Red blood cell (erythrocyte) count - Cells_EBV-transformed_lymphocytes
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 |
| Rmd | 3a7fbc1 | wesleycrouse | 2021-09-08 | generating reports for known annotations |
| 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 Red blood cell (erythrocyte) count using Cells_EBV-transformed_lymphocytes 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-30010_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 Cells_EBV-transformed_lymphocytes 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] 10542#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1060 714 603 380 468 626 514 378 386 418 621 606 198 357 355
16 17 18 19 20 21 22
481 665 163 850 307 106 286 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8357048Load 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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0179135205 0.0002131108 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
23.44062 23.28752 #report sample size
print(sample_size)[1] 350475#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10542 8696600#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01263037 0.12314626 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.03590177 2.76236203Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
5498 CNIH4 1_114 1.000 37.30 1.1e-04 -6.70
1914 DOT1L 19_3 0.999 163.55 4.7e-04 11.68
9703 ZSCAN23 6_22 0.997 52.28 1.5e-04 -5.29
4293 OSER1 20_28 0.993 30.25 8.6e-05 -5.54
5336 SAE1 19_33 0.991 57.27 1.6e-04 8.58
282 TYMP 22_24 0.990 67.67 1.9e-04 -12.53
1716 GDPD3 16_24 0.989 201.56 5.7e-04 16.34
8822 ODF3B 22_24 0.988 247.80 7.0e-04 -20.20
6470 C12orf43 12_74 0.983 33.50 9.4e-05 -6.42
2603 ASF1A 6_79 0.982 36.26 1.0e-04 5.85
4151 TRAF3 14_54 0.978 30.86 8.6e-05 5.04
6818 RPS6KA4 11_36 0.973 30.01 8.3e-05 -4.78
1247 ZC3HC1 7_79 0.965 27.70 7.6e-05 4.69
1361 MARCH2 19_8 0.964 25.70 7.1e-05 -4.14
6112 ZFP36L2 2_27 0.963 40.60 1.1e-04 6.07
1386 SMARCB1 22_7 0.957 34.60 9.4e-05 -5.71
9716 WNT7B 22_20 0.950 128.41 3.5e-04 -12.33
9341 HIST1H1B 6_21 0.949 84.76 2.3e-04 -7.34
4063 LSM4 19_15 0.944 67.67 1.8e-04 -8.24
7826 SNTB2 16_37 0.939 26.52 7.1e-05 -4.52
7101 GNL3 3_36 0.926 65.99 1.7e-04 8.56
3458 NT5C3A 7_25 0.917 40.81 1.1e-04 6.43
1076 B4GALT1 9_25 0.896 26.69 6.8e-05 5.03
8223 ZNF217 20_31 0.895 34.62 8.8e-05 5.77
920 TNRC6C 17_43 0.884 33.45 8.4e-05 4.56
1585 PCIF1 20_28 0.880 45.59 1.1e-04 -7.07
7256 CDK5 7_94 0.879 65.12 1.6e-04 8.36
7254 NOS3 7_93 0.873 25.27 6.3e-05 -5.09
8249 TEFM 17_18 0.863 24.65 6.1e-05 -4.56
8707 RUFY1 5_108 0.862 19.69 4.8e-05 3.99
4177 NDFIP1 5_84 0.845 28.47 6.9e-05 4.97
6766 NAGS 17_26 0.840 22.72 5.4e-05 -4.39
7036 C4orf36 4_59 0.839 24.86 6.0e-05 -5.08
2531 CDKN1B 12_12 0.830 21.94 5.2e-05 -4.33
1713 ZNF629 16_24 0.825 53.94 1.3e-04 3.67Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
156 SPRTN 1_118 0.000 48573.83 0.0e+00 -7.55
5349 RPS11 19_34 0.000 14321.38 0.0e+00 -5.40
10928 RP11-258F22.1 10_28 0.000 13420.71 0.0e+00 4.06
3052 TSNAX 1_118 0.000 8562.46 0.0e+00 0.62
1907 FCGRT 19_34 0.000 7694.78 0.0e+00 5.87
4578 AHI1 6_89 0.000 7344.77 0.0e+00 3.54
5353 RCN3 19_34 0.000 5592.29 0.0e+00 -5.12
10583 LIN52 14_34 0.000 5372.72 0.0e+00 -5.02
6917 DISC1 1_118 0.000 4111.38 0.0e+00 -2.12
6421 SYN2 3_9 0.000 3945.52 0.0e+00 -0.18
3774 PRRG2 19_34 0.000 3943.53 0.0e+00 -7.54
12104 RP11-508N22.12 10_28 0.000 3696.97 0.0e+00 2.45
2658 HBS1L 6_89 0.000 3488.61 0.0e+00 49.02
5136 PTGR2 14_34 0.000 2635.33 0.0e+00 0.86
395 USP28 11_67 0.000 2560.34 3.9e-14 4.07
10955 LINC01624 6_112 0.351 2551.67 2.6e-03 -5.09
10682 PPP1CB 2_17 0.000 2399.67 9.0e-07 -5.53
5350 RPL13A 19_34 0.000 1722.16 0.0e+00 2.32
9641 HEATR4 14_34 0.000 1610.15 0.0e+00 -1.33
8104 TRMT61B 2_17 0.000 1566.98 0.0e+00 5.28Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
10955 LINC01624 6_112 0.351 2551.67 0.00260 -5.09
8822 ODF3B 22_24 0.988 247.80 0.00070 -20.20
1716 GDPD3 16_24 0.989 201.56 0.00057 16.34
1914 DOT1L 19_3 0.999 163.55 0.00047 11.68
9716 WNT7B 22_20 0.950 128.41 0.00035 -12.33
8901 ZBTB7A 19_4 0.626 158.59 0.00028 13.56
753 TFRC 3_120 0.603 131.68 0.00023 14.74
9341 HIST1H1B 6_21 0.949 84.76 0.00023 -7.34
8667 SMIM19 8_37 0.716 98.80 0.00020 -10.91
282 TYMP 22_24 0.990 67.67 0.00019 -12.53
4063 LSM4 19_15 0.944 67.67 0.00018 -8.24
10577 CPT1B 22_24 0.674 92.52 0.00018 -14.25
7877 RGS14 5_106 0.789 77.24 0.00017 9.28
7882 THBS3 1_76 0.498 122.78 0.00017 -10.94
8372 MTX1 1_76 0.498 122.78 0.00017 -10.94
7101 GNL3 3_36 0.926 65.99 0.00017 8.56
9295 TRAIP 3_35 0.768 72.24 0.00016 8.40
7256 CDK5 7_94 0.879 65.12 0.00016 8.36
1356 SETD1A 16_24 0.396 145.96 0.00016 -10.46
12223 RP11-1072A3.4 16_24 0.396 145.96 0.00016 10.46Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
2658 HBS1L 6_89 0.000 3488.61 0.0e+00 49.02
2129 TFR2 7_62 0.000 373.50 6.5e-15 -26.84
2672 BYSL 6_32 0.001 326.66 6.6e-07 21.11
3617 MED20 6_32 0.000 358.95 9.6e-16 -20.44
8822 ODF3B 22_24 0.988 247.80 7.0e-04 -20.20
2130 MOSPD3 7_62 0.000 168.18 4.9e-15 -19.90
3176 ALDH8A1 6_89 0.000 1524.42 2.6e-17 18.68
2289 WNT3 17_27 0.000 293.15 1.9e-08 17.97
6776 SYCE2 19_10 0.001 227.46 4.6e-07 17.96
8921 FARSA 19_10 0.001 222.05 4.4e-07 -17.64
8930 CALR 19_10 0.001 218.75 4.3e-07 17.54
6573 ARHGAP27 17_27 0.000 278.26 7.6e-09 -17.36
2131 PCOLCE 7_62 0.000 146.70 1.4e-12 -17.15
4057 SCO2 22_24 0.005 204.81 2.9e-06 16.54
1716 GDPD3 16_24 0.989 201.56 5.7e-04 16.34
8275 GNB2 7_62 0.000 273.03 1.1e-09 -15.77
12324 PRICKLE4 6_32 0.000 260.57 2.0e-15 14.76
753 TFRC 3_120 0.603 131.68 2.3e-04 14.74
10987 ARL17B 17_27 0.000 126.94 1.1e-07 14.65
10577 CPT1B 22_24 0.674 92.52 1.8e-04 -14.25Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.04344527#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
2658 HBS1L 6_89 0.000 3488.61 0.0e+00 49.02
2129 TFR2 7_62 0.000 373.50 6.5e-15 -26.84
2672 BYSL 6_32 0.001 326.66 6.6e-07 21.11
3617 MED20 6_32 0.000 358.95 9.6e-16 -20.44
8822 ODF3B 22_24 0.988 247.80 7.0e-04 -20.20
2130 MOSPD3 7_62 0.000 168.18 4.9e-15 -19.90
3176 ALDH8A1 6_89 0.000 1524.42 2.6e-17 18.68
2289 WNT3 17_27 0.000 293.15 1.9e-08 17.97
6776 SYCE2 19_10 0.001 227.46 4.6e-07 17.96
8921 FARSA 19_10 0.001 222.05 4.4e-07 -17.64
8930 CALR 19_10 0.001 218.75 4.3e-07 17.54
6573 ARHGAP27 17_27 0.000 278.26 7.6e-09 -17.36
2131 PCOLCE 7_62 0.000 146.70 1.4e-12 -17.15
4057 SCO2 22_24 0.005 204.81 2.9e-06 16.54
1716 GDPD3 16_24 0.989 201.56 5.7e-04 16.34
8275 GNB2 7_62 0.000 273.03 1.1e-09 -15.77
12324 PRICKLE4 6_32 0.000 260.57 2.0e-15 14.76
753 TFRC 3_120 0.603 131.68 2.3e-04 14.74
10987 ARL17B 17_27 0.000 126.94 1.1e-07 14.65
10577 CPT1B 22_24 0.674 92.52 1.8e-04 -14.25Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 6_89"
genename region_tag susie_pip mu2 PVE z
299 TBPL1 6_89 0 13.64 0.0e+00 -1.12
3176 ALDH8A1 6_89 0 1524.42 2.6e-17 18.68
2658 HBS1L 6_89 0 3488.61 0.0e+00 49.02
4578 AHI1 6_89 0 7344.77 0.0e+00 3.54
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 7_62"
genename region_tag susie_pip mu2 PVE z
2131 PCOLCE 7_62 0 146.70 1.4e-12 -17.15
2130 MOSPD3 7_62 0 168.18 4.9e-15 -19.90
2129 TFR2 7_62 0 373.50 6.5e-15 -26.84
8275 GNB2 7_62 0 273.03 1.1e-09 -15.77
5749 GIGYF1 7_62 0 158.48 5.9e-13 10.55
8269 POP7 7_62 0 160.73 6.9e-12 12.04
5748 SLC12A9 7_62 0 25.89 6.9e-16 -12.67
9926 EPHB4 7_62 0 25.89 6.9e-16 -12.67
1095 TRIP6 7_62 0 27.05 8.4e-16 5.77
1097 SRRT 7_62 0 27.67 6.9e-15 -2.65
8659 UFSP1 7_62 0 33.30 1.9e-15 8.29
1096 ACHE 7_62 0 37.70 2.8e-15 3.27
7947 TRIM56 7_62 0 43.86 2.2e-13 -2.57
2138 SERPINE1 7_62 0 44.83 1.8e-13 -3.89
2139 AP1S1 7_62 0 6.90 1.5e-16 1.98
2143 CLDN15 7_62 0 21.37 6.3e-15 0.19
3908 IFT22 7_62 0 10.20 6.5e-16 2.01
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 6_32"
genename region_tag susie_pip mu2 PVE z
3622 APOBEC2 6_32 0.000 12.74 3.0e-18 0.96
2636 TREML2 6_32 0.000 4.96 4.7e-19 0.27
1325 NCR2 6_32 0.000 8.25 1.1e-18 0.75
4793 FOXP4 6_32 0.000 23.09 1.4e-17 0.45
2671 TFEB 6_32 0.000 81.39 7.7e-16 4.15
3611 RP11-298J23.10 6_32 0.000 6.07 6.9e-19 0.77
12324 PRICKLE4 6_32 0.000 260.57 2.0e-15 14.76
4803 FRS3 6_32 0.000 123.16 3.7e-15 -1.73
10754 TOMM6 6_32 0.000 112.82 1.2e-15 -0.72
7233 USP49 6_32 0.000 27.78 3.1e-18 0.87
3617 MED20 6_32 0.000 358.95 9.6e-16 -20.44
2672 BYSL 6_32 0.001 326.66 6.6e-07 21.11
4825 TAF8 6_32 0.000 152.91 2.8e-12 7.61
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 22_24"
genename region_tag susie_pip mu2 PVE z
10631 RP1-29C18.10 22_24 0.228 23.34 1.5e-05 -4.12
10655 RP1-29C18.9 22_24 0.000 7.84 4.9e-09 1.52
9761 C22orf34 22_24 0.001 12.63 2.0e-08 -2.62
12360 CTA-722E9.1 22_24 0.475 25.18 3.4e-05 -4.36
10940 RP5-983L19.2 22_24 0.000 14.25 1.7e-08 1.83
11703 RP3-522J7.6 22_24 0.000 5.81 2.9e-09 -0.77
1499 ZBED4 22_24 0.000 13.01 1.4e-08 1.66
9322 CRELD2 22_24 0.000 6.65 4.0e-09 0.05
9204 ALG12 22_24 0.023 48.05 3.2e-06 -2.37
1500 MLC1 22_24 0.000 8.67 1.0e-08 0.05
780 PANX2 22_24 0.000 15.76 1.6e-08 1.20
8058 TRABD 22_24 0.005 25.65 3.7e-07 -4.43
781 SELENOO 22_24 0.003 12.56 9.5e-08 -3.84
9722 MAPK12 22_24 0.001 10.23 1.8e-08 1.66
1501 HDAC10 22_24 0.124 27.30 9.7e-06 4.61
9451 MAPK11 22_24 0.000 6.71 4.6e-09 0.83
9955 PLXNB2 22_24 0.000 8.59 5.4e-09 0.06
10580 DENND6B 22_24 0.000 6.37 4.8e-09 0.52
1450 MIOX 22_24 0.000 8.67 7.5e-09 1.40
283 NCAPH2 22_24 0.000 16.19 1.5e-08 0.47
4057 SCO2 22_24 0.005 204.81 2.9e-06 16.54
282 TYMP 22_24 0.990 67.67 1.9e-04 -12.53
8822 ODF3B 22_24 0.988 247.80 7.0e-04 -20.20
12132 CTA-384D8.34 22_24 0.000 59.35 2.8e-08 9.19
12074 CTA-384D8.35 22_24 0.000 60.69 2.9e-08 9.27
4056 KLHDC7B 22_24 0.000 15.35 7.3e-09 -4.62
10577 CPT1B 22_24 0.674 92.52 1.8e-04 -14.25
1457 CHKB 22_24 0.008 54.32 1.3e-06 -10.45
10801 SYCE3 22_24 0.000 16.21 1.6e-08 0.26
1462 ARSA 22_24 0.000 8.83 4.2e-09 -1.15
951 RABL2B 22_24 0.000 9.69 6.3e-09 -2.03
| Version | Author | Date |
|---|---|---|
| 3a7fbc1 | wesleycrouse | 2021-09-08 |
[1] "Region: 17_27"
genename region_tag susie_pip mu2 PVE z
8352 DCAKD 17_27 0.000 42.23 4.6e-09 3.92
9612 HEXIM1 17_27 0.000 23.93 1.7e-10 3.94
9083 ACBD4 17_27 0.000 13.31 1.5e-10 -1.27
9390 FMNL1 17_27 0.000 37.75 1.0e-09 -4.85
6573 ARHGAP27 17_27 0.000 278.26 7.6e-09 -17.36
10890 PLEKHM1 17_27 0.000 11.46 1.4e-10 -0.31
9617 MAPT 17_27 0.000 17.76 3.8e-10 2.56
3292 KANSL1 17_27 0.000 127.02 3.4e-09 9.14
11196 LRRC37A2 17_27 0.015 140.49 5.8e-06 -13.29
8700 LRRC37A 17_27 0.684 38.22 7.5e-05 4.72
10987 ARL17B 17_27 0.000 126.94 1.1e-07 14.65
9513 ARL17A 17_27 0.000 59.95 2.9e-08 2.86
798 NSF 17_27 0.011 42.06 1.3e-06 -0.27
2289 WNT3 17_27 0.000 293.15 1.9e-08 17.97
2298 GOSR2 17_27 0.000 60.07 3.2e-09 8.10
40 CDC27 17_27 0.000 10.66 6.3e-11 2.93
11642 ITGB3 17_27 0.000 6.45 3.3e-11 1.31
8889 EFCAB13 17_27 0.000 6.98 3.5e-11 1.15
5248 NPEPPS 17_27 0.000 9.06 5.2e-11 0.85
2297 KPNB1 17_27 0.000 6.52 5.6e-11 0.43
10357 TBKBP1 17_27 0.008 37.53 9.1e-07 2.20
796 TBX21 17_27 0.000 5.81 3.2e-11 0.72
| 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
19167 rs4655791 1_43 1.000 41.75 1.2e-04 -6.28
49398 rs322933 1_99 1.000 118.81 3.4e-04 -7.46
53650 rs3754140 1_108 1.000 122.73 3.5e-04 -12.18
58263 rs766167074 1_118 1.000 66708.73 1.9e-01 8.67
64417 rs12027924 1_131 1.000 69.61 2.0e-04 -2.03
64423 rs66858280 1_131 1.000 241.81 6.9e-04 -14.00
74241 rs569546056 2_17 1.000 2760.80 7.9e-03 5.27
80307 rs71422190 2_28 1.000 240.73 6.9e-04 18.35
80333 rs10495928 2_28 1.000 360.15 1.0e-03 24.83
85479 rs168565 2_40 1.000 49.46 1.4e-04 -6.52
116695 rs863678 2_106 1.000 73.82 2.1e-04 -9.82
142570 rs3933701 3_12 1.000 115.38 3.3e-04 -10.89
183899 rs9817452 3_97 1.000 34.86 9.9e-05 -5.44
196282 rs9812813 3_120 1.000 135.02 3.9e-04 -10.67
215494 rs145740858 4_39 1.000 90.78 2.6e-04 6.18
215548 rs115306053 4_40 1.000 53.93 1.5e-04 -5.20
215553 rs28651467 4_40 1.000 63.75 1.8e-04 -4.12
230727 rs35518360 4_67 1.000 73.20 2.1e-04 -8.57
230793 rs13140033 4_68 1.000 54.11 1.5e-04 -7.53
231461 rs7699743 4_69 1.000 45.03 1.3e-04 5.79
283275 rs2928169 5_45 1.000 53.52 1.5e-04 -6.79
306512 rs78112077 5_91 1.000 42.09 1.2e-04 -2.84
315753 rs70995354 6_2 1.000 3305.11 9.4e-03 -3.25
315756 rs3929752 6_2 1.000 3301.42 9.4e-03 3.30
324352 rs115740542 6_20 1.000 68.72 2.0e-04 8.77
324754 rs1419642 6_23 1.000 70.06 2.0e-04 -1.74
325145 rs2555469 6_27 1.000 79.56 2.3e-04 5.17
329104 rs3218097 6_32 1.000 725.11 2.1e-03 -29.06
329108 rs9689216 6_32 1.000 173.90 5.0e-04 11.91
329113 rs112233623 6_32 1.000 611.24 1.7e-03 19.90
329146 rs9369327 6_32 1.000 89.21 2.5e-04 -7.59
329581 rs1005230 6_33 1.000 98.66 2.8e-04 10.76
356889 rs113033196 6_89 1.000 416.68 1.2e-03 -32.54
357048 rs199804242 6_89 1.000 33547.37 9.6e-02 5.33
358137 rs590325 6_92 1.000 88.48 2.5e-04 11.89
358147 rs151288714 6_92 1.000 229.13 6.5e-04 -16.83
358154 rs602261 6_93 1.000 61.56 1.8e-04 -5.66
369227 rs13241427 7_2 1.000 56.19 1.6e-04 7.13
384989 rs1990053 7_33 1.000 151.99 4.3e-04 12.87
387000 rs74850530 7_36 1.000 55.26 1.6e-04 -4.11
399245 rs2237572 7_56 1.000 53.75 1.5e-04 6.80
401204 rs141862851 7_61 1.000 167.19 4.8e-04 13.20
401253 rs6465770 7_61 1.000 67.08 1.9e-04 8.20
401281 rs3197597 7_61 1.000 272.65 7.8e-04 -18.46
402351 rs763798411 7_65 1.000 17089.60 4.9e-02 2.68
402354 rs10274607 7_65 1.000 17100.72 4.9e-02 -3.08
402362 rs4997569 7_65 1.000 17107.41 4.9e-02 -2.99
429013 rs17296501 8_23 1.000 108.91 3.1e-04 10.58
466937 rs6415788 9_4 1.000 74.86 2.1e-04 8.96
471292 rs17278406 9_12 1.000 104.11 3.0e-04 10.34
498431 rs11545664 9_66 1.000 38.44 1.1e-04 -6.01
500117 rs115478735 9_70 1.000 478.41 1.4e-03 23.72
515734 rs71007692 10_28 1.000 21116.31 6.0e-02 4.54
517195 rs11239271 10_31 1.000 236.44 6.7e-04 15.84
524830 rs6480402 10_46 1.000 548.25 1.6e-03 -9.16
524850 rs73267649 10_46 1.000 375.84 1.1e-03 5.63
556521 rs369062552 11_21 1.000 80.58 2.3e-04 7.93
556531 rs34830202 11_21 1.000 110.06 3.1e-04 -10.42
581107 rs1176746 11_67 1.000 4570.30 1.3e-02 4.15
581109 rs2307599 11_67 1.000 4571.59 1.3e-02 -3.98
589119 rs35167730 12_2 1.000 143.37 4.1e-04 -12.29
603013 rs1859440 12_30 1.000 73.57 2.1e-04 10.58
604729 rs4326844 12_33 1.000 42.13 1.2e-04 -6.04
614511 rs4842610 12_53 1.000 99.20 2.8e-04 10.30
673539 rs17766755 14_29 1.000 65.67 1.9e-04 7.63
676607 rs7156583 14_34 1.000 47559.82 1.4e-01 2.48
676610 rs369107859 14_34 1.000 47533.73 1.4e-01 -6.40
697476 rs2070895 15_26 1.000 55.70 1.6e-04 7.43
699674 rs11857609 15_30 1.000 110.68 3.2e-04 11.14
701763 rs2955742 15_36 1.000 50.19 1.4e-04 7.04
709666 rs2238368 16_1 1.000 208.04 5.9e-04 -14.88
709694 rs59428751 16_1 1.000 106.43 3.0e-04 11.04
721464 rs62039688 16_27 1.000 80.82 2.3e-04 8.38
721546 rs17616063 16_27 1.000 40.51 1.2e-04 5.42
737391 rs57828851 17_7 1.000 62.90 1.8e-04 8.51
749652 rs11650989 17_36 1.000 82.96 2.4e-04 9.43
773957 rs144596877 18_35 1.000 49.41 1.4e-04 6.99
784312 rs8110787 19_10 1.000 273.04 7.8e-04 20.21
784329 rs111920893 19_10 1.000 86.44 2.5e-04 -10.85
785136 rs146213062 19_12 1.000 56.87 1.6e-04 7.80
790418 rs62126610 19_23 1.000 83.98 2.4e-04 5.89
790419 rs4432367 19_23 1.000 193.91 5.5e-04 -1.58
790422 rs55804555 19_23 1.000 381.59 1.1e-03 -16.11
794444 rs1858742 19_34 1.000 15219.57 4.3e-02 -4.93
794446 rs374141296 19_34 1.000 17177.78 4.9e-02 4.86
794447 rs35443645 19_34 1.000 16984.53 4.8e-02 -5.03
809132 rs73124945 20_24 1.000 40.64 1.2e-04 6.09
812786 rs6099616 20_33 1.000 60.80 1.7e-04 7.80
814581 rs2427539 20_38 1.000 56.16 1.6e-04 5.62
815337 rs2823025 21_2 1.000 46.50 1.3e-04 6.36
821844 rs2834259 21_14 1.000 57.78 1.6e-04 -7.56
822802 rs9981948 21_17 1.000 40.17 1.1e-04 -6.26
847348 rs17534202 1_102 1.000 63.74 1.8e-04 -7.99
860819 rs62160676 2_66 1.000 197.01 5.6e-04 18.60
869563 rs534602560 3_9 1.000 19228.44 5.5e-02 4.05
869640 rs56233336 3_9 1.000 19622.91 5.6e-02 -3.59
889290 rs760400154 5_2 1.000 63316.88 1.8e-01 -5.88
889292 rs563200821 5_2 1.000 63318.42 1.8e-01 6.01
938372 rs17287978 6_34 1.000 92.46 2.6e-04 10.21
938752 rs833805 6_34 1.000 181.64 5.2e-04 -14.51
953682 rs2983226 6_112 1.000 12582.05 3.6e-02 2.75
953692 rs139588569 6_112 1.000 12647.62 3.6e-02 -3.30
964025 rs61739556 7_62 1.000 130.82 3.7e-04 17.14
964139 rs78054447 7_62 1.000 85.87 2.4e-04 11.25
965471 rs147330150 7_62 1.000 88.97 2.5e-04 14.16
977895 rs145700013 7_94 1.000 226.00 6.4e-04 4.79
1018778 rs9549260 13_17 1.000 100.45 2.9e-04 -10.43
1046339 rs67453880 15_31 1.000 698.44 2.0e-03 -4.87
1095978 rs117049004 19_3 1.000 88.75 2.5e-04 0.86
1096102 rs547300749 19_3 1.000 675.12 1.9e-03 2.64
1107120 rs184088518 19_28 1.000 107.57 3.1e-04 10.98
1122606 rs1800961 20_28 1.000 57.13 1.6e-04 -7.74
1137561 rs151305716 20_31 1.000 73.78 2.1e-04 8.75
178939 rs79308158 3_86 0.999 102.58 2.9e-04 12.22
211583 rs10029009 4_32 0.999 45.59 1.3e-04 -6.45
283133 rs10060848 5_45 0.999 40.35 1.2e-04 6.16
329112 rs16895128 6_32 0.999 347.85 9.9e-04 21.91
501365 rs1886296 9_73 0.999 33.24 9.5e-05 -5.48
551661 rs72864006 11_12 0.999 32.86 9.4e-05 4.63
580421 rs10891252 11_66 0.999 41.78 1.2e-04 4.92
733095 rs565911571 16_51 0.999 46.63 1.3e-04 6.74
760571 rs35954636 18_9 0.999 45.55 1.3e-04 -6.06
769205 rs2878889 18_27 0.999 37.20 1.1e-04 6.07
977904 rs2536072 7_94 0.999 124.59 3.6e-04 -4.94
1107128 rs61750953 19_28 0.999 54.98 1.6e-04 6.63
35303 rs76016895 1_73 0.998 51.13 1.5e-04 -7.21
49466 rs74448403 1_100 0.998 31.24 8.9e-05 -4.77
286930 rs244760 5_52 0.998 71.90 2.0e-04 9.82
293638 rs61215818 5_66 0.998 35.92 1.0e-04 6.08
354254 rs10782229 6_84 0.998 41.21 1.2e-04 -3.96
367057 rs1472267 6_108 0.998 31.62 9.0e-05 -5.49
401208 rs3807471 7_61 0.998 60.12 1.7e-04 3.87
530819 rs478839 10_56 0.998 49.38 1.4e-04 -5.72
621804 rs7970581 12_68 0.998 31.68 9.0e-05 5.43
669225 rs2883893 14_20 0.998 48.20 1.4e-04 6.28
692258 rs28714278 15_13 0.998 69.28 2.0e-04 7.92
821931 rs147050753 21_15 0.998 32.24 9.2e-05 5.97
833463 rs9680786 22_18 0.998 52.41 1.5e-04 -6.14
36900 rs138055271 1_78 0.997 31.20 8.9e-05 -4.94
196204 rs406271 3_120 0.997 151.99 4.3e-04 -15.32
354258 rs9388461 6_84 0.997 53.93 1.5e-04 5.16
596453 rs2344157 12_18 0.997 31.34 8.9e-05 5.34
41498 rs9425587 1_84 0.996 35.84 1.0e-04 6.60
53853 rs6673799 1_109 0.996 36.58 1.0e-04 5.63
64751 rs4335411 1_131 0.996 35.07 1.0e-04 -4.96
74244 rs4580350 2_17 0.996 2675.41 7.6e-03 5.25
400967 rs149211972 7_60 0.996 29.85 8.5e-05 -4.94
493936 rs117731582 9_56 0.996 41.65 1.2e-04 -6.48
493995 rs7034523 9_56 0.996 32.96 9.4e-05 5.73
517038 rs3780890 10_31 0.996 36.86 1.0e-04 -6.19
603125 rs567199163 12_30 0.996 43.00 1.2e-04 8.52
793630 rs346738 19_31 0.996 33.44 9.5e-05 6.17
1083827 rs11652522 17_26 0.995 37.17 1.1e-04 -5.85
13330 rs2065944 1_29 0.994 42.51 1.2e-04 7.95
709691 rs1203981 16_1 0.994 69.48 2.0e-04 2.42
1090608 rs2748427 17_43 0.994 130.69 3.7e-04 -13.23
1090624 rs383603 17_43 0.994 52.61 1.5e-04 -8.95
6540 rs603412 1_14 0.993 30.49 8.6e-05 5.25
281184 rs451157 5_41 0.993 41.40 1.2e-04 -6.28
721439 rs11641493 16_27 0.993 30.31 8.6e-05 4.89
937788 rs6905288 6_34 0.993 72.09 2.0e-04 -7.53
53597 rs114775675 1_108 0.992 34.14 9.7e-05 -6.94
100128 rs141849010 2_69 0.992 30.68 8.7e-05 -5.44
317662 rs585312 6_7 0.992 40.50 1.1e-04 -5.80
628171 rs4883568 12_82 0.992 53.65 1.5e-04 -8.51
215549 rs4864911 4_40 0.991 50.13 1.4e-04 -5.46
356892 rs7775698 6_89 0.991 4715.39 1.3e-02 65.30
746125 rs1808192 17_27 0.991 43.85 1.2e-04 4.62
324973 rs3873238 6_23 0.990 37.03 1.0e-04 -6.32
581154 rs17116384 11_67 0.990 77.64 2.2e-04 -7.88
158742 rs13071298 3_48 0.989 58.63 1.7e-04 -7.34
746103 rs8073834 17_27 0.989 40.24 1.1e-04 4.59
317682 rs55792466 6_7 0.988 82.30 2.3e-04 -8.82
746685 rs11658398 17_29 0.988 32.23 9.1e-05 -5.60
12418 rs11589584 1_27 0.987 53.94 1.5e-04 -7.73
13266 rs977747 1_29 0.987 34.65 9.8e-05 6.08
120631 rs62181701 2_112 0.987 30.29 8.5e-05 -5.26
324345 rs707890 6_20 0.987 79.67 2.2e-04 -8.91
494485 rs12375564 9_58 0.987 27.17 7.7e-05 4.23
621274 rs12423752 12_66 0.987 39.24 1.1e-04 6.13
771979 rs62094231 18_31 0.987 31.25 8.8e-05 5.62
145127 rs1667747 3_18 0.986 41.87 1.2e-04 -4.16
550793 rs11022762 11_10 0.986 46.90 1.3e-04 -6.82
734316 rs4547319 16_53 0.986 47.30 1.3e-04 -6.50
322721 rs78381359 6_16 0.985 55.31 1.6e-04 -7.73
685014 rs35604463 14_52 0.985 43.79 1.2e-04 -6.41
67907 rs4669306 2_6 0.984 58.11 1.6e-04 7.59
111746 rs115200150 2_96 0.984 29.62 8.3e-05 -6.06
507268 rs737376 10_12 0.983 37.44 1.1e-04 -5.83
790002 rs9304832 19_22 0.983 31.73 8.9e-05 5.40
964368 rs7778978 7_62 0.983 267.54 7.5e-04 -25.01
144947 rs9819064 3_17 0.982 26.37 7.4e-05 4.82
546095 rs2239681 11_2 0.982 31.05 8.7e-05 -5.48
187968 rs1290790 3_104 0.980 67.60 1.9e-04 7.94
467208 rs10974716 9_5 0.980 33.29 9.3e-05 8.10
524831 rs10998724 10_46 0.980 226.05 6.3e-04 2.57
575184 rs3016490 11_55 0.980 26.65 7.5e-05 -4.83
86517 rs12466865 2_42 0.979 30.95 8.6e-05 -5.38
286934 rs304151 5_52 0.979 60.32 1.7e-04 10.70
726318 rs12708919 16_38 0.979 63.99 1.8e-04 -7.50
793653 rs73036520 19_31 0.979 38.12 1.1e-04 -6.68
812846 rs1328757 20_33 0.979 39.72 1.1e-04 -5.90
910368 rs3864294 6_22 0.979 93.23 2.6e-04 4.91
223408 rs11725113 4_52 0.978 27.35 7.6e-05 3.41
532809 rs11462611 10_61 0.977 51.27 1.4e-04 7.38
80334 rs17034605 2_28 0.975 107.54 3.0e-04 14.01
807255 rs3746615 20_19 0.974 79.40 2.2e-04 -7.95
80281 rs35617557 2_28 0.971 63.22 1.8e-04 -5.58
196279 rs73891224 3_120 0.971 32.10 8.9e-05 -1.84
312320 rs322351 5_103 0.971 29.08 8.1e-05 4.40
730947 rs11862358 16_46 0.971 24.84 6.9e-05 -4.40
152446 rs4974078 3_34 0.970 31.99 8.9e-05 -4.78
84947 rs1641155 2_39 0.969 27.88 7.7e-05 -5.41
467286 rs10815095 9_5 0.969 251.05 6.9e-04 -17.90
701357 rs876383 15_35 0.969 29.12 8.1e-05 5.17
726496 rs34889159 16_38 0.969 40.70 1.1e-04 -6.79
821896 rs4817606 21_15 0.969 28.41 7.9e-05 6.04
37404 rs2494211 1_79 0.967 28.60 7.9e-05 5.05
318449 rs79248374 6_9 0.967 25.48 7.0e-05 -5.00
59465 rs2884425 1_121 0.966 62.84 1.7e-04 6.51
327775 rs4713943 6_29 0.965 32.80 9.0e-05 5.89
369226 rs78148157 7_2 0.965 26.54 7.3e-05 -3.90
623617 rs12830847 12_73 0.964 29.39 8.1e-05 -5.50
621616 rs653178 12_67 0.963 233.05 6.4e-04 17.84
324806 rs1233385 6_23 0.962 88.13 2.4e-04 7.11
784653 rs34438120 19_11 0.962 35.17 9.7e-05 -5.51
34875 rs2236398 1_73 0.961 30.08 8.2e-05 -3.19
451218 rs72671791 8_69 0.961 28.17 7.7e-05 -5.00
123676 rs67409803 2_120 0.960 27.66 7.6e-05 -5.10
182392 rs370612 3_94 0.960 28.25 7.7e-05 5.09
822732 rs8128857 21_16 0.959 31.13 8.5e-05 5.53
400940 rs117645417 7_60 0.958 25.61 7.0e-05 -4.34
215580 rs74995222 4_40 0.957 48.03 1.3e-04 4.77
433752 rs13274178 8_35 0.957 28.21 7.7e-05 -5.02
732940 rs1915035 16_50 0.957 24.94 6.8e-05 4.60
329582 rs28357093 6_33 0.955 33.18 9.0e-05 7.15
729963 rs34252270 16_45 0.955 25.41 6.9e-05 -4.68
602955 rs11608702 12_30 0.954 26.31 7.2e-05 -4.94
734350 rs2608604 16_53 0.954 133.74 3.6e-04 11.87
818519 rs118097076 21_8 0.954 24.72 6.7e-05 4.45
822830 rs9305604 21_17 0.954 28.61 7.8e-05 -5.13
1071358 rs117473347 16_54 0.953 50.89 1.4e-04 6.60
306478 rs74417235 5_91 0.950 36.14 9.8e-05 7.30
692257 rs11853517 15_13 0.949 31.76 8.6e-05 -4.37
17058 rs2261980 1_38 0.948 25.20 6.8e-05 4.63
338791 rs144917451 6_51 0.947 26.89 7.3e-05 4.95
366385 rs4709820 6_107 0.947 164.18 4.4e-04 -12.14
49427 rs567188561 1_99 0.943 36.11 9.7e-05 -7.72
705362 rs2518967 15_42 0.942 25.39 6.8e-05 4.79
319406 rs7453037 6_11 0.939 26.84 7.2e-05 4.97
583667 rs497879 11_74 0.938 25.24 6.8e-05 -4.66
50614 rs4617466 1_103 0.936 30.86 8.2e-05 -5.19
358954 rs13208190 6_94 0.936 27.51 7.3e-05 -4.94
565266 rs546240759 11_34 0.936 36.63 9.8e-05 -7.62
624827 rs4765552 12_75 0.934 28.88 7.7e-05 4.73
312687 rs2594836 5_104 0.933 33.59 8.9e-05 4.37
430648 rs34795012 8_27 0.932 32.87 8.7e-05 5.41
22183 rs12134068 1_48 0.931 26.51 7.0e-05 -4.74
120956 rs192394817 2_113 0.927 24.88 6.6e-05 -4.89
709663 rs2238366 16_1 0.925 35.95 9.5e-05 -4.67
850 rs111652194 1_2 0.924 26.67 7.0e-05 -4.80
451729 rs2570952 8_69 0.924 31.23 8.2e-05 5.26
312725 rs189650 5_104 0.923 44.30 1.2e-04 -6.00
33323 rs60897900 1_71 0.920 40.05 1.1e-04 5.69
539479 rs1925282 10_73 0.920 25.95 6.8e-05 4.73
990537 rs11245982 11_1 0.920 42.89 1.1e-04 -5.83
80422 rs11539645 2_29 0.919 24.88 6.5e-05 -4.73
580464 rs1123066 11_66 0.918 42.98 1.1e-04 -5.39
919142 rs2429657 6_25 0.916 100.16 2.6e-04 -10.16
742073 rs1023682 17_17 0.913 26.66 6.9e-05 -2.96
107671 rs10201197 2_86 0.912 44.75 1.2e-04 -6.93
110964 rs78236918 2_94 0.912 25.11 6.5e-05 4.57
12986 rs1768808 1_28 0.911 86.23 2.2e-04 12.55
1048625 rs11631859 15_37 0.910 35.22 9.1e-05 -6.53
830287 rs9609565 22_12 0.909 62.83 1.6e-04 -10.53
1037615 rs12885878 14_54 0.909 42.62 1.1e-04 -6.85
244003 rs13106087 4_94 0.907 25.80 6.7e-05 4.61
583806 rs1945397 11_75 0.907 27.50 7.1e-05 4.90
627936 rs57565032 12_81 0.907 27.35 7.1e-05 4.90
320657 rs7767676 6_13 0.906 57.12 1.5e-04 -7.53
145143 rs12632081 3_18 0.905 64.09 1.7e-04 5.66
811565 rs2072241 20_32 0.904 29.01 7.5e-05 4.90
1148588 rs35042034 21_18 0.901 49.80 1.3e-04 7.85
128661 rs115997322 2_129 0.898 27.54 7.1e-05 5.63
358155 rs113883411 6_93 0.898 82.88 2.1e-04 7.54
684588 rs7159884 14_51 0.898 29.44 7.5e-05 5.18
80581 rs2166475 2_29 0.896 41.23 1.1e-04 -5.90
41833 rs144869587 1_85 0.893 36.88 9.4e-05 6.18
323940 rs145151542 6_19 0.892 24.89 6.3e-05 3.87
589638 rs594088 12_4 0.891 25.31 6.4e-05 4.58
501858 rs113790047 10_2 0.890 32.55 8.3e-05 5.51
409024 rs9649546 7_80 0.889 26.65 6.8e-05 4.86
467343 rs6476934 9_6 0.889 25.57 6.5e-05 4.86
293651 rs62371569 5_66 0.887 25.33 6.4e-05 -4.80
100471 rs35883727 2_70 0.885 64.66 1.6e-04 -8.23
2574 rs11121022 1_6 0.884 27.27 6.9e-05 -4.87
1020391 rs35870087 13_17 0.884 30.57 7.7e-05 3.54
53345 rs12408694 1_108 0.883 41.15 1.0e-04 -6.28
292299 rs6884480 5_63 0.883 23.56 5.9e-05 4.37
780683 rs2238586 19_2 0.883 31.27 7.9e-05 -5.11
386998 rs12718598 7_36 0.882 299.07 7.5e-04 16.82
153876 rs137980154 3_39 0.881 23.85 6.0e-05 4.09
357064 rs6923513 6_89 0.881 33551.77 8.4e-02 -6.32
98016 rs144442782 2_65 0.879 25.75 6.5e-05 4.85
833464 rs2413715 22_18 0.879 99.61 2.5e-04 -8.81
49770 rs2737649 1_100 0.878 25.75 6.5e-05 -4.76
175298 rs2811366 3_79 0.878 24.33 6.1e-05 -4.41
326430 rs4318925 6_27 0.876 114.41 2.9e-04 10.81
708595 rs11635896 15_48 0.874 24.28 6.1e-05 -4.37
751231 rs147300783 17_39 0.873 25.86 6.4e-05 4.65
534977 rs111604275 10_65 0.872 26.47 6.6e-05 -4.84
675178 rs1275824 14_32 0.872 27.58 6.9e-05 5.14
68353 rs139638572 2_6 0.871 31.04 7.7e-05 5.29
205951 rs963209 4_20 0.870 36.98 9.2e-05 6.64
326930 rs115401532 6_28 0.870 26.22 6.5e-05 4.49
663789 rs797342 14_8 0.870 47.37 1.2e-04 -6.12
741453 rs8078135 17_16 0.870 25.26 6.3e-05 -4.79
286706 rs7700278 5_51 0.868 26.06 6.5e-05 -4.81
495388 rs1861882 9_60 0.866 25.50 6.3e-05 -4.30
622541 rs7972038 12_70 0.866 28.84 7.1e-05 4.94
748173 rs754267414 17_32 0.866 23.80 5.9e-05 4.30
26706 rs12130551 1_56 0.864 24.90 6.1e-05 4.70
15 rs28562941 1_1 0.859 24.27 5.9e-05 4.26
589600 rs61907084 12_4 0.857 25.86 6.3e-05 -4.57
3355 rs205474 1_8 0.853 24.51 6.0e-05 4.49
49370 rs10489689 1_99 0.853 77.74 1.9e-04 5.80
149684 rs111998223 3_27 0.849 55.35 1.3e-04 -7.13
906057 rs75040706 6_21 0.847 32.51 7.9e-05 4.20
709 rs2742690 1_2 0.844 36.60 8.8e-05 -5.94
701661 rs11072556 15_35 0.844 31.79 7.7e-05 4.33
552459 rs11025069 11_13 0.841 32.42 7.8e-05 -6.25
158804 rs7640269 3_48 0.839 27.82 6.7e-05 4.14
14023 rs57401684 1_32 0.838 38.90 9.3e-05 5.66
811506 rs2145697 20_30 0.837 58.95 1.4e-04 10.42
74240 rs7562170 2_17 0.836 2664.02 6.4e-03 -5.15
793158 rs346528 19_30 0.835 47.17 1.1e-04 -6.95
785827 rs113809670 19_14 0.834 27.36 6.5e-05 4.80
700090 rs16952867 15_32 0.831 30.98 7.4e-05 5.38
702314 rs142004961 15_36 0.831 27.56 6.5e-05 5.05
737478 rs7224566 17_7 0.828 37.81 8.9e-05 6.45
34868 rs839605 1_73 0.826 51.55 1.2e-04 -6.55
822200 rs2834711 21_15 0.826 32.56 7.7e-05 -5.75
500133 rs71483206 9_70 0.824 34.47 8.1e-05 5.83
673788 rs8006419 14_30 0.824 76.74 1.8e-04 10.26
49662 rs4915386 1_100 0.821 25.14 5.9e-05 4.29
860807 rs372515880 2_66 0.821 192.48 4.5e-04 17.77
349435 rs13196629 6_73 0.819 80.44 1.9e-04 12.98
158714 rs13079951 3_48 0.818 25.31 5.9e-05 -4.54
358138 rs112784432 6_92 0.818 36.54 8.5e-05 6.94
389754 rs189049917 7_40 0.817 25.14 5.9e-05 4.64
552209 rs66514853 11_13 0.817 27.25 6.4e-05 -4.85
815375 rs926167 21_2 0.817 29.66 6.9e-05 5.75
299204 rs4463178 5_77 0.816 27.32 6.4e-05 -4.88
572055 rs7941337 11_47 0.814 26.16 6.1e-05 -4.75
380103 rs34311813 7_23 0.812 28.48 6.6e-05 5.02
93228 rs4441469 2_54 0.810 26.27 6.1e-05 -4.58
748331 rs3794748 17_32 0.809 28.37 6.6e-05 4.82
307261 rs4704834 5_92 0.808 36.69 8.5e-05 -5.76
459174 rs10956395 8_84 0.808 26.48 6.1e-05 4.85
248134 rs1830456 4_102 0.807 25.61 5.9e-05 -4.67
153796 rs6445819 3_39 0.805 144.78 3.3e-04 12.20
178992 rs146526750 3_87 0.805 26.82 6.2e-05 4.42
8734 rs7525656 1_19 0.801 27.87 6.4e-05 4.74
154200 rs2362899 3_39 0.801 25.39 5.8e-05 -4.35
801618 rs2423508 20_8 0.801 32.47 7.4e-05 5.64SNPs 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
58263 rs766167074 1_118 1.000 66708.73 1.9e-01 8.67
58262 rs971534 1_118 0.713 66307.82 1.3e-01 -8.84
58261 rs2486737 1_118 0.572 66307.66 1.1e-01 -8.83
58260 rs10489611 1_118 0.146 66306.08 2.8e-02 -8.82
58257 rs2790891 1_118 0.127 66301.14 2.4e-02 -8.83
58258 rs2491405 1_118 0.127 66301.14 2.4e-02 -8.83
58254 rs2256908 1_118 0.071 66300.85 1.3e-02 -8.82
58270 rs2211176 1_118 0.103 66281.95 2.0e-02 -8.82
58271 rs2790882 1_118 0.103 66281.95 2.0e-02 -8.82
58269 rs2248646 1_118 0.606 66280.43 1.1e-01 -8.84
58250 rs1076804 1_118 0.000 66200.80 2.1e-05 -8.83
58272 rs1416913 1_118 0.002 66199.35 3.1e-04 -8.84
58275 rs2790874 1_118 0.017 66193.85 3.1e-03 -8.87
58251 rs910824 1_118 0.000 66030.92 1.2e-12 -8.78
58274 rs2808603 1_118 0.000 66025.84 4.7e-08 -8.89
58249 rs2474635 1_118 0.000 65831.36 0.0e+00 -8.75
58268 rs143905935 1_118 0.000 65737.49 0.0e+00 -8.65
58246 rs722302 1_118 0.000 65701.35 0.0e+00 -8.61
58266 rs2739509 1_118 0.000 65038.90 0.0e+00 -8.66
58276 rs2474631 1_118 0.000 63895.27 0.0e+00 -8.30
58273 rs3071894 1_118 0.000 63842.43 0.0e+00 -8.73
889292 rs563200821 5_2 1.000 63318.42 1.8e-01 6.01
889290 rs760400154 5_2 1.000 63316.88 1.8e-01 -5.88
889260 rs116876144 5_2 0.000 63226.53 4.0e-06 5.99
889256 rs79843325 5_2 0.000 63219.97 1.1e-06 5.99
889262 rs113261319 5_2 0.000 63215.22 8.2e-09 5.95
889302 rs118079687 5_2 0.000 63213.38 2.6e-07 6.00
58278 rs2790871 1_118 0.000 63190.76 0.0e+00 -8.90
58279 rs2790870 1_118 0.000 63178.60 0.0e+00 -8.91
889250 rs79635912 5_2 0.000 63173.56 2.1e-09 5.99
58264 rs2739512 1_118 0.000 63081.03 0.0e+00 -8.77
889241 rs117110053 5_2 0.000 63023.07 3.0e-16 6.07
889340 rs112060190 5_2 0.000 62988.08 0.0e+00 6.01
889220 rs117324785 5_2 0.000 62956.83 0.0e+00 6.08
889344 rs117279826 5_2 0.000 62935.37 0.0e+00 6.02
889218 rs117169356 5_2 0.000 62933.76 0.0e+00 6.07
889217 rs117971540 5_2 0.000 62928.89 0.0e+00 6.07
889355 rs111378768 5_2 0.000 62901.12 0.0e+00 6.01
889320 rs576289416 5_2 0.000 62837.10 0.0e+00 5.94
889317 rs111206023 5_2 0.000 62654.05 0.0e+00 6.00
889285 rs554884394 5_2 0.000 62638.09 0.0e+00 6.01
889231 rs117025467 5_2 0.000 62619.03 0.0e+00 6.01
889358 rs117576772 5_2 0.000 62525.37 0.0e+00 5.98
889223 rs116875834 5_2 0.000 62517.64 0.0e+00 6.09
889300 rs372122514 5_2 0.000 62487.25 0.0e+00 -4.98
889342 rs117589613 5_2 0.000 62459.89 0.0e+00 6.06
889351 rs111460998 5_2 0.000 62424.84 0.0e+00 6.06
889173 rs116961707 5_2 0.000 62421.83 0.0e+00 6.06
889359 rs113325004 5_2 0.000 62290.97 0.0e+00 6.05
889242 rs117476328 5_2 0.000 62013.68 0.0e+00 6.01SNPs 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
58263 rs766167074 1_118 1.000 66708.73 0.1900 8.67
889290 rs760400154 5_2 1.000 63316.88 0.1800 -5.88
889292 rs563200821 5_2 1.000 63318.42 0.1800 6.01
676607 rs7156583 14_34 1.000 47559.82 0.1400 2.48
676610 rs369107859 14_34 1.000 47533.73 0.1400 -6.40
58262 rs971534 1_118 0.713 66307.82 0.1300 -8.84
58261 rs2486737 1_118 0.572 66307.66 0.1100 -8.83
58269 rs2248646 1_118 0.606 66280.43 0.1100 -8.84
357048 rs199804242 6_89 1.000 33547.37 0.0960 5.33
357064 rs6923513 6_89 0.881 33551.77 0.0840 -6.32
515734 rs71007692 10_28 1.000 21116.31 0.0600 4.54
869640 rs56233336 3_9 1.000 19622.91 0.0560 -3.59
869563 rs534602560 3_9 1.000 19228.44 0.0550 4.05
402351 rs763798411 7_65 1.000 17089.60 0.0490 2.68
402354 rs10274607 7_65 1.000 17100.72 0.0490 -3.08
402362 rs4997569 7_65 1.000 17107.41 0.0490 -2.99
794446 rs374141296 19_34 1.000 17177.78 0.0490 4.86
794447 rs35443645 19_34 1.000 16984.53 0.0480 -5.03
515743 rs11011452 10_28 0.722 21219.35 0.0440 -4.44
794444 rs1858742 19_34 1.000 15219.57 0.0430 -4.93
953682 rs2983226 6_112 1.000 12582.05 0.0360 2.75
953692 rs139588569 6_112 1.000 12647.62 0.0360 -3.30
515733 rs2474565 10_28 0.535 21217.57 0.0320 -4.45
58260 rs10489611 1_118 0.146 66306.08 0.0280 -8.82
58257 rs2790891 1_118 0.127 66301.14 0.0240 -8.83
58258 rs2491405 1_118 0.127 66301.14 0.0240 -8.83
953703 rs59421548 6_112 0.663 12519.76 0.0240 2.83
515740 rs2472183 10_28 0.370 21217.63 0.0220 -4.45
58270 rs2211176 1_118 0.103 66281.95 0.0200 -8.82
58271 rs2790882 1_118 0.103 66281.95 0.0200 -8.82
869635 rs28897669 3_9 0.312 19534.39 0.0170 4.35
869637 rs28897670 3_9 0.287 19534.76 0.0160 4.34
58254 rs2256908 1_118 0.071 66300.85 0.0130 -8.82
356892 rs7775698 6_89 0.991 4715.39 0.0130 65.30
581107 rs1176746 11_67 1.000 4570.30 0.0130 4.15
581109 rs2307599 11_67 1.000 4571.59 0.0130 -3.98
869632 rs73813138 3_9 0.214 19535.15 0.0120 4.34
953698 rs62424359 6_112 0.337 12571.73 0.0120 2.89
357047 rs2327654 6_89 0.119 33547.64 0.0110 -6.31
869631 rs35839040 3_9 0.188 19535.31 0.0100 4.34
315753 rs70995354 6_2 1.000 3305.11 0.0094 -3.25
315756 rs3929752 6_2 1.000 3301.42 0.0094 3.30
74241 rs569546056 2_17 1.000 2760.80 0.0079 5.27
74244 rs4580350 2_17 0.996 2675.41 0.0076 5.25
74240 rs7562170 2_17 0.836 2664.02 0.0064 -5.15
58275 rs2790874 1_118 0.017 66193.85 0.0031 -8.87
870148 rs35240997 3_9 0.481 1580.71 0.0022 9.25
329104 rs3218097 6_32 1.000 725.11 0.0021 -29.06
1046339 rs67453880 15_31 1.000 698.44 0.0020 -4.87
870089 rs2067819 3_9 0.410 1581.68 0.0019 9.23SNPs 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
356892 rs7775698 6_89 0.991 4715.39 1.3e-02 65.30
356894 rs9402685 6_89 0.009 4709.70 1.2e-04 65.22
356893 rs56293029 6_89 0.000 4604.26 0.0e+00 64.57
356899 rs6925841 6_89 0.000 914.18 4.9e-18 -39.29
356900 rs9321485 6_89 0.000 891.66 5.6e-19 -38.59
356904 rs6909822 6_89 0.000 770.16 0.0e+00 -37.04
356907 rs12204380 6_89 0.000 753.42 0.0e+00 -36.83
215541 rs218251 4_39 0.526 1065.27 1.6e-03 34.44
215538 rs218237 4_39 0.333 1064.35 1.0e-03 34.42
215540 rs218240 4_39 0.144 1062.33 4.4e-04 34.40
215539 rs218239 4_39 0.002 652.91 3.6e-06 34.35
356889 rs113033196 6_89 1.000 416.68 1.2e-03 -32.54
356845 rs6899500 6_89 0.000 1525.86 0.0e+00 -32.02
964097 rs2075672 7_62 0.642 537.99 9.9e-04 30.80
964089 rs9801017 7_62 0.209 534.94 3.2e-04 30.75
964087 rs7385804 7_62 0.149 533.98 2.3e-04 30.74
964161 rs221790 7_62 0.000 514.62 3.8e-10 30.65
964144 rs221788 7_62 0.000 509.53 1.0e-10 30.63
964188 rs221799 7_62 0.000 511.69 3.2e-10 30.59
964216 rs2432930 7_62 0.000 512.20 3.6e-10 30.59
964224 rs221772 7_62 0.000 512.03 3.5e-10 30.59
964225 rs221771 7_62 0.000 512.22 3.6e-10 30.59
964192 rs221801 7_62 0.000 510.52 2.8e-10 30.57
964275 rs1617640 7_62 0.000 509.78 3.8e-10 30.54
964238 rs10277087 7_62 0.000 507.88 2.4e-10 30.53
964241 rs504141 7_62 0.000 507.68 2.4e-10 30.53
964244 rs1734909 7_62 0.000 507.63 2.4e-10 30.53
964274 rs576236 7_62 0.000 508.32 3.1e-10 30.53
964263 rs554055 7_62 0.000 507.61 2.7e-10 30.52
964265 rs56189543 7_62 0.000 507.70 2.7e-10 30.52
964284 rs551238 7_62 0.000 508.53 3.8e-10 30.52
964252 rs568733 7_62 0.000 506.23 2.1e-10 30.50
964260 rs1734908 7_62 0.000 505.89 2.0e-10 30.50
964272 rs35495521 7_62 0.000 507.22 2.5e-10 30.49
964277 rs507392 7_62 0.000 505.13 2.2e-10 30.48
964269 rs475339 7_62 0.000 504.48 1.8e-10 30.47
964243 rs1734910 7_62 0.000 500.71 9.6e-11 30.42
356891 rs55731938 6_89 0.000 327.12 0.0e+00 -30.01
964137 rs371423364 7_62 0.000 475.93 3.2e-12 29.42
964073 rs4548095 7_62 0.000 479.74 1.2e-13 29.39
964062 rs4729597 7_62 0.000 486.72 9.5e-13 29.38
964211 rs66481397 7_62 0.000 460.20 3.0e-12 29.38
964066 rs35142847 7_62 0.000 478.63 4.5e-14 29.20
329104 rs3218097 6_32 1.000 725.11 2.1e-03 -29.06
964136 rs567170062 7_62 0.000 394.97 1.9e-15 26.61
329089 rs10947992 6_32 0.000 613.84 2.0e-15 -26.48
329092 rs9381097 6_32 0.000 612.90 2.0e-15 -26.46
329082 rs9349202 6_32 0.000 607.16 1.7e-15 -26.29
964293 rs760985353 7_62 0.000 340.75 1.2e-16 26.27
964175 rs221795 7_62 0.000 342.43 1.2e-16 26.24Gene 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] 35if (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 arginine metabolic process (GO:0006525)
2 positive regulation of protein sumoylation (GO:0033235)
3 nucleoside metabolic process (GO:0009116)
4 pyrimidine nucleoside catabolic process (GO:0046135)
5 pyrimidine-containing compound metabolic process (GO:0072527)
6 nucleoside catabolic process (GO:0009164)
7 negative regulation of gene expression (GO:0010629)
8 pyrimidine nucleoside metabolic process (GO:0006213)
9 positive regulation of nuclear-transcribed mRNA catabolic process, deadenylation-dependent decay (GO:1900153)
10 regulation of nuclear-transcribed mRNA catabolic process, deadenylation-dependent decay (GO:1900151)
11 positive regulation of protein modification by small protein conjugation or removal (GO:1903322)
12 pyrimidine-containing compound catabolic process (GO:0072529)
13 regulation of protein sumoylation (GO:0033233)
14 regulation of nervous system process (GO:0031644)
15 positive regulation of histone acetylation (GO:0035066)
Overlap Adjusted.P.value Genes
1 2/10 0.01502710 NOS3;NAGS
2 2/10 0.01502710 SAE1;GNL3
3 2/10 0.01502710 NT5C3A;TYMP
4 2/11 0.01502710 NT5C3A;TYMP
5 2/12 0.01502710 NT5C3A;TYMP
6 2/12 0.01502710 NT5C3A;TYMP
7 5/322 0.01502710 RPS6KA4;TNRC6C;NDFIP1;PCIF1;ZFP36L2
8 2/15 0.01502710 NT5C3A;TYMP
9 2/15 0.01502710 TNRC6C;ZFP36L2
10 2/15 0.01502710 TNRC6C;ZFP36L2
11 3/77 0.01502710 NDFIP1;SAE1;GNL3
12 2/17 0.01668230 NT5C3A;TYMP
13 2/18 0.01730491 SAE1;GNL3
14 2/19 0.01793959 NOS3;TYMP
15 2/23 0.02466416 RPS6KA4;SMARCB1
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value Genes
1 arginine binding (GO:0034618) 2/8 0.008854511 NOS3;NAGSLSM4 gene(s) from the input list not found in DisGeNET CURATEDC12orf43 gene(s) from the input list not found in DisGeNET CURATEDOSER1 gene(s) from the input list not found in DisGeNET CURATEDMARCH2 gene(s) from the input list not found in DisGeNET CURATEDTNRC6C gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDSNTB2 gene(s) from the input list not found in DisGeNET CURATEDSAE1 gene(s) from the input list not found in DisGeNET CURATEDZSCAN23 gene(s) from the input list not found in DisGeNET CURATEDPCIF1 gene(s) from the input list not found in DisGeNET CURATEDC4orf36 gene(s) from the input list not found in DisGeNET CURATEDGNL3 gene(s) from the input list not found in DisGeNET CURATEDTEFM gene(s) from the input list not found in DisGeNET CURATEDNDFIP1 gene(s) from the input list not found in DisGeNET CURATEDHIST1H1B gene(s) from the input list not found in DisGeNET CURATEDWNT7B gene(s) from the input list not found in DisGeNET CURATEDZNF629 gene(s) from the input list not found in DisGeNET CURATED Description
58 Hypertension, Renovascular
109 Premature ventricular contractions
136 Chloracne
138 Hyperammonemia, type III
187 Atypical Teratoid Rhabdoid Tumor
192 Small Intestinal Neuroendocrine Neoplasm
202 Teratoid Tumor, Atypical
203 RHABDOID TUMOR PREDISPOSITION SYNDROME 1 (disorder)
206 Uridine 5-Prime Monophosphate Hydrolase Deficiency, Hemolytic Anemia due to
212 Multiple Endocrine Neoplasia, Type IV
FDR Ratio BgRatio
58 0.02748145 1/18 1/9703
109 0.02748145 1/18 1/9703
136 0.02748145 2/18 33/9703
138 0.02748145 1/18 1/9703
187 0.02748145 1/18 1/9703
192 0.02748145 1/18 1/9703
202 0.02748145 1/18 1/9703
203 0.02748145 1/18 1/9703
206 0.02748145 1/18 1/9703
212 0.02748145 1/18 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULLSensitivity, specificity and precision for silver standard genes
library("readxl")
known_annotations <- read_xlsx("data/summary_known_genes_annotations.xlsx", sheet="RBC count")
known_annotations <- unique(known_annotations$`Gene Symbol`)
unrelated_genes <- ctwas_gene_res$genename[!(ctwas_gene_res$genename %in% known_annotations)]
#number of genes in known annotations
print(length(known_annotations))[1] 71#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 37#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] 4.575849#number of ctwas genes
length(ctwas_genes)[1] 35#number of TWAS genes
length(twas_genes)[1] 458#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z
8707 RUFY1 5_108 0.862 19.69 4.8e-05 3.99
2531 CDKN1B 12_12 0.830 21.94 5.2e-05 -4.33
1713 ZNF629 16_24 0.825 53.94 1.3e-04 3.67
7826 SNTB2 16_37 0.939 26.52 7.1e-05 -4.52
8249 TEFM 17_18 0.863 24.65 6.1e-05 -4.56
6766 NAGS 17_26 0.840 22.72 5.4e-05 -4.39
920 TNRC6C 17_43 0.884 33.45 8.4e-05 4.56
1361 MARCH2 19_8 0.964 25.70 7.1e-05 -4.14#sensitivity / recall
sensitivity <- rep(NA,2)
names(sensitivity) <- c("ctwas", "TWAS")
sensitivity["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
sensitivity["TWAS"] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
sensitivity ctwas TWAS
0.01408451 0.05633803 #specificity
specificity <- rep(NA,2)
names(specificity) <- c("ctwas", "TWAS")
specificity["ctwas"] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
specificity["TWAS"] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
specificity ctwas TWAS
0.9967634 0.9567825 #precision / PPV
precision <- rep(NA,2)
names(precision) <- c("ctwas", "TWAS")
precision["ctwas"] <- sum(ctwas_genes %in% known_annotations)/length(ctwas_genes)
precision["TWAS"] <- sum(twas_genes %in% known_annotations)/length(twas_genes)
precision ctwas TWAS
0.028571429 0.008733624 #ROC curves
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res$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] 71#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 37#number of bystander genes
print(length(unrelated_genes))[1] 1909#number of bystander genes with imputed expression
print(sum(unrelated_genes %in% ctwas_gene_res$genename))[1] 901#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] 4.575849#number of ctwas genes
length(ctwas_genes)[1] 35#number of ctwas genes in known annotations or bystanders
sum(ctwas_genes %in% c(known_annotations, unrelated_genes))[1] 3#number of ctwas genes
length(twas_genes)[1] 458#number of TWAS genes
sum(twas_genes %in% c(known_annotations, unrelated_genes))[1] 71#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
0.02702703 0.10810811 #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
0.9977802 0.9256382 #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
0.33333333 0.05633803
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