Alkaline phosphatase (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 | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 03e541c | wesleycrouse | 2021-07-29 | Cleaning up report generation |
| Rmd | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
| html | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Alkaline phosphatase (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-30610_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0104984229 0.0001911337 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
94.47464 23.67386 #report sample size
print(sample_size)[1] 344292#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.03196242 0.11430507 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02168881 0.55700002Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
7258 ICA1L 2_120 1.000 95.49 2.8e-04 -10.69
6089 FADS1 11_34 0.999 425.10 1.2e-03 19.90
1652 PCIF1 20_28 0.999 79.38 2.3e-04 -8.55
5665 CNIH4 1_114 0.997 38.13 1.1e-04 -5.74
5095 DNAJC13 3_82 0.996 57.66 1.7e-04 -8.71
1185 TGDS 13_47 0.996 223.19 6.5e-04 16.68
10100 SELL 1_83 0.991 35.02 1.0e-04 6.09
10757 MAFB 20_24 0.991 94.16 2.7e-04 -10.36
1822 AXIN1 16_1 0.978 36.11 1.0e-04 5.03
9863 LAMP1 13_62 0.977 33.91 9.6e-05 -5.67
4360 TRIM5 11_4 0.970 143.93 4.1e-04 -10.36
10765 ZDHHC18 1_18 0.960 45.93 1.3e-04 -6.99
10954 NYNRIN 14_3 0.959 46.86 1.3e-04 -5.27
1778 KPNA3 13_21 0.956 27.35 7.6e-05 4.90
8329 GPRC5C 17_41 0.946 56.13 1.5e-04 7.19
2312 LIPA 10_57 0.934 33.07 9.0e-05 5.59
7483 CHMP4C 8_58 0.923 26.88 7.2e-05 4.77
10682 CES1 16_29 0.921 36.78 9.8e-05 -3.69
925 NFKB2 10_65 0.898 213.35 5.6e-04 -16.02
3223 CENPF 1_109 0.892 23.15 6.0e-05 4.41
11815 MPV17L2 19_14 0.886 52.17 1.3e-04 -7.46
5851 MYLK4 6_3 0.884 36.07 9.3e-05 -5.43
1224 KIF16B 20_12 0.882 44.24 1.1e-04 6.31
3323 NEK6 9_64 0.849 31.64 7.8e-05 5.05
9223 ZBTB7A 19_4 0.845 48.65 1.2e-04 -6.49
8590 CTSW 11_36 0.802 29.61 6.9e-05 4.65Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
5543 NBPF3 1_15 0.000 2519.31 0.0e+00 54.75
2723 ALDH5A1 6_18 0.000 1523.60 0.0e+00 38.11
6652 RER1 1_2 0.000 1387.32 6.4e-07 6.37
8866 ABO 9_70 0.001 1298.17 2.9e-06 25.25
5934 MFHAS1 8_11 0.000 1037.74 0.0e+00 8.64
3725 MRS2 6_18 0.000 692.00 1.1e-18 -15.10
11790 CLDN23 8_11 0.000 661.59 0.0e+00 8.63
9034 MAMSTR 19_33 0.000 603.84 7.8e-07 32.47
11701 RP11-10A14.5 8_11 0.000 597.04 0.0e+00 9.79
1948 ERI1 8_11 0.000 542.25 0.0e+00 -9.89
2097 RASIP1 19_33 0.000 529.35 1.5e-07 -31.45
4403 CLEC10A 17_6 0.000 488.33 1.3e-16 -12.49
6089 FADS1 11_34 0.999 425.10 1.2e-03 19.90
11160 LINC00339 1_15 0.000 385.07 0.0e+00 -11.61
12434 RP5-965G21.4 20_18 0.005 363.95 4.9e-06 19.00
1656 PYGB 20_18 0.011 342.04 1.1e-05 -18.39
4137 MAU2 19_15 0.000 299.08 1.0e-07 16.00
9448 CLDN7 17_6 0.000 293.26 0.0e+00 9.57
6876 ADAMTS13 9_70 0.000 289.47 1.9e-07 -25.72
4636 FADS2 11_34 0.003 278.43 2.5e-06 15.57Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
6089 FADS1 11_34 0.999 425.10 0.00120 19.90
1185 TGDS 13_47 0.996 223.19 0.00065 16.68
925 NFKB2 10_65 0.898 213.35 0.00056 -16.02
4360 TRIM5 11_4 0.970 143.93 0.00041 -10.36
7258 ICA1L 2_120 1.000 95.49 0.00028 -10.69
10757 MAFB 20_24 0.991 94.16 0.00027 -10.36
1652 PCIF1 20_28 0.999 79.38 0.00023 -8.55
7786 CATSPER2 15_16 0.778 78.18 0.00018 -8.61
5095 DNAJC13 3_82 0.996 57.66 0.00017 -8.71
8329 GPRC5C 17_41 0.946 56.13 0.00015 7.19
6912 IL6R 1_75 0.325 143.79 0.00014 8.12
10765 ZDHHC18 1_18 0.960 45.93 0.00013 -6.99
10505 UGT2B17 4_48 0.641 67.70 0.00013 -10.60
10954 NYNRIN 14_3 0.959 46.86 0.00013 -5.27
11815 MPV17L2 19_14 0.886 52.17 0.00013 -7.46
9297 GPBAR1 2_129 0.602 68.27 0.00012 7.95
9223 ZBTB7A 19_4 0.845 48.65 0.00012 -6.49
4712 AMHR2 12_33 0.789 47.43 0.00011 8.37
5665 CNIH4 1_114 0.997 38.13 0.00011 -5.74
1145 ACHE 7_62 0.594 60.98 0.00011 7.90Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5543 NBPF3 1_15 0.000 2519.31 0.0e+00 54.75
2723 ALDH5A1 6_18 0.000 1523.60 0.0e+00 38.11
9034 MAMSTR 19_33 0.000 603.84 7.8e-07 32.47
2097 RASIP1 19_33 0.000 529.35 1.5e-07 -31.45
6876 ADAMTS13 9_70 0.000 289.47 1.9e-07 -25.72
8866 ABO 9_70 0.001 1298.17 2.9e-06 25.25
6089 FADS1 11_34 0.999 425.10 1.2e-03 19.90
2517 ST3GAL4 11_77 0.000 239.99 1.7e-10 19.50
5995 GBGT1 9_70 0.000 137.25 6.3e-08 19.12
12434 RP5-965G21.4 20_18 0.005 363.95 4.9e-06 19.00
5996 SURF1 9_70 0.000 123.69 5.3e-08 18.55
1656 PYGB 20_18 0.011 342.04 1.1e-05 -18.39
1185 TGDS 13_47 0.996 223.19 6.5e-04 16.68
6000 REXO4 9_70 0.000 71.23 4.6e-08 16.29
6302 GPR180 13_47 0.005 212.06 3.4e-06 16.22
925 NFKB2 10_65 0.898 213.35 5.6e-04 -16.02
4137 MAU2 19_15 0.000 299.08 1.0e-07 16.00
7930 TMC4 19_37 0.001 226.16 8.1e-07 -15.97
5503 NTN5 19_33 0.001 173.30 2.9e-07 15.77
4636 FADS2 11_34 0.003 278.43 2.5e-06 15.57Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.0408292#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
5543 NBPF3 1_15 0.000 2519.31 0.0e+00 54.75
2723 ALDH5A1 6_18 0.000 1523.60 0.0e+00 38.11
9034 MAMSTR 19_33 0.000 603.84 7.8e-07 32.47
2097 RASIP1 19_33 0.000 529.35 1.5e-07 -31.45
6876 ADAMTS13 9_70 0.000 289.47 1.9e-07 -25.72
8866 ABO 9_70 0.001 1298.17 2.9e-06 25.25
6089 FADS1 11_34 0.999 425.10 1.2e-03 19.90
2517 ST3GAL4 11_77 0.000 239.99 1.7e-10 19.50
5995 GBGT1 9_70 0.000 137.25 6.3e-08 19.12
12434 RP5-965G21.4 20_18 0.005 363.95 4.9e-06 19.00
5996 SURF1 9_70 0.000 123.69 5.3e-08 18.55
1656 PYGB 20_18 0.011 342.04 1.1e-05 -18.39
1185 TGDS 13_47 0.996 223.19 6.5e-04 16.68
6000 REXO4 9_70 0.000 71.23 4.6e-08 16.29
6302 GPR180 13_47 0.005 212.06 3.4e-06 16.22
925 NFKB2 10_65 0.898 213.35 5.6e-04 -16.02
4137 MAU2 19_15 0.000 299.08 1.0e-07 16.00
7930 TMC4 19_37 0.001 226.16 8.1e-07 -15.97
5503 NTN5 19_33 0.001 173.30 2.9e-07 15.77
4636 FADS2 11_34 0.003 278.43 2.5e-06 15.57Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 1_15"
genename region_tag susie_pip mu2 PVE z
5543 NBPF3 1_15 0 2519.31 0 54.75
917 RAP1GAP 1_15 0 269.55 0 3.57
1270 USP48 1_15 0 33.61 0 -0.20
10051 LDLRAD2 1_15 0 76.87 0 -3.01
5544 HSPG2 1_15 0 46.93 0 -8.09
5542 CELA3A 1_15 0 14.59 0 0.49
11160 LINC00339 1_15 0 385.07 0 -11.61
769 CDC42 1_15 0 45.18 0 -3.96
7080 WNT4 1_15 0 61.55 0 -2.23
9720 ZBTB40 1_15 0 10.28 0 -2.56
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_18"
genename region_tag susie_pip mu2 PVE z
3725 MRS2 6_18 0 692.00 1.1e-18 -15.10
2723 ALDH5A1 6_18 0 1523.60 0.0e+00 38.11
4959 KIAA0319 6_18 0 88.79 0.0e+00 2.98
2670 TDP2 6_18 0 54.38 0.0e+00 1.22
2727 ACOT13 6_18 0 12.91 0.0e+00 -5.83
2730 C6orf62 6_18 0 125.59 0.0e+00 11.76
2732 GMNN 6_18 0 21.36 0.0e+00 -4.28
2690 FAM65B 6_18 0 54.43 0.0e+00 -6.96
11977 C6orf229 6_18 0 185.49 0.0e+00 -11.15
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_33"
genename region_tag susie_pip mu2 PVE z
2050 PRKD2 19_33 0.000 10.02 3.7e-09 1.09
1257 STRN4 19_33 0.000 5.71 1.4e-09 -0.37
9389 FKRP 19_33 0.001 28.39 6.8e-08 2.14
400 AP2S1 19_33 0.000 5.13 1.2e-09 0.36
6825 ARHGAP35 19_33 0.000 5.03 1.1e-09 0.34
5502 SAE1 19_33 0.006 27.86 5.1e-07 3.94
2055 BBC3 19_33 0.226 59.61 3.9e-05 4.92
2053 CCDC9 19_33 0.000 19.03 1.8e-08 2.41
11894 INAFM1 19_33 0.000 5.62 1.3e-09 0.08
4639 C5AR2 19_33 0.000 12.42 6.0e-09 -1.28
4635 DHX34 19_33 0.000 7.13 2.0e-09 -0.22
2077 MEIS3 19_33 0.000 5.27 1.2e-09 -0.41
2074 NAPA 19_33 0.000 11.46 4.6e-09 0.79
3238 ZNF541 19_33 0.000 6.05 1.4e-09 -0.71
572 GLTSCR1 19_33 0.000 6.36 1.6e-09 0.27
294 EHD2 19_33 0.000 5.07 1.1e-09 0.20
2066 GLTSCR2 19_33 0.000 9.15 3.7e-09 -1.21
2073 SULT2A1 19_33 0.741 47.22 1.0e-04 -7.90
2089 PLA2G4C 19_33 0.000 6.04 1.3e-09 1.83
2086 LIG1 19_33 0.000 5.01 1.1e-09 -0.53
9808 C19orf68 19_33 0.000 7.13 1.9e-09 -0.79
2091 CABP5 19_33 0.000 5.39 1.2e-09 1.20
2085 CARD8 19_33 0.000 14.95 8.3e-09 1.86
5501 EMP3 19_33 0.000 9.76 2.6e-09 3.36
2084 CCDC114 19_33 0.000 13.26 3.1e-09 -4.36
2081 GRWD1 19_33 0.000 12.81 3.1e-09 3.24
2080 CYTH2 19_33 0.000 12.75 3.0e-09 -3.46
9493 KCNJ14 19_33 0.000 16.10 4.6e-09 -3.75
5504 LMTK3 19_33 0.000 7.01 1.6e-09 -2.47
1173 SULT2B1 19_33 0.000 6.63 1.5e-09 1.73
573 SPHK2 19_33 0.000 83.49 1.9e-08 12.91
574 CA11 19_33 0.000 28.09 3.1e-08 6.62
5503 NTN5 19_33 0.001 173.30 2.9e-07 15.77
9034 MAMSTR 19_33 0.000 603.84 7.8e-07 32.47
2097 RASIP1 19_33 0.000 529.35 1.5e-07 -31.45
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 9_70"
genename region_tag susie_pip mu2 PVE z
3800 DDX31 9_70 0.001 43.93 1.1e-07 -3.34
7622 SPACA9 9_70 0.002 32.76 1.7e-07 -2.91
7623 TSC1 9_70 0.000 5.90 2.6e-09 -0.52
7624 GFI1B 9_70 0.000 11.15 8.6e-09 0.98
6002 GTF3C5 9_70 0.001 20.16 4.0e-08 1.63
5995 GBGT1 9_70 0.000 137.25 6.3e-08 19.12
8866 ABO 9_70 0.001 1298.17 2.9e-06 25.25
5998 SURF6 9_70 0.000 167.09 1.3e-07 -2.57
6001 RPL7A 9_70 0.001 77.06 1.4e-07 -11.01
5996 SURF1 9_70 0.000 123.69 5.3e-08 18.55
5997 SURF2 9_70 0.000 46.92 3.9e-08 -7.21
5994 SURF4 9_70 0.001 118.27 3.7e-07 13.85
10687 STKLD1 9_70 0.000 45.85 3.6e-08 7.16
6876 ADAMTS13 9_70 0.000 289.47 1.9e-07 -25.72
6000 REXO4 9_70 0.000 71.23 4.6e-08 16.29
3633 DBH 9_70 0.000 5.54 2.7e-09 0.59
3632 SARDH 9_70 0.000 9.84 8.0e-09 0.13
6868 VAV2 9_70 0.000 5.73 2.8e-09 -0.65
11444 LINC00094 9_70 0.000 19.28 2.7e-08 -2.38
8258 BRD3 9_70 0.000 8.37 4.5e-09 -1.48
10249 WDR5 9_70 0.000 4.98 2.1e-09 -0.13
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_34"
genename region_tag susie_pip mu2 PVE z
10165 FAM111B 11_34 0.010 19.29 5.5e-07 -1.88
7794 FAM111A 11_34 0.003 7.46 6.3e-08 -0.60
2506 DTX4 11_34 0.003 6.44 4.7e-08 -0.74
10468 MPEG1 11_34 0.002 5.04 3.3e-08 0.27
2515 MS4A6A 11_34 0.033 30.23 2.9e-06 2.76
7815 PATL1 11_34 0.002 5.10 3.3e-08 -0.28
7817 STX3 11_34 0.004 9.60 9.8e-08 1.10
7818 MRPL16 11_34 0.003 8.06 7.1e-08 -0.87
4634 GIF 11_34 0.002 5.43 3.7e-08 -0.09
4638 TCN1 11_34 0.003 6.25 4.8e-08 -0.15
6096 MS4A2 11_34 0.006 14.04 2.4e-07 -1.82
11819 AP001257.1 11_34 0.003 6.27 4.7e-08 0.76
11116 MS4A4E 11_34 0.023 27.08 1.8e-06 2.92
2516 MS4A4A 11_34 0.003 7.45 6.6e-08 0.86
7825 MS4A6E 11_34 0.003 9.33 9.1e-08 -1.23
7826 MS4A7 11_34 0.015 24.25 1.1e-06 2.57
7827 MS4A14 11_34 0.003 6.84 5.1e-08 -0.98
2519 CCDC86 11_34 0.005 11.22 1.5e-07 0.86
9570 PTGDR2 11_34 0.005 12.20 1.6e-07 1.59
6093 ZP1 11_34 0.002 6.58 4.7e-08 -1.00
2520 PRPF19 11_34 0.015 24.77 1.1e-06 -2.50
2521 TMEM109 11_34 0.007 15.59 3.0e-07 -1.73
2546 SLC15A3 11_34 0.003 10.58 9.5e-08 -1.89
2547 CD5 11_34 0.017 31.12 1.5e-06 3.37
8008 VPS37C 11_34 0.004 13.75 1.8e-07 -1.99
11874 PGA5 11_34 0.008 15.28 3.6e-07 -0.31
11340 PGA3 11_34 0.005 11.40 1.7e-07 0.00
8009 VWCE 11_34 0.004 10.31 1.2e-07 0.07
6088 TMEM138 11_34 0.004 16.56 1.9e-07 2.43
7030 CYB561A3 11_34 0.004 16.56 1.9e-07 2.43
9981 TMEM216 11_34 0.005 11.74 1.8e-07 -0.67
11871 RP11-286N22.8 11_34 0.010 18.22 5.3e-07 -1.44
4631 DAGLA 11_34 0.002 26.90 1.8e-07 -4.83
3765 MYRF 11_34 0.002 45.92 3.0e-07 6.64
4636 FADS2 11_34 0.003 278.43 2.5e-06 15.57
4637 TMEM258 11_34 0.003 106.68 8.3e-07 9.77
6089 FADS1 11_34 0.999 425.10 1.2e-03 19.90
11190 FADS3 11_34 0.003 12.34 1.2e-07 -2.12
8011 BEST1 11_34 0.003 23.34 2.4e-07 3.54
6092 INCENP 11_34 0.027 35.77 2.8e-06 3.55
7032 ASRGL1 11_34 0.015 20.27 8.9e-07 -1.09
| 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
5824 rs77025042 1_14 1.000 200.61 5.8e-04 -13.03
5840 rs148717955 1_14 1.000 511.69 1.5e-03 5.52
5846 rs72657133 1_14 1.000 1196.52 3.5e-03 -23.99
31706 rs6679677 1_70 1.000 80.70 2.3e-04 -7.89
52468 rs1223802 1_108 1.000 111.25 3.2e-04 -10.30
62455 rs12239046 1_131 1.000 88.39 2.6e-04 9.72
64420 rs10183939 2_2 1.000 38.28 1.1e-04 -6.00
72181 rs780093 2_16 1.000 420.75 1.2e-03 -21.28
92448 rs2860399 2_55 1.000 63.39 1.8e-04 -5.70
92461 rs2176569 2_55 1.000 45.13 1.3e-04 3.49
103726 rs2277882 2_79 1.000 79.51 2.3e-04 -7.09
103779 rs1257220 2_79 1.000 131.75 3.8e-04 -10.61
113652 rs1862069 2_102 1.000 136.80 4.0e-04 -16.41
122168 rs2041080 2_117 1.000 50.59 1.5e-04 10.17
222201 rs6811535 4_52 1.000 52.06 1.5e-04 7.67
272743 rs1428967 5_25 1.000 112.21 3.3e-04 11.11
324123 rs151189505 6_17 1.000 129.03 3.7e-04 10.73
324360 rs9393530 6_18 1.000 204.37 5.9e-04 0.12
324472 rs10946700 6_18 1.000 2001.13 5.8e-03 44.85
324653 rs114584234 6_19 1.000 148.77 4.3e-04 13.26
324657 rs7738816 6_19 1.000 58.38 1.7e-04 9.22
324664 rs9461081 6_19 1.000 134.57 3.9e-04 -13.60
325143 rs115740542 6_20 1.000 122.32 3.6e-04 11.58
370554 rs12208357 6_103 1.000 72.74 2.1e-04 6.41
430773 rs2428 8_11 1.000 2786.21 8.1e-03 15.16
430778 rs758184196 8_11 1.000 2897.15 8.4e-03 -3.89
431030 rs330096 8_12 1.000 337.58 9.8e-04 19.73
431246 rs2048656 8_13 1.000 160.71 4.7e-04 13.82
431932 rs10105588 8_14 1.000 178.64 5.2e-04 -4.87
431940 rs10092177 8_14 1.000 265.14 7.7e-04 -12.10
431942 rs779417490 8_14 1.000 253.95 7.4e-04 -4.17
464202 rs10505348 8_79 1.000 200.43 5.8e-04 19.99
466255 rs13252684 8_83 1.000 374.46 1.1e-03 16.92
466256 rs6987702 8_83 1.000 345.17 1.0e-03 15.28
475419 rs4471106 9_6 1.000 146.50 4.3e-04 14.15
486714 rs11791806 9_27 1.000 37.23 1.1e-04 -5.43
498402 rs2183745 9_50 1.000 363.42 1.1e-03 -21.03
498419 rs146562086 9_50 1.000 76.17 2.2e-04 -8.01
498433 rs35381859 9_50 1.000 173.91 5.1e-04 7.49
498490 rs10448294 9_50 1.000 115.33 3.3e-04 -0.68
508103 rs115478735 9_70 1.000 3945.43 1.1e-02 -108.55
535646 rs2186235 10_51 1.000 48.47 1.4e-04 7.07
551536 rs72636980 11_1 1.000 142.20 4.1e-04 13.97
551576 rs55642248 11_1 1.000 205.73 6.0e-04 -13.11
589125 rs116891075 11_77 1.000 45.83 1.3e-04 -8.07
589173 rs240536 11_77 1.000 89.54 2.6e-04 -14.18
589181 rs10893498 11_77 1.000 317.88 9.2e-04 -18.84
589190 rs10790802 11_77 1.000 394.43 1.1e-03 25.30
589193 rs112282958 11_77 1.000 80.76 2.3e-04 -11.11
592482 rs2191159 12_1 1.000 238.35 6.9e-04 15.88
592483 rs6489532 12_1 1.000 56.07 1.6e-04 5.60
593832 rs61909253 12_5 1.000 44.74 1.3e-04 -5.67
623448 rs117615171 12_59 1.000 35.75 1.0e-04 5.58
658466 rs139406059 13_48 1.000 78.54 2.3e-04 3.68
687960 rs11439803 14_49 1.000 207.46 6.0e-04 0.83
687967 rs1243165 14_49 1.000 223.99 6.5e-04 4.28
691095 rs729183 14_54 1.000 39.30 1.1e-04 5.51
702513 rs62000868 15_27 1.000 76.26 2.2e-04 -9.31
702519 rs2070895 15_27 1.000 161.18 4.7e-04 -12.96
730495 rs1512627 16_37 1.000 49.27 1.4e-04 6.30
734589 rs2255451 16_49 1.000 59.36 1.7e-04 -8.23
738387 rs11078597 17_2 1.000 121.44 3.5e-04 -12.61
739960 rs144129583 17_7 1.000 180.69 5.2e-04 13.93
749641 rs755736 17_29 1.000 122.65 3.6e-04 -7.79
785543 rs2163856 19_9 1.000 64.72 1.9e-04 -7.07
794085 rs11671669 19_30 1.000 100.70 2.9e-04 11.96
794091 rs10853742 19_30 1.000 300.90 8.7e-04 -18.79
795189 rs814573 19_31 1.000 132.44 3.8e-04 -12.40
795190 rs117664574 19_31 1.000 47.72 1.4e-04 7.98
796435 rs601338 19_33 1.000 1305.55 3.8e-03 -50.96
796481 rs116922356 19_34 1.000 48.82 1.4e-04 9.13
796483 rs55975925 19_34 1.000 152.81 4.4e-04 -12.91
833991 rs16996442 22_14 1.000 45.01 1.3e-04 7.43
844401 rs199779538 1_2 1.000 2381.97 6.9e-03 -3.23
849666 rs149344982 1_15 1.000 5614.82 1.6e-02 -75.15
849771 rs72659192 1_15 1.000 1621.02 4.7e-03 52.94
891483 rs201939100 4_48 1.000 63.76 1.9e-04 -2.32
930808 rs45516493 10_65 1.000 97.11 2.8e-04 -13.44
930919 rs72845815 10_66 1.000 151.60 4.4e-04 -13.00
942064 rs11601507 11_4 1.000 282.00 8.2e-04 16.49
988261 rs11621792 14_3 1.000 193.60 5.6e-04 -13.84
1029018 rs201685059 16_29 1.000 842.61 2.4e-03 4.82
1032501 rs9302635 16_38 1.000 376.09 1.1e-03 17.64
1051742 rs55714927 17_6 1.000 1517.78 4.4e-03 36.59
1051924 rs5409 17_6 1.000 327.14 9.5e-04 9.00
1052012 rs78173576 17_6 1.000 188.18 5.5e-04 15.28
1053846 rs201963278 17_23 1.000 444.84 1.3e-03 3.44
29706 rs1730862 1_66 0.999 32.86 9.5e-05 -5.52
36746 rs61804205 1_79 0.999 47.60 1.4e-04 7.48
193099 rs56328339 3_115 0.999 35.28 1.0e-04 -5.73
232519 rs4698813 4_71 0.999 33.22 9.6e-05 4.04
245758 rs72727873 4_98 0.999 40.25 1.2e-04 -4.44
321243 rs10456776 6_13 0.999 57.47 1.7e-04 -7.65
367363 rs6557156 6_99 0.999 33.02 9.6e-05 6.08
384481 rs11983782 7_20 0.999 41.12 1.2e-04 -6.32
486718 rs2812357 9_27 0.999 42.12 1.2e-04 6.36
625306 rs1215606 12_64 0.999 33.95 9.9e-05 5.68
738687 rs2240731 17_3 0.999 38.49 1.1e-04 -6.17
775917 rs62098355 18_34 0.999 42.01 1.2e-04 8.78
787136 rs10405035 19_12 0.999 34.55 1.0e-04 -5.70
849148 rs4654745 1_15 0.999 2671.16 7.7e-03 -51.71
30305 rs507482 1_67 0.998 68.87 2.0e-04 -8.07
122180 rs7595923 2_118 0.998 34.63 1.0e-04 6.90
407141 rs1207731 7_59 0.998 31.80 9.2e-05 -5.32
430972 rs2929451 8_11 0.998 1743.87 5.1e-03 -16.29
431043 rs13265179 8_12 0.998 389.88 1.1e-03 28.39
541247 rs7069475 10_64 0.998 46.90 1.4e-04 -8.16
753777 rs1801689 17_38 0.998 31.15 9.0e-05 -4.95
771404 rs2878889 18_27 0.998 36.04 1.0e-04 -6.11
782913 rs35254404 19_2 0.998 32.50 9.4e-05 -4.43
844408 rs7519807 1_2 0.998 2376.34 6.9e-03 -3.16
5783 rs3026894 1_14 0.997 223.40 6.5e-04 4.48
110381 rs13383985 2_94 0.997 43.50 1.3e-04 -6.42
301128 rs4705986 5_80 0.997 37.91 1.1e-04 -6.01
389739 rs6974574 7_28 0.997 33.00 9.6e-05 -4.93
610310 rs7397189 12_36 0.997 40.61 1.2e-04 -6.46
1127026 rs1800961 20_28 0.997 37.34 1.1e-04 -5.76
41285 rs6682862 1_87 0.996 58.80 1.7e-04 7.68
609390 rs930900 12_33 0.996 87.94 2.5e-04 11.32
687388 rs11624512 14_46 0.996 62.55 1.8e-04 -7.98
734580 rs74032329 16_49 0.996 34.97 1.0e-04 -5.33
749651 rs73987397 17_29 0.996 79.37 2.3e-04 -0.64
775922 rs56051253 18_34 0.996 59.60 1.7e-04 -8.96
308772 rs13167291 5_93 0.995 59.83 1.7e-04 7.57
415764 rs3757387 7_79 0.995 41.98 1.2e-04 6.42
572592 rs695110 11_42 0.995 50.57 1.5e-04 -6.76
822679 rs12482821 21_15 0.995 29.93 8.6e-05 -4.85
51618 rs74704885 1_107 0.994 41.83 1.2e-04 -5.25
57741 rs564212 1_122 0.994 44.43 1.3e-04 7.15
613859 rs113479946 12_42 0.994 35.33 1.0e-04 -5.71
702442 rs1318175 15_27 0.994 69.43 2.0e-04 10.19
725683 rs17616063 16_27 0.994 30.73 8.9e-05 5.32
140290 rs56395424 3_9 0.993 40.44 1.2e-04 -6.28
172337 rs189174 3_74 0.993 61.38 1.8e-04 7.69
225276 rs13134099 4_58 0.993 29.24 8.4e-05 4.99
283766 rs4133339 5_45 0.992 44.85 1.3e-04 6.71
509351 rs1886296 9_73 0.992 30.12 8.7e-05 4.68
53870 rs884127 1_112 0.991 41.34 1.2e-04 6.44
333211 rs78470916 6_32 0.991 31.31 9.0e-05 4.84
422774 rs7807051 7_94 0.991 31.40 9.0e-05 5.34
707030 rs2472297 15_35 0.991 29.12 8.4e-05 -4.95
82081 rs12611996 2_36 0.990 57.42 1.7e-04 -7.71
592484 rs7137297 12_1 0.990 81.00 2.3e-04 -9.53
5878 rs34957055 1_16 0.989 31.25 9.0e-05 -5.42
34631 rs35717427 1_75 0.988 34.61 9.9e-05 0.64
629845 rs2393775 12_74 0.988 396.72 1.1e-03 -24.49
635824 rs9552620 13_3 0.988 26.80 7.7e-05 4.84
276396 rs1499279 5_31 0.986 54.94 1.6e-04 -7.57
329453 rs9272364 6_26 0.985 82.54 2.4e-04 8.97
331174 rs78945013 6_29 0.985 27.65 7.9e-05 -5.07
702423 rs12594571 15_27 0.985 46.20 1.3e-04 -7.06
431517 rs11777976 8_13 0.984 167.75 4.8e-04 -15.73
610246 rs1874888 12_35 0.984 28.35 8.1e-05 5.24
135023 rs12619647 2_144 0.981 36.21 1.0e-04 -6.85
754693 rs113408695 17_39 0.981 30.16 8.6e-05 5.21
754497 rs8072180 17_39 0.980 46.68 1.3e-04 -9.11
795192 rs77719426 19_31 0.980 39.21 1.1e-04 6.57
837334 rs135577 22_21 0.980 31.86 9.1e-05 4.48
415307 rs17864212 7_79 0.979 30.97 8.8e-05 4.75
432235 rs4841659 8_15 0.979 102.82 2.9e-04 15.90
824400 rs2836882 21_18 0.978 47.08 1.3e-04 -6.71
59883 rs12044944 1_126 0.976 26.29 7.5e-05 -4.78
295330 rs12521324 5_69 0.976 29.25 8.3e-05 5.03
445823 rs140753685 8_42 0.975 28.69 8.1e-05 4.94
244971 rs59435073 4_98 0.974 52.48 1.5e-04 -7.43
436333 rs11986461 8_21 0.974 31.21 8.8e-05 -5.93
592492 rs11513717 12_1 0.974 44.07 1.2e-04 1.23
430214 rs2928619 8_10 0.973 43.94 1.2e-04 6.51
324961 rs75080831 6_19 0.972 53.42 1.5e-04 8.29
480985 rs776756 9_14 0.972 27.26 7.7e-05 -4.45
739321 rs140384878 17_4 0.971 25.75 7.3e-05 4.77
276422 rs67715745 5_31 0.970 27.54 7.8e-05 4.93
801643 rs6140010 20_5 0.970 39.96 1.1e-04 -6.12
324228 rs34350323 6_17 0.969 43.72 1.2e-04 5.16
486601 rs11557154 9_27 0.969 46.46 1.3e-04 -6.99
589209 rs113120553 11_78 0.967 32.63 9.2e-05 4.51
775930 rs2957132 18_34 0.967 28.43 8.0e-05 -5.10
639893 rs11424749 13_10 0.966 31.12 8.7e-05 5.35
774080 rs1217565 18_30 0.966 35.05 9.8e-05 -5.56
73766 rs17820747 2_20 0.965 38.19 1.1e-04 -5.66
507273 rs8181197 9_68 0.965 63.91 1.8e-04 8.09
796856 rs34122194 19_34 0.964 27.65 7.7e-05 4.98
97079 rs10170168 2_66 0.963 39.92 1.1e-04 -3.38
43400 rs146203975 1_92 0.962 45.28 1.3e-04 -6.84
383352 rs7796210 7_18 0.962 32.83 9.2e-05 5.51
441905 rs11997272 8_34 0.962 25.60 7.2e-05 -4.47
589196 rs3802821 11_78 0.962 27.26 7.6e-05 3.60
324197 rs554542699 6_17 0.959 33.31 9.3e-05 4.54
331297 rs9470183 6_29 0.959 25.61 7.1e-05 4.10
509499 rs914738 9_74 0.959 26.13 7.3e-05 4.74
318469 rs6597256 6_7 0.958 40.03 1.1e-04 -5.57
87209 rs3796098 2_47 0.957 28.14 7.8e-05 4.88
756695 rs11658216 17_44 0.957 26.18 7.3e-05 4.75
530718 rs9414798 10_42 0.956 101.53 2.8e-04 -14.18
791632 rs17841839 19_23 0.956 72.36 2.0e-04 10.03
324694 rs34888581 6_19 0.953 34.80 9.6e-05 -5.03
795261 rs77332277 19_31 0.952 45.50 1.3e-04 7.13
1050754 rs185342176 17_6 0.952 192.02 5.3e-04 13.69
592152 rs12277680 11_84 0.951 29.60 8.2e-05 -4.91
774471 rs12373325 18_31 0.950 70.69 2.0e-04 -9.66
325213 rs1155207 6_20 0.949 39.26 1.1e-04 -4.57
232518 rs34254189 4_71 0.948 26.48 7.3e-05 3.06
642991 rs116944862 13_17 0.948 30.12 8.3e-05 -2.20
754962 rs189323 17_40 0.946 25.10 6.9e-05 3.87
776174 rs7242402 18_35 0.943 24.96 6.8e-05 4.60
135001 rs61747382 2_144 0.942 34.60 9.5e-05 6.60
730450 rs79829970 16_37 0.942 25.91 7.1e-05 4.55
687365 rs67868394 14_46 0.941 28.21 7.7e-05 5.32
324500 rs78808915 6_18 0.940 1560.87 4.3e-03 -35.32
363185 rs62432712 6_92 0.939 25.90 7.1e-05 4.68
736935 rs7206699 16_54 0.938 41.33 1.1e-04 6.27
499893 rs2900388 9_53 0.935 40.47 1.1e-04 -2.86
596932 rs2417261 12_12 0.935 26.56 7.2e-05 -4.81
691094 rs149136706 14_54 0.934 51.41 1.4e-04 6.56
499919 rs10991458 9_53 0.932 50.31 1.4e-04 4.42
301009 rs6894249 5_79 0.931 27.27 7.4e-05 -4.87
540081 rs10786262 10_61 0.927 31.57 8.5e-05 5.22
570634 rs72917317 11_38 0.927 29.50 7.9e-05 5.31
464256 rs7017788 8_79 0.926 43.81 1.2e-04 8.60
318397 rs2765359 6_7 0.925 35.74 9.6e-05 4.79
586797 rs7104819 11_71 0.925 28.61 7.7e-05 3.32
72378 rs7606480 2_19 0.923 44.00 1.2e-04 -6.65
73763 rs564066844 2_20 0.920 24.94 6.7e-05 -4.40
430794 rs13265731 8_11 0.919 2656.89 7.1e-03 13.94
173386 rs72964564 3_76 0.918 32.59 8.7e-05 5.40
812622 rs2585441 20_32 0.917 24.85 6.6e-05 -4.63
263843 rs112622661 5_9 0.915 23.77 6.3e-05 -4.43
823118 rs928287 21_16 0.915 46.33 1.2e-04 -6.52
20432 rs145366123 1_48 0.913 29.74 7.9e-05 5.41
324558 rs9358773 6_18 0.911 175.07 4.6e-04 18.18
732097 rs557791532 16_41 0.911 25.12 6.6e-05 4.51
540088 rs2039616 10_62 0.910 29.08 7.7e-05 5.09
601631 rs146970907 12_18 0.908 29.48 7.8e-05 5.27
71638 rs368027631 2_15 0.905 30.52 8.0e-05 -5.39
148933 rs2844400 3_27 0.904 23.63 6.2e-05 -4.23
555057 rs7102759 11_8 0.903 27.04 7.1e-05 -4.83
199777 rs113840252 4_9 0.902 26.08 6.8e-05 -4.82
54148 rs61830291 1_112 0.900 34.87 9.1e-05 5.60
489880 rs11144105 9_35 0.899 24.81 6.5e-05 4.53
994866 rs113154361 15_25 0.899 29.59 7.7e-05 5.02
280608 rs253232 5_40 0.898 25.13 6.6e-05 -4.54
390921 rs12155027 7_30 0.897 24.60 6.4e-05 -4.59
823473 rs219783 21_16 0.897 42.31 1.1e-04 -6.43
735342 rs60239983 16_50 0.896 25.40 6.6e-05 -4.64
10319 rs368949592 1_25 0.885 25.47 6.5e-05 -4.05
81354 rs75536720 2_34 0.884 24.50 6.3e-05 -4.46
151850 rs116643069 3_35 0.884 29.94 7.7e-05 -4.67
755371 rs2384955 17_42 0.880 25.71 6.6e-05 4.76
857804 rs2993063 1_18 0.879 42.90 1.1e-04 7.23
996300 rs371481929 15_43 0.879 34.91 8.9e-05 -4.60
752880 rs12452590 17_36 0.877 23.70 6.0e-05 -4.28
795178 rs3729640 19_31 0.876 43.99 1.1e-04 -6.69
750165 rs16950448 17_29 0.873 26.05 6.6e-05 -4.57
218673 rs186589299 4_45 0.871 24.21 6.1e-05 -4.35
435511 rs2015440 8_20 0.870 26.93 6.8e-05 -4.81
1053805 rs16532 17_23 0.869 341.26 8.6e-04 6.07
326263 rs6456964 6_23 0.866 26.58 6.7e-05 -4.79
601171 rs10842642 12_18 0.866 26.80 6.7e-05 -4.69
51564 rs1962918 1_107 0.863 29.01 7.3e-05 -5.64
597384 rs4764086 12_12 0.863 46.63 1.2e-04 6.80
652126 rs9592980 13_36 0.862 62.81 1.6e-04 7.94
198950 rs115976359 4_7 0.861 24.31 6.1e-05 -4.45
137558 rs12497013 3_4 0.858 27.40 6.8e-05 -4.78
203026 rs10034719 4_16 0.855 26.67 6.6e-05 4.72
539522 rs10509670 10_60 0.854 39.38 9.8e-05 -6.08
431467 rs2975676 8_13 0.852 38.71 9.6e-05 -1.41
496392 rs150962029 9_48 0.852 24.64 6.1e-05 -4.49
380111 rs115412782 7_13 0.851 24.01 5.9e-05 4.15
1029198 rs12600137 16_29 0.851 817.89 2.0e-03 1.54
770240 rs73425984 18_24 0.848 27.39 6.7e-05 4.84
733568 rs17689455 16_44 0.845 329.63 8.1e-04 18.92
480969 rs71506880 9_14 0.844 32.73 8.0e-05 5.00
8028 rs75339626 1_21 0.842 24.00 5.9e-05 4.30
99585 rs6724056 2_70 0.838 32.02 7.8e-05 5.40
535215 rs1248889 10_50 0.837 55.76 1.4e-04 9.01
642973 rs34001253 13_16 0.837 48.12 1.2e-04 -8.90
756724 rs9900494 17_44 0.837 26.29 6.4e-05 -4.68
24793 rs34303579 1_55 0.835 24.93 6.0e-05 -3.66
608225 rs12300763 12_31 0.834 26.40 6.4e-05 4.61
827689 rs7290147 22_1 0.831 25.54 6.2e-05 -4.49
375053 rs78894484 7_3 0.825 30.37 7.3e-05 6.04
749632 rs34759387 17_29 0.820 36.90 8.8e-05 7.42
54530 rs12132342 1_115 0.814 25.19 6.0e-05 4.78
210774 rs17578029 4_31 0.813 26.39 6.2e-05 5.10
97413 rs12467534 2_67 0.810 27.08 6.4e-05 -5.12
413414 rs3801387 7_72 0.808 24.20 5.7e-05 -4.18
47049 rs2994256 1_98 0.807 28.68 6.7e-05 -4.93
373821 rs2880362 6_110 0.801 25.93 6.0e-05 4.58SNPs 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
849666 rs149344982 1_15 1.000 5614.82 1.6e-02 -75.15
508099 rs677355 9_70 0.499 4427.98 6.4e-03 -102.39
508098 rs34357864 9_70 0.800 4425.89 1.0e-02 -102.31
508102 rs674302 9_70 0.172 4423.27 2.2e-03 -102.40
508100 rs676457 9_70 0.161 4423.11 2.1e-03 -102.40
849830 rs141957574 1_15 0.000 4396.98 0.0e+00 -66.61
508101 rs782455289 9_70 0.003 4392.91 3.9e-05 -101.92
849788 rs72659199 1_15 0.000 4167.10 0.0e+00 -50.20
849819 rs144999112 1_15 0.000 4150.18 0.0e+00 -49.86
849836 rs72660309 1_15 0.000 4091.98 0.0e+00 -49.53
849866 rs148785605 1_15 0.000 4082.65 0.0e+00 -49.46
849888 rs72660315 1_15 0.000 4011.25 0.0e+00 -49.04
849889 rs115051087 1_15 0.000 3996.42 0.0e+00 -48.95
508103 rs115478735 9_70 1.000 3945.43 1.1e-02 -108.55
849931 rs72660324 1_15 0.000 3757.20 0.0e+00 -47.32
849957 rs72660333 1_15 0.000 3733.80 0.0e+00 -47.08
849959 rs182287953 1_15 0.000 3707.35 0.0e+00 -46.77
849963 rs72660334 1_15 0.000 3706.13 0.0e+00 -46.87
508107 rs495828 9_70 0.000 3326.20 2.0e-06 -99.78
849969 rs1130564 1_15 0.000 3258.54 0.0e+00 -39.05
849991 rs199787255 1_15 0.000 3162.23 0.0e+00 -39.47
849901 rs72660319 1_15 0.000 3027.37 0.0e+00 -43.20
430778 rs758184196 8_11 1.000 2897.15 8.4e-03 -3.89
430773 rs2428 8_11 1.000 2786.21 8.1e-03 15.16
849369 rs12132412 1_15 0.000 2697.80 0.0e+00 -42.82
849148 rs4654745 1_15 0.999 2671.16 7.7e-03 -51.71
849144 rs1566523 1_15 0.001 2658.03 1.0e-05 -51.61
430794 rs13265731 8_11 0.919 2656.89 7.1e-03 13.94
430790 rs6993494 8_11 0.081 2645.82 6.2e-04 13.92
849210 rs4654748 1_15 0.000 2613.30 0.0e+00 -51.25
849384 rs1697421 1_15 0.000 2581.53 0.0e+00 -47.71
849319 rs199855186 1_15 0.000 2510.90 0.0e+00 -49.32
849312 rs56414407 1_15 0.000 2497.97 0.0e+00 -48.18
849253 rs6687836 1_15 0.000 2494.60 0.0e+00 -49.42
849287 rs6426713 1_15 0.000 2494.06 0.0e+00 -49.42
849246 rs1827293 1_15 0.000 2487.25 0.0e+00 -49.36
849306 rs3820293 1_15 0.000 2487.18 0.0e+00 -49.28
849302 rs3820296 1_15 0.000 2484.56 0.0e+00 -49.26
849258 rs2004380 1_15 0.000 2484.45 0.0e+00 -49.26
849150 rs12047493 1_15 0.000 2478.12 0.0e+00 -49.86
849153 rs10916993 1_15 0.000 2474.81 0.0e+00 -49.90
849123 rs12734589 1_15 0.000 2471.11 0.0e+00 -49.80
849122 rs12759170 1_15 0.000 2459.05 0.0e+00 -49.71
849715 rs115257434 1_15 0.000 2451.88 0.0e+00 -46.05
849155 rs3820597 1_15 0.000 2411.10 0.0e+00 -48.86
430752 rs1703982 8_11 0.000 2410.76 5.5e-15 -14.16
849305 rs3820294 1_15 0.000 2404.66 0.0e+00 -48.74
849631 rs181708801 1_15 0.000 2389.26 0.0e+00 -45.95
849307 rs3820292 1_15 0.000 2387.93 0.0e+00 -48.26
844401 rs199779538 1_2 1.000 2381.97 6.9e-03 -3.23SNPs 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
849666 rs149344982 1_15 1.000 5614.82 0.01600 -75.15
508103 rs115478735 9_70 1.000 3945.43 0.01100 -108.55
508098 rs34357864 9_70 0.800 4425.89 0.01000 -102.31
430778 rs758184196 8_11 1.000 2897.15 0.00840 -3.89
430773 rs2428 8_11 1.000 2786.21 0.00810 15.16
849148 rs4654745 1_15 0.999 2671.16 0.00770 -51.71
430794 rs13265731 8_11 0.919 2656.89 0.00710 13.94
844401 rs199779538 1_2 1.000 2381.97 0.00690 -3.23
844408 rs7519807 1_2 0.998 2376.34 0.00690 -3.16
508099 rs677355 9_70 0.499 4427.98 0.00640 -102.39
324472 rs10946700 6_18 1.000 2001.13 0.00580 44.85
430972 rs2929451 8_11 0.998 1743.87 0.00510 -16.29
849771 rs72659192 1_15 1.000 1621.02 0.00470 52.94
1051742 rs55714927 17_6 1.000 1517.78 0.00440 36.59
324500 rs78808915 6_18 0.940 1560.87 0.00430 -35.32
796435 rs601338 19_33 1.000 1305.55 0.00380 -50.96
5846 rs72657133 1_14 1.000 1196.52 0.00350 -23.99
508077 rs10793962 9_70 0.657 1465.47 0.00280 9.89
796437 rs1688264 19_33 0.771 1224.91 0.00270 -50.10
1029018 rs201685059 16_29 1.000 842.61 0.00240 4.82
508102 rs674302 9_70 0.172 4423.27 0.00220 -102.40
849561 rs56222534 1_15 0.432 1759.06 0.00220 -43.49
508100 rs676457 9_70 0.161 4423.11 0.00210 -102.40
1029198 rs12600137 16_29 0.851 817.89 0.00200 1.54
5840 rs148717955 1_14 1.000 511.69 0.00150 5.52
508079 rs8176759 9_70 0.343 1465.22 0.00150 9.85
530731 rs10640079 10_42 0.423 1211.27 0.00150 37.62
849563 rs11586977 1_15 0.300 1758.25 0.00150 -43.48
849799 rs1772710 1_15 0.514 858.36 0.00130 -22.96
1053846 rs201963278 17_23 1.000 444.84 0.00130 3.44
72181 rs780093 2_16 1.000 420.75 0.00120 -21.28
466254 rs2980858 8_83 0.724 573.74 0.00120 -17.16
431043 rs13265179 8_12 0.998 389.88 0.00110 28.39
466255 rs13252684 8_83 1.000 374.46 0.00110 16.92
498402 rs2183745 9_50 1.000 363.42 0.00110 -21.03
589190 rs10790802 11_77 1.000 394.43 0.00110 25.30
629845 rs2393775 12_74 0.988 396.72 0.00110 -24.49
1032501 rs9302635 16_38 1.000 376.09 0.00110 17.64
466256 rs6987702 8_83 1.000 345.17 0.00100 15.28
431030 rs330096 8_12 1.000 337.58 0.00098 19.73
1051924 rs5409 17_6 1.000 327.14 0.00095 9.00
849556 rs145377039 1_15 0.183 1757.13 0.00094 -43.46
589181 rs10893498 11_77 1.000 317.88 0.00092 -18.84
849794 rs1313341 1_15 0.360 857.74 0.00090 -22.94
794091 rs10853742 19_30 1.000 300.90 0.00087 -18.79
1053805 rs16532 17_23 0.869 341.26 0.00086 6.07
796436 rs571689 19_33 0.229 1228.29 0.00082 -50.15
942064 rs11601507 11_4 1.000 282.00 0.00082 16.49
733568 rs17689455 16_44 0.845 329.63 0.00081 18.92
431940 rs10092177 8_14 1.000 265.14 0.00077 -12.10SNPs 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
508103 rs115478735 9_70 1.000 3945.43 1.1e-02 -108.55
508100 rs676457 9_70 0.161 4423.11 2.1e-03 -102.40
508102 rs674302 9_70 0.172 4423.27 2.2e-03 -102.40
508099 rs677355 9_70 0.499 4427.98 6.4e-03 -102.39
508098 rs34357864 9_70 0.800 4425.89 1.0e-02 -102.31
508101 rs782455289 9_70 0.003 4392.91 3.9e-05 -101.92
508107 rs495828 9_70 0.000 3326.20 2.0e-06 -99.78
849666 rs149344982 1_15 1.000 5614.82 1.6e-02 -75.15
508149 rs3758348 9_70 0.000 1464.33 9.2e-07 -69.56
508158 rs17474001 9_70 0.000 1370.99 8.2e-07 -67.32
849830 rs141957574 1_15 0.000 4396.98 0.0e+00 -66.61
508104 rs559723 9_70 0.000 1876.80 3.0e-07 -66.38
508088 rs2073828 9_70 0.001 1522.96 3.0e-06 62.33
849143 rs1976403 1_15 0.000 2350.43 0.0e+00 61.06
849119 rs10916988 1_15 0.000 2299.07 0.0e+00 -60.58
508097 rs7036642 9_70 0.000 1335.79 1.0e-06 59.55
849313 rs2800936 1_15 0.000 2308.24 0.0e+00 -57.18
849379 rs10799702 1_15 0.000 1992.25 0.0e+00 53.88
849771 rs72659192 1_15 1.000 1621.02 4.7e-03 52.94
849148 rs4654745 1_15 0.999 2671.16 7.7e-03 -51.71
849144 rs1566523 1_15 0.001 2658.03 1.0e-05 -51.61
849210 rs4654748 1_15 0.000 2613.30 0.0e+00 -51.25
796435 rs601338 19_33 1.000 1305.55 3.8e-03 -50.96
849788 rs72659199 1_15 0.000 4167.10 0.0e+00 -50.20
796436 rs571689 19_33 0.229 1228.29 8.2e-04 -50.15
796437 rs1688264 19_33 0.771 1224.91 2.7e-03 -50.10
849132 rs10916990 1_15 0.000 2364.33 0.0e+00 -49.95
849153 rs10916993 1_15 0.000 2474.81 0.0e+00 -49.90
849150 rs12047493 1_15 0.000 2478.12 0.0e+00 -49.86
849819 rs144999112 1_15 0.000 4150.18 0.0e+00 -49.86
849123 rs12734589 1_15 0.000 2471.11 0.0e+00 -49.80
849158 rs4654746 1_15 0.000 2357.25 0.0e+00 -49.76
849122 rs12759170 1_15 0.000 2459.05 0.0e+00 -49.71
849836 rs72660309 1_15 0.000 4091.98 0.0e+00 -49.53
849866 rs148785605 1_15 0.000 4082.65 0.0e+00 -49.46
849253 rs6687836 1_15 0.000 2494.60 0.0e+00 -49.42
849287 rs6426713 1_15 0.000 2494.06 0.0e+00 -49.42
849160 rs10799691 1_15 0.000 1657.36 0.0e+00 -49.40
849246 rs1827293 1_15 0.000 2487.25 0.0e+00 -49.36
849319 rs199855186 1_15 0.000 2510.90 0.0e+00 -49.32
849306 rs3820293 1_15 0.000 2487.18 0.0e+00 -49.28
849258 rs2004380 1_15 0.000 2484.45 0.0e+00 -49.26
849302 rs3820296 1_15 0.000 2484.56 0.0e+00 -49.26
849888 rs72660315 1_15 0.000 4011.25 0.0e+00 -49.04
849889 rs115051087 1_15 0.000 3996.42 0.0e+00 -48.95
849169 rs150401191 1_15 0.000 1590.79 0.0e+00 48.89
849155 rs3820597 1_15 0.000 2411.10 0.0e+00 -48.86
849305 rs3820294 1_15 0.000 2404.66 0.0e+00 -48.74
849307 rs3820292 1_15 0.000 2387.93 0.0e+00 -48.26
849312 rs56414407 1_15 0.000 2497.97 0.0e+00 -48.18Gene 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] 26if (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"
Term Overlap Adjusted.P.value
1 azurophil granule membrane (GO:0035577) 2/58 0.0472403
2 lytic vacuole (GO:0000323) 3/219 0.0472403
3 lysosome (GO:0005764) 4/477 0.0472403
Genes
1 LAMP1;DNAJC13
2 LAMP1;CTSW;LIPA
3 LAMP1;CTSW;DNAJC13;LIPA
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value
1 transcription corepressor binding (GO:0001222) 2/29 0.04584271
Genes
1 NEK6;ZBTB7ALAMP1 gene(s) from the input list not found in DisGeNET CURATEDCTSW gene(s) from the input list not found in DisGeNET CURATEDICA1L gene(s) from the input list not found in DisGeNET CURATEDZDHHC18 gene(s) from the input list not found in DisGeNET CURATEDMPV17L2 gene(s) from the input list not found in DisGeNET CURATEDPCIF1 gene(s) from the input list not found in DisGeNET CURATEDNYNRIN gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDGPRC5C gene(s) from the input list not found in DisGeNET CURATED Description
7 Cholesterol Ester Storage Disease
37 Wolman Disease
61 Secondary Adrenal Insufficiency
74 Caudal Duplication Anomaly
76 Catel Manzke syndrome
77 Jejunal Atresia with Microcephaly and Ocular Anomalies
81 Osteolysis, Hereditary, Of Carpal Bones With Or Without Nephropathy
84 Acid cholesteryl ester hydrolase deficiency, type 2
89 IMMUNODEFICIENCY, COMMON VARIABLE, 10
92 DUANE RETRACTION SYNDROME 3 WITH OR WITHOUT DEAFNESS
FDR Ratio BgRatio
7 0.01664605 1/17 1/9703
37 0.01664605 1/17 1/9703
61 0.01664605 1/17 1/9703
74 0.01664605 1/17 1/9703
76 0.01664605 1/17 1/9703
77 0.01664605 1/17 1/9703
81 0.01664605 1/17 1/9703
84 0.01664605 1/17 1/9703
89 0.01664605 1/17 1/9703
92 0.01664605 1/17 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