Urate (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 | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| html | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| Rmd | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| html | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| Rmd | 837dd01 | wesleycrouse | 2021-09-01 | adding additional fixedsigma report |
| Rmd | d0a5417 | wesleycrouse | 2021-08-30 | adding new reports to the index |
| Rmd | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 1c62980 | wesleycrouse | 2021-08-11 | Updating reports |
| Rmd | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| html | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| Rmd | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
| html | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Urate (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-30880_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0069694825 0.0002153448 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
17.43942 18.05307 #report sample size
print(sample_size)[1] 343836#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.00385343 0.09833775 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.01619088 0.72192971Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
11575 DNAJC3-AS1 13_48 0.984 38.79 1.1e-04 -6.74
8238 CHCHD7 8_44 0.969 25.45 7.2e-05 4.81
3212 CCND2 12_4 0.969 50.41 1.4e-04 -7.13
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
1552 PPM1A 14_27 0.955 25.32 7.0e-05 -4.75
9062 KLHDC7A 1_13 0.924 24.74 6.7e-05 4.58
10303 UGT2B17 4_48 0.915 25.94 6.9e-05 4.68
10684 FAM216A 12_67 0.898 32.47 8.5e-05 -4.17
10625 MSH5 6_26 0.891 53.21 1.4e-04 -5.67
5639 ARL6IP5 3_46 0.882 54.11 1.4e-04 -7.54
3426 CCRL2 3_32 0.880 32.64 8.4e-05 -5.80
9635 TLCD2 17_2 0.870 24.89 6.3e-05 4.79
7794 TMC4 19_37 0.855 26.90 6.7e-05 -4.86
8431 PRSS27 16_3 0.853 21.12 5.2e-05 -4.03
10567 GIGYF2 2_137 0.845 28.68 7.0e-05 -4.84
5415 SYTL1 1_19 0.834 47.88 1.2e-04 -6.75
1233 CERS4 19_7 0.799 33.32 7.7e-05 -5.73
10004 SLC35E2B 1_1 0.784 22.91 5.2e-05 4.19
10574 ZDHHC18 1_18 0.770 45.77 1.0e-04 8.19
3459 FAM35A 10_55 0.759 46.32 1.0e-04 -8.35Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
12674 GHET1 7_92 0.000 5943.86 0.0e+00 -1.93
4556 TMEM60 7_49 0.000 5544.01 0.0e+00 3.84
4634 EGLN1 1_118 0.000 5091.54 0.0e+00 -2.19
3058 EXOC8 1_118 0.000 4255.18 0.0e+00 2.49
742 WDR1 4_11 0.000 1386.23 0.0e+00 52.73
10227 ZNF786 7_92 0.000 1296.83 0.0e+00 -1.72
2412 SLC2A9 4_11 0.000 1293.52 0.0e+00 -45.30
6419 PDIA4 7_92 0.000 1293.08 0.0e+00 -0.75
10903 APTR 7_49 0.000 1067.89 0.0e+00 1.99
9811 RSBN1L 7_49 0.000 594.18 0.0e+00 1.64
92 PHTF2 7_49 0.000 425.49 0.0e+00 0.39
10693 ZNF425 7_92 0.000 366.66 0.0e+00 -0.60
2452 NRXN2 11_36 0.000 322.31 2.3e-08 19.86
3201 SPP1 4_59 0.000 288.51 1.6e-07 19.69
7888 BATF2 11_36 0.013 248.31 9.6e-06 10.45
2662 TRIM38 6_20 0.006 225.05 3.8e-06 -20.02
2887 NRBP1 2_16 0.026 219.88 1.6e-05 15.98
10831 ARL2 11_36 0.000 205.30 2.3e-07 -9.02
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
2443 SNX15 11_36 0.005 204.02 2.9e-06 6.51Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
5639 ARL6IP5 3_46 0.882 54.11 1.4e-04 -7.54
10625 MSH5 6_26 0.891 53.21 1.4e-04 -5.67
3212 CCND2 12_4 0.969 50.41 1.4e-04 -7.13
5415 SYTL1 1_19 0.834 47.88 1.2e-04 -6.75
11575 DNAJC3-AS1 13_48 0.984 38.79 1.1e-04 -6.74
10574 ZDHHC18 1_18 0.770 45.77 1.0e-04 8.19
2891 SNX17 2_16 0.223 158.09 1.0e-04 -12.95
3459 FAM35A 10_55 0.759 46.32 1.0e-04 -8.35
5400 EPHA2 1_11 0.733 45.40 9.7e-05 -5.19
10684 FAM216A 12_67 0.898 32.47 8.5e-05 -4.17
3426 CCRL2 3_32 0.880 32.64 8.4e-05 -5.80
8037 LMAN2 5_106 0.640 44.70 8.3e-05 7.93
1233 CERS4 19_7 0.799 33.32 7.7e-05 -5.73
840 MARK3 14_54 0.707 35.11 7.2e-05 -5.91
8238 CHCHD7 8_44 0.969 25.45 7.2e-05 4.81
6951 FAAP20 1_2 0.728 33.51 7.1e-05 -5.69
10567 GIGYF2 2_137 0.845 28.68 7.0e-05 -4.84
1552 PPM1A 14_27 0.955 25.32 7.0e-05 -4.75
10303 UGT2B17 4_48 0.915 25.94 6.9e-05 4.68Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
742 WDR1 4_11 0.000 1386.23 0.0e+00 52.73
2412 SLC2A9 4_11 0.000 1293.52 0.0e+00 -45.30
2662 TRIM38 6_20 0.006 225.05 3.8e-06 -20.02
2452 NRXN2 11_36 0.000 322.31 2.3e-08 19.86
3201 SPP1 4_59 0.000 288.51 1.6e-07 19.69
3641 SLC17A1 6_20 0.023 164.90 1.1e-05 16.91
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
2887 NRBP1 2_16 0.026 219.88 1.6e-05 15.98
2660 SLC17A2 6_20 0.005 123.71 1.8e-06 -13.58
2891 SNX17 2_16 0.223 158.09 1.0e-04 -12.95
8041 SLC50A1 1_76 0.001 122.82 3.0e-07 -12.74
10571 ASAH2B 10_33 0.021 146.05 8.8e-06 12.46
9736 BTN3A2 6_20 0.014 88.18 3.7e-06 -12.29
7139 PPM1K 4_59 0.000 72.63 2.6e-09 -12.23
12337 RP1-86C11.7 6_21 0.000 197.23 1.5e-07 -12.06
7944 STIP1 11_36 0.000 196.78 5.7e-08 -11.67
7040 INHBB 2_70 0.029 99.03 8.3e-06 11.62
8489 RNASEH2C 11_36 0.000 195.72 2.8e-10 -11.60
8284 RBKS 2_16 0.015 111.97 4.8e-06 11.06
9169 HIST1H2AC 6_20 0.005 51.87 7.2e-07 -10.66Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.0243097#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
742 WDR1 4_11 0.000 1386.23 0.0e+00 52.73
2412 SLC2A9 4_11 0.000 1293.52 0.0e+00 -45.30
2662 TRIM38 6_20 0.006 225.05 3.8e-06 -20.02
2452 NRXN2 11_36 0.000 322.31 2.3e-08 19.86
3201 SPP1 4_59 0.000 288.51 1.6e-07 19.69
3641 SLC17A1 6_20 0.023 164.90 1.1e-05 16.91
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
2887 NRBP1 2_16 0.026 219.88 1.6e-05 15.98
2660 SLC17A2 6_20 0.005 123.71 1.8e-06 -13.58
2891 SNX17 2_16 0.223 158.09 1.0e-04 -12.95
8041 SLC50A1 1_76 0.001 122.82 3.0e-07 -12.74
10571 ASAH2B 10_33 0.021 146.05 8.8e-06 12.46
9736 BTN3A2 6_20 0.014 88.18 3.7e-06 -12.29
7139 PPM1K 4_59 0.000 72.63 2.6e-09 -12.23
12337 RP1-86C11.7 6_21 0.000 197.23 1.5e-07 -12.06
7944 STIP1 11_36 0.000 196.78 5.7e-08 -11.67
7040 INHBB 2_70 0.029 99.03 8.3e-06 11.62
8489 RNASEH2C 11_36 0.000 195.72 2.8e-10 -11.60
8284 RBKS 2_16 0.015 111.97 4.8e-06 11.06
9169 HIST1H2AC 6_20 0.005 51.87 7.2e-07 -10.66Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 4_11"
genename region_tag susie_pip mu2 PVE z
2412 SLC2A9 4_11 0 1293.52 0 -45.30
742 WDR1 4_11 0 1386.23 0 52.73
8982 ZNF518B 4_11 0 49.50 0 -2.20
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 6_20"
genename region_tag susie_pip mu2 PVE z
5755 SLC17A4 6_20 0.013 27.10 9.9e-07 3.06
3641 SLC17A1 6_20 0.023 164.90 1.1e-05 16.91
3640 SLC17A3 6_20 0.064 83.09 1.5e-05 -7.47
2660 SLC17A2 6_20 0.005 123.71 1.8e-06 -13.58
2662 TRIM38 6_20 0.006 225.05 3.8e-06 -20.02
12334 U91328.19 6_20 0.023 14.93 9.9e-07 -2.63
12379 U91328.22 6_20 0.005 84.87 1.2e-06 -4.40
9850 HIST1H1C 6_20 0.005 87.71 1.2e-06 4.09
167 HFE 6_20 0.006 28.49 4.7e-07 -6.38
9169 HIST1H2AC 6_20 0.005 51.87 7.2e-07 -10.66
6602 HIST1H2BD 6_20 0.006 33.14 6.3e-07 -5.23
12482 HIST1H2BE 6_20 0.005 10.90 1.6e-07 -3.03
12602 HIST1H2BF 6_20 0.004 7.50 9.6e-08 -2.39
12502 HIST1H3E 6_20 0.011 11.46 3.6e-07 -0.28
12544 HIST1H2BH 6_20 0.004 13.83 1.8e-07 -1.79
6604 HIST1H4H 6_20 0.005 15.24 2.2e-07 4.56
9736 BTN3A2 6_20 0.014 88.18 3.7e-06 -12.29
3635 BTN2A2 6_20 0.005 50.02 7.5e-07 -8.79
298 BTN3A1 6_20 0.005 16.34 2.3e-07 -4.69
2597 BTN3A3 6_20 0.004 6.52 8.5e-08 1.20
2702 BTN2A1 6_20 0.009 13.40 3.7e-07 -1.80
9373 HMGN4 6_20 0.008 9.49 2.2e-07 0.93
5765 ABT1 6_20 0.009 9.44 2.5e-07 0.13
9231 ZNF322 6_20 0.009 12.57 3.2e-07 1.06
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_36"
genename region_tag susie_pip mu2 PVE z
3808 FLRT1 11_36 0.000 25.23 2.1e-15 5.44
4357 MACROD1 11_36 0.000 65.93 6.5e-13 5.16
7944 STIP1 11_36 0.000 196.78 5.7e-08 -11.67
6021 FERMT3 11_36 0.000 61.53 3.8e-13 5.00
3801 PRDX5 11_36 0.000 10.73 3.2e-16 -2.09
8550 FKBP2 11_36 0.000 12.01 3.1e-16 1.22
11826 RP11-783K16.13 11_36 0.000 66.29 4.0e-13 7.17
6022 PLCB3 11_36 0.000 70.83 7.5e-12 2.17
12 BAD 11_36 0.000 6.80 1.5e-16 1.19
10975 TEX40 11_36 0.000 24.36 4.1e-15 -2.73
8509 TRMT112 11_36 0.000 31.92 5.1e-15 -3.51
7892 CCDC88B 11_36 0.000 23.72 4.0e-15 2.51
2452 NRXN2 11_36 0.000 322.31 2.3e-08 19.86
680 PYGM 11_36 0.000 37.57 7.4e-13 -6.54
7890 SF1 11_36 0.000 8.53 2.2e-16 1.32
8291 CDC42BPG 11_36 0.000 108.61 1.2e-10 -6.30
2446 EHD1 11_36 0.000 108.03 1.7e-11 -6.56
2445 ATG2A 11_36 0.000 136.03 2.3e-10 -7.20
679 PPP2R5B 11_36 0.000 82.18 1.4e-12 6.15
7891 MAJIN 11_36 0.000 48.48 5.6e-14 3.37
7888 BATF2 11_36 0.013 248.31 9.6e-06 10.45
10831 ARL2 11_36 0.000 205.30 2.3e-07 -9.02
2443 SNX15 11_36 0.005 204.02 2.9e-06 6.51
7887 SAC3D1 11_36 0.000 6.93 1.5e-16 -0.77
6024 VPS51 11_36 0.000 17.30 4.0e-16 4.50
8625 ZNHIT2 11_36 0.000 38.07 2.6e-15 6.57
223 CAPN1 11_36 0.000 11.70 2.5e-16 3.45
6023 CDC42EP2 11_36 0.000 8.56 2.0e-16 -3.49
8585 TIGD3 11_36 0.000 12.68 2.7e-16 -4.06
11554 NEAT1 11_36 0.000 20.57 4.4e-15 -1.49
7886 LTBP3 11_36 0.000 13.50 6.1e-16 -1.46
8869 FAM89B 11_36 0.000 12.36 4.8e-16 1.36
8545 EHBP1L1 11_36 0.000 19.76 4.1e-15 -0.21
8534 MAP3K11 11_36 0.000 45.06 6.5e-14 5.06
10828 SIPA1 11_36 0.000 67.69 6.7e-12 4.16
8502 RELA 11_36 0.000 48.72 1.4e-15 -8.93
8489 RNASEH2C 11_36 0.000 195.72 2.8e-10 -11.60
11745 AP5B1 11_36 0.000 72.43 6.7e-14 7.03
8457 EFEMP2 11_36 0.000 16.55 3.4e-16 -5.33
8447 CTSW 11_36 0.000 25.55 2.4e-15 -2.27
8444 FIBP 11_36 0.000 83.58 7.1e-11 1.45
8753 C11orf68 11_36 0.000 13.77 7.9e-16 -0.77
8743 SART1 11_36 0.000 38.49 7.4e-16 7.82
8728 BANF1 11_36 0.000 41.94 1.3e-15 7.11
8733 EIF1AD 11_36 0.000 28.95 1.0e-15 -6.66
8725 CST6 11_36 0.000 23.78 4.2e-15 -1.02
1132 SF3B2 11_36 0.000 16.42 4.2e-16 3.24
8705 PACS1 11_36 0.000 10.02 2.2e-16 -2.19
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 4_59"
genename region_tag susie_pip mu2 PVE z
5683 SLC10A6 4_59 0.000 46.04 2.6e-08 -4.89
7135 C4orf36 4_59 0.000 9.91 3.0e-10 0.04
8206 HSD17B13 4_59 0.000 20.14 1.3e-09 -3.25
8205 NUDT9 4_59 0.000 9.57 2.7e-10 -3.48
6212 SPARCL1 4_59 0.000 13.20 3.9e-10 -3.65
3201 SPP1 4_59 0.000 288.51 1.6e-07 19.69
3200 PKD2 4_59 0.002 40.80 2.8e-07 -2.35
7139 PPM1K 4_59 0.000 72.63 2.6e-09 -12.23
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_76"
genename region_tag susie_pip mu2 PVE z
5524 KCNN3 1_76 0.001 11.92 4.5e-08 3.22
12211 RP11-307C12.13 1_76 0.002 17.31 1.2e-07 4.17
6789 SHC1 1_76 0.001 7.36 2.1e-08 -0.78
5514 ADAM15 1_76 0.003 24.49 1.9e-07 -0.62
7073 DCST2 1_76 0.025 18.61 1.3e-06 5.23
7074 DCST1 1_76 0.007 15.21 3.2e-07 4.22
5523 EFNA3 1_76 0.003 31.28 2.7e-07 4.67
8041 SLC50A1 1_76 0.001 122.82 3.0e-07 -12.74
8042 EFNA1 1_76 0.001 64.23 2.0e-07 6.74
9069 DPM3 1_76 0.002 31.54 1.8e-07 4.80
8040 THBS3 1_76 0.964 205.02 5.7e-04 16.74
8924 GBA 1_76 0.001 74.68 1.8e-07 2.93
6795 FAM189B 1_76 0.001 9.09 2.9e-08 -1.43
8829 CLK2 1_76 0.001 86.03 2.8e-07 -1.35
4294 DAP3 1_76 0.001 8.86 2.0e-08 0.93
7076 YY1AP1 1_76 0.004 34.03 3.8e-07 5.82
3021 GON4L 1_76 0.001 69.72 2.3e-07 -0.93
4300 SYT11 1_76 0.011 80.89 2.6e-06 -1.33
5527 RIT1 1_76 0.002 64.70 2.9e-07 -1.40
3022 ARHGEF2 1_76 0.004 45.85 5.3e-07 -1.56
7094 SSR2 1_76 0.001 8.31 2.2e-08 -2.69
3023 LAMTOR2 1_76 0.001 6.60 1.7e-08 -0.72
6798 SLC25A44 1_76 0.002 16.30 1.0e-07 2.37
6797 PMF1 1_76 0.009 30.38 8.3e-07 3.31
7093 TMEM79 1_76 0.005 22.88 3.2e-07 -2.46
6796 PAQR6 1_76 0.005 23.42 3.5e-07 -2.15
10515 SMG5 1_76 0.002 16.19 1.1e-07 -1.67
10442 GLMP 1_76 0.005 22.75 3.3e-07 -2.03
7092 TSACC 1_76 0.001 5.31 1.3e-08 0.50
| 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
6294 rs79598313 1_18 1.000 93.44 2.7e-04 11.36
11285 rs10788884 1_30 1.000 41.18 1.2e-04 6.38
32725 rs185073199 1_73 1.000 306.70 8.9e-04 -18.84
57243 rs766167074 1_118 1.000 5685.07 1.7e-02 -2.94
72541 rs780093 2_16 1.000 430.18 1.3e-03 -22.26
113494 rs7565788 2_103 1.000 140.03 4.1e-04 -11.91
113556 rs6433115 2_103 1.000 50.48 1.5e-04 7.40
115849 rs863678 2_106 1.000 101.88 3.0e-04 6.01
117790 rs11690832 2_110 1.000 59.09 1.7e-04 8.04
127449 rs2068218 2_128 1.000 44.81 1.3e-04 -6.60
199976 rs141435299 4_10 1.000 511.27 1.5e-03 -1.03
200180 rs57136958 4_11 1.000 377.26 1.1e-03 -10.08
200236 rs13115469 4_11 1.000 9480.84 2.8e-02 133.38
200271 rs3775948 4_11 1.000 9725.80 2.8e-02 131.05
200297 rs75968456 4_11 1.000 659.02 1.9e-03 -2.69
200600 rs6831973 4_12 1.000 85.44 2.5e-04 -9.83
200626 rs76285604 4_12 1.000 134.07 3.9e-04 -11.85
200713 rs142309009 4_12 1.000 73.32 2.1e-04 -9.06
225574 rs149027545 4_59 1.000 2231.75 6.5e-03 53.88
225640 rs10022462 4_60 1.000 99.15 2.9e-04 -12.99
230022 rs35518360 4_67 1.000 45.34 1.3e-04 -6.68
276576 rs255749 5_31 1.000 71.91 2.1e-04 8.64
277347 rs10077826 5_33 1.000 35.85 1.0e-04 -5.73
281784 rs10942549 5_43 1.000 170.57 5.0e-04 -15.21
318593 rs630258 6_7 1.000 163.21 4.7e-04 -17.15
325060 rs7754961 6_19 1.000 122.96 3.6e-04 15.01
325099 rs12213398 6_19 1.000 45.81 1.3e-04 -4.69
325743 rs6908155 6_21 1.000 62.92 1.8e-04 -2.88
328906 rs2856992 6_27 1.000 50.41 1.5e-04 -5.75
358175 rs10782229 6_84 1.000 42.78 1.2e-04 5.77
373398 rs78148157 7_2 1.000 114.99 3.3e-04 -9.55
373399 rs13241427 7_2 1.000 86.19 2.5e-04 8.10
399842 rs761767938 7_49 1.000 6206.94 1.8e-02 -3.88
399850 rs1544459 7_49 1.000 6242.46 1.8e-02 -4.00
449493 rs2941484 8_56 1.000 159.09 4.6e-04 15.49
484112 rs56030777 9_25 1.000 104.08 3.0e-04 -4.38
497675 rs1800977 9_53 1.000 48.13 1.4e-04 -6.96
524397 rs35182775 10_33 1.000 203.32 5.9e-04 15.09
527532 rs11510917 10_39 1.000 214.51 6.2e-04 -19.07
527545 rs1171619 10_39 1.000 332.66 9.7e-04 21.17
533780 rs149429992 10_52 1.000 7775.47 2.3e-02 2.53
544826 rs10886117 10_72 1.000 54.68 1.6e-04 7.40
562269 rs369062552 11_21 1.000 192.36 5.6e-04 11.72
562279 rs34830202 11_21 1.000 191.76 5.6e-04 -12.09
598568 rs11056397 12_13 1.000 35.26 1.0e-04 -5.84
611264 rs6581124 12_35 1.000 101.89 3.0e-04 -10.40
611283 rs7397189 12_36 1.000 340.88 9.9e-04 -20.09
630291 rs2701194 12_74 1.000 71.55 2.1e-04 8.03
634624 rs76734539 12_82 1.000 60.46 1.8e-04 7.62
647399 rs566812111 13_25 1.000 2938.63 8.5e-03 2.56
676810 rs72681869 14_20 1.000 65.44 1.9e-04 -8.26
709562 rs2472297 15_35 1.000 66.82 1.9e-04 -9.72
709804 rs145727191 15_35 1.000 51.53 1.5e-04 8.91
709833 rs2955742 15_36 1.000 42.91 1.2e-04 7.22
717310 rs59646751 15_48 1.000 149.48 4.3e-04 14.47
730557 rs12927956 16_27 1.000 68.93 2.0e-04 7.55
759024 rs3794748 17_32 1.000 171.00 5.0e-04 -13.59
794944 rs10401485 19_7 1.000 66.75 1.9e-04 9.35
827264 rs209955 20_32 1.000 36.39 1.1e-04 5.89
839063 rs219783 21_17 1.000 88.38 2.6e-04 -9.60
937590 rs140927145 7_92 1.000 8749.76 2.5e-02 -2.70
958461 rs542984928 11_36 1.000 241.16 7.0e-04 23.70
32727 rs1058534 1_73 0.999 39.27 1.1e-04 4.74
551845 rs3842748 11_2 0.999 84.95 2.5e-04 -8.12
571635 rs1203614 11_37 0.999 50.14 1.5e-04 6.02
647403 rs12430288 13_25 0.999 2966.38 8.6e-03 2.63
754251 rs2688 17_22 0.999 32.12 9.3e-05 -5.49
760345 rs11650989 17_36 0.999 40.76 1.2e-04 7.88
803393 rs56287732 19_23 0.999 41.14 1.2e-04 5.41
958490 rs12363578 11_36 0.999 494.47 1.4e-03 -26.87
150985 rs113569731 3_33 0.998 34.67 1.0e-04 6.69
200719 rs61795273 4_12 0.998 54.55 1.6e-04 -7.25
314395 rs139078584 5_106 0.998 32.11 9.3e-05 6.53
533782 rs2152629 10_52 0.998 7794.14 2.3e-02 2.47
774641 rs527616 18_14 0.998 31.06 9.0e-05 -5.57
200396 rs4140694 4_11 0.997 862.78 2.5e-03 16.31
243507 rs4521364 4_95 0.997 56.31 1.6e-04 -5.84
617762 rs1848968 12_48 0.997 45.86 1.3e-04 -6.75
847453 rs740219 22_10 0.997 35.50 1.0e-04 6.06
318416 rs200823080 6_6 0.996 33.69 9.8e-05 5.68
550630 rs2767419 10_85 0.996 34.75 1.0e-04 -5.65
762453 rs113408695 17_39 0.996 31.77 9.2e-05 -5.55
806859 rs814573 19_32 0.996 31.06 9.0e-05 -5.56
406243 rs45467892 7_61 0.995 41.78 1.2e-04 -6.54
205121 rs358256 4_20 0.994 33.87 9.8e-05 5.69
325576 rs13191326 6_21 0.994 231.18 6.7e-04 13.59
937586 rs6954405 7_92 0.994 8729.37 2.5e-02 -2.37
87127 rs7561263 2_46 0.993 35.30 1.0e-04 -6.03
173655 rs11712964 3_78 0.993 30.31 8.8e-05 5.45
711351 rs7174325 15_38 0.993 28.54 8.2e-05 4.89
828985 rs1407040 20_34 0.993 29.63 8.6e-05 -5.21
84689 rs778147 2_40 0.992 47.99 1.4e-04 6.79
803247 rs71176165 19_23 0.992 69.52 2.0e-04 -9.09
92278 rs10196697 2_54 0.991 32.46 9.4e-05 -5.64
180547 rs145422957 3_92 0.991 30.16 8.7e-05 -5.47
803431 rs889140 19_23 0.991 29.72 8.6e-05 -4.20
512622 rs72777711 10_10 0.990 30.40 8.8e-05 -5.28
717317 rs8037855 15_48 0.990 64.84 1.9e-04 10.82
399846 rs11972122 7_49 0.988 5768.82 1.7e-02 -3.92
691061 rs73349296 14_50 0.988 43.23 1.2e-04 -6.59
449531 rs2941432 8_56 0.987 69.92 2.0e-04 11.61
736271 rs72799826 16_38 0.987 33.06 9.5e-05 -5.84
178134 rs115604285 3_87 0.986 32.63 9.4e-05 6.16
714094 rs113404146 15_42 0.986 34.61 9.9e-05 5.81
589923 rs73022311 11_77 0.985 26.53 7.6e-05 -4.89
683267 rs10151620 14_34 0.985 36.53 1.0e-04 6.06
390782 rs700752 7_34 0.983 48.41 1.4e-04 6.67
33769 rs1979575 1_75 0.980 26.03 7.4e-05 -4.39
230797 rs2903386 4_69 0.979 34.33 9.8e-05 5.82
792922 rs2074457 19_3 0.979 26.64 7.6e-05 4.44
394303 rs140420256 7_39 0.976 25.42 7.2e-05 4.74
539944 rs7900763 10_64 0.976 26.79 7.6e-05 -4.72
211123 rs12639940 4_32 0.974 25.87 7.3e-05 -4.41
630300 rs80019595 12_74 0.974 54.14 1.5e-04 -6.72
888117 rs17050272 2_70 0.974 114.74 3.2e-04 12.28
609350 rs1878234 12_31 0.972 29.37 8.3e-05 -5.23
237787 rs62323480 4_83 0.970 25.85 7.3e-05 4.40
151688 rs62259692 3_36 0.969 35.31 9.9e-05 7.65
205918 rs34811474 4_21 0.969 35.27 9.9e-05 -5.82
429354 rs17151140 8_13 0.969 55.61 1.6e-04 5.11
711207 rs8024096 15_37 0.969 28.33 8.0e-05 5.11
735932 rs56259873 16_37 0.968 90.15 2.5e-04 10.62
774682 rs162000 18_14 0.968 25.57 7.2e-05 4.95
247504 rs139212650 4_102 0.966 24.92 7.0e-05 4.61
329108 rs1126511 6_27 0.966 49.78 1.4e-04 5.39
141873 rs6778028 3_12 0.961 25.37 7.1e-05 4.59
422421 rs11761498 7_98 0.961 32.77 9.2e-05 5.26
479485 rs10964603 9_16 0.961 25.16 7.0e-05 -4.52
277508 rs536916238 5_33 0.960 31.95 8.9e-05 -0.30
439750 rs111965375 8_34 0.955 25.74 7.2e-05 5.22
225594 rs28366540 4_59 0.952 558.03 1.5e-03 -33.87
332608 rs10223666 6_34 0.952 191.31 5.3e-04 14.34
572083 rs6591334 11_37 0.952 38.49 1.1e-04 -5.74
70531 rs62112223 2_12 0.951 30.01 8.3e-05 -5.32
828854 rs73185042 20_34 0.950 25.29 7.0e-05 -4.69
316738 rs1272694 6_3 0.947 34.69 9.6e-05 -5.95
326523 rs13437375 6_23 0.943 29.91 8.2e-05 2.72
277490 rs173964 5_33 0.941 104.18 2.9e-04 8.09
424792 rs117950418 8_4 0.936 24.65 6.7e-05 -4.62
681701 rs34528648 14_32 0.936 32.14 8.7e-05 5.45
719548 rs2601781 16_4 0.934 25.44 6.9e-05 4.71
32400 rs146141366 1_73 0.933 32.24 8.7e-05 -6.41
441040 rs12543287 8_37 0.932 34.56 9.4e-05 -5.67
717293 rs75422555 15_47 0.932 31.12 8.4e-05 -6.41
505789 rs201421930 9_69 0.931 34.84 9.4e-05 -5.88
72738 rs7606480 2_17 0.926 25.69 6.9e-05 -4.89
158345 rs56324130 3_49 0.926 23.92 6.4e-05 -4.43
550576 rs75184896 10_84 0.926 26.63 7.2e-05 5.47
236957 rs72680231 4_81 0.924 24.44 6.6e-05 -4.66
464077 rs921719 8_83 0.924 27.01 7.3e-05 -5.17
66954 rs139638572 2_6 0.922 27.95 7.5e-05 4.44
610539 rs11608918 12_33 0.922 25.08 6.7e-05 4.71
352403 rs12196331 6_71 0.921 28.69 7.7e-05 5.24
236094 rs41278087 4_79 0.917 24.04 6.4e-05 -4.60
465396 rs36041912 8_85 0.917 24.31 6.5e-05 -4.56
716994 rs116887089 15_47 0.916 24.33 6.5e-05 4.45
186960 rs1290790 3_104 0.915 69.86 1.9e-04 6.15
271834 rs13170671 5_23 0.913 70.77 1.9e-04 8.54
522692 rs58434594 10_30 0.912 27.69 7.3e-05 4.83
155878 rs56145049 3_43 0.910 24.80 6.6e-05 -4.67
315649 rs6873880 5_109 0.906 28.18 7.4e-05 -5.03
420509 rs10224210 7_94 0.905 131.87 3.5e-04 11.82
230088 rs13140033 4_68 0.904 24.32 6.4e-05 -4.46
212858 rs112161979 4_35 0.900 23.67 6.2e-05 4.56
346480 rs118165878 6_58 0.899 23.86 6.2e-05 4.44
805755 rs11671669 19_28 0.896 24.00 6.3e-05 4.49
190228 rs17593458 3_110 0.895 25.72 6.7e-05 4.52
504688 rs11545664 9_66 0.895 25.56 6.7e-05 4.37
709774 rs553274247 15_35 0.891 122.44 3.2e-04 -5.27
194788 rs7642977 3_119 0.890 29.72 7.7e-05 5.14
151871 rs2276816 3_36 0.889 32.28 8.3e-05 -0.96
281193 rs6886422 5_42 0.889 25.33 6.5e-05 -4.52
783269 rs784218 18_30 0.889 27.03 7.0e-05 4.29
811810 rs2003700 20_1 0.878 24.14 6.2e-05 4.41
562194 rs3781846 11_21 0.877 34.31 8.8e-05 -6.10
32718 rs36107432 1_73 0.875 91.25 2.3e-04 -10.06
287232 rs3952745 5_53 0.875 32.65 8.3e-05 -6.44
572424 rs10752584 11_38 0.874 30.10 7.7e-05 4.73
325013 rs62392365 6_19 0.873 72.24 1.8e-04 11.55
798747 rs11668601 19_14 0.871 35.15 8.9e-05 7.11
354504 rs1997649 6_76 0.870 23.80 6.0e-05 4.28
199559 rs28680668 4_9 0.864 33.07 8.3e-05 5.66
331859 rs17328707 6_32 0.863 24.27 6.1e-05 -4.44
543559 rs11196217 10_70 0.863 25.08 6.3e-05 -4.56
551832 rs11042594 11_2 0.861 67.84 1.7e-04 6.63
571658 rs509533 11_37 0.857 45.29 1.1e-04 7.93
613719 rs113897089 12_40 0.857 29.33 7.3e-05 5.22
6777 rs7516039 1_20 0.856 25.69 6.4e-05 4.71
697307 rs62004133 15_8 0.854 25.07 6.2e-05 -4.56
960801 rs5792371 11_36 0.853 267.41 6.6e-04 18.90
227624 rs11722010 4_63 0.850 25.08 6.2e-05 -4.49
739611 rs76862947 16_44 0.849 139.30 3.4e-04 -11.76
958110 rs78915580 11_36 0.849 65.82 1.6e-04 -7.09
949639 rs3824359 9_73 0.846 43.67 1.1e-04 6.60
794928 rs11085216 19_7 0.844 61.29 1.5e-04 -8.96
24773 rs9432440 1_58 0.842 25.38 6.2e-05 4.55
302516 rs356486 5_82 0.842 29.54 7.2e-05 5.01
827294 rs570322378 20_32 0.841 27.15 6.6e-05 4.45
40689 rs604388 1_87 0.840 24.57 6.0e-05 4.43
446808 rs71553284 8_50 0.839 25.37 6.2e-05 4.39
123087 rs56059523 2_120 0.838 29.74 7.2e-05 4.32
399114 rs1179610 7_48 0.834 25.92 6.3e-05 4.72
784839 rs17773471 18_33 0.834 35.74 8.7e-05 7.37
103431 rs71862935 2_79 0.832 27.39 6.6e-05 -4.89
735867 rs72799382 16_37 0.832 42.87 1.0e-04 -6.78
111131 rs12467636 2_96 0.830 35.50 8.6e-05 5.79
579952 rs57569860 11_52 0.829 23.66 5.7e-05 3.53
151474 rs78342753 3_35 0.828 34.08 8.2e-05 5.19
87142 rs72837990 2_46 0.826 24.50 5.9e-05 -4.42
484081 rs10971408 9_25 0.826 28.16 6.8e-05 -3.63
601350 rs4077798 12_17 0.825 30.31 7.3e-05 -5.30
814218 rs235763 20_5 0.825 25.95 6.2e-05 -4.66
735501 rs12599580 16_36 0.824 28.33 6.8e-05 5.66
448127 rs34650318 8_54 0.819 26.07 6.2e-05 -4.51
141191 rs9846767 3_11 0.816 25.01 5.9e-05 4.59
331155 rs2815103 6_30 0.815 28.30 6.7e-05 4.85
406907 rs7803747 7_63 0.815 26.81 6.4e-05 5.00
446574 rs17397411 8_50 0.814 25.24 6.0e-05 4.24
435750 rs4871905 8_24 0.813 131.14 3.1e-04 12.10
547435 rs1693628 10_78 0.812 26.32 6.2e-05 -4.38
639726 rs9315009 13_9 0.807 36.74 8.6e-05 5.93SNPs 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
200271 rs3775948 4_11 1.000 9725.80 2.8e-02 131.05
200238 rs6838021 4_11 0.000 9644.63 0.0e+00 133.98
200239 rs6823324 4_11 0.000 9625.69 0.0e+00 -133.15
200242 rs11723439 4_11 0.000 9485.78 0.0e+00 -132.41
200236 rs13115469 4_11 1.000 9480.84 2.8e-02 133.38
937590 rs140927145 7_92 1.000 8749.76 2.5e-02 -2.70
937586 rs6954405 7_92 0.994 8729.37 2.5e-02 -2.37
937585 rs6953180 7_92 0.301 8727.34 7.6e-03 -2.31
937584 rs11971515 7_92 0.092 8725.68 2.3e-03 -2.27
937583 rs11974627 7_92 0.102 8724.43 2.6e-03 -2.27
937588 rs528981137 7_92 0.006 8721.80 1.6e-04 -2.16
937589 rs549027364 7_92 0.007 8721.59 1.8e-04 -2.16
200261 rs7439210 4_11 0.000 8715.70 0.0e+00 -128.12
937580 rs7807788 7_92 0.000 8510.18 1.1e-07 -2.43
937574 rs11546289 7_92 0.000 8486.58 2.5e-09 -2.37
937575 rs13243678 7_92 0.000 8478.19 9.5e-10 -2.36
937576 rs11546290 7_92 0.000 8472.68 3.1e-10 -2.34
937572 rs6952916 7_92 0.000 8432.27 5.9e-12 -2.29
937569 rs7793916 7_92 0.000 8421.15 4.0e-12 -2.30
937570 rs7781312 7_92 0.000 8402.16 5.7e-13 -2.28
937568 rs13247593 7_92 0.000 8393.44 1.1e-11 -2.38
937571 rs6965276 7_92 0.000 8347.45 9.9e-15 -2.28
937593 rs112579924 7_92 0.000 8308.78 0.0e+00 -1.71
937567 rs568222600 7_92 0.000 8271.15 5.6e-17 -2.28
937598 rs12667507 7_92 0.000 8261.11 0.0e+00 2.10
937565 rs112799047 7_92 0.000 8147.73 0.0e+00 -2.31
937566 rs10233906 7_92 0.000 8137.38 0.0e+00 -2.31
937563 rs112684174 7_92 0.000 8128.78 0.0e+00 -2.32
937564 rs112634954 7_92 0.000 8120.51 0.0e+00 -2.32
937558 rs6965440 7_92 0.000 8107.64 0.0e+00 -2.33
937557 rs6464930 7_92 0.000 8096.21 0.0e+00 -2.35
937552 rs6978068 7_92 0.000 8056.14 0.0e+00 -2.28
200329 rs11723742 4_11 0.000 7991.99 0.0e+00 -116.64
937543 rs10233535 7_92 0.000 7957.75 0.0e+00 -2.31
937544 rs56932055 7_92 0.000 7955.42 0.0e+00 -2.32
937548 rs6958855 7_92 0.000 7954.44 0.0e+00 -2.34
937551 rs6963547 7_92 0.000 7951.80 0.0e+00 -2.37
937533 rs10269104 7_92 0.000 7950.62 0.0e+00 -2.30
937549 rs1551926 7_92 0.000 7928.96 0.0e+00 2.45
200413 rs17389602 4_11 0.000 7843.29 0.0e+00 -117.34
200415 rs78917351 4_11 0.000 7831.73 0.0e+00 -117.31
937577 rs2306169 7_92 0.000 7802.72 0.0e+00 2.54
533782 rs2152629 10_52 0.998 7794.14 2.3e-02 2.47
937579 rs2306170 7_92 0.000 7786.32 0.0e+00 2.33
533778 rs7913261 10_52 0.565 7777.68 1.3e-02 2.51
533780 rs149429992 10_52 1.000 7775.47 2.3e-02 2.53
533783 rs1360953 10_52 0.000 7699.10 1.9e-08 2.50
937587 rs6953222 7_92 0.000 7697.89 0.0e+00 -1.43
533788 rs76574695 10_52 0.000 7697.50 1.6e-07 2.40
937582 rs34909003 7_92 0.000 7646.92 0.0e+00 -1.86SNPs 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
200236 rs13115469 4_11 1.000 9480.84 0.02800 133.38
200271 rs3775948 4_11 1.000 9725.80 0.02800 131.05
937586 rs6954405 7_92 0.994 8729.37 0.02500 -2.37
937590 rs140927145 7_92 1.000 8749.76 0.02500 -2.70
533780 rs149429992 10_52 1.000 7775.47 0.02300 2.53
533782 rs2152629 10_52 0.998 7794.14 0.02300 2.47
399842 rs761767938 7_49 1.000 6206.94 0.01800 -3.88
399850 rs1544459 7_49 1.000 6242.46 0.01800 -4.00
57243 rs766167074 1_118 1.000 5685.07 0.01700 -2.94
399846 rs11972122 7_49 0.988 5768.82 0.01700 -3.92
533778 rs7913261 10_52 0.565 7777.68 0.01300 2.51
647403 rs12430288 13_25 0.999 2966.38 0.00860 2.63
647399 rs566812111 13_25 1.000 2938.63 0.00850 2.56
937585 rs6953180 7_92 0.301 8727.34 0.00760 -2.31
225574 rs149027545 4_59 1.000 2231.75 0.00650 53.88
57241 rs2486737 1_118 0.369 5647.25 0.00610 -3.17
57242 rs971534 1_118 0.357 5647.23 0.00590 -3.16
57240 rs10489611 1_118 0.303 5647.08 0.00500 -3.16
57237 rs2790891 1_118 0.290 5646.71 0.00480 -3.16
57238 rs2491405 1_118 0.290 5646.71 0.00480 -3.16
57234 rs2256908 1_118 0.277 5646.68 0.00450 -3.16
647402 rs1579715 13_25 0.395 2936.01 0.00340 -2.77
57249 rs2248646 1_118 0.181 5644.36 0.00300 -3.14
57250 rs2211176 1_118 0.173 5644.56 0.00280 -3.14
57251 rs2790882 1_118 0.173 5644.56 0.00280 -3.14
937583 rs11974627 7_92 0.102 8724.43 0.00260 -2.27
200396 rs4140694 4_11 0.997 862.78 0.00250 16.31
937584 rs11971515 7_92 0.092 8725.68 0.00230 -2.27
200297 rs75968456 4_11 1.000 659.02 0.00190 -2.69
57230 rs1076804 1_118 0.090 5638.03 0.00150 -3.14
199976 rs141435299 4_10 1.000 511.27 0.00150 -1.03
225594 rs28366540 4_59 0.952 558.03 0.00150 -33.87
958490 rs12363578 11_36 0.999 494.47 0.00140 -26.87
72541 rs780093 2_16 1.000 430.18 0.00130 -22.26
57252 rs1416913 1_118 0.072 5637.35 0.00120 -3.13
57246 rs2739509 1_118 0.071 5544.70 0.00110 -3.30
200180 rs57136958 4_11 1.000 377.26 0.00110 -10.08
199971 rs146530806 4_10 0.572 603.57 0.00100 6.59
611283 rs7397189 12_36 1.000 340.88 0.00099 -20.09
199969 rs144362537 4_10 0.558 604.18 0.00098 6.57
527545 rs1171619 10_39 1.000 332.66 0.00097 21.17
199970 rs34658640 4_10 0.536 604.12 0.00094 6.57
32725 rs185073199 1_73 1.000 306.70 0.00089 -18.84
57255 rs2790874 1_118 0.052 5636.55 0.00085 -3.12
958461 rs542984928 11_36 1.000 241.16 0.00070 23.70
325576 rs13191326 6_21 0.994 231.18 0.00067 13.59
960801 rs5792371 11_36 0.853 267.41 0.00066 18.90
527532 rs11510917 10_39 1.000 214.51 0.00062 -19.07
524397 rs35182775 10_33 1.000 203.32 0.00059 15.09
325178 rs1165209 6_20 0.270 726.02 0.00057 32.67SNPs 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
200238 rs6838021 4_11 0 9644.63 0.000 133.98
200236 rs13115469 4_11 1 9480.84 0.028 133.38
200239 rs6823324 4_11 0 9625.69 0.000 -133.15
200242 rs11723439 4_11 0 9485.78 0.000 -132.41
200271 rs3775948 4_11 1 9725.80 0.028 131.05
200261 rs7439210 4_11 0 8715.70 0.000 -128.12
200413 rs17389602 4_11 0 7843.29 0.000 -117.34
200415 rs78917351 4_11 0 7831.73 0.000 -117.31
200329 rs11723742 4_11 0 7991.99 0.000 -116.64
200467 rs11722185 4_11 0 7641.23 0.000 -115.64
200453 rs11727390 4_11 0 7646.53 0.000 -115.57
200459 rs4697745 4_11 0 7029.54 0.000 -110.85
200490 rs546391476 4_11 0 6944.82 0.000 -110.51
200441 rs10489070 4_11 0 6824.81 0.000 -109.37
200279 rs9291642 4_11 0 5583.73 0.000 103.74
200376 rs4697717 4_11 0 6460.03 0.000 -103.72
200398 rs887732 4_11 0 6243.84 0.000 -103.53
200301 rs7349721 4_11 0 5356.95 0.000 100.68
200245 rs7375643 4_11 0 4135.50 0.000 -90.99
200265 rs11723970 4_11 0 4129.19 0.000 90.75
200244 rs7375599 4_11 0 4106.76 0.000 -90.71
200497 rs5856057 4_11 0 4541.67 0.000 -86.60
200259 rs6449177 4_11 0 3550.06 0.000 -85.49
200272 rs34501273 4_11 0 3884.81 0.000 84.99
200274 rs3733586 4_11 0 3885.37 0.000 84.99
200275 rs35438220 4_11 0 3884.22 0.000 84.99
200278 rs12507330 4_11 0 3883.30 0.000 84.97
200269 rs17187075 4_11 0 3735.98 0.000 84.09
200241 rs12498956 4_11 0 3433.44 0.000 -83.90
200283 rs3756236 4_11 0 3717.48 0.000 83.79
200298 rs11727199 4_11 0 3718.14 0.000 83.20
200299 rs10939665 4_11 0 3715.67 0.000 83.17
200300 rs12508991 4_11 0 3716.39 0.000 83.17
200312 rs35501905 4_11 0 3713.33 0.000 83.12
200233 rs35955619 4_11 0 3188.83 0.000 -82.89
200251 rs6844316 4_11 0 3331.64 0.000 -82.84
200256 rs4295261 4_11 0 3333.13 0.000 -82.84
200249 rs34297373 4_11 0 3330.44 0.000 -82.82
200252 rs28837683 4_11 0 3326.06 0.000 -82.81
200258 rs7434391 4_11 0 3333.44 0.000 -82.79
200295 rs13148371 4_11 0 3694.27 0.000 82.73
200243 rs7376155 4_11 0 3307.88 0.000 -82.67
200247 rs4314284 4_11 0 3307.91 0.000 -82.67
200316 rs7678211 4_11 0 3821.79 0.000 82.60
200337 rs4235356 4_11 0 3340.56 0.000 -80.91
200286 rs6827785 4_11 0 3246.53 0.000 79.26
200311 rs13120348 4_11 0 3235.31 0.000 78.51
200303 rs13122689 4_11 0 3225.75 0.000 78.50
200304 rs12504565 4_11 0 3226.18 0.000 78.50
200296 rs12506122 4_11 0 3239.20 0.000 78.48Gene 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] 16if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)PPM1A gene(s) from the input list not found in DisGeNET CURATEDTLCD2 gene(s) from the input list not found in DisGeNET CURATEDTMC4 gene(s) from the input list not found in DisGeNET CURATEDFAM216A gene(s) from the input list not found in DisGeNET CURATEDPRSS27 gene(s) from the input list not found in DisGeNET CURATEDSYTL1 gene(s) from the input list not found in DisGeNET CURATEDCHCHD7 gene(s) from the input list not found in DisGeNET CURATEDDNAJC3-AS1 gene(s) from the input list not found in DisGeNET CURATED Description
64 BONE MINERAL DENSITY QUANTITATIVE TRAIT LOCUS 12
67 PROSTATE CANCER, SUSCEPTIBILITY TO
70 MEGALENCEPHALY-POLYMICROGYRIA-POLYDACTYLY-HYDROCEPHALUS SYNDROME 3
71 PARKINSON DISEASE 11, AUTOSOMAL DOMINANT, SUSCEPTIBILITY TO
73 PREMATURE OVARIAN FAILURE 13
35 POLYDACTYLY, POSTAXIAL
58 Perisylvian syndrome
59 Megalanecephaly Polymicrogyria-Polydactyly Hydrocephalus Syndrome
60 POSTAXIAL POLYDACTYLY, TYPE B
62 Alcohol Toxicity
FDR Ratio BgRatio
64 0.01220367 1/8 1/9703
67 0.01220367 1/8 1/9703
70 0.01220367 1/8 1/9703
71 0.01220367 1/8 1/9703
73 0.01220367 1/8 1/9703
35 0.02031744 1/8 4/9703
58 0.02031744 1/8 4/9703
59 0.02031744 1/8 4/9703
60 0.02031744 1/8 3/9703
62 0.02031744 1/8 2/9703******************************************
* *
* 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