CURATEDKIF26B gene(s) from the input list not found in DisGeNET CURATEDPPM1J gene(s) from the input list not found in DisGeNET CURATEDCDK5RAP3 gene(s) from the input list not found in DisGeNET CURATEDGTDC1 gene(s) from the input list not found in DisGeNET CURATEDTMEM176B gene(s) from the input list not found in DisGeNET CURATEDPRIM1 gene(s) from the input list not found in DisGeNET CURATEDTNKS gene(s) from the input list not found in DisGeNET CURATEDDLEU1 gene(s) from the input list not found in DisGeNET CURATEDAMZ2 gene(s) from the input list not found in DisGeNET CURATEDPPDPF gene(s) from the input list not found in DisGeNET CURATED Description
38 Opisthorchiasis
45 Rickettsia Infections
58 Tick-Borne Infections
59 Tick-Borne Diseases
80 Opisthorchis felineus Infection
81 Opisthorchis viverrini Infection
94 Dilatation of the bladder
125 DEAFNESS, AUTOSOMAL RECESSIVE 32
134 GLUCOCORTICOID DEFICIENCY 4 WITH OR WITHOUT MINERALOCORTICOID DEFICIENCY
138 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 20
FDR Ratio BgRatio
38 0.02445606 1/18 1/9703
45 0.02445606 1/18 1/9703
58 0.02445606 1/18 1/9703
59 0.02445606 1/18 1/9703
80 0.02445606 1/18 1/9703
81 0.02445606 1/18 1/9703
94 0.02445606 1/18 1/9703
125 0.02445606 1/18 1/9703
134 0.02445606 1/18 1/9703
138 0.02445606 1/18 1/9703******************************************
* *
* 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