SHBG (quantile) - Liver
wesleycrouse
2021-09-09
Last updated: 2021-09-09
Checks: 6 1
Knit directory: ctwas_applied/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| html | cbf7408 | wesleycrouse | 2021-09-08 | adding enrichment to reports |
| Rmd | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| html | 4970e3e | wesleycrouse | 2021-09-08 | updating reports |
| Rmd | 627a4e1 | wesleycrouse | 2021-09-07 | adding heritability |
| Rmd | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| html | dfd2b5f | wesleycrouse | 2021-09-07 | regenerating reports |
| Rmd | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| html | 61b53b3 | wesleycrouse | 2021-09-06 | updated PVE calculation |
| Rmd | 837dd01 | wesleycrouse | 2021-09-01 | adding additional fixedsigma report |
| Rmd | d0a5417 | wesleycrouse | 2021-08-30 | adding new reports to the index |
| Rmd | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 0922de7 | wesleycrouse | 2021-08-18 | updating all reports with locus plots |
| html | 1c62980 | wesleycrouse | 2021-08-11 | Updating reports |
| Rmd | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| html | 5981e80 | wesleycrouse | 2021-08-11 | Adding more reports |
| Rmd | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 05a98b7 | wesleycrouse | 2021-08-07 | adding additional results |
| html | 03e541c | wesleycrouse | 2021-07-29 | Cleaning up report generation |
| Rmd | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
| html | 276893d | wesleycrouse | 2021-07-29 | Updating reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait SHBG (quantile) using Liver gene weights.
The GWAS was conducted by the Neale Lab, and the biomarker traits we analyzed are discussed here. Summary statistics were obtained from IEU OpenGWAS using GWAS ID: ukb-d-30830_irnt. Results were obtained from from IEU rather than Neale Lab because they are in a standardard format (GWAS VCF). Note that 3 of the 34 biomarker traits were not available from IEU and were excluded from analysis.
The weights are mashr GTEx v8 models on Liver eQTL obtained from PredictDB. We performed a full harmonization of the variants, including recovering strand ambiguous variants. This procedure is discussed in a separate document. (TO-DO: add report that describes harmonization)
LD matrices were computed from a 10% subset of Neale lab subjects. Subjects were matched using the plate and well information from genotyping. We included only biallelic variants with MAF>0.01 in the original Neale Lab GWAS. (TO-DO: add more details [number of subjects, variants, etc])
Weight QC
TO-DO: add enhanced QC reporting (total number of weights, why each variant was missing for all genes)
qclist_all <- list()
qc_files <- paste0(results_dir, "/", list.files(results_dir, pattern="exprqc.Rd"))
for (i in 1:length(qc_files)){
load(qc_files[i])
chr <- unlist(strsplit(rev(unlist(strsplit(qc_files[i], "_")))[1], "[.]"))[1]
qclist_all[[chr]] <- cbind(do.call(rbind, lapply(qclist,unlist)), as.numeric(substring(chr,4)))
}
qclist_all <- data.frame(do.call(rbind, qclist_all))
colnames(qclist_all)[ncol(qclist_all)] <- "chr"
rm(qclist, wgtlist, z_gene_chr)
#number of imputed weights
nrow(qclist_all)[1] 10901#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1070 768 652 417 494 611 548 408 405 434 634 629 195 365 354
16 17 18 19 20 21 22
526 663 160 859 306 114 289 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8366205Load ctwas results
#load ctwas results
ctwas_res <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.susieIrss.txt"))
#make unique identifier for regions
ctwas_res$region_tag <- paste(ctwas_res$region_tag1, ctwas_res$region_tag2, sep="_")
#compute PVE for each gene/SNP
ctwas_res$PVE = ctwas_res$susie_pip*ctwas_res$mu2/sample_size #check PVE calculation
#separate gene and SNP results
ctwas_gene_res <- ctwas_res[ctwas_res$type == "gene", ]
ctwas_gene_res <- data.frame(ctwas_gene_res)
ctwas_snp_res <- ctwas_res[ctwas_res$type == "SNP", ]
ctwas_snp_res <- data.frame(ctwas_snp_res)
#add gene information to results
sqlite <- RSQLite::dbDriver("SQLite")
db = RSQLite::dbConnect(sqlite, paste0("/project2/compbio/predictdb/mashr_models/mashr_", weight, ".db"))
query <- function(...) RSQLite::dbGetQuery(db, ...)
gene_info <- query("select gene, genename from extra")
gene_info <- query("select gene, genename, gene_type from extra")
RSQLite::dbDisconnect(db)
ctwas_gene_res <- cbind(ctwas_gene_res, gene_info[sapply(ctwas_gene_res$id, match, gene_info$gene), c("genename", "gene_type")])
#add z score to results
load(paste0(results_dir, "/", analysis_id, "_expr_z_gene.Rd"))
ctwas_gene_res$z <- z_gene[ctwas_gene_res$id,]$z
#load(paste0(results_dir, "/", analysis_id, "_expr_z_snp.Rd")) #for new version, stored after harmonization
z_snp <- readRDS(paste0(results_dir, "/", trait_id, ".RDS")) #for old version, unharmonized
z_snp <- z_snp[z_snp$id %in% ctwas_snp_res$id,] #subset snps to those included in analysis, note some are duplicated, need to match which allele was used
ctwas_snp_res$z <- z_snp$z[match(ctwas_snp_res$id, z_snp$id)] #for duplicated snps, this arbitrarily uses the first allele
ctwas_snp_res$z_flag <- as.numeric(ctwas_snp_res$id %in% z_snp$id[duplicated(z_snp$id)]) #mark the unclear z scores, flag=1
#formatting and rounding for tables
ctwas_gene_res$z <- round(ctwas_gene_res$z,2)
ctwas_snp_res$z <- round(ctwas_snp_res$z,2)
ctwas_gene_res$susie_pip <- round(ctwas_gene_res$susie_pip,3)
ctwas_snp_res$susie_pip <- round(ctwas_snp_res$susie_pip,3)
ctwas_gene_res$mu2 <- round(ctwas_gene_res$mu2,2)
ctwas_snp_res$mu2 <- round(ctwas_snp_res$mu2,2)
ctwas_gene_res$PVE <- signif(ctwas_gene_res$PVE, 2)
ctwas_snp_res$PVE <- signif(ctwas_snp_res$PVE, 2)
#merge gene and snp results with added information
ctwas_gene_res$z_flag=NA
ctwas_snp_res$genename=NA
ctwas_snp_res$gene_type=NA
ctwas_res <- rbind(ctwas_gene_res,
ctwas_snp_res[,colnames(ctwas_gene_res)])
#store columns to report
report_cols <- colnames(ctwas_gene_res)[!(colnames(ctwas_gene_res) %in% c("type", "region_tag1", "region_tag2", "cs_index", "gene_type", "z_flag", "id", "chrom", "pos"))]
first_cols <- c("genename", "region_tag")
report_cols <- c(first_cols, report_cols[!(report_cols %in% first_cols)])
report_cols_snps <- c("id", report_cols[-1])
#get number of SNPs from s1 results; adjust for thin
ctwas_res_s1 <- data.table::fread(paste0(results_dir, "/", analysis_id, "_ctwas.s1.susieIrss.txt"))
n_snps <- sum(ctwas_res_s1$type=="SNP")/thin
rm(ctwas_res_s1)Check convergence of parameters
library(ggplot2)
library(cowplot)
********************************************************Note: As of version 1.0.0, cowplot does not change the default ggplot2 theme anymore. To recover the previous behavior, execute:
theme_set(theme_cowplot())********************************************************load(paste0(results_dir, "/", analysis_id, "_ctwas.s2.susieIrssres.Rd"))
df <- data.frame(niter = rep(1:ncol(group_prior_rec), 2),
value = c(group_prior_rec[1,], group_prior_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_rec)))
df$group <- as.factor(df$group)
df$value[df$group=="SNP"] <- df$value[df$group=="SNP"]*thin #adjust parameter to account for thin argument
p_pi <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(pi)) +
ggtitle("Prior mean") +
theme_cowplot()
df <- data.frame(niter = rep(1:ncol(group_prior_var_rec), 2),
value = c(group_prior_var_rec[1,], group_prior_var_rec[2,]),
group = rep(c("Gene", "SNP"), each = ncol(group_prior_var_rec)))
df$group <- as.factor(df$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0233427958 0.0001676304 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
23.01551 31.86722 #report sample size
print(sample_size)[1] 312215#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01875798 0.14880895 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05162072 3.80924281Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$susie_pip, xlim=c(0,1), main="Distribution of Gene PIPs")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#genes with PIP>0.8 or 20 highest PIPs
head(ctwas_gene_res[order(-ctwas_gene_res$susie_pip),report_cols], max(sum(ctwas_gene_res$susie_pip>0.8), 20)) genename region_tag susie_pip mu2 PVE z
1144 ASAP3 1_16 1.000 44.36 1.4e-04 7.83
1114 SRRT 7_62 0.998 129.45 4.1e-04 11.98
10856 ZNF845 19_36 0.996 36.27 1.2e-04 5.89
10090 SULT1A1 16_23 0.993 23.75 7.6e-05 -3.50
9017 ERN1 17_37 0.992 26.99 8.6e-05 -4.84
1954 AES 19_4 0.991 63.06 2.0e-04 -8.00
8378 ZNF217 20_31 0.991 27.73 8.8e-05 4.93
3133 DHDDS 1_18 0.989 47.15 1.5e-04 3.85
2678 TFEB 6_32 0.988 124.69 3.9e-04 11.06
2173 TMEM176B 7_93 0.987 94.90 3.0e-04 -8.91
3273 NRDE2 14_45 0.987 22.93 7.2e-05 4.51
11889 RP11-327J17.2 15_46 0.985 157.32 5.0e-04 -10.46
5415 SYTL1 1_19 0.982 121.02 3.8e-04 11.21
3212 CCND2 12_4 0.981 196.86 6.2e-04 14.46
8428 PDZD3 11_71 0.978 31.94 1.0e-04 2.29
666 COASY 17_25 0.978 42.99 1.3e-04 -6.27
6509 NTAN1 16_15 0.977 66.59 2.1e-04 -8.87
12621 RP11-714M23.2 18_30 0.976 29.30 9.2e-05 6.58
9457 CBX6 22_15 0.975 22.71 7.1e-05 -3.58
3774 ZNF436 1_16 0.974 30.06 9.4e-05 -6.86
12074 RP11-131K5.2 17_12 0.974 76.99 2.4e-04 -8.86
10303 UGT2B17 4_48 0.973 78.09 2.4e-04 11.80
2204 AKNA 9_59 0.966 60.08 1.9e-04 -7.78
1946 STX10 19_10 0.959 23.38 7.2e-05 4.53
2261 GBF1 10_65 0.958 40.64 1.2e-04 6.22
9102 ZFPM1 16_53 0.957 34.72 1.1e-04 5.59
8502 RELA 11_36 0.951 28.34 8.6e-05 4.81
8238 CHCHD7 8_44 0.950 22.24 6.8e-05 -4.60
6100 ALLC 2_2 0.949 25.25 7.7e-05 4.74
4239 TRIM5 11_4 0.947 36.73 1.1e-04 -5.03
5632 CAND2 3_9 0.939 22.63 6.8e-05 4.69
2731 PCDHB15 5_83 0.938 18.80 5.6e-05 -3.86
5161 NAA30 14_26 0.938 19.33 5.8e-05 3.99
9855 PALM3 19_11 0.938 40.14 1.2e-04 6.12
4736 HLX 1_112 0.937 54.54 1.6e-04 -8.08
5400 EPHA2 1_11 0.931 98.22 2.9e-04 -10.17
8803 DLEU1 13_21 0.923 29.45 8.7e-05 -6.00
4608 REPS1 6_92 0.910 28.06 8.2e-05 4.95
8716 ARHGAP1 11_28 0.905 22.55 6.5e-05 -4.34
4271 H3F3B 17_42 0.899 45.58 1.3e-04 7.09
6592 MRAS 3_85 0.893 20.57 5.9e-05 4.01
3861 UBR4 1_13 0.892 23.83 6.8e-05 -4.54
10557 LINC01270 20_30 0.889 23.73 6.8e-05 4.35
7671 NAV2 11_14 0.888 18.42 5.2e-05 -3.77
10731 EXOC3L4 14_54 0.886 20.44 5.8e-05 -4.09
9635 TLCD2 17_2 0.881 201.10 5.7e-04 7.05
49 LIG3 17_21 0.876 19.92 5.6e-05 3.91
6526 TMED6 16_37 0.871 20.85 5.8e-05 -4.41
5639 ARL6IP5 3_46 0.868 21.33 5.9e-05 4.22
6792 ADAR 1_75 0.866 109.13 3.0e-04 -10.70
5089 SCAF11 12_29 0.866 20.32 5.6e-05 4.41
5224 EFL1 15_38 0.858 26.22 7.2e-05 -5.18
12704 EXOC3L2 19_32 0.851 37.78 1.0e-04 -6.02
7965 ADAM9 8_34 0.850 19.21 5.2e-05 3.73
6481 UBE2L6 11_32 0.847 19.84 5.4e-05 3.54
6494 PHKG2 16_24 0.847 34.45 9.3e-05 4.88
5358 CCDC97 19_28 0.847 20.03 5.4e-05 4.02
5038 SCARB2 4_52 0.846 62.40 1.7e-04 9.72
990 KIAA0141 5_84 0.846 20.03 5.4e-05 3.90
10004 SLC35E2B 1_1 0.838 21.07 5.7e-05 -4.07
3800 NR1D1 17_23 0.825 43.49 1.1e-04 6.78
6223 GPR180 13_47 0.819 61.01 1.6e-04 7.86
4925 IFT172 2_16 0.812 54.69 1.4e-04 -9.79
7825 SRR 17_3 0.804 22.40 5.8e-05 4.35
1116 ATXN7L3 17_26 0.804 18.06 4.6e-05 2.57
3539 ATF1 12_31 0.801 47.22 1.2e-04 -8.67Genes 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 |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#genes with 20 largest effect sizes
head(ctwas_gene_res[order(-ctwas_gene_res$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
5389 RPS11 19_34 0 137431.25 0.0e+00 -6.80
1227 FLT3LG 19_34 0 118630.64 0.0e+00 6.13
5393 RCN3 19_34 0 44814.90 0.0e+00 8.52
1931 FCGRT 19_34 0 40894.36 0.0e+00 7.29
3804 PRRG2 19_34 0 19977.64 0.0e+00 4.54
11357 TUSC8 13_18 0 18736.12 0.0e+00 -4.36
3805 SCAF1 19_34 0 13594.89 0.0e+00 2.92
3803 PRMT1 19_34 0 13560.41 0.0e+00 4.56
3270 ALDH6A1 14_34 0 13313.06 5.1e-09 -5.00
4556 TMEM60 7_49 0 13227.64 0.0e+00 -3.87
3802 IRF3 19_34 0 13212.07 0.0e+00 2.85
11199 LINC00271 6_89 0 11567.86 0.0e+00 2.05
1940 SLC17A7 19_34 0 9579.39 0.0e+00 0.57
10602 RNF5 6_26 0 6492.48 3.0e-08 3.09
10742 LIN52 14_34 0 5965.34 1.6e-10 -3.67
11007 PPT2 6_26 0 5615.15 1.4e-07 -2.72
10848 CLIC1 6_26 0 4897.91 2.5e-08 -0.76
1932 PIH1D1 19_34 0 4146.87 0.0e+00 -1.16
11541 C4A 6_26 0 4062.21 1.8e-06 0.65
4604 AHI1 6_89 0 3995.77 0.0e+00 0.70Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
5799 SLC22A3 6_104 0.479 2403.08 0.00370 14.15
3212 CCND2 12_4 0.981 196.86 0.00062 14.46
9635 TLCD2 17_2 0.881 201.10 0.00057 7.05
10495 PRMT6 1_66 0.526 307.24 0.00052 -18.10
11889 RP11-327J17.2 15_46 0.985 157.32 0.00050 -10.46
10712 ZBTB10 8_57 0.783 181.44 0.00046 14.64
1114 SRRT 7_62 0.998 129.45 0.00041 11.98
2678 TFEB 6_32 0.988 124.69 0.00039 11.06
5415 SYTL1 1_19 0.982 121.02 0.00038 11.21
6792 ADAR 1_75 0.866 109.13 0.00030 -10.70
2173 TMEM176B 7_93 0.987 94.90 0.00030 -8.91
5400 EPHA2 1_11 0.931 98.22 0.00029 -10.17
10303 UGT2B17 4_48 0.973 78.09 0.00024 11.80
12074 RP11-131K5.2 17_12 0.974 76.99 0.00024 -8.86
6778 PKN3 9_66 0.740 91.17 0.00022 -9.80
6509 NTAN1 16_15 0.977 66.59 0.00021 -8.87
1058 GCKR 2_16 0.494 126.01 0.00020 14.34
10987 C2orf16 2_16 0.494 126.01 0.00020 14.34
1954 AES 19_4 0.991 63.06 0.00020 -8.00
2204 AKNA 9_59 0.966 60.08 0.00019 -7.78Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
6880 TNFSF13 17_7 0.000 3120.75 0.0e+00 76.93
3991 ATP1B2 17_7 0.000 3037.00 0.0e+00 -68.57
11399 TNFSF12 17_7 0.000 2123.88 0.0e+00 47.66
5311 WRAP53 17_7 0.000 1906.91 0.0e+00 -43.17
6883 EIF4A1 17_7 0.000 2074.67 0.0e+00 -40.42
9477 DNAH2 17_7 0.000 1018.23 0.0e+00 37.37
6881 SENP3 17_7 0.000 775.17 0.0e+00 23.93
7355 BRI3 7_60 0.033 324.25 3.4e-05 -23.22
9851 PLSCR3 17_6 0.000 299.35 0.0e+00 19.38
2887 NRBP1 2_16 0.011 313.76 1.1e-05 -18.63
10495 PRMT6 1_66 0.526 307.24 5.2e-04 -18.10
5313 SAT2 17_7 0.000 1607.07 0.0e+00 -16.77
9229 TMEM102 17_7 0.000 142.62 0.0e+00 -16.57
9052 RMI1 9_41 0.068 226.42 4.9e-05 -15.79
773 ACAP1 17_6 0.000 263.05 0.0e+00 -15.68
8284 RBKS 2_16 0.036 179.20 2.1e-05 -15.40
8651 MSL2 3_84 0.031 229.62 2.3e-05 15.34
8389 THOP1 19_3 0.064 209.30 4.3e-05 14.92
7656 CATSPER2 15_16 0.112 230.64 8.3e-05 -14.91
5414 GPN2 1_18 0.009 172.71 4.9e-06 -14.65Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$z[order(ctwas_gene_res$z)]
exp_z <- qnorm((1:nrow(ctwas_gene_res))/nrow(ctwas_gene_res))
plot(exp_z, obs_z, xlab="Expected z", ylab="Observed z", main="Gene z score Q-Q plot")
abline(a=0,b=1)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#plot z score vs PIP
plot(abs(ctwas_gene_res$z), ctwas_gene_res$susie_pip, xlab="abs(z)", ylab="PIP")
abline(v=sig_thresh, col="red", lty=2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.03504266#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
6880 TNFSF13 17_7 0.000 3120.75 0.0e+00 76.93
3991 ATP1B2 17_7 0.000 3037.00 0.0e+00 -68.57
11399 TNFSF12 17_7 0.000 2123.88 0.0e+00 47.66
5311 WRAP53 17_7 0.000 1906.91 0.0e+00 -43.17
6883 EIF4A1 17_7 0.000 2074.67 0.0e+00 -40.42
9477 DNAH2 17_7 0.000 1018.23 0.0e+00 37.37
6881 SENP3 17_7 0.000 775.17 0.0e+00 23.93
7355 BRI3 7_60 0.033 324.25 3.4e-05 -23.22
9851 PLSCR3 17_6 0.000 299.35 0.0e+00 19.38
2887 NRBP1 2_16 0.011 313.76 1.1e-05 -18.63
10495 PRMT6 1_66 0.526 307.24 5.2e-04 -18.10
5313 SAT2 17_7 0.000 1607.07 0.0e+00 -16.77
9229 TMEM102 17_7 0.000 142.62 0.0e+00 -16.57
9052 RMI1 9_41 0.068 226.42 4.9e-05 -15.79
773 ACAP1 17_6 0.000 263.05 0.0e+00 -15.68
8284 RBKS 2_16 0.036 179.20 2.1e-05 -15.40
8651 MSL2 3_84 0.031 229.62 2.3e-05 15.34
8389 THOP1 19_3 0.064 209.30 4.3e-05 14.92
7656 CATSPER2 15_16 0.112 230.64 8.3e-05 -14.91
5414 GPN2 1_18 0.009 172.71 4.9e-06 -14.65Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 17_7"
genename region_tag susie_pip mu2 PVE z
9229 TMEM102 17_7 0 142.62 0 -16.57
6882 FGF11 17_7 0 113.48 0 2.88
11885 SLC35G6 17_7 0 878.91 0 12.59
11399 TNFSF12 17_7 0 2123.88 0 47.66
6880 TNFSF13 17_7 0 3120.75 0 76.93
6881 SENP3 17_7 0 775.17 0 23.93
6883 EIF4A1 17_7 0 2074.67 0 -40.42
5313 SAT2 17_7 0 1607.07 0 -16.77
3991 ATP1B2 17_7 0 3037.00 0 -68.57
5311 WRAP53 17_7 0 1906.91 0 -43.17
9477 DNAH2 17_7 0 1018.23 0 37.37
7853 TMEM88 17_7 0 37.51 0 5.89
9115 AC025335.1 17_7 0 42.41 0 5.02
8143 KCNAB3 17_7 0 89.20 0 -3.14
8142 CNTROB 17_7 0 67.21 0 -1.44
10982 VAMP2 17_7 0 66.96 0 -2.88
9063 TMEM107 17_7 0 120.38 0 -4.38
9059 AURKB 17_7 0 107.05 0 -4.87
9053 CTC1 17_7 0 65.77 0 3.94
9046 PFAS 17_7 0 26.18 0 3.29
12191 RP11-849F2.9 17_7 0 34.30 0 0.15
3703 SLC25A35 17_7 0 70.52 0 3.11
9538 KRBA2 17_7 0 11.59 0 -2.31
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 7_60"
genename region_tag susie_pip mu2 PVE z
79 TAC1 7_60 0.023 9.70 7.0e-07 2.70
725 ASNS 7_60 0.023 5.58 4.1e-07 0.89
7356 LMTK2 7_60 0.025 8.19 6.6e-07 1.63
9166 BHLHA15 7_60 0.026 6.84 5.8e-07 0.93
7355 BRI3 7_60 0.033 324.25 3.4e-05 -23.22
86 BAIAP2L1 7_60 0.027 69.56 6.0e-06 -11.74
2136 NPTX2 7_60 0.023 5.23 3.9e-07 -0.47
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 17_6"
genename region_tag susie_pip mu2 PVE z
10506 KIAA0753 17_6 0 5.60 0.0e+00 -0.40
3990 TXNDC17 17_6 0 55.86 0.0e+00 -3.38
11821 C17orf100 17_6 0 5.41 0.0e+00 0.56
12016 CTC-281F24.1 17_6 0 21.81 0.0e+00 2.03
5309 SLC13A5 17_6 0 6.62 0.0e+00 -0.89
4278 XAF1 17_6 0 10.01 0.0e+00 0.55
8894 FBXO39 17_6 0 7.80 0.0e+00 -0.54
2357 ALOX12 17_6 0 35.71 0.0e+00 2.22
12018 RP11-589P10.5 17_6 0 20.07 0.0e+00 1.76
10974 RNASEK 17_6 0 35.33 0.0e+00 2.15
6879 BCL6B 17_6 0 38.45 0.0e+00 2.36
8630 SLC16A13 17_6 0 7.26 0.0e+00 1.20
4276 CLEC10A 17_6 0 34.85 0.0e+00 1.00
4279 DLG4 17_6 0 22.17 0.0e+00 1.76
42 DVL2 17_6 0 23.81 0.0e+00 0.42
8173 ELP5 17_6 0 74.11 0.0e+00 8.14
8775 CTDNEP1 17_6 0 80.90 0.0e+00 6.80
9272 CLDN7 17_6 0 109.96 0.0e+00 -1.41
75 YBX2 17_6 0 13.50 0.0e+00 -3.55
9270 SLC2A4 17_6 0 13.50 0.0e+00 -3.55
4275 EIF5A 17_6 0 60.16 0.0e+00 3.18
4277 GPS2 17_6 0 21.00 0.0e+00 2.02
10937 NEURL4 17_6 0 122.02 0.0e+00 -8.09
773 ACAP1 17_6 0 263.05 0.0e+00 -15.68
8627 TNK1 17_6 0 157.50 0.0e+00 4.55
9851 PLSCR3 17_6 0 299.35 0.0e+00 19.38
10735 TMEM256 17_6 0 26.05 0.0e+00 -1.98
8136 NLGN2 17_6 0 208.56 4.5e-14 -9.76
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_16"
genename region_tag susie_pip mu2 PVE z
2881 CENPA 2_16 0.014 10.69 4.8e-07 -2.68
11149 OST4 2_16 0.154 29.97 1.5e-05 -4.08
4939 EMILIN1 2_16 0.011 25.21 9.2e-07 6.68
4927 KHK 2_16 0.017 10.74 6.0e-07 -2.86
4935 PREB 2_16 0.016 21.43 1.1e-06 -4.87
4941 ATRAID 2_16 0.021 131.28 8.9e-06 12.02
4936 SLC5A6 2_16 0.022 134.37 9.5e-06 -12.12
1060 CAD 2_16 0.012 75.23 2.8e-06 -7.43
2885 SLC30A3 2_16 0.066 56.63 1.2e-05 -6.31
7169 UCN 2_16 0.016 36.82 1.8e-06 -8.00
2891 SNX17 2_16 0.019 182.96 1.1e-05 13.27
7170 ZNF513 2_16 0.012 59.87 2.3e-06 -6.60
2887 NRBP1 2_16 0.011 313.76 1.1e-05 -18.63
4925 IFT172 2_16 0.812 54.69 1.4e-04 -9.79
1058 GCKR 2_16 0.494 126.01 2.0e-04 14.34
10987 C2orf16 2_16 0.494 126.01 2.0e-04 14.34
10407 GPN1 2_16 0.014 85.01 3.8e-06 -7.72
8847 CCDC121 2_16 0.013 15.32 6.2e-07 3.11
6575 BRE 2_16 0.013 35.27 1.5e-06 7.87
8284 RBKS 2_16 0.036 179.20 2.1e-05 -15.40
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_66"
genename region_tag susie_pip mu2 PVE z
10495 PRMT6 1_66 0.526 307.24 0.00052 -18.1
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
SNPs with highest PIPs
#snps with PIP>0.8 or 20 highest PIPs
head(ctwas_snp_res[order(-ctwas_snp_res$susie_pip),report_cols_snps],
max(sum(ctwas_snp_res$susie_pip>0.8), 20)) id region_tag susie_pip mu2 PVE z
50432 rs1223802 1_108 1.000 62.51 2.0e-04 -10.01
141327 rs11719769 3_18 1.000 90.80 2.9e-04 -8.77
148350 rs113569731 3_33 1.000 46.20 1.5e-04 -7.57
149053 rs62259692 3_36 1.000 47.49 1.5e-04 -6.89
194198 rs114524202 4_4 1.000 67.92 2.2e-04 11.06
194214 rs3748034 4_4 1.000 92.67 3.0e-04 13.72
194215 rs3752442 4_4 1.000 100.52 3.2e-04 -15.97
194229 rs36205397 4_4 1.000 101.63 3.3e-04 17.79
221078 rs28529445 4_58 1.000 86.14 2.8e-04 -9.95
221265 rs71633359 4_59 1.000 195.78 6.3e-04 -16.88
227970 rs17039766 4_72 1.000 45.76 1.5e-04 6.65
273660 rs58477254 5_33 1.000 50.69 1.6e-04 -7.31
332488 rs112354376 6_46 1.000 1674.96 5.4e-03 -3.29
332489 rs208453 6_46 1.000 1664.12 5.3e-03 -0.53
354432 rs199804242 6_89 1.000 53751.81 1.7e-01 -3.57
362060 rs60425481 6_104 1.000 14665.15 4.7e-02 10.63
392966 rs761767938 7_49 1.000 15156.68 4.9e-02 4.57
392974 rs1544459 7_49 1.000 15257.92 4.9e-02 4.45
394370 rs3839804 7_51 1.000 48.28 1.5e-04 -6.55
407725 rs125124 7_80 1.000 56.02 1.8e-04 7.98
433640 rs12543287 8_37 1.000 149.18 4.8e-04 8.71
436999 rs4738679 8_45 1.000 83.95 2.7e-04 9.38
443715 rs382796 8_57 1.000 94.49 3.0e-04 13.00
499261 rs1886296 9_73 1.000 67.42 2.2e-04 7.78
524725 rs2186235 10_51 1.000 117.64 3.8e-04 -11.10
559377 rs12804411 11_38 1.000 129.49 4.1e-04 12.03
587247 rs66720652 12_15 1.000 105.40 3.4e-04 9.08
596678 rs7397189 12_36 1.000 139.68 4.5e-04 12.07
607417 rs61935502 12_55 1.000 64.34 2.1e-04 -7.76
609830 rs375115050 12_59 1.000 109.51 3.5e-04 -11.08
613205 rs75622376 12_67 1.000 146.82 4.7e-04 12.42
632245 rs566812111 13_25 1.000 6188.72 2.0e-02 -2.84
632249 rs12430288 13_25 1.000 6236.94 2.0e-02 -2.67
660511 rs72681869 14_20 1.000 88.33 2.8e-04 9.60
667268 rs13379043 14_34 1.000 73.23 2.3e-04 7.25
667399 rs369107859 14_34 1.000 19214.82 6.2e-02 -0.33
673272 rs11439803 14_48 1.000 511.68 1.6e-03 2.33
673279 rs1243165 14_48 1.000 569.33 1.8e-03 6.12
675537 rs35007880 14_52 1.000 66.17 2.1e-04 -8.22
685453 rs4363819 15_21 1.000 50.86 1.6e-04 -3.50
685472 rs2414183 15_22 1.000 217.48 7.0e-04 -13.29
685714 rs72743115 15_22 1.000 134.65 4.3e-04 -11.63
720781 rs889639 16_48 1.000 42.58 1.4e-04 6.61
720799 rs2255451 16_48 1.000 75.26 2.4e-04 8.90
725019 rs35985803 17_6 1.000 326.42 1.0e-03 -19.38
725034 rs7223885 17_6 1.000 403.47 1.3e-03 -22.29
725035 rs968580 17_6 1.000 245.12 7.9e-04 -13.61
725036 rs73233955 17_6 1.000 284.12 9.1e-04 -11.43
725059 rs116560331 17_7 1.000 1912.32 6.1e-03 -64.18
725085 rs11078694 17_7 1.000 3238.52 1.0e-02 -82.59
725101 rs62059839 17_7 1.000 4847.89 1.6e-02 88.82
725112 rs72829446 17_7 1.000 1015.62 3.3e-03 30.00
730050 rs56032910 17_19 1.000 741.77 2.4e-03 -2.90
730051 rs3744618 17_19 1.000 612.95 2.0e-03 -2.45
732806 rs1814451 17_29 1.000 43.78 1.4e-04 3.96
732810 rs1000787 17_29 1.000 76.79 2.5e-04 -6.38
736695 rs1801689 17_38 1.000 134.55 4.3e-04 -11.80
768162 rs10401485 19_7 1.000 113.27 3.6e-04 -10.90
770730 rs141356897 19_14 1.000 264.86 8.5e-04 16.44
775705 rs889140 19_23 1.000 53.01 1.7e-04 5.95
776298 rs4806075 19_24 1.000 145.40 4.7e-04 -4.36
776968 rs140965448 19_26 1.000 41.63 1.3e-04 -5.90
790553 rs547713677 20_20 1.000 57.54 1.8e-04 3.60
793312 rs3212201 20_28 1.000 188.67 6.0e-04 14.22
861340 rs140584594 1_67 1.000 99.78 3.2e-04 -10.15
882660 rs1260326 2_16 1.000 705.60 2.3e-03 30.11
934745 rs139439683 5_106 1.000 5535.82 1.8e-02 -2.67
934880 rs13172121 5_106 1.000 5539.97 1.8e-02 -2.57
949273 rs9279507 6_26 1.000 10380.12 3.3e-02 -2.07
975840 rs17256042 7_93 1.000 58.39 1.9e-04 -2.68
998633 rs72766607 9_70 1.000 47.93 1.5e-04 -7.01
1006028 rs10995596 10_42 1.000 99247.57 3.2e-01 10.99
1006046 rs773090945 10_42 1.000 99389.15 3.2e-01 11.08
1028485 rs11601507 11_4 1.000 57.60 1.8e-04 7.02
1083354 rs78750369 13_18 1.000 26018.73 8.3e-02 3.38
1083359 rs7320922 13_18 1.000 25931.57 8.3e-02 3.38
1091118 rs36179992 13_21 1.000 60.90 2.0e-04 7.08
1108289 rs11621792 14_3 1.000 241.34 7.7e-04 -15.30
1142647 rs56332871 15_46 1.000 687.40 2.2e-03 25.92
1178612 rs11078597 17_2 1.000 255.76 8.2e-04 13.37
1210825 rs17637241 17_28 1.000 243.86 7.8e-04 16.29
1260449 rs61371437 19_34 1.000 144284.05 4.6e-01 6.86
1260458 rs113176985 19_34 1.000 144531.16 4.6e-01 7.00
1260461 rs374141296 19_34 1.000 145307.45 4.7e-01 6.36
82524 rs12466865 2_42 0.999 77.99 2.5e-04 -11.93
129106 rs11682084 2_135 0.999 34.25 1.1e-04 -5.80
351722 rs58321169 6_84 0.999 39.84 1.3e-04 -6.49
545844 rs34623292 11_10 0.999 39.69 1.3e-04 -7.89
730037 rs62062359 17_19 0.999 81.70 2.6e-04 -3.62
771021 rs11668601 19_14 0.999 93.67 3.0e-04 -9.58
944275 rs1050556 6_25 0.999 82.21 2.6e-04 -8.33
1230277 rs60018147 19_4 0.999 40.54 1.3e-04 6.21
94205 rs3789066 2_66 0.998 46.60 1.5e-04 -6.67
399009 rs4268041 7_60 0.998 342.27 1.1e-03 23.71
443782 rs2400362 8_57 0.998 81.89 2.6e-04 11.26
520057 rs4746440 10_43 0.998 31.69 1.0e-04 5.27
685446 rs8032322 15_21 0.998 51.97 1.7e-04 -4.17
221061 rs116755775 4_58 0.997 34.09 1.1e-04 6.48
392970 rs11972122 7_49 0.997 13951.88 4.5e-02 3.99
399355 rs138124694 7_61 0.997 47.13 1.5e-04 7.46
737637 rs8070232 17_39 0.997 59.15 1.9e-04 -1.02
587454 rs56020380 12_16 0.996 75.30 2.4e-04 -8.11
609810 rs11837065 12_59 0.996 33.79 1.1e-04 -6.16
639943 rs7323648 13_40 0.996 31.27 1.0e-04 5.28
676363 rs4983559 14_55 0.996 49.04 1.6e-04 -7.16
743097 rs117823974 18_3 0.996 30.67 9.8e-05 -5.10
1083355 rs1555718 13_18 0.995 25905.97 8.3e-02 3.36
1132583 rs12591786 15_27 0.995 43.46 1.4e-04 6.42
443646 rs11994858 8_57 0.994 91.59 2.9e-04 10.84
541977 rs2239681 11_2 0.994 48.20 1.5e-04 7.93
558081 rs1047739 11_34 0.994 42.66 1.4e-04 6.18
463923 rs1016565 9_1 0.993 31.31 1.0e-04 -5.39
466157 rs1616572 9_7 0.993 33.04 1.1e-04 -5.83
587322 rs10841577 12_15 0.993 32.23 1.0e-04 -4.82
587615 rs4149081 12_16 0.992 301.35 9.6e-04 -18.14
685708 rs8040040 15_22 0.992 65.22 2.1e-04 -7.71
869765 rs2642438 1_112 0.992 75.65 2.4e-04 10.16
218580 rs6838435 4_51 0.991 45.87 1.5e-04 -6.60
780131 rs11084395 19_38 0.991 29.87 9.5e-05 4.96
325485 rs1005230 6_33 0.990 29.52 9.4e-05 -5.10
608289 rs55692966 12_56 0.990 30.53 9.7e-05 5.25
45152 rs10801583 1_98 0.989 40.49 1.3e-04 -8.37
498398 rs34755157 9_71 0.987 30.04 9.5e-05 5.10
588345 rs78444263 12_18 0.987 138.90 4.4e-04 -11.99
53498 rs3845509 1_115 0.986 32.40 1.0e-04 5.24
725387 rs1465650 17_8 0.986 27.62 8.7e-05 -4.73
141332 rs6803476 3_18 0.985 30.80 9.7e-05 -3.70
407734 rs12533527 7_80 0.979 27.39 8.6e-05 -5.03
485183 rs78648697 9_45 0.979 28.21 8.8e-05 -4.98
13979 rs79574044 1_38 0.977 27.03 8.5e-05 -5.13
383037 rs150560724 7_32 0.976 29.96 9.4e-05 -5.04
894642 rs2249407 3_9 0.972 51.42 1.6e-04 -5.13
75100 rs34636718 2_26 0.971 54.40 1.7e-04 7.24
286641 rs114964731 5_60 0.971 29.81 9.3e-05 -5.22
483285 rs796003 9_41 0.971 287.33 8.9e-04 17.80
661722 rs12881212 14_23 0.970 26.94 8.4e-05 -4.76
221209 rs13120301 4_59 0.968 81.29 2.5e-04 -14.39
730052 rs62063894 17_19 0.968 99.43 3.1e-04 -4.37
238804 rs1579737 4_94 0.964 30.77 9.5e-05 5.36
776297 rs1688031 19_24 0.964 103.55 3.2e-04 11.19
172069 rs6794445 3_80 0.960 26.40 8.1e-05 4.56
241501 rs72727873 4_98 0.960 30.55 9.4e-05 -5.19
273514 rs173964 5_33 0.958 205.66 6.3e-04 -12.13
184697 rs149368105 3_105 0.956 47.51 1.5e-04 -7.98
354448 rs6923513 6_89 0.956 53785.74 1.6e-01 -3.26
83065 rs62143990 2_43 0.955 30.39 9.3e-05 5.32
232737 rs68018489 4_80 0.954 27.74 8.5e-05 -5.03
770613 rs138466679 19_14 0.953 34.43 1.1e-04 5.73
2634 rs10746487 1_6 0.952 25.42 7.8e-05 4.61
1115581 rs1005421 14_45 0.952 45.33 1.4e-04 6.54
649483 rs750598 13_59 0.951 28.71 8.7e-05 5.12
240145 rs34690971 4_96 0.949 86.25 2.6e-04 -9.47
94281 rs2166862 2_66 0.948 31.70 9.6e-05 -5.35
50385 rs340835 1_108 0.947 69.16 2.1e-04 -7.18
725100 rs112885647 17_7 0.946 1870.19 5.7e-03 -48.76
775699 rs16968072 19_23 0.945 29.45 8.9e-05 -3.03
313207 rs55792466 6_7 0.944 39.40 1.2e-04 6.88
465348 rs10758593 9_4 0.943 27.11 8.2e-05 -4.98
600719 rs2137537 12_44 0.940 24.48 7.4e-05 -4.42
766280 rs4807612 19_2 0.940 42.37 1.3e-04 6.30
794036 rs6066141 20_29 0.940 32.15 9.7e-05 5.65
667396 rs7156583 14_34 0.936 19231.91 5.8e-02 -4.20
673299 rs72692809 14_48 0.935 50.51 1.5e-04 7.82
6520 rs7516039 1_20 0.934 26.80 8.0e-05 -4.86
813797 rs78668392 22_9 0.934 24.80 7.4e-05 3.78
613206 rs147598676 12_67 0.933 63.00 1.9e-04 7.93
323420 rs41270056 6_28 0.926 27.73 8.2e-05 4.92
129516 rs62192912 2_137 0.921 29.06 8.6e-05 4.42
8541 rs71642659 1_24 0.913 28.31 8.3e-05 6.02
148593 rs4974078 3_35 0.910 42.14 1.2e-04 -7.36
546574 rs201519335 11_12 0.908 32.32 9.4e-05 2.32
319891 rs9379832 6_20 0.907 32.76 9.5e-05 6.62
610651 rs4764939 12_62 0.906 177.34 5.1e-04 -13.66
295561 rs10057561 5_77 0.903 28.57 8.3e-05 -5.24
202150 rs2946394 4_20 0.901 24.93 7.2e-05 4.27
81006 rs35510572 2_39 0.900 24.94 7.2e-05 4.09
1244716 rs117090198 19_27 0.896 29.92 8.6e-05 4.74
272577 rs1694060 5_31 0.894 29.71 8.5e-05 -4.71
1210832 rs12952581 17_28 0.892 350.61 1.0e-03 -28.36
239342 rs11727676 4_94 0.889 24.76 7.1e-05 -4.45
616213 rs2393775 12_74 0.889 184.37 5.3e-04 14.43
732827 rs62079262 17_29 0.889 28.99 8.3e-05 -3.73
415539 rs11761498 7_98 0.887 25.00 7.1e-05 -4.45
776836 rs149349299 19_25 0.887 46.03 1.3e-04 -6.44
809441 rs9975329 21_22 0.887 26.52 7.5e-05 4.75
8717 rs4660293 1_24 0.886 84.39 2.4e-04 -10.04
383022 rs149901303 7_32 0.879 24.63 6.9e-05 -4.28
10993 rs112681075 1_33 0.878 26.37 7.4e-05 4.58
23072 rs164899 1_55 0.877 28.16 7.9e-05 -5.42
77106 rs55761545 2_31 0.877 30.52 8.6e-05 -5.48
399091 rs117501142 7_60 0.865 24.41 6.8e-05 4.39
498459 rs12351482 9_71 0.865 98.24 2.7e-04 10.02
732834 rs112147932 17_29 0.861 26.55 7.3e-05 -4.19
346284 rs117864346 6_73 0.851 30.37 8.3e-05 5.16
407483 rs4731639 7_79 0.849 34.82 9.5e-05 5.78
109516 rs1460670 2_99 0.847 26.32 7.1e-05 4.61
1142730 rs142035705 15_46 0.845 35.84 9.7e-05 -6.70
735317 rs2632527 17_34 0.841 25.81 7.0e-05 -4.53
202141 rs112396442 4_20 0.839 25.03 6.7e-05 -4.25
322081 rs3094124 6_24 0.836 29.70 8.0e-05 -4.67
184718 rs234043 3_106 0.833 39.30 1.0e-04 -5.98
408139 rs4731855 7_80 0.833 25.60 6.8e-05 -4.41
493525 rs2808798 9_58 0.832 24.88 6.6e-05 4.41
408971 rs2551778 7_82 0.831 47.39 1.3e-04 -6.65
811039 rs175169 22_4 0.830 35.86 9.5e-05 6.10
576100 rs10750224 11_75 0.821 25.88 6.8e-05 4.43
243335 rs17285611 4_102 0.817 41.55 1.1e-04 -2.34
117789 rs10202868 2_113 0.806 55.44 1.4e-04 -7.73
1092359 rs536338 13_21 0.805 41.21 1.1e-04 -6.60
443685 rs28435511 8_57 0.804 71.82 1.8e-04 -5.24
843576 rs114165349 1_18 0.803 464.76 1.2e-03 -21.20
546146 rs7946907 11_11 0.802 27.87 7.2e-05 4.85SNPs with largest effect sizes
#plot PIP vs effect size
plot(ctwas_snp_res$susie_pip, ctwas_snp_res$mu2, xlab="PIP", ylab="mu^2", main="SNP PIPs vs Effect Size")
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#SNPs with 50 largest effect sizes
head(ctwas_snp_res[order(-ctwas_snp_res$mu2),report_cols_snps],50) id region_tag susie_pip mu2 PVE z
1260461 rs374141296 19_34 1 145307.5 4.7e-01 6.36
1260458 rs113176985 19_34 1 144531.2 4.6e-01 7.00
1260449 rs61371437 19_34 1 144284.0 4.6e-01 6.86
1260451 rs35295508 19_34 0 144129.7 1.6e-14 7.05
1260465 rs2946865 19_34 0 143717.6 0.0e+00 6.96
1260439 rs739349 19_34 0 143707.6 0.0e+00 6.83
1260440 rs756628 19_34 0 143707.6 0.0e+00 6.83
1260456 rs73056069 19_34 0 143628.0 0.0e+00 7.13
1260436 rs739347 19_34 0 143436.7 0.0e+00 6.80
1260453 rs2878354 19_34 0 143304.9 0.0e+00 7.15
1260437 rs2073614 19_34 0 143285.3 0.0e+00 6.76
1260442 rs2077300 19_34 0 142903.7 0.0e+00 6.92
1260432 rs4802613 19_34 0 142653.8 0.0e+00 6.77
1260446 rs73056059 19_34 0 142633.2 0.0e+00 6.97
1260466 rs60815603 19_34 0 141661.2 0.0e+00 7.20
1260469 rs1316885 19_34 0 140990.9 0.0e+00 7.11
1260471 rs60746284 19_34 0 140786.4 0.0e+00 7.33
1260474 rs2946863 19_34 0 140731.1 0.0e+00 7.04
1260430 rs10403394 19_34 0 140666.3 0.0e+00 6.80
1260431 rs17555056 19_34 0 140609.9 0.0e+00 6.75
1260467 rs35443645 19_34 0 140607.2 0.0e+00 7.08
1260447 rs73056062 19_34 0 138969.4 0.0e+00 6.99
1260477 rs553431297 19_34 0 136947.8 0.0e+00 6.78
1260460 rs112283514 19_34 0 136592.7 0.0e+00 6.51
1260462 rs11270139 19_34 0 135655.2 0.0e+00 7.17
1260427 rs10421294 19_34 0 127049.0 0.0e+00 6.07
1260426 rs8108175 19_34 0 127031.7 0.0e+00 6.07
1260419 rs59192944 19_34 0 126790.8 0.0e+00 6.07
1260425 rs1858742 19_34 0 126788.5 0.0e+00 6.04
1260416 rs55991145 19_34 0 126701.0 0.0e+00 6.08
1260411 rs3786567 19_34 0 126651.4 0.0e+00 6.08
1260410 rs4801801 19_34 0 126602.1 0.0e+00 6.05
1260407 rs2271952 19_34 0 126601.3 0.0e+00 6.08
1260406 rs2271953 19_34 0 126461.8 0.0e+00 6.04
1260408 rs2271951 19_34 0 126455.9 0.0e+00 6.05
1260397 rs60365978 19_34 0 126339.9 0.0e+00 6.02
1260403 rs4802612 19_34 0 125840.1 0.0e+00 6.16
1260413 rs2517977 19_34 0 125696.9 0.0e+00 5.83
1260400 rs55893003 19_34 0 125493.7 0.0e+00 6.16
1260392 rs55992104 19_34 0 122543.1 0.0e+00 6.04
1260386 rs60403475 19_34 0 122512.7 0.0e+00 6.03
1260389 rs4352151 19_34 0 122506.7 0.0e+00 6.01
1260383 rs11878448 19_34 0 122419.5 0.0e+00 6.01
1260377 rs9653100 19_34 0 122379.3 0.0e+00 6.04
1260373 rs4802611 19_34 0 122298.8 0.0e+00 6.03
1260365 rs7251338 19_34 0 122112.7 0.0e+00 6.02
1260364 rs59269605 19_34 0 122099.4 0.0e+00 6.05
1260385 rs1042120 19_34 0 121795.9 0.0e+00 6.13
1260381 rs113220577 19_34 0 121688.0 0.0e+00 6.12
1260375 rs9653118 19_34 0 121499.2 0.0e+00 6.16SNPs 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
1260461 rs374141296 19_34 1.000 145307.45 0.4700 6.36
1260449 rs61371437 19_34 1.000 144284.05 0.4600 6.86
1260458 rs113176985 19_34 1.000 144531.16 0.4600 7.00
1006028 rs10995596 10_42 1.000 99247.57 0.3200 10.99
1006046 rs773090945 10_42 1.000 99389.15 0.3200 11.08
354432 rs199804242 6_89 1.000 53751.81 0.1700 -3.57
354448 rs6923513 6_89 0.956 53785.74 0.1600 -3.26
354431 rs2327654 6_89 0.724 53782.97 0.1200 -3.25
1083354 rs78750369 13_18 1.000 26018.73 0.0830 3.38
1083355 rs1555718 13_18 0.995 25905.97 0.0830 3.36
1083359 rs7320922 13_18 1.000 25931.57 0.0830 3.38
667399 rs369107859 14_34 1.000 19214.82 0.0620 -0.33
667396 rs7156583 14_34 0.936 19231.91 0.0580 -4.20
392966 rs761767938 7_49 1.000 15156.68 0.0490 4.57
392974 rs1544459 7_49 1.000 15257.92 0.0490 4.45
362060 rs60425481 6_104 1.000 14665.15 0.0470 10.63
392970 rs11972122 7_49 0.997 13951.88 0.0450 3.99
949273 rs9279507 6_26 1.000 10380.12 0.0330 -2.07
362057 rs3127598 6_104 0.560 14600.20 0.0260 -6.70
362065 rs3106167 6_104 0.477 14600.14 0.0220 -6.70
632245 rs566812111 13_25 1.000 6188.72 0.0200 -2.84
632249 rs12430288 13_25 1.000 6236.94 0.0200 -2.67
362056 rs3106169 6_104 0.413 14600.15 0.0190 -6.71
934745 rs139439683 5_106 1.000 5535.82 0.0180 -2.67
934880 rs13172121 5_106 1.000 5539.97 0.0180 -2.57
949259 rs3130291 6_26 0.500 10377.22 0.0170 -2.70
725101 rs62059839 17_7 1.000 4847.89 0.0160 88.82
667408 rs2159704 14_34 0.249 19219.19 0.0150 -4.21
949262 rs3130292 6_26 0.444 10377.24 0.0150 -2.70
667406 rs72627160 14_34 0.225 19212.34 0.0140 -4.23
362049 rs11755965 6_104 0.280 14596.33 0.0130 -6.70
934836 rs7703057 5_106 0.721 5370.96 0.0120 -2.87
725085 rs11078694 17_7 1.000 3238.52 0.0100 -82.59
1004999 rs10822163 10_42 0.252 9007.81 0.0073 47.75
725059 rs116560331 17_7 1.000 1912.32 0.0061 -64.18
725100 rs112885647 17_7 0.946 1870.19 0.0057 -48.76
332488 rs112354376 6_46 1.000 1674.96 0.0054 -3.29
332489 rs208453 6_46 1.000 1664.12 0.0053 -0.53
948710 rs35337578 6_26 0.577 2084.83 0.0039 -6.67
725112 rs72829446 17_7 1.000 1015.62 0.0033 30.00
1004992 rs12355784 10_42 0.106 8998.38 0.0031 47.73
948709 rs17207867 6_26 0.394 2080.68 0.0026 -6.62
730050 rs56032910 17_19 1.000 741.77 0.0024 -2.90
882660 rs1260326 2_16 1.000 705.60 0.0023 30.11
1005012 rs10761750 10_42 0.075 8996.26 0.0022 47.72
1142647 rs56332871 15_46 1.000 687.40 0.0022 25.92
730051 rs3744618 17_19 1.000 612.95 0.0020 -2.45
673279 rs1243165 14_48 1.000 569.33 0.0018 6.12
1005008 rs10822164 10_42 0.061 8990.86 0.0018 47.72
673272 rs11439803 14_48 1.000 511.68 0.0016 2.33SNPs 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
725101 rs62059839 17_7 1.000 4847.89 1.6e-02 88.82
725095 rs149932962 17_7 0.000 3958.80 0.0e+00 84.44
725081 rs8073177 17_7 0.000 3201.34 4.6e-18 82.80
725079 rs9892862 17_7 0.000 3188.32 0.0e+00 82.78
725085 rs11078694 17_7 1.000 3238.52 1.0e-02 -82.59
725082 rs62059797 17_7 0.000 2883.47 0.0e+00 74.35
725080 rs35049113 17_7 0.000 2868.25 0.0e+00 74.25
725086 rs12941509 17_7 0.000 2841.92 0.0e+00 73.74
725091 rs4968212 17_7 0.000 2787.40 0.0e+00 -72.06
725089 rs62059801 17_7 0.000 2688.24 0.0e+00 71.11
725121 rs1641549 17_7 0.000 2029.01 0.0e+00 -69.17
725050 rs34474914 17_7 0.000 2237.50 0.0e+00 64.95
725074 rs142700974 17_7 0.000 1981.83 3.7e-11 -64.87
725059 rs116560331 17_7 1.000 1912.32 6.1e-03 -64.18
725111 rs745412832 17_7 0.000 1343.51 0.0e+00 60.54
725043 rs13290 17_7 0.000 742.81 0.0e+00 58.64
725090 rs12601581 17_7 0.000 1857.53 0.0e+00 -55.97
725103 rs1642797 17_7 0.000 2325.41 0.0e+00 54.83
725104 rs1642808 17_7 0.000 2311.44 0.0e+00 54.70
725105 rs1641538 17_7 0.000 2311.07 0.0e+00 54.69
725106 rs1641531 17_7 0.000 2309.52 0.0e+00 54.67
725107 rs1641528 17_7 0.000 2310.87 0.0e+00 54.67
725108 rs1641522 17_7 0.000 2313.63 0.0e+00 54.66
725040 rs11652328 17_7 0.000 1441.22 0.0e+00 53.21
725044 rs34706172 17_7 0.000 610.04 0.0e+00 52.30
725096 rs58614441 17_7 0.000 2380.36 0.0e+00 -49.67
725046 rs3829603 17_7 0.000 716.28 0.0e+00 49.05
725047 rs12600863 17_7 0.000 682.63 0.0e+00 48.93
725100 rs112885647 17_7 0.946 1870.19 5.7e-03 -48.76
725102 rs6257 17_7 0.054 1863.74 3.2e-04 -48.70
725118 rs4968186 17_7 0.000 1125.62 0.0e+00 48.27
1004999 rs10822163 10_42 0.252 9007.81 7.3e-03 47.75
1004992 rs12355784 10_42 0.106 8998.38 3.1e-03 47.73
1004701 rs10995477 10_42 0.014 9023.36 3.9e-04 47.72
1005004 rs6479896 10_42 0.051 8993.97 1.5e-03 47.72
1005008 rs10822164 10_42 0.061 8990.86 1.8e-03 47.72
1005012 rs10761750 10_42 0.075 8996.26 2.2e-03 47.72
1004710 rs4595427 10_42 0.005 9015.53 1.5e-04 47.70
1005027 rs7076310 10_42 0.024 8990.11 6.9e-04 47.70
1005037 rs4310508 10_42 0.023 8986.80 6.6e-04 47.70
1005266 rs10761771 10_42 0.022 9000.40 6.3e-04 47.70
1004707 rs4400684 10_42 0.003 9021.50 7.3e-05 47.69
1004714 rs4405189 10_42 0.003 9023.96 8.9e-05 47.69
1004814 rs10822153 10_42 0.012 9024.89 3.6e-04 47.69
1005023 rs2893919 10_42 0.004 8934.73 1.1e-04 47.69
1005024 rs2393966 10_42 0.004 8937.59 1.0e-04 47.69
1005040 rs7910927 10_42 0.001 8870.24 3.9e-05 47.69
1005199 rs10509186 10_42 0.012 8992.50 3.4e-04 47.69
1005211 rs10740126 10_42 0.012 8992.00 3.4e-04 47.69
1005228 rs7092784 10_42 0.012 8992.07 3.5e-04 47.69Gene 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] 66if (length(genes)>0){
GO_enrichment <- enrichr(genes, dbs)
for (db in dbs){
print(db)
df <- GO_enrichment[[db]]
df <- df[df$Adjusted.P.value<0.05,c("Term", "Overlap", "Adjusted.P.value", "Genes")]
print(df)
}
#DisGeNET enrichment
# devtools::install_bitbucket("ibi_group/disgenet2r")
library(disgenet2r)
disgenet_api_key <- get_disgenet_api_key(
email = "wesleycrouse@gmail.com",
password = "uchicago1" )
Sys.setenv(DISGENET_API_KEY= disgenet_api_key)
res_enrich <-disease_enrichment(entities=genes, vocabulary = "HGNC",
database = "CURATED" )
df <- res_enrich@qresult[1:10, c("Description", "FDR", "Ratio", "BgRatio")]
print(df)
#WebGestalt enrichment
library(WebGestaltR)
background <- ctwas_gene_res$genename
#listGeneSet()
databases <- c("pathway_KEGG", "disease_GLAD4U", "disease_OMIM")
enrichResult <- WebGestaltR(enrichMethod="ORA", organism="hsapiens",
interestGene=genes, referenceGene=background,
enrichDatabase=databases, interestGeneType="genesymbol",
referenceGeneType="genesymbol", isOutput=F)
print(enrichResult[,c("description", "size", "overlap", "FDR", "database", "userId")])
}Uploading data to Enrichr... Done.
Querying GO_Biological_Process_2021... Done.
Querying GO_Cellular_Component_2021... Done.
Querying GO_Molecular_Function_2021... Done.
Parsing results... Done.
[1] "GO_Biological_Process_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)SLC35E2B gene(s) from the input list not found in DisGeNET CURATEDRP11-131K5.2 gene(s) from the input list not found in DisGeNET CURATEDATXN7L3 gene(s) from the input list not found in DisGeNET CURATEDSTX10 gene(s) from the input list not found in DisGeNET CURATEDRP11-714M23.2 gene(s) from the input list not found in DisGeNET CURATEDEXOC3L4 gene(s) from the input list not found in DisGeNET CURATEDCHCHD7 gene(s) from the input list not found in DisGeNET CURATEDSYTL1 gene(s) from the input list not found in DisGeNET CURATEDCCDC97 gene(s) from the input list not found in DisGeNET CURATEDPDZD3 gene(s) from the input list not found in DisGeNET CURATEDZFPM1 gene(s) from the input list not found in DisGeNET CURATEDTMEM176B gene(s) from the input list not found in DisGeNET CURATEDKIAA0141 gene(s) from the input list not found in DisGeNET CURATEDNAA30 gene(s) from the input list not found in DisGeNET CURATEDTLCD2 gene(s) from the input list not found in DisGeNET CURATEDHLX gene(s) from the input list not found in DisGeNET CURATEDAKNA gene(s) from the input list not found in DisGeNET CURATEDDLEU1 gene(s) from the input list not found in DisGeNET CURATEDRP11-327J17.2 gene(s) from the input list not found in DisGeNET CURATEDH3F3B gene(s) from the input list not found in DisGeNET CURATEDLINC01270 gene(s) from the input list not found in DisGeNET CURATEDCBX6 gene(s) from the input list not found in DisGeNET CURATEDPALM3 gene(s) from the input list not found in DisGeNET CURATEDNTAN1 gene(s) from the input list not found in DisGeNET CURATEDNRDE2 gene(s) from the input list not found in DisGeNET CURATEDSCAF11 gene(s) from the input list not found in DisGeNET CURATEDZNF845 gene(s) from the input list not found in DisGeNET CURATEDAES gene(s) from the input list not found in DisGeNET CURATEDALLC gene(s) from the input list not found in DisGeNET CURATEDTMED6 gene(s) from the input list not found in DisGeNET CURATEDGBF1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio
51 Hydrocephalus 0.03472222 2/35
69 Noonan Syndrome 0.03472222 2/35
97 LEOPARD Syndrome 0.03472222 2/35
107 Renal Cell Dysplasia 0.03472222 1/35
127 Symmetrical dyschromatosis of extremities 0.03472222 1/35
136 Anhydramnios 0.03472222 1/35
169 CONE-ROD DYSTROPHY 9 0.03472222 1/35
182 CATARACT, POSTERIOR POLAR, 1 0.03472222 1/35
190 BONE MINERAL DENSITY QUANTITATIVE TRAIT LOCUS 12 0.03472222 1/35
193 Age-related cortical cataract 0.03472222 1/35
BgRatio
51 9/9703
69 24/9703
97 22/9703
107 1/9703
127 1/9703
136 1/9703
169 1/9703
182 1/9703
190 1/9703
193 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!NULL
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.0.0
[5] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.57.0
[3] fs_1.3.1 bit64_4.0.5
[5] doParallel_1.0.16 progress_1.2.2
[7] httr_1.4.1 rprojroot_2.0.2
[9] GenomeInfoDb_1.20.0 doRNG_1.8.2
[11] tools_3.6.1 utf8_1.2.1
[13] R6_2.5.0 DBI_1.1.1
[15] BiocGenerics_0.30.0 colorspace_1.4-1
[17] withr_2.4.1 tidyselect_1.1.0
[19] prettyunits_1.0.2 bit_4.0.4
[21] curl_3.3 compiler_3.6.1
[23] git2r_0.26.1 Biobase_2.44.0
[25] DelayedArray_0.10.0 rtracklayer_1.44.0
[27] labeling_0.3 scales_1.1.0
[29] readr_1.4.0 apcluster_1.4.8
[31] stringr_1.4.0 digest_0.6.20
[33] Rsamtools_2.0.0 svglite_1.2.2
[35] rmarkdown_1.13 XVector_0.24.0
[37] pkgconfig_2.0.3 htmltools_0.3.6
[39] fastmap_1.1.0 BSgenome_1.52.0
[41] rlang_0.4.11 RSQLite_2.2.7
[43] generics_0.0.2 farver_2.1.0
[45] jsonlite_1.6 BiocParallel_1.18.0
[47] dplyr_1.0.7 VariantAnnotation_1.30.1
[49] RCurl_1.98-1.1 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.1 Matrix_1.2-18
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.22.1 fansi_0.5.0
[57] gdtools_0.1.9 lifecycle_1.0.0
[59] stringi_1.4.3 whisker_0.3-2
[61] yaml_2.2.0 SummarizedExperiment_1.14.1
[63] zlibbioc_1.30.0 plyr_1.8.4
[65] grid_3.6.1 blob_1.2.1
[67] parallel_3.6.1 promises_1.0.1
[69] crayon_1.4.1 lattice_0.20-38
[71] Biostrings_2.52.0 GenomicFeatures_1.36.3
[73] hms_1.1.0 knitr_1.23
[75] pillar_1.6.1 igraph_1.2.4.1
[77] GenomicRanges_1.36.0 rjson_0.2.20
[79] rngtools_1.5 codetools_0.2-16
[81] reshape2_1.4.3 biomaRt_2.40.1
[83] stats4_3.6.1 XML_3.98-1.20
[85] glue_1.4.2 evaluate_0.14
[87] data.table_1.14.0 foreach_1.5.1
[89] vctrs_0.3.8 httpuv_1.5.1
[91] gtable_0.3.0 purrr_0.3.4
[93] assertthat_0.2.1 cachem_1.0.5
[95] xfun_0.8 later_0.8.0
[97] tibble_3.1.2 iterators_1.0.13
[99] GenomicAlignments_1.20.1 AnnotationDbi_1.46.0
[101] memoise_2.0.0 IRanges_2.18.1
[103] workflowr_1.6.2 ellipsis_0.3.2