LDL direct (quantile) - Liver
wesleycrouse
2021-09-7
Last updated: 2021-11-12
Checks: 7 0
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | b16d97c | wesleycrouse | 2021-11-12 | sort1 additional |
| html | e6dc4d4 | wesleycrouse | 2021-11-12 | LDL sort1 |
| Rmd | d7c5250 | wesleycrouse | 2021-11-12 | SORT1 analysis |
Overview
These are the results of a ctwas analysis of the UK Biobank trait LDL direct using Liver_SORT1 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 Liver_SORT1 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] 10902#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1071 768 652 417 494 611 548 408 405 434 634 629 195 365 354
16 17 18 19 20 21 22
526 663 160 859 306 114 289 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8365438Load 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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0097361600 0.0001740824 #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
44.804384 9.714547 #report sample size
print(sample_size)[1] 343621#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10902 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
0.01383996 0.04280033 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.02576947 0.33525503Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z
12008 HPR 16_38 1.0000000 209.84524 6.106880e-04 -17.240252
5563 ABCG8 2_27 0.9999667 313.61660 9.126513e-04 -20.293982
3721 INSIG2 2_69 0.9997835 62.50606 1.818647e-04 -9.364196
12687 RP4-781K5.7 1_121 0.9997323 203.71281 5.926828e-04 -15.108415
5544 CNIH4 1_114 0.9996225 48.38269 1.407493e-04 6.721857
5991 FADS1 11_34 0.9995362 160.57923 4.670982e-04 12.825883
10657 TRIM39 6_24 0.9986852 72.25254 2.099916e-04 8.848422
1999 PRKD2 19_33 0.9960497 32.48400 9.416095e-05 5.289849
7410 ABCA1 9_53 0.9953955 70.36807 2.038410e-04 7.982017
9390 GAS6 13_62 0.9881812 71.35449 2.052004e-04 -8.923688
1597 PLTP 20_28 0.9877966 61.56981 1.769928e-04 -5.732491
12715 SORT1 1_67 0.9873012 1681.66147 4.831795e-03 -41.793474
8531 TNKS 8_12 0.9843992 73.76708 2.113266e-04 11.026034
7040 INHBB 2_70 0.9822550 74.04749 2.116678e-04 -8.518936
2092 SP4 7_19 0.9770594 102.38295 2.911179e-04 10.693191
4704 DDX56 7_32 0.9746378 58.70502 1.665094e-04 9.446271
6093 CSNK1G3 5_75 0.9746220 84.22981 2.389034e-04 9.116291
6996 ACP6 1_73 0.9686707 25.67817 7.238700e-05 4.648193
6220 PELO 5_31 0.9671689 72.14660 2.030666e-04 8.426917
8865 FUT2 19_33 0.9654285 104.78618 2.944045e-04 -11.927107
233 NPC1L1 7_32 0.9639516 89.79973 2.519130e-04 -10.761931
11790 CYP2A6 19_28 0.9618743 32.00395 8.958642e-05 5.407028
3247 KDSR 18_35 0.9552673 24.68796 6.863260e-05 -4.526287
3562 ACVR1C 2_94 0.9388258 26.34290 7.197288e-05 -4.737778
6778 PKN3 9_66 0.9359865 47.70775 1.299507e-04 -6.620563
1114 SRRT 7_62 0.9266916 33.00835 8.901832e-05 5.547715
6391 TTC39B 9_13 0.9259989 23.04988 6.211543e-05 -4.287139
6957 USP1 1_39 0.8944443 253.87996 6.608487e-04 16.258211
3300 C10orf88 10_77 0.8796486 35.77485 9.158142e-05 -6.634448
9062 KLHDC7A 1_13 0.8184864 22.59315 5.381564e-05 4.124187
9072 SPTY2D1 11_13 0.8096215 33.54398 7.903455e-05 -5.557123
8931 CRACR2B 11_1 0.8018274 22.03492 5.141771e-05 -3.989585
8418 POP7 7_62 0.8015962 40.08302 9.350534e-05 -5.845258
num_eqtl
12008 2
5563 1
3721 3
12687 1
5544 2
5991 2
10657 3
1999 2
7410 1
9390 1
1597 1
12715 1
8531 2
7040 1
2092 1
4704 2
6093 1
6996 2
6220 2
8865 1
233 1
11790 1
3247 1
3562 2
6778 1
1114 2
6391 3
6957 1
3300 2
9062 1
9072 1
8931 1
8418 1Genes with largest effect sizes
#plot PIP vs effect size
plot(ctwas_gene_res$susie_pip, ctwas_gene_res$mu2, xlab="PIP", ylab="mu^2", main="Gene PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE
12715 SORT1 1_67 9.873012e-01 1681.6615 4.831795e-03
4435 PSRC1 1_67 2.464714e-02 1673.2741 1.200201e-04
5436 PSMA5 1_67 7.880027e-03 1212.6447 2.780876e-05
4562 SRPK2 7_65 0.000000e+00 518.4576 0.000000e+00
6970 ATXN7L2 1_67 9.823180e-03 367.0754 1.049368e-05
5563 ABCG8 2_27 9.999667e-01 313.6166 9.126513e-04
11364 RP11-325F22.2 7_65 0.000000e+00 297.6048 0.000000e+00
781 PVR 19_32 0.000000e+00 295.7017 0.000000e+00
6957 USP1 1_39 8.944443e-01 253.8800 6.608487e-04
4317 ANGPTL3 1_39 1.149945e-01 249.6543 8.354808e-05
11684 RP11-136O12.2 8_83 3.004591e-03 235.9050 2.062732e-06
3441 POLK 5_45 4.086732e-03 217.4555 2.586228e-06
12008 HPR 16_38 1.000000e+00 209.8452 6.106880e-04
12687 RP4-781K5.7 1_121 9.997323e-01 203.7128 5.926828e-04
5431 SYPL2 1_67 1.643337e-02 198.5261 9.494332e-06
5377 GEMIN7 19_32 0.000000e+00 193.9781 0.000000e+00
5991 FADS1 11_34 9.995362e-01 160.5792 4.670982e-04
5240 NLRC5 16_31 8.890738e-02 159.6861 4.131667e-05
538 ZNF112 19_32 0.000000e+00 147.0610 0.000000e+00
11245 AC067959.1 2_13 3.200605e-09 145.4737 1.354993e-12
z num_eqtl
12715 -41.7934744 1
4435 -41.6873361 1
5436 -35.4138115 2
4562 -1.4622459 1
6970 -19.2427445 2
5563 -20.2939818 1
11364 0.9704489 2
781 -10.0782525 2
6957 16.2582110 1
4317 16.1322287 1
11684 14.4041325 1
3441 17.5157647 1
12008 -17.2402523 2
12687 -15.1084154 1
5431 -14.1478749 2
5377 10.9432287 2
5991 12.8258829 2
5240 11.8602110 1
538 10.3860543 1
11245 -2.3287171 1Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
12715 SORT1 1_67 0.9873012 1681.66147 0.0048317954 -41.793474
5563 ABCG8 2_27 0.9999667 313.61660 0.0009126513 -20.293982
6957 USP1 1_39 0.8944443 253.87996 0.0006608487 16.258211
12008 HPR 16_38 1.0000000 209.84524 0.0006106880 -17.240252
12687 RP4-781K5.7 1_121 0.9997323 203.71281 0.0005926828 -15.108415
5991 FADS1 11_34 0.9995362 160.57923 0.0004670982 12.825883
8865 FUT2 19_33 0.9654285 104.78618 0.0002944045 -11.927107
2092 SP4 7_19 0.9770594 102.38295 0.0002911179 10.693191
233 NPC1L1 7_32 0.9639516 89.79973 0.0002519130 -10.761931
6093 CSNK1G3 5_75 0.9746220 84.22981 0.0002389034 9.116291
7040 INHBB 2_70 0.9822550 74.04749 0.0002116678 -8.518936
8531 TNKS 8_12 0.9843992 73.76708 0.0002113266 11.026034
10657 TRIM39 6_24 0.9986852 72.25254 0.0002099916 8.848422
9390 GAS6 13_62 0.9881812 71.35449 0.0002052004 -8.923688
7410 ABCA1 9_53 0.9953955 70.36807 0.0002038410 7.982017
6220 PELO 5_31 0.9671689 72.14660 0.0002030666 8.426917
3721 INSIG2 2_69 0.9997835 62.50606 0.0001818647 -9.364196
1597 PLTP 20_28 0.9877966 61.56981 0.0001769928 -5.732491
4704 DDX56 7_32 0.9746378 58.70502 0.0001665094 9.446271
5544 CNIH4 1_114 0.9996225 48.38269 0.0001407493 6.721857
num_eqtl
12715 1
5563 1
6957 1
12008 2
12687 1
5991 2
8865 1
2092 1
233 1
6093 1
7040 1
8531 2
10657 3
9390 1
7410 1
6220 2
3721 3
1597 1
4704 2
5544 2Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE
12715 SORT1 1_67 9.873012e-01 1681.6615 4.831795e-03
4435 PSRC1 1_67 2.464714e-02 1673.2741 1.200201e-04
5436 PSMA5 1_67 7.880027e-03 1212.6447 2.780876e-05
5563 ABCG8 2_27 9.999667e-01 313.6166 9.126513e-04
6970 ATXN7L2 1_67 9.823180e-03 367.0754 1.049368e-05
3441 POLK 5_45 4.086732e-03 217.4555 2.586228e-06
12008 HPR 16_38 1.000000e+00 209.8452 6.106880e-04
6957 USP1 1_39 8.944443e-01 253.8800 6.608487e-04
4317 ANGPTL3 1_39 1.149945e-01 249.6543 8.354808e-05
12687 RP4-781K5.7 1_121 9.997323e-01 203.7128 5.926828e-04
9978 ANKDD1B 5_45 4.085134e-03 144.6236 1.719355e-06
11684 RP11-136O12.2 8_83 3.004591e-03 235.9050 2.062732e-06
5431 SYPL2 1_67 1.643337e-02 198.5261 9.494332e-06
1930 PPP1R37 19_32 0.000000e+00 125.3261 0.000000e+00
5991 FADS1 11_34 9.995362e-01 160.5792 4.670982e-04
7955 FEN1 11_34 6.376688e-03 145.1988 2.694501e-06
4507 FADS2 11_34 6.376688e-03 145.1988 2.694501e-06
4112 YIPF2 19_9 2.205268e-09 126.5638 8.122524e-13
8865 FUT2 19_33 9.654285e-01 104.7862 2.944045e-04
5240 NLRC5 16_31 8.890738e-02 159.6861 4.131667e-05
z num_eqtl
12715 -41.79347 1
4435 -41.68734 1
5436 -35.41381 2
5563 -20.29398 1
6970 -19.24274 2
3441 17.51576 1
12008 -17.24025 2
6957 16.25821 1
4317 16.13223 1
12687 -15.10842 1
9978 15.06698 2
11684 14.40413 1
5431 -14.14787 2
1930 -12.89212 2
5991 12.82588 2
7955 12.07264 1
4507 12.07264 1
4112 11.94206 1
8865 -11.92711 1
5240 11.86021 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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.0199046#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE
12715 SORT1 1_67 9.873012e-01 1681.6615 4.831795e-03
4435 PSRC1 1_67 2.464714e-02 1673.2741 1.200201e-04
5436 PSMA5 1_67 7.880027e-03 1212.6447 2.780876e-05
5563 ABCG8 2_27 9.999667e-01 313.6166 9.126513e-04
6970 ATXN7L2 1_67 9.823180e-03 367.0754 1.049368e-05
3441 POLK 5_45 4.086732e-03 217.4555 2.586228e-06
12008 HPR 16_38 1.000000e+00 209.8452 6.106880e-04
6957 USP1 1_39 8.944443e-01 253.8800 6.608487e-04
4317 ANGPTL3 1_39 1.149945e-01 249.6543 8.354808e-05
12687 RP4-781K5.7 1_121 9.997323e-01 203.7128 5.926828e-04
9978 ANKDD1B 5_45 4.085134e-03 144.6236 1.719355e-06
11684 RP11-136O12.2 8_83 3.004591e-03 235.9050 2.062732e-06
5431 SYPL2 1_67 1.643337e-02 198.5261 9.494332e-06
1930 PPP1R37 19_32 0.000000e+00 125.3261 0.000000e+00
5991 FADS1 11_34 9.995362e-01 160.5792 4.670982e-04
7955 FEN1 11_34 6.376688e-03 145.1988 2.694501e-06
4507 FADS2 11_34 6.376688e-03 145.1988 2.694501e-06
4112 YIPF2 19_9 2.205268e-09 126.5638 8.122524e-13
8865 FUT2 19_33 9.654285e-01 104.7862 2.944045e-04
5240 NLRC5 16_31 8.890738e-02 159.6861 4.131667e-05
z num_eqtl
12715 -41.79347 1
4435 -41.68734 1
5436 -35.41381 2
5563 -20.29398 1
6970 -19.24274 2
3441 17.51576 1
12008 -17.24025 2
6957 16.25821 1
4317 16.13223 1
12687 -15.10842 1
9978 15.06698 2
11684 14.40413 1
5431 -14.14787 2
1930 -12.89212 2
5991 12.82588 2
7955 12.07264 1
4507 12.07264 1
4112 11.94206 1
8865 -11.92711 1
5240 11.86021 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: 1_67"
genename region_tag susie_pip mu2 PVE z
4434 VAV3 1_67 0.065099832 24.320182 4.607517e-06 -2.1042470
1073 SLC25A24 1_67 0.008611429 6.469190 1.621233e-07 0.9234769
6966 FAM102B 1_67 0.007746337 6.075857 1.369696e-07 -1.1378586
3009 STXBP3 1_67 0.017008399 18.337108 9.076420e-07 2.9982594
3438 GPSM2 1_67 0.008080202 9.107535 2.141625e-07 -1.9348222
3437 CLCC1 1_67 0.008069139 11.438836 2.686144e-07 2.5660415
10286 TAF13 1_67 0.010760437 9.452750 2.960114e-07 -1.5591453
10955 TMEM167B 1_67 0.013756111 11.324506 4.533517e-07 -1.5270485
315 SARS 1_67 0.014473303 94.891762 3.996837e-06 9.5234950
12715 SORT1 1_67 0.987301191 1681.661467 4.831795e-03 -41.7934744
5436 PSMA5 1_67 0.007880027 1212.644656 2.780876e-05 -35.4138115
5431 SYPL2 1_67 0.016433368 198.526066 9.494332e-06 -14.1478749
6970 ATXN7L2 1_67 0.009823180 367.075403 1.049368e-05 -19.2427445
4435 PSRC1 1_67 0.024647145 1673.274100 1.200201e-04 -41.6873361
8615 CYB561D1 1_67 0.063213063 127.989209 2.354510e-05 10.6827516
9259 AMIGO1 1_67 0.018715231 27.987319 1.524322e-06 -3.9630816
6445 GPR61 1_67 0.007845449 23.052571 5.263292e-07 4.2425343
587 GNAI3 1_67 0.054241321 31.679097 5.000614e-06 -3.8408490
7977 GSTM4 1_67 0.014374335 30.871560 1.291417e-06 4.7825961
10821 GSTM2 1_67 0.008660380 14.362332 3.619780e-07 2.9726102
4430 GSTM1 1_67 0.018905914 29.235760 1.608542e-06 4.2590068
4433 GSTM3 1_67 0.007854604 20.899567 4.777293e-07 -3.9546683
4432 GSTM5 1_67 0.014116282 14.798554 6.079389e-07 2.3798227
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "Region: 2_27"
genename region_tag susie_pip mu2 PVE
12661 LINC01126 2_27 1.311448e-05 17.794481 6.791362e-10
2977 THADA 2_27 1.774105e-06 8.185093 4.225938e-11
6208 PLEKHH2 2_27 6.426620e-06 16.108349 3.012687e-10
11022 C1GALT1C1L 2_27 3.979730e-06 24.312863 2.815853e-10
4930 DYNC2LI1 2_27 8.642848e-07 8.220899 2.067743e-11
5563 ABCG8 2_27 9.999667e-01 313.616602 9.126513e-04
4943 LRPPRC 2_27 2.558980e-06 12.554554 9.349503e-11
z
12661 0.91913800
2977 -2.34643541
6208 -2.96266114
11022 3.06095256
4930 -0.02538894
5563 -20.29398177
4943 -0.91853212
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "Region: 5_45"
genename region_tag susie_pip mu2 PVE
8340 ENC1 5_45 0.001035337 5.039858 1.518520e-08
7307 GFM2 5_45 0.001320858 6.976638 2.681778e-08
7306 NSA2 5_45 0.002589266 15.645763 1.178946e-07
10458 FAM169A 5_45 0.001100229 6.142066 1.966608e-08
3441 POLK 5_45 0.004086732 217.455496 2.586228e-06
12287 CTC-366B18.4 5_45 0.049947636 104.949701 1.525515e-05
9978 ANKDD1B 5_45 0.004085134 144.623555 1.719355e-06
6186 POC5 5_45 0.004045610 49.433109 5.819990e-07
11757 AC113404.1 5_45 0.001796895 13.679364 7.153341e-08
5717 IQGAP2 5_45 0.005523966 22.930980 3.686328e-07
7281 F2RL2 5_45 0.001225391 6.622802 2.361765e-08
9219 F2R 5_45 0.002030873 11.367751 6.718581e-08
7287 F2RL1 5_45 0.011051116 27.615939 8.881499e-07
5718 CRHBP 5_45 0.001234901 6.618927 2.378702e-08
7288 AGGF1 5_45 0.001164389 6.093567 2.064857e-08
4314 ZBED3 5_45 0.005668933 21.527896 3.551593e-07
2729 PDE8B 5_45 0.001128430 5.782481 1.898931e-08
7289 WDR41 5_45 0.001113305 5.646907 1.829553e-08
4313 AP3B1 5_45 0.004175976 18.483106 2.246225e-07
z
8340 -0.4000089
7307 -0.4062418
7306 -2.0511430
10458 -0.9826944
3441 17.5157647
12287 -10.7732063
9978 15.0669830
6186 -7.0119331
11757 2.3250769
5717 2.5652287
7281 0.5923159
9219 -1.2065901
7287 2.2468261
5718 -0.6287222
7288 -0.5067707
4314 -1.8752115
2729 0.4406481
7289 -0.4097230
4313 1.7055957
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "Region: 16_38"
genename region_tag susie_pip mu2 PVE z
9196 CMTR2 16_38 0.005337161 17.92485 2.784109e-07 3.141964
5237 CHST4 16_38 0.002247854 13.04318 8.532411e-08 5.644697
7757 ZNF23 16_38 0.002629850 14.09995 1.079118e-07 -2.837908
6535 ZNF19 16_38 0.006317105 22.57561 4.150285e-07 -1.744263
10432 TAT 16_38 0.002454268 22.40243 1.600064e-07 5.466577
5236 MARVELD3 16_38 0.003036604 21.35162 1.886858e-07 -2.077911
366 PHLPP2 16_38 0.005090025 51.93105 7.692497e-07 -7.224850
11042 ATXN1L 16_38 0.002283953 56.92601 3.783713e-07 -8.126354
1752 ZNF821 16_38 0.002273640 46.15333 3.053831e-07 7.585503
12612 PKD1L3 16_38 0.003294069 99.19138 9.508828e-07 4.998967
12008 HPR 16_38 1.000000000 209.84524 6.106880e-04 -17.240252
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "Region: 1_39"
genename region_tag susie_pip mu2 PVE z
6956 TM2D1 1_39 0.056959707 23.071148 3.824347e-06 2.1432487
4316 KANK4 1_39 0.008972516 5.075472 1.325290e-07 0.5123038
6957 USP1 1_39 0.894444325 253.879959 6.608487e-04 16.2582110
4317 ANGPTL3 1_39 0.114994538 249.654254 8.354808e-05 16.1322287
3024 DOCK7 1_39 0.010009135 24.336915 7.088958e-07 4.4594815
3733 ATG4C 1_39 0.024969688 81.344478 5.911007e-06 -8.6477262
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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
14015 rs2495502 1_34 1.0000000 283.39830 8.247409e-04
68004 rs1042034 2_13 1.0000000 233.15915 6.785358e-04
68010 rs934197 2_13 1.0000000 415.42498 1.208963e-03
69740 rs780093 2_16 1.0000000 160.58172 4.673222e-04
365662 rs12208357 6_103 1.0000000 234.68244 6.829688e-04
402311 rs763798411 7_65 1.0000000 3396.71235 9.885055e-03
753628 rs113408695 17_39 1.0000000 143.15204 4.165986e-04
787044 rs73013176 19_9 1.0000000 237.05896 6.898849e-04
797185 rs62117204 19_32 1.0000000 825.46404 2.402251e-03
797203 rs111794050 19_32 1.0000000 763.44994 2.221779e-03
797236 rs814573 19_32 1.0000000 2204.07229 6.414254e-03
797238 rs113345881 19_32 1.0000000 772.04516 2.246793e-03
797241 rs12721109 19_32 1.0000000 1341.06609 3.902748e-03
1025547 rs964184 11_70 1.0000000 238.93791 6.953530e-04
753654 rs8070232 17_39 1.0000000 144.02681 4.191444e-04
789854 rs2285626 19_15 1.0000000 245.81587 7.153692e-04
807513 rs34507316 20_13 1.0000000 78.16426 2.274723e-04
67955 rs11679386 2_12 1.0000000 127.40733 3.707786e-04
68013 rs548145 2_13 1.0000000 656.37348 1.910167e-03
68090 rs1848922 2_13 1.0000000 229.85857 6.689305e-04
500307 rs115478735 9_70 1.0000000 302.07219 8.790854e-04
1097413 rs1800961 20_28 1.0000000 70.80780 2.060636e-04
752712 rs1801689 17_38 1.0000000 79.68987 2.319121e-04
796899 rs73036721 19_30 1.0000000 57.37649 1.669761e-04
75418 rs72800939 2_28 1.0000000 55.14435 1.604801e-04
440113 rs4738679 8_45 1.0000000 106.62314 3.102929e-04
787082 rs137992968 19_9 1.0000000 112.56039 3.275713e-04
582936 rs4937122 11_77 0.9999999 77.06485 2.242728e-04
14026 rs10888896 1_34 0.9999999 131.44541 3.825302e-04
365846 rs56393506 6_104 0.9999999 88.86741 2.586204e-04
7471 rs79598313 1_18 0.9999996 46.26124 1.346286e-04
459774 rs13252684 8_83 0.9999991 216.74642 6.307712e-04
52932 rs2807848 1_112 0.9999990 58.52565 1.703202e-04
438718 rs140753685 8_42 0.9999981 54.26658 1.579254e-04
796944 rs62115478 19_30 0.9999959 179.71675 5.230065e-04
789879 rs3794991 19_15 0.9999931 212.34134 6.179479e-04
13985 rs11580527 1_34 0.9999819 87.73827 2.553298e-04
14033 rs471705 1_34 0.9999643 207.61741 6.041831e-04
347099 rs9496567 6_67 0.9999493 38.26875 1.113634e-04
317884 rs11376017 6_13 0.9998645 64.29661 1.870895e-04
787108 rs4804149 19_10 0.9998583 45.33233 1.319067e-04
787068 rs3745677 19_9 0.9998138 88.64445 2.579235e-04
807512 rs6075251 20_13 0.9997609 51.25984 1.491399e-04
365810 rs117733303 6_104 0.9994569 97.23060 2.828052e-04
538661 rs17875416 10_71 0.9991968 37.10628 1.078993e-04
787073 rs1569372 19_9 0.9990931 268.63428 7.810659e-04
787161 rs322144 19_10 0.9989403 54.42858 1.582293e-04
603362 rs7397189 12_36 0.9988891 33.42230 9.715697e-05
789838 rs12981966 19_15 0.9986767 90.09468 2.618450e-04
787065 rs147985405 19_9 0.9984711 2244.59923 6.522207e-03
428445 rs1495743 8_20 0.9975054 40.03842 1.162285e-04
789519 rs2302209 19_14 0.9967822 42.18165 1.223613e-04
321970 rs454182 6_22 0.9961194 31.79158 9.216027e-05
279291 rs7701166 5_45 0.9959927 32.16840 9.324077e-05
440081 rs56386732 8_45 0.9953284 34.15926 9.894531e-05
401241 rs3197597 7_61 0.9951083 31.93647 9.248632e-05
812466 rs76981217 20_24 0.9948789 35.06138 1.015125e-04
607728 rs148481241 12_44 0.9919763 26.93591 7.775948e-05
619946 rs653178 12_67 0.9918978 91.31515 2.635907e-04
322407 rs3130253 6_23 0.9891939 28.48415 8.199833e-05
1052542 rs12445804 16_12 0.9889191 33.19453 9.553171e-05
136562 rs709149 3_9 0.9842266 35.13157 1.006266e-04
402322 rs4997569 7_65 0.9829557 3420.88216 9.785711e-03
728365 rs4396539 16_37 0.9817537 26.83979 7.668349e-05
279232 rs10062361 5_45 0.9809662 198.65357 5.671144e-04
143572 rs9834932 3_24 0.9787030 64.78782 1.845290e-04
812470 rs73124945 20_24 0.9782549 32.06393 9.128284e-05
812417 rs6029132 20_24 0.9779037 38.62864 1.099324e-04
624035 rs11057830 12_76 0.9778745 25.37370 7.220831e-05
243844 rs114756490 4_100 0.9644687 25.75683 7.229378e-05
459763 rs79658059 8_83 0.9607192 258.87917 7.237922e-04
564013 rs6591179 11_36 0.9597312 25.78495 7.201719e-05
385473 rs141379002 7_33 0.9597292 25.04656 6.995473e-05
820471 rs62219001 21_2 0.9590058 25.62942 7.152870e-05
221115 rs1458038 4_54 0.9578839 51.20624 1.427434e-04
475021 rs1556516 9_16 0.9540145 71.53687 1.986119e-04
756787 rs4969183 17_44 0.9529555 47.80066 1.325644e-04
588845 rs11048034 12_9 0.9494114 34.77366 9.607825e-05
467826 rs7024888 9_3 0.9442346 25.75505 7.077219e-05
321431 rs75080831 6_19 0.9414962 55.50200 1.520714e-04
622900 rs1169300 12_74 0.9403212 66.55848 1.821377e-04
322378 rs28986304 6_23 0.9401426 41.98181 1.148617e-04
618039 rs1196760 12_63 0.9388555 25.37039 6.931804e-05
68007 rs78610189 2_13 0.9211001 58.28823 1.562457e-04
349835 rs12199109 6_73 0.9187283 24.37639 6.517437e-05
192740 rs5855544 3_120 0.9183709 23.51557 6.284835e-05
424122 rs117037226 8_11 0.9089191 23.58510 6.238545e-05
14016 rs1887552 1_34 0.9064797 326.56512 8.614859e-04
365656 rs9456502 6_103 0.9048365 32.52889 8.565637e-05
194527 rs36205397 4_4 0.8917378 37.33934 9.690009e-05
505257 rs10905277 10_8 0.8890510 27.52765 7.122233e-05
168565 rs189174 3_74 0.8879801 42.98935 1.110924e-04
724473 rs821840 16_31 0.8876263 154.64172 3.994635e-04
538372 rs12244851 10_70 0.8848188 35.55355 9.154985e-05
803158 rs74273659 20_5 0.8839709 24.37799 6.271280e-05
787149 rs322125 19_10 0.8839093 98.44547 2.532350e-04
576653 rs201912654 11_59 0.8672199 39.31461 9.922098e-05
196752 rs2002574 4_10 0.8654715 24.48735 6.167581e-05
789928 rs12984303 19_15 0.8638363 24.54658 6.170819e-05
815969 rs10641149 20_32 0.8632498 26.79960 6.732635e-05
118659 rs7569317 2_120 0.8624280 43.75055 1.098062e-04
1058530 rs763665 16_38 0.8577432 137.83737 3.440682e-04
67807 rs6531234 2_12 0.8552413 41.73982 1.038866e-04
827712 rs2835302 21_17 0.8505265 25.61363 6.339853e-05
787118 rs58495388 19_10 0.8502362 33.27765 8.234033e-05
800968 rs34003091 19_39 0.8469877 101.75007 2.508027e-04
839800 rs145678077 22_17 0.8439364 24.90997 6.117912e-05
812435 rs6102034 20_24 0.8436944 95.23413 2.338289e-04
483007 rs11144506 9_35 0.8431092 26.72443 6.557113e-05
356038 rs9321207 6_86 0.8403145 30.12016 7.365792e-05
582939 rs74612335 11_77 0.8386661 75.15697 1.834335e-04
279255 rs3843482 5_45 0.8331412 389.96981 9.455182e-04
811211 rs11167269 20_21 0.8262414 55.46835 1.333744e-04
931949 rs535137438 5_31 0.8224762 31.27791 7.486543e-05
532551 rs10882161 10_59 0.8096986 29.44109 6.937413e-05
753639 rs9303012 17_39 0.8093213 135.16292 3.183456e-04
807493 rs78348000 20_13 0.8011579 29.84630 6.958712e-05
z
14015 -6.292225
68004 -16.573036
68010 -33.060888
69740 14.142603
365662 -12.282337
402311 -3.272149
753628 -12.768796
787044 16.232742
797185 44.672230
797203 33.599649
797236 -55.537887
797238 34.318568
797241 46.325818
1025547 16.661098
753654 8.091491
789854 18.215134
807513 6.814661
67955 -11.909428
68013 -33.086010
68090 -25.412292
500307 -19.011790
1097413 8.896957
752712 -9.396430
796899 7.787947
75418 7.845728
440113 11.699924
787082 10.752566
582936 -12.147947
14026 -11.893801
365846 -14.088321
7471 -7.024638
459774 -11.964411
52932 7.882775
438718 -7.799241
796944 14.326186
789879 21.492060
13985 11.167216
14033 -16.262997
347099 6.340216
317884 8.507919
787108 -6.519414
787068 -9.335807
807512 2.329832
365810 -10.097959
538661 6.266313
787073 -10.005506
787161 -3.946578
603362 5.770964
789838 -1.822895
787065 48.935175
428445 6.515969
789519 -6.636049
321970 -4.779053
279291 2.484790
440081 7.012272
401241 5.045242
812466 -7.692477
607728 -5.095452
619946 -11.050062
322407 -5.641451
1052542 -5.772374
136562 6.781974
402322 2.984117
728365 5.232860
279232 -20.320600
143572 8.481579
812470 7.775426
812417 6.762459
624035 -4.929635
243844 -4.988910
459763 16.022043
564013 -4.893333
385473 -4.896981
820471 4.948445
221115 7.417851
475021 8.992146
756787 -7.169275
588845 -6.133690
467826 5.055827
321431 7.906709
622900 -8.685477
322378 -7.382502
618039 4.866700
68007 8.385467
349835 -4.857045
192740 4.593724
424122 -4.192202
14016 9.868570
365656 -5.963991
194527 -6.159378
505257 -5.125802
168565 -6.767794
724473 13.475251
538372 4.883085
803158 -4.646762
787149 7.470403
576653 6.305597
196752 4.558284
789928 -4.516645
815969 -5.075761
118659 -7.900653
1058530 11.285714
67807 7.170830
827712 4.653743
787118 -5.531347
800968 10.423688
839800 4.868601
812435 11.189979
483007 -5.042667
356038 -5.401634
582939 -11.904831
279255 -25.034352
811211 7.795037
931949 5.067634
532551 5.475649
753639 -2.259115
807493 -5.220624SNPs 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
402322 rs4997569 7_65 9.829557e-01 3420.882 9.785711e-03
402314 rs10274607 7_65 6.568395e-02 3411.352 6.520877e-04
402317 rs13230660 7_65 1.763701e-01 3408.700 1.749581e-03
402329 rs6952534 7_65 7.708635e-03 3406.742 7.642528e-05
402328 rs4730069 7_65 1.970167e-03 3403.406 1.951359e-05
402311 rs763798411 7_65 1.000000e+00 3396.712 9.885055e-03
402321 rs10242713 7_65 6.069594e-05 3390.662 5.989139e-07
402324 rs10249965 7_65 8.715420e-07 3363.587 8.531223e-09
402336 rs1013016 7_65 0.000000e+00 3216.926 0.000000e+00
402361 rs8180737 7_65 0.000000e+00 3065.938 0.000000e+00
402354 rs17778396 7_65 0.000000e+00 3064.282 0.000000e+00
402355 rs2237621 7_65 0.000000e+00 3063.058 0.000000e+00
402388 rs10224564 7_65 0.000000e+00 3057.255 0.000000e+00
402326 rs71562637 7_65 0.000000e+00 3056.533 0.000000e+00
402373 rs10255779 7_65 0.000000e+00 3056.267 0.000000e+00
402390 rs78132606 7_65 0.000000e+00 3040.831 0.000000e+00
402393 rs4610671 7_65 0.000000e+00 3035.828 0.000000e+00
402395 rs12669532 7_65 0.000000e+00 2912.556 0.000000e+00
402352 rs2237618 7_65 0.000000e+00 2858.276 0.000000e+00
402397 rs118089279 7_65 0.000000e+00 2835.054 0.000000e+00
402384 rs73188303 7_65 0.000000e+00 2827.128 0.000000e+00
787065 rs147985405 19_9 9.984711e-01 2244.599 6.522207e-03
402394 rs560364150 7_65 0.000000e+00 2237.943 0.000000e+00
787060 rs73015020 19_9 8.949230e-04 2232.714 5.814858e-06
787058 rs138175288 19_9 4.220323e-04 2230.913 2.739988e-06
787061 rs77140532 19_9 6.329720e-05 2227.531 4.103256e-07
787059 rs138294113 19_9 1.041836e-04 2226.911 6.751850e-07
787063 rs10412048 19_9 1.308379e-05 2224.251 8.469105e-08
787062 rs112552009 19_9 3.159260e-05 2223.220 2.044035e-07
797236 rs814573 19_32 1.000000e+00 2204.072 6.414254e-03
787057 rs55997232 19_9 1.647129e-08 2203.737 1.056349e-10
402380 rs10261738 7_65 0.000000e+00 1848.199 0.000000e+00
787066 rs17248769 19_9 1.554718e-06 1690.831 7.650188e-09
787067 rs2228671 19_9 1.068626e-06 1679.767 5.223904e-09
797231 rs34878901 19_32 0.000000e+00 1526.404 0.000000e+00
874797 rs12740374 1_67 4.242375e-04 1445.136 1.784178e-06
874793 rs7528419 1_67 4.907785e-04 1442.138 2.059741e-06
874804 rs646776 1_67 4.449647e-04 1441.323 1.866411e-06
874803 rs629301 1_67 4.297103e-04 1437.930 1.798182e-06
797228 rs8106922 19_32 0.000000e+00 1437.533 0.000000e+00
874815 rs583104 1_67 4.532478e-04 1397.560 1.843430e-06
402335 rs368909701 7_65 0.000000e+00 1395.874 0.000000e+00
874818 rs4970836 1_67 4.466012e-04 1394.716 1.812700e-06
874820 rs1277930 1_67 4.563564e-04 1390.073 1.846130e-06
874821 rs599839 1_67 4.709081e-04 1389.161 1.903746e-06
874801 rs3832016 1_67 3.230354e-04 1350.618 1.269705e-06
874798 rs660240 1_67 3.206728e-04 1343.461 1.253740e-06
797241 rs12721109 19_32 1.000000e+00 1341.066 3.902748e-03
874816 rs602633 1_67 3.563030e-04 1322.388 1.371193e-06
797156 rs62120566 19_32 0.000000e+00 1321.045 0.000000e+00
z
402322 2.9841166
402314 2.8669582
402317 2.9479628
402329 2.8884240
402328 2.8658735
402311 -3.2721491
402321 2.8123983
402324 2.8497381
402336 -2.3988524
402361 2.8328454
402354 2.7980012
402355 2.8029605
402388 2.7911904
402326 2.6635936
402373 2.8135791
402390 2.7728082
402393 2.7249742
402395 2.7702573
402352 2.4663255
402397 2.6667208
402384 2.4217031
787065 48.9351750
402394 1.8694582
787060 48.7956295
787058 48.7806894
787061 48.7379874
787059 48.7519286
787063 48.7012269
787062 48.7051628
797236 -55.5378874
787057 48.5243103
402380 2.6665109
787066 40.8424908
787067 40.7026250
797231 -16.3492722
874797 41.7934744
874793 41.7369129
874804 -41.7333995
874803 -41.6873361
797228 -15.6770531
874815 -41.0870961
402335 0.7778883
874818 -41.0454951
874820 -40.9759931
874821 -40.9589874
874801 -40.3959842
874798 -40.2895814
797241 46.3258178
874816 -39.9564086
797156 33.7353904SNPs 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
402311 rs763798411 7_65 1.00000000 3396.71235 0.0098850546
402322 rs4997569 7_65 0.98295573 3420.88216 0.0097857107
787065 rs147985405 19_9 0.99847109 2244.59923 0.0065222074
797236 rs814573 19_32 1.00000000 2204.07229 0.0064142537
797241 rs12721109 19_32 1.00000000 1341.06609 0.0039027478
797185 rs62117204 19_32 1.00000000 825.46404 0.0024022514
797238 rs113345881 19_32 1.00000000 772.04516 0.0022467927
797203 rs111794050 19_32 1.00000000 763.44994 0.0022217790
68013 rs548145 2_13 1.00000000 656.37348 0.0019101670
402317 rs13230660 7_65 0.17637011 3408.69952 0.0017495808
68010 rs934197 2_13 1.00000000 415.42498 0.0012089627
279255 rs3843482 5_45 0.83314121 389.96981 0.0009455182
500307 rs115478735 9_70 1.00000000 302.07219 0.0008790854
14016 rs1887552 1_34 0.90647969 326.56512 0.0008614859
14015 rs2495502 1_34 1.00000000 283.39830 0.0008247409
787073 rs1569372 19_9 0.99909305 268.63428 0.0007810659
459763 rs79658059 8_83 0.96071923 258.87917 0.0007237922
789854 rs2285626 19_15 1.00000000 245.81587 0.0007153692
1025547 rs964184 11_70 1.00000000 238.93791 0.0006953530
787044 rs73013176 19_9 1.00000000 237.05896 0.0006898849
365662 rs12208357 6_103 1.00000000 234.68244 0.0006829688
68004 rs1042034 2_13 1.00000000 233.15915 0.0006785358
68090 rs1848922 2_13 1.00000000 229.85857 0.0006689305
402314 rs10274607 7_65 0.06568395 3411.35159 0.0006520877
459774 rs13252684 8_83 0.99999914 216.74642 0.0006307712
789879 rs3794991 19_15 0.99999308 212.34134 0.0006179479
14033 rs471705 1_34 0.99996426 207.61741 0.0006041831
279232 rs10062361 5_45 0.98096616 198.65357 0.0005671144
796944 rs62115478 19_30 0.99999588 179.71675 0.0005230065
907819 rs6544713 2_27 0.76455919 223.20976 0.0004966433
69740 rs780093 2_16 1.00000000 160.58172 0.0004673222
753654 rs8070232 17_39 1.00000000 144.02681 0.0004191444
365676 rs3818678 6_103 0.75522628 190.32636 0.0004183082
753628 rs113408695 17_39 1.00000000 143.15204 0.0004165986
724473 rs821840 16_31 0.88762627 154.64172 0.0003994635
14026 rs10888896 1_34 0.99999990 131.44541 0.0003825302
67955 rs11679386 2_12 1.00000000 127.40733 0.0003707786
1058530 rs763665 16_38 0.85774323 137.83737 0.0003440682
304134 rs12657266 5_92 0.74890902 153.03976 0.0003335444
1058537 rs77303550 16_38 0.70612966 160.90896 0.0003306625
787082 rs137992968 19_9 0.99999998 112.56039 0.0003275713
753639 rs9303012 17_39 0.80932130 135.16292 0.0003183456
440113 rs4738679 8_45 0.99999998 106.62314 0.0003102929
459762 rs2980875 8_83 0.57334932 184.71361 0.0003082042
365810 rs117733303 6_104 0.99945686 97.23060 0.0002828052
619946 rs653178 12_67 0.99189778 91.31515 0.0002635907
789838 rs12981966 19_15 0.99867667 90.09468 0.0002618450
365846 rs56393506 6_104 0.99999988 88.86741 0.0002586204
787068 rs3745677 19_9 0.99981385 88.64445 0.0002579235
13985 rs11580527 1_34 0.99998185 87.73827 0.0002553298
z
402311 -3.272149
402322 2.984117
787065 48.935175
797236 -55.537887
797241 46.325818
797185 44.672230
797238 34.318568
797203 33.599649
68013 -33.086010
402317 2.947963
68010 -33.060888
279255 -25.034352
500307 -19.011790
14016 9.868570
14015 -6.292225
787073 -10.005506
459763 16.022043
789854 18.215134
1025547 16.661098
787044 16.232742
365662 -12.282337
68004 -16.573036
68090 -25.412292
402314 2.866958
459774 -11.964411
789879 21.492060
14033 -16.262997
279232 -20.320600
796944 14.326186
907819 20.377651
69740 14.142603
753654 8.091491
365676 9.947776
753628 -12.768796
724473 13.475251
14026 -11.893801
67955 -11.909428
1058530 11.285714
304134 -13.894754
1058537 13.732910
787082 10.752566
753639 -2.259115
440113 11.699924
459762 22.102229
365810 -10.097959
619946 -11.050062
789838 -1.822895
365846 -14.088321
787068 -9.335807
13985 11.167216SNPs with largest z scores
#histogram of (abs) SNP z scores
hist(abs(ctwas_snp_res$z))
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50) id region_tag susie_pip mu2 PVE
797236 rs814573 19_32 1.000000e+00 2204.0723 6.414254e-03
787065 rs147985405 19_9 9.984711e-01 2244.5992 6.522207e-03
787060 rs73015020 19_9 8.949230e-04 2232.7142 5.814858e-06
787058 rs138175288 19_9 4.220323e-04 2230.9134 2.739988e-06
787059 rs138294113 19_9 1.041836e-04 2226.9115 6.751850e-07
787061 rs77140532 19_9 6.329720e-05 2227.5310 4.103256e-07
787062 rs112552009 19_9 3.159260e-05 2223.2203 2.044035e-07
787063 rs10412048 19_9 1.308379e-05 2224.2510 8.469105e-08
787057 rs55997232 19_9 1.647129e-08 2203.7367 1.056349e-10
797241 rs12721109 19_32 1.000000e+00 1341.0661 3.902748e-03
797185 rs62117204 19_32 1.000000e+00 825.4640 2.402251e-03
797172 rs1551891 19_32 0.000000e+00 505.0338 0.000000e+00
874797 rs12740374 1_67 4.242375e-04 1445.1360 1.784178e-06
874793 rs7528419 1_67 4.907785e-04 1442.1381 2.059741e-06
874804 rs646776 1_67 4.449647e-04 1441.3230 1.866411e-06
874803 rs629301 1_67 4.297103e-04 1437.9299 1.798182e-06
874815 rs583104 1_67 4.532478e-04 1397.5602 1.843430e-06
874818 rs4970836 1_67 4.466012e-04 1394.7162 1.812700e-06
874820 rs1277930 1_67 4.563564e-04 1390.0734 1.846130e-06
874821 rs599839 1_67 4.709081e-04 1389.1613 1.903746e-06
787066 rs17248769 19_9 1.554718e-06 1690.8311 7.650188e-09
787067 rs2228671 19_9 1.068626e-06 1679.7674 5.223904e-09
874801 rs3832016 1_67 3.230354e-04 1350.6175 1.269705e-06
874798 rs660240 1_67 3.206728e-04 1343.4608 1.253740e-06
874816 rs602633 1_67 3.563030e-04 1322.3880 1.371193e-06
787056 rs9305020 19_9 4.507505e-14 1277.3679 1.675608e-16
797232 rs405509 19_32 0.000000e+00 976.8097 0.000000e+00
874784 rs4970834 1_67 6.395977e-04 998.6813 1.858892e-06
797238 rs113345881 19_32 1.000000e+00 772.0452 2.246793e-03
797156 rs62120566 19_32 0.000000e+00 1321.0446 0.000000e+00
797203 rs111794050 19_32 1.000000e+00 763.4499 2.221779e-03
68013 rs548145 2_13 1.000000e+00 656.3735 1.910167e-03
797209 rs4802238 19_32 0.000000e+00 977.7255 0.000000e+00
68010 rs934197 2_13 1.000000e+00 415.4250 1.208963e-03
797150 rs188099946 19_32 0.000000e+00 1266.3237 0.000000e+00
797220 rs2972559 19_32 0.000000e+00 1298.6714 0.000000e+00
797144 rs201314191 19_32 0.000000e+00 1174.4706 0.000000e+00
874805 rs3902354 1_67 3.762837e-04 853.0124 9.340951e-07
874794 rs11102967 1_67 3.694905e-04 849.2612 9.131978e-07
874819 rs4970837 1_67 4.316205e-04 845.9641 1.062611e-06
797211 rs56394238 19_32 0.000000e+00 968.7922 0.000000e+00
797188 rs2965169 19_32 0.000000e+00 367.3413 0.000000e+00
797212 rs3021439 19_32 0.000000e+00 864.3578 0.000000e+00
874789 rs611917 1_67 3.401829e-04 800.1403 7.921343e-07
68040 rs12997242 2_13 5.728673e-11 384.1172 6.403805e-14
797219 rs12162222 19_32 0.000000e+00 1112.8952 0.000000e+00
68014 rs478588 2_13 1.461258e-10 604.3616 2.570064e-13
797149 rs62119327 19_32 0.000000e+00 1034.6875 0.000000e+00
68015 rs56350433 2_13 5.923595e-12 351.2369 6.054883e-15
68020 rs56079819 2_13 5.935585e-12 350.4366 6.053315e-15
z
797236 -55.53789
787065 48.93517
787060 48.79563
787058 48.78069
787059 48.75193
787061 48.73799
787062 48.70516
787063 48.70123
787057 48.52431
797241 46.32582
797185 44.67223
797172 42.26680
874797 41.79347
874793 41.73691
874804 -41.73340
874803 -41.68734
874815 -41.08710
874818 -41.04550
874820 -40.97599
874821 -40.95899
787066 40.84249
787067 40.70262
874801 -40.39598
874798 -40.28958
874816 -39.95641
787056 34.84073
797232 34.63979
874784 34.62492
797238 34.31857
797156 33.73539
797203 33.59965
68013 -33.08601
797209 -33.07569
68010 -33.06089
797150 33.04407
797220 -32.28660
797144 32.06858
874805 -32.00383
874794 -31.93893
874819 -31.85593
797211 -31.55187
797188 31.38057
797212 -31.04506
874789 30.97527
68040 -30.81528
797219 -30.49671
68014 -30.48811
797149 30.41868
68015 -30.23229
68020 -30.19307Gene 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] 33if (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"
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
Term Overlap
1 peptidyl-serine phosphorylation (GO:0018105) 5/156
2 peptidyl-serine modification (GO:0018209) 5/169
3 lipid transport (GO:0006869) 4/109
4 cholesterol transport (GO:0030301) 3/51
5 intestinal cholesterol absorption (GO:0030299) 2/9
6 cellular response to sterol depletion (GO:0071501) 2/9
7 negative regulation of cholesterol storage (GO:0010887) 2/10
8 protein phosphorylation (GO:0006468) 6/496
9 intestinal lipid absorption (GO:0098856) 2/11
10 cholesterol homeostasis (GO:0042632) 3/71
11 sterol homeostasis (GO:0055092) 3/72
12 cholesterol metabolic process (GO:0008203) 3/77
13 regulation of cholesterol storage (GO:0010885) 2/16
14 response to insulin (GO:0032868) 3/84
15 activin receptor signaling pathway (GO:0032924) 2/19
16 negative regulation of lipid storage (GO:0010888) 2/20
17 sterol transport (GO:0015918) 2/21
18 cholesterol efflux (GO:0033344) 2/24
19 regulation of DNA biosynthetic process (GO:2000278) 2/29
20 cellular protein modification process (GO:0006464) 7/1025
21 regulation of cholesterol efflux (GO:0010874) 2/33
22 secondary alcohol biosynthetic process (GO:1902653) 2/34
23 organic substance transport (GO:0071702) 3/136
24 cholesterol biosynthetic process (GO:0006695) 2/35
25 sterol biosynthetic process (GO:0016126) 2/38
Adjusted.P.value Genes
1 0.001778112 CSNK1G3;TNKS;PKN3;PRKD2;GAS6
2 0.001778112 CSNK1G3;TNKS;PKN3;PRKD2;GAS6
3 0.004505375 ABCA1;ABCG8;NPC1L1;PLTP
4 0.007029494 ABCA1;ABCG8;NPC1L1
5 0.007029494 ABCG8;NPC1L1
6 0.007029494 INSIG2;NPC1L1
7 0.007144915 ABCA1;TTC39B
8 0.007144915 CSNK1G3;ACVR1C;TNKS;PKN3;PRKD2;GAS6
9 0.007144915 ABCG8;NPC1L1
10 0.009177895 ABCA1;ABCG8;TTC39B
11 0.009177895 ABCA1;ABCG8;TTC39B
12 0.010261090 ABCA1;INSIG2;NPC1L1
13 0.010736790 ABCA1;TTC39B
14 0.011366853 INSIG2;SORT1;INHBB
15 0.013218960 ACVR1C;INHBB
16 0.013755552 ABCA1;TTC39B
17 0.014294428 ABCG8;NPC1L1
18 0.017688424 ABCA1;ABCG8
19 0.024523690 TNKS;PRKD2
20 0.026889526 CSNK1G3;ACVR1C;TNKS;PKN3;PRKD2;FUT2;GAS6
21 0.028088337 PLTP;TTC39B
22 0.028088337 INSIG2;NPC1L1
23 0.028088337 ABCA1;ABCG8;PLTP
24 0.028276991 INSIG2;NPC1L1
25 0.031974245 INSIG2;NPC1L1
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
Term Overlap Adjusted.P.value
1 high-density lipoprotein particle (GO:0034364) 2/19 0.01685639
2 endoplasmic reticulum membrane (GO:0005789) 6/712 0.01843438
Genes
1 HPR;PLTP
2 ABCA1;CYP2A6;INSIG2;KDSR;FADS1;CNIH4
[1] "GO_Molecular_Function_2021" Term Overlap
1 cholesterol transfer activity (GO:0120020) 3/18
2 sterol transfer activity (GO:0120015) 3/19
3 phosphatidylcholine transporter activity (GO:0008525) 2/18
Adjusted.P.value Genes
1 0.0001655238 ABCA1;ABCG8;PLTP
2 0.0001655238 ABCA1;ABCG8;PLTP
3 0.0112569839 ABCA1;PLTPCNIH4 gene(s) from the input list not found in DisGeNET CURATEDPELO gene(s) from the input list not found in DisGeNET CURATEDACP6 gene(s) from the input list not found in DisGeNET CURATEDTTC39B gene(s) from the input list not found in DisGeNET CURATEDUSP1 gene(s) from the input list not found in DisGeNET CURATEDTNKS gene(s) from the input list not found in DisGeNET CURATEDPKN3 gene(s) from the input list not found in DisGeNET CURATEDTRIM39 gene(s) from the input list not found in DisGeNET CURATEDSPTY2D1 gene(s) from the input list not found in DisGeNET CURATEDCSNK1G3 gene(s) from the input list not found in DisGeNET CURATEDRP4-781K5.7 gene(s) from the input list not found in DisGeNET CURATEDDDX56 gene(s) from the input list not found in DisGeNET CURATEDHPR gene(s) from the input list not found in DisGeNET CURATEDC10orf88 gene(s) from the input list not found in DisGeNET CURATEDCRACR2B gene(s) from the input list not found in DisGeNET CURATEDNPC1L1 gene(s) from the input list not found in DisGeNET CURATEDPOP7 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
5 Blood Platelet Disorders 0.01370851 2/16 16/9703
24 Hypercholesterolemia, Familial 0.01370851 2/16 18/9703
39 Opisthorchiasis 0.01370851 1/16 1/9703
46 Tangier Disease 0.01370851 1/16 1/9703
61 Caliciviridae Infections 0.01370851 1/16 1/9703
67 Infections, Calicivirus 0.01370851 1/16 1/9703
81 Opisthorchis felineus Infection 0.01370851 1/16 1/9703
82 Opisthorchis viverrini Infection 0.01370851 1/16 1/9703
93 Hypoalphalipoproteinemias 0.01370851 1/16 1/9703
99 Tangier Disease Neuropathy 0.01370851 1/16 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR database
1 Coronary Artery Disease 154 10 1.358080e-07 disease_GLAD4U
2 Dyslipidaemia 85 8 3.890274e-07 disease_GLAD4U
3 Coronary Disease 172 9 3.367936e-06 disease_GLAD4U
4 Hypercholesterolemia 61 6 3.097224e-05 disease_GLAD4U
5 Arteriosclerosis 174 8 4.663087e-05 disease_GLAD4U
6 Myocardial Ischemia 182 8 5.505551e-05 disease_GLAD4U
7 Arterial Occlusive Diseases 175 7 6.079951e-04 disease_GLAD4U
8 Hyperlipidemias 65 5 7.717716e-04 disease_GLAD4U
9 Cholesterol metabolism 32 4 1.052462e-03 pathway_KEGG
10 Heart Diseases 228 7 2.458608e-03 disease_GLAD4U
11 Cardiovascular Diseases 283 7 9.055220e-03 disease_GLAD4U
12 Fat digestion and absorption 23 3 1.241474e-02 pathway_KEGG
13 Vascular Diseases 235 6 2.464145e-02 disease_GLAD4U
14 Obesity 172 5 4.851973e-02 disease_GLAD4U
userId
1 SORT1;ABCG8;INSIG2;NPC1L1;TTC39B;ABCA1;SPTY2D1;FADS1;FUT2;PLTP
2 SORT1;ABCG8;INSIG2;NPC1L1;TTC39B;ABCA1;FADS1;PLTP
3 SORT1;ABCG8;INSIG2;NPC1L1;TTC39B;ABCA1;FADS1;FUT2;PLTP
4 SORT1;ABCG8;INSIG2;NPC1L1;ABCA1;PLTP
5 SORT1;ABCG8;NPC1L1;TTC39B;ABCA1;FADS1;HPR;PLTP
6 SORT1;ABCG8;INSIG2;NPC1L1;TTC39B;ABCA1;FADS1;PLTP
7 SORT1;ABCG8;NPC1L1;TTC39B;ABCA1;FADS1;PLTP
8 SORT1;ABCG8;NPC1L1;ABCA1;PLTP
9 SORT1;ABCG8;ABCA1;PLTP
10 SORT1;ABCG8;NPC1L1;TTC39B;ABCA1;FADS1;PLTP
11 SORT1;ABCG8;TTC39B;ABCA1;FADS1;GAS6;PLTP
12 ABCG8;NPC1L1;ABCA1
13 SORT1;ABCG8;NPC1L1;ABCA1;GAS6;PLTP
14 INSIG2;TTC39B;ABCA1;FADS1;PLTPSensitivity, 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] 47#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] 4.582873#number of ctwas genes
length(ctwas_genes)[1] 33#number of TWAS genes
length(twas_genes)[1] 217#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z
9062 KLHDC7A 1_13 0.8184864 22.59315 5.381564e-05 4.124187
6391 TTC39B 9_13 0.9259989 23.04988 6.211543e-05 -4.287139
8931 CRACR2B 11_1 0.8018274 22.03492 5.141771e-05 -3.989585
3247 KDSR 18_35 0.9552673 24.68796 6.863260e-05 -4.526287
num_eqtl
9062 1
6391 3
8931 1
3247 1#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.1014493 0.2898551 #specificity
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9976048 0.9818517 #precision / PPV
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.2121212 0.0921659 #ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res$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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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: GenomeInfoDb# 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)
#
# #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$hgnc_symbol[bystanders]
# bystanders <- unique(bystanders[!(bystanders %in% known_annotations)])
# unrelated_genes <- bystanders
#
# #save gene lists
# save(known_annotations, file=paste0(results_dir, "/known_annotations_SORT1.Rd"))
# save(unrelated_genes, file=paste0(results_dir, "/bystanders_SORT1.Rd"))
load(paste0(results_dir, "/known_annotations_SORT1.Rd"))
load(paste0(results_dir, "/bystanders_SORT1.Rd"))
#remove genes without imputed expression from bystander list
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$genename]
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 47#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 571#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$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.582873#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 9#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 68#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.1489362 0.4255319 #specificity / (1 - False Positive Rate)
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9964974 0.9159370 #precision / PPV / (1 - False Discovery Rate)
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.7777778 0.2941176 #store sensitivity and specificity calculations for plots
sensitivity_plot <- sensitivity
specificity_plot <- specificity
#precision / PPV by PIP bin
pip_range <- c(0.2, 0.4, 0.6, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes in PIP bin
ctwas_genes <- ctwas_gene_res_subset$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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#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$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
PIP Manhattan Plot
library(tibble)
library(tidyverse)── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──✔ tidyr 1.1.0 ✔ dplyr 1.0.7
✔ readr 1.4.0 ✔ stringr 1.4.0
✔ purrr 0.3.4 ✔ forcats 0.4.0── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::collapse() masks IRanges::collapse()
✖ dplyr::combine() masks BiocGenerics::combine()
✖ dplyr::desc() masks IRanges::desc()
✖ tidyr::expand() masks S4Vectors::expand()
✖ tidyr::extract() masks disgenet2r::extract()
✖ dplyr::filter() masks stats::filter()
✖ dplyr::first() masks S4Vectors::first()
✖ dplyr::lag() masks stats::lag()
✖ BiocGenerics::Position() masks ggplot2::Position(), base::Position()
✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce()
✖ dplyr::rename() masks S4Vectors::rename()
✖ dplyr::select() masks biomaRt::select()
✖ dplyr::slice() masks IRanges::slice()full.gene.pip.summary <- data.frame(gene_name = ctwas_gene_res$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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
Locus Plots - 5_45 - Thin
locus_plot("5_45", label="TWAS")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
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_SORT1.Rd"))
load(paste0(results_dir, "/bystanders_SORT1.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,])
}
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ITIH4"
[1] "3_36"
genename region_tag susie_pip mu2 PVE z
2847 RRP9 3_36 0.008912679 8.053289 2.088824e-07 -0.9533837
374 PARP3 3_36 0.008813915 7.946108 2.038185e-07 0.9395160
11516 ACY1 3_36 0.007063585 5.902054 1.213246e-07 -0.5115172
7244 POC1A 3_36 0.006621583 5.439133 1.048122e-07 0.6100943
11578 TWF2 3_36 0.010426443 10.279732 3.119165e-07 -1.4613887
7245 PPM1M 3_36 0.008144510 7.075939 1.677140e-07 -1.0722712
7915 GLYCTK 3_36 0.020740001 14.425844 8.707035e-07 -1.6364989
7247 WDR82 3_36 0.006887422 5.638483 1.130158e-07 -0.3708077
2853 DNAH1 3_36 0.023205709 17.785965 1.201137e-06 2.8547462
158 PHF7 3_36 0.010706220 9.513164 2.964022e-07 1.0713405
159 SEMA3G 3_36 0.010798965 9.205085 2.892879e-07 -0.4278002
2856 TNNC1 3_36 0.074836640 20.834388 4.537486e-06 -3.4591550
160 NISCH 3_36 0.006431110 5.078652 9.505057e-08 0.2448166
161 STAB1 3_36 0.089874339 21.813723 5.705396e-06 3.5822738
7918 NT5DC2 3_36 0.006439435 5.124729 9.603709e-08 -0.5772516
7203 GNL3 3_36 0.081279075 24.021784 5.682040e-06 -3.6426177
7204 PBRM1 3_36 0.008572620 6.953344 1.734713e-07 -0.8048656
239 GLT8D1 3_36 0.025262850 17.372092 1.277188e-06 2.5357598
2861 NEK4 3_36 0.029870402 18.721242 1.627406e-06 -2.8779547
482 ITIH1 3_36 0.079833141 27.970002 6.498244e-06 3.3942500
6912 ITIH3 3_36 0.114733859 31.481630 1.051161e-05 3.5156979
481 ITIH4 3_36 0.007120365 6.630771 1.373999e-07 0.8376918
12349 MUSTN1 3_36 0.009317317 8.974420 2.433423e-07 2.0802688
10835 TMEM110 3_36 0.007528361 6.948477 1.522336e-07 1.5371064
7202 SFMBT1 3_36 0.009982870 9.825276 2.854437e-07 2.0068207
7201 PRKCD 3_36 0.007211035 6.210166 1.303230e-07 -0.6434864
7200 TKT 3_36 0.017086334 13.589481 6.757283e-07 2.5841158
12386 DCP1A 3_36 0.007488504 6.484291 1.413116e-07 0.5186961
236 CHDH 3_36 0.006480354 5.067465 9.556740e-08 0.1444517
486 IL17RB 3_36 0.006663043 5.296065 1.026943e-07 0.1956411
2783 ACTR8 3_36 0.006661519 5.259447 1.019609e-07 0.3348511
num_eqtl
2847 1
374 1
11516 1
7244 1
11578 2
7245 3
7915 1
7247 1
2853 2
158 1
159 1
2856 2
160 2
161 1
7918 2
7203 2
7204 1
239 2
2861 1
482 1
6912 1
481 3
12349 2
10835 2
7202 1
7201 1
7200 1
12386 2
236 1
486 2
2783 3
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "EPHX2"
[1] "8_27"
genename region_tag susie_pip mu2 PVE z
11425 PNMA2 8_27 0.006147874 4.946154 8.849381e-08 -0.21331428
1295 DPYSL2 8_27 0.006115659 4.894635 8.711318e-08 0.04738342
3371 ADRA1A 8_27 0.008801745 8.466693 2.168717e-07 -0.92728371
1869 TRIM35 8_27 0.015125046 13.787497 6.068794e-07 1.42379941
3374 EPHX2 8_27 0.006430530 5.386957 1.008116e-07 -0.24570047
3368 CLU 8_27 0.007487306 6.879418 1.498986e-07 0.59866869
7893 SCARA3 8_27 0.019713918 16.397585 9.407476e-07 -1.50201507
8304 ESCO2 8_27 0.010205907 9.920080 2.946369e-07 1.06980762
5838 CCDC25 8_27 0.012295854 11.750613 4.204744e-07 1.25261686
7894 PBK 8_27 0.006767015 5.887178 1.159377e-07 0.48335074
7895 SCARA5 8_27 0.006113499 4.891171 8.702079e-08 -0.01387977
9998 NUGGC 8_27 0.037028402 22.632887 2.438907e-06 2.06999161
num_eqtl
11425 1
1295 1
3371 2
1869 2
3374 2
3368 1
7893 2
8304 2
5838 1
7894 1
7895 1
9998 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ABCA1"
[1] "9_53"
genename region_tag susie_pip mu2 PVE z
7410 ABCA1 9_53 0.995395541 70.368069 2.038410e-04 7.9820172
2193 FKTN 9_53 0.001193971 7.325906 2.545514e-08 -0.7642857
1314 TMEM38B 9_53 0.001903917 7.860778 4.355459e-08 0.7019380
num_eqtl
7410 1
2193 1
1314 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LPL"
[1] "8_21"
genename region_tag susie_pip mu2 PVE z
5836 CSGALNACT1 8_21 0.007456082 5.829327 1.264880e-07 -0.8624862
1906 INTS10 8_21 0.009985748 7.775824 2.259682e-07 -0.5466864
8739 LPL 8_21 0.023413721 16.909622 1.152191e-06 -1.8179375
num_eqtl
5836 1
1906 1
8739 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "APOA5"
[1] "11_70"
genename region_tag susie_pip mu2 PVE z
4868 BUD13 11_70 0.005760699 36.876902 6.182297e-07 4.11527976
3154 APOA1 11_70 0.004278331 6.652390 8.282709e-08 1.11150616
7898 PAFAH1B2 11_70 0.005499480 7.725576 1.236439e-07 -0.01722766
6005 SIDT2 11_70 0.004139980 5.468270 6.588226e-08 0.50104522
6006 TAGLN 11_70 0.004614639 18.478031 2.481497e-07 -1.55444774
6785 PCSK7 11_70 0.012766000 16.431533 6.104544e-07 0.97935688
7745 RNF214 11_70 0.004787354 6.579102 9.166055e-08 -0.52468931
2466 CEP164 11_70 0.004664222 5.763477 7.823193e-08 -0.30209785
9720 BACE1 11_70 0.004495693 21.121393 2.763373e-07 -4.13706265
4881 FXYD2 11_70 0.004684096 6.128819 8.354547e-08 -0.37435241
2465 APOA5 11_70 0.032499003 145.137431 1.372681e-05 -11.35991043
num_eqtl
4868 1
3154 2
7898 2
6005 1
6006 1
6785 1
7745 1
2466 2
9720 1
4881 2
2465 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "MTTP"
[1] "4_66"
genename region_tag susie_pip mu2 PVE
7980 TSPAN5 4_66 0.014674407 11.179397 4.774185e-07
6091 EIF4E 4_66 0.009482212 6.919558 1.909450e-07
7222 METAP1 4_66 0.007832039 5.097756 1.161914e-07
12374 RP11-571L19.8 4_66 0.008322181 5.636996 1.365228e-07
8496 ADH6 4_66 0.009968595 7.562593 2.193941e-07
10115 ADH1B 4_66 0.014780501 11.319478 4.868956e-07
11584 ADH1C 4_66 0.143197702 32.307606 1.346360e-05
10057 ADH7 4_66 0.009291555 10.533608 2.848301e-07
5055 MTTP 4_66 0.010983181 8.019871 2.563397e-07
5686 TRMT10A 4_66 0.011475866 9.119569 3.045651e-07
z num_eqtl
7980 -1.2321573 2
6091 0.9082871 1
7222 -0.1831346 1
12374 -0.4759060 3
8496 0.7334699 2
10115 -1.1153042 1
11584 -3.1932254 3
10057 1.9684512 2
5055 -0.7972018 1
5686 -1.1240076 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "DHCR7"
[1] "11_40"
genename region_tag susie_pip mu2 PVE
8487 DHCR7 11_40 0.01773926 6.126264 3.162652e-07
8486 NADSYN1 11_40 0.01846773 6.522820 3.505656e-07
11530 KRTAP5-7 11_40 0.05750340 17.790367 2.977137e-06
11761 KRTAP5-9 11_40 0.01702918 5.723847 2.836626e-07
10650 KRTAP5-10 11_40 0.02462858 9.363322 6.711036e-07
6613 FAM86C1 11_40 0.01812508 6.338261 3.343262e-07
11744 RP11-849H4.2 11_40 0.01598480 5.100621 2.372742e-07
4859 RNF121 11_40 0.08438209 21.653507 5.317394e-06
4851 IL18BP 11_40 0.01565080 4.892748 2.228486e-07
4852 NUMA1 11_40 0.01679770 5.589051 2.732172e-07
9490 LRTOMT 11_40 0.01736402 5.915645 2.989322e-07
2462 FOLR3 11_40 0.01725682 5.854641 2.940229e-07
7453 INPPL1 11_40 0.01567344 4.906976 2.238198e-07
6900 CLPB 11_40 0.02252713 8.482540 5.560991e-07
11125 LINC01537 11_40 0.02069388 7.644949 4.604016e-07
z num_eqtl
8487 0.65130261 1
8486 0.72652201 1
11530 2.05073754 1
11761 0.41678412 1
10650 -1.13422621 2
6613 0.45043836 1
11744 -0.32070634 1
4859 2.08692398 2
4851 -0.10323637 2
4852 0.44145029 1
9490 -0.65197454 1
2462 -0.63775295 1
7453 0.05040952 1
6900 1.03637029 1
11125 -0.84083998 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LIPA"
[1] "10_57"
genename region_tag susie_pip mu2 PVE z
3295 IFIT2 10_57 0.010965069 6.795438 2.168449e-07 -0.6053359
3294 IFIT3 10_57 0.009447275 5.332101 1.465970e-07 -0.3100521
9655 IFIT1 10_57 0.022963912 14.073769 9.405385e-07 1.4103375
2253 LIPA 10_57 0.014680323 9.664353 4.128846e-07 1.0134814
6227 IFIT5 10_57 0.009249289 5.124159 1.379276e-07 0.2173750
6228 PANK1 10_57 0.015037652 9.901033 4.332921e-07 -1.6480922
11305 RP11-80H5.9 10_57 0.017494284 11.390715 5.799192e-07 -1.8221939
4960 KIF20B 10_57 0.013172614 8.598397 3.296171e-07 -1.5022578
11224 LINC00865 10_57 0.017715849 11.514908 5.936668e-07 1.6098123
10558 IFIT1B 10_57 0.010816977 6.661864 2.097114e-07 -0.6164140
num_eqtl
3295 1
3294 1
9655 2
2253 1
6227 3
6228 1
11305 1
4960 1
11224 1
10558 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LDLRAP1"
[1] "1_18"
genename region_tag susie_pip mu2 PVE
3127 SYF2 1_18 0.007421533 6.668378 1.440238e-07
9783 RHD 1_18 0.006831065 43.925912 8.732317e-07
9453 TMEM50A 1_18 0.073989028 97.506077 2.099517e-05
9940 RHCE 1_18 0.007318047 64.649717 1.376836e-06
10578 TMEM57 1_18 0.384331155 100.841359 1.127884e-04
6571 LDLRAP1 1_18 0.007455503 8.855688 1.921408e-07
11070 RP11-70P17.1 1_18 0.017573873 17.547450 8.974325e-07
3130 MAN1C1 1_18 0.006859267 6.059395 1.209560e-07
6933 SELENON 1_18 0.030657158 20.024234 1.786521e-06
3129 MTFR1L 1_18 0.012751504 12.670202 4.701812e-07
8700 PDIK1L 1_18 0.035796331 23.470168 2.444978e-06
10206 FAM110D 1_18 0.007329688 6.264318 1.336225e-07
5406 CNKSR1 1_18 0.012521149 12.521539 4.562703e-07
4099 CEP85 1_18 0.013538518 14.905762 5.872805e-07
5405 SH3BGRL3 1_18 0.022295299 18.546477 1.203358e-06
8065 CD52 1_18 0.015681040 13.740455 6.270415e-07
6581 UBXN11 1_18 0.015681040 13.740455 6.270415e-07
8797 AIM1L 1_18 0.006748074 5.518993 1.083827e-07
8795 ZNF683 1_18 0.006513347 5.410474 1.025557e-07
3133 DHDDS 1_18 0.010598647 13.547692 4.178651e-07
10473 HMGN2 1_18 0.006663401 5.607062 1.087306e-07
3132 RPS6KA1 1_18 0.006440604 5.224797 9.793012e-08
525 PIGV 1_18 0.007151950 11.000297 2.289545e-07
10574 ZDHHC18 1_18 0.006769204 8.649976 1.704013e-07
5414 GPN2 1_18 0.008348650 13.689703 3.326064e-07
11698 TRNP1 1_18 0.033770287 19.267182 1.893535e-06
z num_eqtl
3127 0.7426202 1
9783 -6.4603360 1
9453 10.0815103 1
9940 8.1134433 2
10578 -10.2641908 1
6571 1.9336337 2
11070 2.9461337 1
3130 -1.0646970 1
6933 -2.1819486 1
3129 2.2702594 2
8700 3.2275633 1
10206 0.6465781 1
5406 2.3608913 2
4099 2.3661103 1
5405 -2.8355236 1
8065 -1.2648216 1
6581 -1.2648216 1
8797 0.4474766 1
8795 0.5504045 1
3133 2.6796586 2
10473 0.5156134 1
3132 0.3917090 1
525 -2.2722478 2
10574 1.8553085 1
5414 2.5894296 1
11698 1.1357927 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ANGPTL3"
[1] "1_39"
genename region_tag susie_pip mu2 PVE z
6956 TM2D1 1_39 0.056959707 23.071148 3.824347e-06 2.1432487
4316 KANK4 1_39 0.008972516 5.075472 1.325290e-07 0.5123038
6957 USP1 1_39 0.894444325 253.879959 6.608487e-04 16.2582110
4317 ANGPTL3 1_39 0.114994538 249.654254 8.354808e-05 16.1322287
3024 DOCK7 1_39 0.010009135 24.336915 7.088958e-07 4.4594815
3733 ATG4C 1_39 0.024969688 81.344478 5.911007e-06 -8.6477262
num_eqtl
6956 1
4316 1
6957 1
4317 1
3024 1
3733 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "APOB"
[1] "2_13"
genename region_tag susie_pip mu2 PVE z
1053 APOB 2_13 1.618250e-11 62.92961 2.963609e-15 -11.725895
11245 AC067959.1 2_13 3.200605e-09 145.47372 1.354993e-12 -2.328717
num_eqtl
1053 1
11245 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "APOE"
[1] "19_32"
genename region_tag susie_pip mu2 PVE z num_eqtl
6721 ZNF233 19_32 0 115.540308 0 -9.2725820 2
6722 ZNF235 19_32 0 106.459190 0 -9.2122953 1
538 ZNF112 19_32 0 147.061017 0 10.3860543 1
12133 ZNF285 19_32 0 14.844074 0 0.9962471 2
12637 ZNF229 19_32 0 91.589291 0 10.9591492 2
7760 ZNF180 19_32 0 28.966742 0 -3.9159702 3
781 PVR 19_32 0 295.701702 0 -10.0782525 2
9745 CEACAM19 19_32 0 64.977515 0 9.4554813 2
9810 BCAM 19_32 0 109.853221 0 4.6421318 1
4048 NECTIN2 19_32 0 109.049177 0 6.2443536 2
4050 TOMM40 19_32 0 25.471829 0 -1.4020544 1
4049 APOE 19_32 0 47.814697 0 -2.0092826 1
11300 APOC2 19_32 0 57.109667 0 -9.1630690 2
8231 ZNF296 19_32 0 111.900459 0 5.4593536 1
5377 GEMIN7 19_32 0 193.978064 0 10.9432287 2
104 MARK4 19_32 0 24.156056 0 -2.2463768 1
1930 PPP1R37 19_32 0 125.326110 0 -12.8921201 2
109 TRAPPC6A 19_32 0 30.419699 0 1.8816459 1
9989 BLOC1S3 19_32 0 11.134819 0 2.3014119 1
12704 EXOC3L2 19_32 0 25.621613 0 -1.3436507 1
1933 CKM 19_32 0 15.790117 0 -1.5738464 1
1937 ERCC2 19_32 0 11.393491 0 2.3297330 2
3143 CD3EAP 19_32 0 27.197891 0 -3.0806361 1
3738 FOSB 19_32 0 18.939030 0 -2.3658041 1
196 ERCC1 19_32 0 14.630613 0 -0.2091619 1
10862 PPM1N 19_32 0 31.392476 0 5.4808308 1
3741 RTN2 19_32 0 31.851486 0 5.5300783 1
3742 VASP 19_32 0 12.782928 0 1.8957985 1
3739 OPA3 19_32 0 13.745582 0 -0.4654901 2
1942 KLC3 19_32 0 10.261197 0 1.7718715 1
10863 CEACAM16 19_32 0 7.492021 0 1.8740580 1
12131 APOC4 19_32 0 49.134556 0 8.0662459 2
10965 IGSF23 19_32 0 12.715131 0 1.9670520 1
8908 GPR4 19_32 0 66.214998 0 -3.5802828 1
3740 SNRPD2 19_32 0 10.032421 0 1.0366923 1
189 QPCTL 19_32 0 24.612260 0 -2.0303487 2
1949 DMPK 19_32 0 20.547022 0 -1.8090245 1
9659 DMWD 19_32 0 19.622427 0 -1.7547946 1
3743 SYMPK 19_32 0 4.904062 0 -0.0525717 1
8809 MYPOP 19_32 0 21.246603 0 1.8490001 1
1963 CCDC61 19_32 0 21.113500 0 1.8414612 2
3628 HIF3A 19_32 0 20.433740 0 -1.8024680 2
190 PPP5C 19_32 0 13.448632 0 1.3374649 1
8073 CCDC8 19_32 0 7.392266 0 0.7230949 2
9281 PNMAL1 19_32 0 20.657761 0 -1.8154111 4
10682 PNMAL2 19_32 0 5.246629 0 -0.2727077 1
11190 PPP5D1 19_32 0 6.392915 0 -0.5603345 1
6726 CALM3 19_32 0 54.624374 0 3.2242313 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "NPC1L1"
[1] "7_32"
genename region_tag susie_pip mu2 PVE z
7330 STK17A 7_32 0.006414090 5.405182 1.008941e-07 0.5439997
2177 COA1 7_32 0.011779535 9.889774 3.390274e-07 -0.7042755
2178 BLVRA 7_32 0.006315331 5.151026 9.466951e-08 0.4660052
541 MRPS24 7_32 0.007219831 6.241177 1.311336e-07 0.3827818
2179 URGCP 7_32 0.007395391 6.536478 1.406777e-07 -0.6697027
927 UBE2D4 7_32 0.009713232 9.447748 2.670622e-07 1.1906995
11147 AC004951.6 7_32 0.009988303 8.243453 2.396190e-07 0.2209151
4706 DBNL 7_32 0.008351048 6.910010 1.679345e-07 0.1009981
3488 POLM 7_32 0.006250639 5.193249 9.446782e-08 0.5460441
2183 AEBP1 7_32 0.022616023 20.450642 1.345995e-06 -2.6280619
2184 POLD2 7_32 0.014036368 13.082079 5.343820e-07 -1.4227083
2185 MYL7 7_32 0.007671582 6.668056 1.488691e-07 0.4396483
2186 GCK 7_32 0.006292970 5.111982 9.361928e-08 -0.2515709
500 CAMK2B 7_32 0.011448736 9.069547 3.021784e-07 -1.5162371
233 NPC1L1 7_32 0.963951568 89.799732 2.519130e-04 -10.7619311
4704 DDX56 7_32 0.974637816 58.705019 1.665094e-04 9.4462712
6619 TMED4 7_32 0.011779695 45.305762 1.553130e-06 7.5475920
2101 OGDH 7_32 0.008233040 19.553123 4.684860e-07 0.1499623
num_eqtl
7330 1
2177 2
2178 1
541 1
2179 2
927 1
11147 1
4706 2
3488 3
2183 1
2184 2
2185 1
2186 1
500 2
233 1
4704 2
6619 2
2101 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "SOAT1"
[1] "1_89"
genename region_tag susie_pip mu2 PVE
5477 FAM163A 1_89 0.009167159 4.894659 1.305803e-07
3000 FAM20B 1_89 0.009869807 5.619835 1.614182e-07
9716 TOR3A 1_89 0.010997748 6.682763 2.138849e-07
5473 ABL2 1_89 0.009168965 4.896592 1.306576e-07
488 SOAT1 1_89 0.009182909 4.911512 1.312550e-07
8120 TOR1AIP2 1_89 0.011769789 7.349455 2.517353e-07
5476 TOR1AIP1 1_89 0.009194003 4.923366 1.317307e-07
11939 RP11-533E19.5 1_89 0.013686639 8.832932 3.518212e-07
4640 CEP350 1_89 0.044025422 20.382847 2.611492e-06
3008 QSOX1 1_89 0.036018911 18.387061 1.927362e-06
3408 LHX4 1_89 0.010832293 6.533828 2.059721e-07
11184 ACBD6 1_89 0.010823793 6.526117 2.055676e-07
5474 XPR1 1_89 0.026064750 15.183148 1.151690e-06
6245 MR1 1_89 0.009943485 5.692874 1.647368e-07
z num_eqtl
5477 -0.07283498 1
3000 0.42412943 2
9716 -0.64642141 1
5473 -0.04638378 1
488 -0.14955596 1
8120 0.78824459 1
5476 -0.06998621 1
11939 1.29962295 2
4640 2.27414775 2
3008 -1.74016201 2
3408 -0.58114341 1
11184 -0.61546219 2
5474 1.57571425 2
6245 0.40422440 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "MYLIP"
[1] "6_13"
genename region_tag susie_pip mu2 PVE z
12277 RP11-560J1.2 6_13 0.006258658 6.277863 1.143440e-07 0.5773850
400 DTNBP1 6_13 0.024828937 19.136034 1.382708e-06 1.8923854
124 MYLIP 6_13 0.005437696 39.355857 6.227943e-07 6.1101946
4817 GMPR 6_13 0.009852449 9.816480 2.814623e-07 0.2573808
num_eqtl
12277 2
400 1
124 2
4817 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "OSBPL5"
[1] "11_2"
genename region_tag susie_pip mu2 PVE z
926 TOLLIP 11_2 0.005892172 11.279305 1.934096e-07 -1.1132790
9307 MOB2 11_2 0.021035050 23.395361 1.432167e-06 2.2732312
9530 DUSP8 11_2 0.005016946 9.712891 1.418105e-07 1.2015225
10759 KRTAP5-1 11_2 0.010207639 16.802551 4.991382e-07 -1.7341826
11527 IFITM10 11_2 0.003760600 7.181078 7.858996e-08 -0.8538633
3146 CTSD 11_2 0.006277922 11.914524 2.176772e-07 1.2201456
4093 TNNI2 11_2 0.003443352 6.123122 6.135849e-08 0.4977574
12709 PRR33 11_2 0.003103652 6.436119 5.813228e-08 1.2367544
4092 TNNT3 11_2 0.003110993 5.117300 4.632977e-08 -0.1271889
7744 IGF2 11_2 0.003170213 5.256745 4.849820e-08 0.1447958
9455 ASCL2 11_2 0.004706846 8.845565 1.211646e-07 -0.8044614
2490 C11orf21 11_2 0.004270528 8.041761 9.994315e-08 -0.7150719
9508 TSSC4 11_2 0.004275455 8.294521 1.032034e-07 -0.9083240
9251 PHLDA2 11_2 0.037010195 28.576194 3.077840e-06 -2.5765310
10734 NAP1L4 11_2 0.020732883 23.353782 1.409085e-06 -2.2381727
264 OSBPL5 11_2 0.008807622 15.130551 3.878231e-07 -1.6475511
67 ZNF195 11_2 0.005812778 11.170732 1.889669e-07 1.1809650
9140 TH 11_2 0.017305764 22.322118 1.124208e-06 2.0988645
10758 FAM99A 11_2 0.003545402 6.356068 6.558045e-08 0.5299554
11117 AP006285.6 11_2 0.003969445 7.584440 8.761403e-08 -0.9091754
11209 AP006285.7 11_2 0.004604045 8.906721 1.193377e-07 0.8556168
10757 KRTAP5-6 11_2 0.003056331 4.950785 4.403468e-08 0.1958622
num_eqtl
926 2
9307 2
9530 1
10759 1
11527 1
3146 2
4093 1
12709 2
4092 2
7744 1
9455 1
2490 2
9508 1
9251 1
10734 1
264 1
67 2
9140 1
10758 2
11117 3
11209 1
10757 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "SCARB1"
[1] "12_76"
genename region_tag susie_pip mu2 PVE
10916 RP11-83B20.1 12_76 0.087880449 27.427537 7.014543e-06
783 SCARB1 12_76 0.009051033 6.717003 1.769270e-07
6070 UBC 12_76 0.013557335 9.017802 3.557913e-07
989 AACS 12_76 0.008772012 4.959455 1.266058e-07
z num_eqtl
10916 -2.2707583 1
783 -1.3579091 1
6070 0.9059691 1
989 -0.1677513 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "VDAC3"
[1] "8_37"
genename region_tag susie_pip mu2 PVE z
726 AP3M2 8_37 0.009690732 5.042460 1.422065e-07 -0.1640405
1883 PLAT 8_37 0.009714433 5.066447 1.432324e-07 0.2157926
916 VDAC3 8_37 0.021066166 12.682981 7.775479e-07 -1.3606126
7961 SLC20A2 8_37 0.009633496 4.984289 1.397357e-07 -0.1602583
8811 SMIM19 8_37 0.013404866 8.230773 3.210875e-07 -0.9418962
4215 THAP1 8_37 0.009696744 5.048550 1.424665e-07 0.2765875
7909 HOOK3 8_37 0.037378181 18.357303 1.996859e-06 1.9222889
3375 RNF170 8_37 0.037378181 18.357303 1.996859e-06 1.9222889
num_eqtl
726 2
1883 1
916 1
7961 1
8811 1
4215 2
7909 1
3375 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LRP2"
[1] "2_103"
genename region_tag susie_pip mu2 PVE z
985 LRP2 2_103 0.02217809 7.636701 4.928902e-07 0.79845416
7041 BBS5 2_103 0.02342284 8.175811 5.573022e-07 0.88589081
11395 KLHL41 2_103 0.01745463 5.275211 2.679605e-07 -0.32742036
4985 FASTKD1 2_103 0.02567410 9.082462 6.786083e-07 0.94743244
4984 PPIG 2_103 0.02959246 10.487462 9.031746e-07 -1.40458148
6343 CCDC173 2_103 0.02842244 10.088118 8.344337e-07 -1.44045674
10808 KLHL23 2_103 0.04614336 14.899351 2.000769e-06 1.90247786
5602 PHOSPHO2 2_103 0.01753032 5.317839 2.712973e-07 0.29104367
4982 SSB 2_103 0.03091274 10.919763 9.823607e-07 1.22773041
4981 METTL5 2_103 0.01683652 4.920086 2.410712e-07 0.11571328
5601 UBR3 2_103 0.01683593 4.919741 2.410458e-07 0.07450539
num_eqtl
985 1
7041 1
11395 1
4985 1
4984 2
6343 2
10808 2
5602 1
4982 1
4981 1
5601 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "CETP"
[1] "16_31"
genename region_tag susie_pip mu2 PVE
11561 RP11-461O7.1 16_31 0.003204156 5.972640 5.569297e-08
1124 GNAO1 16_31 0.003204823 6.140440 5.726956e-08
6695 AMFR 16_31 0.003883669 7.473990 8.447243e-08
7710 NUDT21 16_31 0.003243357 6.413318 6.053378e-08
3681 BBS2 16_31 0.022262951 23.691814 1.534975e-06
1122 MT3 16_31 0.002931993 5.273953 4.500072e-08
8094 MT1E 16_31 0.003038882 5.705955 5.046178e-08
10727 MT1M 16_31 0.004316483 12.028226 1.510956e-07
10725 MT1A 16_31 0.004544415 11.127910 1.471675e-07
10386 MT1F 16_31 0.121676003 38.641857 1.368306e-05
9805 MT1X 16_31 0.002802285 5.055912 4.123179e-08
1740 NUP93 16_31 0.021078005 24.521710 1.504183e-06
438 HERPUD1 16_31 0.006119693 24.441227 4.352842e-07
1120 CETP 16_31 0.056359403 121.048764 1.985396e-05
5240 NLRC5 16_31 0.088907377 159.686144 4.131667e-05
5239 CPNE2 16_31 0.002979777 5.481558 4.753441e-08
8472 FAM192A 16_31 0.003111837 6.218458 5.631444e-08
6698 RSPRY1 16_31 0.004338276 11.198711 1.413857e-07
1745 PLLP 16_31 0.017878458 25.352215 1.319065e-06
81 CX3CL1 16_31 0.003038169 6.159428 5.445937e-08
1747 CCL17 16_31 0.004492805 8.998202 1.176505e-07
52 CIAPIN1 16_31 0.012172545 20.135497 7.132866e-07
1154 COQ9 16_31 0.004452740 9.338385 1.210095e-07
3685 DOK4 16_31 0.003676532 7.914669 8.468207e-08
4628 CCDC102A 16_31 0.002926457 5.388759 4.589351e-08
10722 ADGRG1 16_31 0.008452685 15.679673 3.857021e-07
6688 CES5A 16_31 0.002843469 5.460433 4.518517e-08
9366 ADGRG3 16_31 0.004909044 10.382723 1.483298e-07
5241 KATNB1 16_31 0.014466440 20.952899 8.821168e-07
5242 KIFC3 16_31 0.027079246 27.084949 2.134445e-06
7571 ZNF319 16_31 0.002825783 4.978056 4.093727e-08
1754 USB1 16_31 0.002938331 5.360702 4.583980e-08
1753 MMP15 16_31 0.008726601 16.009518 4.065778e-07
729 CFAP20 16_31 0.002875097 5.146633 4.306218e-08
730 CSNK2A2 16_31 0.002833587 5.007791 4.129553e-08
9278 GINS3 16_31 0.003615780 7.395250 7.781713e-08
1757 NDRG4 16_31 0.002839241 5.025067 4.152067e-08
3680 CNOT1 16_31 0.028066316 27.090515 2.212702e-06
1759 SLC38A7 16_31 0.005199281 10.880930 1.646378e-07
3684 GOT2 16_31 0.023688956 25.558053 1.761952e-06
z num_eqtl
11561 0.1973122 1
1124 -0.5287206 1
6695 -0.1575098 1
7710 -0.6747743 2
3681 -1.9263988 2
1122 0.2341288 1
8094 0.5732896 1
10727 2.0216456 1
10725 1.5829980 2
10386 -2.7354541 1
9805 -0.4099722 1
1740 2.2770780 2
438 3.8389063 2
1120 10.0796427 1
5240 11.8602110 1
5239 0.2383750 1
8472 -0.7860456 1
6698 -1.8323801 1
1745 -2.6585007 2
81 -0.8286220 1
1747 0.7431888 1
52 -2.0356089 2
1154 -0.9549661 2
3685 -0.9956520 2
4628 0.4043649 2
10722 -1.5429173 3
6688 -0.6790309 2
9366 -1.0805254 1
5241 -1.8723683 2
5242 -2.2243116 1
7571 0.1401284 1
1754 0.3145636 1
1753 -1.5466217 1
729 0.2411647 2
730 -0.1322483 2
9278 -0.7182699 2
1757 0.1679672 1
3680 -2.4928488 2
1759 1.2166483 1
3684 2.3111934 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "PLTP"
[1] "20_28"
genename region_tag susie_pip mu2 PVE
6007 JPH2 20_28 0.001852802 4.974997 2.682514e-08
4309 OSER1 20_28 0.002214811 6.655706 4.289939e-08
11031 OSER1-AS1 20_28 0.003014773 10.290390 9.028316e-08
10216 FITM2 20_28 0.001911355 7.639780 4.249545e-08
4310 SERINC3 20_28 0.003697268 10.983824 1.181829e-07
7974 PKIG 20_28 0.011661025 20.494334 6.954899e-07
10148 ADA 20_28 0.001900094 6.295141 3.480975e-08
12701 RP11-445H22.3 20_28 0.002347942 7.416846 5.067888e-08
3615 KCNK15 20_28 0.002246072 6.627410 4.331993e-08
7691 YWHAB 20_28 0.002604384 7.952601 6.027463e-08
292 TOMM34 20_28 0.001882193 5.177950 2.836236e-08
1617 STK4 20_28 0.002029729 5.817996 3.436621e-08
3588 SLPI 20_28 0.002117132 6.088002 3.750965e-08
3613 RBPJL 20_28 0.004630587 13.922251 1.876142e-07
3594 MATN4 20_28 0.002006962 5.528553 3.229022e-08
3591 SDC4 20_28 0.593701674 24.729865 4.272778e-05
10561 SYS1 20_28 0.001857654 4.937691 2.669372e-08
11528 DBNDD2 20_28 0.002261249 7.557779 4.973508e-08
3616 TP53TG5 20_28 0.002080417 7.348546 4.449099e-08
3589 WFDC3 20_28 0.002509208 12.679418 9.258835e-08
1683 DNTTIP1 20_28 0.006782956 16.294182 3.216414e-07
8697 UBE2C 20_28 0.002805178 10.120379 8.261853e-08
3587 SNX21 20_28 0.029736350 30.060443 2.601377e-06
1685 ACOT8 20_28 0.002380100 7.991865 5.535588e-08
7964 ZSWIM1 20_28 0.275222785 30.975062 2.480943e-05
1597 PLTP 20_28 0.987796601 61.569807 1.769928e-04
1598 PCIF1 20_28 0.001885312 21.324128 1.169970e-07
10331 ZNF335 20_28 0.001935285 5.281502 2.974560e-08
1600 MMP9 20_28 0.007202366 18.171321 3.808746e-07
3595 NCOA5 20_28 0.003370356 10.757165 1.055101e-07
1608 CD40 20_28 0.005659928 14.207615 2.340197e-07
z num_eqtl
6007 0.34475118 1
4309 -0.72359138 2
11031 1.30139067 3
10216 1.70850449 1
4310 1.06666707 2
7974 -1.92973723 1
10148 -1.11873945 1
12701 -0.77170625 1
3615 -0.43953756 2
7691 0.92140948 1
292 -0.21559970 1
1617 -0.65248556 1
3588 -0.43426645 2
3613 1.21973824 1
3594 -0.72142554 1
3591 -3.92072709 1
10561 -0.53036749 1
11528 0.76276385 1
3616 -1.26808126 2
3589 0.89942952 1
1683 1.68660209 2
8697 -1.29063071 1
3587 -2.25095415 1
1685 0.21164457 2
7964 -0.64131988 1
1597 -5.73249075 1
1598 2.96018585 1
10331 0.03190689 1
1600 1.76632544 1
3595 1.06921473 1
1608 -1.05986939 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "VAPA"
[1] "18_7"
genename region_tag susie_pip mu2 PVE z
10773 RAB12 18_7 0.005669417 4.963550 8.189381e-08 0.123074043
8980 NDUFV2 18_7 0.014693904 14.314634 6.121217e-07 1.381942467
1703 ANKRD12 18_7 0.007509173 7.719828 1.687019e-07 -0.889300700
240 RALBP1 18_7 0.008844314 9.325868 2.400345e-07 1.171373949
7947 RAB31 18_7 0.005628537 4.892601 8.014116e-08 -0.002933481
1691 VAPA 18_7 0.006682102 6.575116 1.278606e-07 0.657289426
4446 NAPG 18_7 0.007219661 7.334089 1.540931e-07 0.841503954
num_eqtl
10773 1
8980 2
1703 2
240 1
7947 2
1691 1
4446 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "KPNB1"
[1] "17_27"
genename region_tag susie_pip mu2 PVE
8499 DCAKD 17_27 0.007295915 5.154351 1.094395e-07
12583 AC142472.6 17_27 0.011448028 9.536381 3.177127e-07
6678 ARHGAP27 17_27 0.009937412 8.187470 2.367791e-07
11062 PLEKHM1 17_27 0.007185413 4.929695 1.030842e-07
12113 RP11-798G7.6 17_27 0.007337732 5.328459 1.137847e-07
3310 KANSL1 17_27 0.007337732 5.328459 1.137847e-07
9773 MAPT 17_27 0.007952718 5.896543 1.364688e-07
8846 LRRC37A 17_27 0.008112452 5.901095 1.393173e-07
11381 LRRC37A2 17_27 0.013594627 10.712992 4.238365e-07
9663 ARL17A 17_27 0.009161622 7.315193 1.950377e-07
802 NSF 17_27 0.010877140 10.526122 3.331988e-07
2301 WNT3 17_27 0.014727755 13.095719 5.612886e-07
2310 GOSR2 17_27 0.019773773 13.779865 7.929664e-07
41 CDC27 17_27 0.007920754 8.440671 1.945646e-07
11884 ITGB3 17_27 0.009508899 9.519123 2.634192e-07
9041 EFCAB13 17_27 0.011965142 57.621477 2.006423e-06
5281 NPEPPS 17_27 0.010242051 15.882260 4.733905e-07
2309 KPNB1 17_27 0.020782622 89.848478 5.434147e-06
10511 TBKBP1 17_27 0.016936476 90.170965 4.444369e-06
z num_eqtl
8499 -0.15093721 1
12583 -0.86874169 1
6678 1.16027409 2
11062 0.03569373 1
12113 -0.08580432 1
3310 -0.08580432 1
9773 -0.72635506 1
8846 -0.35529825 1
11381 2.39235673 1
9663 1.91935258 2
802 -2.06053407 1
2301 -1.55730420 1
2310 1.29775360 2
41 -1.62384444 1
11884 -1.58019328 2
9041 7.36590043 4
5281 -3.02425642 1
2309 -9.51317987 2
10511 -9.31233452 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ALDH2"
[1] "12_67"
genename region_tag susie_pip mu2 PVE
5112 TCHP 12_67 0.024551426 13.970132 9.981540e-07
5111 GIT2 12_67 0.028408805 15.795647 1.305902e-06
8639 C12orf76 12_67 0.010065551 6.904105 2.022392e-07
3515 IFT81 12_67 0.014681787 12.233365 5.226912e-07
10093 ANAPC7 12_67 0.009610337 6.533553 1.827294e-07
2531 ARPC3 12_67 0.011906193 8.239795 2.855023e-07
10684 FAM216A 12_67 0.009063171 5.613735 1.480650e-07
2532 GPN3 12_67 0.011364178 8.543498 2.825492e-07
2533 VPS29 12_67 0.011444432 8.620619 2.871131e-07
10683 TCTN1 12_67 0.027201544 17.021122 1.347417e-06
3517 HVCN1 12_67 0.008921524 5.669699 1.472039e-07
9717 PPP1CC 12_67 0.008699185 5.274093 1.335201e-07
10375 FAM109A 12_67 0.008732629 5.860945 1.489474e-07
2536 SH2B3 12_67 0.071501763 57.685407 1.200337e-05
10680 ATXN2 12_67 0.042796306 18.570768 2.312898e-06
2541 ALDH2 12_67 0.020766191 32.769841 1.980393e-06
11290 MAPKAPK5-AS1 12_67 0.020066937 31.847532 1.859847e-06
10370 TMEM116 12_67 0.038333402 32.410717 3.615649e-06
1191 ERP29 12_67 0.038333402 32.410717 3.615649e-06
2544 NAA25 12_67 0.041829146 33.498264 4.077759e-06
8505 HECTD4 12_67 0.039386207 33.640810 3.855946e-06
9084 PTPN11 12_67 0.011489634 10.378259 3.470172e-07
z num_eqtl
5112 -1.4944146 2
5111 -1.8046506 2
8639 -1.0008849 1
3515 -2.3268452 2
10093 -1.0505294 1
2531 1.1143107 1
10684 -0.6987263 1
2532 -1.4783205 1
2533 1.4871406 1
10683 2.1771229 1
3517 -0.8757995 1
9717 0.7231339 1
10375 0.8704329 1
2536 -7.8354247 1
10680 -0.7777805 1
2541 -6.4436064 1
11290 6.3728846 1
10370 5.8049447 1
1191 -5.8049447 1
2544 5.8544343 1
8505 -5.7749393 2
9084 2.2253869 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "APOA1"
[1] "11_70"
genename region_tag susie_pip mu2 PVE z
4868 BUD13 11_70 0.005760699 36.876902 6.182297e-07 4.11527976
3154 APOA1 11_70 0.004278331 6.652390 8.282709e-08 1.11150616
7898 PAFAH1B2 11_70 0.005499480 7.725576 1.236439e-07 -0.01722766
6005 SIDT2 11_70 0.004139980 5.468270 6.588226e-08 0.50104522
6006 TAGLN 11_70 0.004614639 18.478031 2.481497e-07 -1.55444774
6785 PCSK7 11_70 0.012766000 16.431533 6.104544e-07 0.97935688
7745 RNF214 11_70 0.004787354 6.579102 9.166055e-08 -0.52468931
2466 CEP164 11_70 0.004664222 5.763477 7.823193e-08 -0.30209785
9720 BACE1 11_70 0.004495693 21.121393 2.763373e-07 -4.13706265
4881 FXYD2 11_70 0.004684096 6.128819 8.354547e-08 -0.37435241
2465 APOA5 11_70 0.032499003 145.137431 1.372681e-05 -11.35991043
num_eqtl
4868 1
3154 2
7898 2
6005 1
6006 1
6785 1
7745 1
2466 2
9720 1
4881 2
2465 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "STARD3"
[1] "17_23"
genename region_tag susie_pip mu2 PVE
12452 EPOP 17_23 0.008828798 4.964151 1.275460e-07
12620 PSMB3 17_23 0.027491186 16.148740 1.291970e-06
12575 PIP4K2B 17_23 0.013522666 9.153051 3.602040e-07
12450 CWC25 17_23 0.015484658 10.486090 4.725367e-07
16 LASP1 17_23 0.041860534 20.319049 2.475303e-06
12051 LINC00672 17_23 0.283902976 40.138551 3.316286e-05
6848 PLXDC1 17_23 0.010327090 6.503367 1.954504e-07
2297 FBXL20 17_23 0.019490995 12.752867 7.233728e-07
3731 MED1 17_23 0.013638291 9.236893 3.666116e-07
4202 STARD3 17_23 0.008991146 5.143031 1.345719e-07
8601 TCAP 17_23 0.012254194 8.184631 2.918799e-07
5343 PNMT 17_23 0.013071583 8.819499 3.354999e-07
5341 ERBB2 17_23 0.011726386 7.751891 2.645405e-07
6849 PGAP3 17_23 0.017422902 11.647436 5.905697e-07
5342 GRB7 17_23 0.009220048 5.389875 1.446213e-07
6850 IKZF3 17_23 0.145210688 32.923351 1.391307e-05
8390 ORMDL3 17_23 0.027480145 16.144734 1.291131e-06
12065 RP11-387H17.4 17_23 0.009107332 5.269052 1.396510e-07
7860 GSDMA 17_23 0.029113792 16.716128 1.416298e-06
2299 CSF3 17_23 0.086516718 27.605103 6.950399e-06
3800 NR1D1 17_23 0.011905826 7.901065 2.737572e-07
9964 MSL1 17_23 0.011174564 7.278216 2.366878e-07
2300 RAPGEFL1 17_23 0.013131803 8.864586 3.387686e-07
8318 WIPF2 17_23 0.008763262 4.891010 1.247339e-07
1306 CDC6 17_23 0.010894501 7.028750 2.228465e-07
5344 IGFBP4 17_23 0.010563096 6.725286 2.067389e-07
4201 TNS4 17_23 0.008765883 4.893945 1.248461e-07
12085 RP5-1028K7.2 17_23 0.034839186 18.495303 1.875209e-06
3799 CCR7 17_23 0.009538106 5.722828 1.588522e-07
793 SMARCE1 17_23 0.009140545 5.304816 1.411116e-07
10827 KRT222 17_23 0.008795849 4.927446 1.261304e-07
z num_eqtl
12452 -0.10662081 2
12620 1.72896001 2
12575 -1.00157125 1
12450 1.08708711 3
16 2.06384703 1
12051 3.53577816 2
6848 -0.56588683 1
2297 1.93901635 1
3731 -1.49493107 2
4202 -0.38811898 2
8601 1.04770013 1
5343 -1.18909299 2
5341 -1.13724896 2
6849 -2.14338495 2
5342 0.49199960 3
6850 3.46618563 1
8390 2.64808902 2
12065 0.05180697 2
7860 2.77074527 2
2299 -3.20456334 1
3800 0.81175389 1
9964 0.98720359 1
2300 0.89662465 1
8318 -0.18843428 2
1306 0.50375876 2
5344 -0.57588470 1
4201 0.02859681 1
12085 1.93533443 1
3799 -0.44220762 1
793 -0.32396665 2
10827 0.11407193 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "PPARG"
[1] "3_9"
genename region_tag susie_pip mu2 PVE z
10251 ATG7 3_9 0.005863072 11.279321 1.924547e-07 1.4232154
5615 TAMM41 3_9 0.004285657 9.651761 1.203772e-07 1.3225877
6517 TIMP4 3_9 0.005068659 8.137228 1.200300e-07 0.2250754
4231 PPARG 3_9 0.002950426 10.891923 9.352110e-08 -2.5953663
6362 TSEN2 3_9 0.033836996 29.748883 2.929428e-06 4.4713068
856 MKRN2 3_9 0.005642229 14.942806 2.453597e-07 -3.4863426
11068 MKRN2OS 3_9 0.039627647 28.911089 3.334134e-06 -4.7387006
4230 RAF1 3_9 0.002819330 5.486407 4.501469e-08 0.8372135
5632 CAND2 3_9 0.021913744 27.707542 1.766993e-06 -3.2762482
5633 RPL32 3_9 0.003803163 7.956967 8.806692e-08 -0.8436264
num_eqtl
10251 2
5615 3
6517 1
4231 1
6362 1
856 2
11068 2
4230 1
5632 1
5633 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LPIN3"
[1] "20_25"
genename region_tag susie_pip mu2 PVE z
10499 TOP1 20_25 0.012372927 20.23536 7.286243e-07 -3.533405
3599 PLCG1 20_25 0.046442227 22.49403 3.040189e-06 2.065730
8628 ZHX3 20_25 0.007581188 12.81247 2.826770e-07 -2.767903
4307 LPIN3 20_25 0.011921879 47.36111 1.643187e-06 6.600722
9463 EMILIN3 20_25 0.027281344 95.64737 7.593799e-06 9.450280
3598 CHD6 20_25 0.010044191 11.65434 3.406613e-07 -2.247872
num_eqtl
10499 2
3599 2
8628 1
4307 2
9463 2
3598 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "SORT1"
[1] "1_67"
genename region_tag susie_pip mu2 PVE z
4434 VAV3 1_67 0.065099832 24.320182 4.607517e-06 -2.1042470
1073 SLC25A24 1_67 0.008611429 6.469190 1.621233e-07 0.9234769
6966 FAM102B 1_67 0.007746337 6.075857 1.369696e-07 -1.1378586
3009 STXBP3 1_67 0.017008399 18.337108 9.076420e-07 2.9982594
3438 GPSM2 1_67 0.008080202 9.107535 2.141625e-07 -1.9348222
3437 CLCC1 1_67 0.008069139 11.438836 2.686144e-07 2.5660415
10286 TAF13 1_67 0.010760437 9.452750 2.960114e-07 -1.5591453
10955 TMEM167B 1_67 0.013756111 11.324506 4.533517e-07 -1.5270485
315 SARS 1_67 0.014473303 94.891762 3.996837e-06 9.5234950
4435 PSRC1 1_67 0.024647145 1673.274100 1.200201e-04 -41.6873361
5436 PSMA5 1_67 0.007880027 1212.644656 2.780876e-05 -35.4138115
5431 SYPL2 1_67 0.016433368 198.526066 9.494332e-06 -14.1478749
6970 ATXN7L2 1_67 0.009823180 367.075403 1.049368e-05 -19.2427445
8615 CYB561D1 1_67 0.063213063 127.989209 2.354510e-05 10.6827516
9259 AMIGO1 1_67 0.018715231 27.987319 1.524322e-06 -3.9630816
6445 GPR61 1_67 0.007845449 23.052571 5.263292e-07 4.2425343
587 GNAI3 1_67 0.054241321 31.679097 5.000614e-06 -3.8408490
7977 GSTM4 1_67 0.014374335 30.871560 1.291417e-06 4.7825961
10821 GSTM2 1_67 0.008660380 14.362332 3.619780e-07 2.9726102
4430 GSTM1 1_67 0.018905914 29.235760 1.608542e-06 4.2590068
4433 GSTM3 1_67 0.007854604 20.899567 4.777293e-07 -3.9546683
4432 GSTM5 1_67 0.014116282 14.798554 6.079389e-07 2.3798227
12715 SORT1 1_67 0.987301191 1681.661467 4.831795e-03 -41.7934744
num_eqtl
4434 1
1073 2
6966 1
3009 1
3438 1
3437 2
10286 1
10955 1
315 1
4435 1
5436 2
5431 2
6970 2
8615 3
9259 1
6445 1
587 1
7977 3
10821 2
4430 1
4433 3
4432 5
12715 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "FADS2"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
9982 FAM111B 11_34 0.004473671 5.029481 6.547983e-08
7662 FAM111A 11_34 0.006610236 8.672723 1.668371e-07
2444 DTX4 11_34 0.004504280 5.112576 6.701707e-08
10267 MPEG1 11_34 0.004535836 5.241120 6.918337e-08
7684 PATL1 11_34 0.062436117 30.278853 5.501684e-06
7687 STX3 11_34 0.004422795 4.937794 6.355506e-08
7688 MRPL16 11_34 0.006813281 8.877090 1.760140e-07
5997 MS4A2 11_34 0.008397351 10.906049 2.665202e-07
2453 MS4A6A 11_34 0.005002286 6.032979 8.782550e-08
10924 MS4A4E 11_34 0.005747724 7.597092 1.270760e-07
7697 MS4A7 11_34 0.004397638 4.910668 6.284639e-08
7698 MS4A14 11_34 0.025928734 21.804577 1.645316e-06
2455 CCDC86 11_34 0.005785125 7.386093 1.243506e-07
2456 PRPF19 11_34 0.008960933 12.093005 3.153609e-07
2457 TMEM109 11_34 0.010241876 13.173172 3.926360e-07
2480 SLC15A3 11_34 0.004713723 6.044231 8.291353e-08
2481 CD5 11_34 0.004532367 5.291874 6.979992e-08
7874 VPS37C 11_34 0.005273098 6.314732 9.690387e-08
7875 VWCE 11_34 0.004740535 5.867788 8.095098e-08
6902 CYB561A3 11_34 0.005995808 10.249129 1.788360e-07
5990 TMEM138 11_34 0.005995808 10.249129 1.788360e-07
9789 TMEM216 11_34 0.004401847 4.951549 6.343023e-08
5996 CPSF7 11_34 0.005172782 9.944039 1.496950e-07
11817 RP11-286N22.8 11_34 0.004941196 5.969003 8.583297e-08
6903 PPP1R32 11_34 0.005377305 6.576369 1.029132e-07
11812 RP11-794G24.1 11_34 0.011355935 12.517096 4.136631e-07
3676 DKFZP434K028 11_34 0.004413466 5.958618 7.653244e-08
4508 TMEM258 11_34 0.034858621 66.046365 6.700071e-06
7955 FEN1 11_34 0.006376688 145.198765 2.694501e-06
4507 FADS2 11_34 0.006376688 145.198765 2.694501e-06
5991 FADS1 11_34 0.999536200 160.579227 4.670982e-04
11004 FADS3 11_34 0.009838644 21.356696 6.114904e-07
7876 BEST1 11_34 0.004701537 18.832400 2.576712e-07
5994 INCENP 11_34 0.004408432 5.798857 7.439553e-08
6904 ASRGL1 11_34 0.004535271 5.204785 6.869519e-08
1196 GANAB 11_34 0.007548814 72.351397 1.589447e-06
z num_eqtl
9982 -0.130372989 1
7662 0.788300174 2
2444 0.272926929 2
10267 0.288859011 1
7684 3.303999343 2
7687 0.001285218 2
7688 0.989371951 2
5997 -1.135206653 1
2453 0.544252801 1
10924 0.848247159 1
7697 -0.132073393 2
7698 -1.857701655 3
2455 -0.651729299 3
2456 1.430603519 2
2457 1.421831985 1
2480 0.821410772 1
2481 0.346138465 1
7874 0.024014132 1
7875 -0.638825054 2
6902 -1.782804562 1
5990 -1.782804562 1
9789 -0.251085346 2
5996 -2.061044578 1
11817 -0.427047808 1
6903 -0.382653253 1
11812 0.447753087 1
3676 1.073921044 1
4508 -6.946921109 2
7955 12.072635202 1
4507 12.072635202 1
5991 12.825882927 2
11004 3.289416818 1
7876 -3.744804132 1
5994 -0.969291005 2
6904 -0.250084386 1
1196 -8.204723304 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "CD36"
[1] "7_51"
genename region_tag susie_pip mu2 PVE z
11275 AC003988.1 7_51 0.01031357 5.223114 1.567685e-07 -0.1655722
4557 CD36 7_51 0.01019033 5.105037 1.513936e-07 -0.2565559
830 SEMA3C 7_51 0.01303533 7.524757 2.854531e-07 -0.8034967
num_eqtl
11275 1
4557 1
830 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "CYP27A1"
[1] "2_129"
genename region_tag susie_pip mu2 PVE
3881 VIL1 2_129 0.763699944 27.026402 6.006636e-05
9895 RUFY4 2_129 0.011442676 9.066223 3.019078e-07
9189 CXCR2 2_129 0.018287052 12.276389 6.533330e-07
7090 CXCR1 2_129 0.008884523 6.046207 1.563282e-07
7091 ARPC2 2_129 0.031841608 17.311693 1.604186e-06
3882 AAMP 2_129 0.038309380 18.542799 2.067287e-06
3883 PNKD 2_129 0.054373381 22.024557 3.485089e-06
4655 TMBIM1 2_129 0.040696625 19.094782 2.261483e-06
243 SLC11A1 2_129 0.008170071 5.223184 1.241885e-07
4649 USP37 2_129 0.035570735 20.587521 2.131166e-06
5618 CNOT9 2_129 0.018681776 17.677950 9.611039e-07
2934 PLCD4 2_129 0.021724924 18.250786 1.153879e-06
813 BCS1L 2_129 0.008337444 6.052047 1.468438e-07
2936 ZNF142 2_129 0.008267565 5.945377 1.430465e-07
7095 STK36 2_129 0.011916534 15.777607 5.471563e-07
4656 CYP27A1 2_129 0.008274146 8.427972 2.029395e-07
12345 RP11-459I19.1 2_129 0.011464801 15.500748 5.171773e-07
12356 RP11-33O4.1 2_129 0.008713959 6.549260 1.660841e-07
9840 NHEJ1 2_129 0.025684066 17.265619 1.290524e-06
10864 SLC23A3 2_129 0.009736132 6.643068 1.882242e-07
5617 FAM134A 2_129 0.023177900 14.752830 9.951069e-07
2941 CNPPD1 2_129 0.010657232 7.798462 2.418654e-07
2943 ABCB6 2_129 0.026426528 17.136106 1.317870e-06
10508 ATG9A 2_129 0.012610803 9.734087 3.572385e-07
7101 ANKZF1 2_129 0.013436809 10.384134 4.060567e-07
7104 GLB1L 2_129 0.008407253 5.452794 1.334116e-07
3880 TUBA4A 2_129 0.009769496 6.887493 1.958184e-07
4654 DNAJB2 2_129 0.008698513 5.839548 1.478239e-07
3580 DNPEP 2_129 0.025658967 16.697455 1.246837e-06
8699 DES 2_129 0.034829083 19.722359 1.999039e-06
758 SPEG 2_129 0.008027319 5.098423 1.191041e-07
5620 GMPPA 2_129 0.010075513 7.327313 2.148484e-07
3579 CHPF 2_129 0.008411898 5.622050 1.376287e-07
3582 OBSL1 2_129 0.013937549 10.874333 4.410719e-07
z num_eqtl
3881 4.72553123 1
9895 1.02099255 2
9189 -1.47518963 1
7090 0.56376400 1
7091 -1.94043222 1
3882 -1.90836173 1
3883 -2.20803733 1
4655 -1.92761030 2
243 0.05100451 1
4649 -3.97558895 3
5618 3.65097314 1
2934 -3.71953627 1
813 -0.95838574 1
2936 0.93284395 1
7095 3.44963509 2
4656 1.82913381 2
12345 3.40999367 1
12356 -0.93933690 1
9840 1.96641989 2
10864 0.30848047 1
5617 -1.33896546 1
2941 -0.77901195 2
2943 1.84732093 1
10508 -1.13410218 1
7101 -1.21232145 2
7104 -0.12917630 1
3880 -0.56762321 1
4654 -0.48915334 1
3580 1.83086497 2
8699 2.01667108 1
758 0.08326323 1
5620 -0.73851578 1
3579 0.41418410 1
3582 -1.30597047 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "NPC1"
[1] "18_12"
genename region_tag susie_pip mu2 PVE
4477 CABLES1 18_12 0.01032410 5.056290 1.519163e-07
4476 TMEM241 18_12 0.02805115 14.903677 1.216646e-06
1708 RIOK3 18_12 0.02100112 12.044525 7.361265e-07
5304 C18orf8 18_12 0.02842276 15.034173 1.243558e-06
5306 NPC1 18_12 0.06410504 23.134063 4.315831e-06
454 LAMA3 18_12 0.01015168 4.890870 1.444922e-07
7914 TTC39C 18_12 0.03939709 18.272028 2.094938e-06
6311 CABYR 18_12 0.01015293 4.892076 1.445455e-07
12078 RP11-799B12.4 18_12 0.01019975 4.937265 1.465535e-07
z num_eqtl
4477 -0.14555775 1
4476 -1.84256728 2
1708 -1.34902775 1
5304 1.67075330 2
5306 -2.39576123 1
454 0.01316175 2
7914 1.78195458 3
6311 -0.02760888 1
12078 0.06691488 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ABCG8"
[1] "2_27"
genename region_tag susie_pip mu2 PVE
5563 ABCG8 2_27 9.999667e-01 313.616602 9.126513e-04
12661 LINC01126 2_27 1.311448e-05 17.794481 6.791362e-10
2977 THADA 2_27 1.774105e-06 8.185093 4.225938e-11
6208 PLEKHH2 2_27 6.426620e-06 16.108349 3.012687e-10
11022 C1GALT1C1L 2_27 3.979730e-06 24.312863 2.815853e-10
4930 DYNC2LI1 2_27 8.642848e-07 8.220899 2.067743e-11
4943 LRPPRC 2_27 2.558980e-06 12.554554 9.349503e-11
z num_eqtl
5563 -20.29398177 1
12661 0.91913800 1
2977 -2.34643541 2
6208 -2.96266114 2
11022 3.06095256 2
4930 -0.02538894 1
4943 -0.91853212 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "NCEH1"
[1] "3_106"
genename region_tag susie_pip mu2 PVE z
11015 LINC02068 3_106 0.01304671 7.915898 3.005533e-07 0.8738216
5661 NCEH1 3_106 0.01190421 7.014947 2.430218e-07 -0.6532732
num_eqtl
11015 2
5661 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "STAR"
[1] "8_34"
genename region_tag susie_pip mu2 PVE
5843 PROSC 8_34 0.016235811 12.429322 5.872753e-07
4029 ASH2L 8_34 0.059082233 25.237307 4.339305e-06
5842 STAR 8_34 0.069925808 26.930315 5.480235e-06
8727 LSM1 8_34 0.007540176 4.892712 1.073622e-07
5850 NSD3 8_34 0.007540845 4.893583 1.073908e-07
12297 RP11-350N15.5 8_34 0.007637312 5.018305 1.115367e-07
7411 LETM2 8_34 0.008060424 5.547421 1.301276e-07
900 FGFR1 8_34 0.011970997 9.431667 3.285784e-07
5846 TACC1 8_34 0.007567141 4.927741 1.085176e-07
8068 PLEKHA2 8_34 0.020595205 14.773190 8.854432e-07
8067 TM2D2 8_34 0.011055904 8.650104 2.783146e-07
7965 ADAM9 8_34 0.236223769 39.581651 2.721058e-05
z num_eqtl
5843 -1.18549708 1
4029 -2.41270520 1
5842 -2.50033778 1
8727 0.14152273 1
5850 -0.06923734 2
12297 0.26974556 2
7411 -0.48761003 2
900 -0.93406568 1
5846 -0.11708259 2
8068 1.82472982 1
8067 0.85605170 2
7965 3.23657677 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "FADS1"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
9982 FAM111B 11_34 0.004473671 5.029481 6.547983e-08
7662 FAM111A 11_34 0.006610236 8.672723 1.668371e-07
2444 DTX4 11_34 0.004504280 5.112576 6.701707e-08
10267 MPEG1 11_34 0.004535836 5.241120 6.918337e-08
7684 PATL1 11_34 0.062436117 30.278853 5.501684e-06
7687 STX3 11_34 0.004422795 4.937794 6.355506e-08
7688 MRPL16 11_34 0.006813281 8.877090 1.760140e-07
5997 MS4A2 11_34 0.008397351 10.906049 2.665202e-07
2453 MS4A6A 11_34 0.005002286 6.032979 8.782550e-08
10924 MS4A4E 11_34 0.005747724 7.597092 1.270760e-07
7697 MS4A7 11_34 0.004397638 4.910668 6.284639e-08
7698 MS4A14 11_34 0.025928734 21.804577 1.645316e-06
2455 CCDC86 11_34 0.005785125 7.386093 1.243506e-07
2456 PRPF19 11_34 0.008960933 12.093005 3.153609e-07
2457 TMEM109 11_34 0.010241876 13.173172 3.926360e-07
2480 SLC15A3 11_34 0.004713723 6.044231 8.291353e-08
2481 CD5 11_34 0.004532367 5.291874 6.979992e-08
7874 VPS37C 11_34 0.005273098 6.314732 9.690387e-08
7875 VWCE 11_34 0.004740535 5.867788 8.095098e-08
6902 CYB561A3 11_34 0.005995808 10.249129 1.788360e-07
5990 TMEM138 11_34 0.005995808 10.249129 1.788360e-07
9789 TMEM216 11_34 0.004401847 4.951549 6.343023e-08
5996 CPSF7 11_34 0.005172782 9.944039 1.496950e-07
11817 RP11-286N22.8 11_34 0.004941196 5.969003 8.583297e-08
6903 PPP1R32 11_34 0.005377305 6.576369 1.029132e-07
11812 RP11-794G24.1 11_34 0.011355935 12.517096 4.136631e-07
3676 DKFZP434K028 11_34 0.004413466 5.958618 7.653244e-08
4508 TMEM258 11_34 0.034858621 66.046365 6.700071e-06
7955 FEN1 11_34 0.006376688 145.198765 2.694501e-06
4507 FADS2 11_34 0.006376688 145.198765 2.694501e-06
5991 FADS1 11_34 0.999536200 160.579227 4.670982e-04
11004 FADS3 11_34 0.009838644 21.356696 6.114904e-07
7876 BEST1 11_34 0.004701537 18.832400 2.576712e-07
5994 INCENP 11_34 0.004408432 5.798857 7.439553e-08
6904 ASRGL1 11_34 0.004535271 5.204785 6.869519e-08
1196 GANAB 11_34 0.007548814 72.351397 1.589447e-06
z num_eqtl
9982 -0.130372989 1
7662 0.788300174 2
2444 0.272926929 2
10267 0.288859011 1
7684 3.303999343 2
7687 0.001285218 2
7688 0.989371951 2
5997 -1.135206653 1
2453 0.544252801 1
10924 0.848247159 1
7697 -0.132073393 2
7698 -1.857701655 3
2455 -0.651729299 3
2456 1.430603519 2
2457 1.421831985 1
2480 0.821410772 1
2481 0.346138465 1
7874 0.024014132 1
7875 -0.638825054 2
6902 -1.782804562 1
5990 -1.782804562 1
9789 -0.251085346 2
5996 -2.061044578 1
11817 -0.427047808 1
6903 -0.382653253 1
11812 0.447753087 1
3676 1.073921044 1
4508 -6.946921109 2
7955 12.072635202 1
4507 12.072635202 1
5991 12.825882927 2
11004 3.289416818 1
7876 -3.744804132 1
5994 -0.969291005 2
6904 -0.250084386 1
1196 -8.204723304 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "VDAC2"
[1] "10_49"
genename region_tag susie_pip mu2 PVE
8458 AGAP5 10_49 0.018576665 12.246864 6.620838e-07
3503 PLAU 10_49 0.026684463 15.821873 1.228674e-06
9575 AP3M1 10_49 0.008829801 4.932942 1.267585e-07
6446 ADK 10_49 0.008846789 4.951812 1.274882e-07
7476 VDAC2 10_49 0.080939322 26.897009 6.335543e-06
7477 COMTD1 10_49 0.080366331 26.825123 6.273879e-06
11089 ZNF503-AS1 10_49 0.008951822 5.067678 1.320203e-07
12363 RP11-399K21.14 10_49 0.016952769 11.345662 5.597457e-07
5936 C10orf11 10_49 0.150380842 33.256677 1.455431e-05
z num_eqtl
8458 -1.3182844 1
3503 -1.6531888 1
9575 -0.1414585 1
6446 0.1263619 2
7476 2.9474923 1
7477 2.9437974 1
11089 0.3935661 1
12363 -1.4701044 2
5936 2.9550655 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LIPC"
[1] "15_26"
genename region_tag susie_pip mu2 PVE z
7547 LIPC 15_26 0.004680373 41.827281 5.697186e-07 -5.9117767
4905 ADAM10 15_26 0.005389507 7.037328 1.103766e-07 0.8412995
4889 SLTM 15_26 0.004643243 5.487403 7.414956e-08 -0.7158866
6536 RNF111 15_26 0.004428083 4.975819 6.412104e-08 -0.2997052
8386 LDHAL6B 15_26 0.004475923 5.075357 6.611036e-08 -0.4439394
num_eqtl
7547 2
4905 2
4889 1
6536 1
8386 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "SOAT2"
[1] "12_33"
genename region_tag susie_pip mu2 PVE
7834 KRT1 12_33 0.01346060 6.908483 2.706246e-07
8189 KRT78 12_33 0.01112877 5.163405 1.672260e-07
2519 KRT18 12_33 0.01610739 9.004353 4.220832e-07
8188 KRT8 12_33 0.03818378 17.190068 1.910191e-06
544 EIF4B 12_33 0.01103557 4.993670 1.603743e-07
2521 TNS2 12_33 0.01395140 7.347316 2.983093e-07
7838 SPRYD3 12_33 0.02003675 10.432033 6.082981e-07
7839 IGFBP6 12_33 0.01109662 5.046586 1.629703e-07
7840 SOAT2 12_33 0.02416931 12.458540 8.762977e-07
11843 RP11-1136G11.8 12_33 0.01104268 5.025887 1.615130e-07
5138 ZNF740 12_33 0.07370299 22.925885 4.917354e-06
5133 CSAD 12_33 0.02184813 11.197921 7.119869e-07
5131 ITGB7 12_33 0.01091073 4.892336 1.553426e-07
9332 MFSD5 12_33 0.03955257 15.879703 1.827836e-06
4595 ESPL1 12_33 0.01870917 10.577800 5.759308e-07
10724 PRR13 12_33 0.15465803 18.801868 8.462405e-06
5124 TARBP2 12_33 0.02387096 13.310904 9.246935e-07
4579 ATP5G2 12_33 0.01309335 9.341404 3.559453e-07
203 CALCOCO1 12_33 0.02525959 13.337260 9.804224e-07
11586 FLJ12825 12_33 0.01295854 6.590990 2.485576e-07
3549 SMUG1 12_33 0.01198160 5.829366 2.032622e-07
11649 RP11-834C11.4 12_33 0.01144393 5.279919 1.758421e-07
1308 CBX5 12_33 0.01253510 6.268348 2.286657e-07
z num_eqtl
7834 -0.570714701 1
8189 -0.349999201 1
2519 1.038536540 1
8188 2.113112572 1
544 -0.221941333 1
2521 -0.962272505 2
7838 1.338767531 1
7839 -0.512279235 1
7840 -1.851220053 1
11843 0.012887459 1
5138 2.546520305 2
5133 -1.178701229 1
5131 0.219057094 2
9332 1.273981567 2
4595 1.636840591 3
10724 -3.775263261 1
5124 -3.023920328 1
4579 2.116508926 2
203 -1.413276345 1
11586 -0.529012775 2
3549 0.396812394 1
11649 -0.009489289 1
1308 0.652766171 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "CYP7A1"
[1] "8_45"
genename region_tag susie_pip mu2 PVE z
10938 UBXN2B 8_45 0.009100805 25.96038 6.875608e-07 -3.437080
7859 CYP7A1 8_45 0.005477161 73.34546 1.169093e-06 -7.392476
num_eqtl
10938 3
7859 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "TNKS"
[1] "8_12"
genename region_tag susie_pip mu2 PVE z
11738 RP11-115J16.2 8_12 0.004472655 39.34125 5.120753e-07 7.146749
8531 TNKS 8_12 0.984399158 73.76708 2.113266e-04 11.026034
num_eqtl
11738 1
8531 2
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "ADH1B"
[1] "4_66"
genename region_tag susie_pip mu2 PVE
7980 TSPAN5 4_66 0.014674407 11.179397 4.774185e-07
6091 EIF4E 4_66 0.009482212 6.919558 1.909450e-07
7222 METAP1 4_66 0.007832039 5.097756 1.161914e-07
12374 RP11-571L19.8 4_66 0.008322181 5.636996 1.365228e-07
8496 ADH6 4_66 0.009968595 7.562593 2.193941e-07
10115 ADH1B 4_66 0.014780501 11.319478 4.868956e-07
11584 ADH1C 4_66 0.143197702 32.307606 1.346360e-05
10057 ADH7 4_66 0.009291555 10.533608 2.848301e-07
5055 MTTP 4_66 0.010983181 8.019871 2.563397e-07
5686 TRMT10A 4_66 0.011475866 9.119569 3.045651e-07
z num_eqtl
7980 -1.2321573 2
6091 0.9082871 1
7222 -0.1831346 1
12374 -0.4759060 3
8496 0.7334699 2
10115 -1.1153042 1
11584 -3.1932254 3
10057 1.9684512 2
5055 -0.7972018 1
5686 -1.1240076 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "LPA"
[1] "6_104"
genename region_tag susie_pip mu2 PVE z
10435 LPA 6_104 5.130003e-06 64.969050 9.699390e-10 8.1196160
3449 PLG 6_104 6.083324e-06 17.973715 3.181992e-10 2.4097623
11043 RP1-81D8.3 6_104 6.907513e-01 51.497870 1.035217e-04 -7.2217829
5799 SLC22A3 6_104 1.427439e-06 33.716653 1.400627e-10 -6.5929784
1074 MAP3K4 6_104 7.636543e-07 6.705596 1.490234e-11 0.7795492
num_eqtl
10435 1
3449 1
11043 2
5799 1
1074 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "VDAC1"
[1] "5_80"
genename region_tag susie_pip mu2 PVE
11643 RP11-215P8.4 5_80 0.01208028 6.379661 2.242822e-07
7311 SEPT8 5_80 0.02248021 12.495947 8.175039e-07
7312 SHROOM1 5_80 0.01402345 7.846346 3.202157e-07
7313 GDF9 5_80 0.01038665 4.895420 1.479741e-07
760 AFF4 5_80 0.01525774 8.676374 3.852555e-07
6400 ZCCHC10 5_80 0.01051868 5.019498 1.536533e-07
8217 HSPA4 5_80 0.01038586 4.894673 1.479402e-07
2763 C5orf15 5_80 0.01110367 5.551218 1.793804e-07
10837 VDAC1 5_80 0.04021829 18.256995 2.136846e-06
978 TCF7 5_80 0.02805202 14.684092 1.198758e-06
2759 SKP1 5_80 0.01871745 10.689171 5.822519e-07
2761 PPP2CA 5_80 0.02118079 11.908353 7.340306e-07
102 CDKL3 5_80 0.01301332 7.111095 2.693052e-07
3214 UBE2B 5_80 0.01301332 7.111095 2.693052e-07
11352 CDKN2AIPNL 5_80 0.01907718 10.876819 6.038602e-07
11660 LINC01843 5_80 0.01097258 5.434537 1.735369e-07
7340 CAMLG 5_80 0.01039039 4.898965 1.481346e-07
9275 C5orf24 5_80 0.01048438 4.987424 1.521737e-07
4283 PCBD2 5_80 0.01389182 7.753574 3.134594e-07
681 PITX1 5_80 0.01175911 6.114825 2.092564e-07
z num_eqtl
11643 0.60776683 1
7311 1.30260276 1
7312 0.89963346 2
7313 0.03182872 1
760 1.02990725 1
6400 -0.15509357 1
8217 -0.07597330 2
2763 0.41873673 1
10837 1.82176093 1
978 1.55983246 1
2759 -1.31317545 1
2761 1.40300081 2
102 0.87889673 1
3214 0.87889673 1
11352 -1.07306079 2
11660 0.36441021 1
7340 0.10493598 1
9275 -0.20162988 1
4283 0.78727304 1
681 0.52096477 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "FADS3"
[1] "11_34"
genename region_tag susie_pip mu2 PVE
9982 FAM111B 11_34 0.004473671 5.029481 6.547983e-08
7662 FAM111A 11_34 0.006610236 8.672723 1.668371e-07
2444 DTX4 11_34 0.004504280 5.112576 6.701707e-08
10267 MPEG1 11_34 0.004535836 5.241120 6.918337e-08
7684 PATL1 11_34 0.062436117 30.278853 5.501684e-06
7687 STX3 11_34 0.004422795 4.937794 6.355506e-08
7688 MRPL16 11_34 0.006813281 8.877090 1.760140e-07
5997 MS4A2 11_34 0.008397351 10.906049 2.665202e-07
2453 MS4A6A 11_34 0.005002286 6.032979 8.782550e-08
10924 MS4A4E 11_34 0.005747724 7.597092 1.270760e-07
7697 MS4A7 11_34 0.004397638 4.910668 6.284639e-08
7698 MS4A14 11_34 0.025928734 21.804577 1.645316e-06
2455 CCDC86 11_34 0.005785125 7.386093 1.243506e-07
2456 PRPF19 11_34 0.008960933 12.093005 3.153609e-07
2457 TMEM109 11_34 0.010241876 13.173172 3.926360e-07
2480 SLC15A3 11_34 0.004713723 6.044231 8.291353e-08
2481 CD5 11_34 0.004532367 5.291874 6.979992e-08
7874 VPS37C 11_34 0.005273098 6.314732 9.690387e-08
7875 VWCE 11_34 0.004740535 5.867788 8.095098e-08
6902 CYB561A3 11_34 0.005995808 10.249129 1.788360e-07
5990 TMEM138 11_34 0.005995808 10.249129 1.788360e-07
9789 TMEM216 11_34 0.004401847 4.951549 6.343023e-08
5996 CPSF7 11_34 0.005172782 9.944039 1.496950e-07
11817 RP11-286N22.8 11_34 0.004941196 5.969003 8.583297e-08
6903 PPP1R32 11_34 0.005377305 6.576369 1.029132e-07
11812 RP11-794G24.1 11_34 0.011355935 12.517096 4.136631e-07
3676 DKFZP434K028 11_34 0.004413466 5.958618 7.653244e-08
4508 TMEM258 11_34 0.034858621 66.046365 6.700071e-06
7955 FEN1 11_34 0.006376688 145.198765 2.694501e-06
4507 FADS2 11_34 0.006376688 145.198765 2.694501e-06
5991 FADS1 11_34 0.999536200 160.579227 4.670982e-04
11004 FADS3 11_34 0.009838644 21.356696 6.114904e-07
7876 BEST1 11_34 0.004701537 18.832400 2.576712e-07
5994 INCENP 11_34 0.004408432 5.798857 7.439553e-08
6904 ASRGL1 11_34 0.004535271 5.204785 6.869519e-08
1196 GANAB 11_34 0.007548814 72.351397 1.589447e-06
z num_eqtl
9982 -0.130372989 1
7662 0.788300174 2
2444 0.272926929 2
10267 0.288859011 1
7684 3.303999343 2
7687 0.001285218 2
7688 0.989371951 2
5997 -1.135206653 1
2453 0.544252801 1
10924 0.848247159 1
7697 -0.132073393 2
7698 -1.857701655 3
2455 -0.651729299 3
2456 1.430603519 2
2457 1.421831985 1
2480 0.821410772 1
2481 0.346138465 1
7874 0.024014132 1
7875 -0.638825054 2
6902 -1.782804562 1
5990 -1.782804562 1
9789 -0.251085346 2
5996 -2.061044578 1
11817 -0.427047808 1
6903 -0.382653253 1
11812 0.447753087 1
3676 1.073921044 1
4508 -6.946921109 2
7955 12.072635202 1
4507 12.072635202 1
5991 12.825882927 2
11004 3.289416818 1
7876 -3.744804132 1
5994 -0.969291005 2
6904 -0.250084386 1
1196 -8.204723304 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "APOC2"
[1] "19_32"
genename region_tag susie_pip mu2 PVE z num_eqtl
6721 ZNF233 19_32 0 115.540308 0 -9.2725820 2
6722 ZNF235 19_32 0 106.459190 0 -9.2122953 1
538 ZNF112 19_32 0 147.061017 0 10.3860543 1
12133 ZNF285 19_32 0 14.844074 0 0.9962471 2
12637 ZNF229 19_32 0 91.589291 0 10.9591492 2
7760 ZNF180 19_32 0 28.966742 0 -3.9159702 3
781 PVR 19_32 0 295.701702 0 -10.0782525 2
9745 CEACAM19 19_32 0 64.977515 0 9.4554813 2
9810 BCAM 19_32 0 109.853221 0 4.6421318 1
4048 NECTIN2 19_32 0 109.049177 0 6.2443536 2
4050 TOMM40 19_32 0 25.471829 0 -1.4020544 1
4049 APOE 19_32 0 47.814697 0 -2.0092826 1
11300 APOC2 19_32 0 57.109667 0 -9.1630690 2
8231 ZNF296 19_32 0 111.900459 0 5.4593536 1
5377 GEMIN7 19_32 0 193.978064 0 10.9432287 2
104 MARK4 19_32 0 24.156056 0 -2.2463768 1
1930 PPP1R37 19_32 0 125.326110 0 -12.8921201 2
109 TRAPPC6A 19_32 0 30.419699 0 1.8816459 1
9989 BLOC1S3 19_32 0 11.134819 0 2.3014119 1
12704 EXOC3L2 19_32 0 25.621613 0 -1.3436507 1
1933 CKM 19_32 0 15.790117 0 -1.5738464 1
1937 ERCC2 19_32 0 11.393491 0 2.3297330 2
3143 CD3EAP 19_32 0 27.197891 0 -3.0806361 1
3738 FOSB 19_32 0 18.939030 0 -2.3658041 1
196 ERCC1 19_32 0 14.630613 0 -0.2091619 1
10862 PPM1N 19_32 0 31.392476 0 5.4808308 1
3741 RTN2 19_32 0 31.851486 0 5.5300783 1
3742 VASP 19_32 0 12.782928 0 1.8957985 1
3739 OPA3 19_32 0 13.745582 0 -0.4654901 2
1942 KLC3 19_32 0 10.261197 0 1.7718715 1
10863 CEACAM16 19_32 0 7.492021 0 1.8740580 1
12131 APOC4 19_32 0 49.134556 0 8.0662459 2
10965 IGSF23 19_32 0 12.715131 0 1.9670520 1
8908 GPR4 19_32 0 66.214998 0 -3.5802828 1
3740 SNRPD2 19_32 0 10.032421 0 1.0366923 1
189 QPCTL 19_32 0 24.612260 0 -2.0303487 2
1949 DMPK 19_32 0 20.547022 0 -1.8090245 1
9659 DMWD 19_32 0 19.622427 0 -1.7547946 1
3743 SYMPK 19_32 0 4.904062 0 -0.0525717 1
8809 MYPOP 19_32 0 21.246603 0 1.8490001 1
1963 CCDC61 19_32 0 21.113500 0 1.8414612 2
3628 HIF3A 19_32 0 20.433740 0 -1.8024680 2
190 PPP5C 19_32 0 13.448632 0 1.3374649 1
8073 CCDC8 19_32 0 7.392266 0 0.7230949 2
9281 PNMAL1 19_32 0 20.657761 0 -1.8154111 4
10682 PNMAL2 19_32 0 5.246629 0 -0.2727077 1
11190 PPP5D1 19_32 0 6.392915 0 -0.5603345 1
6726 CALM3 19_32 0 54.624374 0 3.2242313 2#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]
}
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "USP1"
[1] "1_39"
genename region_tag susie_pip mu2 PVE z
6956 TM2D1 1_39 0.056959707 23.071148 3.824347e-06 2.1432487
4316 KANK4 1_39 0.008972516 5.075472 1.325290e-07 0.5123038
6957 USP1 1_39 0.894444325 253.879959 6.608487e-04 16.2582110
4317 ANGPTL3 1_39 0.114994538 249.654254 8.354808e-05 16.1322287
3024 DOCK7 1_39 0.010009135 24.336915 7.088958e-07 4.4594815
3733 ATG4C 1_39 0.024969688 81.344478 5.911007e-06 -8.6477262
num_eqtl
6956 1
4316 1
6957 1
4317 1
3024 1
3733 1
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
[1] "DDX56"
[1] "7_32"
genename region_tag susie_pip mu2 PVE z
7330 STK17A 7_32 0.006414090 5.405182 1.008941e-07 0.5439997
2177 COA1 7_32 0.011779535 9.889774 3.390274e-07 -0.7042755
2178 BLVRA 7_32 0.006315331 5.151026 9.466951e-08 0.4660052
541 MRPS24 7_32 0.007219831 6.241177 1.311336e-07 0.3827818
2179 URGCP 7_32 0.007395391 6.536478 1.406777e-07 -0.6697027
927 UBE2D4 7_32 0.009713232 9.447748 2.670622e-07 1.1906995
11147 AC004951.6 7_32 0.009988303 8.243453 2.396190e-07 0.2209151
4706 DBNL 7_32 0.008351048 6.910010 1.679345e-07 0.1009981
3488 POLM 7_32 0.006250639 5.193249 9.446782e-08 0.5460441
2183 AEBP1 7_32 0.022616023 20.450642 1.345995e-06 -2.6280619
2184 POLD2 7_32 0.014036368 13.082079 5.343820e-07 -1.4227083
2185 MYL7 7_32 0.007671582 6.668056 1.488691e-07 0.4396483
2186 GCK 7_32 0.006292970 5.111982 9.361928e-08 -0.2515709
500 CAMK2B 7_32 0.011448736 9.069547 3.021784e-07 -1.5162371
233 NPC1L1 7_32 0.963951568 89.799732 2.519130e-04 -10.7619311
4704 DDX56 7_32 0.974637816 58.705019 1.665094e-04 9.4462712
6619 TMED4 7_32 0.011779695 45.305762 1.553130e-06 7.5475920
2101 OGDH 7_32 0.008233040 19.553123 4.684860e-07 0.1499623
num_eqtl
7330 1
2177 2
2178 1
541 1
2179 2
927 1
11147 1
4706 2
3488 3
2183 1
2184 2
2185 1
2186 1
500 2
233 1
4704 2
6619 2
2101 2Genes with many eQTL
#distribution of number of eQTL for all imputed genes (after dropping ambiguous variants)
table(ctwas_gene_res$num_eqtl)
1 2 3 4 5
7237 3251 389 20 5 #all genes with 4+ eQTL
ctwas_gene_res[ctwas_gene_res$num_eqtl>3,] chrom id pos type region_tag1 region_tag2
9872 3 ENSG00000188086.13 46740510 gene 3 33
9154 3 ENSG00000180376.16 56557027 gene 3 39
5031 4 ENSG00000138744.14 75919602 gene 4 51
7252 4 ENSG00000164111.14 121685788 gene 4 78
11216 6 ENSG00000231852.6 32037872 gene 6 26
9089 6 ENSG00000179344.16 32668036 gene 6 26
3487 7 ENSG00000122674.11 5882180 gene 7 9
10043 7 ENSG00000196247.11 64665954 gene 7 44
2211 9 ENSG00000107099.15 211762 gene 9 1
4764 9 ENSG00000136866.13 113056669 gene 9 58
4473 10 ENSG00000134463.14 11740178 gene 10 10
3820 14 ENSG00000126790.11 59473099 gene 14 27
11861 14 ENSG00000258572.1 95515864 gene 14 49
9432 16 ENSG00000183549.10 20409006 gene 16 19
5259 16 ENSG00000140995.16 89919436 gene 16 54
7826 17 ENSG00000167723.14 3557863 gene 17 3
9041 17 ENSG00000178852.15 47322830 gene 17 27
9330 17 ENSG00000182534.13 76686803 gene 17 43
8584 17 ENSG00000173818.16 80415678 gene 17 45
9281 19 ENSG00000182013.17 46471505 gene 19 32
11086 20 ENSG00000225978.3 63102057 gene 20 37
1478 22 ENSG00000100299.17 50625049 gene 22 24
4432 1 ENSG00000134201.10 109704237 gene 1 67
9631 11 ENSG00000185522.8 559466 gene 11 1
3791 13 ENSG00000126231.13 113146308 gene 13 62
cs_index susie_pip mu2 region_tag PVE
9872 0 0.009863521 5.773608 3_33 1.657294e-07
9154 0 0.013519448 6.218520 3_39 2.446619e-07
5031 0 0.009321968 5.941198 4_51 1.611766e-07
7252 0 0.019523387 13.423365 4_78 7.626703e-07
11216 0 0.009027205 26.703609 6_26 7.015257e-07
9089 0 0.003276226 24.319509 6_26 2.318723e-07
3487 0 0.017259447 18.888890 7_9 9.487540e-07
10043 0 0.005324793 6.312502 7_44 9.781930e-08
2211 0 0.014732313 7.493704 9_1 3.212830e-07
4764 0 0.022576649 9.775593 9_58 6.422778e-07
4473 0 0.142022523 34.074317 10_10 1.408331e-05
3820 0 0.014622780 5.068625 14_27 2.156952e-07
11861 0 0.009144977 5.139739 14_49 1.367867e-07
9432 0 0.011139180 5.648715 16_19 1.831147e-07
5259 0 0.064534531 20.326723 16_54 3.817507e-06
7826 0 0.011933100 8.059574 17_3 2.798889e-07
9041 0 0.011965142 57.621477 17_27 2.006423e-06
9330 0 0.008853452 5.284860 17_43 1.361653e-07
8584 0 0.011760702 4.906819 17_45 1.679398e-07
9281 0 0.000000000 20.657761 19_32 0.000000e+00
11086 0 0.014281821 7.966535 20_37 3.311108e-07
1478 0 0.010575096 5.389966 22_24 1.658787e-07
4432 0 0.014116282 14.798554 1_67 6.079389e-07
9631 0 0.011363631 5.542449 11_1 1.832901e-07
3791 0 0.016664063 13.265298 13_62 6.433069e-07
genename gene_type z num_eqtl
9872 PRSS45 protein_coding 0.44793194 4
9154 CCDC66 protein_coding -0.92699620 4
5031 NAAA protein_coding -0.45129769 4
7252 ANXA5 protein_coding -1.37617072 5
11216 CYP21A2 protein_coding 3.53603409 4
9089 HLA-DQB1 protein_coding 5.01066331 4
3487 CCZ1 protein_coding 1.62284981 5
10043 ZNF107 protein_coding -0.52202720 4
2211 DOCK8 protein_coding -0.79491899 5
4764 ZFP37 protein_coding -1.16499741 4
4473 ECHDC3 protein_coding 3.24989823 5
3820 L3HYPDH protein_coding -0.25254601 4
11861 RP11-1070N10.3 lincRNA 0.25358234 4
9432 ACSM5 protein_coding -0.21729721 4
5259 DEF8 protein_coding 1.97803190 4
7826 TRPV3 protein_coding 0.89399610 4
9041 EFCAB13 protein_coding 7.36590043 4
9330 MXRA7 protein_coding -0.27986200 4
8584 ENDOV protein_coding 0.06847957 4
9281 PNMAL1 protein_coding -1.81541107 4
11086 HAR1A lincRNA 0.85044108 4
1478 ARSA protein_coding 0.08026791 4
4432 GSTM5 protein_coding 2.37982269 5
9631 LMNTD2 protein_coding 0.63337315 4
3791 PROZ protein_coding 2.48358935 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$susie_pip>0.8)
1 2 3
0.57575758 0.33333333 0.09090909 #genes with 2+ eQTL and PIP>0.8
ctwas_gene_res[ctwas_gene_res$num_eqtl>1 & ctwas_gene_res$susie_pip>0.8,] chrom id pos type region_tag1 region_tag2
6996 1 ENSG00000162836.11 147646379 gene 1 73
5544 1 ENSG00000143771.11 224356827 gene 1 114
3721 2 ENSG00000125629.14 118088372 gene 2 69
3562 2 ENSG00000123612.15 157625480 gene 2 94
6220 5 ENSG00000152684.10 52787392 gene 5 31
10657 6 ENSG00000204599.14 30324306 gene 6 24
4704 7 ENSG00000136271.10 44575121 gene 7 32
1114 7 ENSG00000087087.18 100875204 gene 7 62
8531 8 ENSG00000173273.15 9315699 gene 8 12
6391 9 ENSG00000155158.20 15280189 gene 9 13
3300 10 ENSG00000119965.12 122945179 gene 10 77
5991 11 ENSG00000149485.18 61829161 gene 11 34
12008 16 ENSG00000261701.6 72063820 gene 16 38
1999 19 ENSG00000105287.12 46713856 gene 19 33
cs_index susie_pip mu2 region_tag PVE genename
6996 1 0.9686707 25.67817 1_73 7.238700e-05 ACP6
5544 1 0.9996225 48.38269 1_114 1.407493e-04 CNIH4
3721 1 0.9997835 62.50606 2_69 1.818647e-04 INSIG2
3562 1 0.9388258 26.34290 2_94 7.197288e-05 ACVR1C
6220 1 0.9671689 72.14660 5_31 2.030666e-04 PELO
10657 1 0.9986852 72.25254 6_24 2.099916e-04 TRIM39
4704 2 0.9746378 58.70502 7_32 1.665094e-04 DDX56
1114 2 0.9266916 33.00835 7_62 8.901832e-05 SRRT
8531 1 0.9843992 73.76708 8_12 2.113266e-04 TNKS
6391 0 0.9259989 23.04988 9_13 6.211543e-05 TTC39B
3300 1 0.8796486 35.77485 10_77 9.158142e-05 C10orf88
5991 1 0.9995362 160.57923 11_34 4.670982e-04 FADS1
12008 1 1.0000000 209.84524 16_38 6.106880e-04 HPR
1999 2 0.9960497 32.48400 19_33 9.416095e-05 PRKD2
gene_type z num_eqtl
6996 protein_coding 4.648193 2
5544 protein_coding 6.721857 2
3721 protein_coding -9.364196 3
3562 protein_coding -4.737778 2
6220 protein_coding 8.426917 2
10657 protein_coding 8.848422 3
4704 protein_coding 9.446271 2
1114 protein_coding 5.547715 2
8531 protein_coding 11.026034 2
6391 protein_coding -4.287139 3
3300 protein_coding -6.634448 2
5991 protein_coding 12.825883 2
12008 protein_coding -17.240252 2
1999 protein_coding 5.289849 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)
#
# for (db in dbs){
# print(db)
# df <- GO_enrichment[[db]]
# df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
# plotEnrich(GO_enrichment[[db]])
# print(df)
# }
#
# GO_known_annotations <- do.call(rbind, GO_enrichment)
# GO_known_annotations <- GO_known_annotations[GO_known_annotations$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)
#
# 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)]
#
# save(overlap_genes, file=paste0(results_dir, "/overlap_genes_SORT1.Rd"))
load(paste0(results_dir, "/overlap_genes_SORT1.Rd"))
overlap_genes <- overlap_genes[!(names(overlap_genes) %in% known_annotations)]
overlap_genes
GAS6 INSIG2 TTC39B INHBB ACP6 HPR PRKD2 CYP2A6 ACVR1C
14 12 10 8 5 4 4 3 2
KDSR CNIH4 CSNK1G3 SPTY2D1
2 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$hgnc_symbol[bystanders_extended]
# bystanders_extended <- unique(bystanders_extended[!(bystanders_extended %in% known_annotations)])
#
# save(bystanders_extended, file=paste0(results_dir, "/bystanders_extended_SORT1.Rd"))
load(paste0(results_dir, "/bystanders_extended_SORT1.Rd"))
#add extended bystanders list to output
out_table$bystander <- F
out_table$bystander[out_table$genename %in% bystanders_extended] <- T
#reload GO overlaps with silver standard
load(paste0(results_dir, "/overlap_genes_SORT1.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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#number of SNPs at PIP>0.8 threshold
sum(out_table$susie_pip>0.8)[1] 33#number of SNPs at PIP>0.5 threshold
sum(out_table$susie_pip>0.5)[1] 59#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
12008 HPR 16_38 1.0000000 -17.240252 2 FALSE
5563 ABCG8 2_27 0.9999667 -20.293982 1 TRUE
3721 INSIG2 2_69 0.9997835 -9.364196 3 FALSE
12687 RP4-781K5.7 1_121 0.9997323 -15.108415 1 FALSE
5544 CNIH4 1_114 0.9996225 6.721857 2 FALSE
5991 FADS1 11_34 0.9995362 12.825883 2 TRUE
10657 TRIM39 6_24 0.9986852 8.848422 3 FALSE
1999 PRKD2 19_33 0.9960497 5.289849 2 FALSE
7410 ABCA1 9_53 0.9953955 7.982017 1 TRUE
9390 GAS6 13_62 0.9881812 -8.923688 1 FALSE
1597 PLTP 20_28 0.9877966 -5.732491 1 TRUE
12715 SORT1 1_67 0.9873012 -41.793474 1 TRUE
8531 TNKS 8_12 0.9843992 11.026034 2 TRUE
7040 INHBB 2_70 0.9822550 -8.518936 1 FALSE
2092 SP4 7_19 0.9770594 10.693191 1 FALSE
4704 DDX56 7_32 0.9746378 9.446271 2 FALSE
6093 CSNK1G3 5_75 0.9746220 9.116291 1 FALSE
6996 ACP6 1_73 0.9686707 4.648193 2 FALSE
6220 PELO 5_31 0.9671689 8.426917 2 FALSE
8865 FUT2 19_33 0.9654285 -11.927107 1 FALSE
233 NPC1L1 7_32 0.9639516 -10.761931 1 TRUE
11790 CYP2A6 19_28 0.9618743 5.407028 1 FALSE
3247 KDSR 18_35 0.9552673 -4.526287 1 FALSE
3562 ACVR1C 2_94 0.9388258 -4.737778 2 FALSE
6778 PKN3 9_66 0.9359865 -6.620563 1 FALSE
1114 SRRT 7_62 0.9266916 5.547715 2 FALSE
6391 TTC39B 9_13 0.9259989 -4.287139 3 FALSE
6957 USP1 1_39 0.8944443 16.258211 1 FALSE
3300 C10orf88 10_77 0.8796486 -6.634448 2 FALSE
9062 KLHDC7A 1_13 0.8184864 4.124187 1 FALSE
9072 SPTY2D1 11_13 0.8096215 -5.557123 1 FALSE
8931 CRACR2B 11_1 0.8018274 -3.989585 1 FALSE
8418 POP7 7_62 0.8015962 -5.845258 1 FALSE
GO_overlap_silver bystander
12008 4 FALSE
5563 16 FALSE
3721 12 FALSE
12687 0 FALSE
5544 1 FALSE
5991 11 FALSE
10657 0 FALSE
1999 4 FALSE
7410 38 FALSE
9390 14 FALSE
1597 20 FALSE
12715 9 FALSE
8531 0 FALSE
7040 8 FALSE
2092 0 FALSE
4704 0 TRUE
6093 1 FALSE
6996 5 FALSE
6220 0 FALSE
8865 0 FALSE
233 11 FALSE
11790 3 FALSE
3247 2 FALSE
3562 2 FALSE
6778 0 FALSE
1114 0 FALSE
6391 10 FALSE
6957 0 TRUE
3300 0 FALSE
9062 0 FALSE
9072 1 FALSE
8931 0 FALSE
8418 0 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
12008 HPR 16_38 1.0000000 -17.240252 2 FALSE
5563 ABCG8 2_27 0.9999667 -20.293982 1 TRUE
3721 INSIG2 2_69 0.9997835 -9.364196 3 FALSE
12687 RP4-781K5.7 1_121 0.9997323 -15.108415 1 FALSE
5544 CNIH4 1_114 0.9996225 6.721857 2 FALSE
5991 FADS1 11_34 0.9995362 12.825883 2 TRUE
10657 TRIM39 6_24 0.9986852 8.848422 3 FALSE
1999 PRKD2 19_33 0.9960497 5.289849 2 FALSE
7410 ABCA1 9_53 0.9953955 7.982017 1 TRUE
9390 GAS6 13_62 0.9881812 -8.923688 1 FALSE
1597 PLTP 20_28 0.9877966 -5.732491 1 TRUE
12715 SORT1 1_67 0.9873012 -41.793474 1 TRUE
8531 TNKS 8_12 0.9843992 11.026034 2 TRUE
7040 INHBB 2_70 0.9822550 -8.518936 1 FALSE
2092 SP4 7_19 0.9770594 10.693191 1 FALSE
4704 DDX56 7_32 0.9746378 9.446271 2 FALSE
6093 CSNK1G3 5_75 0.9746220 9.116291 1 FALSE
6996 ACP6 1_73 0.9686707 4.648193 2 FALSE
6220 PELO 5_31 0.9671689 8.426917 2 FALSE
8865 FUT2 19_33 0.9654285 -11.927107 1 FALSE
233 NPC1L1 7_32 0.9639516 -10.761931 1 TRUE
11790 CYP2A6 19_28 0.9618743 5.407028 1 FALSE
3247 KDSR 18_35 0.9552673 -4.526287 1 FALSE
3562 ACVR1C 2_94 0.9388258 -4.737778 2 FALSE
6778 PKN3 9_66 0.9359865 -6.620563 1 FALSE
1114 SRRT 7_62 0.9266916 5.547715 2 FALSE
6391 TTC39B 9_13 0.9259989 -4.287139 3 FALSE
6957 USP1 1_39 0.8944443 16.258211 1 FALSE
3300 C10orf88 10_77 0.8796486 -6.634448 2 FALSE
9062 KLHDC7A 1_13 0.8184864 4.124187 1 FALSE
9072 SPTY2D1 11_13 0.8096215 -5.557123 1 FALSE
8931 CRACR2B 11_1 0.8018274 -3.989585 1 FALSE
8418 POP7 7_62 0.8015962 -5.845258 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
12008 HPR 4 FALSE
5563 ABCG8 16 FALSE
3721 INSIG2 12 FALSE
12687 RP4-781K5.7 0 FALSE
5544 CNIH4 1 FALSE
5991 FADS1 11 FALSE
10657 TRIM39 0 FALSE
1999 PRKD2 4 FALSE
7410 ABCA1 38 FALSE
9390 GAS6 14 FALSE
1597 PLTP 20 FALSE
12715 SORT1 9 FALSE
8531 TNKS 0 FALSE
7040 INHBB 8 FALSE
2092 SP4 0 FALSE
4704 DDX56 0 TRUE
6093 CSNK1G3 1 FALSE
6996 ACP6 5 FALSE
6220 PELO 0 FALSE
8865 FUT2 0 FALSE
233 NPC1L1 11 FALSE
11790 CYP2A6 3 FALSE
3247 KDSR 2 FALSE
3562 ACVR1C 2 FALSE
6778 PKN3 0 FALSE
1114 SRRT 0 FALSE
6391 TTC39B 10 FALSE
6957 USP1 0 TRUE
3300 C10orf88 0 FALSE
9062 KLHDC7A 0 FALSE
9072 SPTY2D1 1 FALSE
8931 CRACR2B 0 FALSE
8418 POP7 0 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] 33GO_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]]))
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df) Term Overlap
1 peptidyl-serine phosphorylation (GO:0018105) 5/156
2 peptidyl-serine modification (GO:0018209) 5/169
3 lipid transport (GO:0006869) 4/109
4 cholesterol transport (GO:0030301) 3/51
5 intestinal cholesterol absorption (GO:0030299) 2/9
6 cellular response to sterol depletion (GO:0071501) 2/9
7 negative regulation of cholesterol storage (GO:0010887) 2/10
8 protein phosphorylation (GO:0006468) 6/496
9 intestinal lipid absorption (GO:0098856) 2/11
10 cholesterol homeostasis (GO:0042632) 3/71
11 sterol homeostasis (GO:0055092) 3/72
12 cholesterol metabolic process (GO:0008203) 3/77
13 regulation of cholesterol storage (GO:0010885) 2/16
14 response to insulin (GO:0032868) 3/84
15 activin receptor signaling pathway (GO:0032924) 2/19
16 negative regulation of lipid storage (GO:0010888) 2/20
17 sterol transport (GO:0015918) 2/21
18 cholesterol efflux (GO:0033344) 2/24
19 regulation of DNA biosynthetic process (GO:2000278) 2/29
20 cellular protein modification process (GO:0006464) 7/1025
21 regulation of cholesterol efflux (GO:0010874) 2/33
22 secondary alcohol biosynthetic process (GO:1902653) 2/34
23 organic substance transport (GO:0071702) 3/136
24 cholesterol biosynthetic process (GO:0006695) 2/35
25 sterol biosynthetic process (GO:0016126) 2/38
Adjusted.P.value Genes
1 0.001778112 CSNK1G3;TNKS;PKN3;PRKD2;GAS6
2 0.001778112 CSNK1G3;TNKS;PKN3;PRKD2;GAS6
3 0.004505375 ABCA1;ABCG8;NPC1L1;PLTP
4 0.007029494 ABCA1;ABCG8;NPC1L1
5 0.007029494 ABCG8;NPC1L1
6 0.007029494 INSIG2;NPC1L1
7 0.007144915 ABCA1;TTC39B
8 0.007144915 CSNK1G3;ACVR1C;TNKS;PKN3;PRKD2;GAS6
9 0.007144915 ABCG8;NPC1L1
10 0.009177895 ABCA1;ABCG8;TTC39B
11 0.009177895 ABCA1;ABCG8;TTC39B
12 0.010261090 ABCA1;INSIG2;NPC1L1
13 0.010736790 ABCA1;TTC39B
14 0.011366853 INSIG2;SORT1;INHBB
15 0.013218960 ACVR1C;INHBB
16 0.013755552 ABCA1;TTC39B
17 0.014294428 ABCG8;NPC1L1
18 0.017688424 ABCA1;ABCG8
19 0.024523690 TNKS;PRKD2
20 0.026889526 CSNK1G3;ACVR1C;TNKS;PKN3;PRKD2;FUT2;GAS6
21 0.028088337 PLTP;TTC39B
22 0.028088337 INSIG2;NPC1L1
23 0.028088337 ABCA1;ABCG8;PLTP
24 0.028276991 INSIG2;NPC1L1
25 0.031974245 INSIG2;NPC1L1#}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")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
out_table[out_table$region_tag=="8_12",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
11738 RP11-115J16.2 8_12 0.004472655 7.146749 1 FALSE
8531 TNKS 8_12 0.984399158 11.026034 2 TRUEout_table[out_table$region_tag=="8_12",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
11738 RP11-115J16.2 NA FALSE
8531 TNKS 0 FALSEFADS1 is a silver standard gene (assumed true positive gene) that is correctly detected. There are 5 significant TWAS genes at this locus, including FADS2, another silver standard gene. FADS2 is not detected due to its high LD with FADS1. The remaining 3 bystander genes at this locus have low PIP using cTWAS.
#FADS1 gene
locus_plot3("11_34", focus="FADS1")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
out_table[out_table$region_tag=="11_34",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
9982 FAM111B 11_34 0.004473671 -0.130372989 1 FALSE
7662 FAM111A 11_34 0.006610236 0.788300174 2 FALSE
2444 DTX4 11_34 0.004504280 0.272926929 2 FALSE
10267 MPEG1 11_34 0.004535836 0.288859011 1 FALSE
7684 PATL1 11_34 0.062436117 3.303999343 2 FALSE
7687 STX3 11_34 0.004422795 0.001285218 2 FALSE
7688 MRPL16 11_34 0.006813281 0.989371951 2 FALSE
5997 MS4A2 11_34 0.008397351 -1.135206653 1 FALSE
2453 MS4A6A 11_34 0.005002286 0.544252801 1 FALSE
10924 MS4A4E 11_34 0.005747724 0.848247159 1 FALSE
7697 MS4A7 11_34 0.004397638 -0.132073393 2 FALSE
7698 MS4A14 11_34 0.025928734 -1.857701655 3 FALSE
2455 CCDC86 11_34 0.005785125 -0.651729299 3 FALSE
2456 PRPF19 11_34 0.008960933 1.430603519 2 FALSE
2457 TMEM109 11_34 0.010241876 1.421831985 1 FALSE
2480 SLC15A3 11_34 0.004713723 0.821410772 1 FALSE
2481 CD5 11_34 0.004532367 0.346138465 1 FALSE
7874 VPS37C 11_34 0.005273098 0.024014132 1 FALSE
7875 VWCE 11_34 0.004740535 -0.638825054 2 FALSE
6902 CYB561A3 11_34 0.005995808 -1.782804562 1 FALSE
5990 TMEM138 11_34 0.005995808 -1.782804562 1 FALSE
9789 TMEM216 11_34 0.004401847 -0.251085346 2 FALSE
5996 CPSF7 11_34 0.005172782 -2.061044578 1 FALSE
11817 RP11-286N22.8 11_34 0.004941196 -0.427047808 1 FALSE
6903 PPP1R32 11_34 0.005377305 -0.382653253 1 FALSE
11812 RP11-794G24.1 11_34 0.011355935 0.447753087 1 FALSE
3676 DKFZP434K028 11_34 0.004413466 1.073921044 1 FALSE
4508 TMEM258 11_34 0.034858621 -6.946921109 2 FALSE
7955 FEN1 11_34 0.006376688 12.072635202 1 FALSE
4507 FADS2 11_34 0.006376688 12.072635202 1 TRUE
5991 FADS1 11_34 0.999536200 12.825882927 2 TRUE
11004 FADS3 11_34 0.009838644 3.289416818 1 TRUE
7876 BEST1 11_34 0.004701537 -3.744804132 1 FALSE
5994 INCENP 11_34 0.004408432 -0.969291005 2 FALSE
6904 ASRGL1 11_34 0.004535271 -0.250084386 1 FALSE
1196 GANAB 11_34 0.007548814 -8.204723304 1 FALSEout_table[out_table$region_tag=="11_34",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
9982 FAM111B NA FALSE
7662 FAM111A NA FALSE
2444 DTX4 NA FALSE
10267 MPEG1 NA FALSE
7684 PATL1 NA FALSE
7687 STX3 NA FALSE
7688 MRPL16 NA FALSE
5997 MS4A2 NA FALSE
2453 MS4A6A NA FALSE
10924 MS4A4E NA FALSE
7697 MS4A7 NA FALSE
7698 MS4A14 NA FALSE
2455 CCDC86 NA TRUE
2456 PRPF19 NA TRUE
2457 TMEM109 NA TRUE
2480 SLC15A3 NA TRUE
2481 CD5 NA TRUE
7874 VPS37C NA TRUE
7875 VWCE NA TRUE
6902 CYB561A3 NA TRUE
5990 TMEM138 NA TRUE
9789 TMEM216 NA TRUE
5996 CPSF7 NA TRUE
11817 RP11-286N22.8 NA FALSE
6903 PPP1R32 NA TRUE
11812 RP11-794G24.1 NA FALSE
3676 DKFZP434K028 NA FALSE
4508 TMEM258 NA TRUE
7955 FEN1 NA TRUE
4507 FADS2 NA FALSE
5991 FADS1 11 FALSE
11004 FADS3 NA FALSE
7876 BEST1 NA TRUE
5994 INCENP NA TRUE
6904 ASRGL1 NA TRUE
1196 GANAB NA TRUE#number of significant TWAS genes at this locus
sum(abs(out_table$z[out_table$region_tag=="11_34"])>sig_thresh)[1] 5cTWAS avoids false positives when SNPs in a region are (considerably) more significant
POLK is a gene that is significant using TWAS but not detected using TWAS. cTWAS places a high posterior probability on SNPs are this locus. OpenTargets suggets that the causal gene at this locus is HMGCR (note: different GWAS, similar population), which is not imputed in our dataset. cTWAS selected the variants at this locus because the causal gene is not imputed. Note that MR-JTI claims POLK is causal using their method, and their paper includes a discussion of its potential relevance to LDL.
locus_plot("5_45", label="TWAS")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#locus_plot("5_45", label="TWAS", rerun_ctwas = T)
out_table[out_table$region_tag=="5_45",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
8340 ENC1 5_45 0.001035337 -0.4000089 1 FALSE
7307 GFM2 5_45 0.001320858 -0.4062418 2 FALSE
7306 NSA2 5_45 0.002589266 -2.0511430 3 FALSE
10458 FAM169A 5_45 0.001100229 -0.9826944 2 FALSE
3441 POLK 5_45 0.004086732 17.5157647 1 FALSE
12287 CTC-366B18.4 5_45 0.049947636 -10.7732063 2 FALSE
9978 ANKDD1B 5_45 0.004085134 15.0669830 2 FALSE
6186 POC5 5_45 0.004045610 -7.0119331 1 FALSE
11757 AC113404.1 5_45 0.001796895 2.3250769 1 FALSE
5717 IQGAP2 5_45 0.005523966 2.5652287 2 FALSE
7281 F2RL2 5_45 0.001225391 0.5923159 1 FALSE
9219 F2R 5_45 0.002030873 -1.2065901 2 FALSE
7287 F2RL1 5_45 0.011051116 2.2468261 3 FALSE
5718 CRHBP 5_45 0.001234901 -0.6287222 2 FALSE
7288 AGGF1 5_45 0.001164389 -0.5067707 2 FALSE
4314 ZBED3 5_45 0.005668933 -1.8752115 1 FALSE
2729 PDE8B 5_45 0.001128430 0.4406481 3 FALSE
7289 WDR41 5_45 0.001113305 -0.4097230 1 FALSE
4313 AP3B1 5_45 0.004175976 1.7055957 1 FALSEout_table[out_table$region_tag=="5_45",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
8340 ENC1 NA TRUE
7307 GFM2 NA TRUE
7306 NSA2 NA TRUE
10458 FAM169A NA TRUE
3441 POLK NA TRUE
12287 CTC-366B18.4 NA FALSE
9978 ANKDD1B NA TRUE
6186 POC5 NA TRUE
11757 AC113404.1 NA FALSE
5717 IQGAP2 NA FALSE
7281 F2RL2 NA FALSE
9219 F2R NA FALSE
7287 F2RL1 NA FALSE
5718 CRHBP NA FALSE
7288 AGGF1 NA FALSE
4314 ZBED3 NA FALSE
2729 PDE8B NA FALSE
7289 WDR41 NA FALSE
4313 AP3B1 NA FALSEcTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
load(paste0(results_dir, "/known_annotations_SORT1.Rd"))
load(paste0(results_dir, "/bystanders_SORT1.Rd"))
#remove genes without imputed expression from bystander list
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$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$z)>sig_thresh]
#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.1489362 0.4255319 #specificity / (1 - False Positive Rate)
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9964974 0.9159370 #precision / PPV / (1 - False Discovery Rate)
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.7777778 0.2941176 #store sensitivity and specificity calculations for plots
sensitivity_plot <- sensitivity
specificity_plot <- specificity
#precision / PPV by PIP threshold
pip_range <- c(0.5, 0.8, 1)
precision_range <- rep(NA, length(pip_range))
number_detected <- rep(NA, length(pip_range))
for (i in 1:length(pip_range)){
pip_upper <- pip_range[i]
if (i==1){
pip_lower <- 0
} else {
pip_lower <- pip_range[i-1]
}
#assign ctwas genes using PIP threshold
ctwas_genes <- ctwas_gene_res_subset$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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#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)
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
#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
27 22
Nearby SNP(s) Detected (PIP > 0.8)
11 7
Nearby Bystander Gene Nearby Silver Standard Gene
1 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$Outcome) #exclude genes not imputed
bp<- ggplot(df, aes(x=Outcome, y=Frequency, fill=Outcome)) + geom_bar(width = 1, stat = "identity", position=position_dodge()) +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) + theme(legend.position = "none")
bp
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
pie <- ggplot(df, aes(x="", y=Frequency, fill=Outcome)) + geom_bar(width = 1, stat = "identity")
pie <- pie + coord_polar("y", start=0) + theme_minimal() + theme(axis.title.y=element_blank())
pie
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
locus_plot3(focus="KPNB1", region_tag="17_27")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
locus_plot3(focus="LPIN3", region_tag="20_25")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
locus_plot3(focus="LIPC", region_tag="15_26")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
cTWAS detects some genes that are not significant using a stringent TWAS threshold
TTC39B is a member of the Dyslipidaemia term in the disease_GLAD4U. This gene was not included in our silver standard. This gene is not significant using TWAS but is detected by cTWAS.
locus_plot3(focus="TTC39B", region_tag="9_13")
| Version | Author | Date |
|---|---|---|
| e6dc4d4 | wesleycrouse | 2021-11-12 |
out_table[out_table$region_tag=="9_13",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
7397 FREM1 9_13 0.008139599 -0.7657943 1 FALSE
6391 TTC39B 9_13 0.925998942 -4.2871392 3 FALSE
7406 PSIP1 9_13 0.006964747 -0.1267424 1 FALSE
7407 CCDC171 9_13 0.014092852 1.2312339 3 FALSEout_table[out_table$region_tag=="9_13",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
7397 FREM1 NA FALSE
6391 TTC39B 10 FALSE
7406 PSIP1 NA FALSE
7407 CCDC171 NA FALSEcTWAS distinguishes SORT1 and PSRC1
locus_plot3(focus="SORT1", region_tag="1_67")
out_table[out_table$region_tag=="1_67",report_cols[-(7:8)]] genename region_tag susie_pip z num_eqtl silver
4434 VAV3 1_67 0.065099832 -2.1042470 1 FALSE
1073 SLC25A24 1_67 0.008611429 0.9234769 2 FALSE
6966 FAM102B 1_67 0.007746337 -1.1378586 1 FALSE
3009 STXBP3 1_67 0.017008399 2.9982594 1 FALSE
3438 GPSM2 1_67 0.008080202 -1.9348222 1 FALSE
3437 CLCC1 1_67 0.008069139 2.5660415 2 FALSE
10286 TAF13 1_67 0.010760437 -1.5591453 1 FALSE
10955 TMEM167B 1_67 0.013756111 -1.5270485 1 FALSE
315 SARS 1_67 0.014473303 9.5234950 1 FALSE
4435 PSRC1 1_67 0.024647145 -41.6873361 1 FALSE
5436 PSMA5 1_67 0.007880027 -35.4138115 2 FALSE
5431 SYPL2 1_67 0.016433368 -14.1478749 2 FALSE
6970 ATXN7L2 1_67 0.009823180 -19.2427445 2 FALSE
8615 CYB561D1 1_67 0.063213063 10.6827516 3 FALSE
9259 AMIGO1 1_67 0.018715231 -3.9630816 1 FALSE
6445 GPR61 1_67 0.007845449 4.2425343 1 FALSE
587 GNAI3 1_67 0.054241321 -3.8408490 1 FALSE
7977 GSTM4 1_67 0.014374335 4.7825961 3 FALSE
10821 GSTM2 1_67 0.008660380 2.9726102 2 FALSE
4430 GSTM1 1_67 0.018905914 4.2590068 1 FALSE
4433 GSTM3 1_67 0.007854604 -3.9546683 3 FALSE
4432 GSTM5 1_67 0.014116282 2.3798227 5 FALSE
12715 SORT1 1_67 0.987301191 -41.7934744 1 TRUEout_table[out_table$region_tag=="1_67",report_cols[c(1,7:8)]] genename GO_overlap_silver bystander
4434 VAV3 NA FALSE
1073 SLC25A24 NA FALSE
6966 FAM102B NA TRUE
3009 STXBP3 NA TRUE
3438 GPSM2 NA TRUE
3437 CLCC1 NA TRUE
10286 TAF13 NA TRUE
10955 TMEM167B NA TRUE
315 SARS NA FALSE
4435 PSRC1 NA TRUE
5436 PSMA5 NA TRUE
5431 SYPL2 NA TRUE
6970 ATXN7L2 NA TRUE
8615 CYB561D1 NA TRUE
9259 AMIGO1 NA TRUE
6445 GPR61 NA TRUE
587 GNAI3 NA TRUE
7977 GSTM4 NA TRUE
10821 GSTM2 NA TRUE
4430 GSTM1 NA TRUE
4433 GSTM3 NA TRUE
4432 GSTM5 NA TRUE
12715 SORT1 9 FALSE
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