Body mass index (BMI) - Brain_Hypothalamus
wesleycrouse
2021-09-7
Last updated: 2021-09-13
Checks: 7 0
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 72c8ef7 | wesleycrouse | 2021-09-13 | changing mart for biomart |
| Rmd | a49c40e | wesleycrouse | 2021-09-13 | updating with bystander analysis |
| html | 7e22565 | wesleycrouse | 2021-09-13 | updating reports |
| html | 3a7fbc1 | wesleycrouse | 2021-09-08 | generating reports for known annotations |
| Rmd | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Body mass index (BMI) using Brain_Hypothalamus 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-a-248. 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 Brain_Hypothalamus 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] 11083#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1107 757 667 439 549 622 531 429 416 429 660 610 213 357 369
16 17 18 19 20 21 22
492 680 168 851 327 134 276 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8005053Load ctwas results
#load ctwas results
ctwas_res <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.susieIrss.txt"))
#make unique identifier for regions
ctwas_res$region_tag <- paste(ctwas_res$region_tag1, ctwas_res$region_tag2, sep="_")
#load z scores for SNPs and collect sample size
load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd"))
sample_size <- z_snp$ss
sample_size <- as.numeric(names(which.max(table(sample_size))))
#compute PVE for each gene/SNP
ctwas_res$PVE = ctwas_res$susie_pip*ctwas_res$mu2/sample_size
#separate gene and SNP results
ctwas_gene_res <- ctwas_res[ctwas_res$type == "gene", ]
ctwas_gene_res <- data.frame(ctwas_gene_res)
ctwas_snp_res <- ctwas_res[ctwas_res$type == "SNP", ]
ctwas_snp_res <- data.frame(ctwas_snp_res)
#add gene information to results
sqlite <- RSQLite::dbDriver("SQLite")
db = RSQLite::dbConnect(sqlite, paste0("/project2/compbio/predictdb/mashr_models/mashr_", weight, ".db"))
query <- function(...) RSQLite::dbGetQuery(db, ...)
gene_info <- query("select gene, genename from extra")
gene_info <- query("select gene, genename, gene_type from extra")
RSQLite::dbDisconnect(db)
ctwas_gene_res <- cbind(ctwas_gene_res, gene_info[sapply(ctwas_gene_res$id, match, gene_info$gene), c("genename", "gene_type")])
#add z scores to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$id,]$z
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,]
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)]
#formatting and rounding for tables
ctwas_gene_res$z <- round(ctwas_gene_res$z,2)
ctwas_snp_res$z <- round(ctwas_snp_res$z,2)
ctwas_gene_res$susie_pip <- round(ctwas_gene_res$susie_pip,3)
ctwas_snp_res$susie_pip <- round(ctwas_snp_res$susie_pip,3)
ctwas_gene_res$mu2 <- round(ctwas_gene_res$mu2,2)
ctwas_snp_res$mu2 <- round(ctwas_snp_res$mu2,2)
ctwas_gene_res$PVE <- signif(ctwas_gene_res$PVE, 2)
ctwas_snp_res$PVE <- signif(ctwas_snp_res$PVE, 2)
#merge gene and snp results with added information
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
#get number of SNPs from s1 results; adjust for thin
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
library(ggplot2)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************load(paste0(results_dir, "/", analysis_id, "_ctwas.s2.susieIrssres.Rd"))
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
value = c(group_prior_rec[1,], group_prior_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$group <- as.factor(df$group)
df$value[df$group=="SNP"] <- df$value[df$group=="SNP"]*thin #adjust parameter to account for thin argument
p_pi <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(pi)) +
ggtitle("Prior mean") +
theme_cowplot()
df <- data.frame(niter = rep(1:ncol(group_prior_var_rec), 2),
value = c(group_prior_var_rec[1,], group_prior_var_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_var_rec)))
df$group <- as.factor(df$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0049608595 0.0002982899 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.52429 17.75596 #report sample size
print(sample_size)[1] 336107#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11083 7535010#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.002539502 0.118737825 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.01272535 15.94833250Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#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
7493 PPM1M 3_36 1.000 244.75 7.3e-04 4.54
3276 CCND2 12_4 0.940 28.26 7.9e-05 -5.12
7598 ZNF12 7_9 0.781 26.90 6.3e-05 5.09
7840 ALKBH3 11_27 0.768 28.50 6.5e-05 -5.13
13153 RP11-1109F11.3 12_54 0.765 30.71 7.0e-05 6.46
8379 CENPX 17_46 0.711 23.79 5.0e-05 4.11
8812 RARG 12_33 0.703 25.49 5.3e-05 -4.11
241 ISL1 5_30 0.701 26.16 5.5e-05 -5.01
7356 SERPINI1 3_103 0.698 23.16 4.8e-05 -4.06
4821 DCAF7 17_38 0.656 30.16 5.9e-05 5.44
3176 PRRC2C 1_84 0.625 28.14 5.2e-05 -5.17
584 NGFR 17_29 0.625 28.07 5.2e-05 -4.01
11412 NCKIPSD 3_34 0.600 26.30 4.7e-05 4.49
13194 CTC-498M16.4 5_52 0.592 52.95 9.3e-05 7.71
5798 ECE2 3_113 0.583 28.52 4.9e-05 -5.29
5712 THSD7B 2_81 0.549 27.38 4.5e-05 5.32
11611 HRAT92 7_1 0.548 24.31 4.0e-05 -3.93
7806 R3HCC1L 10_62 0.546 39.55 6.4e-05 7.44
5498 CARM1 19_9 0.543 32.91 5.3e-05 5.02
155 CSDE1 1_71 0.535 22.42 3.6e-05 -4.74Genes 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 |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
7665 CCDC171 9_13 0 50550.12 0.0e+00 8.03
8735 NEGR1 1_46 0 43383.72 0.0e+00 -10.70
9420 STX19 3_59 0 31106.49 0.0e+00 -5.06
7889 LEO1 15_21 0 27969.54 3.4e-14 4.60
5271 MFAP1 15_16 0 23764.59 0.0e+00 4.30
13397 LINC02019 3_35 0 22551.06 7.4e-17 -4.47
5098 TMOD3 15_21 0 22268.83 0.0e+00 5.41
4029 TMOD2 15_21 0 21601.60 0.0e+00 5.23
1293 WDR76 15_16 0 21486.56 0.0e+00 4.74
11601 CKMT1A 15_16 0 21284.13 0.0e+00 4.13
2876 CISH 3_35 0 20260.39 0.0e+00 -3.80
3017 PLCL1 2_117 0 18664.08 0.0e+00 -5.64
1015 CCNT2 2_80 0 18644.85 8.6e-17 3.69
2875 HEMK1 3_35 0 17517.21 0.0e+00 -3.88
4998 TUBGCP4 15_16 0 16916.45 0.0e+00 3.43
9416 DHFR2 3_59 0 16605.15 0.0e+00 2.76
9414 NSUN3 3_59 0 15678.49 0.0e+00 4.76
8261 ADAL 15_16 0 14821.09 0.0e+00 -2.86
125 CACNA2D2 3_35 0 14198.82 0.0e+00 -4.01
5136 CNOT6L 4_52 0 14094.61 0.0e+00 3.42Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
7493 PPM1M 3_36 1.000 244.75 7.3e-04 4.54
2953 LANCL1 2_124 0.042 4670.09 5.8e-04 -3.71
9392 FAM220A 7_8 0.216 445.76 2.9e-04 -1.29
13194 CTC-498M16.4 5_52 0.592 52.95 9.3e-05 7.71
2896 SPCS1 3_36 0.081 348.59 8.4e-05 -5.07
4791 RAC1 7_8 0.158 178.70 8.4e-05 -5.51
3276 CCND2 12_4 0.940 28.26 7.9e-05 -5.12
13153 RP11-1109F11.3 12_54 0.765 30.71 7.0e-05 6.46
12160 ATP5J2 7_62 0.458 50.81 6.9e-05 -7.12
2926 ITGB6 2_96 0.499 45.97 6.8e-05 5.45
7840 ALKBH3 11_27 0.768 28.50 6.5e-05 -5.13
7806 R3HCC1L 10_62 0.546 39.55 6.4e-05 7.44
7598 ZNF12 7_9 0.781 26.90 6.3e-05 5.09
13639 DHRS11 17_22 0.347 60.57 6.3e-05 -8.14
4821 DCAF7 17_38 0.656 30.16 5.9e-05 5.44
241 ISL1 5_30 0.701 26.16 5.5e-05 -5.01
8812 RARG 12_33 0.703 25.49 5.3e-05 -4.11
5498 CARM1 19_9 0.543 32.91 5.3e-05 5.02
3176 PRRC2C 1_84 0.625 28.14 5.2e-05 -5.17
584 NGFR 17_29 0.625 28.07 5.2e-05 -4.01Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7489 MST1R 3_35 0.002 1050.67 5.7e-06 -12.63
38 RBM6 3_35 0.000 906.71 9.6e-07 12.54
9046 KCTD13 16_24 0.045 109.28 1.5e-05 -11.49
9045 ASPHD1 16_24 0.009 101.21 2.7e-06 -11.34
7484 RNF123 3_35 0.000 823.16 1.2e-14 -10.96
6178 TAOK2 16_24 0.016 92.70 4.5e-06 10.74
8735 NEGR1 1_46 0.000 43383.72 0.0e+00 -10.70
10430 CLN3 16_23 0.052 86.01 1.3e-05 10.45
11930 NPIPB7 16_23 0.052 86.01 1.3e-05 10.45
8365 INO80E 16_24 0.015 78.23 3.5e-06 10.10
8032 ZNF646 16_24 0.051 75.84 1.2e-05 -10.00
7487 CAMKV 3_35 0.000 1446.64 1.4e-18 9.85
5486 SAE1 19_33 0.004 97.46 1.0e-06 9.85
2753 COL4A3BP 5_44 0.020 68.99 4.1e-06 -9.83
458 PRSS8 16_24 0.011 70.69 2.3e-06 -9.76
1830 KAT8 16_24 0.009 68.78 1.9e-06 -9.71
11411 LAT 16_23 0.106 83.00 2.6e-05 -9.55
8031 ZNF668 16_24 0.011 70.05 2.3e-06 9.55
2458 MTCH2 11_29 0.008 81.32 1.8e-06 -9.51
10711 SULT1A2 16_23 0.043 80.36 1.0e-05 -9.45Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.02012091#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7489 MST1R 3_35 0.002 1050.67 5.7e-06 -12.63
38 RBM6 3_35 0.000 906.71 9.6e-07 12.54
9046 KCTD13 16_24 0.045 109.28 1.5e-05 -11.49
9045 ASPHD1 16_24 0.009 101.21 2.7e-06 -11.34
7484 RNF123 3_35 0.000 823.16 1.2e-14 -10.96
6178 TAOK2 16_24 0.016 92.70 4.5e-06 10.74
8735 NEGR1 1_46 0.000 43383.72 0.0e+00 -10.70
10430 CLN3 16_23 0.052 86.01 1.3e-05 10.45
11930 NPIPB7 16_23 0.052 86.01 1.3e-05 10.45
8365 INO80E 16_24 0.015 78.23 3.5e-06 10.10
8032 ZNF646 16_24 0.051 75.84 1.2e-05 -10.00
7487 CAMKV 3_35 0.000 1446.64 1.4e-18 9.85
5486 SAE1 19_33 0.004 97.46 1.0e-06 9.85
2753 COL4A3BP 5_44 0.020 68.99 4.1e-06 -9.83
458 PRSS8 16_24 0.011 70.69 2.3e-06 -9.76
1830 KAT8 16_24 0.009 68.78 1.9e-06 -9.71
11411 LAT 16_23 0.106 83.00 2.6e-05 -9.55
8031 ZNF668 16_24 0.011 70.05 2.3e-06 9.55
2458 MTCH2 11_29 0.008 81.32 1.8e-06 -9.51
10711 SULT1A2 16_23 0.043 80.36 1.0e-05 -9.45Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 3_35"
genename region_tag susie_pip mu2 PVE z
2843 USP4 3_35 0.000 382.53 0.0e+00 2.17
11932 GPX1 3_35 0.000 79.24 0.0e+00 -5.84
5786 TCTA 3_35 0.000 1761.03 0.0e+00 -0.82
671 RHOA 3_35 0.000 343.56 0.0e+00 -8.35
5785 AMT 3_35 0.000 77.23 0.0e+00 -0.16
5787 NICN1 3_35 0.000 65.14 0.0e+00 5.57
11724 BSN-AS2 3_35 0.000 370.01 0.0e+00 5.67
7482 APEH 3_35 0.000 176.23 0.0e+00 3.42
8898 MST1 3_35 0.000 504.57 0.0e+00 4.77
7484 RNF123 3_35 0.000 823.16 1.2e-14 -10.96
10102 FAM212A 3_35 0.000 59.54 0.0e+00 2.35
9891 TRAIP 3_35 0.000 756.55 0.0e+00 -6.90
7487 CAMKV 3_35 0.000 1446.64 1.4e-18 9.85
7489 MST1R 3_35 0.002 1050.67 5.7e-06 -12.63
38 RBM6 3_35 0.000 906.71 9.6e-07 12.54
10406 SLC38A3 3_35 0.000 1504.12 0.0e+00 6.73
208 SEMA3B 3_35 0.000 2779.61 0.0e+00 2.68
10243 HYAL3 3_35 0.000 56.46 0.0e+00 5.62
12211 NAT6 3_35 0.000 68.10 0.0e+00 -7.17
679 RASSF1 3_35 0.000 2345.02 0.0e+00 2.76
125 CACNA2D2 3_35 0.000 14198.82 0.0e+00 -4.01
2875 HEMK1 3_35 0.000 17517.21 0.0e+00 -3.88
2877 MAPKAPK3 3_35 0.000 250.87 0.0e+00 -3.08
2876 CISH 3_35 0.000 20260.39 0.0e+00 -3.80
13397 LINC02019 3_35 0.000 22551.06 7.4e-17 -4.47
5789 MANF 3_35 0.000 107.52 0.0e+00 3.78
12724 RBM15B 3_35 0.000 98.84 0.0e+00 -3.58
7490 RAD54L2 3_35 0.000 189.71 0.0e+00 -3.26
7491 TEX264 3_35 0.000 10476.85 0.0e+00 5.21
7492 GRM2 3_35 0.000 144.73 0.0e+00 1.98
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 16_24"
genename region_tag susie_pip mu2 PVE z
12759 RP11-426C22.5 16_24 0.012 9.21 3.2e-07 1.45
10764 SPN 16_24 0.014 14.07 5.9e-07 2.68
1824 QPRT 16_24 0.008 6.31 1.4e-07 -1.75
10146 C16orf54 16_24 0.032 16.90 1.6e-06 0.85
13675 PAGR1 16_24 0.022 15.44 9.9e-07 0.87
8026 PRRT2 16_24 0.043 20.03 2.6e-06 -0.94
1828 CDIPT 16_24 0.008 16.30 3.7e-07 -4.38
8365 INO80E 16_24 0.015 78.23 3.5e-06 10.10
9044 SEZ6L2 16_24 0.008 19.97 4.7e-07 -5.26
9045 ASPHD1 16_24 0.009 101.21 2.7e-06 -11.34
9046 KCTD13 16_24 0.045 109.28 1.5e-05 -11.49
6178 TAOK2 16_24 0.016 92.70 4.5e-06 10.74
6177 DOC2A 16_24 0.012 30.66 1.1e-06 5.85
6176 FAM57B 16_24 0.008 9.35 2.1e-07 2.95
6175 PPP4C 16_24 0.013 47.36 1.8e-06 7.40
6174 TBX6 16_24 0.008 13.13 3.1e-07 -3.53
1224 YPEL3 16_24 0.013 45.87 1.7e-06 -7.28
1748 MAPK3 16_24 0.010 66.63 2.0e-06 8.83
1747 CORO1A 16_24 0.013 13.85 5.4e-07 2.87
9545 SEPT1 16_24 0.011 18.94 6.3e-07 3.79
9535 ZNF771 16_24 0.010 9.35 2.8e-07 1.51
8419 ZNF768 16_24 0.010 6.40 1.9e-07 -0.20
8418 ZNF747 16_24 0.016 33.70 1.6e-06 5.80
6678 ZNF689 16_24 0.013 30.85 1.2e-06 -6.01
6679 PRR14 16_24 0.008 6.86 1.7e-07 -0.49
1746 ZNF629 16_24 0.011 20.28 6.6e-07 4.34
1379 BCL7C 16_24 0.008 9.11 2.2e-07 1.28
1378 SETD1A 16_24 0.008 27.14 6.2e-07 -5.63
13501 RP11-1072A3.4 16_24 0.008 27.14 6.2e-07 5.63
1827 STX4 16_24 0.008 29.63 6.9e-07 5.78
12790 RP11-196G11.2 16_24 0.008 27.60 6.2e-07 -5.65
8031 ZNF668 16_24 0.011 70.05 2.3e-06 9.55
8032 ZNF646 16_24 0.051 75.84 1.2e-05 -10.00
1829 BCKDK 16_24 0.009 12.66 3.3e-07 -3.44
1830 KAT8 16_24 0.009 68.78 1.9e-06 -9.71
458 PRSS8 16_24 0.011 70.69 2.3e-06 -9.76
9270 TRIM72 16_24 0.020 47.02 2.8e-06 7.27
8408 PYDC1 16_24 0.019 46.30 2.7e-06 -7.22
8407 ITGAM 16_24 0.033 34.55 3.4e-06 5.20
5332 ITGAX 16_24 0.021 14.42 9.0e-07 1.63
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 1_46"
genename region_tag susie_pip mu2 PVE z
8735 NEGR1 1_46 0 43383.72 0 -10.7
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 16_23"
genename region_tag susie_pip mu2 PVE z
900 GTF3C1 16_23 0.005 5.54 8.3e-08 0.48
406 KIAA0556 16_23 0.005 4.93 6.9e-08 -0.29
8310 GSG1L 16_23 0.007 9.20 1.9e-07 1.12
8309 XPO6 16_23 0.012 12.40 4.5e-07 0.71
10430 CLN3 16_23 0.052 86.01 1.3e-05 10.45
11930 NPIPB7 16_23 0.052 86.01 1.3e-05 10.45
11298 EIF3CL 16_23 0.009 60.23 1.7e-06 -8.73
10728 IL27 16_23 0.014 68.25 2.9e-06 9.14
9162 NUPR1 16_23 0.020 72.43 4.4e-06 -9.33
9201 SGF29 16_23 0.005 5.99 8.4e-08 -1.33
10711 SULT1A2 16_23 0.043 80.36 1.0e-05 -9.45
9286 CD19 16_23 0.005 5.94 8.6e-08 1.16
11411 LAT 16_23 0.106 83.00 2.6e-05 -9.55
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 19_33"
genename region_tag susie_pip mu2 PVE z
2015 PRKD2 19_33 0.004 5.12 5.4e-08 0.27
9631 FKRP 19_33 0.010 15.07 4.5e-07 -1.71
1229 STRN4 19_33 0.008 12.93 3.2e-07 1.51
4204 NPAS1 19_33 0.003 4.75 4.8e-08 -0.02
4202 TMEM160 19_33 0.006 14.36 2.4e-07 -1.98
5486 SAE1 19_33 0.004 97.46 1.0e-06 9.85
4203 ZC3H4 19_33 0.089 33.13 8.7e-06 1.43
2019 CCDC9 19_33 0.007 10.37 2.1e-07 0.38
4599 C5AR2 19_33 0.004 5.73 6.6e-08 -0.13
4596 DHX34 19_33 0.003 5.95 6.0e-08 1.08
2041 MEIS3 19_33 0.004 5.97 6.4e-08 -0.90
3214 ZNF541 19_33 0.005 7.64 1.1e-07 0.94
563 GLTSCR1 19_33 0.004 6.00 7.0e-08 0.67
288 EHD2 19_33 0.003 4.71 4.7e-08 -0.01
2037 SULT2A1 19_33 0.004 7.08 9.2e-08 0.95
2053 PLA2G4C 19_33 0.052 30.46 4.7e-06 -2.63
2051 LIG1 19_33 0.008 12.33 2.9e-07 1.35
10082 C19orf68 19_33 0.036 27.21 2.9e-06 2.38
2050 CARD8 19_33 0.003 4.75 4.8e-08 0.04
9367 ZNF114 19_33 0.003 4.73 4.8e-08 0.03
5485 EMP3 19_33 0.004 5.40 5.8e-08 0.46
2049 CCDC114 19_33 0.003 4.78 4.8e-08 0.22
9730 KCNJ14 19_33 0.060 31.31 5.6e-06 -3.03
2044 CYTH2 19_33 0.011 15.26 4.9e-07 1.96
5488 LMTK3 19_33 0.003 4.81 4.9e-08 0.07
13078 CTC-273B12.10 19_33 0.003 4.81 4.9e-08 -0.07
1148 SULT2B1 19_33 0.006 9.84 1.7e-07 -1.00
2057 FAM83E 19_33 0.004 6.90 8.7e-08 1.01
564 SPHK2 19_33 0.007 11.21 2.2e-07 1.21
565 CA11 19_33 0.004 5.73 6.4e-08 -0.47
9236 FUT2 19_33 0.004 5.66 6.1e-08 0.91
9233 MAMSTR 19_33 0.005 8.09 1.2e-07 1.58
9051 FUT1 19_33 0.116 28.66 9.9e-06 3.37
2058 RASIP1 19_33 0.008 12.30 3.0e-07 2.01
9726 IZUMO1 19_33 0.005 7.19 9.7e-08 0.52
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag susie_pip mu2 PVE z
15430 rs12140153 1_39 1.000 66.95 2.0e-04 -7.64
16297 rs7526141 1_41 1.000 7867.00 2.3e-02 3.66
16303 rs6662904 1_41 1.000 7811.51 2.3e-02 -3.97
18757 rs71658797 1_48 1.000 68.02 2.0e-04 9.19
22190 rs7532966 1_54 1.000 4619.65 1.4e-02 3.03
22196 rs2061480 1_54 1.000 4695.91 1.4e-02 -3.06
24289 rs41285694 1_60 1.000 28198.83 8.4e-02 -5.00
24292 rs2154403 1_60 1.000 28224.41 8.4e-02 -4.94
28142 rs2618039 1_69 1.000 40.40 1.2e-04 6.27
31329 rs111360475 1_76 1.000 2692.80 8.0e-03 2.73
31332 rs72704175 1_76 1.000 2757.14 8.2e-03 -3.17
31470 rs1778830 1_77 1.000 45.14 1.3e-04 5.61
32046 rs1557615 1_78 1.000 8111.50 2.4e-02 2.78
32048 rs857716 1_78 1.000 8084.64 2.4e-02 -2.72
35492 rs10919376 1_83 1.000 8367.68 2.5e-02 2.49
36432 rs75222047 1_85 1.000 16772.48 5.0e-02 3.96
36451 rs2179109 1_85 1.000 16675.33 5.0e-02 -3.93
36454 rs60240702 1_85 1.000 16638.22 5.0e-02 -3.96
40780 rs12754976 1_95 1.000 13447.65 4.0e-02 3.47
41980 rs12122131 1_97 1.000 45.13 1.3e-04 1.20
45640 rs17014375 1_106 1.000 18204.62 5.4e-02 5.71
45642 rs1507336 1_106 1.000 18067.57 5.4e-02 -5.77
46753 rs41296674 1_108 1.000 3265.82 9.7e-03 3.35
57401 rs192754985 2_1 1.000 70.72 2.1e-04 -9.32
64889 rs10865322 2_15 1.000 284.30 8.5e-04 14.46
64914 rs72807675 2_15 1.000 151.79 4.5e-04 9.41
64920 rs10192245 2_15 1.000 257.80 7.7e-04 -4.83
64938 rs2304429 2_15 1.000 105.99 3.2e-04 -3.21
66727 rs56232255 2_20 1.000 1520.34 4.5e-03 2.22
66736 rs72798763 2_20 1.000 1477.39 4.4e-03 -2.15
73076 rs10205893 2_33 1.000 17810.95 5.3e-02 -3.69
73077 rs9711404 2_33 1.000 17770.01 5.3e-02 3.71
74300 rs9808435 2_36 1.000 7700.88 2.3e-02 3.61
74303 rs3821112 2_36 1.000 7573.43 2.3e-02 -3.63
74836 rs1117259 2_37 1.000 4414.99 1.3e-02 3.05
74840 rs13410457 2_37 1.000 4347.72 1.3e-02 3.37
74842 rs1030334 2_37 1.000 4447.88 1.3e-02 -2.89
77567 rs79792870 2_44 1.000 1460.92 4.3e-03 -3.83
77568 rs3885079 2_44 1.000 2128.27 6.3e-03 4.80
79785 rs78841558 2_49 1.000 10976.39 3.3e-02 3.00
79788 rs76340200 2_49 1.000 11003.01 3.3e-02 -2.90
80171 rs6739222 2_50 1.000 12337.24 3.7e-02 -3.57
80173 rs4853289 2_50 1.000 12407.67 3.7e-02 3.48
80182 rs6547116 2_50 1.000 12083.56 3.6e-02 -3.38
81555 rs13407148 2_53 1.000 27215.33 8.1e-02 -4.64
81567 rs7593114 2_53 1.000 27282.39 8.1e-02 4.67
84470 rs11123893 2_59 1.000 11379.99 3.4e-02 3.03
84472 rs12053430 2_59 1.000 11351.64 3.4e-02 -2.97
85037 rs17417204 2_61 1.000 11281.87 3.4e-02 3.78
85044 rs79795961 2_61 1.000 11341.02 3.4e-02 -3.85
87219 rs45586833 2_67 1.000 12297.67 3.7e-02 -3.41
87220 rs45627132 2_67 1.000 12256.92 3.6e-02 3.35
88244 rs1979190 2_68 1.000 7977.18 2.4e-02 3.40
88246 rs334844 2_68 1.000 8034.01 2.4e-02 3.24
89752 rs17367752 2_71 1.000 16508.04 4.9e-02 3.50
92880 rs10175818 2_80 1.000 18971.31 5.6e-02 -3.74
92881 rs6737606 2_80 1.000 18932.13 5.6e-02 3.73
95861 rs12992878 2_87 1.000 8917.52 2.7e-02 -2.88
95865 rs4358069 2_87 1.000 8949.37 2.7e-02 3.00
99188 rs7576002 2_96 1.000 3165.74 9.4e-03 -2.93
99687 rs62187751 2_97 1.000 22526.67 6.7e-02 4.07
103887 rs1918345 2_107 1.000 1065.24 3.2e-03 2.85
104740 rs72888751 2_109 1.000 2790.01 8.3e-03 2.34
104748 rs16867321 2_109 1.000 9720.80 2.9e-02 -4.65
107902 rs72912537 2_114 1.000 49.50 1.5e-04 3.02
108787 rs7587598 2_117 1.000 19417.85 5.8e-02 5.70
108793 rs1595824 2_117 1.000 19384.14 5.8e-02 -5.59
111682 rs6749711 2_124 1.000 1573.96 4.7e-03 -0.25
111683 rs2007748 2_124 1.000 5912.06 1.8e-02 -3.64
117753 rs878444 2_136 1.000 5027.22 1.5e-02 4.48
117755 rs10170567 2_136 1.000 6318.68 1.9e-02 4.07
117759 rs6723277 2_136 1.000 6399.49 1.9e-02 -4.28
128319 rs6550786 3_17 1.000 8033.77 2.4e-02 -2.65
128320 rs6550787 3_17 1.000 8056.87 2.4e-02 2.62
134728 rs113569731 3_33 1.000 40.72 1.2e-04 5.41
135222 rs60898829 3_35 1.000 22931.95 6.8e-02 4.51
140456 rs17008124 3_48 1.000 113.56 3.4e-04 -1.30
140459 rs66590721 3_48 1.000 95.71 2.8e-04 2.36
140461 rs11720703 3_48 1.000 630.82 1.9e-03 -4.01
140462 rs7635544 3_48 1.000 588.66 1.8e-03 2.93
143466 rs7635267 3_54 1.000 54696.47 1.6e-01 6.70
143468 rs6793322 3_54 1.000 54557.44 1.6e-01 -6.58
143517 rs34666414 3_55 1.000 4329.89 1.3e-02 -2.38
144082 rs66855166 3_56 1.000 6816.39 2.0e-02 -4.00
144096 rs142356570 3_56 1.000 6831.65 2.0e-02 4.08
144169 rs116560608 3_56 1.000 488.47 1.5e-03 -1.04
145526 rs9872445 3_59 1.000 108672.61 3.2e-01 8.95
147931 rs9875534 3_65 1.000 12730.85 3.8e-02 -3.07
149161 rs607878 3_67 1.000 8118.97 2.4e-02 2.69
152096 rs9846585 3_73 1.000 8198.24 2.4e-02 2.48
154178 rs11706854 3_78 1.000 7996.86 2.4e-02 2.83
154183 rs62270870 3_78 1.000 8027.39 2.4e-02 -2.86
155656 rs10934978 3_81 1.000 70082.07 2.1e-01 7.19
155659 rs9828720 3_81 1.000 69935.22 2.1e-01 -7.22
158667 rs6440207 3_88 1.000 8653.67 2.6e-02 -2.53
161598 rs896015 3_95 1.000 18711.99 5.6e-02 -4.20
163531 rs4856719 3_99 1.000 37336.21 1.1e-01 5.61
163532 rs4856720 3_99 1.000 37232.78 1.1e-01 -5.66
163998 rs13091802 3_100 1.000 12662.93 3.8e-02 3.07
167119 rs1388475 3_107 1.000 5510.87 1.6e-02 4.01
167120 rs13071192 3_107 1.000 5438.38 1.6e-02 -3.95
167122 rs13081434 3_107 1.000 5324.01 1.6e-02 -3.93
172327 rs35941299 3_118 1.000 9898.39 2.9e-02 -2.88
172328 rs35544990 3_118 1.000 9870.56 2.9e-02 2.91
174722 rs73791842 4_3 1.000 6633.68 2.0e-02 2.44
174723 rs28843679 4_3 1.000 6634.46 2.0e-02 -2.45
176671 rs28649910 4_9 1.000 3078.27 9.2e-03 2.17
180094 rs988964 4_17 1.000 52.83 1.6e-04 2.31
180956 rs9631706 4_19 1.000 610.46 1.8e-03 3.81
180957 rs77006543 4_19 1.000 143.79 4.3e-04 1.23
180959 rs60734157 4_19 1.000 670.59 2.0e-03 -3.88
182224 rs34811474 4_21 1.000 106.53 3.2e-04 -10.76
183146 rs567993 4_24 1.000 9672.23 2.9e-02 -2.77
188642 rs2348930 4_35 1.000 12301.11 3.7e-02 3.30
191156 rs1441067 4_41 1.000 7227.45 2.2e-02 -2.80
191159 rs71601696 4_41 1.000 7254.28 2.2e-02 2.64
196715 rs28706514 4_51 1.000 386.99 1.2e-03 -3.38
196716 rs13101593 4_51 1.000 402.38 1.2e-03 2.17
197368 rs6827822 4_52 1.000 15799.77 4.7e-02 -3.49
201596 rs35992541 4_63 1.000 2468.23 7.3e-03 3.40
201606 rs13145086 4_63 1.000 2593.30 7.7e-03 -3.88
201609 rs13121813 4_63 1.000 2450.14 7.3e-03 3.55
203956 rs35518360 4_67 1.000 86.54 2.6e-04 10.86
204014 rs13140033 4_68 1.000 76.65 2.3e-04 8.96
206252 rs2199342 4_72 1.000 13975.63 4.2e-02 3.40
206253 rs421160 4_72 1.000 14012.12 4.2e-02 -3.37
206878 rs4273539 4_73 1.000 27559.02 8.2e-02 4.60
206884 rs4428352 4_73 1.000 27620.23 8.2e-02 -4.57
210796 rs72616928 4_84 1.000 7009.81 2.1e-02 2.35
211044 rs4975290 4_85 1.000 5426.41 1.6e-02 -2.04
212668 rs75943774 4_89 1.000 6556.17 2.0e-02 2.43
215108 rs2165301 4_94 1.000 7063.63 2.1e-02 3.08
219719 rs11100346 4_104 1.000 36750.16 1.1e-01 5.20
225836 rs1862375 4_116 1.000 267.09 7.9e-04 -3.79
225865 rs3113743 4_116 1.000 352.12 1.0e-03 -3.60
229429 rs2115271 5_1 1.000 9760.92 2.9e-02 -3.03
229431 rs6962 5_1 1.000 9776.44 2.9e-02 3.06
241553 rs388377 5_28 1.000 1853.08 5.5e-03 -1.87
244694 rs1202500 5_35 1.000 9282.55 2.8e-02 2.64
244695 rs1202499 5_35 1.000 9262.64 2.8e-02 -2.63
246147 rs10063060 5_39 1.000 20544.75 6.1e-02 4.04
248523 rs34360 5_44 1.000 96.91 2.9e-04 11.50
256714 rs1345613 5_64 1.000 66238.35 2.0e-01 6.99
256716 rs1477283 5_64 1.000 66375.33 2.0e-01 -6.99
264358 rs329123 5_80 1.000 44.27 1.3e-04 -6.70
268852 rs973184 5_89 1.000 14446.18 4.3e-02 3.29
269889 rs7714176 5_92 1.000 5668.80 1.7e-02 2.11
269894 rs280460 5_92 1.000 5681.98 1.7e-02 -2.08
270484 rs2946171 5_93 1.000 11543.29 3.4e-02 -3.63
270485 rs2946173 5_93 1.000 11492.38 3.4e-02 3.59
274288 rs17703470 5_103 1.000 9764.34 2.9e-02 3.19
274291 rs10078465 5_103 1.000 9845.16 2.9e-02 -3.31
274301 rs10068324 5_103 1.000 9802.20 2.9e-02 3.15
275336 rs60793167 5_105 1.000 1177.51 3.5e-03 -0.08
275339 rs273077 5_105 1.000 2060.22 6.1e-03 -2.86
284250 rs9393470 6_17 1.000 30152.40 9.0e-02 4.81
285580 rs34662244 6_22 1.000 14770.85 4.4e-02 3.35
285598 rs67297533 6_22 1.000 14802.45 4.4e-02 -3.31
287666 rs17200698 6_25 1.000 37.66 1.1e-04 -5.62
288206 rs9272679 6_26 1.000 620.92 1.8e-03 -2.78
288236 rs3134996 6_26 1.000 570.65 1.7e-03 3.76
288757 rs3130266 6_28 1.000 5224.94 1.6e-02 3.12
288758 rs3130267 6_28 1.000 5129.99 1.5e-02 -3.16
290785 rs3734554 6_32 1.000 86.86 2.6e-04 9.47
291641 rs140163521 6_33 1.000 35.71 1.1e-04 -5.84
293849 rs1751684 6_38 1.000 1360.55 4.0e-03 -3.86
293879 rs67271941 6_38 1.000 1159.26 3.4e-03 4.28
295505 rs41271856 6_42 1.000 11914.18 3.5e-02 -2.99
302452 rs12208210 6_56 1.000 1257.24 3.7e-03 1.04
302460 rs4324818 6_56 1.000 6921.15 2.1e-02 3.50
302461 rs1935000 6_56 1.000 5866.77 1.7e-02 -3.37
309722 rs7755970 6_73 1.000 15318.49 4.6e-02 3.83
309729 rs9320282 6_73 1.000 15376.26 4.6e-02 -4.01
312472 rs76690375 6_80 1.000 9210.11 2.7e-02 -2.78
312478 rs60033756 6_80 1.000 9186.25 2.7e-02 2.70
316366 rs1535118 6_89 1.000 6990.56 2.1e-02 2.52
316372 rs6928242 6_89 1.000 6988.62 2.1e-02 -2.36
317942 rs9389867 6_93 1.000 16212.70 4.8e-02 3.46
317943 rs9376575 6_93 1.000 16178.16 4.8e-02 -3.46
322405 rs9371421 6_101 1.000 4714.33 1.4e-02 3.57
322407 rs9322572 6_101 1.000 3839.76 1.1e-02 -3.77
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175315 rs146902503 4_5 0.956 24.16 6.9e-05 4.55
210802 rs72616936 4_84 0.956 7024.95 2.0e-02 -2.35
72198 rs7561278 2_31 0.955 25.88 7.4e-05 -5.14
85852 rs66511537 2_64 0.955 26.54 7.5e-05 -4.67
593539 rs72681869 14_20 0.954 24.89 7.1e-05 -4.70
661688 rs7218876 17_22 0.954 25.71 7.3e-05 5.20
515797 rs10047413 11_69 0.951 25.26 7.1e-05 4.76
75473 rs4672266 2_39 0.950 29.35 8.3e-05 -5.16
170221 rs61587156 3_114 0.950 87.94 2.5e-04 -9.65
263763 rs34914000 5_79 0.949 27.40 7.7e-05 5.24
350221 rs4730115 7_49 0.949 31.56 8.9e-05 5.59
645812 rs11642529 16_38 0.949 51.57 1.5e-04 4.64
75248 rs13021589 2_38 0.948 23.42 6.6e-05 3.98
162227 rs9834519 3_97 0.945 31.69 8.9e-05 -5.68
290777 rs4714342 6_31 0.944 42.36 1.2e-04 6.71
76245 rs146416612 2_40 0.942 26.10 7.3e-05 4.74
492915 rs34906813 11_20 0.942 24.49 6.9e-05 -2.55
614926 rs28531819 15_16 0.942 24683.79 6.9e-02 -4.30
145684 rs114467488 3_60 0.940 24.41 6.8e-05 -4.41
166328 rs8192675 3_104 0.940 45.41 1.3e-04 7.22
307353 rs7754315 6_68 0.940 24.51 6.9e-05 4.62
10297 rs35042446 1_25 0.939 24.43 6.8e-05 4.49
35493 rs12133032 1_83 0.939 8384.86 2.3e-02 -2.53
462374 rs2110019 10_38 0.939 9388.19 2.6e-02 -2.67
664230 rs2555111 17_28 0.939 32.46 9.1e-05 4.19
696129 rs56356382 19_4 0.938 171.99 4.8e-04 -7.74
59232 rs116648994 2_4 0.937 25.86 7.2e-05 4.76
144300 rs9813436 3_57 0.937 33.33 9.3e-05 6.99
274789 rs2340010 5_104 0.937 24.54 6.8e-05 4.74
281057 rs9381812 6_11 0.936 36.53 1.0e-04 -5.98
629002 rs75422555 15_47 0.936 25.22 7.0e-05 -4.82
605577 rs942066 14_47 0.934 66.27 1.8e-04 8.96
522553 rs78470967 12_5 0.933 22.88 6.4e-05 4.11
217022 rs919013 4_98 0.932 33.18 9.2e-05 5.57
750941 rs11209950 1_46 0.932 56245.78 1.6e-01 8.98
230582 rs1691101 5_3 0.931 29.71 8.2e-05 5.12
639436 rs11642933 16_24 0.931 118.31 3.3e-04 11.97
703375 rs17530179 19_21 0.929 25.48 7.0e-05 5.41
548997 rs111260184 12_66 0.928 32.71 9.0e-05 5.28
394766 rs9774039 8_56 0.926 854.68 2.4e-03 -1.45
638211 rs11074653 16_21 0.925 492.07 1.4e-03 -5.91
515484 rs1048932 11_69 0.924 50.02 1.4e-04 -6.75
780890 rs4759228 12_35 0.923 57.54 1.6e-04 -7.55
388082 rs35707769 8_40 0.921 26.01 7.1e-05 -4.76
412547 rs4072917 8_93 0.921 68.12 1.9e-04 5.49
295506 rs139359522 6_42 0.920 11888.09 3.3e-02 2.99
708331 rs8044 19_35 0.920 29.37 8.0e-05 -5.14
304515 rs1209058 6_61 0.916 27.45 7.5e-05 -5.32
356577 rs112588752 7_64 0.914 12143.78 3.3e-02 -3.30
300032 rs72942627 6_51 0.912 26.66 7.2e-05 -4.95
664416 rs150619217 17_28 0.911 24.15 6.5e-05 -4.40
188678 rs1392615 4_36 0.910 45.39 1.2e-04 6.91
60762 rs62120278 2_7 0.908 27.73 7.5e-05 5.00
234858 rs56846084 5_13 0.908 23.52 6.4e-05 4.46
253445 rs34976806 5_56 0.907 40.18 1.1e-04 -6.36
682339 rs10853457 18_21 0.905 25.33 6.8e-05 -4.47
11559 rs3014211 1_28 0.904 26.66 7.2e-05 5.39
268854 rs10044974 5_89 0.904 14413.69 3.9e-02 -3.30
213059 rs10018743 4_90 0.902 53.29 1.4e-04 7.45
576167 rs9602895 13_43 0.902 20875.27 5.6e-02 4.10
24055 rs158636 1_59 0.901 27.60 7.4e-05 4.96
283220 rs3806114 6_15 0.901 26.55 7.1e-05 -4.57
689295 rs73963711 18_35 0.901 27.56 7.4e-05 5.09
416629 rs2254372 9_9 0.900 28.05 7.5e-05 5.01
423818 rs10969334 9_22 0.899 35.02 9.4e-05 -5.68
730343 rs62207573 21_6 0.898 17744.71 4.7e-02 3.62
324737 rs4709728 6_106 0.897 24.23 6.5e-05 4.58
666545 rs7224664 17_34 0.897 25.55 6.8e-05 -4.76
196697 rs6531902 4_51 0.896 125.46 3.3e-04 -2.04
615399 rs7183417 15_17 0.895 39.01 1.0e-04 6.14
261502 rs1582931 5_75 0.894 37.99 1.0e-04 -6.13
430124 rs111228813 9_40 0.893 1080.39 2.9e-03 3.61
688437 rs17773471 18_33 0.893 76.23 2.0e-04 12.58
725241 rs6127658 20_33 0.893 25.91 6.9e-05 -4.64
95552 rs115590225 2_86 0.892 34.17 9.1e-05 5.90
686903 rs11152029 18_30 0.892 39.35 1.0e-04 5.96
39045 rs12032144 1_92 0.890 29.47 7.8e-05 5.31
381716 rs3174040 8_24 0.889 26.92 7.1e-05 -5.04
161596 rs1727936 3_95 0.887 18774.11 5.0e-02 4.19
363100 rs3909553 7_79 0.887 23.53 6.2e-05 -4.14
521273 rs73041988 11_84 0.887 40.33 1.1e-04 -7.08
614934 rs16977724 15_16 0.884 24666.04 6.5e-02 -4.34
37280 rs74128490 1_87 0.882 36.99 9.7e-05 -7.67
112365 rs13001305 2_125 0.880 31.13 8.1e-05 1.00
668311 rs117808679 17_38 0.880 25.61 6.7e-05 4.67
307348 rs9496567 6_67 0.879 24.86 6.5e-05 4.56
318616 rs3819810 6_94 0.879 25.05 6.5e-05 4.55
622123 rs876383 15_35 0.879 27.20 7.1e-05 -5.48
20656 rs28726372 1_51 0.878 30.19 7.9e-05 5.44
404710 rs2737251 8_78 0.878 48.43 1.3e-04 -7.15
377839 rs13273446 8_17 0.877 6878.29 1.8e-02 5.76
696510 rs77233515 19_5 0.877 24.90 6.5e-05 -4.57
451540 rs7075283 10_14 0.876 30.73 8.0e-05 5.41
736295 rs2837398 21_20 0.876 28.67 7.5e-05 5.13
383857 rs2543601 8_29 0.875 35.88 9.3e-05 -5.63
727923 rs113298497 20_38 0.875 24.25 6.3e-05 -4.26
738136 rs9637192 21_23 0.873 45.38 1.2e-04 6.82
437986 rs10119402 9_55 0.872 26.76 6.9e-05 -4.76
623211 rs1838200 15_37 0.872 41.03 1.1e-04 -6.32
641459 rs62034144 16_29 0.871 26.20 6.8e-05 -4.39
760758 rs66530305 3_36 0.871 49.63 1.3e-04 -5.33
293833 rs11757533 6_38 0.870 1257.41 3.3e-03 4.44
474257 rs2495708 10_64 0.870 53.41 1.4e-04 -3.56
285090 rs4486004 6_20 0.869 30.61 7.9e-05 -6.45
410292 rs4909854 8_88 0.869 26.05 6.7e-05 -4.77
433315 rs6479463 9_47 0.868 27.50 7.1e-05 4.50
186609 rs34494851 4_31 0.867 29.66 7.6e-05 -5.26
393722 rs7845090 8_53 0.867 64.99 1.7e-04 -8.23
490454 rs8176786 11_14 0.866 23.82 6.1e-05 4.41
200064 rs149027545 4_59 0.864 25.36 6.5e-05 -4.72
250199 rs10942267 5_48 0.863 37.48 9.6e-05 -5.73
672411 rs12601423 17_45 0.863 46.60 1.2e-04 -6.08
197913 rs13123414 4_53 0.862 36.45 9.4e-05 6.46
405771 rs182730147 8_79 0.862 23.98 6.1e-05 -4.46
560072 rs77229024 13_7 0.862 25.46 6.5e-05 -4.51
723132 rs34696920 20_30 0.862 33.99 8.7e-05 -5.67
713686 rs6054392 20_5 0.860 68.84 1.8e-04 8.48
180965 rs73101743 4_19 0.858 565.97 1.4e-03 3.10
788637 rs2414122 15_21 0.858 28577.43 7.3e-02 -4.60
419264 rs2153726 9_13 0.857 69522.79 1.8e-01 7.36
275332 rs114999886 5_105 0.856 650.77 1.7e-03 -0.21
410883 rs73712231 8_90 0.855 24.24 6.2e-05 -4.40
414174 rs57263785 9_3 0.852 24.41 6.2e-05 -4.50
4502 rs9435734 1_12 0.851 27.85 7.0e-05 -4.86
349850 rs28796334 7_49 0.851 51.74 1.3e-04 7.55
302783 rs907843 6_57 0.850 37.22 9.4e-05 -6.17
419573 rs10756793 9_14 0.850 58.36 1.5e-04 -7.49
183148 rs292065 4_24 0.847 9694.83 2.4e-02 2.75
672131 rs1285245 17_45 0.847 30.03 7.6e-05 -5.23
415383 rs117723031 9_6 0.846 25.92 6.5e-05 -4.80
649935 rs11150461 16_46 0.846 27.58 6.9e-05 -5.16
714784 rs6039798 20_8 0.845 24.58 6.2e-05 4.42
128755 rs7619139 3_18 0.844 27.98 7.0e-05 5.26
274644 rs40796 5_104 0.842 27.22 6.8e-05 -4.89
480863 rs75274506 10_78 0.842 28.13 7.0e-05 -5.29
559687 rs1974200 13_7 0.841 34.79 8.7e-05 -5.70
678606 rs57440424 18_12 0.839 52.51 1.3e-04 -7.45
54525 rs12140220 1_125 0.838 24.02 6.0e-05 -4.38
607920 rs12880358 14_52 0.838 28.10 7.0e-05 5.35
143840 rs114593013 3_55 0.837 60.84 1.5e-04 -7.66
633445 rs72772932 16_8 0.837 31.98 8.0e-05 -5.45
306682 rs17057669 6_66 0.836 44.82 1.1e-04 -6.30
719673 rs2424994 20_21 0.833 25.69 6.4e-05 -5.03
364838 rs161342 7_84 0.832 14522.93 3.6e-02 -3.28
26975 rs4914945 1_66 0.831 36.71 9.1e-05 -5.89
517528 rs10750224 11_75 0.828 23.18 5.7e-05 -4.21
630934 rs181434970 16_3 0.827 23.46 5.8e-05 4.20
674320 rs73379103 18_3 0.826 31.38 7.7e-05 5.35
774973 rs115507748 11_1 0.824 14342.80 3.5e-02 3.40
116573 rs12479233 2_134 0.822 25.48 6.2e-05 -5.12
662972 rs665268 17_25 0.822 30.79 7.5e-05 5.67
225911 rs2613009 4_116 0.821 33.38 8.2e-05 -4.31
700484 rs113230003 19_15 0.821 36.73 9.0e-05 -7.17
147932 rs61581234 3_65 0.820 12704.57 3.1e-02 3.07
445324 rs1009473 9_72 0.815 24.72 6.0e-05 4.29
738555 rs73152864 21_24 0.812 24.31 5.9e-05 4.35
10054 rs113603865 1_24 0.811 40.04 9.7e-05 6.68
177549 rs150164330 4_11 0.811 24.51 5.9e-05 -4.42
579249 rs729174 13_48 0.811 45.47 1.1e-04 6.23
463239 rs117734506 10_41 0.810 26.26 6.3e-05 -4.86
637163 rs72771047 16_18 0.809 55.58 1.3e-04 -7.82
585407 rs1268403 14_2 0.808 24.79 6.0e-05 -4.43
265754 rs6874378 5_84 0.807 26.76 6.4e-05 4.62
525425 rs10840733 12_14 0.807 32.26 7.7e-05 -5.29
574361 rs11620422 13_40 0.804 25.66 6.1e-05 -4.48
616024 rs11854184 15_19 0.804 24.13 5.8e-05 -4.34
375468 rs7826654 8_11 0.803 53.14 1.3e-04 8.03
373388 rs78686130 8_7 0.802 25.35 6.0e-05 -4.33
451489 rs10904750 10_13 0.802 24.67 5.9e-05 -4.59
145529 rs12495822 3_59 0.801 108896.41 2.6e-01 -8.96SNPs with largest effect sizes
#plot PIP vs effect size
plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
145529 rs12495822 3_59 0.801 108896.41 2.6e-01 -8.96
145523 rs4857339 3_59 0.694 108896.30 2.2e-01 -8.95
145526 rs9872445 3_59 1.000 108672.61 3.2e-01 8.95
145522 rs9289735 3_59 0.000 108589.14 0.0e+00 -8.99
145517 rs3887491 3_59 0.000 106304.67 0.0e+00 -8.85
145509 rs1584927 3_59 0.000 105450.50 0.0e+00 -8.85
155656 rs10934978 3_81 1.000 70082.07 2.1e-01 7.19
155659 rs9828720 3_81 1.000 69935.22 2.1e-01 -7.22
419266 rs9407657 9_13 1.000 69686.82 2.1e-01 -7.40
419264 rs2153726 9_13 0.857 69522.79 1.8e-01 7.36
419263 rs7024440 9_13 0.746 69521.92 1.5e-01 7.36
419262 rs7032634 9_13 0.383 69508.35 7.9e-02 7.36
419261 rs9406540 9_13 0.023 69479.68 4.8e-03 7.35
419260 rs7866641 9_13 0.000 69125.15 2.6e-14 7.37
419259 rs7849380 9_13 0.000 68563.08 0.0e+00 7.29
419258 rs13296360 9_13 0.000 68532.13 0.0e+00 7.36
256716 rs1477283 5_64 1.000 66375.33 2.0e-01 -6.99
256714 rs1345613 5_64 1.000 66238.35 2.0e-01 6.99
155660 rs62279048 3_81 0.000 66083.49 0.0e+00 6.76
155674 rs10934981 3_81 0.000 63283.03 0.0e+00 7.81
700394 rs11673702 19_14 1.000 62755.69 1.9e-01 7.16
700395 rs11670228 19_14 1.000 62594.86 1.9e-01 -7.12
419242 rs7019851 9_13 0.000 58460.88 0.0e+00 7.70
419250 rs7861802 9_13 0.000 58404.47 0.0e+00 7.67
419251 rs2382540 9_13 0.000 58396.48 0.0e+00 7.72
419247 rs12378499 9_13 0.000 58347.72 0.0e+00 7.66
419245 rs10962153 9_13 0.000 57841.83 0.0e+00 7.47
419249 rs10962156 9_13 0.000 56531.80 0.0e+00 7.76
750941 rs11209950 1_46 0.932 56245.78 1.6e-01 8.98
750919 rs6661921 1_46 0.160 56235.50 2.7e-02 8.98
750954 rs1841499 1_46 0.019 56233.84 3.1e-03 8.96
750918 rs990871 1_46 0.467 56230.63 7.8e-02 8.98
750920 rs6687024 1_46 0.205 56226.30 3.4e-02 8.99
750933 rs10889947 1_46 0.000 56208.43 1.1e-05 8.96
750957 rs11209951 1_46 0.000 56155.45 5.6e-11 8.91
750962 rs10789336 1_46 0.000 56148.22 8.0e-10 8.93
750904 rs2012697 1_46 0.000 56121.18 4.0e-09 8.97
750958 rs11209952 1_46 0.000 56120.61 3.4e-15 8.89
750921 rs6699744 1_46 1.000 56078.00 1.7e-01 -9.04
419236 rs36013000 9_13 0.000 55489.26 0.0e+00 7.74
419232 rs1396706 9_13 0.000 55264.82 0.0e+00 7.81
145530 rs12485753 3_59 0.000 54900.10 0.0e+00 -7.85
145531 rs6762431 3_59 0.000 54818.08 0.0e+00 -7.82
143466 rs7635267 3_54 1.000 54696.47 1.6e-01 6.70
143468 rs6793322 3_54 1.000 54557.44 1.6e-01 -6.58
155691 rs7428670 3_81 0.000 54348.48 0.0e+00 8.28
145515 rs4857299 3_59 0.000 54301.51 0.0e+00 -8.20
145513 rs12638900 3_59 0.000 54281.06 0.0e+00 -8.21
145511 rs2198619 3_59 0.000 54275.95 0.0e+00 -8.20
143465 rs62260874 3_54 0.000 54243.12 0.0e+00 6.79SNPs with highest PVE
#SNPs with 50 highest pve
head(ctwas_snp_res[order(-ctwas_snp_res$PVE),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
145526 rs9872445 3_59 1.000 108672.61 0.320 8.95
145529 rs12495822 3_59 0.801 108896.41 0.260 -8.96
145523 rs4857339 3_59 0.694 108896.30 0.220 -8.95
155656 rs10934978 3_81 1.000 70082.07 0.210 7.19
155659 rs9828720 3_81 1.000 69935.22 0.210 -7.22
419266 rs9407657 9_13 1.000 69686.82 0.210 -7.40
256714 rs1345613 5_64 1.000 66238.35 0.200 6.99
256716 rs1477283 5_64 1.000 66375.33 0.200 -6.99
700394 rs11673702 19_14 1.000 62755.69 0.190 7.16
700395 rs11670228 19_14 1.000 62594.86 0.190 -7.12
419264 rs2153726 9_13 0.857 69522.79 0.180 7.36
750921 rs6699744 1_46 1.000 56078.00 0.170 -9.04
143466 rs7635267 3_54 1.000 54696.47 0.160 6.70
143468 rs6793322 3_54 1.000 54557.44 0.160 -6.58
750941 rs11209950 1_46 0.932 56245.78 0.160 8.98
419263 rs7024440 9_13 0.746 69521.92 0.150 7.36
463839 rs10740103 10_42 1.000 50803.94 0.150 6.14
163531 rs4856719 3_99 1.000 37336.21 0.110 5.61
163532 rs4856720 3_99 1.000 37232.78 0.110 -5.66
219719 rs11100346 4_104 1.000 36750.16 0.110 5.20
284250 rs9393470 6_17 1.000 30152.40 0.090 4.81
284251 rs9295599 6_17 0.997 30064.26 0.089 -4.81
788634 rs2959291 15_21 1.000 28637.24 0.085 4.60
24289 rs41285694 1_60 1.000 28198.83 0.084 -5.00
24292 rs2154403 1_60 1.000 28224.41 0.084 -4.94
24281 rs61789077 1_60 0.998 28067.28 0.083 5.06
206878 rs4273539 4_73 1.000 27559.02 0.082 4.60
206884 rs4428352 4_73 1.000 27620.23 0.082 -4.57
219721 rs13105180 4_104 0.747 36826.22 0.082 -5.20
219723 rs1523563 4_104 0.752 36826.20 0.082 -5.21
81555 rs13407148 2_53 1.000 27215.33 0.081 -4.64
81567 rs7593114 2_53 1.000 27282.39 0.081 4.67
419262 rs7032634 9_13 0.383 69508.35 0.079 7.36
750918 rs990871 1_46 0.467 56230.63 0.078 8.98
614928 rs12441984 15_16 1.000 24636.90 0.073 4.31
788637 rs2414122 15_21 0.858 28577.43 0.073 -4.60
502402 rs584108 11_38 1.000 24105.67 0.072 4.32
502404 rs669659 11_38 1.000 24072.94 0.072 -4.31
502407 rs592697 11_38 1.000 24072.92 0.072 4.38
614926 rs28531819 15_16 0.942 24683.79 0.069 -4.30
135222 rs60898829 3_35 1.000 22931.95 0.068 4.51
470598 rs56330831 10_55 1.000 22778.95 0.068 4.82
99687 rs62187751 2_97 1.000 22526.67 0.067 4.07
470599 rs17106600 10_55 1.000 22679.55 0.067 -5.02
470601 rs2114828 10_55 1.000 22676.14 0.067 -4.85
596256 rs10150849 14_27 1.000 22649.89 0.067 -4.26
596260 rs11848764 14_27 1.000 22637.07 0.067 4.26
596263 rs34456481 14_27 1.000 22656.34 0.067 -4.18
99689 rs62188765 2_97 0.977 22573.36 0.066 -4.09
614934 rs16977724 15_16 0.884 24666.04 0.065 -4.34SNPs with largest z scores
#SNPs with 50 largest z scores
head(ctwas_snp_res[order(-abs(ctwas_snp_res$z)),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
641365 rs17817712 16_29 0.736 766.63 1.7e-03 28.57
641369 rs79994966 16_29 0.176 763.40 4.0e-04 28.52
641368 rs62033403 16_29 0.089 762.19 2.0e-04 28.50
641361 rs9937354 16_29 0.012 769.43 2.7e-05 28.14
641362 rs9928094 16_29 0.012 769.40 2.7e-05 28.14
641371 rs62033413 16_29 0.000 699.76 9.4e-07 27.32
641364 rs9933509 16_29 0.000 695.86 7.1e-07 27.16
641367 rs7201850 16_29 0.000 694.69 6.9e-07 27.14
641372 rs9922708 16_29 0.000 638.19 5.9e-07 26.03
641360 rs7206790 16_29 0.006 559.95 1.0e-05 23.65
688406 rs7240682 18_33 0.572 234.50 4.0e-04 18.11
688408 rs12967878 18_33 0.428 233.78 3.0e-04 18.09
688411 rs921971 18_33 0.000 212.14 1.5e-12 17.25
688414 rs1942872 18_33 0.000 211.37 1.4e-12 17.23
688412 rs12954782 18_33 0.000 211.09 1.3e-12 17.22
688421 rs12970134 18_33 0.000 204.26 6.3e-13 17.04
688422 rs11665439 18_33 0.000 200.37 4.7e-13 16.88
688424 rs12964203 18_33 0.000 198.64 4.2e-13 16.85
688401 rs72982988 18_33 0.000 184.43 3.1e-14 16.67
688397 rs8084085 18_33 0.000 182.08 1.4e-14 16.15
57453 rs6735049 2_1 0.070 208.26 4.3e-05 16.01
57451 rs12997450 2_1 0.063 208.07 3.9e-05 16.00
57454 rs6731688 2_1 0.067 208.15 4.1e-05 16.00
57452 rs6725549 2_1 0.057 207.75 3.5e-05 15.99
57456 rs7588007 2_1 0.053 207.58 3.3e-05 15.99
57461 rs11127487 2_1 0.062 207.96 3.8e-05 15.99
57455 rs5017300 2_1 0.051 207.44 3.1e-05 15.98
57459 rs62105304 2_1 0.047 207.27 2.9e-05 15.98
57450 rs66906321 2_1 0.056 209.17 3.5e-05 15.97
57458 rs13013021 2_1 0.046 207.20 2.8e-05 15.97
57462 rs7558910 2_1 0.049 207.41 3.0e-05 15.97
57463 rs11127489 2_1 0.046 207.23 2.8e-05 15.97
57464 rs10172769 2_1 0.046 207.23 2.8e-05 15.97
57470 rs13396935 2_1 0.050 207.59 3.1e-05 -15.97
57471 rs13412194 2_1 0.055 207.80 3.4e-05 -15.97
57446 rs939583 2_1 0.023 207.65 1.4e-05 15.96
57448 rs6711039 2_1 0.024 207.75 1.5e-05 15.96
57457 rs12992672 2_1 0.036 206.63 2.2e-05 15.96
57467 rs1320337 2_1 0.036 206.67 2.2e-05 -15.95
57468 rs4613321 2_1 0.034 206.52 2.1e-05 -15.94
57466 rs4854349 2_1 0.028 206.15 1.7e-05 15.93
37273 rs545608 1_87 0.995 216.83 6.4e-04 15.87
57469 rs2867112 2_1 0.002 200.73 1.2e-06 -15.65
37264 rs571567 1_87 0.008 205.60 5.1e-06 15.50
37266 rs532504 1_87 0.006 203.51 3.7e-06 15.44
64889 rs10865322 2_15 1.000 284.30 8.5e-04 14.46
37258 rs2094510 1_87 0.004 167.41 2.1e-06 14.03
688431 rs718475 18_33 0.062 94.05 1.7e-05 13.57
688387 rs9959028 18_33 0.000 124.40 4.7e-15 -13.50
64885 rs6717671 2_15 0.000 258.98 4.3e-10 13.48Gene set enrichment for genes with PIP>0.8
#GO enrichment analysis
library(enrichR)Welcome to enrichR
Checking connection ... Enrichr ... Connection is Live!
FlyEnrichr ... Connection is available!
WormEnrichr ... Connection is available!
YeastEnrichr ... Connection is available!
FishEnrichr ... Connection is available!dbs <- c("GO_Biological_Process_2021", "GO_Cellular_Component_2021", "GO_Molecular_Function_2021")
genes <- ctwas_gene_res$genename[ctwas_gene_res$susie_pip>0.8]
#number of genes for gene set enrichment
length(genes)[1] 2if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
Term
1 positive regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0045737)
2 positive regulation of cyclin-dependent protein kinase activity (GO:1904031)
3 positive regulation of G1/S transition of mitotic cell cycle (GO:1900087)
4 positive regulation of cell cycle G1/S phase transition (GO:1902808)
5 regulation of cyclin-dependent protein kinase activity (GO:1904029)
6 positive regulation of mitotic cell cycle phase transition (GO:1901992)
7 positive regulation of cell cycle (GO:0045787)
8 regulation of G1/S transition of mitotic cell cycle (GO:2000045)
9 regulation of cyclin-dependent protein serine/threonine kinase activity (GO:0000079)
10 positive regulation of protein serine/threonine kinase activity (GO:0071902)
11 regulation of protein serine/threonine kinase activity (GO:0071900)
12 protein dephosphorylation (GO:0006470)
13 dephosphorylation (GO:0016311)
14 mitotic cell cycle phase transition (GO:0044772)
15 positive regulation of protein modification process (GO:0031401)
16 positive regulation of phosphorylation (GO:0042327)
17 regulation of protein phosphorylation (GO:0001932)
18 positive regulation of protein phosphorylation (GO:0001934)
19 negative regulation of programmed cell death (GO:0043069)
Overlap Adjusted.P.value Genes
1 1/17 0.02165276 CCND2
2 1/20 0.02165276 CCND2
3 1/26 0.02165276 CCND2
4 1/35 0.02185607 CCND2
5 1/54 0.02214841 CCND2
6 1/58 0.02214841 CCND2
7 1/66 0.02214841 CCND2
8 1/71 0.02214841 CCND2
9 1/82 0.02273140 CCND2
10 1/106 0.02515765 CCND2
11 1/111 0.02515765 CCND2
12 1/139 0.02885817 PPM1M
13 1/153 0.02931101 PPM1M
14 1/209 0.03547647 CCND2
15 1/214 0.03547647 CCND2
16 1/253 0.03885823 CCND2
17 1/266 0.03885823 CCND2
18 1/371 0.04965504 CCND2
19 1/381 0.04965504 CCND2
[1] "GO_Cellular_Component_2021"
Term Overlap
1 cyclin-dependent protein kinase holoenzyme complex (GO:0000307) 1/30
2 serine/threonine protein kinase complex (GO:1902554) 1/37
Adjusted.P.value Genes
1 0.01478646 CCND2
2 0.01478646 CCND2
[1] "GO_Molecular_Function_2021"
Term
1 cyclin-dependent protein serine/threonine kinase regulator activity (GO:0016538)
2 manganese ion binding (GO:0030145)
3 protein serine/threonine phosphatase activity (GO:0004722)
4 protein kinase regulator activity (GO:0019887)
5 transition metal ion binding (GO:0046914)
6 kinase binding (GO:0019900)
7 protein kinase binding (GO:0019901)
Overlap Adjusted.P.value Genes
1 1/44 0.01444442 CCND2
2 1/48 0.01444442 PPM1M
3 1/62 0.01444442 PPM1M
4 1/98 0.01710823 CCND2
5 1/445 0.04996091 PPM1M
6 1/461 0.04996091 CCND2
7 1/506 0.04996091 CCND2PPM1M gene(s) from the input list not found in DisGeNET CURATED Description
6 Communicating Hydrocephalus
19 POLYDACTYLY, POSTAXIAL
22 Hydrocephalus Ex-Vacuo
24 Post-Traumatic Hydrocephalus
25 Obstructive Hydrocephalus
30 Cerebral ventriculomegaly
32 Perisylvian syndrome
33 Megalanecephaly Polymicrogyria-Polydactyly Hydrocephalus Syndrome
34 POSTAXIAL POLYDACTYLY, TYPE B
36 Alcohol Toxicity
FDR Ratio BgRatio
6 0.002020202 1/1 7/9703
19 0.002020202 1/1 4/9703
22 0.002020202 1/1 7/9703
24 0.002020202 1/1 7/9703
25 0.002020202 1/1 7/9703
30 0.002020202 1/1 7/9703
32 0.002020202 1/1 4/9703
33 0.002020202 1/1 4/9703
34 0.002020202 1/1 3/9703
36 0.002020202 1/1 2/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULLSensitivity, specificity and precision for silver standard genes
library("readxl")
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="BMI")
known_annotations <- unique(known_annotations$`Gene Symbol`)
unrelated_genes <- ctwas_gene_res$genename[!(ctwas_gene_res$genename %in% known_annotations)]
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 22#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] 4.586313#number of ctwas genes
length(ctwas_genes)[1] 2#number of TWAS genes
length(twas_genes)[1] 223#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z
7493 PPM1M 3_36 1 244.75 0.00073 4.54#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.00000000 0.07317073 #specificity
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9998192 0.9801103 #precision / PPV
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.00000000 0.01345291 #ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res$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 |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
Sensitivity, specificity and precision for silver standard genes - bystanders only
This section first uses all silver standard genes to identify bystander genes within 1Mb. The silver standard and bystander gene lists are then subset to only genes with imputed expression in this analysis. Then, the ctwas and TWAS gene lists from this analysis are subset to only genes that are in the (subset) silver standard and bystander genes. These gene lists are then used to compute sensitivity, specificity and precision for ctwas and TWAS.
library(biomaRt)
library(GenomicRanges)Loading required package: stats4Loading required package: BiocGenericsLoading required package: parallel
Attaching package: 'BiocGenerics'The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLBThe following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabsThe following objects are masked from 'package:base':
anyDuplicated, append, as.data.frame, basename, cbind,
colnames, dirname, do.call, duplicated, eval, evalq, Filter,
Find, get, grep, grepl, intersect, is.unsorted, lapply, Map,
mapply, match, mget, order, paste, pmax, pmax.int, pmin,
pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
setdiff, sort, table, tapply, union, unique, unsplit, which,
which.max, which.minLoading required package: S4Vectors
Attaching package: 'S4Vectors'The following object is masked from 'package:base':
expand.gridLoading required package: IRangesLoading required package: GenomeInfoDbensembl <- useEnsembl(biomart="ENSEMBL_MART_ENSEMBL", dataset="hsapiens_gene_ensembl")
G_list <- getBM(filters= "chromosome_name", attributes= c("hgnc_symbol","chromosome_name","start_position","end_position","gene_biotype"), values=1:22, mart=ensembl)
G_list <- G_list[G_list$hgnc_symbol!="",]
G_list <- G_list[G_list$gene_biotype %in% c("protein_coding","lncRNA"),]
G_list$start <- G_list$start_position
G_list$end <- G_list$end_position
G_list_granges <- makeGRangesFromDataFrame(G_list, keep.extra.columns=T)
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 <- bystanders[!(bystanders %in% known_annotations)]
unrelated_genes <- bystanders
#number of genes in known annotations
print(length(known_annotations))[1] 41#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 22#number of bystander genes
print(length(unrelated_genes))[1] 748#number of bystander genes with imputed expression
print(sum(unrelated_genes %in% ctwas_gene_res$genename))[1] 371#remove genes without imputed expression from gene lists
known_annotations <- known_annotations[known_annotations %in% ctwas_gene_res$genename]
unrelated_genes <- unrelated_genes[unrelated_genes %in% ctwas_gene_res$genename]
#assign ctwas and TWAS 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]
#significance threshold for TWAS
print(sig_thresh)[1] 4.586313#number of ctwas genes
length(ctwas_genes)[1] 2#number of ctwas genes in known annotations or bystanders
sum(ctwas_genes %in% c(known_annotations, unrelated_genes))[1] 0#number of ctwas genes
length(twas_genes)[1] 223#number of TWAS genes
sum(twas_genes %in% c(known_annotations, unrelated_genes))[1] 17#remove genes not in known or bystander lists from results
ctwas_genes <- ctwas_genes[ctwas_genes %in% c(known_annotations, unrelated_genes)]
twas_genes <- twas_genes[twas_genes %in% c(known_annotations, unrelated_genes)]
#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
0.0000000 0.1363636 #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
1.0000000 0.9622642 #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
NaN 0.1764706
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] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0 IRanges_2.18.1
[4] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[7] readxl_1.3.1 WebGestaltR_0.4.4 disgenet2r_0.99.2
[10] enrichR_3.0 cowplot_1.0.0 ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.3.1 bit64_4.0.5
[4] doParallel_1.0.16 progress_1.2.2 httr_1.4.1
[7] rprojroot_2.0.2 tools_3.6.1 doRNG_1.8.2
[10] utf8_1.2.1 R6_2.5.0 DBI_1.1.1
[13] colorspace_1.4-1 withr_2.4.1 tidyselect_1.1.0
[16] prettyunits_1.0.2 bit_4.0.4 curl_3.3
[19] compiler_3.6.1 git2r_0.26.1 Biobase_2.44.0
[22] labeling_0.3 scales_1.1.0 readr_1.4.0
[25] stringr_1.4.0 apcluster_1.4.8 digest_0.6.20
[28] rmarkdown_1.13 svglite_1.2.2 XVector_0.24.0
[31] pkgconfig_2.0.3 htmltools_0.3.6 fastmap_1.1.0
[34] rlang_0.4.11 RSQLite_2.2.7 farver_2.1.0
[37] generics_0.0.2 jsonlite_1.6 dplyr_1.0.7
[40] RCurl_1.98-1.1 magrittr_2.0.1 GenomeInfoDbData_1.2.1
[43] Matrix_1.2-18 Rcpp_1.0.6 munsell_0.5.0
[46] fansi_0.5.0 gdtools_0.1.9 lifecycle_1.0.0
[49] stringi_1.4.3 whisker_0.3-2 yaml_2.2.0
[52] zlibbioc_1.30.0 plyr_1.8.4 grid_3.6.1
[55] blob_1.2.1 promises_1.0.1 crayon_1.4.1
[58] lattice_0.20-38 hms_1.1.0 knitr_1.23
[61] pillar_1.6.1 igraph_1.2.4.1 rjson_0.2.20
[64] rngtools_1.5 reshape2_1.4.3 codetools_0.2-16
[67] XML_3.98-1.20 glue_1.4.2 evaluate_0.14
[70] data.table_1.14.0 vctrs_0.3.8 httpuv_1.5.1
[73] foreach_1.5.1 cellranger_1.1.0 gtable_0.3.0
[76] purrr_0.3.4 assertthat_0.2.1 cachem_1.0.5
[79] xfun_0.8 later_0.8.0 tibble_3.1.2
[82] iterators_1.0.13 AnnotationDbi_1.46.0 memoise_2.0.0
[85] workflowr_1.6.2 ellipsis_0.3.2