LDL direct (quantile) - Adipose_Visceral_Omentum
wesleycrouse
2021-09-7
Last updated: 2021-11-15
Checks: 6 1
Knit directory: ctwas_applied/
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Overview
These are the results of a ctwas analysis of the UK Biobank trait LDL direct using Adipose_Visceral_Omentum 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-30780_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 Adipose_Visceral_Omentum 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] 12810#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1254 879 741 489 607 727 644 458 473 519 787 751 243 420 418
16 17 18 19 20 21 22
587 800 198 989 370 144 312 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7874317Load ctwas results
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)
#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
1.601932e-02 7.465574e-05 #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
9.810884 34.580704 #report sample size
print(sample_size)[1] 343621#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 12810 8696600#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
0.005858973 0.065338147 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.03372035 0.54983647Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z
2252 PRKD2 19_33 0.9990989 29.27528 8.511965e-05 5.316724
6227 CNIH4 1_114 0.9916593 36.75923 1.060838e-04 6.201835
7878 ACP6 1_73 0.9881237 22.14907 6.369233e-05 4.575774
1003 TPD52 8_57 0.9812117 21.94017 6.265027e-05 -4.557712
1163 GSK3B 3_74 0.9750098 43.57087 1.236305e-04 6.835676
3740 C10orf88 10_77 0.9675624 32.45930 9.139835e-05 -6.783901
1553 MZF1 19_39 0.9648108 28.16926 7.909298e-05 -4.742966
402 TUBG2 17_25 0.9570282 20.51179 5.712794e-05 4.434366
9315 KCNK3 2_16 0.9525992 23.25301 6.446287e-05 -4.821789
3305 FN1 2_127 0.9418621 21.45337 5.880349e-05 -4.446065
9946 ZNF575 19_30 0.9308837 26.19545 7.096457e-05 -5.954341
6406 USP53 4_77 0.9248454 24.58220 6.616225e-05 -4.856546
14455 AC007950.2 15_29 0.9135056 31.25232 8.308331e-05 5.555780
13062 ATP5J2 7_61 0.9089457 34.74097 9.189676e-05 -5.116980
1542 SCD 10_64 0.8945838 19.60935 5.105103e-05 -4.541468
3734 GPAM 10_70 0.8925750 21.37803 5.553063e-05 4.133221
3645 CCND2 12_4 0.8874327 19.93184 5.147580e-05 -4.065830
2080 CTSH 15_37 0.8853807 18.41185 4.744034e-05 3.805849
12483 FXYD7 19_24 0.8819526 19.45446 4.993267e-05 -3.872239
266 NPC1L1 7_32 0.8707786 100.50633 2.546956e-04 11.631021
1508 CWF19L1 10_64 0.8687056 29.13781 7.366309e-05 5.747567
756 EVI5 1_56 0.8564938 41.00879 1.022166e-04 -6.589915
6974 PELO 5_30 0.8524653 63.70825 1.580493e-04 8.522224
9463 POP7 7_62 0.8450469 35.57880 8.749683e-05 5.858772
7444 TMED4 7_32 0.8423169 38.14671 9.350890e-05 7.608826
8291 NOS3 7_93 0.8379598 19.38306 4.726783e-05 3.856590
2174 SARS2 19_26 0.8249021 21.80207 5.233841e-05 4.480159
5866 FURIN 15_42 0.8215001 20.23083 4.836617e-05 -4.391033
5626 PARP9 3_76 0.8120411 41.98828 9.922621e-05 -5.774700
3681 KDSR 18_35 0.8103019 19.33886 4.560347e-05 -3.912562
13192 LINC01184 5_78 0.8057176 19.17555 4.496256e-05 -3.918269
11839 FAM3D 3_40 0.8027527 18.66547 4.360548e-05 -3.889457
num_eqtl
2252 2
6227 2
7878 4
1003 2
1163 1
3740 1
1553 2
402 2
9315 1
3305 1
9946 2
6406 1
14455 1
13062 2
1542 1
3734 1
3645 1
2080 4
12483 1
266 1
1508 1
756 2
6974 1
9463 1
7444 2
8291 2
2174 1
5866 1
5626 1
3681 3
13192 2
11839 1Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
5137 SRPK2 7_65 0.000000e+00 31995.3681 0.000000e+00 -2.6189650
4573 NECTIN2 19_31 0.000000e+00 1648.6094 0.000000e+00 -35.7740463
77 KMT2E 7_65 0.000000e+00 1172.7041 0.000000e+00 1.5487658
4575 TOMM40 19_31 0.000000e+00 1005.7112 0.000000e+00 -14.0286334
4576 APOC1 19_31 0.000000e+00 474.0764 0.000000e+00 -9.1150442
5564 ABCG5 2_27 2.715998e-03 431.7488 3.412565e-06 -20.2939818
3092 COL4A3BP 5_44 2.595991e-02 363.0208 2.742553e-05 -23.9950413
9013 PCSK9 1_34 4.996004e-15 354.0076 5.147017e-18 13.4587025
4574 APOE 19_31 0.000000e+00 303.2005 0.000000e+00 0.6519443
8722 GATAD2A 19_15 1.672333e-02 286.9939 1.396741e-05 -17.2984151
2340 ATP13A1 19_15 6.335397e-02 243.8491 4.495886e-05 -17.2714942
6037 GEMIN7 19_31 0.000000e+00 205.8472 0.000000e+00 13.2439035
7733 SPC24 19_9 0.000000e+00 205.4392 0.000000e+00 -10.5595998
3904 POLK 5_44 1.323527e-02 190.3275 7.330855e-06 17.5157647
4647 YIPF2 19_9 1.635359e-13 180.2447 8.578192e-17 11.7711942
5001 PSRC1 1_67 7.323688e-07 176.3153 3.757856e-10 -22.0965140
6446 TIMD4 5_92 8.255199e-02 156.5669 3.761386e-05 -13.8823626
1279 CETP 16_30 5.943840e-02 154.1376 2.666220e-05 13.3791897
3273 NRBP1 2_16 1.850969e-02 144.9038 7.805473e-06 8.1485391
4518 CDKN2D 19_9 0.000000e+00 144.0020 0.000000e+00 -10.2988017
num_eqtl
5137 1
4573 1
77 1
4575 2
4576 1
5564 1
3092 1
9013 3
4574 1
8722 1
2340 1
6037 2
7733 1
3904 1
4647 2
5001 2
6446 1
1279 1
3273 1
4518 1Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
266 NPC1L1 7_32 0.8707786 100.50633 2.546956e-04 11.631021
6974 PELO 5_30 0.8524653 63.70825 1.580493e-04 8.522224
3279 PPM1G 2_16 0.4415502 118.76322 1.526098e-04 5.730640
1163 GSK3B 3_74 0.9750098 43.57087 1.236305e-04 6.835676
6227 CNIH4 1_114 0.9916593 36.75923 1.060838e-04 6.201835
756 EVI5 1_56 0.8564938 41.00879 1.022166e-04 -6.589915
5626 PARP9 3_76 0.8120411 41.98828 9.922621e-05 -5.774700
10128 PLEC 8_94 0.7699725 41.76936 9.359516e-05 -6.601667
7444 TMED4 7_32 0.8423169 38.14671 9.350890e-05 7.608826
13062 ATP5J2 7_61 0.9089457 34.74097 9.189676e-05 -5.116980
3277 SNX17 2_16 0.2688332 117.41033 9.185643e-05 5.753118
3740 C10orf88 10_77 0.9675624 32.45930 9.139835e-05 -6.783901
9463 POP7 7_62 0.8450469 35.57880 8.749683e-05 5.858772
1276 PGS1 17_44 0.6298748 46.87376 8.592198e-05 7.140667
2252 PRKD2 19_33 0.9990989 29.27528 8.511965e-05 5.316724
643 SPHK2 19_33 0.6717137 42.78513 8.363680e-05 -8.660309
14455 AC007950.2 15_29 0.9135056 31.25232 8.308331e-05 5.555780
1553 MZF1 19_39 0.9648108 28.16926 7.909298e-05 -4.742966
1508 CWF19L1 10_64 0.8687056 29.13781 7.366309e-05 5.747567
9946 ZNF575 19_30 0.9308837 26.19545 7.096457e-05 -5.954341
num_eqtl
266 1
6974 1
3279 1
1163 1
6227 2
756 2
5626 1
10128 2
7444 2
13062 2
3277 1
3740 1
9463 1
1276 1
2252 2
643 2
14455 1
1553 2
1508 1
9946 2Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE
4573 NECTIN2 19_31 0.000000e+00 1648.60943 0.000000e+00
3092 COL4A3BP 5_44 2.595991e-02 363.02084 2.742553e-05
5001 PSRC1 1_67 7.323688e-07 176.31532 3.757856e-10
5564 ABCG5 2_27 2.715998e-03 431.74884 3.412565e-06
363 SARS 1_67 2.260060e-07 135.37304 8.903740e-11
3904 POLK 5_44 1.323527e-02 190.32752 7.330855e-06
8722 GATAD2A 19_15 1.672333e-02 286.99388 1.396741e-05
2340 ATP13A1 19_15 6.335397e-02 243.84907 4.495886e-05
14117 CTC-366B18.4 5_44 1.418545e-02 129.35359 5.340009e-06
14528 ZNF229 19_31 0.000000e+00 125.38401 0.000000e+00
4575 TOMM40 19_31 0.000000e+00 1005.71122 0.000000e+00
6446 TIMD4 5_92 8.255199e-02 156.56695 3.761386e-05
9013 PCSK9 1_34 4.996004e-15 354.00755 5.147017e-18
1279 CETP 16_30 5.943840e-02 154.13758 2.666220e-05
6037 GEMIN7 19_31 0.000000e+00 205.84724 0.000000e+00
11052 CEACAM19 19_31 0.000000e+00 64.75321 0.000000e+00
4647 YIPF2 19_9 1.635359e-13 180.24470 8.578192e-17
6096 SYPL2 1_67 1.177502e-07 58.30445 1.997945e-11
266 NPC1L1 7_32 8.707786e-01 100.50633 2.546956e-04
6035 NTN5 19_33 8.596474e-02 99.99560 2.501621e-05
z num_eqtl
4573 -35.77405 1
3092 -23.99504 1
5001 -22.09651 2
5564 -20.29398 1
363 -17.77828 2
3904 17.51576 1
8722 -17.29842 1
2340 -17.27149 1
14117 -15.55068 1
14528 14.49981 1
4575 -14.02863 2
6446 -13.88236 1
9013 13.45870 3
1279 13.37919 1
6037 13.24390 2
11052 11.79782 2
4647 11.77119 2
6096 11.72818 3
266 11.63102 1
6035 11.50875 1Comparing 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)
#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)
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.01678376#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE
4573 NECTIN2 19_31 0.000000e+00 1648.60943 0.000000e+00
3092 COL4A3BP 5_44 2.595991e-02 363.02084 2.742553e-05
5001 PSRC1 1_67 7.323688e-07 176.31532 3.757856e-10
5564 ABCG5 2_27 2.715998e-03 431.74884 3.412565e-06
363 SARS 1_67 2.260060e-07 135.37304 8.903740e-11
3904 POLK 5_44 1.323527e-02 190.32752 7.330855e-06
8722 GATAD2A 19_15 1.672333e-02 286.99388 1.396741e-05
2340 ATP13A1 19_15 6.335397e-02 243.84907 4.495886e-05
14117 CTC-366B18.4 5_44 1.418545e-02 129.35359 5.340009e-06
14528 ZNF229 19_31 0.000000e+00 125.38401 0.000000e+00
4575 TOMM40 19_31 0.000000e+00 1005.71122 0.000000e+00
6446 TIMD4 5_92 8.255199e-02 156.56695 3.761386e-05
9013 PCSK9 1_34 4.996004e-15 354.00755 5.147017e-18
1279 CETP 16_30 5.943840e-02 154.13758 2.666220e-05
6037 GEMIN7 19_31 0.000000e+00 205.84724 0.000000e+00
11052 CEACAM19 19_31 0.000000e+00 64.75321 0.000000e+00
4647 YIPF2 19_9 1.635359e-13 180.24470 8.578192e-17
6096 SYPL2 1_67 1.177502e-07 58.30445 1.997945e-11
266 NPC1L1 7_32 8.707786e-01 100.50633 2.546956e-04
6035 NTN5 19_33 8.596474e-02 99.99560 2.501621e-05
z num_eqtl
4573 -35.77405 1
3092 -23.99504 1
5001 -22.09651 2
5564 -20.29398 1
363 -17.77828 2
3904 17.51576 1
8722 -17.29842 1
2340 -17.27149 1
14117 -15.55068 1
14528 14.49981 1
4575 -14.02863 2
6446 -13.88236 1
9013 13.45870 3
1279 13.37919 1
6037 13.24390 2
11052 11.79782 2
4647 11.77119 2
6096 11.72818 3
266 11.63102 1
6035 11.50875 1Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
report_cols_region <- report_cols[!(report_cols %in% c("num_eqtl"))]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$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_region])
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: 19_31"
genename region_tag susie_pip mu2 PVE z
7553 ZNF233 19_31 0 131.643247 0 -10.0229697
7554 ZNF235 19_31 0 35.913626 0 -6.2627967
633 ZNF112 19_31 0 47.819226 0 4.8629559
13922 ZNF285 19_31 0 10.133291 0 -1.3318721
14528 ZNF229 19_31 0 125.384012 0 14.4998133
8710 ZNF180 19_31 0 43.730327 0 4.0279244
905 PVR 19_31 0 31.813106 0 -3.0943045
11052 CEACAM19 19_31 0 64.753213 0 11.7978210
11128 BCAM 19_31 0 96.429711 0 4.6421318
4573 NECTIN2 19_31 0 1648.609427 0 -35.7740463
4575 TOMM40 19_31 0 1005.711219 0 -14.0286334
4574 APOE 19_31 0 303.200452 0 0.6519443
4576 APOC1 19_31 0 474.076449 0 -9.1150442
12884 APOC2 19_31 0 18.135579 0 -2.2896080
2176 CLPTM1 19_31 0 50.178070 0 2.5751726
9234 ZNF296 19_31 0 94.996588 0 5.4593536
122 MARK4 19_31 0 21.181957 0 -2.2463768
6037 GEMIN7 19_31 0 205.847243 0 13.2439035
2178 PPP1R37 19_31 0 42.423234 0 -5.9455792
11348 BLOC1S3 19_31 0 11.973214 0 2.7250151
2189 KLC3 19_31 0 13.524015 0 -3.6648287
2184 ERCC2 19_31 0 13.966069 0 1.5340117
2182 PPP1R13L 19_31 0 22.778963 0 -3.0806361
3573 CD3EAP 19_31 0 11.426032 0 2.5646694
227 ERCC1 19_31 0 7.409485 0 -1.6350316
4228 FOSB 19_31 0 16.099633 0 -2.3658041
12327 PPM1N 19_31 0 24.361748 0 -2.6089113
4231 RTN2 19_31 0 6.949994 0 -2.0700710
4233 VASP 19_31 0 33.645255 0 -2.7026884
4229 OPA3 19_31 0 6.808430 0 1.5059074
[1] "Region: 5_44"
genename region_tag susie_pip mu2 PVE
9360 ENC1 5_44 0.01016091 4.668557 1.380497e-07
8214 NSA2 5_44 0.01518137 9.160767 4.047279e-07
8215 GFM2 5_44 0.01176127 5.882642 2.013478e-07
11871 FAM169A 5_44 0.01070872 5.096627 1.588330e-07
9983 GCNT4 5_44 0.01353265 8.945941 3.523133e-07
3904 POLK 5_44 0.01323527 190.327516 7.330855e-06
3092 COL4A3BP 5_44 0.02595991 363.020845 2.742553e-05
14117 CTC-366B18.4 5_44 0.01418545 129.353591 5.340009e-06
11338 ANKDD1B 5_44 0.01037025 15.483522 4.672822e-07
3905 SV2C 5_44 0.13940412 33.030361 1.340014e-05
13441 AC113404.1 5_44 0.01580063 11.797146 5.424649e-07
6425 IQGAP2 5_44 0.01829098 11.787918 6.274720e-07
z
9360 -0.4000089
8214 -1.5373676
8215 0.5648206
11871 0.1096504
9983 -1.7429762
3904 17.5157647
3092 -23.9950413
14117 -15.5506781
11338 2.9537749
3905 4.2165904
13441 2.3250769
6425 -2.1788866
[1] "Region: 1_67"
genename region_tag susie_pip mu2 PVE
12627 RP11-356N1.2 1_67 1.304537e-07 6.851387 2.601090e-12
1223 SLC25A24 1_67 6.417211e-07 14.911375 2.784738e-11
7839 HENMT1 1_67 3.871372e-07 15.825908 1.783010e-11
3416 STXBP3 1_67 1.554058e-07 15.012036 6.789333e-12
3900 CLCC1 1_67 1.398833e-07 5.489588 2.234734e-12
11677 TAF13 1_67 1.508761e-07 9.939408 4.364165e-12
12440 TMEM167B 1_67 1.528715e-07 5.494436 2.444387e-12
3419 KIAA1324 1_67 2.480353e-07 19.360714 1.397511e-11
363 SARS 1_67 2.260060e-07 135.373041 8.903740e-11
6104 CELSR2 1_67 5.286519e-07 113.723036 1.749599e-10
5001 PSRC1 1_67 7.323688e-07 176.315318 3.757856e-10
5003 SORT1 1_67 1.746323e-07 5.800528 2.947897e-12
6096 SYPL2 1_67 1.177502e-07 58.304452 1.997945e-11
7843 ATXN7L2 1_67 4.472802e-07 10.700754 1.392882e-11
10463 AMIGO1 1_67 5.210579e-07 25.569247 3.877254e-11
687 GNAI3 1_67 4.317367e-07 49.524698 6.222446e-11
4996 GSTM1 1_67 2.166034e-03 59.592966 3.756475e-07
8954 GSTM4 1_67 2.557416e-07 15.286858 1.137732e-11
12282 GSTM2 1_67 2.681935e-07 23.418731 1.827814e-11
4998 GSTM5 1_67 1.515841e-07 7.772201 3.428609e-12
4999 GSTM3 1_67 2.121061e-07 6.780908 4.185634e-12
z
12627 -1.23494808
1223 -0.09485475
7839 -1.85374050
3416 2.98447183
3900 -0.52733642
11677 -1.55914526
12440 1.84229721
3419 4.94390244
363 -17.77828114
6104 5.65177064
5001 -22.09651395
5003 -0.45551677
6096 11.72818164
7843 0.17069498
10463 -3.96308159
687 7.97926365
4996 7.68447989
8954 2.22512920
12282 4.14108479
4998 1.58897772
4999 -3.43690762
[1] "Region: 2_27"
genename region_tag susie_pip mu2 PVE
14565 LINC01126 2_27 0.0419225031 24.029336 2.931631e-06
3379 THADA 2_27 0.0013108049 5.680737 2.167020e-08
12512 C1GALT1C1L 2_27 0.0009562864 10.212103 2.841996e-08
6962 PLEKHH2 2_27 0.0080093432 24.276118 5.658437e-07
5556 DYNC2LI1 2_27 0.0015444811 9.199794 4.135052e-08
6249 ABCG8 2_27 0.7227631910 33.272060 6.998356e-05
5564 ABCG5 2_27 0.0027159977 431.748837 3.412565e-06
5570 LRPPRC 2_27 0.0012135925 9.102509 3.214803e-08
z
14565 0.5696005
3379 0.1649722
12512 1.1627524
6962 -2.4287605
5556 0.1268456
6249 6.5141780
5564 -20.2939818
5570 0.5202811
[1] "Region: 19_15"
genename region_tag susie_pip mu2 PVE z
4615 LSM4 19_15 0.01667072 4.694035 2.277304e-07 -0.04485155
4611 SSBP4 19_15 0.04472249 12.632132 1.644080e-06 -1.36527088
4613 PGPEP1 19_15 0.01736268 5.000955 2.526911e-07 -0.17636082
4612 GDF15 19_15 0.02138838 7.185379 4.472474e-07 0.99468054
9847 LRRC25 19_15 0.01979227 6.132966 3.532534e-07 -0.76178608
2317 ISYNA1 19_15 0.07195848 17.654436 3.697057e-06 2.08351436
2318 ELL 19_15 0.05479083 15.108314 2.409041e-06 1.88929817
2332 KXD1 19_15 0.02162117 6.834563 4.300413e-07 0.45146333
12491 UBA52 19_15 0.01654971 4.763213 2.294092e-07 0.37012117
88 C19orf60 19_15 0.02317689 7.193408 4.851880e-07 0.11838519
2328 TMEM59L 19_15 0.03598812 11.965986 1.253222e-06 1.52323891
89 CRLF1 19_15 0.02599402 8.840885 6.687897e-07 1.24791128
8721 KLHL26 19_15 0.03606686 14.048594 1.474557e-06 2.15688437
2319 CRTC1 19_15 0.02581776 9.191383 6.905890e-07 -1.32083615
2320 COMP 19_15 0.02046306 6.485941 3.862459e-07 0.49443686
54 UPF1 19_15 0.03548194 13.660618 1.410581e-06 2.05330389
2322 COPE 19_15 0.01655592 4.742140 2.284799e-07 -0.43225989
510 HOMER3 19_15 0.09140093 17.998846 4.787575e-06 1.24493998
669 SUGP2 19_15 0.01868862 7.556890 4.109988e-07 1.34264890
2325 ARMC6 19_15 0.01681132 5.095694 2.493018e-07 -0.03517232
10413 SLC25A42 19_15 0.07575909 16.842634 3.713343e-06 -1.25354593
667 TMEM161A 19_15 0.01962503 5.972855 3.411242e-07 -0.13086869
13442 BORCS8 19_15 0.01640666 18.220128 8.699454e-07 3.87529133
12348 MEF2B 19_15 0.10888140 39.939631 1.265546e-05 5.62947886
666 RFXANK 19_15 0.07331963 22.740741 4.852273e-06 -3.39933452
11158 HAPLN4 19_15 0.01796278 20.022195 1.046660e-06 3.94830824
12347 TM6SF2 19_15 0.06121640 86.712813 1.544797e-05 -9.76402244
8722 GATAD2A 19_15 0.01672333 286.993885 1.396741e-05 -17.29841508
10112 TSSK6 19_15 0.02250480 12.208485 7.995713e-07 1.60453456
13280 YJEFN3 19_15 0.02147848 24.176651 1.511193e-06 -4.43456926
7572 CILP2 19_15 0.02118831 12.028170 7.416793e-07 1.67751329
2337 PBX4 19_15 0.23177850 33.073358 2.230857e-05 4.57934973
1369 GMIP 19_15 0.02201833 9.801210 6.280357e-07 -1.69505042
10481 ZNF101 19_15 0.05858381 14.449604 2.463507e-06 2.48239944
2340 ATP13A1 19_15 0.06335397 243.849070 4.495886e-05 -17.27149418
2335 ZNF14 19_15 0.08605150 30.847238 7.724938e-06 4.88424969
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
15512 rs2495502 1_34 1.0000000 416.42536 1.211874e-03
31756 rs611917 1_67 1.0000000 1058.30049 3.079848e-03
71921 rs1042034 2_13 1.0000000 264.89530 7.708938e-04
71927 rs934197 2_13 1.0000000 412.90030 1.201615e-03
323276 rs115740542 6_20 1.0000000 179.13955 5.213289e-04
368681 rs12208357 6_103 1.0000000 287.92952 8.379276e-04
406538 rs763798411 7_65 1.0000000 43351.79029 1.261616e-01
759365 rs113408695 17_39 1.0000000 164.75187 4.794581e-04
792327 rs73013176 19_9 1.0000000 240.21500 6.990696e-04
792365 rs137992968 19_9 1.0000000 238.72707 6.947395e-04
795162 rs3794991 19_15 1.0000000 504.94944 1.469495e-03
800369 rs62117204 19_31 1.0000000 828.59034 2.411350e-03
800387 rs111794050 19_31 1.0000000 819.59983 2.385186e-03
800420 rs814573 19_31 1.0000000 2401.71797 6.989439e-03
800422 rs113345881 19_31 1.0000000 837.18307 2.436356e-03
800425 rs12721109 19_31 1.0000000 1445.06140 4.205393e-03
810553 rs34507316 20_13 1.0000000 99.65543 2.900155e-04
324009 rs454182 6_22 1.0000000 158.84336 4.622633e-04
759391 rs8070232 17_39 1.0000000 204.28712 5.945129e-04
71872 rs11679386 2_12 1.0000000 167.47528 4.873837e-04
504011 rs115478735 9_70 1.0000000 346.78056 1.009195e-03
72007 rs1848922 2_13 1.0000000 243.53464 7.087304e-04
71930 rs548145 2_13 1.0000000 719.56009 2.094052e-03
495296 rs2437818 9_53 1.0000000 83.04996 2.416906e-04
406549 rs4997569 7_65 1.0000000 43377.63905 1.262369e-01
758449 rs1801689 17_39 1.0000000 88.53077 2.576408e-04
443350 rs4738679 8_45 1.0000000 119.07886 3.465413e-04
428056 rs7012814 8_12 1.0000000 101.31613 2.948485e-04
59253 rs822928 1_121 1.0000000 120.31527 3.501395e-04
79368 rs72800939 2_28 1.0000000 62.30751 1.813263e-04
15523 rs10888896 1_34 1.0000000 152.61491 4.441373e-04
587705 rs4937122 11_77 0.9999997 81.20242 2.363138e-04
8327 rs79598313 1_18 0.9999996 51.93049 1.511272e-04
441955 rs140753685 8_42 0.9999996 62.21001 1.810424e-04
55628 rs2807848 1_112 0.9999990 62.05114 1.805800e-04
733330 rs12149380 16_38 0.9999964 137.61950 4.004965e-04
795193 rs113619686 19_15 0.9999928 76.87070 2.237062e-04
462210 rs6470359 8_83 0.9999910 336.50472 9.792815e-04
15482 rs11580527 1_34 0.9999890 93.13585 2.710394e-04
462213 rs13252684 8_83 0.9999875 279.41529 8.131395e-04
15530 rs471705 1_34 0.9999864 227.23788 6.612948e-04
567431 rs174553 11_34 0.9999805 150.50141 4.379781e-04
810552 rs6075251 20_13 0.9999803 72.10892 2.098460e-04
323255 rs72834643 6_20 0.9999754 53.57379 1.559057e-04
350118 rs9496567 6_67 0.9999685 42.81962 1.246090e-04
1052986 rs73045960 19_32 0.9999520 129.64546 3.772739e-04
59203 rs6586405 1_121 0.9999511 54.53416 1.586966e-04
319547 rs11376017 6_13 0.9999431 74.12906 2.157168e-04
792444 rs322144 19_10 0.9998977 73.02669 2.124993e-04
704146 rs2070895 15_26 0.9998361 64.58694 1.879290e-04
368865 rs56393506 6_104 0.9998318 136.28359 3.965435e-04
792391 rs4804149 19_10 0.9997902 50.41207 1.466776e-04
368829 rs117733303 6_104 0.9997137 99.51179 2.895146e-04
733373 rs57186116 16_38 0.9996703 76.02905 2.211855e-04
540937 rs17875416 10_71 0.9995198 41.76312 1.214800e-04
495269 rs2297400 9_53 0.9992742 44.40749 1.291401e-04
608591 rs7397189 12_36 0.9991893 37.53755 1.091526e-04
281255 rs7701166 5_44 0.9990396 40.57905 1.179791e-04
1052324 rs62115559 19_30 0.9989979 214.59150 6.238747e-04
794802 rs2302209 19_14 0.9987956 46.91823 1.363762e-04
382062 rs56130071 7_19 0.9983525 106.66626 3.099069e-04
462201 rs2980875 8_83 0.9978210 613.06254 1.780237e-03
31824 rs41313290 1_67 0.9978157 44.72350 1.298693e-04
431682 rs1495743 8_21 0.9975279 44.75903 1.299350e-04
792348 rs147985405 19_9 0.9971798 2880.68797 8.359687e-03
584637 rs75542613 11_70 0.9971573 38.87458 1.128105e-04
738825 rs2255451 16_48 0.9971191 42.76111 1.240842e-04
584632 rs3135506 11_70 0.9967147 162.35784 4.709388e-04
815506 rs76981217 20_24 0.9959836 37.33675 1.082204e-04
625175 rs653178 12_67 0.9956669 111.26015 3.223844e-04
1051896 rs55840997 19_30 0.9947002 71.64502 2.073951e-04
443318 rs56386732 8_45 0.9935777 36.37333 1.051732e-04
79245 rs13430143 2_27 0.9934546 105.89810 3.061657e-04
664684 rs3934835 13_62 0.9925982 63.62177 1.837805e-04
325231 rs28780090 6_24 0.9910717 54.23840 1.564344e-04
31090 rs1730862 1_66 0.9904827 31.42552 9.058363e-05
140546 rs709149 3_9 0.9898370 38.54520 1.110336e-04
815457 rs6029132 20_24 0.9887883 43.08875 1.239902e-04
147556 rs9834932 3_24 0.9862678 72.92535 2.093118e-04
612957 rs148481241 12_45 0.9861123 29.63099 8.503405e-05
325254 rs62407548 6_24 0.9858753 72.43641 2.078257e-04
593614 rs11048034 12_9 0.9824846 42.78517 1.223318e-04
666492 rs2332328 14_3 0.9778872 49.29997 1.402994e-04
281196 rs10062361 5_44 0.9765936 231.78846 6.587581e-04
603678 rs2638250 12_25 0.9724866 29.22661 8.271463e-05
79248 rs4076834 2_27 0.9703966 490.24454 1.384466e-03
477621 rs1556516 9_16 0.9687335 80.99319 2.283353e-04
628129 rs1169300 12_74 0.9663765 74.81160 2.103951e-04
222691 rs1458038 4_54 0.9663424 56.95198 1.601622e-04
324446 rs3130253 6_23 0.9652028 29.99204 8.424515e-05
323094 rs75080831 6_19 0.9640699 63.66311 1.786145e-04
1052407 rs185920692 19_30 0.9636959 58.17640 1.631575e-04
324417 rs28986304 6_23 0.9623004 47.32673 1.325371e-04
245213 rs114756490 4_100 0.9615821 28.19237 7.889296e-05
629264 rs11057830 12_76 0.9572866 28.19944 7.856024e-05
815510 rs73124945 20_24 0.9440501 33.67087 9.250597e-05
824147 rs62219001 21_2 0.9439210 28.38197 7.796478e-05
469757 rs7024888 9_3 0.9430358 28.19331 7.737391e-05
733371 rs9652628 16_38 0.9355348 139.51408 3.798379e-04
638151 rs1012130 13_10 0.9337754 49.93406 1.356937e-04
1012842 rs2908806 17_7 0.9288374 39.64833 1.071729e-04
749264 rs117859452 17_17 0.9246138 28.50999 7.671456e-05
368675 rs9456502 6_103 0.9223760 35.19508 9.447356e-05
508961 rs10905277 10_8 0.9115563 30.35722 8.053151e-05
554351 rs7943121 11_13 0.9093865 32.89274 8.705001e-05
792432 rs322125 19_10 0.9005713 123.40519 3.234237e-04
15513 rs1887552 1_34 0.9003838 460.58783 1.206870e-03
31755 rs614174 1_67 0.8995990 620.26288 1.623847e-03
389431 rs141379002 7_33 0.8995917 28.13509 7.365701e-05
71924 rs78610189 2_13 0.8979165 64.49599 1.685345e-04
568261 rs6591179 11_36 0.8948508 28.78390 7.495844e-05
495289 rs2777788 9_53 0.8948326 68.72333 1.789642e-04
623268 rs1196760 12_63 0.8867763 28.36202 7.319333e-05
194316 rs5855544 3_121 0.8851707 26.18777 6.745991e-05
124265 rs7569317 2_120 0.8844178 46.81963 1.205052e-04
196103 rs36205397 4_4 0.8838346 42.96871 1.105207e-04
819645 rs10641149 20_32 0.8823761 29.72784 7.633740e-05
729673 rs821840 16_30 0.8811021 179.08060 4.591928e-04
580901 rs201912654 11_59 0.8773061 42.31775 1.080424e-04
352854 rs12199109 6_73 0.8767410 27.85584 7.107352e-05
1052479 rs12981080 19_32 0.8678001 63.26139 1.597639e-04
485607 rs11144506 9_35 0.8677570 29.31721 7.403569e-05
502519 rs13289095 9_66 0.8676225 47.71374 1.204743e-04
281219 rs3843482 5_44 0.8623902 448.48013 1.125556e-03
359057 rs9321207 6_86 0.8613011 33.21406 8.325250e-05
810533 rs78348000 20_13 0.8594311 32.64757 8.165489e-05
759376 rs9303012 17_39 0.8506970 204.62334 5.065827e-04
587708 rs74612335 11_77 0.8436282 78.84034 1.935619e-04
934939 rs542985909 7_61 0.8429711 41.53143 1.018849e-04
638143 rs1799955 13_10 0.8371824 83.39094 2.031698e-04
40355 rs1795240 1_84 0.8357339 28.18533 6.855062e-05
806198 rs74273659 20_5 0.8343665 27.61683 6.705805e-05
831388 rs2835302 21_16 0.8333008 28.06221 6.805249e-05
733311 rs12708919 16_38 0.8325730 161.84423 3.921388e-04
815475 rs6102034 20_24 0.8303202 105.66307 2.553225e-04
71724 rs6531234 2_12 0.8241415 43.91344 1.053221e-04
814251 rs11167269 20_21 0.8229157 63.58430 1.522739e-04
536255 rs10882161 10_59 0.8206680 31.78043 7.590102e-05
832525 rs149577713 21_19 0.8205595 34.87742 8.328653e-05
815651 rs11086801 20_25 0.8155315 118.99192 2.824090e-04
428067 rs13265179 8_12 0.8102350 39.21164 9.245838e-05
z
15512 -6.292225
31756 30.975273
71921 -16.573036
71927 -33.060888
323276 12.532321
368681 -12.282337
406538 -3.272149
759365 -12.768796
792327 16.232742
792365 10.752566
795162 21.492060
800369 44.672230
800387 33.599649
800420 -55.537887
800422 34.318568
800425 46.325818
810553 6.814661
324009 -4.779053
759391 8.091491
71872 -11.909428
504011 -19.011790
72007 -25.412292
71930 -33.086010
495296 -6.333973
406549 2.984117
758449 -9.396430
443350 11.699924
428056 -10.906064
59253 -12.369141
79368 7.845728
15523 -11.893801
587705 -12.147947
8327 -7.024638
441955 -7.799241
55628 7.882775
733330 4.164582
795193 -0.593903
462210 -9.646876
15482 11.167216
462213 -11.964411
15530 -16.262997
567431 12.656944
810552 2.329832
323255 6.048695
350118 6.340216
1052986 12.817911
59203 -8.960936
319547 8.507919
792444 -3.946578
704146 -7.734663
368865 -14.088321
792391 -6.519414
368829 -10.097959
733373 -7.714555
540937 6.266313
495269 -6.605676
608591 5.770964
281255 2.484790
1052324 14.946045
794802 -6.636049
382062 -10.978916
462201 22.102229
31824 5.646803
431682 6.515969
792348 48.935175
584637 6.534400
738825 6.362828
584632 -12.372986
815506 -7.692477
625175 -11.050062
1051896 8.331826
443318 7.012272
79245 3.344504
664684 -7.943873
325231 -6.871357
31090 5.284644
140546 6.781974
815457 6.762459
147556 8.481579
612957 -5.095452
325254 -8.257335
593614 -6.133690
666492 -7.035042
281196 -20.320600
603678 5.037754
79248 20.108567
477621 8.992146
628129 -8.685477
222691 7.417851
324446 -5.641451
323094 7.906709
1052407 7.096889
324417 -7.382502
245213 -4.988910
629264 -4.929635
815510 7.775426
824147 4.948445
469757 5.055827
733371 -11.950504
638151 2.781022
1012842 6.026359
749264 3.851670
368675 -5.963991
508961 -5.125802
554351 -5.557494
792432 7.470403
15513 9.868570
31755 -7.395089
389431 -4.896981
71924 8.385467
568261 -4.893333
495289 5.737015
623268 4.866700
194316 4.593724
124265 -7.900653
196103 -6.159378
819645 -5.075761
729673 13.475251
580901 6.305597
352854 -4.857045
1052479 9.714563
485607 -5.042667
502519 6.643823
281219 -25.034352
359057 -5.401634
810533 -5.220624
759376 -2.259115
587708 -11.904831
934939 5.295309
638143 6.693636
40355 4.846186
806198 -4.646762
831388 4.653743
733311 -11.302762
815475 11.189979
71724 7.170830
814251 7.795037
536255 5.475649
832525 -3.316824
815651 -10.975177
428067 7.414877SNPs 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")
#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
406549 rs4997569 7_65 1.000000e+00 43377.639 1.262369e-01
406538 rs763798411 7_65 1.000000e+00 43351.790 1.261616e-01
406541 rs10274607 7_65 6.802521e-01 43319.992 8.575877e-02
406556 rs6952534 7_65 2.342571e-14 43252.602 2.948664e-15
406544 rs13230660 7_65 1.002215e-03 43221.242 1.260603e-04
406555 rs4730069 7_65 0.000000e+00 43215.053 0.000000e+00
406548 rs10242713 7_65 0.000000e+00 43056.211 0.000000e+00
406551 rs10249965 7_65 0.000000e+00 42710.840 0.000000e+00
406563 rs1013016 7_65 0.000000e+00 40942.368 0.000000e+00
406588 rs8180737 7_65 0.000000e+00 38855.382 0.000000e+00
406581 rs17778396 7_65 0.000000e+00 38845.290 0.000000e+00
406582 rs2237621 7_65 0.000000e+00 38827.992 0.000000e+00
406553 rs71562637 7_65 0.000000e+00 38821.414 0.000000e+00
406615 rs10224564 7_65 0.000000e+00 38757.015 0.000000e+00
406600 rs10255779 7_65 0.000000e+00 38737.326 0.000000e+00
406617 rs78132606 7_65 0.000000e+00 38551.882 0.000000e+00
406620 rs4610671 7_65 0.000000e+00 38502.672 0.000000e+00
406622 rs12669532 7_65 0.000000e+00 36904.190 0.000000e+00
406579 rs2237618 7_65 0.000000e+00 36299.602 0.000000e+00
406624 rs118089279 7_65 0.000000e+00 35939.694 0.000000e+00
406611 rs73188303 7_65 0.000000e+00 35911.991 0.000000e+00
406621 rs560364150 7_65 0.000000e+00 28478.718 0.000000e+00
406607 rs10261738 7_65 0.000000e+00 23270.989 0.000000e+00
406562 rs368909701 7_65 0.000000e+00 17856.440 0.000000e+00
406561 rs2299297 7_65 0.000000e+00 14073.543 0.000000e+00
406547 rs6961668 7_65 0.000000e+00 12909.744 0.000000e+00
406605 rs56384866 7_65 0.000000e+00 11575.319 0.000000e+00
406629 rs147367948 7_65 0.000000e+00 9745.532 0.000000e+00
406533 rs145194740 7_65 0.000000e+00 9337.336 0.000000e+00
406529 rs11762333 7_65 0.000000e+00 9166.538 0.000000e+00
406610 rs34356406 7_65 0.000000e+00 8327.804 0.000000e+00
406603 rs143717474 7_65 0.000000e+00 6391.370 0.000000e+00
406606 rs2385557 7_65 0.000000e+00 6358.588 0.000000e+00
406614 rs10224539 7_65 0.000000e+00 6275.067 0.000000e+00
406530 rs12333765 7_65 0.000000e+00 6270.293 0.000000e+00
406525 rs60551932 7_65 0.000000e+00 6160.036 0.000000e+00
406574 rs67180946 7_65 0.000000e+00 6144.313 0.000000e+00
406594 rs55898317 7_65 0.000000e+00 6133.827 0.000000e+00
406602 rs67162771 7_65 0.000000e+00 6132.567 0.000000e+00
406598 rs7780006 7_65 0.000000e+00 6125.448 0.000000e+00
406599 rs17640711 7_65 0.000000e+00 6125.349 0.000000e+00
406589 rs7808226 7_65 0.000000e+00 6125.172 0.000000e+00
406597 rs67154126 7_65 0.000000e+00 6124.252 0.000000e+00
406590 rs7782673 7_65 0.000000e+00 6123.122 0.000000e+00
406587 rs67529088 7_65 0.000000e+00 6121.526 0.000000e+00
406583 rs67505443 7_65 0.000000e+00 6120.131 0.000000e+00
406580 rs7776832 7_65 0.000000e+00 6117.622 0.000000e+00
406584 rs112675813 7_65 0.000000e+00 6103.565 0.000000e+00
406536 rs2106500 7_65 0.000000e+00 6068.738 0.000000e+00
406540 rs6976394 7_65 0.000000e+00 6055.628 0.000000e+00
z
406549 2.98411662
406538 -3.27214912
406541 2.86695815
406556 2.88842403
406544 2.94796276
406555 2.86587353
406548 2.81239831
406551 2.84973811
406563 -2.39885238
406588 2.83284539
406581 2.79800123
406582 2.80296048
406553 2.66359357
406615 2.79119041
406600 2.81357915
406617 2.77280824
406620 2.72497424
406622 2.77025728
406579 2.46632548
406624 2.66672085
406611 2.42170311
406621 1.86945819
406607 2.66651092
406562 0.77788834
406561 -0.79635059
406547 3.23185861
406605 1.88257817
406629 -0.13863917
406533 -0.26515806
406529 -0.05367528
406610 2.10990687
406603 -1.55752731
406606 -1.59367067
406614 1.51838531
406530 2.64663965
406525 2.57501100
406574 -1.56995763
406594 -1.54112280
406602 -1.51684679
406598 -1.48700886
406599 -1.49652454
406589 -1.53349729
406597 -1.50258409
406590 -1.54265604
406587 -1.56434174
406583 -1.59189384
406580 -1.56236222
406584 -1.55422350
406536 1.77217605
406540 1.69154398SNPs 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
406549 rs4997569 7_65 1.0000000 43377.6391 0.1262368687
406538 rs763798411 7_65 1.0000000 43351.7903 0.1261616440
406541 rs10274607 7_65 0.6802521 43319.9925 0.0857587692
792348 rs147985405 19_9 0.9971798 2880.6880 0.0083596866
800420 rs814573 19_31 1.0000000 2401.7180 0.0069894389
800425 rs12721109 19_31 1.0000000 1445.0614 0.0042053932
31756 rs611917 1_67 1.0000000 1058.3005 0.0030798481
800422 rs113345881 19_31 1.0000000 837.1831 0.0024363559
800369 rs62117204 19_31 1.0000000 828.5903 0.0024113495
800387 rs111794050 19_31 1.0000000 819.5998 0.0023851855
71930 rs548145 2_13 1.0000000 719.5601 0.0020940516
462201 rs2980875 8_83 0.9978210 613.0625 0.0017802365
31755 rs614174 1_67 0.8995990 620.2629 0.0016238469
795162 rs3794991 19_15 1.0000000 504.9494 0.0014694953
79248 rs4076834 2_27 0.9703966 490.2445 0.0013844661
15512 rs2495502 1_34 1.0000000 416.4254 0.0012118740
15513 rs1887552 1_34 0.9003838 460.5878 0.0012068699
71927 rs934197 2_13 1.0000000 412.9003 0.0012016154
281219 rs3843482 5_44 0.8623902 448.4801 0.0011255565
504011 rs115478735 9_70 1.0000000 346.7806 0.0010091949
462210 rs6470359 8_83 0.9999910 336.5047 0.0009792815
792338 rs8102273 19_9 0.5843999 568.5845 0.0009669977
31757 rs658435 1_67 0.4936838 615.6500 0.0008845106
368681 rs12208357 6_103 1.0000000 287.9295 0.0008379276
792353 rs34008246 19_9 0.6951331 412.8440 0.0008351686
462213 rs13252684 8_83 0.9999875 279.4153 0.0008131395
71921 rs1042034 2_13 1.0000000 264.8953 0.0007708938
72007 rs1848922 2_13 1.0000000 243.5346 0.0007087304
792327 rs73013176 19_9 1.0000000 240.2150 0.0006990696
792365 rs137992968 19_9 1.0000000 238.7271 0.0006947395
792335 rs68010235 19_9 0.4155999 559.9146 0.0006772009
15530 rs471705 1_34 0.9999864 227.2379 0.0006612948
281196 rs10062361 5_44 0.9765936 231.7885 0.0006587581
1052324 rs62115559 19_30 0.9989979 214.5915 0.0006238747
759391 rs8070232 17_39 1.0000000 204.2871 0.0005945129
870392 rs1260326 2_16 0.6552363 302.3539 0.0005765458
368695 rs3818678 6_103 0.7781102 239.3214 0.0005419297
323276 rs115740542 6_20 1.0000000 179.1396 0.0005213289
759376 rs9303012 17_39 0.8506970 204.6233 0.0005065827
71872 rs11679386 2_12 1.0000000 167.4753 0.0004873837
759365 rs113408695 17_39 1.0000000 164.7519 0.0004794581
584632 rs3135506 11_70 0.9967147 162.3578 0.0004709388
324009 rs454182 6_22 1.0000000 158.8434 0.0004622633
729673 rs821840 16_30 0.8811021 179.0806 0.0004591928
15523 rs10888896 1_34 1.0000000 152.6149 0.0004441373
567431 rs174553 11_34 0.9999805 150.5014 0.0004379781
733330 rs12149380 16_38 0.9999964 137.6195 0.0004004965
368865 rs56393506 6_104 0.9998318 136.2836 0.0003965435
733311 rs12708919 16_38 0.8325730 161.8442 0.0003921388
305797 rs12657266 5_92 0.7275630 179.5833 0.0003802391
z
406549 2.984117
406538 -3.272149
406541 2.866958
792348 48.935175
800420 -55.537887
800425 46.325818
31756 30.975273
800422 34.318568
800369 44.672230
800387 33.599649
71930 -33.086010
462201 22.102229
31755 -7.395089
795162 21.492060
79248 20.108567
15512 -6.292225
15513 9.868570
71927 -33.060888
281219 -25.034352
504011 -19.011790
462210 -9.646876
792338 14.167679
31757 -7.357780
368681 -12.282337
792353 -12.967795
462213 -11.964411
71921 -16.573036
72007 -25.412292
792327 16.232742
792365 10.752566
792335 13.918807
15530 -16.262997
281196 -20.320600
1052324 14.946045
759391 8.091491
870392 14.553497
368695 9.947776
323276 12.532321
759376 -2.259115
71872 -11.909428
759365 -12.768796
584632 -12.372986
324009 -4.779053
729673 13.475251
15523 -11.893801
567431 12.656944
733330 4.164582
368865 -14.088321
733311 -11.302762
305797 -13.894754SNPs with largest z scores
#histogram of (abs) SNP z scores
hist(abs(ctwas_snp_res$z))
#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50) id region_tag susie_pip mu2 PVE
800420 rs814573 19_31 1.000000e+00 2401.7180 6.989439e-03
792348 rs147985405 19_9 9.971798e-01 2880.6880 8.359687e-03
792343 rs73015020 19_9 2.288662e-03 2868.9692 1.910855e-05
792341 rs138175288 19_9 3.507913e-04 2865.3735 2.925165e-06
792342 rs138294113 19_9 7.700365e-05 2862.4189 6.414530e-07
792344 rs77140532 19_9 7.785806e-05 2862.3416 6.485528e-07
792345 rs112552009 19_9 1.061088e-05 2858.5604 8.827117e-08
792346 rs10412048 19_9 1.527069e-05 2859.1319 1.270613e-07
792340 rs55997232 19_9 4.638042e-10 2839.1379 3.832141e-12
800425 rs12721109 19_31 1.000000e+00 1445.0614 4.205393e-03
800369 rs62117204 19_31 1.000000e+00 828.5903 2.411350e-03
800356 rs1551891 19_31 0.000000e+00 478.6955 0.000000e+00
792349 rs17248769 19_9 0.000000e+00 2057.4990 0.000000e+00
792350 rs2228671 19_9 0.000000e+00 2046.0949 0.000000e+00
792339 rs9305020 19_9 0.000000e+00 1822.1218 0.000000e+00
800416 rs405509 19_31 0.000000e+00 965.6516 0.000000e+00
800422 rs113345881 19_31 1.000000e+00 837.1831 2.436356e-03
800340 rs62120566 19_31 0.000000e+00 1406.6368 0.000000e+00
800387 rs111794050 19_31 1.000000e+00 819.5998 2.385186e-03
71930 rs548145 2_13 1.000000e+00 719.5601 2.094052e-03
800393 rs4802238 19_31 0.000000e+00 981.5570 0.000000e+00
71927 rs934197 2_13 1.000000e+00 412.9003 1.201615e-03
800334 rs188099946 19_31 0.000000e+00 1347.8800 0.000000e+00
800404 rs2972559 19_31 0.000000e+00 1351.9306 0.000000e+00
800328 rs201314191 19_31 0.000000e+00 1248.2640 0.000000e+00
800395 rs56394238 19_31 0.000000e+00 985.1874 0.000000e+00
800372 rs2965169 19_31 0.000000e+00 326.4180 0.000000e+00
800396 rs3021439 19_31 0.000000e+00 867.5409 0.000000e+00
31756 rs611917 1_67 1.000000e+00 1058.3005 3.079848e-03
71957 rs12997242 2_13 2.130296e-12 374.1321 2.319451e-15
800403 rs12162222 19_31 0.000000e+00 1153.4375 0.000000e+00
71931 rs478588 2_13 2.309486e-11 665.7481 4.474511e-14
800333 rs62119327 19_31 0.000000e+00 1096.5732 0.000000e+00
71932 rs56350433 2_13 5.185852e-13 349.7071 5.277701e-16
71937 rs56079819 2_13 5.178080e-13 348.9293 5.258072e-16
71941 rs2337383 2_13 5.274670e-13 341.8990 5.248237e-16
71942 rs56090741 2_13 5.268008e-13 341.3666 5.233446e-16
71946 rs7568899 2_13 5.443423e-13 332.4222 5.266020e-16
71947 rs62135036 2_13 5.456746e-13 332.1847 5.275136e-16
71953 rs11687710 2_13 5.409007e-13 331.3858 5.216410e-16
71958 rs532300 2_13 1.007194e-12 611.6368 1.792781e-15
71959 rs558130 2_13 1.007194e-12 611.6365 1.792780e-15
71960 rs533211 2_13 1.007194e-12 611.6365 1.792780e-15
71981 rs574461 2_13 1.034950e-12 611.2060 1.840887e-15
71983 rs494465 2_13 1.029066e-12 611.0355 1.829911e-15
71961 rs528113 2_13 1.001754e-12 611.3482 1.782256e-15
71966 rs1652418 2_13 1.000089e-12 611.0141 1.778321e-15
71968 rs563696 2_13 9.962031e-13 610.8926 1.771059e-15
71956 rs312979 2_13 9.752199e-13 610.8510 1.733637e-15
71970 rs479545 2_13 9.823253e-13 610.5373 1.745371e-15
z
800420 -55.53789
792348 48.93517
792343 48.79563
792341 48.78069
792342 48.75193
792344 48.73799
792345 48.70516
792346 48.70123
792340 48.52431
800425 46.32582
800369 44.67223
800356 42.26680
792349 40.84249
792350 40.70262
792339 34.84073
800416 34.63979
800422 34.31857
800340 33.73539
800387 33.59965
71930 -33.08601
800393 -33.07569
71927 -33.06089
800334 33.04407
800404 -32.28660
800328 32.06858
800395 -31.55187
800372 31.38057
800396 -31.04506
31756 30.97527
71957 -30.81528
800403 -30.49671
71931 -30.48811
800333 30.41868
71932 -30.23229
71937 -30.19307
71941 -29.88780
71942 -29.86248
71946 -29.70268
71947 -29.69428
71953 -29.63286
71958 -29.56783
71959 -29.56783
71960 -29.56783
71981 -29.56585
71983 -29.56317
71961 -29.56210
71966 -29.55946
71968 -29.55712
71956 -29.55544
71970 -29.55270Gene set enrichment for genes with PIP>0.8
#GO enrichment analysis
library(enrichR)Welcome to enrichR
Checking connection ... Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
#number of genes for gene set enrichment
length(genes)[1] 32if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
print(plotEnrich(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 alditol phosphate metabolic process (GO:0052646)
2 negative regulation of transforming growth factor beta production (GO:0071635)
Overlap Adjusted.P.value Genes
1 2/5 0.01270895 GPAM;ACP6
2 2/10 0.02845289 FN1;FURIN
[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)TMED4 gene(s) from the input list not found in DisGeNET CURATEDEVI5 gene(s) from the input list not found in DisGeNET CURATEDMZF1 gene(s) from the input list not found in DisGeNET CURATEDUSP53 gene(s) from the input list not found in DisGeNET CURATEDACP6 gene(s) from the input list not found in DisGeNET CURATEDATP5J2 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDPELO gene(s) from the input list not found in DisGeNET CURATEDPARP9 gene(s) from the input list not found in DisGeNET CURATEDZNF575 gene(s) from the input list not found in DisGeNET CURATEDC10orf88 gene(s) from the input list not found in DisGeNET CURATEDNPC1L1 gene(s) from the input list not found in DisGeNET CURATEDLINC01184 gene(s) from the input list not found in DisGeNET CURATEDTUBG2 gene(s) from the input list not found in DisGeNET CURATEDPOP7 gene(s) from the input list not found in DisGeNET CURATEDAC007950.2 gene(s) from the input list not found in DisGeNET CURATEDFXYD7 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
21 Carcinoma 0.02116266 3/15 164/9703
47 Diabetic Nephropathy 0.02116266 2/15 44/9703
53 Nodular glomerulosclerosis 0.02116266 2/15 41/9703
56 Cardiomegaly 0.02116266 3/15 82/9703
68 Pulmonary Hypertension 0.02116266 2/15 40/9703
69 Hypertension, Renovascular 0.02116266 1/15 1/9703
86 Animal Mammary Neoplasms 0.02116266 3/15 142/9703
87 Mammary Neoplasms, Experimental 0.02116266 3/15 155/9703
117 Ureteral obstruction 0.02116266 2/15 24/9703
130 Premature ventricular contractions 0.02116266 1/15 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!
NULLSensitivity, specificity and precision for silver standard genes
library("readxl")
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="LDL")New names:
* `` -> ...4
* `` -> ...5known_annotations <- unique(known_annotations$`Gene Symbol`)
unrelated_genes <- ctwas_gene_res$genename[!(ctwas_gene_res$genename %in% known_annotations)]
#number of genes in known annotations
print(length(known_annotations))[1] 69#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 48#assign ctwas, TWAS, and bystander genes
ctwas_genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
twas_genes <- ctwas_gene_res$genename[abs(ctwas_gene_res$z)>sig_thresh]
novel_genes <- ctwas_genes[!(ctwas_genes %in% twas_genes)]
#significance threshold for TWAS
print(sig_thresh)[1] 4.616471#number of ctwas genes
length(ctwas_genes)[1] 32#number of TWAS genes
length(twas_genes)[1] 215#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z
7878 ACP6 1_73 0.9881237 22.14907 6.369233e-05 4.575774
3305 FN1 2_127 0.9418621 21.45337 5.880349e-05 -4.446065
11839 FAM3D 3_40 0.8027527 18.66547 4.360548e-05 -3.889457
13192 LINC01184 5_78 0.8057176 19.17555 4.496256e-05 -3.918269
8291 NOS3 7_93 0.8379598 19.38306 4.726783e-05 3.856590
1003 TPD52 8_57 0.9812117 21.94017 6.265027e-05 -4.557712
1542 SCD 10_64 0.8945838 19.60935 5.105103e-05 -4.541468
3734 GPAM 10_70 0.8925750 21.37803 5.553063e-05 4.133221
3645 CCND2 12_4 0.8874327 19.93184 5.147580e-05 -4.065830
2080 CTSH 15_37 0.8853807 18.41185 4.744034e-05 3.805849
5866 FURIN 15_42 0.8215001 20.23083 4.836617e-05 -4.391033
402 TUBG2 17_25 0.9570282 20.51179 5.712794e-05 4.434366
3681 KDSR 18_35 0.8103019 19.33886 4.560347e-05 -3.912562
12483 FXYD7 19_24 0.8819526 19.45446 4.993267e-05 -3.872239
2174 SARS2 19_26 0.8249021 21.80207 5.233841e-05 4.480159
num_eqtl
7878 4
3305 1
11839 1
13192 2
8291 2
1003 2
1542 1
3734 1
3645 1
2080 4
5866 1
402 2
3681 3
12483 1
2174 1#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.01449275 0.18840580 #specificity
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9975709 0.9841718 #precision / PPV
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.03125000 0.06046512 #ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1))
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res$genename[abs(ctwas_gene_res$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=2)
Sensitivity, specificity and precision for silver standard genes - bystanders only
This section first uses imputed silver standard genes to identify bystander genes within 1Mb. The bystander gene list is then subset to only genes with imputed expression in this analysis. Then, the ctwas and TWAS gene lists from this analysis are subset to only genes that are in the (subset) silver standard and bystander genes. These gene lists are then used to compute sensitivity, specificity and precision for ctwas and TWAS.
library(biomaRt)
library(GenomicRanges)Loading required package: stats4Loading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter,
Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,
mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following object is masked from 'package:base':
expand.gridLoading required package: IRangesLoading required package: GenomeInfoDbensembl <- useEnsembl(biomart="ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl")
G_list <- getBM(filters= "chromosome_name", attributes= c("hgnc_symbol","chromosome_name","start_position","end_position","gene_biotype"), values=1:22, mart=ensembl)
G_list <- G_list[G_list$hgnc_symbol!="",]
G_list <- G_list[G_list$gene_biotype %in% c("protein_coding","lncRNA"),]
G_list$start <- G_list$start_position
G_list$end <- G_list$end_position
G_list_granges <- makeGRangesFromDataFrame(G_list, keep.extra.columns=T)
#remove genes without imputed expression from gene lists
known_annotations <- known_annotations[known_annotations %in% ctwas_gene_res$genename]
known_annotations_positions <- G_list[G_list$hgnc_symbol %in% known_annotations,]
half_window <- 1000000
known_annotations_positions$start <- known_annotations_positions$start_position - half_window
known_annotations_positions$end <- known_annotations_positions$end_position + half_window
known_annotations_positions$start[known_annotations_positions$start<1] <- 1
known_annotations_granges <- makeGRangesFromDataFrame(known_annotations_positions, keep.extra.columns=T)
bystanders <- findOverlaps(known_annotations_granges,G_list_granges)
bystanders <- unique(subjectHits(bystanders))
bystanders <- G_list$hgnc_symbol[bystanders]
bystanders <- unique(bystanders[!(bystanders %in% known_annotations)])
unrelated_genes <- bystanders
#save gene lists
save(known_annotations, file=paste0(results_dir, "/known_annotations.Rd"))
save(unrelated_genes, file=paste0(results_dir, "/bystanders.Rd"))
load(paste0(results_dir, "/known_annotations.Rd"))
load(paste0(results_dir, "/bystanders.Rd"))
#remove genes without imputed expression from bystander list
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$genename]
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 48#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 710#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.616471#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 2#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 51#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.02083333 0.27083333 #specificity / (1 - False Positive Rate)
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9985915 0.9464789 #precision / PPV / (1 - False Discovery Rate)
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.500000 0.254902 #store sensitivity and specificity calculations for plots
sensitivity_plot <- sensitivity
specificity_plot <- specificity
#precision / PPV by PIP bin
pip_range <- c(0.2, 0.4, 0.6, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes in PIP bin
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip_lower & ctwas_gene_res_subset$susie_pip<pip_upper]
precision_range[i] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
}
names(precision_range) <- paste(c(0, pip_range[-length(pip_range)]), pip_range,sep=" - ")
barplot(precision_range, ylim=c(0,1), main="Precision by PIP Range", xlab="PIP Range", ylab="Precision")
abline(h=0.2, lty=2)
abline(h=0.4, lty=2)
abline(h=0.6, lty=2)
abline(h=0.8, lty=2)
barplot(precision_range, add=T, col="darkgrey")
#precision / PPV by PIP threshold
#pip_range <- c(0.2, 0.4, 0.6, 0.8, 1)
pip_range <- c(0.5, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
number_detected <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes using PIP threshold
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip_lower]
number_detected[i] <- length(ctwas_genes)
precision_range[i] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
}
names(precision_range) <- paste0(">= ", c(0, pip_range[-length(pip_range)]))
precision_range <- precision_range*100
precision_range <- c(precision_range, precision["TWAS"]*100)
names(precision_range)[4] <- "TWAS Bonferroni"
number_detected <- c(number_detected, length(twas_genes))
barplot(precision_range, ylim=c(0,100), main="Precision for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% of Detected Genes in Silver Standard")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)
#false discovery rate by PIP threshold
barplot(100-precision_range, ylim=c(0,100), main="False Discovery Rate for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% Bystanders in Detected Genes")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(100-precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)
#ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)
abline(a=0,b=1,lty=3)
#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
PIP Manhattan Plot
library(tibble)
library(tidyverse)── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──✔ tidyr 1.1.0 ✔ dplyr 1.0.7
✔ readr 1.4.0 ✔ stringr 1.4.0
✔ purrr 0.3.4 ✔ forcats 0.4.0── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::collapse() masks IRanges::collapse()
✖ dplyr::combine() masks BiocGenerics::combine()
✖ dplyr::desc() masks IRanges::desc()
✖ tidyr::expand() masks S4Vectors::expand()
✖ tidyr::extract() masks disgenet2r::extract()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::first() masks S4Vectors::first()
✖ dplyr::lag() masks stats::lag()
✖ BiocGenerics::Position() masks ggplot2::Position(), base::Position()
✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
✖ dplyr::rename() masks S4Vectors::rename()
✖ dplyr::select() masks biomaRt::select()
✖ dplyr::slice() masks IRanges::slice()full.gene.pip.summary <- data.frame(gene_name = ctwas_gene_res$genename,
gene_pip = ctwas_gene_res$susie_pip,
gene_id = ctwas_gene_res$id,
chr = as.integer(ctwas_gene_res$chrom),
start = ctwas_gene_res$pos / 1e3,
is_highlight = F, stringsAsFactors = F) %>% as_tibble()
full.gene.pip.summary$is_highlight <- full.gene.pip.summary$gene_pip > 0.80
don <- full.gene.pip.summary %>%
# Compute chromosome size
group_by(chr) %>%
summarise(chr_len=max(start)) %>%
# Calculate cumulative position of each chromosome
mutate(tot=cumsum(chr_len)-chr_len) %>%
dplyr::select(-chr_len) %>%
# Add this info to the initial dataset
left_join(full.gene.pip.summary, ., by=c("chr"="chr")) %>%
# Add a cumulative position of each SNP
arrange(chr, start) %>%
mutate( BPcum=start+tot)
axisdf <- don %>% group_by(chr) %>% summarize(center=( max(BPcum) + min(BPcum) ) / 2 )
x_axis_labels <- axisdf$chr
x_axis_labels[seq(1,21,2)] <- ""
ggplot(don, aes(x=BPcum, y=gene_pip)) +
# Show all points
ggrastr::geom_point_rast(aes(color=as.factor(chr)), size=2) +
scale_color_manual(values = rep(c("grey", "skyblue"), 22 )) +
# custom X axis:
# scale_x_continuous(label = axisdf$chr,
# breaks= axisdf$center,
# guide = guide_axis(n.dodge = 2)) +
scale_x_continuous(label = x_axis_labels,
breaks = axisdf$center) +
scale_y_continuous(expand = c(0, 0), limits = c(0,1.25), breaks=(1:5)*0.2, minor_breaks=(1:10)*0.1) + # remove space between plot area and x axis
# Add highlighted points
ggrastr::geom_point_rast(data=subset(don, is_highlight==T), color="orange", size=2) +
# Add label using ggrepel to avoid overlapping
ggrepel::geom_label_repel(data=subset(don, is_highlight==T),
aes(label=gene_name),
size=4,
min.segment.length = 0,
label.size = NA,
fill = alpha(c("white"),0)) +
# Custom the theme:
theme_bw() +
theme(
text = element_text(size = 14),
legend.position="none",
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()
) +
xlab("Chromosome") +
ylab("cTWAS PIP")
Locus Plots - 1_67
library(ctwas)
Attaching package: 'ctwas'The following object is masked _by_ '.GlobalEnv':
z_snplocus_plot <- function(region_tag, rerun_ctwas = F, plot_eqtl = T, label="cTWAS"){
region_tag1 <- unlist(strsplit(region_tag, "_"))[1]
region_tag2 <- unlist(strsplit(region_tag, "_"))[2]
a <- ctwas_res[ctwas_res$region_tag==region_tag,]
regionlist <- readRDS(paste0(results_dir, "/", analysis_id, "_ctwas.regionlist.RDS"))
region <- regionlist[[as.numeric(region_tag1)]][[region_tag2]]
R_snp_info <- do.call(rbind, lapply(region$regRDS, function(x){data.table::fread(paste0(tools::file_path_sans_ext(x), ".Rvar"))}))
if (isTRUE(rerun_ctwas)){
ld_exprfs <- paste0(results_dir, "/", analysis_id, "_expr_chr", 1:22, ".expr.gz")
temp_reg <- data.frame("chr" = paste0("chr",region_tag1), "start" = region$start, "stop" = region$stop)
write.table(temp_reg,
#file= paste0(results_dir, "/", analysis_id, "_ctwas.temp.reg.txt") ,
file= "temp_reg.txt",
row.names=F, col.names=T, sep="\t", quote = F)
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
z_gene_temp <- z_gene[z_gene$id %in% a$id[a$type=="gene"],]
z_snp_temp <- z_snp[z_snp$id %in% R_snp_info$id,]
ctwas_rss(z_gene_temp, z_snp_temp, ld_exprfs, ld_pgenfs = NULL,
ld_R_dir = dirname(region$regRDS)[1],
ld_regions_custom = "temp_reg.txt", thin = 1,
outputdir = ".", outname = "temp", ncore = 1, ncore.rerun = 1, prob_single = 0,
group_prior = estimated_group_prior, group_prior_var = estimated_group_prior_var,
estimate_group_prior = F, estimate_group_prior_var = F)
a <- data.table::fread("temp.susieIrss.txt", header = T)
rownames(z_snp_temp) <- z_snp_temp$id
z_snp_temp <- z_snp_temp[a$id[a$type=="SNP"],]
z_gene_temp <- z_gene_temp[a$id[a$type=="gene"],]
a$z <- NA
a$z[a$type=="SNP"] <- z_snp_temp$z
a$z[a$type=="gene"] <- z_gene_temp$z
}
a$ifcausal <- 0
focus <- a$id[a$type=="gene"][which.max(abs(a$z[a$type=="gene"]))]
a$ifcausal <- as.numeric(a$id==focus)
a$PVALUE <- (-log(2) - pnorm(abs(a$z), lower.tail=F, log.p=T))/log(10)
R_gene <- readRDS(region$R_g_file)
R_snp_gene <- readRDS(region$R_sg_file)
R_snp <- as.matrix(Matrix::bdiag(lapply(region$regRDS, readRDS)))
rownames(R_gene) <- region$gid
colnames(R_gene) <- region$gid
rownames(R_snp_gene) <- R_snp_info$id
colnames(R_snp_gene) <- region$gid
rownames(R_snp) <- R_snp_info$id
colnames(R_snp) <- R_snp_info$id
a$r2max <- NA
a$r2max[a$type=="gene"] <- R_gene[focus,a$id[a$type=="gene"]]
a$r2max[a$type=="SNP"] <- R_snp_gene[a$id[a$type=="SNP"],focus]
r2cut <- 0.4
colorsall <- c("#7fc97f", "#beaed4", "#fdc086")
layout(matrix(1:2, ncol = 1), widths = 1, heights = c(1.5,1.5), respect = FALSE)
par(mar = c(0, 4.1, 4.1, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 19, xlab=paste0("Chromosome ", region_tag1, " Position"),frame.plot=FALSE, col = "white", ylim= c(-0.1,1.1), ylab = "cTWAS PIP", xaxt = 'n')
grid()
points(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 21, xlab="Genomic position", bg = colorsall[1])
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$susie_pip[a$type == "SNP" & a$r2max >r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$susie_pip[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$susie_pip[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$susie_pip[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$susie_pip[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
if (isTRUE(plot_eqtl)){
for (cgene in a[a$type=="gene" & a$ifcausal == 1, ]$id){
load(paste0(results_dir, "/",analysis_id, "_expr_chr", region_tag1, ".exprqc.Rd"))
eqtls <- rownames(wgtlist[[cgene]])
points(a[a$id %in% eqtls,]$pos, rep( -0.15, nrow(a[a$id %in% eqtls,])), pch = "|", col = "salmon", cex = 1.5)
}
}
legend(min(a$pos), y= 1.1 ,c("Gene", "SNP"), pch = c(22,21), title="Shape Legend", bty ='n', cex=0.6, title.adj = 0)
legend(min(a$pos), y= 0.7 ,c("Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = 19, col = c("salmon", "purple", colorsall[1]), title="Color Legend", bty ='n', cex=0.6, title.adj = 0)
if (label=="cTWAS"){
text(a$pos[a$id==focus], a$susie_pip[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
par(mar = c(4.1, 4.1, 0.5, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 21, xlab=paste0("Chromosome ", region_tag1, " Position"), frame.plot=FALSE, bg = colorsall[1], ylab = "TWAS -log10(p value)", panel.first = grid(), ylim =c(0, max(a$PVALUE)*1.2))
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$PVALUE[a$type == "SNP" & a$r2max > r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$PVALUE[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$PVALUE[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$PVALUE[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$PVALUE[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
abline(h=-log10(alpha/nrow(ctwas_gene_res)), col ="red", lty = 2)
if (label=="TWAS"){
text(a$pos[a$id==focus], a$PVALUE[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
}
locus_plot("1_67", label="TWAS")
Locus Plots - 5_45 - Thin
locus_plot("5_45", label="TWAS")
Locus Plots - 5_45 - Re-run
#locus_plot("5_45", label="TWAS", rerun_ctwas = T)Locus Plots - 8_12
locus_plot4 <- function(region_tag, rerun_ctwas = F, plot_eqtl = T, label="cTWAS"){
region_tag1 <- unlist(strsplit(region_tag, "_"))[1]
region_tag2 <- unlist(strsplit(region_tag, "_"))[2]
a <- ctwas_res[ctwas_res$region_tag==region_tag,]
regionlist <- readRDS(paste0(results_dir, "/", analysis_id, "_ctwas.regionlist.RDS"))
region <- regionlist[[as.numeric(region_tag1)]][[region_tag2]]
R_snp_info <- do.call(rbind, lapply(region$regRDS, function(x){data.table::fread(paste0(tools::file_path_sans_ext(x), ".Rvar"))}))
if (isTRUE(rerun_ctwas)){
ld_exprfs <- paste0(results_dir, "/", analysis_id, "_expr_chr", 1:22, ".expr.gz")
temp_reg <- data.frame("chr" = paste0("chr",region_tag1), "start" = region$start, "stop" = region$stop)
write.table(temp_reg,
#file= paste0(results_dir, "/", analysis_id, "_ctwas.temp.reg.txt") ,
file= "temp_reg.txt",
row.names=F, col.names=T, sep="\t", quote = F)
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
z_gene_temp <- z_gene[z_gene$id %in% a$id[a$type=="gene"],]
z_snp_temp <- z_snp[z_snp$id %in% R_snp_info$id,]
ctwas_rss(z_gene_temp, z_snp_temp, ld_exprfs, ld_pgenfs = NULL,
ld_R_dir = dirname(region$regRDS)[1],
ld_regions_custom = "temp_reg.txt", thin = 1,
outputdir = ".", outname = "temp", ncore = 1, ncore.rerun = 1, prob_single = 0,
group_prior = estimated_group_prior, group_prior_var = estimated_group_prior_var,
estimate_group_prior = F, estimate_group_prior_var = F)
a <- data.table::fread("temp.susieIrss.txt", header = T)
rownames(z_snp_temp) <- z_snp_temp$id
z_snp_temp <- z_snp_temp[a$id[a$type=="SNP"],]
z_gene_temp <- z_gene_temp[a$id[a$type=="gene"],]
a$z <- NA
a$z[a$type=="SNP"] <- z_snp_temp$z
a$z[a$type=="gene"] <- z_gene_temp$z
}
a$ifcausal <- 0
focus <- a$id[a$type=="gene"][which.max(abs(a$z[a$type=="gene"]))]
a$ifcausal <- as.numeric(a$id==focus)
a$PVALUE <- (-log(2) - pnorm(abs(a$z), lower.tail=F, log.p=T))/log(10)
R_gene <- readRDS(region$R_g_file)
R_snp_gene <- readRDS(region$R_sg_file)
R_snp <- as.matrix(Matrix::bdiag(lapply(region$regRDS, readRDS)))
rownames(R_gene) <- region$gid
colnames(R_gene) <- region$gid
rownames(R_snp_gene) <- R_snp_info$id
colnames(R_snp_gene) <- region$gid
rownames(R_snp) <- R_snp_info$id
colnames(R_snp) <- R_snp_info$id
a$r2max <- NA
a$r2max[a$type=="gene"] <- R_gene[focus,a$id[a$type=="gene"]]
a$r2max[a$type=="SNP"] <- R_snp_gene[a$id[a$type=="SNP"],focus]
r2cut <- 0.4
colorsall <- c("#7fc97f", "#beaed4", "#fdc086")
layout(matrix(1:2, ncol = 1), widths = 1, heights = c(1.5,1.5), respect = FALSE)
par(mar = c(0, 4.1, 4.1, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 19, xlab=paste0("Chromosome ", region_tag1, " Position"),frame.plot=FALSE, col = "white", ylim= c(-0.1,1.1), ylab = "cTWAS PIP", xaxt = 'n')
grid()
points(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 21, xlab="Genomic position", bg = colorsall[1])
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$susie_pip[a$type == "SNP" & a$r2max >r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$susie_pip[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$susie_pip[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$susie_pip[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$susie_pip[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
if (isTRUE(plot_eqtl)){
for (cgene in a[a$type=="gene" & a$ifcausal == 1, ]$id){
load(paste0(results_dir, "/",analysis_id, "_expr_chr", region_tag1, ".exprqc.Rd"))
eqtls <- rownames(wgtlist[[cgene]])
points(a[a$id %in% eqtls,]$pos, rep( -0.15, nrow(a[a$id %in% eqtls,])), pch = "|", col = "salmon", cex = 1.5)
}
}
#legend(min(a$pos), y= 1.1 ,c("Gene", "SNP"), pch = c(22,21), title="Shape Legend", bty ='n', cex=0.6, title.adj = 0)
#legend(min(a$pos), y= 0.7 ,c("Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = 19, col = c("salmon", "purple", colorsall[1]), title="Color Legend", bty ='n', cex=0.6, title.adj = 0)
legend(max(a$pos)-0.2*(max(a$pos)-min(a$pos)), y= 1.1 ,c("Gene", "SNP"), pch = c(22,21), title="Shape Legend", bty ='n', cex=0.6, title.adj = 0)
legend(max(a$pos)-0.2*(max(a$pos)-min(a$pos)), y= 0.7 ,c("Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = 19, col = c("salmon", "purple", colorsall[1]), title="Color Legend", bty ='n', cex=0.6, title.adj = 0)
if (label=="cTWAS"){
text(a$pos[a$id==focus], a$susie_pip[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
par(mar = c(4.1, 4.1, 0.5, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 21, xlab=paste0("Chromosome ", region_tag1, " Position"), frame.plot=FALSE, bg = colorsall[1], ylab = "TWAS -log10(p value)", panel.first = grid(), ylim =c(0, max(a$PVALUE)*1.2))
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$PVALUE[a$type == "SNP" & a$r2max > r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$PVALUE[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$PVALUE[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$PVALUE[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$PVALUE[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
abline(h=-log10(alpha/nrow(ctwas_gene_res)), col ="red", lty = 2)
if (label=="TWAS"){
text(a$pos[a$id==focus], a$PVALUE[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
}
#locus_plot4("8_12", label="cTWAS")locus_plot5 <- function(region_tag, rerun_ctwas = F, plot_eqtl = T, label="cTWAS", focus){
region_tag1 <- unlist(strsplit(region_tag, "_"))[1]
region_tag2 <- unlist(strsplit(region_tag, "_"))[2]
a <- ctwas_res[ctwas_res$region_tag==region_tag,]
regionlist <- readRDS(paste0(results_dir, "/", analysis_id, "_ctwas.regionlist.RDS"))
region <- regionlist[[as.numeric(region_tag1)]][[region_tag2]]
R_snp_info <- do.call(rbind, lapply(region$regRDS, function(x){data.table::fread(paste0(tools::file_path_sans_ext(x), ".Rvar"))}))
if (isTRUE(rerun_ctwas)){
ld_exprfs <- paste0(results_dir, "/", analysis_id, "_expr_chr", 1:22, ".expr.gz")
temp_reg <- data.frame("chr" = paste0("chr",region_tag1), "start" = region$start, "stop" = region$stop)
write.table(temp_reg,
#file= paste0(results_dir, "/", analysis_id, "_ctwas.temp.reg.txt") ,
file= "temp_reg.txt",
row.names=F, col.names=T, sep="\t", quote = F)
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
z_gene_temp <- z_gene[z_gene$id %in% a$id[a$type=="gene"],]
z_snp_temp <- z_snp[z_snp$id %in% R_snp_info$id,]
ctwas_rss(z_gene_temp, z_snp_temp, ld_exprfs, ld_pgenfs = NULL,
ld_R_dir = dirname(region$regRDS)[1],
ld_regions_custom = "temp_reg.txt", thin = 1,
outputdir = ".", outname = "temp", ncore = 1, ncore.rerun = 1, prob_single = 0,
group_prior = estimated_group_prior, group_prior_var = estimated_group_prior_var,
estimate_group_prior = F, estimate_group_prior_var = F)
a <- data.table::fread("temp.susieIrss.txt", header = T)
rownames(z_snp_temp) <- z_snp_temp$id
z_snp_temp <- z_snp_temp[a$id[a$type=="SNP"],]
z_gene_temp <- z_gene_temp[a$id[a$type=="gene"],]
a$z <- NA
a$z[a$type=="SNP"] <- z_snp_temp$z
a$z[a$type=="gene"] <- z_gene_temp$z
}
a$ifcausal <- 0
focus <- a$id[which(a$genename==focus)]
a$ifcausal <- as.numeric(a$id==focus)
a$PVALUE <- (-log(2) - pnorm(abs(a$z), lower.tail=F, log.p=T))/log(10)
R_gene <- readRDS(region$R_g_file)
R_snp_gene <- readRDS(region$R_sg_file)
R_snp <- as.matrix(Matrix::bdiag(lapply(region$regRDS, readRDS)))
rownames(R_gene) <- region$gid
colnames(R_gene) <- region$gid
rownames(R_snp_gene) <- R_snp_info$id
colnames(R_snp_gene) <- region$gid
rownames(R_snp) <- R_snp_info$id
colnames(R_snp) <- R_snp_info$id
a$r2max <- NA
a$r2max[a$type=="gene"] <- R_gene[focus,a$id[a$type=="gene"]]
a$r2max[a$type=="SNP"] <- R_snp_gene[a$id[a$type=="SNP"],focus]
r2cut <- 0.4
colorsall <- c("#7fc97f", "#beaed4", "#fdc086")
layout(matrix(1:2, ncol = 1), widths = 1, heights = c(1.5,1.5), respect = FALSE)
par(mar = c(0, 4.1, 4.1, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 19, xlab=paste0("Chromosome ", region_tag1, " Position"),frame.plot=FALSE, col = "white", ylim= c(-0.1,1.1), ylab = "cTWAS PIP", xaxt = 'n')
grid()
points(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 21, xlab="Genomic position", bg = colorsall[1])
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$susie_pip[a$type == "SNP" & a$r2max >r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$susie_pip[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$susie_pip[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$susie_pip[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$susie_pip[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
if (isTRUE(plot_eqtl)){
for (cgene in a[a$type=="gene" & a$ifcausal == 1, ]$id){
load(paste0(results_dir, "/",analysis_id, "_expr_chr", region_tag1, ".exprqc.Rd"))
eqtls <- rownames(wgtlist[[cgene]])
points(a[a$id %in% eqtls,]$pos, rep( -0.15, nrow(a[a$id %in% eqtls,])), pch = "|", col = "salmon", cex = 1.5)
}
}
legend(max(a$pos)-0.2*(max(a$pos)-min(a$pos)), y= 1.1 ,c("Gene", "SNP"), pch = c(22,21), title="Shape Legend", bty ='n', cex=0.6, title.adj = 0)
legend(max(a$pos)-0.2*(max(a$pos)-min(a$pos)), y= 0.7 ,c("Focal Gene", "R2 > 0.4", "R2 <= 0.4"), pch = 19, col = c("salmon", "purple", colorsall[1]), title="Color Legend", bty ='n', cex=0.6, title.adj = 0)
if (label=="cTWAS"){
text(a$pos[a$id==focus], a$susie_pip[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
par(mar = c(4.1, 4.1, 0.5, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 21, xlab=paste0("Chromosome ", region_tag1, " Position"), frame.plot=FALSE, bg = colorsall[1], ylab = "TWAS -log10(p value)", panel.first = grid(), ylim =c(0, max(a$PVALUE)*1.2))
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$PVALUE[a$type == "SNP" & a$r2max > r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$PVALUE[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$PVALUE[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$PVALUE[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$PVALUE[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
abline(h=-log10(alpha/nrow(ctwas_gene_res)), col ="red", lty = 2)
if (label=="TWAS"){
text(a$pos[a$id==focus], a$PVALUE[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
}
locus_plot5("19_33", focus="PRKD2")
Locus Plots - Exploring known annotations
This section produces locus plots for all silver standard genes with known annotations. The highlighted gene at each region is the silver standard gene. Note that if no genes in a region have PIP>0.8, then only the result using thinned SNPs is displayed.
locus_plot3 <- function(region_tag, rerun_ctwas = F, plot_eqtl = T, label="cTWAS", focus){
region_tag1 <- unlist(strsplit(region_tag, "_"))[1]
region_tag2 <- unlist(strsplit(region_tag, "_"))[2]
a <- ctwas_res[ctwas_res$region_tag==region_tag,]
regionlist <- readRDS(paste0(results_dir, "/", analysis_id, "_ctwas.regionlist.RDS"))
region <- regionlist[[as.numeric(region_tag1)]][[region_tag2]]
R_snp_info <- do.call(rbind, lapply(region$regRDS, function(x){data.table::fread(paste0(tools::file_path_sans_ext(x), ".Rvar"))}))
if (isTRUE(rerun_ctwas)){
ld_exprfs <- paste0(results_dir, "/", analysis_id, "_expr_chr", 1:22, ".expr.gz")
temp_reg <- data.frame("chr" = paste0("chr",region_tag1), "start" = region$start, "stop" = region$stop)
write.table(temp_reg,
#file= paste0(results_dir, "/", analysis_id, "_ctwas.temp.reg.txt") ,
file= "temp_reg.txt",
row.names=F, col.names=T, sep="\t", quote = F)
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
z_gene_temp <- z_gene[z_gene$id %in% a$id[a$type=="gene"],]
z_snp_temp <- z_snp[z_snp$id %in% R_snp_info$id,]
ctwas_rss(z_gene_temp, z_snp_temp, ld_exprfs, ld_pgenfs = NULL,
ld_R_dir = dirname(region$regRDS)[1],
ld_regions_custom = "temp_reg.txt", thin = 1,
outputdir = ".", outname = "temp", ncore = 1, ncore.rerun = 1, prob_single = 0,
group_prior = estimated_group_prior, group_prior_var = estimated_group_prior_var,
estimate_group_prior = F, estimate_group_prior_var = F)
a <- data.table::fread("temp.susieIrss.txt", header = T)
rownames(z_snp_temp) <- z_snp_temp$id
z_snp_temp <- z_snp_temp[a$id[a$type=="SNP"],]
z_gene_temp <- z_gene_temp[a$id[a$type=="gene"],]
a$z <- NA
a$z[a$type=="SNP"] <- z_snp_temp$z
a$z[a$type=="gene"] <- z_gene_temp$z
}
a$ifcausal <- 0
focus <- a$id[which(a$genename==focus)]
a$ifcausal <- as.numeric(a$id==focus)
a$PVALUE <- (-log(2) - pnorm(abs(a$z), lower.tail=F, log.p=T))/log(10)
R_gene <- readRDS(region$R_g_file)
R_snp_gene <- readRDS(region$R_sg_file)
R_snp <- as.matrix(Matrix::bdiag(lapply(region$regRDS, readRDS)))
rownames(R_gene) <- region$gid
colnames(R_gene) <- region$gid
rownames(R_snp_gene) <- R_snp_info$id
colnames(R_snp_gene) <- region$gid
rownames(R_snp) <- R_snp_info$id
colnames(R_snp) <- R_snp_info$id
a$r2max <- NA
a$r2max[a$type=="gene"] <- R_gene[focus,a$id[a$type=="gene"]]
a$r2max[a$type=="SNP"] <- R_snp_gene[a$id[a$type=="SNP"],focus]
r2cut <- 0.4
colorsall <- c("#7fc97f", "#beaed4", "#fdc086")
layout(matrix(1:2, ncol = 1), widths = 1, heights = c(1.5,1.5), respect = FALSE)
par(mar = c(0, 4.1, 4.1, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 19, xlab=paste0("Chromosome ", region_tag1, " Position"),frame.plot=FALSE, col = "white", ylim= c(-0.1,1.1), ylab = "cTWAS PIP", xaxt = 'n')
grid()
points(a$pos[a$type=="SNP"], a$susie_pip[a$type == "SNP"], pch = 21, xlab="Genomic position", bg = colorsall[1])
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$susie_pip[a$type == "SNP" & a$r2max >r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$susie_pip[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$susie_pip[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$susie_pip[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$susie_pip[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
if (isTRUE(plot_eqtl)){
for (cgene in a[a$type=="gene" & a$ifcausal == 1, ]$id){
load(paste0(results_dir, "/",analysis_id, "_expr_chr", region_tag1, ".exprqc.Rd"))
eqtls <- rownames(wgtlist[[cgene]])
points(a[a$id %in% eqtls,]$pos, rep( -0.15, nrow(a[a$id %in% eqtls,])), pch = "|", col = "salmon", cex = 1.5)
}
}
legend(min(a$pos), y= 1.1 ,c("Gene", "SNP"), pch = c(22,21), title="Shape Legend", bty ='n', cex=0.6, title.adj = 0)
legend(min(a$pos), y= 0.7 ,c("Lead TWAS Gene", "R2 > 0.4", "R2 <= 0.4"), pch = 19, col = c("salmon", "purple", colorsall[1]), title="Color Legend", bty ='n', cex=0.6, title.adj = 0)
if (label=="cTWAS"){
text(a$pos[a$id==focus], a$susie_pip[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
par(mar = c(4.1, 4.1, 0.5, 2.1))
plot(a$pos[a$type=="SNP"], a$PVALUE[a$type == "SNP"], pch = 21, xlab=paste0("Chromosome ", region_tag1, " Position"), frame.plot=FALSE, bg = colorsall[1], ylab = "TWAS -log10(p value)", panel.first = grid(), ylim =c(0, max(a$PVALUE)*1.2))
points(a$pos[a$type=="SNP" & a$r2max > r2cut], a$PVALUE[a$type == "SNP" & a$r2max > r2cut], pch = 21, bg = "purple")
points(a$pos[a$type=="SNP" & a$ifcausal == 1], a$PVALUE[a$type == "SNP" & a$ifcausal == 1], pch = 21, bg = "salmon")
points(a$pos[a$type=="gene"], a$PVALUE[a$type == "gene"], pch = 22, bg = colorsall[1], cex = 2)
points(a$pos[a$type=="gene" & a$r2max > r2cut], a$PVALUE[a$type == "gene" & a$r2max > r2cut], pch = 22, bg = "purple", cex = 2)
points(a$pos[a$type=="gene" & a$ifcausal == 1], a$PVALUE[a$type == "gene" & a$ifcausal == 1], pch = 22, bg = "salmon", cex = 2)
abline(h=-log10(alpha/nrow(ctwas_gene_res)), col ="red", lty = 2)
if (label=="TWAS"){
text(a$pos[a$id==focus], a$PVALUE[a$id==focus], labels=ctwas_gene_res$genename[ctwas_gene_res$id==focus], pos=3, cex=0.6)
}
}load(paste0(results_dir, "/known_annotations.Rd"))
load(paste0(results_dir, "/bystanders.Rd"))
for (i in 1:length(known_annotations)){
focus <- known_annotations[i]
region_tag <- ctwas_res$region_tag[which(ctwas_res$genename==focus)]
locus_plot3(region_tag, focus=focus)
mtext(text=region_tag)
print(focus)
print(region_tag)
print(ctwas_gene_res[ctwas_gene_res$region_tag==region_tag,report_cols,])
}
[1] "ITIH4"
[1] "3_36"
genename region_tag susie_pip mu2 PVE z
190 NISCH 3_36 0.03078967 4.881423 4.373929e-07 -0.1143171
191 STAB1 3_36 0.38131140 17.731609 1.967652e-05 3.6621156
271 CHDH 3_36 0.03913369 7.198463 8.198056e-07 1.0082090
274 GLT8D1 3_36 0.04176897 7.458057 9.065668e-07 1.0942021
432 PARP3 3_36 0.03937152 7.067687 8.098038e-07 0.9533837
567 ITIH4 3_36 0.03220946 5.275288 4.944814e-07 1.0884470
568 ITIH1 3_36 0.21275162 22.172092 1.372777e-05 -3.2168754
573 IL17RB 3_36 0.03206673 5.210367 4.862317e-07 0.3500674
3157 SELENOK 3_36 0.03637105 6.509894 6.890490e-07 0.8957058
3158 ACTR8 3_36 0.03248376 5.307413 5.017293e-07 0.4283727
3223 RRP9 3_36 0.03937152 7.067687 8.098038e-07 -0.9533837
3232 DNAH1 3_36 0.02977494 4.693951 4.067333e-07 -0.2468006
3235 TNNC1 3_36 0.23830009 16.004750 1.109924e-05 -3.4428582
3239 NEK4 3_36 0.13563711 18.074034 7.134342e-06 -2.9773988
7777 RPL29 3_36 0.03231116 5.322646 5.004959e-07 -0.4392119
8103 TKT 3_36 0.04149599 7.336503 8.859629e-07 -0.6487825
8104 PRKCD 3_36 0.03372818 5.776812 5.670240e-07 -0.6434864
8105 RFT1 3_36 0.04983891 8.990987 1.304056e-06 0.8853186
8106 SFMBT1 3_36 0.05637825 10.072860 1.652664e-06 -0.9994935
8107 GNL3 3_36 0.07557941 12.753739 2.805184e-06 -2.5465869
8108 PBRM1 3_36 0.04119380 6.692885 8.023530e-07 -0.8048656
8150 POC1A 3_36 0.03064018 4.950297 4.414107e-07 -0.5058147
8151 PPM1M 3_36 0.04909498 8.744020 1.249305e-06 -1.4513993
8881 GLYCTK 3_36 0.03206923 5.135283 4.792622e-07 -0.5091229
8885 NT5DC2 3_36 0.03423998 6.605134 6.581661e-07 0.6567418
8886 SMIM4 3_36 0.03056677 4.863176 4.326033e-07 -0.3777767
10396 GPR62 3_36 0.03234785 5.290531 4.980409e-07 -0.4602688
12296 TMEM110 3_36 0.02995753 4.670413 4.071755e-07 0.3872171
13015 TLR9 3_36 0.11763360 14.715004 5.037465e-06 -1.5730124
13145 ACY1 3_36 0.03185565 5.228488 4.847110e-07 -0.5115172
13232 ABHD14A 3_36 0.04702784 9.192806 1.258124e-06 1.3240881
14229 DCP1A 3_36 0.03738241 6.666728 7.252710e-07 0.7749500
num_eqtl
190 1
191 1
271 1
274 1
432 1
567 2
568 1
573 2
3157 1
3158 2
3223 1
3232 1
3235 2
3239 1
7777 1
8103 1
8104 1
8105 1
8106 1
8107 1
8108 1
8150 1
8151 2
8881 2
8885 2
8886 1
10396 1
12296 2
13015 1
13145 1
13232 1
14229 1
[1] "GHR"
[1] "5_28"
genename region_tag susie_pip mu2 PVE
3076 GHR 5_28 0.04637573 6.886801 9.294555e-07
3077 HMGCS1 5_28 0.07352349 11.172363 2.390515e-06
3080 NNT 5_28 0.04783145 7.172752 9.984347e-07
6898 FBXO4 5_28 0.04744249 7.097210 9.798857e-07
6899 TMEM267 5_28 0.03702731 4.808710 5.181686e-07
6900 CCL28 5_28 0.03809372 5.070344 5.620967e-07
9447 PAIP1 5_28 0.04016977 5.559686 6.499350e-07
9452 ZNF131 5_28 0.04088829 5.723331 6.810329e-07
10031 NIM1K 5_28 0.06032355 9.325966 1.637197e-06
11904 CCDC152 5_28 0.04818282 7.240529 1.015273e-06
12198 C5orf51 5_28 0.04290202 6.167086 7.699775e-07
13307 SELENOP 5_28 0.04089217 5.724207 6.812017e-07
14165 CTD-2035E11.4 5_28 0.05716960 8.826580 1.468513e-06
z num_eqtl
3076 -0.9403419 2
3077 -1.5237127 1
3080 -0.9709429 2
6898 -1.2044106 1
6899 0.2515965 1
6900 0.4194805 1
9447 0.5925832 1
9452 -0.8998649 1
10031 1.4417117 1
11904 -1.3377996 2
12198 0.4012229 1
13307 0.8925146 1
14165 -1.0977611 1
[1] "EPHX2"
[1] "8_27"
genename region_tag susie_pip mu2 PVE z
268 STMN4 8_27 0.03643381 4.663586 4.944757e-07 0.18785714
2106 TRIM35 8_27 0.07631826 11.525446 2.559803e-06 1.38523367
3825 CLU 8_27 0.03803226 5.059225 5.599593e-07 0.40112191
3829 ADRA1A 8_27 0.03593858 4.537536 4.745711e-07 -0.06231638
3832 EPHX2 8_27 0.03757033 4.946598 5.408438e-07 -0.28231248
4976 ELP3 8_27 0.12821406 16.438729 6.133724e-06 2.14839507
6548 CCDC25 8_27 0.03864665 5.206956 5.856203e-07 -0.43534994
8860 PBK 8_27 0.03968918 5.452426 6.297703e-07 0.48335074
8861 SCARA5 8_27 0.03896406 5.282375 5.989820e-07 0.38275862
9319 ESCO2 8_27 0.03639814 4.654562 4.930356e-07 -0.18809073
11361 NUGGC 8_27 0.20839300 21.183850 1.284720e-05 -2.49551570
num_eqtl
268 1
2106 2
3825 2
3829 1
3832 2
4976 2
6548 1
8860 1
8861 1
9319 1
11361 1
[1] "ABCA1"
[1] "9_52"
genename region_tag susie_pip mu2 PVE z
5358 NIPSNAP3A 9_52 0.04605158 8.047124 1.078464e-06 1.0390715
5364 SMC2 9_52 0.03454539 5.395051 5.423829e-07 -0.5519492
8325 NIPSNAP3B 9_52 0.06039336 10.561843 1.856305e-06 1.2257096
8326 ABCA1 9_52 0.30873469 26.431397 2.374793e-05 -3.0910125
num_eqtl
5358 2
5364 2
8325 3
8326 1
[1] "LPL"
[1] "8_21"
genename region_tag susie_pip mu2 PVE
2161 ASAH1 8_21 0.02487708 4.609640 3.337235e-07
6547 CSGALNACT1 8_21 0.24958462 24.106797 1.750966e-05
7232 NAT2 8_21 0.03635848 25.328368 2.679990e-06
9326 NAT1 8_21 0.03264974 6.625381 6.295219e-07
13374 RP11-1080G15.1 8_21 0.02809254 5.764858 4.713026e-07
2144 INTS10 8_21 0.03392676 7.018632 6.929712e-07
9840 LPL 8_21 0.07252143 14.647971 3.091463e-06
z num_eqtl
2161 -0.2610289 1
6547 -2.6909853 2
7232 -4.7711082 1
9326 0.3941094 1
13374 -0.6440697 1
2144 -0.5466864 1
9840 -1.8179375 1
[1] "DHCR7"
[1] "11_40"
genename region_tag susie_pip mu2 PVE
2787 FOLR1 11_40 0.03975218 4.542209 5.254706e-07
2789 FOLR3 11_40 0.06139167 8.568171 1.530798e-06
5474 IL18BP 11_40 0.03977323 4.547092 5.263139e-07
5475 NUMA1 11_40 0.05124637 6.890387 1.027607e-06
5482 RNF121 11_40 0.16722341 18.085384 8.801265e-06
7437 FAM86C1 11_40 0.04345734 5.364843 6.784853e-07
7766 CLPB 11_40 0.05923111 8.234797 1.419460e-06
8374 FOLR2 11_40 0.06869721 9.616499 1.922544e-06
8375 INPPL1 11_40 0.06783789 9.498964 1.875292e-06
9532 NADSYN1 11_40 0.04028011 4.663916 5.467158e-07
9533 DHCR7 11_40 0.04433609 5.549826 7.160727e-07
10750 LRTOMT 11_40 0.04593677 5.877717 7.857592e-07
12074 KRTAP5-10 11_40 0.04909424 6.492897 9.276612e-07
12386 ZNF705E 11_40 0.04176754 4.998575 6.075827e-07
12645 LINC01537 11_40 0.05405387 7.385054 1.161718e-06
13156 KRTAP5-7 11_40 0.16191175 17.770090 8.373139e-06
13416 RP11-849H4.2 11_40 0.04115642 4.862519 5.823972e-07
13446 KRTAP5-9 11_40 0.04256256 5.172676 6.407127e-07
z num_eqtl
2787 0.02189389 1
2789 -1.02010138 2
5474 -0.10595077 2
5475 0.84278892 2
5482 2.10926722 2
7437 -0.55734863 1
7766 1.03637029 1
8374 -1.19777355 2
8375 -1.31200488 1
9532 0.26353734 2
9533 -0.65130261 1
10750 -0.65197454 1
12074 0.91565982 1
12386 0.30405380 1
12645 -0.88109681 1
13156 2.13324635 1
13416 -0.32070634 1
13446 0.61486760 3
[1] "LIPA"
[1] "10_57"
genename region_tag susie_pip mu2 PVE z
2550 LIPA 10_57 0.04031256 7.675012 9.004089e-07 -0.9174128
3731 IFIT3 10_57 0.04023636 7.657545 8.966615e-07 0.8109877
3733 IFIT2 10_57 0.03080308 5.197860 4.659497e-07 0.4143150
5588 KIF20B 10_57 0.02890865 4.614789 3.882397e-07 0.0836273
6982 IFIT5 10_57 0.04324667 8.323610 1.047574e-06 0.9830768
6983 SLC16A12 10_57 0.08706291 14.841367 3.760342e-06 -1.7113595
6984 PANK1 10_57 0.06542503 12.164611 2.316127e-06 -1.6398223
10952 IFIT1 10_57 0.03772732 7.063845 7.755636e-07 0.8329755
11983 IFIT1B 10_57 0.03311754 5.863954 5.651567e-07 -0.6164140
12792 LINC00865 10_57 0.05223558 10.071314 1.530992e-06 1.3703904
14572 LINC01374 10_57 0.04182393 8.014704 9.755122e-07 -1.0512219
num_eqtl
2550 1
3731 1
3733 1
5588 2
6982 3
6983 3
6984 1
10952 1
11983 1
12792 2
14572 1
[1] "APOB"
[1] "2_13"
genename region_tag susie_pip mu2 PVE z
1205 APOB 2_13 4.269285e-11 67.70494 8.411933e-15 5.997104
num_eqtl
1205 2
[1] "APOE"
[1] "19_31"
genename region_tag susie_pip mu2 PVE z num_eqtl
122 MARK4 19_31 0 21.181957 0 -2.2463768 1
227 ERCC1 19_31 0 7.409485 0 -1.6350316 2
633 ZNF112 19_31 0 47.819226 0 4.8629559 1
905 PVR 19_31 0 31.813106 0 -3.0943045 1
2176 CLPTM1 19_31 0 50.178070 0 2.5751726 1
2178 PPP1R37 19_31 0 42.423234 0 -5.9455792 1
2182 PPP1R13L 19_31 0 22.778963 0 -3.0806361 1
2184 ERCC2 19_31 0 13.966069 0 1.5340117 1
2189 KLC3 19_31 0 13.524015 0 -3.6648287 2
3573 CD3EAP 19_31 0 11.426032 0 2.5646694 1
4228 FOSB 19_31 0 16.099633 0 -2.3658041 1
4229 OPA3 19_31 0 6.808430 0 1.5059074 2
4231 RTN2 19_31 0 6.949994 0 -2.0700710 1
4233 VASP 19_31 0 33.645255 0 -2.7026884 1
4573 NECTIN2 19_31 0 1648.609427 0 -35.7740463 1
4574 APOE 19_31 0 303.200452 0 0.6519443 1
4575 TOMM40 19_31 0 1005.711219 0 -14.0286334 2
4576 APOC1 19_31 0 474.076449 0 -9.1150442 1
6037 GEMIN7 19_31 0 205.847243 0 13.2439035 2
7553 ZNF233 19_31 0 131.643247 0 -10.0229697 2
7554 ZNF235 19_31 0 35.913626 0 -6.2627967 2
8710 ZNF180 19_31 0 43.730327 0 4.0279244 1
9234 ZNF296 19_31 0 94.996588 0 5.4593536 1
11052 CEACAM19 19_31 0 64.753213 0 11.7978210 2
11128 BCAM 19_31 0 96.429711 0 4.6421318 1
11348 BLOC1S3 19_31 0 11.973214 0 2.7250151 2
12327 PPM1N 19_31 0 24.361748 0 -2.6089113 1
12884 APOC2 19_31 0 18.135579 0 -2.2896080 1
13922 ZNF285 19_31 0 10.133291 0 -1.3318721 3
14528 ZNF229 19_31 0 125.384012 0 14.4998133 1
[1] "NPC1L1"
[1] "7_32"
genename region_tag susie_pip mu2 PVE z
18 HECW1 7_32 0.02781865 5.801245 4.696536e-07 -0.3769605
266 NPC1L1 7_32 0.87077857 100.506326 2.546956e-04 11.6310213
590 CAMK2B 7_32 0.03205752 6.435120 6.003533e-07 -1.3113038
636 MRPS24 7_32 0.02688409 5.636475 4.409843e-07 0.3827818
1072 UBE2D4 7_32 0.02674845 5.737501 4.466237e-07 0.6290952
2371 OGDH 7_32 0.02806812 11.531669 9.419456e-07 -2.5518108
2452 COA1 7_32 0.06263166 12.220524 2.227430e-06 -1.0972085
2453 BLVRA 7_32 0.02525596 5.282089 3.882306e-07 0.6112755
2458 AEBP1 7_32 0.02852512 6.670093 5.537065e-07 -0.9567966
2461 GCK 7_32 0.03992584 9.537524 1.108179e-06 1.2747958
2463 YKT6 7_32 0.02913308 7.784609 6.599994e-07 1.6794862
3955 POLM 7_32 0.02600385 5.343151 4.043481e-07 -0.4204156
5303 DDX56 7_32 0.04246638 9.385524 1.159909e-06 -0.7865840
5305 DBNL 7_32 0.02968824 6.061190 5.236760e-07 0.1683551
7444 TMED4 7_32 0.84231695 38.146713 9.350890e-05 7.6088259
8238 STK17A 7_32 0.02906015 6.122062 5.177450e-07 0.4764271
12678 AC004951.6 7_32 0.03572632 7.307602 7.597723e-07 0.2209151
12901 LINC00957 7_32 0.02576194 5.348360 4.009770e-07 -0.4692011
num_eqtl
18 1
266 1
590 1
636 1
1072 1
2371 2
2452 2
2453 2
2458 2
2461 2
2463 1
3955 2
5303 1
5305 2
7444 2
8238 2
12678 1
12901 1
[1] "PCSK9"
[1] "1_34"
genename region_tag susie_pip mu2 PVE z
106 TTC22 1_34 0.000000e+00 9.290286 0.000000e+00 -0.1753778
596 YIPF1 1_34 0.000000e+00 5.881413 0.000000e+00 -0.5588436
597 NDC1 1_34 0.000000e+00 33.452327 0.000000e+00 -2.4643141
1144 HSPB11 1_34 0.000000e+00 5.853377 0.000000e+00 0.2303441
3409 TCEANC2 1_34 0.000000e+00 4.855985 0.000000e+00 0.1189682
6094 TMEM61 1_34 2.220446e-16 81.742852 5.282145e-20 -6.5190877
7340 SSBP3 1_34 0.000000e+00 4.644200 0.000000e+00 -0.6008954
7791 ACOT11 1_34 0.000000e+00 9.373849 0.000000e+00 -0.9430103
7792 FAM151A 1_34 0.000000e+00 19.564553 0.000000e+00 2.0257851
7793 PARS2 1_34 0.000000e+00 6.796833 0.000000e+00 -0.6202883
7794 USP24 1_34 6.195444e-11 114.940287 2.072359e-14 2.6580171
9013 PCSK9 1_34 4.996004e-15 354.007553 5.147017e-18 13.4587025
10774 MROH7 1_34 1.554312e-15 57.653621 2.607865e-19 3.8593115
12240 DIO1 1_34 0.000000e+00 30.330326 0.000000e+00 -2.2818792
12445 CYB5RL 1_34 0.000000e+00 13.194047 0.000000e+00 1.2190244
13135 TTC4 1_34 0.000000e+00 4.721135 0.000000e+00 0.4475698
num_eqtl
106 1
596 2
597 1
1144 1
3409 2
6094 1
7340 1
7791 1
7792 1
7793 2
7794 1
9013 3
10774 2
12240 2
12445 3
13135 1
[1] "SOAT1"
[1] "1_89"
genename region_tag susie_pip mu2 PVE
575 SOAT1 1_89 0.06102989 8.212966 1.458690e-06
3408 FAM20B 1_89 0.04661231 5.712560 7.749108e-07
3866 LHX4 1_89 0.05247722 6.810253 1.040050e-06
5233 CEP350 1_89 0.07793805 10.497274 2.380929e-06
6141 ABL2 1_89 0.04240151 4.837480 5.969263e-07
6145 FAM163A 1_89 0.04122992 4.578801 5.493949e-07
7005 MR1 1_89 0.04346623 5.066548 6.408914e-07
7871 TDRD5 1_89 0.04363641 5.102648 6.479850e-07
7872 IER5 1_89 0.07567044 10.220549 2.250717e-06
9111 TOR1AIP2 1_89 0.05166999 6.666511 1.002438e-06
11015 TOR3A 1_89 0.04280450 4.924837 6.134816e-07
12728 ACBD6 1_89 0.21232028 20.145153 1.244751e-05
13674 RP11-533E19.5 1_89 0.07793805 10.497274 2.380929e-06
z num_eqtl
575 1.0093956 2
3408 0.5840899 1
3866 0.7900365 3
5233 1.4557823 1
6141 -0.3650471 2
6145 0.1072614 2
7005 -0.4010016 1
7871 -0.3743691 2
7872 1.2579383 1
9111 0.7882446 1
11015 0.2842174 2
12728 -2.4944034 1
13674 1.4557823 1
[1] "MYLIP"
[1] "6_13"
genename region_tag susie_pip mu2 PVE z
143 MYLIP 6_13 0.05835638 10.968363 1.862732e-06 1.1219050
145 JARID2 6_13 0.03076400 5.440489 4.870809e-07 -0.6250412
462 DTNBP1 6_13 0.37017726 26.951056 2.903393e-05 3.0116911
2994 RBM24 6_13 0.04274513 8.173616 1.016766e-06 0.9044930
4152 ATXN1 6_13 0.06452223 12.052057 2.263033e-06 -1.4308658
5431 GMPR 6_13 0.05867866 9.963612 1.701443e-06 0.2015303
14103 RP11-560J1.2 6_13 0.04059601 7.552062 8.922144e-07 0.8390515
14156 RP1-151F17.2 6_13 0.05675147 10.834632 1.789417e-06 -1.2947856
num_eqtl
143 1
145 1
462 3
2994 1
4152 2
5431 1
14103 2
14156 3
[1] "OSBPL5"
[1] "11_3"
genename region_tag susie_pip mu2 PVE z
81 ZNF195 11_3 0.02172229 6.205832 3.923069e-07 0.62769811
299 OSBPL5 11_3 0.02380442 7.035523 4.873874e-07 -0.76274979
658 TSPAN32 11_3 0.04340867 12.705350 1.605031e-06 1.47312051
1071 TOLLIP 11_3 0.02778176 8.429244 6.815045e-07 -0.97661273
2814 CARS 11_3 0.01887035 4.952995 2.719995e-07 0.43596830
2815 SLC22A18 11_3 0.12759108 22.468275 8.342771e-06 -2.34987486
2816 CD81 11_3 0.01885128 4.914636 2.696203e-07 0.20617703
2818 C11orf21 11_3 0.02404786 7.071441 4.948854e-07 -0.69609976
3576 CTSD 11_3 0.03140041 9.548858 8.725837e-07 1.09935339
4549 CDKN1C 11_3 0.03181921 9.749616 9.028119e-07 -1.13636208
4623 LSP1 11_3 0.05197020 14.479178 2.189871e-06 -1.89370692
4624 TNNT3 11_3 0.07007280 17.473738 3.563326e-06 2.14046358
8700 ART5 11_3 0.02656282 8.061675 6.231889e-07 0.94563803
9740 BRSK2 11_3 0.01800633 4.557829 2.388380e-07 -0.11596505
10321 TH 11_3 0.01882728 4.872775 2.669834e-07 -0.05052331
10455 PHLDA2 11_3 0.34357189 31.415580 3.141109e-05 -3.38048160
10520 MOB2 11_3 0.09410867 19.498553 5.340136e-06 2.26264017
10700 ASCL2 11_3 0.02878914 8.908793 7.463936e-07 1.10077476
10800 DUSP8 11_3 0.06775535 16.573967 3.268063e-06 -2.14473420
12209 KRTAP5-1 11_3 0.04899992 13.591768 1.938169e-06 1.85100113
12719 LINC01150 11_3 0.09508990 19.353209 5.355594e-06 -1.99663571
13154 IFITM10 11_3 0.03081421 9.644677 8.648864e-07 -1.25156464
13435 SLC22A18AS 11_3 0.01843434 4.754973 2.550915e-07 -0.25675540
14632 PRR33 11_3 0.02723999 9.561388 7.579633e-07 -1.74750284
2824 NUP98 11_3 0.02401654 7.152473 4.999043e-07 0.86950651
4548 CHRNA10 11_3 0.01881363 4.942951 2.706320e-07 -0.32964221
4774 TRIM21 11_3 0.01822990 4.656413 2.470336e-07 -0.17964264
6673 PGAP2 11_3 0.02401654 7.152473 4.999043e-07 0.86950651
8702 RRM1 11_3 0.02907398 8.899029 7.529522e-07 1.09666210
8703 OR51E2 11_3 0.13545115 23.020609 9.074440e-06 2.38819625
8704 TRIM68 11_3 0.02115946 6.020173 3.707097e-07 0.59859506
10006 RHOG 11_3 0.01949822 5.272998 2.992078e-07 0.41748273
10378 OR51E1 11_3 0.05180652 14.203185 2.141364e-06 -1.60358931
num_eqtl
81 2
299 1
658 1
1071 1
2814 2
2815 5
2816 1
2818 2
3576 1
4549 1
4623 1
4624 2
8700 3
9740 2
10321 1
10455 3
10520 2
10700 2
10800 1
12209 1
12719 1
13154 2
13435 1
14632 1
2824 1
4548 1
4774 2
6673 1
8702 3
8703 1
8704 1
10006 1
10378 2
[1] "SCARB1"
[1] "12_76"
genename region_tag susie_pip mu2 PVE z
907 SCARB1 12_76 0.03209546 5.803494 5.420675e-07 -0.2308064
1138 AACS 12_76 0.02745900 4.627128 3.697571e-07 0.1677513
5735 TMEM132B 12_76 0.02842419 4.917243 4.067523e-07 -0.2384026
6809 DHX37 12_76 0.02784678 5.018175 4.066690e-07 0.5898098
6810 UBC 12_76 0.04156297 8.249029 9.977683e-07 0.9059691
num_eqtl
907 2
1138 1
5735 1
6809 1
6810 1
[1] "ABCB11"
[1] "2_102"
genename region_tag susie_pip mu2 PVE z
920 ABCB11 2_102 0.04881810 16.883731 2.398665e-06 -3.29039301
6930 SPC25 2_102 0.03471167 5.097828 5.149689e-07 0.06087347
6931 G6PC2 2_102 0.03918795 5.985848 6.826506e-07 0.13649188
7922 NOSTRIN 2_102 0.12718614 16.697971 6.180503e-06 -1.84344148
7925 XIRP2 2_102 0.14515983 18.481837 7.807498e-06 2.15634557
9457 CERS6 2_102 0.04539467 7.440774 9.829770e-07 0.64830473
num_eqtl
920 1
6930 2
6931 2
7922 3
7925 1
9457 1
[1] "CETP"
[1] "16_30"
genename region_tag susie_pip mu2 PVE
65 CIAPIN1 16_30 0.09922573 16.140439 4.660795e-06
509 HERPUD1 16_30 0.03063699 15.012321 1.338487e-06
1279 CETP 16_30 0.05943840 154.137583 2.666220e-05
1282 GNAO1 16_30 0.05474511 9.648534 1.537188e-06
1283 OGFOD1 16_30 0.08482868 13.663370 3.373035e-06
1973 NUP93 16_30 0.03389513 7.189412 7.091710e-07
1978 PLLP 16_30 0.04701067 9.012393 1.232982e-06
1980 CCL22 16_30 0.06277198 12.196698 2.228068e-06
1982 CCL17 16_30 0.04600774 7.987632 1.069472e-06
1985 POLR2C 16_30 0.03705200 6.247704 6.736781e-07
4167 BBS2 16_30 0.07635818 12.164451 2.703139e-06
4168 MT1G 16_30 0.14023188 19.459716 7.941519e-06
4169 MT2A 16_30 0.24484833 30.619487 2.181802e-05
4172 DOK4 16_30 0.03696066 6.776722 7.289197e-07
5217 CCDC102A 16_30 0.03349517 5.741979 5.597113e-07
5890 CPNE2 16_30 0.04209355 7.052151 8.638880e-07
5891 NLRC5 16_30 0.03327940 5.302069 5.135009e-07
7520 AMFR 16_30 0.11866301 20.567751 7.102684e-06
7523 RSPRY1 16_30 0.03943038 8.605903 9.875242e-07
8653 NUDT21 16_30 0.03507034 5.933260 6.055551e-07
9517 FAM192A 16_30 0.05319274 10.172081 1.574644e-06
11123 MT1X 16_30 0.03004306 4.703990 4.112737e-07
12162 ADGRG1 16_30 0.04666851 8.607849 1.169065e-06
12166 MT1H 16_30 0.03215687 5.107977 4.780167e-07
12167 MT1A 16_30 0.08160174 15.578063 3.699416e-06
12169 MT1M 16_30 0.03030993 6.059852 5.345241e-07
13200 RP11-461O7.1 16_30 0.03142816 4.977037 4.552083e-07
13709 RP11-249C24.10 16_30 0.03024031 4.917166 4.327343e-07
z num_eqtl
65 -2.01566091 2
509 3.41395172 2
1279 13.37918969 1
1282 1.22086252 1
1283 1.52977829 2
1973 -1.50724713 1
1978 -1.32251235 1
1980 1.77535072 1
1982 0.74318880 1
1985 -0.55079469 1
4167 -1.14371909 2
4168 -2.39948454 1
4169 4.18897030 1
4172 -0.99412325 1
5217 0.70399476 2
5890 0.17965015 2
5891 0.01238369 2
7520 3.00562598 1
7523 1.63931756 2
8653 -0.68237249 2
9517 1.39362846 2
11123 -0.40997217 1
12162 -1.00646342 1
12166 0.07381601 1
12167 2.49797425 1
12169 1.32616620 1
13200 -0.33504951 3
13709 0.67428781 1
[1] "APOH"
[1] "17_39"
genename region_tag susie_pip mu2 PVE
1435 APOH 17_39 0.0050262325 23.798293 3.481037e-07
7102 PRKCA 17_39 0.0006180851 5.807792 1.044671e-08
7103 CEP112 17_39 0.0008831047 8.704009 2.236927e-08
836 WIPI1 17_39 0.0005764356 6.078915 1.019758e-08
977 CACNG4 17_39 0.0005517705 5.003426 8.034266e-09
2678 PRKAR1A 17_39 0.0045070039 21.583225 2.830900e-07
2680 FAM20A 17_39 0.0028406145 20.079995 1.659955e-07
4672 NOL11 17_39 0.0008673373 8.681743 2.191368e-08
5935 ARSG 17_39 0.0014552540 12.688817 5.373784e-08
5936 ABCA8 17_39 0.0017649595 28.383000 1.457852e-07
7105 ABCA9 17_39 0.0005295935 15.338567 2.364002e-08
7106 ABCA6 17_39 0.0005266745 8.386623 1.285434e-08
7107 ABCA10 17_39 0.0005550710 23.200002 3.747631e-08
7108 ABCA5 17_39 0.0006844813 15.178302 3.023466e-08
9363 BPTF 17_39 0.0007310785 5.183708 1.102871e-08
10548 KPNA2 17_39 0.1397716491 21.747100 8.845874e-06
11065 C17orf58 17_39 0.0006187252 10.488692 1.888597e-08
11505 AMZ2 17_39 0.0005452568 4.645326 7.371191e-09
11577 PSMD12 17_39 0.0010089192 10.554701 3.099007e-08
11776 HELZ 17_39 0.0672606883 41.848455 8.191455e-06
13833 AC145343.2 17_39 0.0027352555 9.652425 7.683421e-08
13895 RP1-193H18.2 17_39 0.0007087302 6.834048 1.409546e-08
13939 RP11-147L13.8 17_39 0.0014059132 7.986356 3.267589e-08
14493 RP11-147L13.13 17_39 0.0039316765 10.995623 1.258108e-07
14547 RP11-147L13.11 17_39 0.0009064142 6.759620 1.783074e-08
14549 RP11-147L13.14 17_39 0.0074289240 16.261207 3.515596e-07
z num_eqtl
1435 -1.91701270 1
7102 -0.44948704 2
7103 0.71069618 2
836 -1.02541152 1
977 0.39545302 2
2678 -2.21221961 2
2680 2.24457257 1
4672 1.13410595 2
5935 1.55860994 1
5936 2.07648383 1
7105 -0.18212085 2
7106 0.06426505 1
7107 0.38591370 2
7108 2.96447847 3
9363 -0.84879222 1
10548 -3.23637841 1
11065 0.76361857 1
11505 0.26588473 3
11577 1.15596036 1
11776 3.16694426 1
13833 1.76097265 1
13895 0.21633277 1
13939 1.44231609 2
14493 1.90531419 1
14547 0.98729755 1
14549 2.29627994 4
[1] "TSPO"
[1] "22_18"
genename region_tag susie_pip mu2 PVE z
1661 POLDIP3 22_18 0.04272540 7.762313 9.651560e-07 0.87032032
1665 CYB5R3 22_18 0.04271872 7.760854 9.648238e-07 0.94437171
1670 PACSIN2 22_18 0.05024041 9.261013 1.354042e-06 1.05059827
1671 TTLL1 22_18 0.03100660 4.809102 4.339488e-07 -0.44754491
1679 MCAT 22_18 0.03017729 4.559980 4.004640e-07 0.01048418
1684 TSPO 22_18 0.03068906 4.714494 4.210552e-07 -0.20946571
1686 TTLL12 22_18 0.05191953 9.565722 1.445336e-06 -1.17273602
4425 A4GALT 22_18 0.03507573 5.943305 6.066735e-07 -0.64748120
10672 SERHL2 22_18 0.03106248 4.825641 4.362259e-07 -0.24263854
11369 RRP7A 22_18 0.03023230 4.576710 4.026659e-07 0.06985354
12726 LINC01315 22_18 0.03412597 5.690631 5.651525e-07 0.74913358
13083 ARFGAP3 22_18 0.03655423 6.323474 6.726880e-07 -0.54526660
num_eqtl
1661 1
1665 3
1670 1
1671 1
1679 1
1684 1
1686 2
4425 3
10672 3
11369 8
12726 1
13083 2
[1] "PLTP"
[1] "20_28"
genename region_tag susie_pip mu2 PVE
331 TOMM34 20_28 0.02481039 4.632292 3.344643e-07
659 WISP2 20_28 0.02554106 4.880421 3.627576e-07
668 CTSA 20_28 0.02713247 5.303569 4.187722e-07
1819 PLTP 20_28 0.51802364 27.464476 4.140389e-05
1820 PCIF1 20_28 0.02724406 13.178628 1.044870e-06
1822 MMP9 20_28 0.07689545 14.964249 3.348697e-06
1829 CD40 20_28 0.53728780 24.754708 3.870661e-05
1838 PABPC1L 20_28 0.20136324 23.464998 1.375058e-05
1839 STK4 20_28 0.03212233 6.873413 6.425394e-07
1905 WFDC2 20_28 0.03080279 6.609210 5.924611e-07
1909 EPPIN 20_28 0.02467687 6.033980 4.333256e-07
1911 DNTTIP1 20_28 0.09544508 15.604311 4.334295e-06
1913 TNNC2 20_28 0.20951928 22.699362 1.384070e-05
1914 ACOT8 20_28 0.03647958 7.508643 7.971344e-07
4063 SNX21 20_28 0.11539576 16.180927 5.433924e-06
4064 SLPI 20_28 0.02470584 4.562105 3.280086e-07
4065 WFDC3 20_28 0.07364804 12.835048 2.750926e-06
4066 TTPAL 20_28 0.11088838 17.963297 5.796855e-06
4068 KCNS1 20_28 0.02701985 5.391787 4.239708e-07
4069 SLC12A5 20_28 0.05284935 17.826927 2.741804e-06
4070 SDC4 20_28 0.02470493 4.572097 3.287149e-07
4078 GDAP1L1 20_28 0.02467118 4.542028 3.261068e-07
4091 RBPJL 20_28 0.02483779 4.608584 3.331201e-07
4092 KCNK15 20_28 0.02503904 4.698052 3.423385e-07
4093 TP53TG5 20_28 0.02919453 6.731988 5.719592e-07
4096 NEURL2 20_28 0.04180188 20.590643 2.504875e-06
4868 OSER1 20_28 0.02721626 5.399054 4.276282e-07
4869 SERINC3 20_28 0.03783059 8.417942 9.267643e-07
6737 SPATA25 20_28 0.07316128 23.893709 5.087274e-06
8634 YWHAB 20_28 0.02506559 4.667626 3.404820e-07
8940 ZSWIM1 20_28 0.05111071 18.204181 2.707717e-06
8942 WFDC13 20_28 0.02519754 5.756900 4.221503e-07
8951 PKIG 20_28 0.02483475 4.591864 3.318709e-07
9786 UBE2C 20_28 0.04396268 9.792529 1.252851e-06
11519 ADA 20_28 0.04137678 9.311068 1.121183e-06
11596 FITM2 20_28 0.02574309 4.930671 3.693915e-07
11731 ZNF335 20_28 0.02650416 5.222738 4.028400e-07
11985 SYS1 20_28 0.02597216 5.097085 3.852567e-07
12521 OSER1-AS1 20_28 0.05721387 12.261832 2.041630e-06
13124 WFDC6 20_28 0.02467687 6.033980 4.333256e-07
13155 DBNDD2 20_28 0.05334355 11.224347 1.742462e-06
14621 RP11-445H22.3 20_28 0.03341477 7.344932 7.142439e-07
z num_eqtl
331 -0.10989698 2
659 -0.25665541 1
668 -0.87509434 1
1819 -5.19351264 2
1820 2.96018585 1
1822 1.76632544 1
1829 3.80524431 3
1838 2.49186414 2
1839 -0.65248556 1
1905 0.93772036 2
1909 -1.16673693 1
1911 1.88090870 3
1913 2.24481047 1
1914 0.45272938 2
4063 0.29970270 1
4064 -0.14754425 2
4065 1.25068638 2
4066 -1.97291211 1
4068 -0.43139755 1
4069 3.01382135 2
4070 -0.04183070 2
4078 0.08808787 1
4091 -0.10166334 1
4092 -0.28027366 2
4093 -1.20929244 2
4096 -4.49707434 3
4868 -0.47113166 2
4869 1.03407932 2
6737 -4.27365922 2
8634 -0.29810641 2
8940 -3.71131870 2
8942 -0.87450731 1
8951 0.14985723 2
9786 1.29063071 1
11519 1.17385615 2
11596 0.27590329 1
11731 0.27617099 2
11985 -0.53036749 1
12521 1.42674981 3
13124 -1.16673693 1
13155 -1.23084720 1
14621 -0.88261946 2
[1] "VAPA"
[1] "18_7"
genename region_tag susie_pip mu2 PVE
276 RALBP1 18_7 0.10132328 10.269646 3.028203e-06
1922 VAPA 18_7 0.06182001 5.632259 1.013286e-06
1933 ANKRD12 18_7 0.05511023 4.564061 7.319880e-07
4468 TWSG1 18_7 0.05502157 4.549116 7.284172e-07
5012 NAPG 18_7 0.08839413 8.979952 2.310031e-06
7156 PPP4R1 18_7 0.10019956 10.163953 2.963799e-06
7157 APCDD1 18_7 0.07762432 7.759002 1.752766e-06
8919 RAB31 18_7 0.05501833 4.548567 7.282866e-07
8926 MTCL1 18_7 0.05521551 4.581783 7.362341e-07
10117 NDUFV2 18_7 0.11580052 11.539087 3.888681e-06
12224 RAB12 18_7 0.11166661 11.192787 3.637323e-06
13810 PPP4R1-AS1 18_7 0.28509356 20.449505 1.696643e-05
13831 RP11-888D10.3 18_7 0.05589681 4.695668 7.638441e-07
13841 RP11-419J16.1 18_7 0.06361627 5.899058 1.092122e-06
z num_eqtl
276 1.38108389 1
1922 -0.59893979 1
1933 0.08112039 2
4468 -0.13930090 3
5012 1.20163310 2
7156 -1.65261189 1
7157 0.94933238 1
8919 0.07211509 3
8926 0.11132967 2
10117 1.33400717 2
12224 -1.39994460 1
13810 2.89522789 2
13831 0.23529202 2
13841 0.60218674 1
[1] "LIPG"
[1] "18_27"
genename region_tag susie_pip mu2 PVE z
1931 LIPG 18_27 0.02114715 5.853059 3.602094e-07 -0.2867060
4979 CTIF 18_27 0.04900208 13.565962 1.934574e-06 -1.5876520
8699 MYO5B 18_27 0.04950871 14.539844 2.094892e-06 -2.0042595
8701 ACAA2 18_27 0.08869210 20.489718 5.288606e-06 2.5182740
13842 RPL17 18_27 0.01829560 4.757610 2.533121e-07 -0.7607734
num_eqtl
1931 1
4979 1
8699 2
8701 1
13842 2
[1] "KPNB1"
[1] "17_27"
genename region_tag susie_pip mu2 PVE
50 CDC27 17_27 0.05018854 7.074295 1.033256e-06
923 TBX21 17_27 0.04831602 6.984934 9.821409e-07
925 NSF 17_27 0.05300045 6.401019 9.872996e-07
2606 WNT3 17_27 0.06245700 9.312947 1.692733e-06
2614 KPNB1 17_27 0.05466416 11.677231 1.857646e-06
2615 GOSR2 17_27 0.06961473 7.445343 1.508364e-06
3753 KANSL1 17_27 0.05509458 5.573146 8.935721e-07
3755 CRHR1 17_27 0.08931397 9.855139 2.561548e-06
5319 NMT1 17_27 0.16659417 16.266207 7.886175e-06
5929 NPEPPS 17_27 0.12235886 64.455364 2.295170e-05
7505 ARHGAP27 17_27 0.07891299 8.610851 1.977493e-06
7725 PLCD3 17_27 0.04793259 4.543611 6.338002e-07
9547 DCAKD 17_27 0.04829679 4.601205 6.467108e-07
9960 LRRC37A 17_27 0.06132540 6.366040 1.136135e-06
10192 EFCAB13 17_27 0.06641369 40.099387 7.750249e-06
10444 ACBD4 17_27 0.04907158 4.738049 6.766279e-07
10776 SPATA32 17_27 0.05532556 6.085539 9.798174e-07
10959 ARL17A 17_27 0.04793456 4.705544 6.564155e-07
11081 HEXIM1 17_27 0.07316136 8.470758 1.803534e-06
11086 MAPT 17_27 0.06147514 8.277266 1.480835e-06
11784 MYL4 17_27 0.19997712 18.318392 1.066075e-05
11928 TBKBP1 17_27 0.05344599 57.739319 8.980636e-06
12558 PLEKHM1 17_27 0.04816174 4.575226 6.412613e-07
12692 ARL17B 17_27 0.05149821 5.152057 7.721347e-07
12991 LRRC37A2 17_27 0.09644636 10.703548 3.004235e-06
13604 ITGB3 17_27 0.05066297 6.141907 9.055538e-07
13896 RP11-798G7.6 17_27 0.06132540 6.366040 1.136135e-06
z num_eqtl
50 1.62384444 1
923 -1.92274600 1
925 -1.64992252 1
2606 -1.51263182 3
2614 2.98994131 1
2615 0.60171896 1
3753 0.14021081 2
3755 2.30200199 1
5319 2.03531470 2
5929 -8.27442112 3
7505 -1.83235871 2
7725 -0.01048238 2
9547 0.12894272 3
9960 0.52435342 1
10192 6.66298655 4
10444 0.06107748 2
10776 -0.84205877 1
10959 1.10880657 3
11081 -0.91072220 1
11086 -1.02110963 1
11784 -2.30641908 2
11928 -8.11699724 1
12558 0.03569373 1
12692 -1.04068857 2
12991 2.39831149 2
13604 1.24015919 2
13896 -0.52435342 1
[1] "ALDH2"
[1] "12_67"
genename region_tag susie_pip mu2 PVE z
1340 MAPKAPK5 12_67 0.03445796 6.877885 6.897072e-07 1.3275064
1360 ERP29 12_67 0.10500695 31.020890 9.479656e-06 -5.8049447
2870 ARPC3 12_67 0.03557198 6.658277 6.892713e-07 1.1143107
2871 GPN3 12_67 0.06639348 10.739925 2.075138e-06 0.5188468
2872 VPS29 12_67 0.06547541 10.634277 2.026313e-06 -0.5087618
2878 ACAD10 12_67 0.03111972 7.385556 6.688662e-07 1.5620324
2879 ALDH2 12_67 0.05536642 31.785981 5.121562e-06 -6.4436064
2882 NAA25 12_67 0.21786490 35.041892 2.221750e-05 5.4351344
3983 IFT81 12_67 0.10304539 15.418844 4.623818e-06 -1.8515363
5742 GIT2 12_67 0.02876130 4.583006 3.836006e-07 -0.1845083
5743 TCHP 12_67 0.11724314 19.165124 6.539121e-06 2.7798610
9554 HECTD4 12_67 0.04177246 29.027556 3.528750e-06 -6.3703196
9716 C12orf76 12_67 0.02890954 4.640939 3.904518e-07 0.2001312
11016 PPP1CC 12_67 0.02886434 4.986526 4.188708e-07 0.7231339
11471 ANAPC7 12_67 0.02911355 5.457609 4.624001e-07 -1.0674363
11521 PPTC7 12_67 0.03851480 8.394721 9.409232e-07 -1.4985220
11777 TMEM116 12_67 0.05791765 17.393075 2.931620e-06 4.2018408
11782 FAM109A 12_67 0.02986795 5.568807 4.840473e-07 -0.8704329
12105 ATXN2 12_67 0.27346353 28.767728 2.289419e-05 4.1148453
12108 TCTN1 12_67 0.07006290 13.542405 2.761240e-06 2.1771229
12109 FAM216A 12_67 0.02905458 5.275373 4.460547e-07 -0.8712340
12871 MAPKAPK5-AS1 12_67 0.05805255 31.861894 5.382861e-06 6.4104295
13545 RP3-473L9.4 12_67 0.03317766 8.420852 8.130591e-07 -1.9604827
num_eqtl
1340 1
1360 1
2870 1
2871 1
2872 1
2878 1
2879 1
2882 2
3983 2
5742 2
5743 1
9554 2
9716 2
11016 1
11471 3
11521 1
11777 3
11782 1
12105 2
12108 1
12109 2
12871 1
13545 1
[1] "APOA1"
[1] "11_70"
genename region_tag susie_pip mu2 PVE z
2754 ZPR1 11_70 0.02300735 88.630710 5.934321e-06 -9.39015626
2792 CEP164 11_70 0.01564838 6.331356 2.883276e-07 -0.76051268
3583 APOA1 11_70 0.01438377 6.599292 2.762425e-07 1.25989673
5491 BUD13 11_70 0.01676924 5.639521 2.752175e-07 1.10679518
5506 FXYD2 11_70 0.01567292 6.342876 2.893054e-07 0.72703920
6730 SIDT2 11_70 0.01889305 9.892234 5.438971e-07 1.78912447
6732 TAGLN 11_70 0.01376665 14.160537 5.673201e-07 0.94033244
7630 SIK3 11_70 0.01726799 15.991642 8.036283e-07 -4.71780675
7634 PCSK7 11_70 0.02348708 20.240512 1.383474e-06 1.21256221
8691 RNF214 11_70 0.01459332 5.910837 2.510286e-07 0.73686934
8863 PAFAH1B2 11_70 0.01376155 11.360313 4.549650e-07 0.59673452
10005 DSCAML1 11_70 0.01764011 7.185564 3.688777e-07 0.54678155
11019 BACE1 11_70 0.01353272 5.522103 2.174753e-07 -0.09221856
num_eqtl
2754 2
2792 2
3583 1
5491 1
5506 1
6730 1
6732 2
7630 2
7634 1
8691 1
8863 3
10005 1
11019 1
[1] "NPC2"
[1] "14_34"
genename region_tag susie_pip mu2 PVE
1114 PSEN1 14_34 0.038548258 19.309657 2.166205e-06
1789 PAPLN 14_34 0.009536150 5.180343 1.437646e-07
3685 DCAF4 14_34 0.012694425 9.138270 3.375960e-07
3686 PROX2 14_34 0.010719520 10.536923 3.287074e-07
3687 FCF1 14_34 0.009742823 5.146890 1.459318e-07
3689 BBOF1 14_34 0.088677330 16.049645 4.141888e-06
3690 NEK9 14_34 0.024316542 18.183629 1.286775e-06
3692 IFT43 14_34 0.013046719 7.881792 2.992586e-07
3693 NPC2 14_34 0.034798880 14.957709 1.514784e-06
3694 DNAL1 14_34 0.010410740 5.506799 1.668404e-07
3695 ACOT2 14_34 0.012007051 7.128056 2.490736e-07
3696 LTBP2 14_34 0.011847203 6.556734 2.260600e-07
3697 AREL1 14_34 0.009742823 5.146890 1.459318e-07
3698 MLH3 14_34 0.018085186 10.601518 5.579706e-07
3699 TTLL5 14_34 0.009947135 5.346808 1.547793e-07
3700 FLVCR2 14_34 0.009193231 4.635208 1.240103e-07
3701 ABCD4 14_34 0.009188970 4.688445 1.253765e-07
3705 RBM25 14_34 0.061533116 21.126165 3.783118e-06
3709 EIF2B2 14_34 0.022722819 17.362555 1.148143e-06
3711 COQ6 14_34 0.010586292 6.435245 1.982573e-07
3712 ZNF410 14_34 0.010095972 5.840310 1.715949e-07
4968 NUMB 14_34 0.009472144 5.003573 1.379269e-07
5804 PTGR2 14_34 0.009908638 5.587656 1.611254e-07
7237 FAM161B 14_34 0.468061813 23.288644 3.172252e-05
7244 BATF 14_34 0.015635605 9.521921 4.332710e-07
8437 ZFYVE1 14_34 0.010504354 11.835607 3.618097e-07
9199 RIOX1 14_34 0.009341533 4.822103 1.310916e-07
9978 PNMA1 14_34 0.011081173 5.870650 1.893181e-07
10036 ACOT4 14_34 0.014657271 11.266101 4.805594e-07
10649 SYNDIG1L 14_34 0.010924767 6.434782 2.045815e-07
10764 ACOT1 14_34 0.010839259 6.394410 2.017067e-07
11107 ENTPD5 14_34 0.133703549 25.727066 1.001045e-05
11109 HEATR4 14_34 0.009175434 4.863457 1.298650e-07
12193 LIN52 14_34 0.010280732 5.933109 1.775116e-07
12194 DPF3 14_34 0.025944166 11.672279 8.812835e-07
13588 RP11-270M14.5 14_34 0.011160145 6.288869 2.042503e-07
13598 RP3-449M8.6 14_34 0.011011554 6.817193 2.184613e-07
13627 LINC01220 14_34 0.010079869 5.545563 1.626750e-07
14005 RP3-449M8.9 14_34 0.009639068 5.134116 1.440194e-07
z num_eqtl
1114 3.87525094 3
1789 0.07441007 2
3685 1.97132622 2
3686 2.58946400 2
3687 -0.41600000 1
3689 -3.80184690 2
3690 -3.22969877 1
3692 0.91864269 1
3693 1.24405347 1
3694 -0.75378449 1
3695 -1.08912101 3
3696 0.25350957 1
3697 0.41600000 1
3698 -1.24835484 1
3699 -0.42323972 1
3700 -0.17583158 3
3701 0.18152469 2
3705 4.27380978 1
3709 -2.95569593 1
3711 -0.54992070 2
3712 -1.37775403 2
4968 -0.15384046 1
5804 -1.58536168 6
7237 -4.43442574 2
7244 -1.11897993 1
8437 2.58136162 1
9199 -0.25456352 1
9978 0.78703034 2
10036 1.46876989 2
10649 0.91820626 1
10764 -1.12091919 2
11107 -3.42239554 3
11109 0.29630206 1
12193 -0.08542499 2
12194 1.71707462 1
13588 0.53073305 1
13598 -0.79146280 2
13627 0.68162560 3
14005 -0.31553630 2
[1] "LRP1"
[1] "12_35"
genename region_tag susie_pip mu2 PVE z
327 HSD17B6 12_35 0.10245295 13.291665 3.963001e-06 -1.86267660
634 CS 12_35 0.04070144 4.676264 5.538971e-07 0.15757464
696 DGKA 12_35 0.19335964 19.430601 1.093383e-05 2.66109583
697 ERBB3 12_35 0.04262929 5.103471 6.331317e-07 -0.35490155
994 RBMS2 12_35 0.05082732 6.730623 9.955723e-07 -0.75825498
995 BAZ2A 12_35 0.15809040 17.453600 8.029912e-06 -2.21535156
1472 MYL6 12_35 0.04840968 6.279218 8.846227e-07 0.85420270
2851 IL23A 12_35 0.05739391 7.858180 1.312527e-06 -1.08015794
2852 ATP5B 12_35 0.06927712 9.611332 1.937732e-06 1.24795230
2902 RAB5B 12_35 0.04212359 4.993278 6.121127e-07 -0.37817770
4011 ORMDL2 12_35 0.06195060 8.568886 1.544864e-06 -1.33072035
4013 CDK2 12_35 0.05427890 7.339946 1.159429e-06 -1.02781608
4014 LRP1 12_35 0.05573169 7.585126 1.230227e-06 -0.65116085
4017 IKZF4 12_35 0.04218559 5.006854 6.146803e-07 0.31972932
5161 DNAJC14 12_35 0.07638906 10.525622 2.339910e-06 1.46751571
5165 GLS2 12_35 0.04009485 4.537720 5.294764e-07 0.02781582
5166 ITGA7 12_35 0.04152427 4.860982 5.874168e-07 -0.15973006
5168 RDH5 12_35 0.04201175 4.968729 6.074861e-07 0.30366641
5170 BLOC1S1 12_35 0.08191856 11.181272 2.665593e-06 -1.31729931
5177 COQ10A 12_35 0.04022294 4.567144 5.346121e-07 0.13655237
5179 PAN2 12_35 0.05739391 7.858180 1.312527e-06 -1.08015794
5182 ZC3H10 12_35 0.08590267 11.627753 2.906851e-06 -1.49702244
5751 SUOX 12_35 0.07462098 10.306254 2.238114e-06 -1.23521491
5753 SLC39A5 12_35 0.05557223 7.558555 1.222410e-06 0.91079453
5755 RDH16 12_35 0.04604580 5.815975 7.793505e-07 -0.50207842
5760 SMARCC2 12_35 0.17014865 18.171427 8.997831e-06 -2.27459226
8622 TAC3 12_35 0.05585396 7.605509 1.236239e-06 0.91962521
8623 MYO1A 12_35 0.09262135 12.337424 3.325492e-06 1.46063080
8625 NEMP1 12_35 0.34921415 25.523134 2.593858e-05 -3.00425167
8626 NAB2 12_35 0.04013910 4.547896 5.312495e-07 -0.03950645
8627 STAT6 12_35 0.04192899 4.950530 6.040688e-07 -0.51566090
9190 SDR9C7 12_35 0.04011794 4.543031 5.304013e-07 -0.08256908
9192 METTL7B 12_35 0.04009395 4.537512 5.294402e-07 0.07617023
9201 PYM1 12_35 0.20058116 19.794563 1.155464e-05 2.68424809
9218 STAT2 12_35 0.05860880 8.052897 1.373521e-06 1.10856128
9941 SPRYD4 12_35 0.06956518 9.650116 1.953641e-06 1.21757705
10939 PMEL 12_35 0.07195132 9.965354 2.086660e-06 -1.29364873
11463 MYL6B 12_35 0.06214227 8.597650 1.554845e-06 -0.96606967
11475 NACA 12_35 0.04040968 4.609880 5.421198e-07 0.09273658
11738 PRIM1 12_35 0.10245295 13.291665 3.963001e-06 -1.86267660
12160 SARNP 12_35 0.06195060 8.568886 1.544864e-06 -1.33072035
12707 RPL41 12_35 0.09112843 12.184106 3.231230e-06 1.77190320
num_eqtl
327 1
634 2
696 1
697 1
994 2
995 2
1472 2
2851 1
2852 1
2902 2
4011 1
4013 2
4014 1
4017 1
5161 1
5165 1
5166 2
5168 1
5170 1
5177 2
5179 1
5182 1
5751 2
5753 2
5755 1
5760 1
8622 2
8623 1
8625 1
8626 1
8627 2
9190 1
9192 1
9201 1
9218 2
9941 1
10939 1
11463 1
11475 2
11738 1
12160 1
12707 1
[1] "VAPB"
[1] "20_34"
genename region_tag susie_pip mu2 PVE z
1293 PHACTR3 20_34 0.08206706 11.395589 2.721610e-06 -1.37646482
1847 NELFCD 20_34 0.19278192 19.598283 1.099524e-05 2.23141397
1848 CTSZ 20_34 0.14469874 16.792485 7.071312e-06 -2.01922879
4072 VAPB 20_34 0.07271816 10.261817 2.171638e-06 -1.27071052
4081 ZNF831 20_34 0.11589627 14.661367 4.944976e-06 1.78634651
4082 EDN3 20_34 0.03926000 4.540936 5.188192e-07 0.05748016
4084 RAB22A 20_34 0.17291136 18.526480 9.322593e-06 2.14716383
4087 STX16 20_34 0.04098274 4.937123 5.888371e-07 -0.36261482
11868 APCDD1L 20_34 0.03931868 4.554710 5.211706e-07 0.04943793
12433 NPEPL1 20_34 0.05470873 7.610456 1.211679e-06 -0.88725727
13968 LINC01711 20_34 0.04500529 5.801931 7.599001e-07 0.59947598
num_eqtl
1293 4
1847 1
1848 1
4072 2
4081 1
4082 1
4084 2
4087 1
11868 1
12433 2
13968 2
[1] "APOC1"
[1] "19_31"
genename region_tag susie_pip mu2 PVE z num_eqtl
122 MARK4 19_31 0 21.181957 0 -2.2463768 1
227 ERCC1 19_31 0 7.409485 0 -1.6350316 2
633 ZNF112 19_31 0 47.819226 0 4.8629559 1
905 PVR 19_31 0 31.813106 0 -3.0943045 1
2176 CLPTM1 19_31 0 50.178070 0 2.5751726 1
2178 PPP1R37 19_31 0 42.423234 0 -5.9455792 1
2182 PPP1R13L 19_31 0 22.778963 0 -3.0806361 1
2184 ERCC2 19_31 0 13.966069 0 1.5340117 1
2189 KLC3 19_31 0 13.524015 0 -3.6648287 2
3573 CD3EAP 19_31 0 11.426032 0 2.5646694 1
4228 FOSB 19_31 0 16.099633 0 -2.3658041 1
4229 OPA3 19_31 0 6.808430 0 1.5059074 2
4231 RTN2 19_31 0 6.949994 0 -2.0700710 1
4233 VASP 19_31 0 33.645255 0 -2.7026884 1
4573 NECTIN2 19_31 0 1648.609427 0 -35.7740463 1
4574 APOE 19_31 0 303.200452 0 0.6519443 1
4575 TOMM40 19_31 0 1005.711219 0 -14.0286334 2
4576 APOC1 19_31 0 474.076449 0 -9.1150442 1
6037 GEMIN7 19_31 0 205.847243 0 13.2439035 2
7553 ZNF233 19_31 0 131.643247 0 -10.0229697 2
7554 ZNF235 19_31 0 35.913626 0 -6.2627967 2
8710 ZNF180 19_31 0 43.730327 0 4.0279244 1
9234 ZNF296 19_31 0 94.996588 0 5.4593536 1
11052 CEACAM19 19_31 0 64.753213 0 11.7978210 2
11128 BCAM 19_31 0 96.429711 0 4.6421318 1
11348 BLOC1S3 19_31 0 11.973214 0 2.7250151 2
12327 PPM1N 19_31 0 24.361748 0 -2.6089113 1
12884 APOC2 19_31 0 18.135579 0 -2.2896080 1
13922 ZNF285 19_31 0 10.133291 0 -1.3318721 3
14528 ZNF229 19_31 0 125.384012 0 14.4998133 1
[1] "STARD3"
[1] "17_23"
genename region_tag susie_pip mu2 PVE
20 LASP1 17_23 0.14026268 16.661085 6.800889e-06
166 MED24 17_23 0.18951003 19.598572 1.080879e-05
916 SMARCE1 17_23 0.03912442 4.678936 5.327400e-07
1491 CDC6 17_23 0.04366715 5.692872 7.234468e-07
2599 FBXL20 17_23 0.07122397 10.237690 2.122015e-06
2601 CSF3 17_23 0.19249013 19.752683 1.106509e-05
2602 PSMD3 17_23 0.04087970 5.083799 6.048064e-07
2603 CASC3 17_23 0.09350636 12.794541 3.481659e-06
2604 RAPGEFL1 17_23 0.38334795 26.913524 3.002507e-05
4219 MED1 17_23 0.05984642 8.614591 1.500352e-06
4220 RPL23 17_23 0.09354986 12.798371 3.484321e-06
4296 THRA 17_23 0.04764936 6.500058 9.013523e-07
4297 CCR7 17_23 0.03923221 4.704319 5.371058e-07
4298 NR1D1 17_23 0.05238866 7.378293 1.124899e-06
4744 TNS4 17_23 0.03853960 4.540085 5.092037e-07
4745 STARD3 17_23 0.06829874 9.845865 1.956982e-06
5996 ERBB2 17_23 0.08745198 12.163390 3.095598e-06
5997 GRB7 17_23 0.10432611 13.830809 4.199145e-06
5999 PNMT 17_23 0.05187085 7.286366 1.099904e-06
6000 STAC2 17_23 0.08102298 11.445212 2.698686e-06
7701 PLXDC1 17_23 0.04547836 6.068514 8.031699e-07
8822 GSDMA 17_23 0.10336428 13.742597 4.133896e-06
9338 WIPF2 17_23 0.03854211 4.540687 5.093043e-07
9426 ORMDL3 17_23 0.17502618 18.815370 9.583763e-06
9670 TCAP 17_23 0.03869805 4.577905 5.155564e-07
11318 MSL1 17_23 0.03975430 4.826299 5.583657e-07
12288 KRT222 17_23 0.03856605 4.546413 5.102634e-07
13814 LINC00672 17_23 0.04620311 6.214764 8.356340e-07
13832 RP11-387H17.4 17_23 0.04412739 5.789914 7.435337e-07
13854 RP5-1028K7.2 17_23 0.12930768 15.878962 5.975396e-06
13868 RP1-56K13.5 17_23 0.07906870 11.216124 2.580879e-06
14308 CWC25 17_23 0.04333639 5.622656 7.091115e-07
14309 EPOP 17_23 0.06467995 9.337893 1.757676e-06
14384 MLLT6 17_23 0.08736469 12.153812 3.090073e-06
14448 PIP4K2B 17_23 0.05487454 7.808423 1.246966e-06
14507 PSMB3 17_23 0.10686912 14.059314 4.372569e-06
14511 CISD3 17_23 0.04565073 6.103455 8.108560e-07
z num_eqtl
20 2.06384703 1
166 2.97033303 1
916 0.08854994 1
1491 0.38875522 2
2599 1.81331217 1
2601 -3.14227041 1
2602 -0.25796073 1
2603 -2.04139093 4
2604 3.35561544 2
4219 -1.58604287 2
4220 1.76434090 1
4296 -1.82594797 2
4297 -0.21456658 1
4298 0.81175389 1
4744 0.02859681 1
4745 1.78242300 1
5996 -2.17233398 2
5997 2.00767975 1
5999 -1.66201018 2
6000 -1.45577160 1
7701 -0.56588683 1
8822 2.79497424 2
9338 -0.15638007 1
9426 3.04520803 1
9670 -0.23425048 2
11318 0.61992771 2
12288 0.11407193 1
13814 0.71355912 1
13832 1.45916671 4
13854 1.89232102 1
13868 1.58167868 1
14308 0.49000658 1
14309 -1.22710237 1
14384 -1.60128295 1
14448 -1.00157125 1
14507 1.72807936 4
14511 -0.60454978 1
[1] "PPARG"
[1] "3_9"
genename region_tag susie_pip mu2 PVE z
991 MKRN2 3_9 0.05454102 15.491809 2.458928e-06 -3.97044447
1318 TMEM40 3_9 0.01598354 6.023704 2.801927e-07 1.39961663
4778 PPARG 3_9 0.01477102 8.518491 3.661792e-07 -2.40991704
6318 TAMM41 3_9 0.02492225 9.872800 7.160573e-07 1.52001314
6339 CAND2 3_9 0.01463955 5.203331 2.216816e-07 -1.21597175
6340 RPL32 3_9 0.01860029 6.650316 3.599832e-07 -0.73455854
7146 TSEN2 3_9 0.01458549 4.610365 1.956937e-07 -0.09737789
7326 SLC6A1 3_9 0.01845216 6.708771 3.602553e-07 0.72153302
7330 TIMP4 3_9 0.02931687 11.942525 1.018906e-06 1.87806557
7331 SYN2 3_9 0.01636296 6.782790 3.229910e-07 2.13905306
11633 ATG7 3_9 0.02822226 10.153580 8.339333e-07 -1.26026609
12570 MKRN2OS 3_9 0.01552657 5.493510 2.482251e-07 -0.75774371
num_eqtl
991 3
1318 1
4778 2
6318 2
6339 3
6340 2
7146 2
7326 2
7330 2
7331 1
11633 2
12570 1
[1] "LPIN3"
[1] "20_25"
genename region_tag susie_pip mu2 PVE z
4076 PLCG1 20_25 0.02436878 11.92753 8.458717e-07 -2.920830
4864 LPIN3 20_25 0.02620015 45.32804 3.456138e-06 7.012083
9703 ZHX3 20_25 0.02652460 11.82503 9.127911e-07 2.767903
10715 EMILIN3 20_25 0.02430993 20.95056 1.482176e-06 4.410633
11916 TOP1 20_25 0.02402170 11.21537 7.840386e-07 -2.826082
num_eqtl
4076 1
4864 1
9703 1
10715 3
11916 1
[1] "SORT1"
[1] "1_67"
genename region_tag susie_pip mu2 PVE
363 SARS 1_67 2.260060e-07 135.373041 8.903740e-11
687 GNAI3 1_67 4.317367e-07 49.524698 6.222446e-11
1223 SLC25A24 1_67 6.417211e-07 14.911375 2.784738e-11
3416 STXBP3 1_67 1.554058e-07 15.012036 6.789333e-12
3419 KIAA1324 1_67 2.480353e-07 19.360714 1.397511e-11
3900 CLCC1 1_67 1.398833e-07 5.489588 2.234734e-12
4996 GSTM1 1_67 2.166034e-03 59.592966 3.756475e-07
4998 GSTM5 1_67 1.515841e-07 7.772201 3.428609e-12
4999 GSTM3 1_67 2.121061e-07 6.780908 4.185634e-12
5001 PSRC1 1_67 7.323688e-07 176.315318 3.757856e-10
5003 SORT1 1_67 1.746323e-07 5.800528 2.947897e-12
6096 SYPL2 1_67 1.177502e-07 58.304452 1.997945e-11
6104 CELSR2 1_67 5.286519e-07 113.723036 1.749599e-10
7839 HENMT1 1_67 3.871372e-07 15.825908 1.783010e-11
7843 ATXN7L2 1_67 4.472802e-07 10.700754 1.392882e-11
8954 GSTM4 1_67 2.557416e-07 15.286858 1.137732e-11
10463 AMIGO1 1_67 5.210579e-07 25.569247 3.877254e-11
11677 TAF13 1_67 1.508761e-07 9.939408 4.364165e-12
12282 GSTM2 1_67 2.681935e-07 23.418731 1.827814e-11
12440 TMEM167B 1_67 1.528715e-07 5.494436 2.444387e-12
12627 RP11-356N1.2 1_67 1.304537e-07 6.851387 2.601090e-12
z num_eqtl
363 -17.77828114 2
687 7.97926365 1
1223 -0.09485475 2
3416 2.98447183 1
3419 4.94390244 1
3900 -0.52733642 3
4996 7.68447989 4
4998 1.58897772 4
4999 -3.43690762 3
5001 -22.09651395 2
5003 -0.45551677 1
6096 11.72818164 3
6104 5.65177064 2
7839 -1.85374050 1
7843 0.17069498 2
8954 2.22512920 5
10463 -3.96308159 1
11677 -1.55914526 1
12282 4.14108479 2
12440 1.84229721 2
12627 -1.23494808 2
[1] "FADS2"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
214 SYT7 11_34 0.02957653 6.140324 5.285167e-07
2767 DTX4 11_34 0.07661538 15.209328 3.391145e-06
2780 CCDC86 11_34 0.03698217 8.457224 9.102077e-07
2781 PRPF19 11_34 0.04830671 10.674871 1.500688e-06
2782 TMEM109 11_34 0.04703556 10.427372 1.427321e-06
2808 SLC15A3 11_34 0.02701115 5.602543 4.404012e-07
5072 DAGLA 11_34 0.02576174 6.607148 4.953470e-07
5077 FADS2 11_34 0.04839042 48.606847 6.845058e-06
5078 TMEM258 11_34 0.04757906 43.646261 6.043426e-06
5079 TCN1 11_34 0.04129509 8.874129 1.066460e-06
6711 TMEM138 11_34 0.03232386 8.817461 8.294439e-07
6712 FADS1 11_34 0.25990394 81.437461 6.159669e-05
6716 INCENP 11_34 0.02539261 5.487253 4.054923e-07
6717 ZP1 11_34 0.09692062 16.143421 4.553361e-06
6720 CPSF7 11_34 0.02774480 5.557581 4.487327e-07
6721 MS4A2 11_34 0.04531472 9.717689 1.281512e-06
7768 CYB561A3 11_34 0.03232386 8.817461 8.294439e-07
7770 ASRGL1 11_34 0.05115776 10.362157 1.542702e-06
8606 FAM111A 11_34 0.02724854 5.251164 4.164081e-07
8628 PATL1 11_34 0.22934575 24.764852 1.652901e-05
8630 STX3 11_34 0.07589893 14.426088 3.186431e-06
8639 MS4A6E 11_34 0.02613818 4.847693 3.687489e-07
8640 MS4A7 11_34 0.04209350 9.229870 1.130657e-06
8641 MS4A14 11_34 0.02819519 5.733358 4.704401e-07
8838 VWCE 11_34 0.02522415 4.607382 3.382137e-07
8839 RAB3IL1 11_34 0.02754594 5.305591 4.253159e-07
10622 PTGDR2 11_34 0.02616381 4.897209 3.728806e-07
11103 TMEM216 11_34 0.02532492 4.634968 3.415978e-07
11653 MPEG1 11_34 0.02583994 4.867720 3.660475e-07
12120 LRRC10B 11_34 0.02507501 4.605025 3.360419e-07
12403 MS4A4E 11_34 0.04678077 10.054666 1.368848e-06
12488 FADS3 11_34 0.02794435 30.157170 2.452476e-06
12724 PGA3 11_34 0.03549351 7.093703 7.327271e-07
13286 RP11-855O10.2 11_34 0.02514013 4.807881 3.517560e-07
13443 AP001257.1 11_34 0.02508306 4.541950 3.315456e-07
13448 AP000442.4 11_34 0.02506955 4.538164 3.310907e-07
13507 RP11-794G24.1 11_34 0.05924703 10.995449 1.895832e-06
13513 RP11-286N22.8 11_34 0.02520318 4.613831 3.384055e-07
13516 PGA5 11_34 0.03140549 6.201678 5.668068e-07
z num_eqtl
214 -0.577860995 1
2767 -1.742131948 1
2780 1.274058697 2
2781 1.446293747 1
2782 1.421831985 1
2808 0.821410772 1
5072 -1.422119392 2
5077 -6.671545473 1
5078 -6.520941409 2
5079 0.916393926 1
6711 -1.782804562 1
6712 9.150337934 1
6716 -1.053905396 3
6717 1.565447050 1
6720 -0.647252767 2
6721 -1.135206653 1
7768 -1.782804562 1
7770 -1.126706101 2
8606 0.321876718 1
8628 3.252550732 2
8630 2.406885497 4
8639 0.035807230 1
8640 -1.016881909 2
8641 -0.602717982 1
8838 -0.158063055 1
8839 -0.464622365 1
10622 -0.262494092 3
11103 -0.145271506 2
11653 0.278217067 2
12120 -0.263629874 1
12403 1.240983470 1
12488 -5.433117266 1
12724 0.119664297 1
13286 0.547874538 1
13443 0.003034195 2
13448 0.095873681 1
13507 0.447753087 1
13513 -0.175548731 1
13516 -0.047803015 1
[1] "ABCG5"
[1] "2_27"
genename region_tag susie_pip mu2 PVE
3379 THADA 2_27 0.0013108049 5.680737 2.167020e-08
5556 DYNC2LI1 2_27 0.0015444811 9.199794 4.135052e-08
5564 ABCG5 2_27 0.0027159977 431.748837 3.412565e-06
5570 LRPPRC 2_27 0.0012135925 9.102509 3.214803e-08
6249 ABCG8 2_27 0.7227631910 33.272060 6.998356e-05
6962 PLEKHH2 2_27 0.0080093432 24.276118 5.658437e-07
12512 C1GALT1C1L 2_27 0.0009562864 10.212103 2.841996e-08
14565 LINC01126 2_27 0.0419225031 24.029336 2.931631e-06
z num_eqtl
3379 0.1649722 1
5556 0.1268456 2
5564 -20.2939818 1
5570 0.5202811 2
6249 6.5141780 2
6962 -2.4287605 1
12512 1.1627524 2
14565 0.5696005 2
[1] "NPC1"
[1] "18_12"
genename region_tag susie_pip mu2 PVE
532 LAMA3 18_12 0.03933004 4.785918 5.477848e-07
1937 RIOK3 18_12 0.04325590 5.664108 7.130126e-07
5044 HRH4 18_12 0.03836259 4.556283 5.086732e-07
5045 TMEM241 18_12 0.24648928 22.293057 1.599145e-05
5046 CABLES1 18_12 0.03893975 4.693958 5.319277e-07
5953 OSBPL1A 18_12 0.05030804 7.061316 1.033816e-06
5955 C18orf8 18_12 0.05367072 7.661220 1.196618e-06
5957 NPC1 18_12 0.15372082 17.606458 7.876350e-06
7081 ANKRD29 18_12 0.03938399 4.798583 5.499879e-07
8880 TTC39C 18_12 0.12281981 15.444176 5.520183e-06
11877 ZNF521 18_12 0.03941480 4.805782 5.512438e-07
13823 LINC01894 18_12 0.08709567 12.183427 3.088064e-06
13844 RP11-799B12.4 18_12 0.03830067 4.541392 5.061925e-07
13845 RP11-403A21.1 18_12 0.03828723 4.538156 5.056543e-07
z num_eqtl
532 0.25172785 2
1937 0.54170720 1
5044 0.04864756 1
5045 -2.74310618 2
5046 -0.14555775 1
5953 0.81580519 1
5955 -1.25897534 3
5957 -2.34727248 2
7081 0.48150608 1
8880 1.73747867 1
11877 -0.24788310 1
13823 -1.49397339 1
13844 -0.06019807 1
13845 -0.01769671 2
[1] "ABCG8"
[1] "2_27"
genename region_tag susie_pip mu2 PVE
3379 THADA 2_27 0.0013108049 5.680737 2.167020e-08
5556 DYNC2LI1 2_27 0.0015444811 9.199794 4.135052e-08
5564 ABCG5 2_27 0.0027159977 431.748837 3.412565e-06
5570 LRPPRC 2_27 0.0012135925 9.102509 3.214803e-08
6249 ABCG8 2_27 0.7227631910 33.272060 6.998356e-05
6962 PLEKHH2 2_27 0.0080093432 24.276118 5.658437e-07
12512 C1GALT1C1L 2_27 0.0009562864 10.212103 2.841996e-08
14565 LINC01126 2_27 0.0419225031 24.029336 2.931631e-06
z num_eqtl
3379 0.1649722 1
5556 0.1268456 2
5564 -20.2939818 1
5570 0.5202811 2
6249 6.5141780 2
6962 -2.4287605 1
12512 1.1627524 2
14565 0.5696005 2
[1] "NCEH1"
[1] "3_106"
genename region_tag susie_pip mu2 PVE z
6366 NCEH1 3_106 0.03418969 6.281943 6.250424e-07 -0.6532732
num_eqtl
6366 1
[1] "FADS1"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
214 SYT7 11_34 0.02957653 6.140324 5.285167e-07
2767 DTX4 11_34 0.07661538 15.209328 3.391145e-06
2780 CCDC86 11_34 0.03698217 8.457224 9.102077e-07
2781 PRPF19 11_34 0.04830671 10.674871 1.500688e-06
2782 TMEM109 11_34 0.04703556 10.427372 1.427321e-06
2808 SLC15A3 11_34 0.02701115 5.602543 4.404012e-07
5072 DAGLA 11_34 0.02576174 6.607148 4.953470e-07
5077 FADS2 11_34 0.04839042 48.606847 6.845058e-06
5078 TMEM258 11_34 0.04757906 43.646261 6.043426e-06
5079 TCN1 11_34 0.04129509 8.874129 1.066460e-06
6711 TMEM138 11_34 0.03232386 8.817461 8.294439e-07
6712 FADS1 11_34 0.25990394 81.437461 6.159669e-05
6716 INCENP 11_34 0.02539261 5.487253 4.054923e-07
6717 ZP1 11_34 0.09692062 16.143421 4.553361e-06
6720 CPSF7 11_34 0.02774480 5.557581 4.487327e-07
6721 MS4A2 11_34 0.04531472 9.717689 1.281512e-06
7768 CYB561A3 11_34 0.03232386 8.817461 8.294439e-07
7770 ASRGL1 11_34 0.05115776 10.362157 1.542702e-06
8606 FAM111A 11_34 0.02724854 5.251164 4.164081e-07
8628 PATL1 11_34 0.22934575 24.764852 1.652901e-05
8630 STX3 11_34 0.07589893 14.426088 3.186431e-06
8639 MS4A6E 11_34 0.02613818 4.847693 3.687489e-07
8640 MS4A7 11_34 0.04209350 9.229870 1.130657e-06
8641 MS4A14 11_34 0.02819519 5.733358 4.704401e-07
8838 VWCE 11_34 0.02522415 4.607382 3.382137e-07
8839 RAB3IL1 11_34 0.02754594 5.305591 4.253159e-07
10622 PTGDR2 11_34 0.02616381 4.897209 3.728806e-07
11103 TMEM216 11_34 0.02532492 4.634968 3.415978e-07
11653 MPEG1 11_34 0.02583994 4.867720 3.660475e-07
12120 LRRC10B 11_34 0.02507501 4.605025 3.360419e-07
12403 MS4A4E 11_34 0.04678077 10.054666 1.368848e-06
12488 FADS3 11_34 0.02794435 30.157170 2.452476e-06
12724 PGA3 11_34 0.03549351 7.093703 7.327271e-07
13286 RP11-855O10.2 11_34 0.02514013 4.807881 3.517560e-07
13443 AP001257.1 11_34 0.02508306 4.541950 3.315456e-07
13448 AP000442.4 11_34 0.02506955 4.538164 3.310907e-07
13507 RP11-794G24.1 11_34 0.05924703 10.995449 1.895832e-06
13513 RP11-286N22.8 11_34 0.02520318 4.613831 3.384055e-07
13516 PGA5 11_34 0.03140549 6.201678 5.668068e-07
z num_eqtl
214 -0.577860995 1
2767 -1.742131948 1
2780 1.274058697 2
2781 1.446293747 1
2782 1.421831985 1
2808 0.821410772 1
5072 -1.422119392 2
5077 -6.671545473 1
5078 -6.520941409 2
5079 0.916393926 1
6711 -1.782804562 1
6712 9.150337934 1
6716 -1.053905396 3
6717 1.565447050 1
6720 -0.647252767 2
6721 -1.135206653 1
7768 -1.782804562 1
7770 -1.126706101 2
8606 0.321876718 1
8628 3.252550732 2
8630 2.406885497 4
8639 0.035807230 1
8640 -1.016881909 2
8641 -0.602717982 1
8838 -0.158063055 1
8839 -0.464622365 1
10622 -0.262494092 3
11103 -0.145271506 2
11653 0.278217067 2
12120 -0.263629874 1
12403 1.240983470 1
12488 -5.433117266 1
12724 0.119664297 1
13286 0.547874538 1
13443 0.003034195 2
13448 0.095873681 1
13507 0.447753087 1
13513 -0.175548731 1
13516 -0.047803015 1
[1] "LRPAP1"
[1] "4_4"
genename region_tag susie_pip mu2 PVE z
1285 NOP14 4_4 0.02217729 5.130826 3.311434e-07 0.39075231
2737 MFSD10 4_4 0.02779628 7.620120 6.164087e-07 -1.19491915
2740 HGFAC 4_4 0.15983693 37.087867 1.725160e-05 -4.89478532
4187 GRK4 4_4 0.02566832 6.099009 4.555929e-07 0.06592133
7542 RGS12 4_4 0.03833834 11.015237 1.228987e-06 1.63953686
8112 LRPAP1 4_4 0.23448150 23.425953 1.598550e-05 -1.53625589
9889 DOK7 4_4 0.02563728 7.314250 5.457102e-07 1.22504669
11328 MSANTD1 4_4 0.03698215 14.269602 1.535763e-06 2.72889398
11611 HTT 4_4 0.02581281 7.201700 5.409918e-07 -1.25772954
13319 AC141928.1 4_4 0.03227388 8.662645 8.136207e-07 -1.09477345
num_eqtl
1285 1
2737 1
2740 1
4187 2
7542 1
8112 2
9889 1
11328 1
11611 1
13319 2
[1] "VDAC2"
[1] "10_49"
genename region_tag susie_pip mu2 PVE
3973 PLAU 10_49 0.03829918 4.551808 5.073337e-07
6646 C10orf11 10_49 0.28892682 23.944115 2.013293e-05
7242 ADK 10_49 0.04202978 5.409321 6.616375e-07
7291 SAMD8 10_49 0.04066367 5.104322 6.040390e-07
8399 VDAC2 10_49 0.04893666 6.816082 9.707097e-07
8400 COMTD1 10_49 0.28667425 23.861852 1.990734e-05
8403 ZNF503 10_49 0.04021982 5.003042 5.855912e-07
8569 NDST2 10_49 0.03972994 4.890005 5.653892e-07
9499 AGAP5 10_49 0.06710370 9.750357 1.904089e-06
9990 SEC24C 10_49 0.05511812 7.918997 1.270237e-06
11540 FUT11 10_49 0.05597011 8.061447 1.313075e-06
12393 ZSWIM8 10_49 0.04536091 6.114036 8.071050e-07
12595 ZNF503-AS1 10_49 0.03904664 4.730019 5.374855e-07
14134 RP11-574K11.29 10_49 0.05065803 7.136327 1.052067e-06
14200 RP11-399K21.14 10_49 0.09343564 12.856674 3.495920e-06
z num_eqtl
3973 0.01717948 1
6646 2.83036765 2
7242 -0.52221619 2
7291 0.38624927 1
8399 0.77120615 1
8400 2.94379743 1
8403 -0.08773738 1
8569 0.33102963 1
9499 -1.31828442 1
9990 -1.07824995 1
11540 1.14727402 2
12393 0.82505158 1
12595 0.39356612 1
14134 0.91321378 2
14200 -1.52846728 1
[1] "LIPC"
[1] "15_26"
genename region_tag susie_pip mu2 PVE z
4479 MINDY2 15_26 0.02182881 7.634644 4.849971e-07 0.9669014
5513 SLTM 15_26 0.01737449 5.539484 2.800926e-07 -0.7278100
5531 ADAM10 15_26 0.01864221 6.224955 3.377179e-07 0.8403684
7354 RNF111 15_26 0.01582669 4.661543 2.147040e-07 -0.2997052
7355 FAM81A 15_26 0.01576415 4.619803 2.119407e-07 0.1345993
7356 MYO1E 15_26 0.01677466 5.143764 2.511047e-07 -0.3257632
8475 LIPC 15_26 0.02726663 10.206871 8.099242e-07 1.2378553
9418 LDHAL6B 15_26 0.01683910 5.443578 2.667618e-07 -0.6263826
14474 RP11-59H7.4 15_26 0.09080818 20.211605 5.341289e-06 2.3147450
num_eqtl
4479 1
5513 2
5531 2
7354 1
7355 1
7356 1
8475 2
9418 2
14474 1
[1] "SOAT2"
[1] "12_33"
genename region_tag susie_pip mu2 PVE
234 CALCOCO1 12_33 0.04349865 6.673368 8.447752e-07
641 EIF4B 12_33 0.03347448 4.583688 4.465285e-07
2858 TNS2 12_33 0.07727981 12.547864 2.821994e-06
4016 NFE2 12_33 0.06737257 11.065282 2.169531e-06
4018 SMUG1 12_33 0.03527512 5.056525 5.190880e-07
5160 ATP5G2 12_33 0.03577473 6.652201 6.925673e-07
5164 AMHR2 12_33 0.25172464 16.030328 1.174325e-05
5180 ESPL1 12_33 0.06202395 10.614144 1.915864e-06
5757 GPR84 12_33 0.05354162 8.820092 1.374311e-06
5758 NPFF 12_33 0.07880492 14.637800 3.356985e-06
5764 ITGB7 12_33 0.03364714 4.616770 4.520710e-07
5766 CSAD 12_33 0.03702400 5.606838 6.041177e-07
5770 ZNF740 12_33 0.19488822 20.418379 1.158050e-05
8797 KRT1 12_33 0.03474626 4.869346 4.923783e-07
8801 SPRYD3 12_33 0.06068610 9.740314 1.720214e-06
8802 IGFBP6 12_33 0.07009177 11.271009 2.299059e-06
8803 SOAT2 12_33 0.06721434 10.962576 2.144346e-06
9188 KRT78 12_33 0.03414282 4.794059 4.763466e-07
9203 KRT4 12_33 0.03369753 4.651609 4.561646e-07
10380 HOXC9 12_33 0.04370225 6.853768 8.716729e-07
10382 HOXC10 12_33 0.03509146 4.993854 5.099852e-07
10554 MFSD5 12_33 0.06453160 9.670646 1.816135e-06
10926 SP1 12_33 0.18436401 15.159719 8.133690e-06
12164 PRR13 12_33 0.12776503 13.611359 5.060970e-06
13225 FLJ12825 12_33 0.05515857 9.122885 1.464420e-06
13257 RP11-834C11.6 12_33 0.14057561 17.621976 7.209163e-06
13308 RP11-834C11.4 12_33 0.03440711 4.777294 4.783552e-07
13550 RP11-1136G11.8 12_33 0.03342918 4.568659 4.444622e-07
13690 AC012531.25 12_33 0.03334476 4.540033 4.405619e-07
14610 RP11-834C11.15 12_33 0.22717448 22.325178 1.475961e-05
z num_eqtl
234 0.483458204 2
641 -0.139976993 3
2858 -1.824491131 1
4016 1.367961353 1
4018 0.396812394 1
5160 1.707705351 2
5164 3.566058337 1
5180 1.676240323 2
5757 1.184293991 1
5758 -2.568783738 1
5764 0.259908099 1
5766 -0.784742392 2
5770 2.396845990 1
8797 -0.201578746 1
8801 1.338767531 1
8802 -1.546273167 1
8803 -1.761993813 2
9188 0.349999201 1
9203 -0.189880079 1
10380 -0.736846459 1
10382 0.359797101 1
10554 0.686952711 2
10926 3.442597770 1
12164 -3.243337845 2
13225 1.259235906 2
13257 -2.158192780 2
13308 -0.009489289 1
13550 -0.012887459 1
13690 0.150041534 1
14610 -2.584553303 1
[1] "ADH1B"
[1] "4_66"
genename region_tag susie_pip mu2 PVE
6393 TRMT10A 4_66 0.03831161 8.287718 9.240291e-07
6833 EIF4E 4_66 0.03271670 6.978369 6.644215e-07
8127 METAP1 4_66 0.02643780 5.071062 3.901617e-07
8960 TSPAN5 4_66 0.02538005 4.686672 3.461604e-07
9543 ADH6 4_66 0.05522000 11.916541 1.914992e-06
11172 ADH1A 4_66 0.04366401 9.141939 1.161669e-06
11492 ADH1B 4_66 0.02648798 5.203736 4.011293e-07
11700 ADH5 4_66 0.03624318 7.913779 8.347001e-07
11747 ADH4 4_66 0.02946235 5.879762 5.041357e-07
13222 ADH1C 4_66 0.02499946 4.538485 3.301885e-07
14211 RP11-571L19.8 4_66 0.02500054 4.541411 3.304156e-07
z num_eqtl
6393 -1.02976981 2
6833 0.96532383 1
8127 0.51219875 2
8960 -0.16756785 3
9543 1.59860575 4
11172 1.04693291 1
11492 0.53981364 1
11700 -0.97711679 2
11747 -0.42497620 3
13222 -0.29740734 2
14211 0.08894637 2
[1] "LCAT"
[1] "16_36"
genename region_tag susie_pip mu2 PVE
415 CDH1 16_36 0.32934649 15.629291 1.498003e-05
631 CDH3 16_36 0.05366482 7.479259 1.168069e-06
771 CBFB 16_36 0.03801916 4.700029 5.200240e-07
892 NFATC3 16_36 0.03773443 4.657802 5.114923e-07
1366 CMTM1 16_36 0.08019688 11.885486 2.773925e-06
1970 ELMO3 16_36 0.03830333 4.743522 5.287589e-07
1972 NUTF2 16_36 0.03741858 4.563092 4.968975e-07
1974 TSNAXIP1 16_36 0.03742694 4.574752 4.982785e-07
1983 CTCF 16_36 0.03746705 4.589078 5.003746e-07
1984 ACD 16_36 0.03820572 4.743496 5.274087e-07
1986 PARD6A 16_36 0.03747537 4.611482 5.029290e-07
1999 SLC7A6OS 16_36 0.03760938 4.661131 5.101616e-07
2000 SLC7A6 16_36 0.04190161 5.429104 6.620323e-07
2002 ESRP2 16_36 0.04850498 6.789740 9.584286e-07
4059 SLC12A4 16_36 0.03737264 4.561188 4.960804e-07
4060 ENKD1 16_36 0.11653953 14.775721 5.011206e-06
4166 LRRC29 16_36 0.03904619 4.925418 5.596829e-07
4170 C16orf70 16_36 0.03857856 4.809904 5.400111e-07
4835 PRMT7 16_36 0.03779811 4.639705 5.103649e-07
5215 DYNC1LI2 16_36 0.05363617 8.593124 1.341310e-06
5216 FHOD1 16_36 0.07186625 10.415130 2.178261e-06
5218 SLC9A5 16_36 0.07534119 10.851155 2.379188e-06
5896 CMTM3 16_36 0.24362752 21.509367 1.525016e-05
5917 RANBP10 16_36 0.04064040 5.286087 6.251909e-07
5918 GFOD2 16_36 0.06026701 9.339783 1.638086e-06
7526 NAE1 16_36 0.03833035 5.110422 5.700590e-07
7535 LRRC36 16_36 0.04022773 5.262811 6.161176e-07
7536 TPPP3 16_36 0.06056629 9.186315 1.619171e-06
7537 ATP6V0D1 16_36 0.06616756 9.779278 1.883095e-06
7539 C16orf86 16_36 0.03731997 4.546847 4.938238e-07
7543 PSKH1 16_36 0.03834535 4.752767 5.303707e-07
8577 BEAN1 16_36 0.04201038 5.738676 7.015984e-07
8578 TK2 16_36 0.06873114 10.343067 2.068822e-06
8585 FAM96B 16_36 0.03750457 4.624201 5.047093e-07
8692 DPEP2 16_36 0.03841614 4.768859 5.331489e-07
8946 KCTD19 16_36 0.04022773 5.262811 6.161176e-07
9525 CES3 16_36 0.06233782 9.319662 1.690721e-06
9527 CES2 16_36 0.03975666 5.190658 6.005547e-07
9528 PDP2 16_36 0.03875205 4.962469 5.596453e-07
10219 EXOC3L1 16_36 0.04297924 5.749451 7.191268e-07
10284 CDH5 16_36 0.03948882 5.063300 5.818730e-07
10836 ZFP90 16_36 0.04585809 8.050219 1.074345e-06
11205 NRN1L 16_36 0.03866181 5.211611 5.863737e-07
11386 KIAA0895L 16_36 0.08341617 12.110367 2.939868e-06
12151 PSMB10 16_36 0.03732024 4.542329 4.933366e-07
12154 E2F4 16_36 0.04327874 5.812062 7.320236e-07
12285 LCAT 16_36 0.03823580 4.730365 5.263628e-07
12453 CKLF 16_36 0.03935060 5.046591 5.779228e-07
12954 B3GNT9 16_36 0.07239502 10.970949 2.311390e-06
13207 RP11-61A14.4 16_36 0.05978971 9.478025 1.649167e-06
13208 LINC00920 16_36 0.05145331 7.298213 1.092824e-06
14399 RP11-615I2.6 16_36 0.08545334 13.070581 3.250456e-06
z num_eqtl
415 3.46949237 1
631 0.31798262 1
771 -0.35723698 2
892 -0.17593790 2
1366 1.78488529 1
1970 -0.09066466 1
1972 -0.03458739 1
1974 -0.06706963 1
1983 0.24004806 1
1984 -0.33605088 2
1986 0.16803524 2
1999 -0.25795348 1
2000 0.02829446 2
2002 0.64884778 1
4059 -0.03325997 2
4060 -1.74717720 2
4166 0.35721061 1
4170 -0.25979672 2
4835 -0.01811452 3
5215 -1.39184631 2
5216 1.44081757 2
5218 1.48414415 2
5896 2.42221238 2
5917 0.28271111 1
5918 1.35972957 1
7526 -0.70397357 1
7535 0.55097659 1
7536 1.17619053 2
7537 -1.29668047 2
7539 -0.15163395 1
7543 -0.12841385 1
8577 0.63744636 1
8578 1.57732510 2
8585 0.43336957 1
8692 0.06611923 1
8946 0.55097659 1
9525 1.18841547 2
9527 -0.60733274 2
9528 -0.53643489 1
10219 0.62177486 1
10284 0.30408751 1
10836 -2.15487379 1
11205 0.73226207 2
11386 -1.69106241 1
12151 -0.08491409 1
12154 -0.63903874 2
12285 0.12182289 1
12453 0.59799566 1
12954 1.45681505 1
13207 -1.31594410 1
13208 0.85653011 1
14399 -1.99233870 1
[1] "VDAC1"
[1] "5_80"
genename region_tag susie_pip mu2 PVE z
120 CDKL3 5_80 0.05798467 6.503553 1.097448e-06 0.878896726
446 JADE2 5_80 0.04703135 4.562061 6.244086e-07 0.063763443
795 PITX1 5_80 0.05177186 5.451476 8.213498e-07 0.520964770
882 AFF4 5_80 0.04721203 4.597538 6.316817e-07 0.180429451
1126 TCF7 5_80 0.04738579 4.631533 6.386945e-07 0.084800679
3128 SKP1 5_80 0.07560118 8.978670 1.975427e-06 -1.240801827
3129 PPP2CA 5_80 0.08958157 10.572858 2.756331e-06 1.401022662
3131 C5orf15 5_80 0.08258649 9.807745 2.357211e-06 1.212487670
3136 TXNDC15 5_80 0.06336692 7.329617 1.351650e-06 -0.887073744
3140 H2AFY 5_80 0.06735333 7.898565 1.548202e-06 -0.959323725
4832 PCBD2 5_80 0.06655898 7.787849 1.508497e-06 0.941614894
8219 SHROOM1 5_80 0.08706960 10.304931 2.611151e-06 1.407744336
8220 GDF9 5_80 0.10909160 12.437970 3.948764e-06 -1.684248040
8221 UQCRQ 5_80 0.10909160 12.437970 3.948764e-06 -1.684248040
8250 CAMLG 5_80 0.04697266 4.550508 6.220502e-07 0.093330125
9221 HSPA4 5_80 0.04704663 4.565067 6.250230e-07 0.004356676
10482 C5orf24 5_80 0.05619043 6.211477 1.015728e-06 0.651805137
12299 VDAC1 5_80 0.09584043 11.210296 3.126700e-06 1.349083916
12956 CDKN2AIPNL 5_80 0.09617976 11.243705 3.147121e-06 -1.312402346
13324 LINC01843 5_80 0.05712330 6.364454 1.058022e-06 0.735872611
num_eqtl
120 1
446 2
795 1
882 1
1126 1
3128 1
3129 2
3131 2
3136 1
3140 1
4832 2
8219 1
8220 1
8221 1
8250 2
9221 2
10482 1
12299 2
12956 2
13324 1
[1] "FADS3"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
214 SYT7 11_34 0.02957653 6.140324 5.285167e-07
2767 DTX4 11_34 0.07661538 15.209328 3.391145e-06
2780 CCDC86 11_34 0.03698217 8.457224 9.102077e-07
2781 PRPF19 11_34 0.04830671 10.674871 1.500688e-06
2782 TMEM109 11_34 0.04703556 10.427372 1.427321e-06
2808 SLC15A3 11_34 0.02701115 5.602543 4.404012e-07
5072 DAGLA 11_34 0.02576174 6.607148 4.953470e-07
5077 FADS2 11_34 0.04839042 48.606847 6.845058e-06
5078 TMEM258 11_34 0.04757906 43.646261 6.043426e-06
5079 TCN1 11_34 0.04129509 8.874129 1.066460e-06
6711 TMEM138 11_34 0.03232386 8.817461 8.294439e-07
6712 FADS1 11_34 0.25990394 81.437461 6.159669e-05
6716 INCENP 11_34 0.02539261 5.487253 4.054923e-07
6717 ZP1 11_34 0.09692062 16.143421 4.553361e-06
6720 CPSF7 11_34 0.02774480 5.557581 4.487327e-07
6721 MS4A2 11_34 0.04531472 9.717689 1.281512e-06
7768 CYB561A3 11_34 0.03232386 8.817461 8.294439e-07
7770 ASRGL1 11_34 0.05115776 10.362157 1.542702e-06
8606 FAM111A 11_34 0.02724854 5.251164 4.164081e-07
8628 PATL1 11_34 0.22934575 24.764852 1.652901e-05
8630 STX3 11_34 0.07589893 14.426088 3.186431e-06
8639 MS4A6E 11_34 0.02613818 4.847693 3.687489e-07
8640 MS4A7 11_34 0.04209350 9.229870 1.130657e-06
8641 MS4A14 11_34 0.02819519 5.733358 4.704401e-07
8838 VWCE 11_34 0.02522415 4.607382 3.382137e-07
8839 RAB3IL1 11_34 0.02754594 5.305591 4.253159e-07
10622 PTGDR2 11_34 0.02616381 4.897209 3.728806e-07
11103 TMEM216 11_34 0.02532492 4.634968 3.415978e-07
11653 MPEG1 11_34 0.02583994 4.867720 3.660475e-07
12120 LRRC10B 11_34 0.02507501 4.605025 3.360419e-07
12403 MS4A4E 11_34 0.04678077 10.054666 1.368848e-06
12488 FADS3 11_34 0.02794435 30.157170 2.452476e-06
12724 PGA3 11_34 0.03549351 7.093703 7.327271e-07
13286 RP11-855O10.2 11_34 0.02514013 4.807881 3.517560e-07
13443 AP001257.1 11_34 0.02508306 4.541950 3.315456e-07
13448 AP000442.4 11_34 0.02506955 4.538164 3.310907e-07
13507 RP11-794G24.1 11_34 0.05924703 10.995449 1.895832e-06
13513 RP11-286N22.8 11_34 0.02520318 4.613831 3.384055e-07
13516 PGA5 11_34 0.03140549 6.201678 5.668068e-07
z num_eqtl
214 -0.577860995 1
2767 -1.742131948 1
2780 1.274058697 2
2781 1.446293747 1
2782 1.421831985 1
2808 0.821410772 1
5072 -1.422119392 2
5077 -6.671545473 1
5078 -6.520941409 2
5079 0.916393926 1
6711 -1.782804562 1
6712 9.150337934 1
6716 -1.053905396 3
6717 1.565447050 1
6720 -0.647252767 2
6721 -1.135206653 1
7768 -1.782804562 1
7770 -1.126706101 2
8606 0.321876718 1
8628 3.252550732 2
8630 2.406885497 4
8639 0.035807230 1
8640 -1.016881909 2
8641 -0.602717982 1
8838 -0.158063055 1
8839 -0.464622365 1
10622 -0.262494092 3
11103 -0.145271506 2
11653 0.278217067 2
12120 -0.263629874 1
12403 1.240983470 1
12488 -5.433117266 1
12724 0.119664297 1
13286 0.547874538 1
13443 0.003034195 2
13448 0.095873681 1
13507 0.447753087 1
13513 -0.175548731 1
13516 -0.047803015 1
[1] "APOC2"
[1] "19_31"
genename region_tag susie_pip mu2 PVE z num_eqtl
122 MARK4 19_31 0 21.181957 0 -2.2463768 1
227 ERCC1 19_31 0 7.409485 0 -1.6350316 2
633 ZNF112 19_31 0 47.819226 0 4.8629559 1
905 PVR 19_31 0 31.813106 0 -3.0943045 1
2176 CLPTM1 19_31 0 50.178070 0 2.5751726 1
2178 PPP1R37 19_31 0 42.423234 0 -5.9455792 1
2182 PPP1R13L 19_31 0 22.778963 0 -3.0806361 1
2184 ERCC2 19_31 0 13.966069 0 1.5340117 1
2189 KLC3 19_31 0 13.524015 0 -3.6648287 2
3573 CD3EAP 19_31 0 11.426032 0 2.5646694 1
4228 FOSB 19_31 0 16.099633 0 -2.3658041 1
4229 OPA3 19_31 0 6.808430 0 1.5059074 2
4231 RTN2 19_31 0 6.949994 0 -2.0700710 1
4233 VASP 19_31 0 33.645255 0 -2.7026884 1
4573 NECTIN2 19_31 0 1648.609427 0 -35.7740463 1
4574 APOE 19_31 0 303.200452 0 0.6519443 1
4575 TOMM40 19_31 0 1005.711219 0 -14.0286334 2
4576 APOC1 19_31 0 474.076449 0 -9.1150442 1
6037 GEMIN7 19_31 0 205.847243 0 13.2439035 2
7553 ZNF233 19_31 0 131.643247 0 -10.0229697 2
7554 ZNF235 19_31 0 35.913626 0 -6.2627967 2
8710 ZNF180 19_31 0 43.730327 0 4.0279244 1
9234 ZNF296 19_31 0 94.996588 0 5.4593536 1
11052 CEACAM19 19_31 0 64.753213 0 11.7978210 2
11128 BCAM 19_31 0 96.429711 0 4.6421318 1
11348 BLOC1S3 19_31 0 11.973214 0 2.7250151 2
12327 PPM1N 19_31 0 24.361748 0 -2.6089113 1
12884 APOC2 19_31 0 18.135579 0 -2.2896080 1
13922 ZNF285 19_31 0 10.133291 0 -1.3318721 3
14528 ZNF229 19_31 0 125.384012 0 14.4998133 1#run APOE locus again using full SNPs
# focus <- "APOE"
# region_tag <- ctwas_res$region_tag[which(ctwas_res$genename==focus)]
#
# locus_plot(region_tag, label="TWAS", rerun_ctwas = T)
#
# mtext(text=region_tag)
#
# print(focus)
# print(region_tag)
# print(ctwas_gene_res[ctwas_gene_res$region_tag==region_tag,report_cols,])Locus Plots - False positives
This section produces locus plots for all bystander genes with PIP>0.8 (false positives). The highlighted gene at each region is the false positive gene.
false_positives <- ctwas_gene_res$genename[ctwas_gene_res$genename %in% unrelated_genes & ctwas_gene_res$susie_pip>0.8]
for (i in 1:length(false_positives)){
focus <- false_positives[i]
region_tag <- ctwas_res$region_tag[which(ctwas_res$genename==focus)]
locus_plot3(region_tag, focus=focus)
mtext(text=region_tag)
print(focus)
print(region_tag)
print(ctwas_gene_res[ctwas_gene_res$region_tag==region_tag,report_cols,])
#genes at this locus that are in known annotations
ctwas_gene_res$genename[ctwas_gene_res$region_tag==region_tag][ctwas_gene_res$genename[ctwas_gene_res$region_tag==region_tag] %in% known_annotations]
}
[1] "TMED4"
[1] "7_32"
genename region_tag susie_pip mu2 PVE z
18 HECW1 7_32 0.02781865 5.801245 4.696536e-07 -0.3769605
266 NPC1L1 7_32 0.87077857 100.506326 2.546956e-04 11.6310213
590 CAMK2B 7_32 0.03205752 6.435120 6.003533e-07 -1.3113038
636 MRPS24 7_32 0.02688409 5.636475 4.409843e-07 0.3827818
1072 UBE2D4 7_32 0.02674845 5.737501 4.466237e-07 0.6290952
2371 OGDH 7_32 0.02806812 11.531669 9.419456e-07 -2.5518108
2452 COA1 7_32 0.06263166 12.220524 2.227430e-06 -1.0972085
2453 BLVRA 7_32 0.02525596 5.282089 3.882306e-07 0.6112755
2458 AEBP1 7_32 0.02852512 6.670093 5.537065e-07 -0.9567966
2461 GCK 7_32 0.03992584 9.537524 1.108179e-06 1.2747958
2463 YKT6 7_32 0.02913308 7.784609 6.599994e-07 1.6794862
3955 POLM 7_32 0.02600385 5.343151 4.043481e-07 -0.4204156
5303 DDX56 7_32 0.04246638 9.385524 1.159909e-06 -0.7865840
5305 DBNL 7_32 0.02968824 6.061190 5.236760e-07 0.1683551
7444 TMED4 7_32 0.84231695 38.146713 9.350890e-05 7.6088259
8238 STK17A 7_32 0.02906015 6.122062 5.177450e-07 0.4764271
12678 AC004951.6 7_32 0.03572632 7.307602 7.597723e-07 0.2209151
12901 LINC00957 7_32 0.02576194 5.348360 4.009770e-07 -0.4692011
num_eqtl
18 1
266 1
590 1
636 1
1072 1
2371 2
2452 2
2453 2
2458 2
2461 2
2463 1
3955 2
5303 1
5305 2
7444 2
8238 2
12678 1
12901 1Genes with many eQTL
#distribution of number of eQTL for all imputed genes (after dropping ambiguous variants)
table(ctwas_gene_res$num_eqtl)
1 2 3 4 5 6 7 8
7354 4158 1043 201 42 8 1 3 #all genes with 4+ eQTL
ctwas_gene_res[ctwas_gene_res$num_eqtl>3,] chrom id pos type region_tag1 region_tag2
3413 1 ENSG00000116237.15 6222890 gene 1 6
9113 1 ENSG00000169914.5 19882290 gene 1 13
7465 1 ENSG00000158825.5 20588925 gene 1 14
1405 1 ENSG00000090686.15 21782050 gene 1 15
11993 1 ENSG00000204138.12 28349072 gene 1 19
11765 1 ENSG00000198198.15 43384246 gene 1 27
3569 1 ENSG00000117834.12 48212526 gene 1 30
13038 1 ENSG00000240563.1 62194851 gene 1 39
3462 1 ENSG00000116791.13 74701793 gene 1 47
3500 1 ENSG00000117133.10 84383981 gene 1 52
4996 1 ENSG00000134184.12 109571336 gene 1 67
4998 1 ENSG00000134201.10 109709124 gene 1 67
8954 1 ENSG00000168765.16 109636098 gene 1 67
7249 1 ENSG00000156171.14 111139496 gene 1 68
12758 1 ENSG00000231246.1 112176665 gene 1 70
6106 1 ENSG00000143149.12 165652366 gene 1 81
13133 1 ENSG00000243709.1 225909765 gene 1 115
6214 1 ENSG00000143653.9 246720005 gene 1 130
3577 2 ENSG00000118004.17 3594750 gene 2 2
3652 2 ENSG00000119185.12 9408522 gene 2 6
1206 2 ENSG00000084676.15 24488675 gene 2 15
12875 2 ENSG00000234690.6 47336895 gene 2 30
5557 2 ENSG00000138039.14 48735152 gene 2 31
3385 2 ENSG00000116031.8 70833920 gene 2 47
4101 2 ENSG00000124356.15 73828835 gene 2 48
6262 2 ENSG00000144026.11 95171674 gene 2 57
3376 2 ENSG00000115947.13 148018368 gene 2 88
10039 2 ENSG00000177483.11 237779632 gene 2 140
8908 2 ENSG00000168397.16 241634894 gene 2 144
10393 2 ENSG00000180902.17 241737769 gene 2 144
6308 3 ENSG00000144455.13 4368850 gene 3 4
8053 3 ENSG00000163702.18 9915771 gene 3 8
8848 3 ENSG00000168026.18 39106300 gene 3 28
8082 3 ENSG00000163820.14 45921260 gene 3 32
10172 3 ENSG00000178700.7 94060750 gene 3 59
11240 3 ENSG00000188313.12 146532105 gene 3 90
13048 3 ENSG00000240875.5 156751124 gene 3 97
3654 3 ENSG00000119227.7 196935912 gene 3 121
4360 4 ENSG00000127415.12 945299 gene 4 2
12217 4 ENSG00000206113.10 2416718 gene 4 3
4806 4 ENSG00000132406.11 4247307 gene 4 5
4106 4 ENSG00000124406.16 42648749 gene 4 34
11810 4 ENSG00000198515.13 47948298 gene 4 37
277 4 ENSG00000018189.12 70701678 gene 4 49
5660 4 ENSG00000138744.14 75936156 gene 4 51
11764 4 ENSG00000198189.10 87337331 gene 4 59
9543 4 ENSG00000172955.17 99213357 gene 4 66
5672 4 ENSG00000138777.19 105426268 gene 4 69
5676 4 ENSG00000138792.9 110367523 gene 4 72
13991 4 ENSG00000269893.6 118278629 gene 4 76
6784 4 ENSG00000150627.15 176065833 gene 4 114
6411 4 ENSG00000145476.15 186191475 gene 4 120
726 5 ENSG00000066230.10 434990 gene 5 1
13309 5 ENSG00000250786.1 9541581 gene 5 8
3113 5 ENSG00000113360.16 31526264 gene 5 22
7202 5 ENSG00000155542.11 56909472 gene 5 33
8299 5 ENSG00000164904.17 126537717 gene 5 77
12094 5 ENSG00000204677.10 178004394 gene 5 107
14146 6 ENSG00000272279.1 1528446 gene 6 2
12734 6 ENSG00000230438.6 2863329 gene 6 3
6464 6 ENSG00000145979.17 13295270 gene 6 12
12089 6 ENSG00000204632.11 29826724 gene 6 24
12062 6 ENSG00000204516.9 31494468 gene 6 25
12066 6 ENSG00000204525.16 31239091 gene 6 25
12067 6 ENSG00000204531.17 31174520 gene 6 25
12021 6 ENSG00000204301.6 32210996 gene 6 26
13169 6 ENSG00000244731.7 31973120 gene 6 26
1530 6 ENSG00000096433.10 33599327 gene 6 28
11865 6 ENSG00000198755.10 35468418 gene 6 28
2985 6 ENSG00000112130.16 37353428 gene 6 29
11419 6 ENSG00000196284.15 45377620 gene 6 34
5141 6 ENSG00000135297.15 73453764 gene 6 51
9574 6 ENSG00000173214.5 111256558 gene 6 74
11308 6 ENSG00000188820.12 116460362 gene 6 77
14566 6 ENSG00000279968.1 138773501 gene 6 92
12854 6 ENSG00000234147.1 140688240 gene 6 93
12735 7 ENSG00000230487.7 1562574 gene 7 3
3940 7 ENSG00000122512.14 6009342 gene 7 9
3954 7 ENSG00000122674.11 5887685 gene 7 9
2427 7 ENSG00000106392.10 7122680 gene 7 9
2402 7 ENSG00000106125.14 30761823 gene 7 24
11733 7 ENSG00000198039.11 64863974 gene 7 44
12290 7 ENSG00000213462.4 65006275 gene 7 44
4395 7 ENSG00000127947.15 77532312 gene 7 49
2358 7 ENSG00000105854.12 95433958 gene 7 58
9222 7 ENSG00000170615.14 103421569 gene 7 63
11130 7 ENSG00000187260.15 151394339 gene 7 94
13747 7 ENSG00000261455.1 152463544 gene 7 94
4332 7 ENSG00000126870.15 158856009 gene 7 99
398 8 ENSG00000036448.9 2044443 gene 8 4
2157 8 ENSG00000104728.15 1824475 gene 8 4
14180 8 ENSG00000272505.1 10421949 gene 8 13
7034 8 ENSG00000153317.14 130442939 gene 8 85
2495 9 ENSG00000107099.15 205428 gene 9 2
14591 9 ENSG00000281769.1 1044481 gene 9 2
6582 9 ENSG00000147853.16 4720017 gene 9 5
11655 9 ENSG00000197646.7 5500129 gene 9 6
11323 9 ENSG00000188921.13 21019896 gene 9 16
2473 9 ENSG00000106733.20 75064122 gene 9 35
12911 9 ENSG00000235641.4 91159887 gene 9 46
3677 9 ENSG00000119509.12 100097520 gene 9 50
2484 9 ENSG00000106853.18 111461149 gene 9 56
9580 9 ENSG00000173258.12 111525084 gene 9 56
3671 9 ENSG00000119431.9 113137846 gene 9 58
6610 9 ENSG00000148288.11 133156531 gene 9 70
9799 9 ENSG00000175164.13 133252254 gene 9 70
12562 10 ENSG00000225383.7 10794743 gene 10 10
11503 10 ENSG00000196693.14 42638488 gene 10 29
3925 10 ENSG00000122375.11 86611799 gene 10 56
12549 10 ENSG00000224914.3 87222627 gene 10 56
6651 10 ENSG00000148690.11 93696291 gene 10 60
3745 10 ENSG00000120008.15 120850879 gene 10 75
2568 10 ENSG00000107902.13 124462247 gene 10 78
9191 10 ENSG00000170430.9 129467281 gene 10 81
6872 10 ENSG00000151640.12 132186469 gene 10 83
11249 10 ENSG00000188385.11 132105155 gene 10 83
6021 11 ENSG00000142102.15 288845 gene 11 1
9995 11 ENSG00000177042.14 688091 gene 11 1
10871 11 ENSG00000185201.16 306862 gene 11 1
13458 11 ENSG00000255328.1 326718 gene 11 1
14045 11 ENSG00000270972.1 324170 gene 11 1
2815 11 ENSG00000110628.13 2899534 gene 11 3
3850 11 ENSG00000121236.20 5590335 gene 11 4
9268 11 ENSG00000170955.9 6320135 gene 11 5
8544 11 ENSG00000166405.14 8166592 gene 11 6
3878 11 ENSG00000121691.4 34438684 gene 11 23
6677 11 ENSG00000149089.12 34915273 gene 11 23
6841 11 ENSG00000151348.13 44066439 gene 11 27
8630 11 ENSG00000166900.15 59754175 gene 11 34
7781 11 ENSG00000162341.16 69004604 gene 11 38
5502 11 ENSG00000137713.15 111765903 gene 11 66
6697 11 ENSG00000149292.16 113289048 gene 11 67
805 12 ENSG00000069493.14 9664279 gene 12 9
13678 12 ENSG00000260423.1 9319927 gene 12 9
12782 12 ENSG00000231887.6 10937478 gene 12 10
42 12 ENSG00000004700.15 21494403 gene 12 16
14420 12 ENSG00000275764.1 27036797 gene 12 18
6831 12 ENSG00000151233.10 42126028 gene 12 27
5183 12 ENSG00000135502.17 57610139 gene 12 36
4355 12 ENSG00000127337.6 69358729 gene 12 42
5269 12 ENSG00000136051.13 105084034 gene 12 63
8957 12 ENSG00000168778.11 123656482 gene 12 75
367 13 ENSG00000032742.17 20519749 gene 13 2
10376 13 ENSG00000180776.15 21458983 gene 13 2
5747 13 ENSG00000139505.11 25285678 gene 13 6
4177 13 ENSG00000125257.13 95301151 gene 13 47
1949 13 ENSG00000102595.19 96029375 gene 13 48
4174 13 ENSG00000125246.15 99606547 gene 13 50
6770 13 ENSG00000150403.17 113476508 gene 13 62
1458 14 ENSG00000092200.12 21272924 gene 14 2
7349 14 ENSG00000157379.13 24291558 gene 14 3
10451 14 ENSG00000181619.11 59444736 gene 14 27
5798 14 ENSG00000139998.14 64972366 gene 14 30
5804 14 ENSG00000140043.11 73832334 gene 14 34
176 14 ENSG00000009830.11 77317924 gene 14 36
8455 14 ENSG00000165934.12 92121369 gene 14 46
1574 14 ENSG00000099814.15 104846598 gene 14 55
13490 15 ENSG00000256061.7 55419003 gene 15 24
8976 15 ENSG00000168904.14 99251231 gene 15 48
13613 15 ENSG00000259363.5 99791048 gene 15 48
3403 16 ENSG00000116176.6 1223025 gene 16 2
5906 16 ENSG00000140988.15 1964282 gene 16 2
11647 16 ENSG00000197599.12 1436461 gene 16 2
598 16 ENSG00000059122.16 2911846 gene 16 3
12322 16 ENSG00000213853.9 10521110 gene 16 11
10714 16 ENSG00000183793.13 14889311 gene 16 15
6754 16 ENSG00000149922.10 30091070 gene 16 24
700 16 ENSG00000065457.10 75617776 gene 16 40
5903 16 ENSG00000140955.10 84184959 gene 16 48
7084 16 ENSG00000154099.17 84144311 gene 16 48
25 16 ENSG00000003249.13 90020025 gene 16 54
5909 16 ENSG00000140995.16 89928005 gene 16 54
5926 17 ENSG00000141252.19 535410 gene 17 1
4803 17 ENSG00000132383.11 1829845 gene 17 3
11614 17 ENSG00000197417.7 3608661 gene 17 3
4504 17 ENSG00000129204.16 5077692 gene 17 5
118 17 ENSG00000006744.18 12992612 gene 17 11
12772 17 ENSG00000231595.1 14193507 gene 17 13
4777 17 ENSG00000132141.13 34935404 gene 17 21
14514 17 ENSG00000278053.4 37621240 gene 17 22
2603 17 ENSG00000108349.16 40116287 gene 17 23
13832 17 ENSG00000264968.1 39919884 gene 17 23
14507 17 ENSG00000277791.4 38751868 gene 17 23
10192 17 ENSG00000178852.15 47297429 gene 17 27
5320 17 ENSG00000136449.13 50508193 gene 17 29
13796 17 ENSG00000263004.1 57078438 gene 17 33
14549 17 ENSG00000278740.1 68142356 gene 17 39
13871 17 ENSG00000266714.6 75590244 gene 17 42
8819 17 ENSG00000167895.14 78130526 gene 17 43
10553 17 ENSG00000182534.13 76681074 gene 17 43
10564 17 ENSG00000182612.10 81636259 gene 17 46
13793 17 ENSG00000262877.4 81389871 gene 17 46
1924 18 ENSG00000101577.9 2963481 gene 18 3
982 18 ENSG00000075643.5 36187019 gene 18 19
5958 18 ENSG00000141469.16 45723869 gene 18 25
13820 18 ENSG00000264247.1 74592401 gene 18 44
8581 18 ENSG00000166573.5 77250127 gene 18 46
4554 19 ENSG00000129946.10 419407 gene 19 1
6008 19 ENSG00000141934.9 285330 gene 19 1
10466 19 ENSG00000181781.9 429404 gene 19 1
10629 19 ENSG00000183186.7 331904 gene 19 1
691 19 ENSG00000065268.10 982793 gene 19 2
1575 19 ENSG00000099817.11 1086315 gene 19 2
9811 19 ENSG00000175221.14 863601 gene 19 2
4517 19 ENSG00000129354.11 10577400 gene 19 9
11006 19 ENSG00000186204.14 15671348 gene 19 13
2271 19 ENSG00000105383.14 51214320 gene 19 36
2292 19 ENSG00000105501.12 51610285 gene 19 36
7614 19 ENSG00000160336.14 53431544 gene 19 36
8795 19 ENSG00000167766.18 52615913 gene 19 36
9266 19 ENSG00000170949.17 53078878 gene 19 36
9267 19 ENSG00000170954.11 53087254 gene 19 36
11358 19 ENSG00000189190.9 52775408 gene 19 36
303 19 ENSG00000022556.15 54952230 gene 19 37
9255 19 ENSG00000170889.13 54199820 gene 19 37
1866 20 ENSG00000101224.17 3777779 gene 20 3
11760 20 ENSG00000198171.12 3195426 gene 20 3
9801 20 ENSG00000175170.14 25865211 gene 20 19
1293 20 ENSG00000087495.16 59577330 gene 20 34
613 20 ENSG00000060491.16 62787722 gene 20 37
1470 20 ENSG00000092758.15 62817114 gene 20 37
1856 20 ENSG00000101190.12 62846860 gene 20 37
1849 20 ENSG00000101161.7 63979203 gene 20 38
7484 21 ENSG00000159110.19 33229937 gene 21 14
12754 21 ENSG00000231106.2 36005333 gene 21 16
6009 21 ENSG00000141956.13 41794590 gene 21 20
10524 21 ENSG00000182240.15 41167887 gene 21 20
7583 21 ENSG00000160200.17 42950409 gene 21 21
10539 21 ENSG00000182362.13 46257739 gene 21 24
10960 22 ENSG00000185838.13 19852673 gene 22 3
1590 22 ENSG00000099940.11 20856819 gene 22 4
1603 22 ENSG00000099994.10 24179365 gene 22 7
11365 22 ENSG00000189269.12 23632630 gene 22 7
14207 22 ENSG00000272733.1 23583592 gene 22 7
1669 22 ENSG00000100263.13 29177609 gene 22 10
10892 22 ENSG00000185339.8 30598854 gene 22 10
1725 22 ENSG00000100418.7 41620695 gene 22 17
11369 22 ENSG00000189306.10 42490805 gene 22 18
12607 22 ENSG00000226328.6 45131964 gene 22 20
165 22 ENSG00000008735.13 50593164 gene 22 24
4748 1 ENSG00000131778.18 147239128 gene 1 73
7878 1 ENSG00000162836.11 147647471 gene 1 73
5604 2 ENSG00000138363.14 215075224 gene 2 127
5625 3 ENSG00000138495.6 119653997 gene 3 74
870 8 ENSG00000071894.16 144385464 gene 8 94
6580 8 ENSG00000147813.15 143578019 gene 8 94
2080 15 ENSG00000103811.15 78936890 gene 15 37
10494 15 ENSG00000182054.9 90102033 gene 15 42
8695 17 ENSG00000167280.16 79075173 gene 17 44
2344 19 ENSG00000105755.7 43524879 gene 19 30
12864 19 ENSG00000234465.10 43577136 gene 19 30
2282 19 ENSG00000105443.13 48447623 gene 19 33
2291 19 ENSG00000105499.13 48107142 gene 19 33
2227 19 ENSG00000105136.20 57487500 gene 19 39
6043 19 ENSG00000142396.10 58305163 gene 19 39
cs_index susie_pip mu2 region_tag PVE
3413 0 3.571893e-02 4.698777 1_6 4.884314e-07
9113 0 6.450283e-02 8.922900 1_13 1.674963e-06
7465 0 3.212455e-02 5.305480 1_14 4.960005e-07
1405 0 3.617667e-02 4.708355 1_15 4.956991e-07
11993 0 3.716398e-02 4.997286 1_19 5.404763e-07
11765 0 9.616695e-02 11.649462 1_27 3.260258e-06
3569 0 2.922805e-02 4.549534 1_30 3.869787e-07
13038 0 4.661585e-02 11.516620 1_39 1.562352e-06
3462 0 1.352907e-01 16.171475 1_47 6.367044e-06
3500 0 3.419791e-02 5.660007 1_52 5.632962e-07
4996 0 2.166034e-03 59.592966 1_67 3.756475e-07
4998 0 1.515841e-07 7.772201 1_67 3.428609e-12
8954 0 2.557416e-07 15.286858 1_67 1.137732e-11
7249 0 6.506628e-02 11.644158 1_68 2.204877e-06
12758 0 5.576449e-02 7.506332 1_70 1.218164e-06
6106 0 8.308141e-02 13.556515 1_81 3.277723e-06
13133 0 5.355526e-02 8.246114 1_115 1.285203e-06
6214 0 3.108501e-01 25.296859 1_130 2.288431e-05
3577 0 5.220674e-02 6.277464 2_2 9.537425e-07
3652 0 4.198544e-02 5.463184 2_6 6.675209e-07
1206 0 6.050737e-02 8.987663 2_15 1.582615e-06
12875 0 3.437102e-02 6.322592 2_30 6.324234e-07
5557 0 5.779600e-02 9.440139 2_31 1.587802e-06
3385 0 9.353280e-02 12.544505 2_47 3.414584e-06
4101 0 9.671327e-02 8.979941 2_48 2.527434e-06
6262 0 4.986015e-02 4.577584 2_57 6.642174e-07
3376 0 3.736830e-02 4.590825 2_88 4.992458e-07
10039 0 3.747327e-02 4.958316 2_140 5.407246e-07
8908 0 5.021127e-02 10.388322 2_144 1.517983e-06
10393 0 3.873274e-02 8.493331 2_144 9.573628e-07
6308 0 7.724756e-02 7.560402 3_4 1.699613e-06
8053 0 4.119068e-02 4.611351 3_8 5.527737e-07
8848 0 5.273458e-02 7.335916 3_28 1.125823e-06
8082 0 7.498464e-02 10.730551 3_32 2.341610e-06
10172 0 3.805262e-02 5.888783 3_59 6.521243e-07
11240 0 3.332503e-02 4.628616 3_90 4.488921e-07
13048 0 3.099578e-02 4.574740 3_97 4.126571e-07
3654 0 1.061043e-01 12.654876 3_121 3.907611e-06
4360 0 4.787865e-02 8.440241 4_2 1.176026e-06
12217 0 3.615073e-02 5.989552 4_3 6.301323e-07
4806 0 3.679902e-02 6.073889 4_5 6.504641e-07
4106 0 1.663900e-01 20.104220 4_34 9.734972e-06
11810 0 5.048286e-02 10.102755 4_37 1.484240e-06
277 0 3.921636e-02 6.190487 4_49 7.065003e-07
5660 0 5.640138e-02 4.765484 4_51 7.821986e-07
11764 0 4.001866e-02 7.010563 4_59 8.164615e-07
9543 0 5.522000e-02 11.916541 4_66 1.914992e-06
5672 0 3.768842e-02 4.537507 4_69 4.976746e-07
5676 0 8.728298e-02 11.894869 4_72 3.021409e-06
13991 0 3.772549e-02 5.489874 4_76 6.027227e-07
6784 0 4.431433e-02 4.831460 4_114 6.230787e-07
6411 0 7.352719e-02 11.310766 4_120 2.420250e-06
726 0 9.508082e-02 14.846214 5_1 4.107986e-06
13309 0 5.500417e-02 9.793529 5_8 1.567672e-06
3113 0 4.244554e-02 5.597349 5_22 6.914086e-07
7202 0 4.268566e-02 9.034766 5_33 1.122327e-06
8299 0 3.984632e-02 6.548742 5_77 7.593927e-07
12094 0 5.770870e-02 9.347444 5_107 1.569837e-06
14146 0 5.697306e-02 11.579462 6_2 1.919898e-06
12734 0 3.704631e-02 5.077711 6_3 5.474359e-07
6464 0 3.384942e-02 4.602670 6_12 4.533999e-07
12089 0 5.801859e-02 27.354965 6_24 4.618741e-06
12062 0 2.296484e-02 15.169715 6_25 1.013821e-06
12066 0 1.055608e-02 8.183072 6_25 2.513849e-07
12067 0 1.866790e-02 13.335546 6_25 7.244803e-07
12021 0 1.464235e-02 9.530104 6_26 4.060960e-07
13169 0 2.331231e-02 23.351422 6_26 1.584233e-06
1530 0 1.992128e-02 13.162595 6_28 7.630956e-07
11865 0 9.613128e-03 5.209942 6_28 1.457531e-07
2985 0 2.566007e-02 4.721226 6_29 3.525599e-07
11419 0 3.739757e-02 5.153960 6_34 5.609249e-07
5141 0 5.786827e-02 7.068613 6_51 1.190406e-06
9574 0 3.822488e-02 4.827104 6_74 5.369739e-07
11308 0 3.131158e-02 4.854373 6_77 4.423422e-07
14566 0 3.144396e-02 5.798241 6_92 5.305835e-07
12854 0 1.070579e-01 15.674794 6_93 4.883606e-06
12735 0 3.242476e-02 5.107422 7_3 4.819465e-07
3940 0 4.438760e-02 6.672328 7_9 8.619048e-07
3954 0 1.357319e-01 16.799950 7_9 6.636056e-06
2427 0 4.411054e-02 6.914719 7_9 8.876408e-07
2402 0 4.619241e-02 4.616531 7_24 6.205927e-07
11733 0 6.461196e-02 7.510453 7_44 1.412210e-06
12290 0 4.805674e-02 4.761151 7_44 6.658656e-07
4395 0 3.883926e-02 4.543065 7_49 5.134997e-07
2358 0 7.005848e-02 11.342704 7_58 2.312584e-06
9222 0 7.235521e-02 13.333498 7_63 2.807593e-06
11130 0 2.363993e-01 22.550257 7_94 1.551379e-05
13747 0 7.432929e-02 11.376058 7_94 2.460776e-06
4332 0 4.674683e-02 7.175186 7_99 9.761255e-07
398 0 1.690045e-01 18.080884 8_4 8.892796e-06
2157 0 1.024087e-01 13.263118 8_4 3.952781e-06
14180 0 2.552053e-02 4.699150 8_13 3.490031e-07
7034 0 4.429832e-02 8.299131 8_85 1.069893e-06
2495 0 4.576179e-02 4.768116 9_2 6.349947e-07
14591 0 9.370778e-02 11.456878 9_2 3.124368e-06
6582 0 2.697859e-02 5.233867 9_5 4.109248e-07
11655 0 1.589141e-01 20.243800 9_6 9.362132e-06
11323 0 2.571925e-02 4.579875 9_16 3.427932e-07
2473 0 2.869029e-02 6.872631 9_35 5.738234e-07
12911 0 3.357705e-02 4.627000 9_46 4.521290e-07
3677 0 5.310248e-02 5.145675 9_50 7.952019e-07
2484 0 1.043743e-01 14.101797 9_56 4.283398e-06
9580 0 3.645474e-02 5.025192 9_56 5.331225e-07
3671 0 4.015043e-02 5.196289 9_58 6.071609e-07
6610 0 4.048563e-02 9.636297 9_70 1.135354e-06
9799 0 2.066049e-01 19.373660 9_70 1.164857e-05
12562 0 8.243195e-02 10.999311 10_10 2.638647e-06
11503 0 4.038557e-02 5.845838 10_29 6.870579e-07
3925 0 4.184787e-02 6.233339 10_56 7.591270e-07
12549 0 4.370137e-02 6.745175 10_56 8.578445e-07
6651 0 8.106676e-02 14.782151 10_60 3.487392e-06
3745 0 3.317147e-02 5.974008 10_75 5.767011e-07
2568 0 1.204207e-01 13.865987 10_78 4.859283e-06
9191 0 6.658017e-02 12.239813 10_81 2.371592e-06
6872 0 6.536556e-02 7.800655 10_83 1.483885e-06
11249 0 9.767022e-02 11.570700 10_83 3.288835e-06
6021 0 5.114983e-02 6.576528 11_1 9.789514e-07
9995 0 1.462482e-01 16.485624 11_1 7.016430e-06
10871 0 4.232995e-02 4.825691 11_1 5.944669e-07
13458 0 7.511865e-02 10.155766 11_1 2.220142e-06
14045 0 5.000231e-02 6.366258 11_1 9.263916e-07
2815 0 1.275911e-01 22.468275 11_3 8.342771e-06
3850 0 9.812151e-02 18.410258 11_4 5.257078e-06
9268 0 4.307929e-02 6.187872 11_5 7.757648e-07
8544 0 3.942149e-02 6.221093 11_6 7.137071e-07
3878 0 3.618744e-02 4.544123 11_23 4.785511e-07
6677 0 3.663615e-02 4.657635 11_23 4.965873e-07
6841 0 1.113233e-01 12.326672 11_27 3.993486e-06
8630 0 7.589893e-02 14.426088 11_34 3.186431e-06
7781 0 3.434782e-02 5.711611 11_38 5.709237e-07
5502 0 1.391233e-01 18.791395 11_66 7.608149e-06
6697 0 3.555880e-02 8.998223 11_67 9.311597e-07
805 0 3.093193e-02 5.839595 12_9 5.256662e-07
13678 0 3.464077e-02 6.383405 12_9 6.435174e-07
12782 0 5.330310e-02 5.758098 12_10 8.932063e-07
42 0 1.202339e-01 12.413015 12_16 4.343346e-06
14420 0 6.174280e-02 9.207311 12_18 1.654396e-06
6831 0 4.431621e-02 4.698632 12_27 6.059745e-07
5183 0 5.069277e-02 9.099575 12_36 1.342417e-06
4355 0 3.866921e-02 4.845907 12_42 5.453316e-07
5269 0 2.921096e-02 7.003965 12_63 5.954017e-07
8957 0 1.711512e-02 6.491706 12_75 3.233398e-07
367 0 6.835979e-02 9.940197 13_2 1.977498e-06
10376 0 3.829058e-02 4.566453 13_2 5.088517e-07
5747 0 4.001410e-02 5.750651 13_6 6.696539e-07
4177 0 7.641115e-02 10.441282 13_47 2.321832e-06
1949 0 6.628056e-02 11.419335 13_48 2.202659e-06
4174 0 6.187219e-02 9.375656 13_50 1.688175e-06
6770 0 2.307350e-02 5.136943 13_62 3.449361e-07
1458 0 8.657405e-02 11.699268 14_2 2.947588e-06
7349 0 3.792453e-02 7.274864 14_3 8.029072e-07
10451 0 3.892066e-02 4.695170 14_27 5.318042e-07
5798 0 3.285318e-02 4.726146 14_30 4.518610e-07
5804 0 9.908638e-03 5.587656 14_34 1.611254e-07
176 0 3.954329e-02 4.577051 14_36 5.267189e-07
8455 0 5.692538e-02 6.055840 14_46 1.003230e-06
1574 0 3.522879e-02 5.156818 14_55 5.286885e-07
13490 0 8.676855e-02 11.196326 15_24 2.827211e-06
8976 0 5.566622e-02 9.150478 15_48 1.482367e-06
13613 0 1.472484e-01 18.340396 15_48 7.859223e-06
3403 0 1.072586e-01 13.215980 16_2 4.125263e-06
5906 0 1.086303e-01 13.336762 16_2 4.216206e-06
11647 0 1.247861e-01 14.659759 16_2 5.323695e-06
598 0 1.015547e-01 13.131818 16_3 3.881014e-06
12322 0 4.822354e-02 5.073198 16_11 7.119692e-07
10714 0 6.172069e-02 12.127005 16_15 2.178234e-06
6754 0 4.809545e-02 5.451828 16_24 7.630736e-07
700 0 4.912756e-02 11.898801 16_40 1.701174e-06
5903 0 2.945834e-02 4.587943 16_48 3.933206e-07
7084 0 3.622124e-02 6.515454 16_48 6.867968e-07
25 0 9.013551e-02 11.907939 16_54 3.123582e-06
5909 0 4.576984e-02 5.587496 16_54 7.442467e-07
5926 0 4.129487e-02 5.114393 17_1 6.146254e-07
4803 0 3.205855e-02 5.673672 17_3 5.293323e-07
11614 0 2.799809e-02 4.557619 17_3 3.713529e-07
4504 0 5.321119e-02 8.372542 17_5 1.296524e-06
118 0 3.922314e-02 7.854684 17_11 8.965848e-07
12772 0 3.798157e-02 6.560320 17_13 7.251340e-07
4777 0 3.922519e-02 4.782755 17_21 5.459634e-07
14514 0 3.691769e-02 5.488084 17_22 5.896246e-07
2603 0 9.350636e-02 12.794541 17_23 3.481659e-06
13832 0 4.412739e-02 5.789914 17_23 7.435337e-07
14507 0 1.068691e-01 14.059314 17_23 4.372569e-06
10192 0 6.641369e-02 40.099387 17_27 7.750249e-06
5320 0 4.943595e-02 5.003351 17_29 7.198204e-07
13796 0 3.838528e-02 5.854589 17_33 6.540056e-07
14549 0 7.428924e-03 16.261207 17_39 3.515596e-07
13871 0 4.631052e-02 7.634581 17_42 1.028928e-06
8819 0 1.389507e-01 14.796733 17_43 5.983383e-06
10553 0 4.805550e-02 4.804189 17_43 6.718673e-07
10564 0 5.415803e-02 4.627335 17_46 7.293132e-07
13793 0 5.415297e-02 4.626468 17_46 7.291085e-07
1924 0 3.636214e-02 4.540276 18_3 4.804543e-07
982 0 3.852646e-02 4.537815 18_19 5.087755e-07
5958 0 3.697720e-02 5.151675 18_25 5.543739e-07
13820 0 4.986956e-02 8.801642 18_44 1.277378e-06
8581 0 3.406076e-02 4.794787 18_46 4.752739e-07
4554 0 2.826108e-02 4.803472 19_1 3.950611e-07
6008 0 1.552501e-01 20.761335 19_1 9.380100e-06
10466 0 3.819289e-02 7.573575 19_1 8.417899e-07
10629 0 3.195237e-02 5.931255 19_1 5.515310e-07
691 0 9.368083e-02 12.261052 19_2 3.342710e-06
1575 0 5.440359e-02 7.177491 19_2 1.136372e-06
9811 0 4.568912e-02 5.560307 19_2 7.393190e-07
4517 0 0.000000e+00 35.096150 19_9 0.000000e+00
11006 0 2.490099e-02 17.408286 19_13 1.261516e-06
2271 0 2.737839e-02 7.750868 19_36 6.175592e-07
2292 0 2.268712e-01 27.750364 19_36 1.832181e-05
7614 0 2.401471e-02 6.548781 19_36 4.576760e-07
8795 0 1.934245e-02 4.568009 19_36 2.571335e-07
9266 0 1.982598e-02 4.793867 19_36 2.765928e-07
9267 0 2.265623e-02 6.015304 19_36 3.966116e-07
11358 0 1.953609e-02 4.659126 19_36 2.648880e-07
303 0 5.756479e-02 7.650768 19_37 1.281688e-06
9255 0 4.205182e-02 4.742482 19_37 5.803777e-07
1866 0 4.107530e-02 5.614445 20_3 6.711320e-07
11760 0 3.887296e-02 5.106145 20_3 5.776451e-07
9801 0 3.900183e-02 8.371330 20_19 9.501664e-07
1293 0 8.206706e-02 11.395589 20_34 2.721610e-06
613 0 4.430817e-02 7.712334 20_37 9.944660e-07
1470 0 3.174732e-02 4.640051 20_37 4.286967e-07
1856 0 3.692146e-02 6.029550 20_37 6.478643e-07
1849 0 2.918503e-02 8.682716 20_38 7.374559e-07
7484 0 2.712785e-02 4.537661 21_14 3.582347e-07
12754 0 5.823457e-02 12.142218 21_16 2.057781e-06
6009 0 1.412952e-01 16.909441 21_20 6.953075e-06
10524 0 3.787510e-02 4.556820 21_20 5.022686e-07
7583 0 6.104725e-02 8.587923 21_21 1.525719e-06
10539 0 7.196138e-02 12.613830 21_24 2.641598e-06
10960 0 4.392127e-02 6.742816 22_3 8.618596e-07
1590 0 3.159023e-02 6.234633 22_4 5.731707e-07
1603 0 3.041868e-02 4.556146 22_7 4.033279e-07
11365 0 3.790313e-02 6.580377 22_7 7.258488e-07
14207 0 4.643799e-02 8.455437 22_7 1.142694e-06
1669 0 9.393020e-02 15.060264 22_10 4.116785e-06
10892 0 1.331419e-01 18.382906 22_10 7.122775e-06
1725 0 3.440809e-02 5.524900 22_17 5.532295e-07
11369 0 3.023230e-02 4.576710 22_18 4.026659e-07
12607 0 5.000839e-02 7.681342 22_20 1.117893e-06
165 0 4.830204e-02 7.078608 22_24 9.950241e-07
4748 0 4.108938e-02 4.624003 1_73 5.529273e-07
7878 1 9.881237e-01 22.149072 1_73 6.369233e-05
5604 0 6.874174e-02 10.856071 2_127 2.171768e-06
5625 0 4.787277e-02 7.459278 3_74 1.039216e-06
870 0 3.297068e-02 7.096389 8_94 6.809036e-07
6580 0 2.756222e-02 4.584770 8_94 3.677495e-07
2080 0 8.853807e-01 18.411852 15_37 4.744034e-05
10494 0 2.938250e-02 6.327887 15_42 5.410878e-07
8695 0 3.677610e-02 7.495126 17_44 8.021673e-07
2344 0 5.559799e-03 13.149563 19_30 2.127604e-07
12864 0 6.358961e-03 16.880984 19_30 3.123951e-07
2282 0 1.347297e-01 23.140300 19_33 9.073035e-06
2291 0 3.195025e-02 12.400172 19_33 1.152981e-06
2227 0 2.716763e-02 5.360616 19_39 4.238252e-07
6043 0 7.555544e-02 14.415357 19_39 3.169651e-06
genename gene_type z num_eqtl
3413 ICMT protein_coding 0.23914469 4
9113 OTUD3 protein_coding -1.09400811 4
7465 CDA protein_coding 0.40576159 4
1405 USP48 protein_coding -0.21715244 4
11993 PHACTR4 protein_coding 0.29462031 4
11765 SZT2 protein_coding 1.46463487 4
3569 SLC5A9 protein_coding 0.05675350 4
13038 L1TD1 protein_coding -1.87764508 4
3462 CRYZ protein_coding -1.95890620 5
3500 RPF1 protein_coding 0.56430924 4
4996 GSTM1 protein_coding 7.68447989 4
4998 GSTM5 protein_coding 1.58897772 4
8954 GSTM4 protein_coding 2.22512920 5
7249 DRAM2 protein_coding 1.46478831 4
12758 RP5-965F6.2 lincRNA 1.03915613 4
6106 ALDH9A1 protein_coding 1.61849594 5
13133 LEFTY1 protein_coding 0.98024965 4
6214 SCCPDH protein_coding -3.05173115 4
3577 COLEC11 protein_coding -0.76179567 4
3652 ITGB1BP1 protein_coding -0.46655683 4
1206 NCOA1 protein_coding 1.25372335 4
12875 AC073283.4 lincRNA 0.70137118 4
5557 LHCGR protein_coding -1.04901496 4
3385 CD207 protein_coding 2.24525108 4
4101 STAMBP protein_coding 1.18479267 4
6262 ZNF514 protein_coding -0.18391525 4
3376 ORC4 protein_coding -0.26848160 4
10039 RBM44 protein_coding 0.31302982 4
8908 ATG4B protein_coding -1.18643960 4
10393 D2HGDH protein_coding 1.20231955 5
6308 SUMF1 protein_coding -0.91619192 4
8053 IL17RC protein_coding -0.13578821 4
8848 TTC21A protein_coding -0.93368283 4
8082 FYCO1 protein_coding 1.39686147 4
10172 DHFR2 protein_coding -0.87865545 4
11240 PLSCR1 protein_coding -0.15509149 4
13048 LINC00886 lincRNA -0.09623607 5
3654 PIGZ protein_coding 1.66265760 4
4360 IDUA protein_coding -1.18870911 5
12217 CFAP99 protein_coding 0.77403046 4
4806 TMEM128 protein_coding -0.65997549 4
4106 ATP8A1 protein_coding 2.30909222 6
11810 CNGA1 protein_coding -1.33380890 4
277 RUFY3 protein_coding -0.73746209 4
5660 NAAA protein_coding -0.22963149 4
11764 HSD17B11 protein_coding 1.07506679 4
9543 ADH6 protein_coding 1.59860575 4
5672 PPA2 protein_coding 0.04308603 4
5676 ENPEP protein_coding 1.43569266 4
13991 SNHG8 lincRNA 0.65863851 4
6784 WDR17 protein_coding 0.29046758 4
6411 CYP4V2 protein_coding -1.38738353 5
726 SLC9A3 protein_coding 0.94659384 4
13309 SNHG18 lincRNA 1.21246390 6
3113 DROSHA protein_coding -0.52179117 4
7202 SETD9 protein_coding -0.97048381 4
8299 ALDH7A1 protein_coding -0.79317806 4
12094 FAM153C protein_coding 1.17868969 4
14146 RP11-157J24.2 lincRNA 1.35990316 4
12734 SERPINB9P1 lincRNA -0.41407488 5
6464 TBC1D7 protein_coding 0.14510476 4
12089 HLA-G protein_coding 6.48768317 5
12062 MICB protein_coding -3.21001507 5
12066 HLA-C protein_coding 3.21156302 5
12067 POU5F1 protein_coding -2.33674614 5
12021 NOTCH4 protein_coding 3.18977327 5
13169 C4A protein_coding 6.31558725 4
1530 ITPR3 protein_coding 2.09585918 4
11865 RPL10A protein_coding 0.98714858 4
2985 RNF8 protein_coding -0.05612723 4
11419 SUPT3H protein_coding -0.46653187 4
5141 MTO1 protein_coding -0.81194322 4
9574 MFSD4B protein_coding 0.07528524 4
11308 FAM26F protein_coding 0.09891516 4
14566 GVQW2 protein_coding -0.67587411 4
12854 RP3-460G2.2 lincRNA -2.08326137 5
12735 PSMG3-AS1 lincRNA -0.37804304 4
3940 PMS2 protein_coding -0.82815080 4
3954 CCZ1 protein_coding 1.87718926 6
2427 C1GALT1 protein_coding 0.77529679 4
2402 MINDY4 protein_coding 0.40895026 4
11733 ZNF273 protein_coding 0.93283738 4
12290 ERV3-1 protein_coding -0.37389717 4
4395 PTPN12 protein_coding -0.03837800 5
2358 PON2 protein_coding 1.44882776 4
9222 SLC26A5 protein_coding 1.50855727 4
11130 WDR86 protein_coding -2.56648509 5
13747 LINC01003 lincRNA -1.38089737 5
4332 WDR60 protein_coding 0.84819199 4
398 MYOM2 protein_coding 2.10625952 4
2157 ARHGEF10 protein_coding -1.59494029 4
14180 RP11-981G7.6 lincRNA 0.49607434 4
7034 ASAP1 protein_coding -1.00858281 4
2495 DOCK8 protein_coding -0.19360565 5
14591 LINC01230 lincRNA 1.40297541 4
6582 AK3 protein_coding -0.46439796 6
11655 PDCD1LG2 protein_coding -2.35139865 4
11323 HACD4 protein_coding 0.07471922 4
2473 NMRK1 protein_coding 0.85354981 5
12911 LINC00484 lincRNA -0.17038733 4
3677 INVS protein_coding 0.63203690 5
2484 PTGR1 protein_coding -1.70069589 6
9580 ZNF483 protein_coding -0.36481127 4
3671 HDHD3 protein_coding 0.40254637 6
6610 GBGT1 protein_coding -2.41325498 4
9799 ABO protein_coding 0.63958745 4
12562 SFTA1P lincRNA -1.42999160 4
11503 ZNF33B protein_coding -0.64192537 4
3925 OPN4 protein_coding -0.32989832 4
12549 LINC00863 lincRNA 0.69241431 4
6651 FRA10AC1 protein_coding -1.70412703 5
3745 WDR11 protein_coding -0.57410882 4
2568 LHPP protein_coding -1.65688926 5
9191 MGMT protein_coding 1.45635325 4
6872 DPYSL4 protein_coding 0.95607904 4
11249 JAKMIP3 protein_coding -1.42449301 4
6021 PGGHG protein_coding 0.74012984 4
9995 TMEM80 protein_coding -2.21351849 5
10871 IFITM2 protein_coding -0.30424663 4
13458 RP11-326C3.12 lincRNA 1.27855864 5
14045 RP11-326C3.15 lincRNA 0.76601070 4
2815 SLC22A18 protein_coding -2.34987486 5
3850 TRIM6 protein_coding -2.61008937 4
9268 PRKCDBP protein_coding -0.64557619 4
8544 RIC3 protein_coding -0.70475882 4
3878 CAT protein_coding -0.05620694 4
6677 APIP protein_coding -0.22045168 4
6841 EXT2 protein_coding -1.53842625 5
8630 STX3 protein_coding 2.40688550 4
7781 TPCN2 protein_coding -0.58987315 4
5502 PPP2R1B protein_coding 2.67130581 4
6697 TTC12 protein_coding -1.28196799 4
805 CLEC2D protein_coding -1.13601425 4
13678 RP13-735L24.1 lincRNA 0.97735501 4
12782 PRH1 protein_coding -0.79691879 4
42 RECQL protein_coding -1.61682371 4
14420 RP11-582E3.6 lincRNA 1.51107079 4
6831 GXYLT1 protein_coding 0.04753781 4
5183 SLC26A10 protein_coding 0.84989496 4
4355 YEATS4 protein_coding 0.29281346 4
5269 WASHC4 protein_coding 1.71114301 4
8957 TCTN2 protein_coding 0.84925790 4
367 IFT88 protein_coding -1.32070275 5
10376 ZDHHC20 protein_coding -0.02606631 4
5747 MTMR6 protein_coding -0.54526219 4
4177 ABCC4 protein_coding -1.56296803 4
1949 UGGT2 protein_coding -1.39566168 4
4174 CLYBL protein_coding 1.11106275 4
6770 TMCO3 protein_coding 0.46750943 4
1458 RPGRIP1 protein_coding 1.42832063 4
7349 DHRS1 protein_coding -1.17957282 4
10451 GPR135 protein_coding -0.21104674 4
5798 RAB15 protein_coding 0.21354143 4
5804 PTGR2 protein_coding -1.58536168 6
176 POMT2 protein_coding -0.09855002 5
8455 CPSF2 protein_coding -0.90528028 4
1574 CEP170B protein_coding 0.52844732 4
13490 DYX1C1 protein_coding 1.49771123 4
8976 LRRC28 protein_coding 1.08124707 4
13613 CTD-2054N24.2 lincRNA 2.15174496 4
3403 TPSG1 protein_coding -1.63534232 4
5906 RPS2 protein_coding 1.61339652 4
11647 CCDC154 protein_coding -1.87354454 4
598 FLYWCH1 protein_coding 1.71642326 4
12322 EMP2 protein_coding 0.38091246 4
10714 NPIPA5 protein_coding 1.80257039 4
6754 TBX6 protein_coding -0.69623140 4
700 ADAT1 protein_coding -1.98147494 4
5903 ADAD2 protein_coding -0.29286768 4
7084 DNAAF1 protein_coding -0.75221023 4
25 DBNDD1 protein_coding -1.67073436 5
5909 DEF8 protein_coding -0.72630781 4
5926 VPS53 protein_coding -0.38672060 5
4803 RPA1 protein_coding 0.33413970 5
11614 SHPK protein_coding -0.01693189 4
4504 USP6 protein_coding -0.91937924 5
118 ELAC2 protein_coding 0.93137470 4
12772 AC005224.2 lincRNA 0.70925667 4
4777 CCT6B protein_coding -0.26096461 4
14514 DDX52 protein_coding 0.76601975 4
2603 CASC3 protein_coding -2.04139093 4
13832 RP11-387H17.4 lincRNA 1.45916671 4
14507 PSMB3 protein_coding 1.72807936 4
10192 EFCAB13 protein_coding 6.66298655 4
5320 MYCBPAP protein_coding -0.40956352 4
13796 RP11-166P13.3 lincRNA -0.56311482 6
14549 RP11-147L13.14 lincRNA 2.29627994 4
13871 MYO15B protein_coding 1.24433890 4
8819 TMC8 protein_coding 1.77178628 4
10553 MXRA7 protein_coding -0.26728373 5
10564 TSPAN10 protein_coding 0.14763829 4
13793 RP11-1055B8.4 lincRNA -0.17396917 4
1924 LPIN2 protein_coding 0.01517188 4
982 MOCOS protein_coding -0.02854379 5
5958 SLC14A1 protein_coding -0.38251841 4
13820 LINC00909 lincRNA -1.07611370 4
8581 GALR1 protein_coding 0.26397367 4
4554 SHC2 protein_coding 0.26825255 4
6008 PLPP2 protein_coding 2.29773776 7
10466 ODF3L2 protein_coding 0.94738763 4
10629 C2CD4C protein_coding 0.57742405 4
691 WDR18 protein_coding 1.63817769 4
1575 POLR2E protein_coding -1.12488490 4
9811 MED16 protein_coding 0.52282620 4
4517 AP1M2 protein_coding -7.10966827 4
11006 CYP4F12 protein_coding 3.32171653 4
2271 CD33 protein_coding 0.90318496 4
2292 SIGLEC5 protein_coding -2.86306920 5
7614 ZNF761 protein_coding -0.66123658 5
8795 ZNF83 protein_coding 0.04914805 8
9266 ZNF160 protein_coding 0.35509602 4
9267 ZNF415 protein_coding 0.72469869 4
11358 ZNF600 protein_coding -0.21631812 5
303 NLRP2 protein_coding 0.92115111 4
9255 RPS9 protein_coding 0.30576708 4
1866 CDC25B protein_coding -0.52160513 4
11760 DDRGK1 protein_coding -0.37714605 4
9801 FAM182B protein_coding 2.06060944 4
1293 PHACTR3 protein_coding -1.37646482 4
613 OGFR protein_coding 0.89039358 4
1470 COL9A3 protein_coding -0.17704675 4
1856 TCFL5 protein_coding -0.66649181 4
1849 PRPF6 protein_coding -0.25606411 5
7484 IFNAR2 protein_coding 0.06513928 5
12754 LINC01436 lincRNA -1.71692910 4
6009 PRDM15 protein_coding 1.84560558 4
10524 BACE2 protein_coding -0.08618446 4
7583 CBS protein_coding -1.03437537 5
10539 YBEY protein_coding 1.54872331 8
10960 GNB1L protein_coding 0.75083134 4
1590 SNAP29 protein_coding -0.81222751 4
1603 SUSD2 protein_coding 0.03724741 4
11365 DRICH1 protein_coding -0.75799079 4
14207 KB-208E9.1 lincRNA -1.05001724 4
1669 RHBDD3 protein_coding -1.77570834 4
10892 TCN2 protein_coding 2.21888777 4
1725 DESI1 protein_coding -0.88117629 4
11369 RRP7A protein_coding 0.06985354 8
12607 NUP50-AS1 lincRNA -0.99741756 5
165 MAPK8IP2 protein_coding 0.68270292 4
4748 CHD1L protein_coding -0.01353759 4
7878 ACP6 protein_coding 4.57577415 4
5604 ATIC protein_coding 1.07472975 4
5625 COX17 protein_coding 0.78106882 4
870 CPSF1 protein_coding 1.04287999 4
6580 NAPRT protein_coding -0.06551042 5
2080 CTSH protein_coding 3.80584896 4
10494 IDH2 protein_coding 0.76246218 4
8695 ENGASE protein_coding -0.76700796 4
2344 ETHE1 protein_coding -0.55179636 4
12864 PINLYP protein_coding 2.24265830 4
2282 CYTH2 protein_coding -2.45492372 4
2291 PLA2G4C protein_coding -1.61112643 4
2227 ZNF419 protein_coding 0.30955099 4
6043 ERVK3-1 protein_coding -1.44360091 4#distribution of number of eQTL for genes with PIP>0.8
table(ctwas_gene_res$num_eqtl[ctwas_gene_res$susie_pip>0.8])/sum(ctwas_gene_res$susie_pip>0.8)
1 2 3 4
0.56250 0.34375 0.03125 0.06250 #genes with 2+ eQTL and PIP>0.8
ctwas_gene_res[ctwas_gene_res$num_eqtl>1 & ctwas_gene_res$susie_pip>0.8,] chrom id pos type region_tag1 region_tag2
756 1 ENSG00000067208.14 92792120 gene 1 56
7878 1 ENSG00000162836.11 147647471 gene 1 73
6227 1 ENSG00000143771.11 224356827 gene 1 114
13192 5 ENSG00000245937.7 128070567 gene 5 78
7444 7 ENSG00000158604.14 44581237 gene 7 32
13062 7 ENSG00000241468.7 99466102 gene 7 61
8291 7 ENSG00000164867.10 150990599 gene 7 93
1003 8 ENSG00000076554.15 80171625 gene 8 57
2080 15 ENSG00000103811.15 78936890 gene 15 37
402 17 ENSG00000037042.8 42652697 gene 17 25
3681 18 ENSG00000119537.15 63366845 gene 18 35
9946 19 ENSG00000176472.10 43533176 gene 19 30
2252 19 ENSG00000105287.12 46716923 gene 19 33
1553 19 ENSG00000099326.8 58572814 gene 19 39
cs_index susie_pip mu2 region_tag PVE genename
756 1 0.8564938 41.00879 1_56 1.022166e-04 EVI5
7878 1 0.9881237 22.14907 1_73 6.369233e-05 ACP6
6227 1 0.9916593 36.75923 1_114 1.060838e-04 CNIH4
13192 0 0.8057176 19.17555 5_78 4.496256e-05 LINC01184
7444 2 0.8423169 38.14671 7_32 9.350890e-05 TMED4
13062 1 0.9089457 34.74097 7_61 9.189676e-05 ATP5J2
8291 0 0.8379598 19.38306 7_93 4.726783e-05 NOS3
1003 1 0.9812117 21.94017 8_57 6.265027e-05 TPD52
2080 0 0.8853807 18.41185 15_37 4.744034e-05 CTSH
402 2 0.9570282 20.51179 17_25 5.712794e-05 TUBG2
3681 0 0.8103019 19.33886 18_35 4.560347e-05 KDSR
9946 4 0.9308837 26.19545 19_30 7.096457e-05 ZNF575
2252 2 0.9990989 29.27528 19_33 8.511965e-05 PRKD2
1553 2 0.9648108 28.16926 19_39 7.909298e-05 MZF1
gene_type z num_eqtl
756 protein_coding -6.589915 2
7878 protein_coding 4.575774 4
6227 protein_coding 6.201835 2
13192 lincRNA -3.918269 2
7444 protein_coding 7.608826 2
13062 protein_coding -5.116980 2
8291 protein_coding 3.856590 2
1003 protein_coding -4.557712 2
2080 protein_coding 3.805849 4
402 protein_coding 4.434366 2
3681 protein_coding -3.912562 3
9946 protein_coding -5.954341 2
2252 protein_coding 5.316724 2
1553 protein_coding -4.742966 2cTWAS genes in GO terms enriched for silver standard genes
#reload silver standard genes
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="LDL")New names:
* `` -> ...4
* `` -> ...5known_annotations <- unique(known_annotations$`Gene Symbol`)
#GO enrichment analysis for silver standard genes
dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
genes <- known_annotations
GO_enrichment <- enrichr(genes, dbs)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.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")]
plotEnrich(GO_enrichment[[db]])
print(df)
}[1] "GO_Biological_Process_2021"
Term
1 cholesterol transport (GO:0030301)
2 cholesterol homeostasis (GO:0042632)
3 sterol homeostasis (GO:0055092)
4 cholesterol efflux (GO:0033344)
5 sterol transport (GO:0015918)
6 cholesterol metabolic process (GO:0008203)
7 sterol metabolic process (GO:0016125)
8 triglyceride-rich lipoprotein particle remodeling (GO:0034370)
9 high-density lipoprotein particle remodeling (GO:0034375)
10 reverse cholesterol transport (GO:0043691)
11 secondary alcohol metabolic process (GO:1902652)
12 regulation of lipoprotein lipase activity (GO:0051004)
13 phospholipid transport (GO:0015914)
14 lipid transport (GO:0006869)
15 acylglycerol homeostasis (GO:0055090)
16 very-low-density lipoprotein particle remodeling (GO:0034372)
17 triglyceride homeostasis (GO:0070328)
18 triglyceride metabolic process (GO:0006641)
19 phospholipid efflux (GO:0033700)
20 chylomicron remodeling (GO:0034371)
21 regulation of cholesterol transport (GO:0032374)
22 chylomicron assembly (GO:0034378)
23 chylomicron remnant clearance (GO:0034382)
24 lipoprotein metabolic process (GO:0042157)
25 diterpenoid metabolic process (GO:0016101)
26 positive regulation of steroid metabolic process (GO:0045940)
27 retinoid metabolic process (GO:0001523)
28 lipid homeostasis (GO:0055088)
29 negative regulation of lipase activity (GO:0060192)
30 positive regulation of cholesterol esterification (GO:0010873)
31 intestinal cholesterol absorption (GO:0030299)
32 negative regulation of cholesterol transport (GO:0032375)
33 intestinal lipid absorption (GO:0098856)
34 acylglycerol metabolic process (GO:0006639)
35 regulation of cholesterol esterification (GO:0010872)
36 phospholipid homeostasis (GO:0055091)
37 very-low-density lipoprotein particle assembly (GO:0034379)
38 positive regulation of lipid metabolic process (GO:0045834)
39 high-density lipoprotein particle assembly (GO:0034380)
40 negative regulation of lipoprotein lipase activity (GO:0051005)
41 negative regulation of lipoprotein particle clearance (GO:0010985)
42 regulation of very-low-density lipoprotein particle remodeling (GO:0010901)
43 intracellular cholesterol transport (GO:0032367)
44 positive regulation of cholesterol transport (GO:0032376)
45 regulation of intestinal cholesterol absorption (GO:0030300)
46 positive regulation of lipoprotein lipase activity (GO:0051006)
47 acylglycerol catabolic process (GO:0046464)
48 positive regulation of lipid biosynthetic process (GO:0046889)
49 positive regulation of triglyceride metabolic process (GO:0090208)
50 positive regulation of triglyceride lipase activity (GO:0061365)
51 regulation of lipid catabolic process (GO:0050994)
52 steroid metabolic process (GO:0008202)
53 positive regulation of lipid catabolic process (GO:0050996)
54 triglyceride catabolic process (GO:0019433)
55 organophosphate ester transport (GO:0015748)
56 phosphatidylcholine metabolic process (GO:0046470)
57 regulation of triglyceride catabolic process (GO:0010896)
58 fatty acid metabolic process (GO:0006631)
59 regulation of fatty acid biosynthetic process (GO:0042304)
60 regulation of sterol transport (GO:0032371)
61 cellular response to low-density lipoprotein particle stimulus (GO:0071404)
62 low-density lipoprotein particle remodeling (GO:0034374)
63 regulation of macrophage derived foam cell differentiation (GO:0010743)
64 organic substance transport (GO:0071702)
65 regulation of cholesterol efflux (GO:0010874)
66 receptor-mediated endocytosis (GO:0006898)
67 secondary alcohol biosynthetic process (GO:1902653)
68 regulation of cholesterol storage (GO:0010885)
69 cholesterol import (GO:0070508)
70 sterol import (GO:0035376)
71 monocarboxylic acid biosynthetic process (GO:0072330)
72 cholesterol biosynthetic process (GO:0006695)
73 positive regulation of cellular metabolic process (GO:0031325)
74 sterol biosynthetic process (GO:0016126)
75 positive regulation of fatty acid metabolic process (GO:0045923)
76 regulation of receptor-mediated endocytosis (GO:0048259)
77 fatty acid biosynthetic process (GO:0006633)
78 regulation of Cdc42 protein signal transduction (GO:0032489)
79 positive regulation of triglyceride catabolic process (GO:0010898)
80 lipid biosynthetic process (GO:0008610)
81 positive regulation of cholesterol efflux (GO:0010875)
82 negative regulation of receptor-mediated endocytosis (GO:0048261)
83 lipoprotein transport (GO:0042953)
84 regulation of lipid metabolic process (GO:0019216)
85 monocarboxylic acid metabolic process (GO:0032787)
86 lipoprotein localization (GO:0044872)
87 lipid catabolic process (GO:0016042)
88 positive regulation of fatty acid biosynthetic process (GO:0045723)
89 intracellular sterol transport (GO:0032366)
90 steroid biosynthetic process (GO:0006694)
91 regulation of lipid biosynthetic process (GO:0046890)
92 regulation of lipase activity (GO:0060191)
93 positive regulation of cellular biosynthetic process (GO:0031328)
94 regulation of amyloid-beta clearance (GO:1900221)
95 phospholipid metabolic process (GO:0006644)
96 regulation of intestinal lipid absorption (GO:1904729)
97 positive regulation of protein catabolic process in the vacuole (GO:1904352)
98 positive regulation of biosynthetic process (GO:0009891)
99 regulation of cholesterol metabolic process (GO:0090181)
100 foam cell differentiation (GO:0090077)
101 positive regulation of cholesterol storage (GO:0010886)
102 macrophage derived foam cell differentiation (GO:0010742)
103 organic hydroxy compound biosynthetic process (GO:1901617)
104 regulation of steroid metabolic process (GO:0019218)
105 organonitrogen compound biosynthetic process (GO:1901566)
106 negative regulation of lipid metabolic process (GO:0045833)
107 regulation of lysosomal protein catabolic process (GO:1905165)
108 positive regulation of amyloid-beta clearance (GO:1900223)
109 cellular lipid catabolic process (GO:0044242)
110 chemical homeostasis (GO:0048878)
111 cholesterol catabolic process (GO:0006707)
112 sterol catabolic process (GO:0016127)
113 steroid hormone biosynthetic process (GO:0120178)
114 regulation of low-density lipoprotein particle clearance (GO:0010988)
115 bile acid metabolic process (GO:0008206)
116 phosphatidylcholine biosynthetic process (GO:0006656)
117 alcohol catabolic process (GO:0046164)
118 organophosphate catabolic process (GO:0046434)
119 regulation of phospholipase activity (GO:0010517)
120 positive regulation of lipid localization (GO:1905954)
121 positive regulation of phospholipid transport (GO:2001140)
122 glycerophospholipid metabolic process (GO:0006650)
123 organic hydroxy compound transport (GO:0015850)
124 negative regulation of macrophage derived foam cell differentiation (GO:0010745)
125 positive regulation of lipid transport (GO:0032370)
126 C21-steroid hormone biosynthetic process (GO:0006700)
127 membrane organization (GO:0061024)
128 positive regulation of endocytosis (GO:0045807)
129 positive regulation of multicellular organismal process (GO:0051240)
130 negative regulation of catabolic process (GO:0009895)
131 negative regulation of lipid catabolic process (GO:0050995)
132 carbohydrate derivative transport (GO:1901264)
133 positive regulation of macrophage derived foam cell differentiation (GO:0010744)
134 protein transport (GO:0015031)
135 fatty acid transport (GO:0015908)
136 positive regulation of lipid storage (GO:0010884)
137 C21-steroid hormone metabolic process (GO:0008207)
138 phospholipid catabolic process (GO:0009395)
139 negative regulation of endocytosis (GO:0045806)
140 regulation of primary metabolic process (GO:0080090)
141 negative regulation of multicellular organismal process (GO:0051241)
142 bile acid biosynthetic process (GO:0006699)
143 regulation of low-density lipoprotein particle receptor catabolic process (GO:0032803)
144 regulation of Rho protein signal transduction (GO:0035023)
145 regulation of small molecule metabolic process (GO:0062012)
146 positive regulation of cellular catabolic process (GO:0031331)
147 artery morphogenesis (GO:0048844)
148 negative regulation of cellular component organization (GO:0051129)
149 glycolipid transport (GO:0046836)
150 positive regulation of lipoprotein particle clearance (GO:0010986)
151 positive regulation of sterol transport (GO:0032373)
152 long-chain fatty acid transport (GO:0015909)
153 response to insulin (GO:0032868)
154 regulation of bile acid metabolic process (GO:1904251)
155 positive regulation of receptor catabolic process (GO:2000646)
156 positive regulation of transport (GO:0051050)
157 negative regulation of endothelial cell proliferation (GO:0001937)
158 negative regulation of endothelial cell migration (GO:0010596)
159 regulation of nitrogen compound metabolic process (GO:0051171)
160 negative regulation of amyloid-beta clearance (GO:1900222)
161 negative regulation of cellular metabolic process (GO:0031324)
162 glycerophospholipid biosynthetic process (GO:0046474)
163 negative regulation of production of molecular mediator of immune response (GO:0002701)
164 unsaturated fatty acid biosynthetic process (GO:0006636)
165 anion transport (GO:0006820)
166 positive regulation of receptor-mediated endocytosis (GO:0048260)
167 negative regulation of cholesterol storage (GO:0010887)
168 regulation of bile acid biosynthetic process (GO:0070857)
169 peptidyl-amino acid modification (GO:0018193)
170 low-density lipoprotein particle receptor catabolic process (GO:0032802)
171 low-density lipoprotein receptor particle metabolic process (GO:0032799)
172 protein oxidation (GO:0018158)
173 positive regulation by host of viral process (GO:0044794)
174 positive regulation of triglyceride biosynthetic process (GO:0010867)
175 receptor internalization (GO:0031623)
176 response to glucose (GO:0009749)
177 positive regulation of cellular component organization (GO:0051130)
178 negative regulation of fatty acid biosynthetic process (GO:0045717)
179 negative regulation of hemostasis (GO:1900047)
180 peptidyl-methionine modification (GO:0018206)
181 ethanol oxidation (GO:0006069)
182 negative regulation of amyloid fibril formation (GO:1905907)
183 negative regulation of protein metabolic process (GO:0051248)
184 unsaturated fatty acid metabolic process (GO:0033559)
185 alpha-linolenic acid metabolic process (GO:0036109)
186 platelet degranulation (GO:0002576)
187 negative regulation of metabolic process (GO:0009892)
188 negative regulation of cell activation (GO:0050866)
189 negative regulation of coagulation (GO:0050819)
190 cGMP-mediated signaling (GO:0019934)
191 intestinal absorption (GO:0050892)
192 receptor metabolic process (GO:0043112)
193 regulation of phagocytosis (GO:0050764)
194 regulation of amyloid fibril formation (GO:1905906)
195 regulation of sequestering of triglyceride (GO:0010889)
196 regulation of triglyceride biosynthetic process (GO:0010866)
197 post-translational protein modification (GO:0043687)
198 regulation of endocytosis (GO:0030100)
199 amyloid fibril formation (GO:1990000)
200 positive regulation of small molecule metabolic process (GO:0062013)
201 cellular response to nutrient levels (GO:0031669)
202 negative regulation of cytokine production involved in immune response (GO:0002719)
203 negative regulation of fatty acid metabolic process (GO:0045922)
204 regulation of cholesterol biosynthetic process (GO:0045540)
205 regulation of steroid biosynthetic process (GO:0050810)
206 positive regulation of catabolic process (GO:0009896)
207 amyloid precursor protein metabolic process (GO:0042982)
208 nitric oxide mediated signal transduction (GO:0007263)
209 positive regulation of nitric-oxide synthase activity (GO:0051000)
210 ethanol metabolic process (GO:0006067)
211 positive regulation of cellular protein catabolic process (GO:1903364)
212 response to fatty acid (GO:0070542)
213 long-term memory (GO:0007616)
214 negative regulation of lipid storage (GO:0010888)
215 linoleic acid metabolic process (GO:0043651)
216 negative regulation of lipid biosynthetic process (GO:0051055)
217 regulation of lipid storage (GO:0010883)
218 regulation of interleukin-1 beta production (GO:0032651)
219 long-chain fatty acid metabolic process (GO:0001676)
220 regulation of cytokine production involved in immune response (GO:0002718)
221 cellular protein metabolic process (GO:0044267)
222 negative regulation of defense response (GO:0031348)
223 transport across blood-brain barrier (GO:0150104)
224 receptor catabolic process (GO:0032801)
225 response to hexose (GO:0009746)
226 regulated exocytosis (GO:0045055)
227 regulation of endothelial cell migration (GO:0010594)
228 negative regulation of protein transport (GO:0051224)
229 positive regulation of binding (GO:0051099)
230 regulation of blood coagulation (GO:0030193)
231 positive regulation of monooxygenase activity (GO:0032770)
232 negative regulation of wound healing (GO:0061045)
233 negative regulation of macromolecule metabolic process (GO:0010605)
234 long-chain fatty acid biosynthetic process (GO:0042759)
235 regulation of developmental growth (GO:0048638)
236 regulation of cell death (GO:0010941)
237 regulation of angiogenesis (GO:0045765)
238 regulation of inflammatory response (GO:0050727)
239 apoptotic cell clearance (GO:0043277)
240 cellular response to peptide hormone stimulus (GO:0071375)
241 negative regulation of blood vessel endothelial cell migration (GO:0043537)
242 phosphate-containing compound metabolic process (GO:0006796)
243 negative regulation of epithelial cell migration (GO:0010633)
244 cellular response to amyloid-beta (GO:1904646)
245 cyclic-nucleotide-mediated signaling (GO:0019935)
246 regulation of receptor internalization (GO:0002090)
247 response to lipid (GO:0033993)
248 regulation of vascular associated smooth muscle cell proliferation (GO:1904705)
249 regulation of protein-containing complex assembly (GO:0043254)
250 ion transport (GO:0006811)
251 negative regulation of response to external stimulus (GO:0032102)
252 regulation of protein binding (GO:0043393)
253 regulation of cellular component biogenesis (GO:0044087)
254 negative regulation of protein secretion (GO:0050709)
255 negative regulation of secretion by cell (GO:1903531)
256 regulation of nitric-oxide synthase activity (GO:0050999)
257 negative regulation of blood coagulation (GO:0030195)
258 cellular response to organic substance (GO:0071310)
259 cellular response to insulin stimulus (GO:0032869)
260 response to amyloid-beta (GO:1904645)
261 establishment of protein localization to extracellular region (GO:0035592)
262 regulation of cellular metabolic process (GO:0031323)
263 regulation of protein metabolic process (GO:0051246)
264 positive regulation of cell differentiation (GO:0045597)
265 negative regulation of cell projection organization (GO:0031345)
266 positive regulation of fat cell differentiation (GO:0045600)
267 negative regulation of BMP signaling pathway (GO:0030514)
268 negative regulation of cellular biosynthetic process (GO:0031327)
269 positive regulation of phagocytosis (GO:0050766)
Overlap Adjusted.P.value
1 28/51 3.291336e-55
2 29/71 1.134840e-52
3 29/72 1.264418e-52
4 16/24 1.003687e-32
5 15/21 2.203564e-31
6 20/77 5.273224e-31
7 19/70 7.882518e-30
8 12/13 1.520527e-27
9 13/18 2.514128e-27
10 12/17 5.731565e-25
11 15/49 2.711706e-24
12 12/21 2.245426e-23
13 15/59 5.665269e-23
14 17/109 2.748742e-22
15 12/25 3.145271e-22
16 9/9 2.283165e-21
17 12/31 7.414146e-21
18 13/55 1.935295e-19
19 9/12 4.196729e-19
20 8/9 5.374944e-18
21 10/25 1.639871e-17
22 8/10 2.436689e-17
23 7/7 1.679428e-16
24 7/9 5.763376e-15
25 11/64 8.362646e-15
26 7/13 2.508790e-13
27 11/92 5.133855e-13
28 10/64 5.133855e-13
29 6/9 3.296018e-12
30 6/9 3.296018e-12
31 6/9 3.296018e-12
32 6/11 1.693836e-11
33 6/11 1.693836e-11
34 8/41 3.013332e-11
35 6/12 3.013332e-11
36 6/12 3.013332e-11
37 6/12 3.013332e-11
38 7/25 4.654994e-11
39 6/13 5.294950e-11
40 5/6 5.459029e-11
41 5/6 5.459029e-11
42 5/6 5.459029e-11
43 6/15 1.393181e-10
44 7/33 3.496187e-10
45 5/8 4.627510e-10
46 5/8 4.627510e-10
47 7/35 5.017364e-10
48 7/35 5.017364e-10
49 6/19 6.556580e-10
50 5/9 9.553575e-10
51 6/21 1.253116e-09
52 9/104 1.493946e-09
53 6/22 1.653549e-09
54 6/23 2.189811e-09
55 6/25 3.733511e-09
56 8/77 3.733511e-09
57 5/12 5.225810e-09
58 9/124 6.552660e-09
59 6/29 9.279729e-09
60 4/5 9.798195e-09
61 5/14 1.207993e-08
62 5/14 1.207993e-08
63 6/31 1.339781e-08
64 9/136 1.355933e-08
65 6/33 1.942867e-08
66 9/143 2.054367e-08
67 6/34 2.282600e-08
68 5/16 2.390304e-08
69 4/6 2.513038e-08
70 4/6 2.513038e-08
71 7/63 2.556641e-08
72 6/35 2.556641e-08
73 8/105 3.517095e-08
74 6/38 4.196695e-08
75 5/18 4.228478e-08
76 6/39 4.816188e-08
77 7/71 5.609765e-08
78 4/8 1.033779e-07
79 4/8 1.033779e-07
80 7/80 1.258618e-07
81 5/23 1.517283e-07
82 5/26 2.906610e-07
83 4/10 2.936601e-07
84 7/92 3.199662e-07
85 8/143 3.471498e-07
86 4/11 4.442138e-07
87 5/29 4.906437e-07
88 4/13 9.252034e-07
89 4/13 9.252034e-07
90 6/65 9.598111e-07
91 5/35 1.249797e-06
92 4/14 1.249797e-06
93 8/180 1.884951e-06
94 4/16 2.212485e-06
95 6/76 2.336232e-06
96 3/5 3.664395e-06
97 3/5 3.664395e-06
98 5/44 3.827136e-06
99 4/21 6.819051e-06
100 3/6 6.952354e-06
101 3/6 6.952354e-06
102 3/6 6.952354e-06
103 5/50 6.991423e-06
104 4/23 9.554179e-06
105 7/158 1.040987e-05
106 4/24 1.121951e-05
107 3/7 1.146235e-05
108 3/7 1.146235e-05
109 4/27 1.788032e-05
110 5/65 2.452122e-05
111 3/9 2.639634e-05
112 3/9 2.639634e-05
113 4/31 3.060279e-05
114 3/10 3.695601e-05
115 4/33 3.856854e-05
116 4/33 3.856854e-05
117 3/11 4.775659e-05
118 3/11 4.775659e-05
119 3/11 4.775659e-05
120 3/11 4.775659e-05
121 3/11 4.775659e-05
122 5/80 6.182763e-05
123 4/40 7.973389e-05
124 3/13 7.973389e-05
125 3/13 7.973389e-05
126 3/15 1.252218e-04
127 7/242 1.420233e-04
128 4/48 1.598563e-04
129 8/345 1.704405e-04
130 4/49 1.709436e-04
131 3/18 2.111817e-04
132 3/18 2.111817e-04
133 3/18 2.111817e-04
134 8/369 2.646441e-04
135 3/20 2.892290e-04
136 3/21 3.341258e-04
137 3/24 4.974036e-04
138 3/24 4.974036e-04
139 3/25 5.597802e-04
140 5/130 5.616142e-04
141 6/214 6.166972e-04
142 3/27 6.934199e-04
143 2/5 7.406583e-04
144 4/73 7.449454e-04
145 3/28 7.586859e-04
146 5/141 7.898802e-04
147 3/30 9.228897e-04
148 4/80 1.034287e-03
149 2/6 1.049782e-03
150 2/6 1.049782e-03
151 2/6 1.049782e-03
152 3/32 1.085012e-03
153 4/84 1.208000e-03
154 2/7 1.428577e-03
155 2/7 1.428577e-03
156 4/91 1.611630e-03
157 3/37 1.625395e-03
158 3/38 1.749224e-03
159 2/8 1.841132e-03
160 2/8 1.841132e-03
161 3/39 1.855101e-03
162 5/177 2.046657e-03
163 2/9 2.304288e-03
164 2/9 2.304288e-03
165 3/43 2.420344e-03
166 3/44 2.575438e-03
167 2/10 2.756295e-03
168 2/10 2.756295e-03
169 2/10 2.756295e-03
170 2/10 2.756295e-03
171 2/10 2.756295e-03
172 2/11 3.303347e-03
173 2/11 3.303347e-03
174 2/11 3.303347e-03
175 3/49 3.337798e-03
176 3/49 3.337798e-03
177 4/114 3.341630e-03
178 2/12 3.781332e-03
179 2/12 3.781332e-03
180 2/12 3.781332e-03
181 2/12 3.781332e-03
182 2/12 3.781332e-03
183 3/52 3.822266e-03
184 3/54 4.245657e-03
185 2/13 4.386588e-03
186 4/125 4.489058e-03
187 3/56 4.645735e-03
188 2/14 4.945884e-03
189 2/14 4.945884e-03
190 2/14 4.945884e-03
191 2/14 4.945884e-03
192 3/58 4.985945e-03
193 3/58 4.985945e-03
194 2/15 5.548828e-03
195 2/15 5.548828e-03
196 2/15 5.548828e-03
197 6/345 5.596173e-03
198 3/61 5.626994e-03
199 3/63 6.147256e-03
200 2/16 6.200855e-03
201 3/66 6.840974e-03
202 2/17 6.840974e-03
203 2/17 6.840974e-03
204 2/17 6.840974e-03
205 2/17 6.840974e-03
206 3/67 7.093595e-03
207 2/18 7.532008e-03
208 2/18 7.532008e-03
209 2/18 7.532008e-03
210 2/19 8.241667e-03
211 2/19 8.241667e-03
212 2/19 8.241667e-03
213 2/19 8.241667e-03
214 2/20 9.094348e-03
215 2/21 9.982653e-03
216 2/22 1.085553e-02
217 2/22 1.085553e-02
218 3/83 1.230478e-02
219 3/83 1.230478e-02
220 2/24 1.273659e-02
221 6/417 1.291939e-02
222 3/85 1.298477e-02
223 3/86 1.336097e-02
224 2/25 1.350640e-02
225 2/25 1.350640e-02
226 4/180 1.399465e-02
227 3/89 1.440735e-02
228 2/26 1.440735e-02
229 3/90 1.479001e-02
230 2/27 1.539039e-02
231 2/28 1.646589e-02
232 2/29 1.757027e-02
233 4/194 1.771224e-02
234 2/30 1.862299e-02
235 2/31 1.977865e-02
236 3/102 2.036757e-02
237 4/203 2.042828e-02
238 4/206 2.141587e-02
239 2/33 2.198466e-02
240 3/106 2.228428e-02
241 2/34 2.311351e-02
242 4/212 2.328380e-02
243 2/35 2.415927e-02
244 2/35 2.415927e-02
245 2/36 2.531623e-02
246 2/36 2.531623e-02
247 3/114 2.646649e-02
248 2/37 2.648825e-02
249 3/116 2.742785e-02
250 3/116 2.742785e-02
251 3/118 2.842391e-02
252 3/118 2.842391e-02
253 2/39 2.842391e-02
254 2/39 2.842391e-02
255 2/39 2.842391e-02
256 2/39 2.842391e-02
257 2/40 2.973753e-02
258 3/123 3.119488e-02
259 3/129 3.533580e-02
260 2/44 3.533580e-02
261 2/46 3.834204e-02
262 2/47 3.980515e-02
263 2/48 4.128649e-02
264 4/258 4.183273e-02
265 2/49 4.262417e-02
266 2/51 4.583569e-02
267 2/52 4.720898e-02
268 2/52 4.720898e-02
269 2/53 4.877011e-02
Genes
1 SCARB1;CETP;LCAT;LIPC;NPC1L1;LIPG;CD36;APOE;LDLRAP1;APOB;LDLR;ABCA1;ABCG8;STARD3;ABCG5;OSBPL5;APOA2;APOA1;APOC3;APOA4;APOA5;NPC1;SOAT1;STAR;NPC2;SOAT2;APOC2;APOC1
2 SCARB1;CETP;MTTP;PCSK9;LPL;LCAT;ABCB11;CYP7A1;LIPC;LIPG;APOE;LDLRAP1;APOB;LDLR;ABCA1;ABCG8;ABCG5;EPHX2;APOA2;APOA1;APOC3;APOA4;APOA5;SOAT1;NPC1;NPC2;SOAT2;APOC2;ANGPTL3
3 SCARB1;CETP;MTTP;PCSK9;LPL;LCAT;ABCB11;CYP7A1;LIPC;LIPG;APOE;LDLRAP1;APOB;LDLR;ABCA1;ABCG8;ABCG5;EPHX2;APOA2;APOA1;APOC3;APOA4;APOA5;SOAT1;NPC1;NPC2;SOAT2;APOC2;ANGPTL3
4 ABCA1;ABCG8;SCARB1;ABCG5;APOA2;APOA1;APOC3;APOA4;APOA5;NPC1;SOAT1;NPC2;SOAT2;APOC2;APOC1;APOE
5 ABCG8;CETP;STARD3;ABCG5;OSBPL5;APOA2;APOA1;LCAT;NPC1;NPC1L1;NPC2;CD36;APOB;LDLRAP1;LDLR
6 ABCA1;STARD3;CETP;OSBPL5;APOA2;APOA1;LCAT;APOA4;HMGCR;APOA5;CYP7A1;CYP27A1;SOAT1;SOAT2;NPC1L1;ANGPTL3;APOE;DHCR7;LDLRAP1;APOB
7 ABCA1;STARD3;CETP;OSBPL5;APOA2;APOA1;LCAT;APOA4;HMGCR;LIPA;CYP7A1;CYP27A1;SOAT1;SOAT2;ANGPTL3;APOE;DHCR7;LDLRAP1;APOB
8 CETP;LIPC;APOC2;APOA2;APOA1;APOC3;LCAT;LPL;APOA4;APOE;APOB;APOA5
9 CETP;SCARB1;APOA2;APOA1;APOC3;LCAT;APOA4;LIPC;APOC2;APOC1;LIPG;APOE;PLTP
10 ABCA1;CETP;SCARB1;LIPC;APOC2;LIPG;APOA2;APOA1;APOC3;LCAT;APOA4;APOE
11 ABCA1;STARD3;CETP;OSBPL5;APOA2;APOA1;LCAT;APOA4;CYP27A1;SOAT1;SOAT2;ANGPTL3;APOE;LDLRAP1;APOB
12 LIPC;SORT1;APOC2;APOH;APOC1;ANGPTL3;APOA1;APOC3;LPL;APOA4;ANGPTL4;APOA5
13 ABCA1;SCARB1;OSBPL5;MTTP;APOA2;APOA1;APOC3;APOA4;APOA5;NPC2;APOC2;APOC1;APOE;LDLR;PLTP
14 ABCA1;SCARB1;ABCG8;CETP;ABCG5;OSBPL5;MTTP;APOA1;APOA4;ABCB11;APOA5;NPC2;NPC1L1;CD36;APOE;LDLR;PLTP
15 CETP;SCARB1;LIPC;APOC2;ANGPTL3;LPL;APOA1;APOC3;APOA4;APOE;ANGPTL4;APOA5
16 CETP;LIPC;APOC2;APOA1;LCAT;LPL;APOA4;APOE;APOA5
17 CETP;SCARB1;LIPC;APOC2;ANGPTL3;LPL;APOA1;APOC3;APOA4;APOE;ANGPTL4;APOA5
18 CETP;APOA2;LPL;APOC3;APOA5;LIPC;LIPI;APOH;LIPG;APOC1;APOE;APOB;LPIN3
19 ABCA1;APOC2;APOC1;APOA2;APOA1;APOC3;APOA4;APOE;APOA5
20 APOC2;APOA2;APOA1;APOC3;LPL;APOA4;APOE;APOB
21 CETP;LRP1;APOC2;LIPG;APOC1;APOA2;TSPO;APOA1;APOA4;APOA5
22 APOC2;MTTP;APOA2;APOA1;APOC3;APOA4;APOE;APOB
23 LIPC;APOC2;APOC1;APOC3;APOE;APOB;LDLR
24 NPC1L1;MTTP;APOA2;APOA1;APOA4;APOE;APOA5
25 LRP1;ADH1B;APOC2;APOA2;APOA1;LPL;APOC3;APOA4;LRP2;APOE;APOB
26 APOC1;APOA2;APOA1;APOA4;APOE;LDLRAP1;APOA5
27 LRP1;ADH1B;APOC2;APOA2;APOA1;LPL;APOC3;APOA4;LRP2;APOE;APOB
28 ABCA1;CETP;LIPG;ANGPTL3;APOA1;APOA4;PPARG;APOE;ABCB11;APOA5
29 SORT1;APOC1;ANGPTL3;APOA2;APOC3;ANGPTL4
30 APOC1;APOA2;APOA1;APOA4;APOE;APOA5
31 ABCG8;ABCG5;NPC1L1;SOAT2;CD36;LDLR
32 ABCG8;ABCG5;APOC2;APOC1;APOA2;APOC3
33 ABCG8;ABCG5;NPC1L1;SOAT2;CD36;LDLR
34 CETP;APOH;APOC1;APOA2;LPL;APOC3;APOE;APOA5
35 APOC1;APOA2;APOA1;APOA4;APOE;APOA5
36 ABCA1;CETP;LIPG;ANGPTL3;APOA1;ABCB11
37 SOAT1;SOAT2;APOC1;MTTP;APOC3;APOB
38 APOA2;ANGPTL3;APOA1;APOA4;PPARG;APOE;APOA5
39 ABCA1;APOA2;APOA1;APOA4;APOE;APOA5
40 SORT1;APOC1;ANGPTL3;APOC3;ANGPTL4
41 LRPAP1;APOC2;APOC1;APOC3;PCSK9
42 APOC2;APOA2;APOA1;APOC3;APOA5
43 ABCA1;NPC1;STAR;NPC2;LDLRAP1;LDLR
44 CETP;LRP1;LIPG;APOA1;PPARG;APOE;PLTP
45 ABCG8;ABCG5;APOA1;APOA4;APOA5
46 APOC2;APOH;APOA1;APOA4;APOA5
47 LIPC;LIPI;LIPG;APOA2;LPL;APOC3;APOA5
48 SCARB1;APOC2;APOA1;APOA4;APOE;LDLR;APOA5
49 SCARB1;APOC2;APOA1;APOA4;APOA5;LDLR
50 APOC2;APOH;APOA1;APOA4;APOA5
51 APOC1;APOA2;ANGPTL3;APOC3;ABCB11;APOA5
52 CYP27A1;STARD3;NPC1;STAR;TSPO;LRP2;ABCB11;LIPA;CYP7A1
53 APOC2;APOA2;ANGPTL3;APOA1;APOA4;APOA5
54 LIPC;LIPI;LIPG;APOC3;LPL;APOA5
55 SCARB1;OSBPL5;NPC2;MTTP;LDLR;PLTP
56 CETP;LIPC;APOA2;APOA1;LCAT;APOA4;APOA5;LPIN3
57 APOC2;APOA1;APOC3;APOA4;APOA5
58 LIPC;LIPI;LIPG;ANGPTL3;LPL;PPARG;CD36;ABCB11;LPIN3
59 APOC2;APOC1;APOA1;APOC3;APOA4;APOA5
60 LRP1;APOC1;TSPO;APOA4
61 ABCA1;LPL;PPARG;CD36;LDLR
62 CETP;LIPC;APOA2;APOB;LPA
63 ABCA1;CETP;LPL;PPARG;CD36;APOB
64 ABCA1;ABCG8;CETP;ABCG5;APOA1;APOA4;LRP2;APOA5;PLTP
65 CETP;LRP1;APOA1;PPARG;APOE;PLTP
66 SCARB1;LRP1;APOA1;CD36;LRP2;APOE;LDLRAP1;APOB;LDLR
67 NPC1L1;APOA1;APOA4;HMGCR;DHCR7;APOA5
68 ABCA1;SCARB1;LPL;PPARG;APOB
69 SCARB1;APOA1;CD36;LDLR
70 SCARB1;APOA1;CD36;LDLR
71 CYP27A1;LIPC;LIPI;LIPG;LPL;ABCB11;CYP7A1
72 NPC1L1;APOA1;APOA4;HMGCR;DHCR7;APOA5
73 APOC1;APOA2;PCSK9;APOA1;APOA4;PPARG;APOE;APOA5
74 NPC1L1;APOA1;APOA4;HMGCR;DHCR7;APOA5
75 APOC2;APOA1;APOA4;PPARG;APOA5
76 LRPAP1;APOC2;APOC1;APOC3;LDLRAP1;APOA5
77 FADS3;LIPC;LIPI;EPHX2;LIPG;LPL;FADS1
78 ABCA1;APOA1;APOC3;APOE
79 APOC2;APOA1;APOA4;APOA5
80 LIPC;STAR;LIPI;LIPG;LPL;HMGCR;FADS1
81 LRP1;APOA1;PPARG;APOE;PLTP
82 LRPAP1;APOC2;APOC1;PCSK9;APOC3
83 LRP1;PPARG;CD36;APOB
84 NPC2;APOC2;APOC1;APOC3;PPARG;HMGCR;DHCR7
85 NPC1;ADH1B;ANGPTL3;LPL;PPARG;VDAC1;CD36;ABCB11
86 LRP1;PPARG;CD36;APOB
87 LIPC;LIPI;LIPG;LPL;APOA4
88 APOC2;APOA1;APOA4;APOA5
89 ABCA1;NPC1;STAR;NPC2
90 CYP27A1;STAR;HMGCR;DHCR7;ABCB11;CYP7A1
91 STAR;APOA1;APOA4;APOE;APOA5
92 LIPC;APOA2;ANGPTL3;LPL
93 SCARB1;STAR;APOC2;APOA1;APOA4;CD36;APOA5;LDLR
94 LRPAP1;LRP1;HMGCR;APOE
95 LIPG;APOA2;ANGPTL3;LPL;LCAT;FADS1
96 APOA1;APOA4;APOA5
97 LRP1;LRP2;LDLR
98 APOA1;APOA4;APOE;CD36;APOA5
99 EPHX2;APOE;LDLRAP1;KPNB1
100 SOAT1;SOAT2;PPARG
101 SCARB1;LPL;APOB
102 SOAT1;SOAT2;PPARG
103 CYP27A1;HMGCR;DHCR7;ABCB11;CYP7A1
104 STAR;EPHX2;APOE;ABCB11
105 VAPA;VAPB;APOA2;APOA1;LCAT;APOE;LPIN3
106 APOC2;APOC1;APOA2;APOC3
107 LRP1;LRP2;LDLR
108 LRPAP1;LRP1;APOE
109 LIPG;APOA2;ANGPTL3;LPIN3
110 CETP;ANGPTL3;APOA4;PPARG;ABCB11
111 CYP27A1;APOE;CYP7A1
112 CYP27A1;APOE;CYP7A1
113 STARD3;STAR;TSPO;DHCR7
114 APOC3;PCSK9;LDLRAP1
115 CYP27A1;NPC1;ABCB11;CYP7A1
116 APOA2;LCAT;APOA1;LPIN3
117 CYP27A1;APOE;CYP7A1
118 LIPG;ANGPTL3;APOA2
119 LRP1;APOC2;ANGPTL3
120 LRP1;LPL;APOB
121 CETP;APOA1;APOE
122 CETP;APOA1;LCAT;APOA4;APOA5
123 ABCG8;ABCG5;NPC2;ABCB11
124 ABCA1;CETP;PPARG
125 CETP;LRP1;APOE
126 STARD3;STAR;TSPO
127 NPC1;VAPA;VAPB;LRP2;LDLRAP1;APOB;LDLR
128 LRP1;APOE;LDLRAP1;APOA5
129 GHR;ABCA1;LRPAP1;LRP1;APOC2;CD36;APOE;APOA5
130 APOC1;APOA2;APOC3;HMGCR
131 APOC1;APOA2;APOC3
132 SCARB1;NPC2;PLTP
133 LPL;CD36;APOB
134 ABCA1;LRP1;MTTP;PPARG;CD36;LRP2;APOE;APOB
135 PPARG;APOE;CD36
136 SCARB1;LPL;APOB
137 STARD3;STAR;TSPO
138 LIPG;APOA2;ANGPTL3
139 APOC2;APOC1;APOC3
140 PPARG;HMGCR;APOE;DHCR7;LDLR
141 LRPAP1;APOA2;APOA1;APOC3;APOA4;HMGCR
142 CYP27A1;ABCB11;CYP7A1
143 PCSK9;APOE
144 ABCA1;APOA1;APOC3;APOE
145 EPHX2;APOE;ABCB11
146 APOC2;APOA1;APOA4;APOE;APOA5
147 LRP1;ANGPTL3;LRP2
148 APOA2;APOA1;APOC3;APOA4
149 NPC2;PLTP
150 LIPG;LDLRAP1
151 CETP;LIPG
152 PPARG;APOE;CD36
153 SORT1;PCSK9;PPARG;LPIN3
154 ABCB11;CYP7A1
155 PCSK9;APOE
156 LRP1;APOA2;APOA1;APOE
157 APOH;PPARG;APOE
158 APOH;PPARG;APOE
159 APOE;LDLR
160 LRPAP1;HMGCR
161 LRPAP1;PCSK9;APOE
162 LIPI;APOA2;APOA1;LCAT;LPIN3
163 APOA2;APOA1
164 FADS3;FADS1
165 TSPO;VDAC2;VDAC1
166 PCSK9;LDLRAP1;APOA5
167 ABCA1;PPARG
168 STAR;CYP7A1
169 APOA2;APOA1
170 MYLIP;PCSK9
171 MYLIP;PCSK9
172 APOA2;APOA1
173 VAPA;APOE
174 SCARB1;LDLR
175 LRP1;CD36;LDLRAP1
176 APOA2;LPL;CYP7A1
177 LRP1;APOC2;APOE;APOA5
178 APOC1;APOC3
179 APOH;APOE
180 APOA2;APOA1
181 ALDH2;ADH1B
182 APOE;LDLR
183 HMGCR;APOE;LDLR
184 FADS3;FADS2;FADS1
185 FADS2;FADS1
186 ITIH4;APOH;APOA1;CD36
187 APOC2;APOC1;APOC3
188 APOE;LDLR
189 APOH;APOE
190 APOE;CD36
191 NPC1L1;CD36
192 LRP1;CD36;LDLRAP1
193 SCARB1;APOA2;APOA1
194 APOE;LDLR
195 LPL;PPARG
196 SCARB1;LDLR
197 APOA2;PCSK9;APOA1;APOE;APOB;APOA5
198 LRPAP1;LRP1;APOE
199 APOA1;APOA4;CD36
200 PPARG;LDLRAP1
201 PCSK9;LPL;FADS1
202 APOA2;APOA1
203 APOC1;APOC3
204 APOE;KPNB1
205 STAR;CYP7A1
206 APOA2;ANGPTL3;APOA5
207 APOE;LDLRAP1
208 APOE;CD36
209 SCARB1;APOE
210 ALDH2;ADH1B
211 PCSK9;APOE
212 LPL;CD36
213 APOE;LDLR
214 ABCA1;PPARG
215 FADS2;FADS1
216 APOC1;APOC3
217 LPL;APOB
218 APOA1;LPL;CD36
219 FADS2;EPHX2;FADS1
220 APOA2;APOA1
221 APOA2;PCSK9;APOA1;APOE;APOB;APOA5
222 APOA1;PPARG;APOE
223 LRP1;CD36;LRP2
224 MYLIP;PCSK9
225 APOA2;LPL
226 ITIH4;APOH;APOA1;CD36
227 SCARB1;APOH;APOE
228 HMGCR;APOE
229 LRP1;PPARG;APOE
230 APOH;APOE
231 SCARB1;APOE
232 APOH;APOE
233 LRPAP1;PCSK9;APOE;LDLR
234 EPHX2;FADS1
235 GHR;APOE
236 LRPAP1;LRP1;CD36
237 APOH;ANGPTL3;PPARG;ANGPTL4
238 APOA1;LPL;PPARG;APOE
239 SCARB1;LRP1
240 PCSK9;PPARG;LPIN3
241 PPARG;APOE
242 EPHX2;ANGPTL3;LPL;LCAT
243 APOH;APOE
244 LRP1;CD36
245 APOE;CD36
246 LRPAP1;PCSK9
247 APOA4;PPARG;CD36
248 PPARG;LDLRAP1
249 ABCA1;CD36;APOE
250 TSPO;VDAC2;VDAC1
251 APOA1;PPARG;APOE
252 LRPAP1;LRP1;LDLRAP1
253 APOE;CD36
254 HMGCR;APOE
255 HMGCR;APOE
256 SCARB1;APOE
257 APOH;APOE
258 GHR;LRP2;LDLRAP1
259 PCSK9;PPARG;LPIN3
260 LRP1;CD36
261 ABCA1;MTTP
262 NPC2;ABCB11
263 APOE;LDLR
264 LPL;PPARG;CD36;APOB
265 MYLIP;APOE
266 LPL;PPARG
267 PPARG;LRP2
268 APOC1;APOC3
269 APOA2;APOA1
[1] "GO_Cellular_Component_2021"
Term Overlap
1 high-density lipoprotein particle (GO:0034364) 12/19
2 chylomicron (GO:0042627) 10/10
3 triglyceride-rich plasma lipoprotein particle (GO:0034385) 10/15
4 very-low-density lipoprotein particle (GO:0034361) 10/15
5 early endosome (GO:0005769) 13/266
6 low-density lipoprotein particle (GO:0034362) 4/7
7 spherical high-density lipoprotein particle (GO:0034366) 4/8
8 endoplasmic reticulum lumen (GO:0005788) 10/285
9 endocytic vesicle membrane (GO:0030666) 8/158
10 endoplasmic reticulum membrane (GO:0005789) 14/712
11 lysosome (GO:0005764) 11/477
12 lytic vacuole (GO:0000323) 8/219
13 endocytic vesicle (GO:0030139) 7/189
14 clathrin-coated endocytic vesicle membrane (GO:0030669) 5/69
15 clathrin-coated endocytic vesicle (GO:0045334) 5/85
16 clathrin-coated vesicle membrane (GO:0030665) 5/90
17 lysosomal membrane (GO:0005765) 8/330
18 intracellular organelle lumen (GO:0070013) 12/848
19 collagen-containing extracellular matrix (GO:0062023) 8/380
20 endocytic vesicle lumen (GO:0071682) 3/21
21 organelle outer membrane (GO:0031968) 5/142
22 ATP-binding cassette (ABC) transporter complex (GO:0043190) 2/6
23 lytic vacuole membrane (GO:0098852) 6/267
24 endosome membrane (GO:0010008) 6/325
25 mitochondrial outer membrane (GO:0005741) 4/126
26 platelet dense granule lumen (GO:0031089) 2/14
27 vesicle (GO:0031982) 5/226
28 endolysosome membrane (GO:0036020) 2/17
29 basolateral plasma membrane (GO:0016323) 4/151
30 cytoplasmic vesicle membrane (GO:0030659) 6/380
31 platelet dense granule (GO:0042827) 2/21
32 lysosomal lumen (GO:0043202) 3/86
33 endolysosome (GO:0036019) 2/25
34 secretory granule lumen (GO:0034774) 5/316
35 brush border membrane (GO:0031526) 2/37
36 mitochondrial envelope (GO:0005740) 3/127
37 extracellular membrane-bounded organelle (GO:0065010) 2/56
38 extracellular vesicle (GO:1903561) 2/59
39 vacuolar lumen (GO:0005775) 3/161
40 caveola (GO:0005901) 2/60
Adjusted.P.value
1 5.261200e-24
2 6.209923e-24
3 9.203512e-21
4 9.203512e-21
5 1.246888e-10
6 7.731648e-08
7 1.321996e-07
8 6.153361e-07
9 8.075627e-07
10 1.200431e-06
11 6.211359e-06
12 7.353950e-06
13 2.938912e-05
14 2.938912e-05
15 7.671871e-05
16 9.509306e-05
17 1.065748e-04
18 1.698308e-04
19 2.574496e-04
20 2.574496e-04
21 6.432532e-04
22 8.164449e-04
23 1.420908e-03
24 3.827987e-03
25 3.898444e-03
26 4.116966e-03
27 4.199032e-03
28 5.675277e-03
29 6.551600e-03
30 6.795770e-03
31 7.845057e-03
32 1.043474e-02
33 1.043474e-02
34 1.423039e-02
35 2.126720e-02
36 2.763580e-02
37 4.460396e-02
38 4.700422e-02
39 4.700422e-02
40 4.700422e-02
Genes
1 CETP;APOC2;APOH;APOC1;APOA2;APOA1;APOC3;LCAT;APOA4;APOE;APOA5;PLTP
2 APOC2;APOH;APOC1;APOA2;APOA1;APOC3;APOA4;APOE;APOB;APOA5
3 APOC2;APOH;APOC1;APOA2;APOA1;APOC3;APOA4;APOE;APOB;APOA5
4 APOC2;APOH;APOC1;APOA2;APOA1;APOC3;APOA4;APOE;APOB;APOA5
5 LRP1;SORT1;APOA2;PCSK9;APOA1;APOC3;APOA4;APOC2;LIPG;APOE;LDLRAP1;APOB;LDLR
6 APOC2;APOE;APOB;APOA5
7 APOC2;APOA2;APOA1;APOC3
8 LRPAP1;LIPC;MTTP;APOA2;PCSK9;APOA1;APOA4;APOE;APOB;APOA5
9 SCARB1;LRP1;CD36;LRP2;APOE;LDLRAP1;APOB;LDLR
10 ABCA1;STARD3;HMGCR;CYP7A1;FADS2;NCEH1;SOAT1;VAPA;SOAT2;VAPB;DHCR7;APOB;FADS1;LPIN3
11 SCARB1;STARD3;NPC1;LRP1;NPC2;SORT1;PCSK9;LRP2;APOB;LIPA;LDLR
12 SCARB1;NPC1;NPC2;SORT1;PCSK9;LRP2;LIPA;LDLR
13 ABCA1;SCARB1;LRP1;APOA1;CD36;APOE;APOB
14 LRP2;APOE;LDLRAP1;APOB;LDLR
15 LRP2;APOE;LDLRAP1;APOB;LDLR
16 LRP2;APOE;LDLRAP1;APOB;LDLR
17 SCARB1;STARD3;NPC1;LRP1;VAPA;PCSK9;LRP2;LDLR
18 CYP27A1;LIPC;ALDH2;MTTP;APOA2;PCSK9;APOA1;APOA4;APOE;APOB;APOA5;KPNB1
19 ITIH4;APOH;ANGPTL3;APOA1;APOC3;APOA4;ANGPTL4;APOE
20 APOA1;APOE;APOB
21 VDAC3;TSPO;VDAC2;VDAC1;DHCR7
22 ABCG8;ABCG5
23 SCARB1;STARD3;NPC1;LRP1;PCSK9;LRP2
24 STARD3;SORT1;PCSK9;ABCB11;APOB;LDLR
25 VDAC3;TSPO;VDAC2;VDAC1
26 ITIH4;APOH
27 ABCA1;CETP;VAPA;APOA1;APOE
28 PCSK9;LDLR
29 LRP1;MTTP;ABCB11;LDLR
30 SCARB1;LRP1;SORT1;CD36;APOB;LDLR
31 ITIH4;APOH
32 NPC2;APOB;LIPA
33 PCSK9;LDLR
34 ITIH4;NPC2;APOH;APOA1;KPNB1
35 LRP2;CD36
36 STAR;VDAC2;VDAC1
37 APOA1;APOE
38 APOA1;APOE
39 NPC2;APOB;LIPA
40 SCARB1;CD36
[1] "GO_Molecular_Function_2021"
Term
1 cholesterol binding (GO:0015485)
2 sterol binding (GO:0032934)
3 cholesterol transfer activity (GO:0120020)
4 sterol transfer activity (GO:0120015)
5 lipoprotein particle receptor binding (GO:0070325)
6 phosphatidylcholine-sterol O-acyltransferase activator activity (GO:0060228)
7 lipoprotein particle binding (GO:0071813)
8 low-density lipoprotein particle binding (GO:0030169)
9 lipase inhibitor activity (GO:0055102)
10 low-density lipoprotein particle receptor binding (GO:0050750)
11 lipoprotein lipase activity (GO:0004465)
12 amyloid-beta binding (GO:0001540)
13 triglyceride lipase activity (GO:0004806)
14 lipase activity (GO:0016298)
15 lipase binding (GO:0035473)
16 apolipoprotein A-I binding (GO:0034186)
17 apolipoprotein receptor binding (GO:0034190)
18 phospholipase A1 activity (GO:0008970)
19 lipase activator activity (GO:0060229)
20 carboxylic ester hydrolase activity (GO:0052689)
21 voltage-gated anion channel activity (GO:0008308)
22 voltage-gated ion channel activity (GO:0005244)
23 phosphatidylcholine transporter activity (GO:0008525)
24 protein heterodimerization activity (GO:0046982)
25 phosphatidylcholine transfer activity (GO:0120019)
26 phospholipase activity (GO:0004620)
27 O-acyltransferase activity (GO:0008374)
28 high-density lipoprotein particle binding (GO:0008035)
29 ceramide transfer activity (GO:0120017)
30 clathrin heavy chain binding (GO:0032050)
31 phospholipase inhibitor activity (GO:0004859)
32 protein homodimerization activity (GO:0042803)
33 anion channel activity (GO:0005253)
34 phospholipid transfer activity (GO:0120014)
35 oxidoreductase activity, acting on paired donors, with incorporation or reduction of molecular oxygen, NAD(P)H as one donor, and incorporation of one atom of oxygen (GO:0016709)
36 steroid hydroxylase activity (GO:0008395)
37 NADP binding (GO:0050661)
38 peptidase inhibitor activity (GO:0030414)
39 endopeptidase regulator activity (GO:0061135)
Overlap Adjusted.P.value
1 17/50 2.288174e-28
2 17/60 4.390322e-27
3 11/18 5.744987e-22
4 11/19 1.020647e-21
5 10/28 4.677379e-17
6 6/6 3.484605e-14
7 8/24 2.055320e-13
8 6/17 3.140441e-10
9 5/10 1.805253e-09
10 6/23 2.016360e-09
11 4/5 9.113232e-09
12 7/80 1.430772e-07
13 5/23 1.612041e-07
14 6/49 1.859889e-07
15 3/5 3.788104e-06
16 3/5 3.788104e-06
17 3/6 7.112969e-06
18 3/10 3.991032e-05
19 3/12 6.897596e-05
20 5/96 1.501407e-04
21 3/16 1.501407e-04
22 3/16 1.501407e-04
23 3/18 2.082322e-04
24 6/188 3.016202e-04
25 2/5 7.035280e-04
26 4/73 7.035280e-04
27 3/30 8.567836e-04
28 2/6 9.653527e-04
29 2/8 1.732107e-03
30 2/9 2.147965e-03
31 2/10 2.592555e-03
32 8/636 7.345938e-03
33 3/68 7.879809e-03
34 2/22 1.181407e-02
35 2/36 2.870121e-02
36 2/36 2.870121e-02
37 2/36 2.870121e-02
38 2/40 3.429432e-02
39 2/46 4.375411e-02
Genes
1 ABCA1;STARD3;CETP;OSBPL5;APOA2;APOA1;APOC3;APOA4;APOA5;NPC1;SOAT1;STAR;NPC2;SOAT2;TSPO;VDAC2;VDAC1
2 ABCA1;STARD3;CETP;OSBPL5;APOA2;APOA1;APOC3;APOA4;APOA5;NPC1;SOAT1;STAR;NPC2;SOAT2;TSPO;VDAC2;VDAC1
3 ABCA1;ABCG8;CETP;ABCG5;NPC2;MTTP;APOA2;APOA1;APOA4;APOB;PLTP
4 ABCA1;ABCG8;CETP;ABCG5;NPC2;MTTP;APOA2;APOA1;APOA4;APOB;PLTP
5 LRPAP1;LRP1;APOA2;PCSK9;APOA1;APOC3;APOE;APOB;LDLRAP1;APOA5
6 APOC1;APOA2;APOA1;APOA4;APOE;APOA5
7 SCARB1;LIPC;APOA2;LPL;PCSK9;CD36;LDLR;PLTP
8 SCARB1;LIPC;PCSK9;CD36;LDLR;PLTP
9 APOC2;APOC1;ANGPTL3;APOA2;APOC3
10 LRPAP1;PCSK9;APOE;APOB;LDLRAP1;APOA5
11 LIPC;LIPI;LIPG;LPL
12 LRPAP1;LRP1;APOA1;CD36;APOE;LDLRAP1;LDLR
13 LIPC;LIPI;LIPG;LCAT;LPL
14 LIPC;LIPI;LIPG;LCAT;LPL;LIPA
15 LRPAP1;APOB;APOA5
16 ABCA1;SCARB1;LCAT
17 APOA2;APOA1;PCSK9
18 LIPC;LIPG;LPL
19 APOC2;APOH;APOA5
20 LIPC;LIPG;LPL;LCAT;LIPA
21 VDAC3;VDAC2;VDAC1
22 VDAC3;VDAC2;VDAC1
23 ABCA1;MTTP;PLTP
24 ABCG8;ABCG5;VAPA;VAPB;MTTP;APOA2
25 MTTP;PLTP
26 LIPC;LIPI;LIPG;LPL
27 SOAT1;SOAT2;LCAT
28 APOA2;PLTP
29 MTTP;PLTP
30 LRP1;LDLR
31 APOC1;ANGPTL3
32 GHR;STARD3;VAPB;EPHX2;APOA2;LPL;APOA4;APOE
33 VDAC3;VDAC2;VDAC1
34 MTTP;PLTP
35 CYP27A1;CYP7A1
36 CYP27A1;CYP7A1
37 HMGCR;DHCR7
38 ITIH4;LPA
39 ITIH4;LPAGO_known_annotations <- do.call(rbind, GO_enrichment)
GO_known_annotations <- GO_known_annotations[GO_known_annotations$Adjusted.P.value<0.05,]
#GO enrichment analysis for cTWAS genes
genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
GO_enrichment <- enrichr(genes, dbs)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.GO_ctwas_genes <- do.call(rbind, GO_enrichment)
#optionally subset to only significant GO terms
#GO_ctwas_genes <- GO_ctwas_genes[GO_ctwas_genes$Adjusted.P.value<0.05,]
#identify cTWAS genes in silver standard enriched GO terms
GO_ctwas_genes <- GO_ctwas_genes[GO_ctwas_genes$Term %in% GO_known_annotations$Term,]
overlap_genes <- lapply(GO_ctwas_genes$Genes, function(x){unlist(strsplit(x, ";"))})
overlap_genes <- -sort(-table(unlist(overlap_genes)))
#ctwas genes in silver standard enriched GO terms, not already in silver standard
overlap_genes[!(names(overlap_genes) %in% known_annotations)]
NOS3 GSK3B GPAM SCD FURIN CTSH FN1 ACP6 PRKD2 KDSR PARP9 CNIH4
14 13 12 10 9 8 8 5 4 2 2 1
EVI5 FAM3D MZF1 SARS2 TMED4 TPD52
1 1 1 1 1 1 save(overlap_genes, file=paste0(results_dir, "/overlap_genes.Rd"))
load(paste0(results_dir, "/overlap_genes.Rd"))
overlap_genes <- overlap_genes[!(names(overlap_genes) %in% known_annotations)]
overlap_genes
NOS3 GSK3B GPAM SCD FURIN CTSH FN1 ACP6 PRKD2 KDSR PARP9 CNIH4
14 13 12 10 9 8 8 5 4 2 2 1
EVI5 FAM3D MZF1 SARS2 TMED4 TPD52
1 1 1 1 1 1 overlap_genes <- names(overlap_genes)
#ctwas_gene_res[ctwas_gene_res$genename %in% overlap_genes, report_cols,]Results for Paper
out_table <- ctwas_gene_res
report_cols <- report_cols[!(report_cols %in% c("mu2", "PVE"))]
report_cols <- c(report_cols,"silver","GO_overlap_silver", "bystander")
#reload silver standard genes
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="LDL")New names:
* `` -> ...4
* `` -> ...5known_annotations <- unique(known_annotations$`Gene Symbol`)
out_table$silver <- F
out_table$silver[out_table$genename %in% known_annotations] <- T
#create extended bystanders list (all silver standard, not just imputed silver standard)
library(biomaRt)
library(GenomicRanges)
ensembl <- useEnsembl(biomart="ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl")
G_list <- getBM(filters= "chromosome_name", attributes= c("hgnc_symbol","chromosome_name","start_position","end_position","gene_biotype"), values=1:22, mart=ensembl)
G_list <- G_list[G_list$hgnc_symbol!="",]
G_list <- G_list[G_list$gene_biotype %in% c("protein_coding","lncRNA"),]
G_list$start <- G_list$start_position
G_list$end <- G_list$end_position
G_list_granges <- makeGRangesFromDataFrame(G_list, keep.extra.columns=T)
known_annotations_positions <- G_list[G_list$hgnc_symbol %in% known_annotations,]
half_window <- 1000000
known_annotations_positions$start <- known_annotations_positions$start_position - half_window
known_annotations_positions$end <- known_annotations_positions$end_position + half_window
known_annotations_positions$start[known_annotations_positions$start<1] <- 1
known_annotations_granges <- makeGRangesFromDataFrame(known_annotations_positions, keep.extra.columns=T)
bystanders_extended <- findOverlaps(known_annotations_granges,G_list_granges)
bystanders_extended <- unique(subjectHits(bystanders_extended))
bystanders_extended <- G_list$hgnc_symbol[bystanders_extended]
bystanders_extended <- unique(bystanders_extended[!(bystanders_extended %in% known_annotations)])
save(bystanders_extended, file=paste0(results_dir, "/bystanders_extended.Rd"))
load(paste0(results_dir, "/bystanders_extended.Rd"))
#add extended bystanders list to output
out_table$bystander <- F
out_table$bystander[out_table$genename %in% bystanders_extended] <- T
#reload GO overlaps with silver standard
load(paste0(results_dir, "/overlap_genes.Rd"))
out_table$GO_overlap_silver <- NA
out_table$GO_overlap_silver[out_table$susie_pip>0.8] <- 0
for (i in names(overlap_genes)){
out_table$GO_overlap_silver[out_table$genename==i] <- overlap_genes[i]
}cTWAS identifies high-confidence liver genes associated with LDL cholesterol
full.gene.pip.summary <- data.frame(gene_name = ctwas_gene_res$genename,
gene_pip = ctwas_gene_res$susie_pip,
gene_id = ctwas_gene_res$id,
chr = as.integer(ctwas_gene_res$chrom),
start = ctwas_gene_res$pos / 1e3,
is_highlight = F, stringsAsFactors = F) %>% as_tibble()
full.gene.pip.summary$is_highlight <- full.gene.pip.summary$gene_pip > 0.80
don <- full.gene.pip.summary %>%
# Compute chromosome size
group_by(chr) %>%
summarise(chr_len=max(start)) %>%
# Calculate cumulative position of each chromosome
mutate(tot=cumsum(chr_len)-chr_len) %>%
dplyr::select(-chr_len) %>%
# Add this info to the initial dataset
left_join(full.gene.pip.summary, ., by=c("chr"="chr")) %>%
# Add a cumulative position of each SNP
arrange(chr, start) %>%
mutate( BPcum=start+tot)
axisdf <- don %>% group_by(chr) %>% summarize(center=( max(BPcum) + min(BPcum) ) / 2 )
x_axis_labels <- axisdf$chr
x_axis_labels[seq(1,21,2)] <- ""
ggplot(don, aes(x=BPcum, y=gene_pip)) +
# Show all points
ggrastr::geom_point_rast(aes(color=as.factor(chr)), size=2) +
scale_color_manual(values = rep(c("grey", "skyblue"), 22 )) +
# custom X axis:
# scale_x_continuous(label = axisdf$chr,
# breaks= axisdf$center,
# guide = guide_axis(n.dodge = 2)) +
scale_x_continuous(label = x_axis_labels,
breaks = axisdf$center) +
scale_y_continuous(expand = c(0, 0), limits = c(0,1.25), breaks=(1:5)*0.2, minor_breaks=(1:10)*0.1) + # remove space between plot area and x axis
# Add highlighted points
ggrastr::geom_point_rast(data=subset(don, is_highlight==T), color="orange", size=2) +
# Add label using ggrepel to avoid overlapping
ggrepel::geom_label_repel(data=subset(don, is_highlight==T),
aes(label=gene_name),
size=4,
min.segment.length = 0,
label.size = NA,
fill = alpha(c("white"),0)) +
# Custom the theme:
theme_bw() +
theme(
text = element_text(size = 14),
legend.position="none",
panel.border = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()
) +
xlab("Chromosome") +
ylab("cTWAS PIP")
#number of SNPs at PIP>0.8 threshold
sum(out_table$susie_pip>0.8)[1] 32#number of SNPs at PIP>0.5 threshold
sum(out_table$susie_pip>0.5)[1] 83#genes with PIP>0.8
head(out_table[order(-out_table$susie_pip),report_cols], sum(out_table$susie_pip>0.8)) genename region_tag susie_pip z num_eqtl silver
2252 PRKD2 19_33 0.9990989 5.316724 2 FALSE
6227 CNIH4 1_114 0.9916593 6.201835 2 FALSE
7878 ACP6 1_73 0.9881237 4.575774 4 FALSE
1003 TPD52 8_57 0.9812117 -4.557712 2 FALSE
1163 GSK3B 3_74 0.9750098 6.835676 1 FALSE
3740 C10orf88 10_77 0.9675624 -6.783901 1 FALSE
1553 MZF1 19_39 0.9648108 -4.742966 2 FALSE
402 TUBG2 17_25 0.9570282 4.434366 2 FALSE
9315 KCNK3 2_16 0.9525992 -4.821789 1 FALSE
3305 FN1 2_127 0.9418621 -4.446065 1 FALSE
9946 ZNF575 19_30 0.9308837 -5.954341 2 FALSE
6406 USP53 4_77 0.9248454 -4.856546 1 FALSE
14455 AC007950.2 15_29 0.9135056 5.555780 1 FALSE
13062 ATP5J2 7_61 0.9089457 -5.116980 2 FALSE
1542 SCD 10_64 0.8945838 -4.541468 1 FALSE
3734 GPAM 10_70 0.8925750 4.133221 1 FALSE
3645 CCND2 12_4 0.8874327 -4.065830 1 FALSE
2080 CTSH 15_37 0.8853807 3.805849 4 FALSE
12483 FXYD7 19_24 0.8819526 -3.872239 1 FALSE
266 NPC1L1 7_32 0.8707786 11.631021 1 TRUE
1508 CWF19L1 10_64 0.8687056 5.747567 1 FALSE
756 EVI5 1_56 0.8564938 -6.589915 2 FALSE
6974 PELO 5_30 0.8524653 8.522224 1 FALSE
9463 POP7 7_62 0.8450469 5.858772 1 FALSE
7444 TMED4 7_32 0.8423169 7.608826 2 FALSE
8291 NOS3 7_93 0.8379598 3.856590 2 FALSE
2174 SARS2 19_26 0.8249021 4.480159 1 FALSE
5866 FURIN 15_42 0.8215001 -4.391033 1 FALSE
5626 PARP9 3_76 0.8120411 -5.774700 1 FALSE
3681 KDSR 18_35 0.8103019 -3.912562 3 FALSE
13192 LINC01184 5_78 0.8057176 -3.918269 2 FALSE
11839 FAM3D 3_40 0.8027527 -3.889457 1 FALSE
GO_overlap_silver bystander
2252 4 FALSE
6227 1 FALSE
7878 5 FALSE
1003 1 FALSE
1163 13 FALSE
3740 0 FALSE
1553 1 FALSE
402 0 FALSE
9315 0 FALSE
3305 8 FALSE
9946 0 FALSE
6406 0 FALSE
14455 0 FALSE
13062 0 FALSE
1542 10 FALSE
3734 12 FALSE
3645 0 FALSE
2080 8 FALSE
12483 0 FALSE
266 11 FALSE
1508 0 FALSE
756 1 FALSE
6974 0 FALSE
9463 0 FALSE
7444 1 TRUE
8291 14 FALSE
2174 1 FALSE
5866 9 FALSE
5626 2 FALSE
3681 2 FALSE
13192 0 FALSE
11839 1 FALSEhead(out_table[order(-out_table$susie_pip),report_cols[-(7:8)]], sum(out_table$susie_pip>0.8)) genename region_tag susie_pip z num_eqtl silver
2252 PRKD2 19_33 0.9990989 5.316724 2 FALSE
6227 CNIH4 1_114 0.9916593 6.201835 2 FALSE
7878 ACP6 1_73 0.9881237 4.575774 4 FALSE
1003 TPD52 8_57 0.9812117 -4.557712 2 FALSE
1163 GSK3B 3_74 0.9750098 6.835676 1 FALSE
3740 C10orf88 10_77 0.9675624 -6.783901 1 FALSE
1553 MZF1 19_39 0.9648108 -4.742966 2 FALSE
402 TUBG2 17_25 0.9570282 4.434366 2 FALSE
9315 KCNK3 2_16 0.9525992 -4.821789 1 FALSE
3305 FN1 2_127 0.9418621 -4.446065 1 FALSE
9946 ZNF575 19_30 0.9308837 -5.954341 2 FALSE
6406 USP53 4_77 0.9248454 -4.856546 1 FALSE
14455 AC007950.2 15_29 0.9135056 5.555780 1 FALSE
13062 ATP5J2 7_61 0.9089457 -5.116980 2 FALSE
1542 SCD 10_64 0.8945838 -4.541468 1 FALSE
3734 GPAM 10_70 0.8925750 4.133221 1 FALSE
3645 CCND2 12_4 0.8874327 -4.065830 1 FALSE
2080 CTSH 15_37 0.8853807 3.805849 4 FALSE
12483 FXYD7 19_24 0.8819526 -3.872239 1 FALSE
266 NPC1L1 7_32 0.8707786 11.631021 1 TRUE
1508 CWF19L1 10_64 0.8687056 5.747567 1 FALSE
756 EVI5 1_56 0.8564938 -6.589915 2 FALSE
6974 PELO 5_30 0.8524653 8.522224 1 FALSE
9463 POP7 7_62 0.8450469 5.858772 1 FALSE
7444 TMED4 7_32 0.8423169 7.608826 2 FALSE
8291 NOS3 7_93 0.8379598 3.856590 2 FALSE
2174 SARS2 19_26 0.8249021 4.480159 1 FALSE
5866 FURIN 15_42 0.8215001 -4.391033 1 FALSE
5626 PARP9 3_76 0.8120411 -5.774700 1 FALSE
3681 KDSR 18_35 0.8103019 -3.912562 3 FALSE
13192 LINC01184 5_78 0.8057176 -3.918269 2 FALSE
11839 FAM3D 3_40 0.8027527 -3.889457 1 FALSEhead(out_table[order(-out_table$susie_pip),report_cols[c(1,7:8)]], sum(out_table$susie_pip>0.8)) genename GO_overlap_silver bystander
2252 PRKD2 4 FALSE
6227 CNIH4 1 FALSE
7878 ACP6 5 FALSE
1003 TPD52 1 FALSE
1163 GSK3B 13 FALSE
3740 C10orf88 0 FALSE
1553 MZF1 1 FALSE
402 TUBG2 0 FALSE
9315 KCNK3 0 FALSE
3305 FN1 8 FALSE
9946 ZNF575 0 FALSE
6406 USP53 0 FALSE
14455 AC007950.2 0 FALSE
13062 ATP5J2 0 FALSE
1542 SCD 10 FALSE
3734 GPAM 12 FALSE
3645 CCND2 0 FALSE
2080 CTSH 8 FALSE
12483 FXYD7 0 FALSE
266 NPC1L1 11 FALSE
1508 CWF19L1 0 FALSE
756 EVI5 1 FALSE
6974 PELO 0 FALSE
9463 POP7 0 FALSE
7444 TMED4 1 TRUE
8291 NOS3 14 FALSE
2174 SARS2 1 FALSE
5866 FURIN 9 FALSE
5626 PARP9 2 FALSE
3681 KDSR 2 FALSE
13192 LINC01184 0 FALSE
11839 FAM3D 1 FALSEHigh-confidence cTWAS genes are enriched for cholesterol-related GO terms
#GO enrichment analysis
library(enrichR)
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] 32GO_enrichment <- enrichr(genes, dbs)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.db <- dbs[1]
#for (db in dbs){
print(db)[1] "GO_Biological_Process_2021" df <- GO_enrichment[[db]]
print(plotEnrich(GO_enrichment[[db]]))
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df) Term
1 alditol phosphate metabolic process (GO:0052646)
2 negative regulation of transforming growth factor beta production (GO:0071635)
Overlap Adjusted.P.value Genes
1 2/5 0.01270895 GPAM;ACP6
2 2/10 0.02845289 FN1;FURIN#}cTWAS avoids false positives when multiple genes are in a region
TNKS is a silver standard (assumed true positive gene) that is correctly detected. The bystander gene RP11-115J16.2 is significant using TWAS but has low PIP using cTWAS.
# #TNKS gene
# locus_plot4("8_12", label="cTWAS")
#
# out_table[out_table$region_tag=="8_12",report_cols[-(7:8)]]
# out_table[out_table$region_tag=="8_12",report_cols[c(1,7:8)]]FADS1 is a silver standard gene (assumed true positive gene) that is correctly detected. There are 5 significant TWAS genes at this locus, including FADS2, another silver standard gene. FADS2 is not detected due to its high LD with FADS1. The remaining 3 bystander genes at this locus have low PIP using cTWAS.
# #FADS1 gene
# locus_plot3("11_34", focus="FADS1")
#
# out_table[out_table$region_tag=="11_34",report_cols[-(7:8)]]
# out_table[out_table$region_tag=="11_34",report_cols[c(1,7:8)]]
#
# #number of significant TWAS genes at this locus
# sum(abs(out_table$z[out_table$region_tag=="11_34"])>sig_thresh)cTWAS avoids false positives when SNPs in a region are (considerably) more significant
POLK is a gene that is significant using TWAS but not detected using TWAS. cTWAS places a high posterior probability on SNPs are this locus. OpenTargets suggets that the causal gene at this locus is HMGCR (note: different GWAS, similar population), which is not imputed in our dataset. cTWAS selected the variants at this locus because the causal gene is not imputed. Note that MR-JTI claims POLK is causal using their method, and their paper includes a discussion of its potential relevance to LDL.
locus_plot("5_45", label="TWAS")
#locus_plot("5_45", label="TWAS", rerun_ctwas = T)
out_table[out_table$region_tag=="5_45",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
4872 AP3B1 5_45 0.11393562 1.72776969 1 FALSE
4873 ZBED3 5_45 0.03919705 -0.12603742 1 FALSE
6426 CRHBP 5_45 0.04776444 0.73384553 2 FALSE
8184 F2RL2 5_45 0.03985237 0.24209270 2 FALSE
8190 F2RL1 5_45 0.13587040 1.96525972 3 FALSE
8191 AGGF1 5_45 0.04035005 -0.31697845 1 FALSE
8192 WDR41 5_45 0.05757220 0.99877957 1 FALSE
9347 TBCA 5_45 0.03894052 -0.09942578 1 FALSE
10420 F2R 5_45 0.04504062 -0.72708071 2 FALSEout_table[out_table$region_tag=="5_45",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
4872 AP3B1 NA FALSE
4873 ZBED3 NA FALSE
6426 CRHBP NA FALSE
8184 F2RL2 NA FALSE
8190 F2RL1 NA FALSE
8191 AGGF1 NA FALSE
8192 WDR41 NA FALSE
9347 TBCA NA FALSE
10420 F2R NA FALSEcTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
load(paste0(results_dir, "/known_annotations.Rd"))
load(paste0(results_dir, "/bystanders.Rd"))
#remove genes without imputed expression from bystander list
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$genename]
#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.02083333 0.27083333 #specificity / (1 - False Positive Rate)
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9985915 0.9464789 #precision / PPV / (1 - False Discovery Rate)
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.500000 0.254902 #store sensitivity and specificity calculations for plots
sensitivity_plot <- sensitivity
specificity_plot <- specificity
#precision / PPV by PIP threshold
pip_range <- c(0.5, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
number_detected <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes using PIP threshold
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip_lower]
number_detected[i] <- length(ctwas_genes)
precision_range[i] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
}
names(precision_range) <- paste0(">= ", c(0, pip_range[-length(pip_range)]))
precision_range <- precision_range*100
precision_range <- c(precision_range, precision["TWAS"]*100)
names(precision_range)[4] <- "TWAS Bonferroni"
number_detected <- c(number_detected, length(twas_genes))
barplot(precision_range, ylim=c(0,100), main="Precision for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% of Detected Genes in Silver Standard")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)
#false discovery rate by PIP threshold
barplot(100-precision_range, ylim=c(0,100), main="False Discovery Rate for Distinguishing Silver Standard and Bystander Genes", xlab="PIP Threshold for Detection", ylab="% Bystanders in Detected Genes")
abline(h=20, lty=2)
abline(h=40, lty=2)
abline(h=60, lty=2)
abline(h=80, lty=2)
xx <- barplot(100-precision_range, add=T, col=c(rep("darkgrey",3), "white"))
text(x = xx, y = rep(0, length(number_detected)), label = paste0(number_detected, " detected"), pos = 3, cex=0.8)
#text(x = xx, y = precision_range, label = paste0(round(precision_range,1), "%"), pos = 3, cex=0.8, offset = 1.5)Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants
For all 69 silver standard genes, sequentially bin each gene using the following criteria: 1) gene not imputed; 2) gene detected by cTWAS at PIP>0.8; 3) gene insignificant by TWAS; 4) gene nearby a detected silver standard gene; 5) gene nearby a detected bystander gene; 6) gene nearby a detected SNP; 7) inconclusive.
#reload silver standard genes
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="LDL")New names:
* `` -> ...4
* `` -> ...5known_annotations <- unique(known_annotations$`Gene Symbol`)
#categorize silver standard genes by case
silver_standard_case <- c()
uncertain_regions <- matrix(NA, 0, 2)
for (i in 1:length(known_annotations)){
current_gene <- known_annotations[i]
if (current_gene %in% ctwas_gene_res$genename) {
if (ctwas_gene_res$susie_pip[ctwas_gene_res$genename == current_gene] > 0.8){
silver_standard_case <- c(silver_standard_case, "Detected (PIP > 0.8)")
} else {
if (abs(ctwas_gene_res$z[ctwas_gene_res$genename == current_gene]) < sig_thresh){
silver_standard_case <- c(silver_standard_case, "Insignificant z-score")
} else {
current_region <- ctwas_gene_res$region_tag[ctwas_gene_res$genename == current_gene]
current_gene_res <- ctwas_gene_res[ctwas_gene_res$region_tag==current_region,]
current_snp_res <- ctwas_snp_res[ctwas_snp_res$region_tag==current_region,]
if (any(current_gene_res$susie_pip>0.8)){
if (any(current_gene_res$genename[current_gene_res$susie_pip>0.8] %in% known_annotations)){
silver_standard_case <- c(silver_standard_case, "Nearby Silver Standard Gene")
} else {
silver_standard_case <- c(silver_standard_case, "Nearby Bystander Gene")
}
} else {
#if (any(current_snp_res$susie_pip>0.8)){
if (sum(current_snp_res$susie_pip)>0.8){
silver_standard_case <- c(silver_standard_case, "Nearby SNP(s)")
} else {
silver_standard_case <- c(silver_standard_case, "Inconclusive")
uncertain_regions <- rbind(uncertain_regions, c(current_gene, ctwas_gene_res$region_tag[ctwas_gene_res$genename == current_gene]))
print(c(current_gene, ctwas_gene_res$region_tag[ctwas_gene_res$genename == current_gene]))
}
}
}
}
} else {
silver_standard_case <- c(silver_standard_case, "Not Imputed")
}
}
names(silver_standard_case) <- known_annotations
#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))silver_standard_case
Insignificant z-score Not Imputed Nearby SNP(s)
35 21 12
Detected (PIP > 0.8)
1 #show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]named character(0)# for (i in 1:nrow(uncertain_regions)){
# locus_plot3(uncertain_regions[i,2], focus=uncertain_regions[i,1])
# }
#pie chart of outcomes for silver standard genes
df <- data.frame(-sort(-table(silver_standard_case)))
names(df) <- c("Outcome", "Frequency")
#df <- df[df$Outcome!="Not Imputed",] #exclude genes not imputed
df$Outcome <- droplevels(df$Outcome) #exclude genes not imputed
bp<- ggplot(df, aes(x=Outcome, y=Frequency, fill=Outcome)) + geom_bar(width = 1, stat = "identity", position=position_dodge()) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + theme(legend.position = "none")
bp
pie <- ggplot(df, aes(x="", y=Frequency, fill=Outcome)) + geom_bar(width = 1, stat = "identity")
pie <- pie + coord_polar("y", start=0) + theme_minimal() + theme(axis.title.y=element_blank())
pie
# locus_plot3(focus="KPNB1", region_tag="17_27")
# locus_plot3(focus="LPIN3", region_tag="20_25")
# locus_plot3(focus="LIPC", region_tag="15_26")cTWAS detects some genes that are not significant using a stringent TWAS threshold
TTC39B is a member of the Dyslipidaemia term in the disease_GLAD4U. This gene was not included in our silver standard. This gene is not significant using TWAS but is detected by cTWAS.
#locus_plot3(focus="TTC39B", region_tag="9_13")
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] ctwas_0.1.31 forcats_0.4.0 stringr_1.4.0
[4] dplyr_1.0.7 purrr_0.3.4 readr_1.4.0
[7] tidyr_1.1.0 tidyverse_1.3.0 tibble_3.1.2
[10] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0 IRanges_2.18.1
[13] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[16] readxl_1.3.1 WebGestaltR_0.4.4 disgenet2r_0.99.2
[19] enrichR_3.0 cowplot_1.0.0 ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_1.4-1 rjson_0.2.20
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.3.1 rstudioapi_0.10 farver_2.1.0
[10] ggrepel_0.8.1 bit64_4.0.5 AnnotationDbi_1.46.0
[13] fansi_0.5.0 lubridate_1.7.4 xml2_1.3.2
[16] logging_0.10-108 codetools_0.2-16 doParallel_1.0.16
[19] cachem_1.0.5 knitr_1.23 jsonlite_1.6
[22] workflowr_1.6.2 apcluster_1.4.8 Cairo_1.5-12.2
[25] broom_0.7.9 dbplyr_1.4.2 compiler_3.6.1
[28] httr_1.4.1 backports_1.1.4 assertthat_0.2.1
[31] Matrix_1.2-18 fastmap_1.1.0 cli_3.0.1
[34] later_0.8.0 htmltools_0.3.6 prettyunits_1.0.2
[37] tools_3.6.1 igraph_1.2.4.1 gtable_0.3.0
[40] glue_1.4.2 GenomeInfoDbData_1.2.1 reshape2_1.4.3
[43] doRNG_1.8.2 Rcpp_1.0.6 Biobase_2.44.0
[46] cellranger_1.1.0 vctrs_0.3.8 svglite_1.2.2
[49] iterators_1.0.13 xfun_0.8 rvest_0.3.5
[52] lifecycle_1.0.0 rngtools_1.5 XML_3.98-1.20
[55] zlibbioc_1.30.0 scales_1.1.0 hms_1.1.0
[58] promises_1.0.1 yaml_2.2.0 curl_3.3
[61] memoise_2.0.0 ggrastr_0.2.3 gdtools_0.1.9
[64] stringi_1.4.3 RSQLite_2.2.7 foreach_1.5.1
[67] rlang_0.4.11 pkgconfig_2.0.3 bitops_1.0-6
[70] evaluate_0.14 lattice_0.20-38 labeling_0.3
[73] bit_4.0.4 tidyselect_1.1.0 plyr_1.8.4
[76] magrittr_2.0.1 R6_2.5.0 generics_0.0.2
[79] DBI_1.1.1 pgenlibr_0.3.1 pillar_1.6.1
[82] haven_2.3.1 whisker_0.3-2 withr_2.4.1
[85] RCurl_1.98-1.1 modelr_0.1.8 crayon_1.4.1
[88] utf8_1.2.1 rmarkdown_1.13 progress_1.2.2
[91] grid_3.6.1 data.table_1.14.0 blob_1.2.1
[94] git2r_0.26.1 reprex_0.3.0 digest_0.6.20
[97] httpuv_1.5.1 munsell_0.5.0 beeswarm_0.2.3
[100] vipor_0.4.5