Urea (quantile) - Liver
wesleycrouse
2021-09-09
Last updated: 2021-09-09
Checks: 6 1
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| html | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| Rmd | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| html | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| Rmd | 627a4e1 | wesleycrouse | 2021-09-07 | adding heritability |
| Rmd | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| html | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| Rmd | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| html | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| Rmd | 837dd01 | wesleycrouse | 2021-09-01 | adding additional fixedsigma report |
| Rmd | d0a5417 | wesleycrouse | 2021-08-30 | adding new reports to the index |
| Rmd | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 1c62980 | wesleycrouse | 2021-08-11 | Updating reports |
| Rmd | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| html | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| Rmd | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 03e541c | wesleycrouse | 2021-07-29 | Cleaning up report generation |
| Rmd | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
| html | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Urea (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-30670_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0052340814 0.0002113812 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
33.20338 12.32724 #report sample size
print(sample_size)[1] 344052#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.005506366 0.065870944 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.01624708 0.53314062Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
7484 NSD1 5_106 0.999 1538.12 4.5e-03 3.31
7040 INHBB 2_70 0.987 79.45 2.3e-04 8.70
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
4335 LGR6 1_102 0.977 46.82 1.3e-04 -5.66
938 CDC14A 1_61 0.960 39.54 1.1e-04 -6.06
3186 TCF21 6_88 0.942 37.95 1.0e-04 6.24
4101 OSBPL2 20_36 0.847 24.88 6.1e-05 4.54
9251 PHLDA2 11_2 0.827 29.90 7.2e-05 5.04
5549 PSEN2 1_116 0.787 25.80 5.9e-05 4.92
3465 PTGFR 1_48 0.780 27.88 6.3e-05 -4.66
10099 SPTSSB 3_99 0.778 32.24 7.3e-05 -5.59
4811 CNPY3 6_33 0.770 25.62 5.7e-05 5.08
770 NFATC3 16_36 0.743 29.59 6.4e-05 5.23
607 PRKCQ 10_7 0.723 23.44 4.9e-05 4.15
3426 CCRL2 3_32 0.694 36.72 7.4e-05 -5.75
11521 GSTA2 6_39 0.690 73.90 1.5e-04 -8.67
11821 C17orf100 17_6 0.617 26.50 4.7e-05 -4.62
5320 ANAPC11 17_46 0.593 23.80 4.1e-05 -4.49
290 NR1H3 11_29 0.580 44.17 7.4e-05 -7.22Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
4634 EGLN1 1_118 0.000 5904.72 1.3e-11 -4.17
3058 EXOC8 1_118 0.000 4944.14 0.0e+00 4.16
4556 TMEM60 7_49 0.000 4337.19 0.0e+00 -3.59
7484 NSD1 5_106 0.999 1538.12 4.5e-03 3.31
11199 LINC00271 6_89 0.000 1338.21 0.0e+00 -1.51
8824 DMRTA1 9_17 0.000 1142.65 0.0e+00 -3.38
10903 APTR 7_49 0.000 934.09 0.0e+00 0.31
8039 PRELID1 5_106 0.000 885.75 1.3e-14 6.62
6807 FGFR4 5_106 0.000 500.46 0.0e+00 -0.51
10840 PPP1CB 2_17 0.004 485.92 5.7e-06 3.27
4604 AHI1 6_89 0.000 460.18 0.0e+00 0.10
9811 RSBN1L 7_49 0.000 459.15 0.0e+00 -1.23
8270 TRMT61B 2_17 0.020 390.01 2.3e-05 -3.69
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
7172 SPDYA 2_17 0.007 337.31 7.1e-06 -3.39
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
11046 RP11-370B11.3 9_17 0.000 231.07 0.0e+00 0.04
8037 LMAN2 5_106 0.000 219.64 1.3e-08 9.58
92 PHTF2 7_49 0.000 185.84 0.0e+00 -2.23
10820 MXD3 5_106 0.000 179.00 0.0e+00 -3.36Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
7484 NSD1 5_106 0.999 1538.12 4.5e-03 3.31
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
7040 INHBB 2_70 0.987 79.45 2.3e-04 8.70
11521 GSTA2 6_39 0.690 73.90 1.5e-04 -8.67
4335 LGR6 1_102 0.977 46.82 1.3e-04 -5.66
938 CDC14A 1_61 0.960 39.54 1.1e-04 -6.06
3186 TCF21 6_88 0.942 37.95 1.0e-04 6.24
3426 CCRL2 3_32 0.694 36.72 7.4e-05 -5.75
290 NR1H3 11_29 0.580 44.17 7.4e-05 -7.22
10099 SPTSSB 3_99 0.778 32.24 7.3e-05 -5.59
9251 PHLDA2 11_2 0.827 29.90 7.2e-05 5.04
3440 ACVR2A 2_88 0.570 40.59 6.7e-05 6.24
770 NFATC3 16_36 0.743 29.59 6.4e-05 5.23
3465 PTGFR 1_48 0.780 27.88 6.3e-05 -4.66
4101 OSBPL2 20_36 0.847 24.88 6.1e-05 4.54
5549 PSEN2 1_116 0.787 25.80 5.9e-05 4.92
5074 ZCRB1 12_27 0.455 44.25 5.9e-05 6.41
5087 SLC38A4 12_29 0.259 78.22 5.9e-05 7.76
1058 GCKR 2_16 0.430 45.91 5.7e-05 -8.67Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
8865 FUT2 19_33 0.017 151.64 7.6e-06 14.34
8862 MAMSTR 19_33 0.002 96.47 6.8e-07 -11.48
2310 GOSR2 17_27 0.001 102.88 3.0e-07 10.44
5788 RSPO3 6_84 0.005 99.50 1.4e-06 -10.25
2041 FAM83E 19_33 0.012 80.84 2.9e-06 -10.07
8042 EFNA1 1_76 0.000 125.16 2.3e-10 9.61
8037 LMAN2 5_106 0.000 219.64 1.3e-08 9.58
337 SH3YL1 2_1 0.008 94.37 2.3e-06 -9.51
8100 NRG4 15_35 0.003 104.91 8.8e-07 -9.50
5192 UBE2Q2 15_35 0.001 96.32 3.3e-07 -9.31
7040 INHBB 2_70 0.987 79.45 2.3e-04 8.70
5042 SHROOM3 4_52 0.040 75.55 8.8e-06 -8.69
1058 GCKR 2_16 0.430 45.91 5.7e-05 -8.67
10987 C2orf16 2_16 0.430 45.91 5.7e-05 -8.67
11521 GSTA2 6_39 0.690 73.90 1.5e-04 -8.67
796 PPP2R3A 3_84 0.025 70.13 5.2e-06 -8.64
7163 CCDC158 4_52 0.033 73.35 7.0e-06 -8.61
12279 CTD-2376I4.1 5_43 0.128 52.48 2.0e-05 8.10Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.01183378#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
8865 FUT2 19_33 0.017 151.64 7.6e-06 14.34
8862 MAMSTR 19_33 0.002 96.47 6.8e-07 -11.48
2310 GOSR2 17_27 0.001 102.88 3.0e-07 10.44
5788 RSPO3 6_84 0.005 99.50 1.4e-06 -10.25
2041 FAM83E 19_33 0.012 80.84 2.9e-06 -10.07
8042 EFNA1 1_76 0.000 125.16 2.3e-10 9.61
8037 LMAN2 5_106 0.000 219.64 1.3e-08 9.58
337 SH3YL1 2_1 0.008 94.37 2.3e-06 -9.51
8100 NRG4 15_35 0.003 104.91 8.8e-07 -9.50
5192 UBE2Q2 15_35 0.001 96.32 3.3e-07 -9.31
7040 INHBB 2_70 0.987 79.45 2.3e-04 8.70
5042 SHROOM3 4_52 0.040 75.55 8.8e-06 -8.69
1058 GCKR 2_16 0.430 45.91 5.7e-05 -8.67
10987 C2orf16 2_16 0.430 45.91 5.7e-05 -8.67
11521 GSTA2 6_39 0.690 73.90 1.5e-04 -8.67
796 PPP2R3A 3_84 0.025 70.13 5.2e-06 -8.64
7163 CCDC158 4_52 0.033 73.35 7.0e-06 -8.61
12279 CTD-2376I4.1 5_43 0.128 52.48 2.0e-05 8.10Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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_76"
genename region_tag susie_pip mu2 PVE z
5524 KCNN3 1_76 0.001 56.38 2.1e-07 -5.07
12211 RP11-307C12.13 1_76 0.001 43.57 7.7e-08 -3.89
6789 SHC1 1_76 0.000 7.33 6.2e-12 -2.05
5514 ADAM15 1_76 0.000 22.45 1.8e-11 4.03
7073 DCST2 1_76 0.000 27.59 3.0e-08 -3.74
7074 DCST1 1_76 0.000 30.81 1.1e-08 -4.40
5523 EFNA3 1_76 0.000 50.02 4.6e-11 1.04
8041 SLC50A1 1_76 0.984 256.52 7.3e-04 -19.43
8042 EFNA1 1_76 0.000 125.16 2.3e-10 9.61
9069 DPM3 1_76 0.000 54.70 4.7e-11 1.21
8040 THBS3 1_76 1.000 379.94 1.1e-03 21.52
8924 GBA 1_76 0.000 108.92 1.4e-09 6.22
6795 FAM189B 1_76 0.000 11.79 1.4e-11 -3.04
8829 CLK2 1_76 0.000 72.72 4.1e-10 -6.90
4294 DAP3 1_76 0.000 6.62 7.2e-12 1.68
7076 YY1AP1 1_76 0.000 8.81 7.6e-12 2.65
3021 GON4L 1_76 0.000 69.81 5.5e-11 7.42
4300 SYT11 1_76 0.000 67.45 7.1e-11 7.31
5527 RIT1 1_76 0.000 65.86 5.4e-11 6.96
3022 ARHGEF2 1_76 0.000 37.32 3.6e-11 5.04
7094 SSR2 1_76 0.000 8.14 1.0e-11 0.97
3023 LAMTOR2 1_76 0.000 6.87 6.2e-12 -2.18
6798 SLC25A44 1_76 0.002 33.78 1.5e-07 5.90
6797 PMF1 1_76 0.338 45.60 4.5e-05 6.68
7093 TMEM79 1_76 0.005 34.45 4.5e-07 -5.85
6796 PAQR6 1_76 0.000 14.50 1.8e-11 -2.05
10515 SMG5 1_76 0.000 8.31 7.0e-12 -1.94
10442 GLMP 1_76 0.000 10.84 1.0e-11 -2.08
7092 TSACC 1_76 0.000 12.21 2.6e-11 0.45
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_33"
genename region_tag susie_pip mu2 PVE z
10231 DACT3 19_33 0.002 5.13 3.0e-08 -0.21
1999 PRKD2 19_33 0.002 4.92 2.8e-08 0.06
1219 STRN4 19_33 0.002 5.51 3.3e-08 -0.35
9210 FKRP 19_33 0.003 8.69 7.5e-08 0.96
1998 SLC1A5 19_33 0.003 8.73 7.4e-08 -1.03
6725 ARHGAP35 19_33 0.003 10.37 1.0e-07 1.04
4115 NPAS1 19_33 0.002 4.87 2.7e-08 0.12
4114 ZC3H4 19_33 0.009 19.21 4.9e-07 -1.81
5375 SAE1 19_33 0.009 19.21 4.9e-07 -1.81
2002 CCDC9 19_33 0.004 11.03 1.2e-07 1.14
10232 C5AR1 19_33 0.002 6.18 4.0e-08 0.55
11840 INAFM1 19_33 0.007 17.37 3.5e-07 1.90
4510 C5AR2 19_33 0.017 25.45 1.3e-06 -2.21
4505 DHX34 19_33 0.003 7.71 5.9e-08 -0.79
3155 ZNF541 19_33 0.004 11.75 1.4e-07 1.13
546 GLTSCR1 19_33 0.002 6.77 4.5e-08 0.78
285 EHD2 19_33 0.002 5.91 3.8e-08 -0.37
2021 SULT2A1 19_33 0.002 5.02 2.8e-08 -0.35
2035 PLA2G4C 19_33 0.002 6.50 4.5e-08 -0.26
2033 LIG1 19_33 0.004 10.65 1.1e-07 -0.94
9623 C19orf68 19_33 0.002 6.61 4.3e-08 -0.81
2032 CARD8 19_33 0.002 6.38 4.0e-08 -0.81
2031 CCDC114 19_33 0.002 5.32 3.1e-08 0.32
5374 EMP3 19_33 0.002 7.38 4.6e-08 -1.54
2028 GRWD1 19_33 0.011 22.35 7.2e-07 -2.37
9317 KCNJ14 19_33 0.008 22.64 5.6e-07 -2.93
2027 CYTH2 19_33 0.007 16.30 3.1e-07 -1.72
5376 LMTK3 19_33 0.006 18.33 2.9e-07 -2.52
1139 SULT2B1 19_33 0.002 5.48 3.4e-08 0.07
2041 FAM83E 19_33 0.012 80.84 2.9e-06 -10.07
547 SPHK2 19_33 0.002 21.24 1.5e-07 4.90
2037 DBP 19_33 0.031 33.07 3.0e-06 -2.99
548 CA11 19_33 0.003 10.25 8.6e-08 1.81
8865 FUT2 19_33 0.017 151.64 7.6e-06 14.34
8862 MAMSTR 19_33 0.002 96.47 6.8e-07 -11.48
9314 IZUMO1 19_33 0.002 5.15 2.9e-08 -0.24
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_27"
genename region_tag susie_pip mu2 PVE z
8499 DCAKD 17_27 0.005 21.77 3.1e-07 -2.11
12583 AC142472.6 17_27 0.019 34.27 1.9e-06 -3.12
6678 ARHGAP27 17_27 0.001 10.97 4.3e-08 -1.26
11062 PLEKHM1 17_27 0.001 5.39 1.3e-08 -0.15
9773 MAPT 17_27 0.001 5.02 1.1e-08 0.88
9663 ARL17A 17_27 0.002 12.01 8.2e-08 -2.53
3310 KANSL1 17_27 0.001 25.67 8.8e-08 -5.40
12113 RP11-798G7.6 17_27 0.001 25.67 8.8e-08 -5.40
8846 LRRC37A 17_27 0.040 49.91 5.8e-06 5.42
11381 LRRC37A2 17_27 0.009 14.49 3.7e-07 -0.65
802 NSF 17_27 0.038 55.03 6.1e-06 6.32
2301 WNT3 17_27 0.001 10.45 2.7e-08 1.23
2310 GOSR2 17_27 0.001 102.88 3.0e-07 10.44
41 CDC27 17_27 0.003 19.23 1.5e-07 -2.25
11884 ITGB3 17_27 0.001 5.18 1.1e-08 0.33
9041 EFCAB13 17_27 0.001 5.08 1.1e-08 0.15
5281 NPEPPS 17_27 0.002 13.20 5.8e-08 1.81
2309 KPNB1 17_27 0.001 6.00 1.4e-08 -0.24
10511 TBKBP1 17_27 0.001 7.21 1.9e-08 -0.59
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_84"
genename region_tag susie_pip mu2 PVE z
2616 TPD52L1 6_84 0.005 6.24 9.1e-08 -0.78
2618 NCOA7 6_84 0.005 4.99 6.6e-08 0.31
2617 HINT3 6_84 0.005 6.83 9.9e-08 -1.08
633 TRMT11 6_84 0.005 5.71 7.9e-08 -0.79
5788 RSPO3 6_84 0.005 99.50 1.4e-06 -10.25
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 5_106"
genename region_tag susie_pip mu2 PVE z
8146 SIMC1 5_106 0.000 17.99 0.0e+00 1.63
3450 KIAA1191 5_106 0.000 6.60 0.0e+00 0.49
8737 ARL10 5_106 0.000 6.64 0.0e+00 -0.70
5758 HIGD2A 5_106 0.000 5.75 0.0e+00 0.06
8738 CLTB 5_106 0.000 6.25 0.0e+00 0.15
8046 GPRIN1 5_106 0.000 7.06 0.0e+00 -0.70
403 TSPAN17 5_106 0.000 9.59 0.0e+00 0.24
2780 UNC5A 5_106 0.000 9.53 0.0e+00 -1.07
6811 HK3 5_106 0.000 13.58 0.0e+00 -0.61
1119 UIMC1 5_106 0.000 21.25 0.0e+00 -0.85
2779 ZNF346 5_106 0.000 13.38 0.0e+00 -0.02
6807 FGFR4 5_106 0.000 500.46 0.0e+00 -0.51
7484 NSD1 5_106 0.999 1538.12 4.5e-03 3.31
8039 PRELID1 5_106 0.000 885.75 1.3e-14 6.62
10820 MXD3 5_106 0.000 179.00 0.0e+00 -3.36
8037 LMAN2 5_106 0.000 219.64 1.3e-08 9.58
10107 PFN3 5_106 0.000 112.28 0.0e+00 -0.87
4159 F12 5_106 0.000 163.28 0.0e+00 3.34
4160 PRR7 5_106 0.000 26.63 0.0e+00 -0.34
2778 DBN1 5_106 0.000 23.93 0.0e+00 -0.01
10157 PDLIM7 5_106 0.000 11.14 0.0e+00 -3.41
12333 RP11-1277A3.3 5_106 0.000 5.34 0.0e+00 1.00
301 B4GALT7 5_106 0.000 6.05 0.0e+00 0.02
8144 FAM153A 5_106 0.000 17.51 0.0e+00 -1.35
| 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
59149 rs766167074 1_118 1.000 5961.82 1.7e-02 -4.11
74828 rs780093 2_16 1.000 70.41 2.0e-04 -10.49
75129 rs569546056 2_17 1.000 580.83 1.7e-03 -2.90
94504 rs4441469 2_54 1.000 36.15 1.1e-04 6.44
100038 rs4849177 2_67 1.000 99.30 2.9e-04 10.12
102108 rs141849010 2_69 1.000 35.69 1.0e-04 5.91
118140 rs847164 2_106 1.000 54.85 1.6e-04 9.07
127508 rs11887861 2_124 1.000 73.51 2.1e-04 7.60
147537 rs7619139 3_18 1.000 78.92 2.3e-04 9.30
159960 rs13066345 3_45 1.000 155.52 4.5e-04 12.30
196231 rs16862782 3_115 1.000 357.82 1.0e-03 -21.08
207051 rs62411644 4_17 1.000 35.61 1.0e-04 -5.87
214751 rs12639940 4_32 1.000 59.03 1.7e-04 8.08
215113 rs11729899 4_33 1.000 48.90 1.4e-04 7.45
276375 rs11740818 5_23 1.000 50.48 1.5e-04 -7.09
313008 rs17645325 5_93 1.000 36.29 1.1e-04 -6.45
366014 rs199804242 6_89 1.000 5468.96 1.6e-02 3.25
376131 rs300143 6_108 1.000 222.91 6.5e-04 -15.74
378443 rs78148157 7_2 1.000 223.14 6.5e-04 -12.26
378444 rs13241427 7_2 1.000 186.93 5.4e-04 11.86
404884 rs10277379 7_49 1.000 4003.60 1.2e-02 6.01
404887 rs761767938 7_49 1.000 4616.27 1.3e-02 3.82
404895 rs1544459 7_49 1.000 4536.92 1.3e-02 4.42
411852 rs10276555 7_63 1.000 67.53 2.0e-04 6.61
441512 rs1397799 8_24 1.000 122.77 3.6e-04 11.95
470902 rs9720968 8_83 1.000 110.28 3.2e-04 10.36
470907 rs10505503 8_83 1.000 48.11 1.4e-04 6.02
486613 rs476924 9_17 1.000 4289.59 1.2e-02 -3.29
486616 rs141465689 9_17 1.000 4271.20 1.2e-02 -3.26
568194 rs369062552 11_21 1.000 148.39 4.3e-04 14.22
568204 rs34830202 11_21 1.000 251.45 7.3e-04 -14.10
618228 rs2657880 12_35 1.000 120.90 3.5e-04 11.72
618383 rs7397189 12_36 1.000 133.69 3.9e-04 -12.27
714683 rs3803487 15_27 1.000 64.11 1.9e-04 8.25
718458 rs2472297 15_35 1.000 62.20 1.8e-04 -9.67
718700 rs145727191 15_35 1.000 89.66 2.6e-04 11.74
718852 rs143513665 15_36 1.000 42.52 1.2e-04 8.93
739801 rs12927956 16_27 1.000 52.32 1.5e-04 6.72
762870 rs1058166 17_22 1.000 74.73 2.2e-04 9.53
762899 rs4794765 17_22 1.000 61.24 1.8e-04 -8.54
765210 rs137906947 17_27 1.000 143.59 4.2e-04 -9.70
783300 rs162000 18_14 1.000 43.74 1.3e-04 6.80
788623 rs4890562 18_25 1.000 64.12 1.9e-04 9.64
788626 rs12458806 18_25 1.000 79.16 2.3e-04 0.67
788631 rs12964854 18_25 1.000 112.59 3.3e-04 9.21
789410 rs9953845 18_26 1.000 72.19 2.1e-04 9.08
815477 rs814573 19_32 1.000 39.68 1.2e-04 -6.35
815741 rs34783010 19_32 1.000 230.58 6.7e-04 -15.84
816729 rs12978750 19_33 1.000 172.40 5.0e-04 -16.37
835569 rs6123359 20_32 1.000 38.20 1.1e-04 6.89
835575 rs2585441 20_32 1.000 39.94 1.2e-04 6.39
837290 rs62205363 20_34 1.000 66.70 1.9e-04 6.34
846666 rs219783 21_17 1.000 129.85 3.8e-04 -11.90
881241 rs139439683 5_106 1.000 1880.65 5.5e-03 -2.93
919642 rs9749331 19_34 1.000 70.53 2.0e-04 -7.22
100030 rs567964928 2_67 0.999 32.12 9.3e-05 5.14
276265 rs4703440 5_23 0.999 63.34 1.8e-04 7.92
453114 rs17397411 8_50 0.999 33.64 9.8e-05 5.06
605668 rs11056397 12_13 0.999 32.87 9.5e-05 -5.38
836748 rs12481011 20_33 0.999 33.47 9.7e-05 4.92
385664 rs542176135 7_17 0.998 45.17 1.3e-04 -6.93
616443 rs863226 12_31 0.998 31.31 9.1e-05 4.54
718729 rs2955742 15_36 0.998 39.75 1.2e-04 8.59
198416 rs7642977 3_119 0.997 37.03 1.1e-04 6.09
281080 rs113088001 5_31 0.997 39.13 1.1e-04 -5.94
376163 rs1445288 6_108 0.997 31.83 9.2e-05 5.23
765209 rs60372268 17_27 0.997 53.92 1.6e-04 -8.11
788484 rs72902699 18_24 0.997 43.87 1.3e-04 -6.89
839293 rs926167 21_2 0.996 41.31 1.2e-04 5.45
399152 rs2709273 7_39 0.995 30.09 8.7e-05 5.43
546883 rs1408345 10_64 0.995 29.06 8.4e-05 5.37
660502 rs9543236 13_35 0.995 31.22 9.0e-05 -5.34
332884 rs4509168 6_26 0.994 32.23 9.3e-05 5.69
426268 rs10224210 7_94 0.992 326.95 9.4e-04 19.30
616450 rs1878234 12_31 0.991 36.40 1.0e-04 4.94
770599 rs11079697 17_39 0.991 29.13 8.4e-05 -5.44
919835 rs12979373 19_34 0.991 35.20 1.0e-04 -6.00
328639 rs793705 6_18 0.989 34.27 9.8e-05 -6.01
378433 rs4724786 7_2 0.988 53.28 1.5e-04 3.34
701648 rs10141666 14_53 0.987 35.50 1.0e-04 -5.98
322671 rs13193887 6_7 0.986 28.35 8.1e-05 4.93
569246 rs2476504 11_23 0.985 26.98 7.7e-05 -4.83
411856 rs6968978 7_63 0.984 28.25 8.1e-05 -3.55
234548 rs35518360 4_67 0.983 29.10 8.3e-05 -5.20
277813 rs115634741 5_26 0.983 33.62 9.6e-05 -7.51
286330 rs4302565 5_43 0.982 27.19 7.8e-05 4.21
744483 rs59156463 16_37 0.982 42.01 1.2e-04 -8.30
763961 rs12948083 17_25 0.982 29.87 8.5e-05 5.32
395827 rs700752 7_34 0.980 43.13 1.2e-04 6.48
881376 rs13172121 5_106 0.980 1882.66 5.4e-03 -3.09
228398 rs13124978 4_56 0.978 26.15 7.4e-05 -4.87
726206 rs59646751 15_48 0.978 75.74 2.2e-04 9.08
710398 rs8030172 15_19 0.972 24.43 6.9e-05 4.70
766052 rs890398 17_29 0.971 26.51 7.5e-05 5.08
814329 rs12982615 19_28 0.970 27.93 7.9e-05 -5.22
598365 rs148884160 11_80 0.968 25.59 7.2e-05 4.82
816748 rs495315 19_33 0.968 54.44 1.5e-04 10.55
765910 rs9895945 17_28 0.966 39.79 1.1e-04 6.52
61584 rs17520491 1_123 0.963 26.04 7.3e-05 4.97
777450 rs4513192 18_3 0.955 34.25 9.5e-05 -5.15
783259 rs527616 18_14 0.955 25.89 7.2e-05 -5.24
845812 rs2154568 21_15 0.952 43.09 1.2e-04 6.46
618364 rs6581124 12_35 0.951 25.99 7.2e-05 -4.95
823859 rs6040069 20_8 0.949 24.37 6.7e-05 -4.67
327008 rs41271299 6_15 0.939 24.90 6.8e-05 4.68
744654 rs139861017 16_37 0.939 26.33 7.2e-05 4.78
836733 rs1884500 20_33 0.939 27.83 7.6e-05 2.53
865550 rs111552903 1_76 0.939 43.32 1.2e-04 -3.27
75132 rs4580350 2_17 0.937 579.68 1.6e-03 2.96
760748 rs112861323 17_18 0.935 24.52 6.7e-05 -4.71
486614 rs34033213 9_17 0.934 4217.97 1.1e-02 -3.16
673200 rs7987209 13_59 0.934 57.79 1.6e-04 7.64
514655 rs113790047 10_2 0.931 40.23 1.1e-04 6.42
372393 rs1449674 6_101 0.927 25.28 6.8e-05 -4.77
837292 rs1407040 20_34 0.924 44.01 1.2e-04 -3.89
512914 rs115478735 9_70 0.918 47.26 1.3e-04 6.96
751423 rs34341288 16_50 0.918 25.09 6.7e-05 -4.80
775586 rs28454947 17_46 0.913 34.74 9.2e-05 6.20
578812 rs10796869 11_38 0.911 46.76 1.2e-04 7.75
291707 rs61552236 5_53 0.909 46.48 1.2e-04 -6.85
568577 rs11031796 11_22 0.905 29.50 7.8e-05 5.12
490977 rs34223057 9_26 0.904 26.38 6.9e-05 4.97
524925 rs79545879 10_20 0.903 24.85 6.5e-05 4.24
744767 rs192776582 16_38 0.901 25.84 6.8e-05 5.14
155013 rs62259692 3_36 0.895 27.03 7.0e-05 4.72
534550 rs10821950 10_42 0.894 39.14 1.0e-04 -6.19
10043 rs56307352 1_21 0.890 25.79 6.7e-05 -4.83
530628 rs4935194 10_33 0.883 24.01 6.2e-05 4.40
196246 rs62278004 3_115 0.881 105.40 2.7e-04 15.80
320818 rs1272694 6_3 0.876 33.07 8.4e-05 -5.73
726248 rs45506098 15_48 0.871 23.65 6.0e-05 4.42
813205 rs117236730 19_25 0.871 23.99 6.1e-05 4.72
347018 rs2815715 6_50 0.869 24.77 6.3e-05 4.71
389323 rs67971665 7_23 0.862 30.03 7.5e-05 -5.52
196229 rs2679508 3_115 0.854 64.94 1.6e-04 -3.66
51120 rs113608553 1_104 0.850 32.33 8.0e-05 -5.54
286323 rs745063 5_43 0.848 140.50 3.5e-04 13.29
285593 rs11743158 5_41 0.847 32.75 8.1e-05 5.38
200390 rs151223318 4_2 0.845 23.91 5.9e-05 -4.28
6670 rs61779072 1_14 0.843 24.33 6.0e-05 4.57
3331 rs115560453 1_7 0.841 24.84 6.1e-05 4.67
264283 rs62331274 5_2 0.839 23.85 5.8e-05 4.36
307115 rs156094 5_83 0.839 28.89 7.0e-05 -5.09
725302 rs4335732 15_46 0.836 25.95 6.3e-05 -4.79
159269 rs28599817 3_43 0.832 131.33 3.2e-04 12.33
329357 rs115740542 6_20 0.829 24.66 5.9e-05 4.42
827576 rs10854249 20_15 0.824 29.01 6.9e-05 -5.10
695677 rs17796675 14_41 0.821 24.23 5.8e-05 4.45
765712 rs3809778 17_28 0.815 30.66 7.3e-05 -5.73
199178 rs13059257 3_120 0.814 42.62 1.0e-04 6.50
528884 rs56059584 10_29 0.810 23.77 5.6e-05 4.44
660106 rs5804585 13_35 0.810 103.13 2.4e-04 -10.42
196247 rs12634556 3_115 0.806 312.60 7.3e-04 -22.53
919644 rs837640 19_34 0.806 40.97 9.6e-05 4.85
919251 rs117643180 17_6 0.803 49.54 1.2e-04 7.00SNPs 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
59149 rs766167074 1_118 1.000 5961.82 1.7e-02 -4.11
59148 rs971534 1_118 0.248 5908.03 4.3e-03 -4.23
59146 rs10489611 1_118 0.248 5907.99 4.3e-03 -4.23
59147 rs2486737 1_118 0.221 5907.92 3.8e-03 -4.23
59140 rs2256908 1_118 0.252 5907.66 4.3e-03 -4.24
59143 rs2790891 1_118 0.216 5907.53 3.7e-03 -4.23
59144 rs2491405 1_118 0.216 5907.53 3.7e-03 -4.23
59156 rs2211176 1_118 0.274 5906.32 4.7e-03 -4.25
59157 rs2790882 1_118 0.274 5906.32 4.7e-03 -4.25
59155 rs2248646 1_118 0.205 5905.84 3.5e-03 -4.24
59136 rs1076804 1_118 0.286 5900.27 4.9e-03 -4.28
59158 rs1416913 1_118 0.205 5899.53 3.5e-03 -4.26
59161 rs2790874 1_118 0.029 5897.00 5.0e-04 -4.19
59137 rs910824 1_118 0.047 5884.99 8.0e-04 -4.26
59160 rs2808603 1_118 0.012 5882.27 2.0e-04 -4.20
59135 rs2474635 1_118 0.011 5867.60 1.9e-04 -4.26
59132 rs722302 1_118 0.003 5855.98 4.9e-05 -4.24
59154 rs143905935 1_118 0.000 5852.55 5.9e-08 -3.97
59152 rs2739509 1_118 0.000 5795.21 1.2e-06 -4.13
59162 rs2474631 1_118 0.000 5691.35 6.6e-13 -3.99
59159 rs3071894 1_118 0.000 5682.17 4.9e-15 -3.88
59164 rs2790871 1_118 0.000 5625.37 3.2e-16 -3.97
59165 rs2790870 1_118 0.000 5623.86 1.7e-16 -3.95
59150 rs2739512 1_118 0.000 5617.18 5.1e-15 -3.99
59130 rs7414807 1_118 0.000 5529.37 3.4e-09 -4.81
59167 rs371976741 1_118 0.000 5506.55 3.6e-18 4.10
366013 rs2327654 6_89 0.776 5489.94 1.2e-02 2.70
366030 rs6923513 6_89 0.684 5489.74 1.1e-02 2.70
366014 rs199804242 6_89 1.000 5468.96 1.6e-02 3.25
366017 rs113527452 6_89 0.069 5463.66 1.1e-03 2.74
366022 rs200796875 6_89 0.000 5427.97 3.2e-08 2.63
366035 rs7756915 6_89 0.000 5398.15 5.0e-08 2.79
59141 rs6659323 1_118 0.000 5361.86 0.0e+00 -4.12
366028 rs6570040 6_89 0.000 5181.21 0.0e+00 2.76
366015 rs6570031 6_89 0.000 5170.39 0.0e+00 2.81
366016 rs9389323 6_89 0.000 5167.78 0.0e+00 2.79
404887 rs761767938 7_49 1.000 4616.27 1.3e-02 3.82
404895 rs1544459 7_49 1.000 4536.92 1.3e-02 4.42
366032 rs9321531 6_89 0.000 4536.09 0.0e+00 2.60
366005 rs9321528 6_89 0.000 4473.43 0.0e+00 2.49
486613 rs476924 9_17 1.000 4289.59 1.2e-02 -3.29
486616 rs141465689 9_17 1.000 4271.20 1.2e-02 -3.26
366037 rs5880262 6_89 0.000 4262.57 0.0e+00 2.93
366033 rs9494389 6_89 0.000 4250.36 0.0e+00 2.12
486614 rs34033213 9_17 0.934 4217.97 1.1e-02 -3.16
404891 rs11972122 7_49 0.000 4161.39 0.0e+00 3.97
404892 rs11406602 7_49 0.000 4159.04 0.0e+00 3.96
486617 rs664198 9_17 0.000 4149.87 6.9e-07 -3.37
486621 rs35519115 9_17 0.000 4131.73 4.2e-07 -3.14
366011 rs2208574 6_89 0.000 4107.71 0.0e+00 2.34SNPs 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
59149 rs766167074 1_118 1.000 5961.82 0.01700 -4.11
366014 rs199804242 6_89 1.000 5468.96 0.01600 3.25
404887 rs761767938 7_49 1.000 4616.27 0.01300 3.82
404895 rs1544459 7_49 1.000 4536.92 0.01300 4.42
366013 rs2327654 6_89 0.776 5489.94 0.01200 2.70
404884 rs10277379 7_49 1.000 4003.60 0.01200 6.01
486613 rs476924 9_17 1.000 4289.59 0.01200 -3.29
486616 rs141465689 9_17 1.000 4271.20 0.01200 -3.26
366030 rs6923513 6_89 0.684 5489.74 0.01100 2.70
486614 rs34033213 9_17 0.934 4217.97 0.01100 -3.16
881241 rs139439683 5_106 1.000 1880.65 0.00550 -2.93
881376 rs13172121 5_106 0.980 1882.66 0.00540 -3.09
59136 rs1076804 1_118 0.286 5900.27 0.00490 -4.28
59156 rs2211176 1_118 0.274 5906.32 0.00470 -4.25
59157 rs2790882 1_118 0.274 5906.32 0.00470 -4.25
59140 rs2256908 1_118 0.252 5907.66 0.00430 -4.24
59146 rs10489611 1_118 0.248 5907.99 0.00430 -4.23
59148 rs971534 1_118 0.248 5908.03 0.00430 -4.23
59147 rs2486737 1_118 0.221 5907.92 0.00380 -4.23
59143 rs2790891 1_118 0.216 5907.53 0.00370 -4.23
59144 rs2491405 1_118 0.216 5907.53 0.00370 -4.23
59155 rs2248646 1_118 0.205 5905.84 0.00350 -4.24
59158 rs1416913 1_118 0.205 5899.53 0.00350 -4.26
75129 rs569546056 2_17 1.000 580.83 0.00170 -2.90
75132 rs4580350 2_17 0.937 579.68 0.00160 2.96
278240 rs11956741 5_27 0.716 532.98 0.00110 -24.85
366017 rs113527452 6_89 0.069 5463.66 0.00110 2.74
196231 rs16862782 3_115 1.000 357.82 0.00100 -21.08
426268 rs10224210 7_94 0.992 326.95 0.00094 19.30
59137 rs910824 1_118 0.047 5884.99 0.00080 -4.26
196247 rs12634556 3_115 0.806 312.60 0.00073 -22.53
568204 rs34830202 11_21 1.000 251.45 0.00073 -14.10
815741 rs34783010 19_32 1.000 230.58 0.00067 -15.84
376131 rs300143 6_108 1.000 222.91 0.00065 -15.74
378443 rs78148157 7_2 1.000 223.14 0.00065 -12.26
378444 rs13241427 7_2 1.000 186.93 0.00054 11.86
59161 rs2790874 1_118 0.029 5897.00 0.00050 -4.19
816729 rs12978750 19_33 1.000 172.40 0.00050 -16.37
278235 rs28856650 5_27 0.296 531.27 0.00046 -24.81
159960 rs13066345 3_45 1.000 155.52 0.00045 12.30
404870 rs17156706 7_49 0.337 454.50 0.00044 -3.20
568194 rs369062552 11_21 1.000 148.39 0.00043 14.22
765210 rs137906947 17_27 1.000 143.59 0.00042 -9.70
75131 rs2169748 2_17 0.233 573.44 0.00039 -2.89
618383 rs7397189 12_36 1.000 133.69 0.00039 -12.27
75128 rs7562170 2_17 0.226 574.94 0.00038 -2.80
846666 rs219783 21_17 1.000 129.85 0.00038 -11.90
441512 rs1397799 8_24 1.000 122.77 0.00036 11.95
718676 rs28607641 15_35 0.552 221.46 0.00036 15.78
286323 rs745063 5_43 0.848 140.50 0.00035 13.29SNPs 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
278240 rs11956741 5_27 0.716 532.98 1.1e-03 -24.85
278235 rs28856650 5_27 0.296 531.27 4.6e-04 -24.81
278212 rs11955175 5_27 0.013 520.66 2.0e-05 -24.60
196247 rs12634556 3_115 0.806 312.60 7.3e-04 -22.53
196249 rs11924549 3_115 0.194 307.88 1.7e-04 -22.45
865988 rs760077 1_76 0.000 345.16 6.0e-08 -21.52
865999 rs2990223 1_76 0.000 344.97 7.5e-08 -21.47
196231 rs16862782 3_115 1.000 357.82 1.0e-03 -21.08
865879 rs6676150 1_76 0.005 318.25 4.8e-06 20.90
196256 rs1027498 3_115 0.000 220.81 2.0e-10 -20.38
866012 rs139558368 1_76 0.000 308.48 3.6e-08 20.25
865832 rs9330264 1_76 0.010 233.91 6.5e-06 -19.37
426268 rs10224210 7_94 0.992 326.95 9.4e-04 19.30
426270 rs10224002 7_94 0.018 320.13 1.6e-05 19.03
865975 rs423144 1_76 0.000 155.37 1.1e-11 -18.28
865973 rs7366775 1_76 0.000 154.47 1.1e-11 -18.25
865958 rs4971101 1_76 0.000 150.47 1.1e-11 -18.10
865959 rs2070803 1_76 0.000 150.26 1.1e-11 -18.10
865982 rs2075571 1_76 0.000 149.12 1.2e-11 -18.09
865955 rs4971100 1_76 0.000 148.76 1.1e-11 -18.03
865946 rs9426886 1_76 0.000 149.48 1.1e-11 -17.94
865952 rs541049493 1_76 0.000 146.59 1.1e-11 -17.87
865945 rs11264341 1_76 0.000 147.45 1.1e-11 -17.85
196242 rs73188608 3_115 0.000 168.83 7.9e-15 -17.82
196243 rs73188616 3_115 0.000 168.73 7.9e-15 -17.82
196248 rs4686916 3_115 0.000 168.59 7.9e-15 -17.82
196250 rs73188638 3_115 0.000 167.63 7.5e-15 -17.79
865910 rs141625351 1_76 0.000 165.33 5.2e-11 17.71
866625 rs12134456 1_76 0.000 233.57 5.2e-10 17.63
866002 rs1057941 1_76 0.000 140.64 9.9e-12 -17.62
196245 rs12233463 3_115 0.000 155.97 3.2e-15 -17.60
865953 rs4971099 1_76 0.000 138.06 8.7e-12 -17.04
426266 rs66497154 7_94 0.005 244.05 3.2e-06 16.71
865967 rs4072037 1_76 0.000 224.81 4.2e-11 -16.46
865944 rs3814316 1_76 0.000 125.05 7.0e-12 -16.45
865972 rs2974937 1_76 0.000 224.63 4.5e-11 -16.43
816729 rs12978750 19_33 1.000 172.40 5.0e-04 -16.37
865965 rs12743084 1_76 0.000 222.86 4.1e-11 -16.34
865943 rs4971059 1_76 0.000 119.42 7.0e-12 -16.33
865978 rs2066981 1_76 0.000 219.50 5.5e-11 -16.21
865984 rs370545 1_76 0.000 219.43 5.5e-11 -16.20
865985 rs914615 1_76 0.000 219.39 5.5e-11 -16.20
865968 rs12411216 1_76 0.000 218.85 4.7e-11 16.17
865936 rs4971091 1_76 0.000 106.33 6.7e-12 -15.89
865938 rs4971093 1_76 0.000 105.51 6.7e-12 -15.86
815741 rs34783010 19_32 1.000 230.58 6.7e-04 -15.84
816737 rs838145 19_33 0.067 182.63 3.6e-05 15.82
196246 rs62278004 3_115 0.881 105.40 2.7e-04 15.80
718676 rs28607641 15_35 0.552 221.46 3.6e-04 15.78
718677 rs7177266 15_35 0.448 220.98 2.9e-04 15.76Gene 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] 9if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
Term
1 metanephric mesenchymal cell differentiation (GO:0072162)
2 diaphragm development (GO:0060539)
3 lung morphogenesis (GO:0060425)
4 negative regulation of gonadotropin secretion (GO:0032277)
5 mesenchymal cell differentiation involved in kidney development (GO:0072161)
6 spleen development (GO:0048536)
7 regulation of RNA polymerase II regulatory region sequence-specific DNA binding (GO:1903025)
8 embryonic digestive tract morphogenesis (GO:0048557)
9 cell differentiation involved in metanephros development (GO:0072202)
10 metanephric mesenchyme development (GO:0072075)
11 intracellular sterol transport (GO:0032366)
12 ovarian follicle development (GO:0001541)
13 carbohydrate transport (GO:0008643)
14 regulation of histone methylation (GO:0031060)
15 negative regulation of androgen receptor signaling pathway (GO:0060766)
16 placenta development (GO:0001890)
17 intracellular cholesterol transport (GO:0032367)
18 digestive tract morphogenesis (GO:0048546)
19 negative regulation of insulin secretion (GO:0046676)
20 vasculature development (GO:0001944)
21 monosaccharide transmembrane transport (GO:0015749)
22 mesonephric tubule development (GO:0072164)
23 negative regulation of peptide hormone secretion (GO:0090278)
24 branching involved in ureteric bud morphogenesis (GO:0001658)
25 activin receptor signaling pathway (GO:0032924)
26 ureteric bud development (GO:0001657)
27 ureteric bud morphogenesis (GO:0060675)
28 embryonic digestive tract development (GO:0048566)
29 sterol transport (GO:0015918)
30 female gonad development (GO:0008585)
31 hexose transmembrane transport (GO:0008645)
32 organophosphate ester transport (GO:0015748)
33 histone methylation (GO:0016571)
34 bile acid biosynthetic process (GO:0006699)
35 regulation of transcription regulatory region DNA binding (GO:2000677)
36 respiratory tube development (GO:0030323)
37 negative regulation of intracellular steroid hormone receptor signaling pathway (GO:0033144)
38 skeletal muscle organ development (GO:0060538)
39 bile acid metabolic process (GO:0008206)
40 negative chemotaxis (GO:0050919)
41 respiratory system development (GO:0060541)
42 lung development (GO:0030324)
43 positive regulation of reproductive process (GO:2000243)
44 regulation of androgen receptor signaling pathway (GO:0060765)
45 skeletal muscle tissue development (GO:0007519)
46 positive regulation of cytokinesis (GO:0032467)
47 negative regulation of protein secretion (GO:0050709)
48 regulation of exit from mitosis (GO:0007096)
49 protein methylation (GO:0006479)
50 branching morphogenesis of an epithelial tube (GO:0048754)
51 positive regulation of cell division (GO:0051781)
52 embryonic organ morphogenesis (GO:0048562)
53 positive regulation of pathway-restricted SMAD protein phosphorylation (GO:0010862)
54 organic hydroxy compound biosynthetic process (GO:1901617)
55 cholesterol transport (GO:0030301)
56 protein homotetramerization (GO:0051289)
57 negative regulation of protein metabolic process (GO:0051248)
Overlap Adjusted.P.value Genes
1 1/5 0.03849410 TCF21
2 1/5 0.03849410 TCF21
3 1/5 0.03849410 TCF21
4 1/5 0.03849410 INHBB
5 1/6 0.03849410 TCF21
6 1/8 0.03849410 TCF21
7 1/9 0.03849410 NSD1
8 1/9 0.03849410 TCF21
9 1/10 0.03849410 TCF21
10 1/11 0.03849410 TCF21
11 1/13 0.03849410 OSBPL2
12 1/14 0.03849410 INHBB
13 1/14 0.03849410 SLC50A1
14 1/14 0.03849410 NSD1
15 1/15 0.03849410 TCF21
16 1/15 0.03849410 PHLDA2
17 1/15 0.03849410 OSBPL2
18 1/16 0.03849410 TCF21
19 1/17 0.03849410 INHBB
20 1/17 0.03849410 TCF21
21 1/17 0.03849410 SLC50A1
22 1/17 0.03849410 TCF21
23 1/18 0.03849410 INHBB
24 1/19 0.03849410 TCF21
25 1/19 0.03849410 INHBB
26 1/19 0.03849410 TCF21
27 1/19 0.03849410 TCF21
28 1/20 0.03906516 TCF21
29 1/21 0.03959608 OSBPL2
30 1/22 0.04009088 INHBB
31 1/23 0.04055305 SLC50A1
32 1/25 0.04213143 OSBPL2
33 1/26 0.04213143 NSD1
34 1/27 0.04213143 OSBPL2
35 1/27 0.04213143 NSD1
36 1/30 0.04384167 TCF21
37 1/32 0.04384167 TCF21
38 1/32 0.04384167 TCF21
39 1/33 0.04384167 OSBPL2
40 1/33 0.04384167 LGR6
41 1/35 0.04384167 TCF21
42 1/35 0.04384167 TCF21
43 1/35 0.04384167 INHBB
44 1/36 0.04384167 TCF21
45 1/37 0.04384167 TCF21
46 1/37 0.04384167 CDC14A
47 1/39 0.04426833 INHBB
48 1/39 0.04426833 CDC14A
49 1/41 0.04557053 NSD1
50 1/44 0.04605592 TCF21
51 1/44 0.04605592 CDC14A
52 1/44 0.04605592 TCF21
53 1/47 0.04823897 INHBB
54 1/50 0.04957595 OSBPL2
55 1/51 0.04957595 OSBPL2
56 1/52 0.04957595 OSBPL2
57 1/52 0.04957595 INHBB
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
Term
1 androgen receptor binding (GO:0050681)
2 nuclear receptor binding (GO:0016922)
3 histone methyltransferase activity (H4-K20 specific) (GO:0042799)
4 Roundabout binding (GO:0048495)
5 phosphatidylinositol transfer activity (GO:0008526)
6 histone methyltransferase activity (H3-K36 specific) (GO:0046975)
7 retinoid X receptor binding (GO:0046965)
8 cholesterol transfer activity (GO:0120020)
9 retinoic acid receptor binding (GO:0042974)
10 sterol transfer activity (GO:0120015)
11 phospholipid transfer activity (GO:0120014)
12 bHLH transcription factor binding (GO:0043425)
13 thyroid hormone receptor binding (GO:0046966)
14 estrogen receptor binding (GO:0030331)
Overlap Adjusted.P.value Genes
1 2/27 0.002575322 NSD1;TCF21
2 2/120 0.025631315 NSD1;TCF21
3 1/5 0.028934537 NSD1
4 1/8 0.028934537 LGR6
5 1/9 0.028934537 OSBPL2
6 1/11 0.028934537 NSD1
7 1/11 0.028934537 NSD1
8 1/18 0.033682910 OSBPL2
9 1/18 0.033682910 NSD1
10 1/19 0.033682910 OSBPL2
11 1/22 0.033682910 OSBPL2
12 1/22 0.033682910 TCF21
13 1/28 0.039524143 NSD1
14 1/35 0.045812150 NSD1SLC50A1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
32 DEAFNESS, AUTOSOMAL RECESSIVE 32 0.008864152 1/8 1/9703
40 DEAFNESS, AUTOSOMAL DOMINANT 67 0.008864152 1/8 1/9703
41 5q35 microduplication syndrome 0.008864152 1/8 1/9703
42 SOTOS SYNDROME 1 0.008864152 1/8 1/9703
35 Chromosome 5, monosomy 5q35 0.013375050 1/8 2/9703
37 Nonsyndromic Deafness 0.013375050 2/8 81/9703
23 Familial Testotoxicosis 0.015184726 1/8 3/9703
21 Sotos' syndrome 0.017709123 1/8 4/9703
22 Weaver syndrome 0.019669705 1/8 5/9703
3 Beckwith-Wiedemann Syndrome 0.024765953 1/8 7/9703******************************************
* *
* 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