IGF-1 (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 IGF-1 (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-30770_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0189019301 0.0002085686 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
27.64842 24.74043 #report sample size
print(sample_size)[1] 342439#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10901 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01663641 0.13105665 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05253737 1.56608472Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
12135 S1PR2 19_9 1.000 149.54 4.4e-04 -15.92
8579 STAT5B 17_25 0.998 32.01 9.3e-05 3.39
9816 C11orf96 11_27 0.993 27.83 8.1e-05 5.47
2173 TMEM176B 7_93 0.991 47.72 1.4e-04 7.28
9017 ERN1 17_37 0.990 93.86 2.7e-04 13.72
1372 IGFALS 16_2 0.989 4778.29 1.4e-02 19.66
10856 ZNF845 19_36 0.988 40.43 1.2e-04 -6.16
8493 OXSR1 3_27 0.987 27.15 7.8e-05 4.94
9390 GAS6 13_62 0.984 55.22 1.6e-04 -7.63
10303 UGT2B17 4_48 0.983 137.64 4.0e-04 -14.40
7905 VASN 16_4 0.981 37.59 1.1e-04 6.23
7651 CASC4 15_17 0.980 97.82 2.8e-04 13.39
1894 TRPS1 8_78 0.973 138.53 3.9e-04 9.81
1954 AES 19_4 0.971 25.72 7.3e-05 4.81
112 SCN4A 17_37 0.970 55.84 1.6e-04 -9.25
247 ZNF582 19_38 0.970 24.64 7.0e-05 -4.59
10927 AC004540.5 7_23 0.962 24.16 6.8e-05 4.49
8040 THBS3 1_76 0.953 27.69 7.7e-05 -4.92
12516 RP11-442O1.3 16_50 0.951 30.29 8.4e-05 -5.40
8060 NPR1 1_75 0.948 22.70 6.3e-05 -5.13
7300 RICTOR 5_26 0.945 85.72 2.4e-04 9.38
906 UBE2K 4_32 0.942 140.90 3.9e-04 -10.77
5972 HIKESHI 11_47 0.937 40.77 1.1e-04 6.49
886 IL4R 16_22 0.937 40.55 1.1e-04 -6.18
4350 KMT5C 19_38 0.933 27.39 7.5e-05 -5.00
625 MPPED2 11_21 0.926 196.51 5.3e-04 3.77
10185 IGF2R 6_103 0.925 79.15 2.1e-04 8.92
6411 LRGUK 7_81 0.922 30.59 8.2e-05 -5.72
1779 CRISPLD2 16_49 0.915 20.36 5.4e-05 -4.15
3133 DHDDS 1_18 0.912 31.95 8.5e-05 -8.25
9855 PALM3 19_11 0.912 24.50 6.5e-05 -4.66
6636 ZNF276 16_54 0.908 21.36 5.7e-05 -4.41
5415 SYTL1 1_19 0.904 61.47 1.6e-04 7.28
3832 MAP2K2 19_4 0.901 25.30 6.7e-05 3.04
2021 SULT2A1 19_33 0.901 56.20 1.5e-04 -7.73
5358 CCDC97 19_28 0.894 34.65 9.0e-05 5.83
6951 FAAP20 1_2 0.888 31.60 8.2e-05 -5.92
4435 PSRC1 1_67 0.875 125.11 3.2e-04 10.55
9121 B3GNT3 19_14 0.874 22.30 5.7e-05 4.24
7040 INHBB 2_70 0.850 80.01 2.0e-04 -8.97
2497 GTF2H1 11_13 0.848 62.28 1.5e-04 -8.37
7682 VPS39 15_15 0.847 20.07 5.0e-05 -3.47
3267 TGFB3 14_35 0.846 20.64 5.1e-05 3.99
10446 LDB1 10_65 0.840 30.71 7.5e-05 -5.24
9635 TLCD2 17_2 0.837 37.12 9.1e-05 -6.24
9102 ZFPM1 16_53 0.831 30.40 7.4e-05 -5.29
4835 RNF144B 6_14 0.830 23.67 5.7e-05 -4.71
3150 KMT2A 11_71 0.823 33.35 8.0e-05 3.88
2870 ACTR1B 2_57 0.820 19.98 4.8e-05 -3.77
10205 TBC1D9B 5_108 0.817 19.57 4.7e-05 -3.70
8996 GEN1 2_10 0.816 46.33 1.1e-04 -6.69
2042 BCAT2 19_34 0.813 39.87 9.5e-05 -6.16
7546 HTRA1 10_77 0.812 21.38 5.1e-05 -4.07Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
8460 ZMAT3 3_110 0.000 26727.87 0.000 -4.39
8275 KCNMB3 3_110 0.000 20674.34 0.000 -3.10
3429 PIK3CA 3_110 0.000 16378.32 0.000 0.44
555 HAGH 16_2 0.000 8558.89 0.000 -8.93
4634 EGLN1 1_118 0.000 5393.85 0.000 2.31
6890 SPSB3 16_2 0.000 5034.67 0.000 10.12
6891 MEIOB 16_2 0.000 4789.74 0.000 2.67
1372 IGFALS 16_2 0.989 4778.29 0.014 19.66
3058 EXOC8 1_118 0.000 4519.66 0.000 -3.21
839 ZNF37A 10_28 0.000 2956.89 0.000 -2.26
8272 MFN1 3_110 0.000 2516.73 0.000 -1.35
11963 RP11-255C15.3 3_110 0.000 1451.59 0.000 -0.41
5256 RPS2 16_2 0.000 1327.95 0.000 -2.01
10766 HN1L 16_2 0.000 955.31 0.000 -2.80
9995 ZNF33A 10_28 0.000 929.05 0.000 4.30
10284 EME2 16_2 0.000 578.22 0.000 -2.93
8736 ZNF25 10_28 0.000 541.74 0.000 1.91
10449 MSRB1 16_2 0.000 533.00 0.000 -2.19
7863 ZNF598 16_2 0.000 497.70 0.000 -1.15
2822 GNB4 3_110 0.000 405.62 0.000 0.12Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1372 IGFALS 16_2 0.989 4778.29 0.01400 19.66
7656 CATSPER2 15_16 0.693 337.29 0.00068 -19.25
625 MPPED2 11_21 0.926 196.51 0.00053 3.77
12135 S1PR2 19_9 1.000 149.54 0.00044 -15.92
10303 UGT2B17 4_48 0.983 137.64 0.00040 -14.40
906 UBE2K 4_32 0.942 140.90 0.00039 -10.77
1894 TRPS1 8_78 0.973 138.53 0.00039 9.81
4435 PSRC1 1_67 0.875 125.11 0.00032 10.55
7651 CASC4 15_17 0.980 97.82 0.00028 13.39
9017 ERN1 17_37 0.990 93.86 0.00027 13.72
9626 PARPBP 12_61 0.783 103.79 0.00024 -10.53
7300 RICTOR 5_26 0.945 85.72 0.00024 9.38
10185 IGF2R 6_103 0.925 79.15 0.00021 8.92
760 AFF4 5_80 0.414 162.49 0.00020 -12.90
7040 INHBB 2_70 0.850 80.01 0.00020 -8.97
5427 PTPRF 1_27 0.765 86.41 0.00019 10.47
6792 ADAR 1_75 0.755 85.42 0.00019 9.55
12687 RP4-781K5.7 1_121 0.771 86.15 0.00019 -8.91
10981 ZGLP1 19_9 0.776 76.91 0.00017 -12.08
5415 SYTL1 1_19 0.904 61.47 0.00016 7.28Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1372 IGFALS 16_2 0.989 4778.29 1.4e-02 19.66
7656 CATSPER2 15_16 0.693 337.29 6.8e-04 -19.25
12694 RP11-210L7.3 12_61 0.000 358.24 7.9e-14 -18.02
1328 NUBP2 16_2 0.000 244.71 1.0e-08 -17.99
1058 GCKR 2_16 0.354 132.72 1.4e-04 16.85
10987 C2orf16 2_16 0.354 132.72 1.4e-04 16.85
12135 S1PR2 19_9 1.000 149.54 4.4e-04 -15.92
10425 AKR1C4 10_6 0.009 278.09 7.2e-06 15.70
7985 LCMT2 15_16 0.108 222.02 7.0e-05 14.68
10303 UGT2B17 4_48 0.983 137.64 4.0e-04 -14.40
5799 SLC22A3 6_104 0.681 74.98 1.5e-04 -14.17
9017 ERN1 17_37 0.990 93.86 2.7e-04 13.72
11256 AP000688.29 21_17 0.000 120.19 1.2e-08 -13.50
9569 SORCS2 4_8 0.010 212.24 6.3e-06 -13.41
7651 CASC4 15_17 0.980 97.82 2.8e-04 13.39
2662 TRIM38 6_20 0.000 91.41 6.3e-08 13.35
4736 HLX 1_112 0.081 120.57 2.8e-05 13.13
1367 STX1B 16_24 0.099 153.76 4.5e-05 -13.03
2891 SNX17 2_16 0.044 160.39 2.1e-05 12.91
760 AFF4 5_80 0.414 162.49 2.0e-04 -12.90Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03843684#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
1372 IGFALS 16_2 0.989 4778.29 1.4e-02 19.66
7656 CATSPER2 15_16 0.693 337.29 6.8e-04 -19.25
12694 RP11-210L7.3 12_61 0.000 358.24 7.9e-14 -18.02
1328 NUBP2 16_2 0.000 244.71 1.0e-08 -17.99
1058 GCKR 2_16 0.354 132.72 1.4e-04 16.85
10987 C2orf16 2_16 0.354 132.72 1.4e-04 16.85
12135 S1PR2 19_9 1.000 149.54 4.4e-04 -15.92
10425 AKR1C4 10_6 0.009 278.09 7.2e-06 15.70
7985 LCMT2 15_16 0.108 222.02 7.0e-05 14.68
10303 UGT2B17 4_48 0.983 137.64 4.0e-04 -14.40
5799 SLC22A3 6_104 0.681 74.98 1.5e-04 -14.17
9017 ERN1 17_37 0.990 93.86 2.7e-04 13.72
11256 AP000688.29 21_17 0.000 120.19 1.2e-08 -13.50
9569 SORCS2 4_8 0.010 212.24 6.3e-06 -13.41
7651 CASC4 15_17 0.980 97.82 2.8e-04 13.39
2662 TRIM38 6_20 0.000 91.41 6.3e-08 13.35
4736 HLX 1_112 0.081 120.57 2.8e-05 13.13
1367 STX1B 16_24 0.099 153.76 4.5e-05 -13.03
2891 SNX17 2_16 0.044 160.39 2.1e-05 12.91
760 AFF4 5_80 0.414 162.49 2.0e-04 -12.90Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 16_2"
genename region_tag susie_pip mu2 PVE z
10103 CACNA1H 16_2 0.000 41.46 0.0e+00 4.38
2996 TPSG1 16_2 0.000 67.38 0.0e+00 1.28
10208 TPSB2 16_2 0.000 48.88 0.0e+00 -2.49
1329 TPSD1 16_2 0.000 56.07 0.0e+00 -0.50
11962 RP11-616M22.7 16_2 0.000 75.00 0.0e+00 2.18
1798 UBE2I 16_2 0.000 52.99 0.0e+00 -1.02
118 BAIAP3 16_2 0.000 98.02 0.0e+00 -1.80
119 TSR3 16_2 0.000 31.70 0.0e+00 1.12
1228 GNPTG 16_2 0.000 31.70 0.0e+00 1.12
10263 CCDC154 16_2 0.000 95.54 0.0e+00 2.08
1789 CLCN7 16_2 0.000 85.46 0.0e+00 -3.87
1564 TELO2 16_2 0.000 35.21 0.0e+00 -5.54
11958 LA16c-385E7.1 16_2 0.000 118.10 0.0e+00 -0.81
4194 TMEM204 16_2 0.000 35.27 0.0e+00 7.14
120 CRAMP1 16_2 0.000 266.53 0.0e+00 1.90
10766 HN1L 16_2 0.000 955.31 0.0e+00 -2.80
5056 MAPK8IP3 16_2 0.000 328.72 0.0e+00 -4.59
1756 NME3 16_2 0.000 112.58 0.0e+00 -1.68
10284 EME2 16_2 0.000 578.22 0.0e+00 -2.93
1328 NUBP2 16_2 0.000 244.71 1.0e-08 -17.99
6890 SPSB3 16_2 0.000 5034.67 0.0e+00 10.12
1372 IGFALS 16_2 0.989 4778.29 1.4e-02 19.66
555 HAGH 16_2 0.000 8558.89 0.0e+00 -8.93
6891 MEIOB 16_2 0.000 4789.74 0.0e+00 2.67
10449 MSRB1 16_2 0.000 533.00 0.0e+00 -2.19
5255 RPL3L 16_2 0.000 10.66 0.0e+00 -2.01
5257 NDUFB10 16_2 0.000 211.68 0.0e+00 -1.08
5256 RPS2 16_2 0.000 1327.95 0.0e+00 -2.01
11769 SNHG9 16_2 0.000 289.34 0.0e+00 1.41
3868 GFER 16_2 0.000 81.63 0.0e+00 -4.00
3869 SYNGR3 16_2 0.000 264.88 0.0e+00 -0.89
7863 ZNF598 16_2 0.000 497.70 0.0e+00 -1.15
584 SLC9A3R2 16_2 0.000 13.63 0.0e+00 -0.01
1780 TSC2 16_2 0.000 83.39 0.0e+00 2.19
585 NTHL1 16_2 0.000 54.15 0.0e+00 2.84
139 PKD1 16_2 0.000 6.21 0.0e+00 0.82
7864 RAB26 16_2 0.000 6.51 0.0e+00 0.63
7865 MLST8 16_2 0.000 39.17 0.0e+00 -4.10
9349 BRICD5 16_2 0.000 82.84 0.0e+00 1.20
9499 PGP 16_2 0.000 90.88 0.0e+00 -1.09
7866 E4F1 16_2 0.000 102.73 0.0e+00 -2.77
7867 DNASE1L2 16_2 0.000 58.10 0.0e+00 2.69
7868 ECI1 16_2 0.000 10.76 0.0e+00 1.96
10761 RNPS1 16_2 0.000 33.60 0.0e+00 -1.23
11965 RP11-304L19.13 16_2 0.000 26.93 0.0e+00 0.38
6893 CCNF 16_2 0.000 25.83 0.0e+00 0.67
6892 C16orf59 16_2 0.000 23.70 0.0e+00 -1.50
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 15_16"
genename region_tag susie_pip mu2 PVE z
1853 ZNF106 15_16 0.038 35.12 3.9e-06 5.21
9202 LRRC57 15_16 0.024 10.97 7.8e-07 2.02
6691 STARD9 15_16 0.018 7.08 3.8e-07 -1.37
5189 CDAN1 15_16 0.021 7.32 4.5e-07 0.73
3962 TTBK2 15_16 0.083 22.84 5.5e-06 -5.26
4903 TMEM62 15_16 0.677 29.04 5.7e-05 -6.28
7984 ADAL 15_16 0.018 61.78 3.2e-06 7.05
7985 LCMT2 15_16 0.108 222.02 7.0e-05 14.68
4898 TUBGCP4 15_16 0.017 68.55 3.5e-06 -7.30
5180 ZSCAN29 15_16 0.170 27.32 1.4e-05 -0.42
7702 MAP1A 15_16 0.017 99.27 4.8e-06 -9.60
7656 CATSPER2 15_16 0.693 337.29 6.8e-04 -19.25
7709 PDIA3 15_16 0.046 23.94 3.2e-06 -1.42
5178 MFAP1 15_16 0.017 72.71 3.6e-06 -7.79
1286 WDR76 15_16 0.018 62.49 3.3e-06 -7.24
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 12_61"
genename region_tag susie_pip mu2 PVE z
7576 SPIC 12_61 0.000 17.33 9.7e-16 1.31
10018 MYBPC1 12_61 0.000 7.55 1.3e-16 -1.30
2575 CHPT1 12_61 0.000 9.47 1.9e-16 0.08
12495 RP11-285E23.2 12_61 0.000 9.54 1.9e-16 0.97
2577 GNPTAB 12_61 0.000 6.45 1.0e-16 -1.30
4674 DRAM1 12_61 0.000 6.50 1.0e-16 1.10
3366 WASHC3 12_61 0.000 115.01 5.0e-12 -0.64
9626 PARPBP 12_61 0.783 103.79 2.4e-04 -10.53
12694 RP11-210L7.3 12_61 0.000 358.24 7.9e-14 -18.02
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_16"
genename region_tag susie_pip mu2 PVE z
2881 CENPA 2_16 0.662 25.01 4.8e-05 -5.90
11149 OST4 2_16 0.004 8.94 1.1e-07 -2.74
4939 EMILIN1 2_16 0.004 22.70 2.7e-07 7.90
4927 KHK 2_16 0.005 8.79 1.3e-07 -3.47
4935 PREB 2_16 0.009 41.90 1.1e-06 -7.67
4941 ATRAID 2_16 0.019 104.75 5.8e-06 7.80
4936 SLC5A6 2_16 0.019 106.18 5.8e-06 -7.87
1060 CAD 2_16 0.009 67.00 1.7e-06 -5.23
2885 SLC30A3 2_16 0.013 77.76 3.1e-06 -9.66
7169 UCN 2_16 0.005 18.32 2.4e-07 -3.54
2891 SNX17 2_16 0.044 160.39 2.1e-05 12.91
7170 ZNF513 2_16 0.327 46.66 4.5e-05 -1.21
2887 NRBP1 2_16 0.004 212.73 2.6e-06 -12.22
4925 IFT172 2_16 0.004 20.01 2.4e-07 -3.60
1058 GCKR 2_16 0.354 132.72 1.4e-04 16.85
10987 C2orf16 2_16 0.354 132.72 1.4e-04 16.85
10407 GPN1 2_16 0.005 57.35 8.6e-07 -4.38
8847 CCDC121 2_16 0.013 16.20 6.2e-07 1.47
6575 BRE 2_16 0.005 18.60 2.6e-07 3.96
8284 RBKS 2_16 0.008 111.05 2.8e-06 -12.26
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 19_9"
genename region_tag susie_pip mu2 PVE z
4124 ZNF317 19_9 0.000 4.84 3.5e-17 -0.07
10020 ZNF699 19_9 0.000 26.93 2.2e-15 2.04
9901 ZNF559 19_9 0.000 12.28 1.9e-16 -1.09
9933 ZNF177 19_9 0.000 14.49 3.0e-16 -1.11
8657 ZNF266 19_9 0.000 13.40 2.5e-16 1.27
10320 ZNF121 19_9 0.000 12.37 2.7e-16 1.14
8317 ZNF561 19_9 0.000 5.46 4.1e-17 -0.48
12616 CTD-3116E22.8 19_9 0.000 5.51 4.2e-17 -0.57
12140 CTD-3116E22.7 19_9 0.000 6.10 5.0e-17 -0.80
10113 ZNF846 19_9 0.000 7.48 7.0e-17 0.90
3858 FBXL12 19_9 0.000 8.08 8.2e-17 -1.02
3857 PIN1 19_9 0.000 48.10 3.6e-14 3.33
1972 OLFM2 19_9 0.000 15.96 4.4e-16 1.84
964 COL5A3 19_9 0.000 5.24 3.9e-17 1.58
4125 PPAN 19_9 0.000 14.19 1.6e-16 -2.21
11524 P2RY11 19_9 0.000 7.07 6.0e-17 1.35
12135 S1PR2 19_9 1.000 149.54 4.4e-04 -15.92
2010 MRPL4 19_9 0.000 62.08 3.7e-12 -1.88
1218 ICAM1 19_9 0.000 46.57 3.3e-16 -11.67
10981 ZGLP1 19_9 0.776 76.91 1.7e-04 -12.08
12143 FDX2 19_9 0.000 8.47 2.3e-16 1.72
2020 TYK2 19_9 0.000 28.82 2.7e-16 -8.20
612 PDE4A 19_9 0.000 56.33 2.1e-13 -3.90
952 KEAP1 19_9 0.000 21.20 1.5e-15 -1.89
9178 S1PR5 19_9 0.000 6.97 5.5e-17 1.25
4113 ATG4D 19_9 0.000 14.28 2.5e-16 -2.40
3997 KRI1 19_9 0.000 9.25 6.6e-17 2.12
4000 AP1M2 19_9 0.000 35.02 6.9e-14 1.21
3999 SLC44A2 19_9 0.000 9.00 7.2e-17 3.99
12108 ILF3-AS1 19_9 0.000 6.40 4.9e-17 -0.60
3998 ILF3 19_9 0.726 67.52 1.4e-04 10.41
10818 QTRT1 19_9 0.008 73.06 1.7e-06 -12.59
1353 TMED1 19_9 0.000 13.35 1.3e-16 -2.58
10897 C19orf38 19_9 0.000 13.35 1.3e-16 2.58
5383 CARM1 19_9 0.000 7.24 6.7e-17 -1.65
4112 YIPF2 19_9 0.000 32.11 1.6e-14 6.15
3874 SMARCA4 19_9 0.000 18.91 1.8e-15 5.36
6872 SPC24 19_9 0.000 47.09 6.6e-14 -4.25
| 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
23245 rs164877 1_55 1.000 310.87 9.1e-04 14.93
33940 rs77369503 1_80 1.000 44.22 1.3e-04 -6.59
48907 rs7548045 1_108 1.000 55.67 1.6e-04 -4.14
52633 rs287613 1_116 1.000 468.58 1.4e-03 3.46
52639 rs71180790 1_116 1.000 464.95 1.4e-03 2.98
53599 rs766167074 1_118 1.000 5864.16 1.7e-02 2.75
82472 rs35641591 2_46 1.000 57.92 1.7e-04 -7.78
125912 rs142215640 2_136 1.000 166.91 4.9e-04 -3.60
142123 rs9854123 3_24 1.000 40.66 1.2e-04 6.29
157616 rs56320121 3_58 1.000 787.64 2.3e-03 -3.10
157632 rs768688512 3_58 1.000 976.27 2.9e-03 -3.54
182763 rs519352 3_105 1.000 84.71 2.5e-04 12.46
182781 rs6445061 3_105 1.000 154.77 4.5e-04 -14.75
185023 rs146797780 3_110 1.000 92556.81 2.7e-01 -5.92
185024 rs7636471 3_110 1.000 92458.89 2.7e-01 -5.66
186840 rs6778003 3_114 1.000 42.84 1.3e-04 -6.08
186873 rs6773553 3_114 1.000 35.35 1.0e-04 4.88
192325 rs114524202 4_4 1.000 37.72 1.1e-04 -6.94
206744 rs116419948 4_35 1.000 75.58 2.2e-04 5.68
266802 rs55681913 5_28 1.000 246.15 7.2e-04 15.62
293834 rs329123 5_80 1.000 56.26 1.6e-04 7.99
316552 rs1980449 6_19 1.000 57.66 1.7e-04 8.94
317193 rs6908155 6_21 1.000 38.25 1.1e-04 1.41
343412 rs657536 6_67 1.000 43.17 1.3e-04 -6.95
346177 rs3800231 6_73 1.000 317.54 9.3e-04 18.75
360849 rs60425481 6_104 1.000 184.30 5.4e-04 -13.20
362947 rs2323036 6_108 1.000 164.19 4.8e-04 14.90
376665 rs11761979 7_24 1.000 52.86 1.5e-04 -7.18
381600 rs185529878 7_33 1.000 83.30 2.4e-04 7.39
381629 rs1542820 7_34 1.000 227.76 6.7e-04 -17.00
381854 rs2107787 7_34 1.000 238.77 7.0e-04 17.50
381950 rs700752 7_34 1.000 2229.11 6.5e-03 47.41
382048 rs79306382 7_35 1.000 37.17 1.1e-04 -6.34
405874 rs125124 7_80 1.000 474.47 1.4e-03 22.58
413494 rs78609178 7_98 1.000 35.45 1.0e-04 -4.52
424410 rs1495743 8_20 1.000 77.81 2.3e-04 -9.05
436079 rs4738679 8_45 1.000 83.50 2.4e-04 -9.59
463772 rs79531507 9_5 1.000 42.00 1.2e-04 -6.61
463792 rs12552790 9_5 1.000 67.51 2.0e-04 -8.04
463836 rs41303235 9_6 1.000 107.38 3.1e-04 9.73
489090 rs143474127 9_54 1.000 47.47 1.4e-04 9.05
512782 rs71007692 10_28 1.000 9582.85 2.8e-02 2.95
529104 rs35443777 10_60 1.000 62.74 1.8e-04 -6.24
530979 rs10883563 10_64 1.000 115.03 3.4e-04 10.79
541789 rs11042594 11_2 1.000 399.03 1.2e-03 17.70
541798 rs7481173 11_2 1.000 176.00 5.1e-04 -0.73
541799 rs17885785 11_2 1.000 360.79 1.1e-03 24.68
541800 rs2239681 11_2 1.000 233.35 6.8e-04 -25.38
541801 rs3842762 11_2 1.000 329.30 9.6e-04 -19.28
584323 rs2856322 12_11 1.000 103.27 3.0e-04 -10.04
591311 rs7302975 12_21 1.000 139.33 4.1e-04 12.91
611880 rs186877434 12_61 1.000 69.84 2.0e-04 -11.39
617713 rs80019595 12_74 1.000 301.85 8.8e-04 19.61
617927 rs140184587 12_75 1.000 48.72 1.4e-04 6.47
634702 rs7999449 13_25 1.000 37832.08 1.1e-01 -4.29
634704 rs775834524 13_25 1.000 37910.53 1.1e-01 -4.23
670829 rs13379043 14_34 1.000 76.31 2.2e-04 -8.81
679479 rs12147987 14_52 1.000 65.77 1.9e-04 -4.57
679487 rs12885370 14_52 1.000 69.05 2.0e-04 -4.81
692403 rs4474658 15_28 1.000 66.89 2.0e-04 -11.20
695616 rs876383 15_35 1.000 58.95 1.7e-04 8.12
703474 rs72767924 15_47 1.000 73.97 2.2e-04 5.08
703476 rs9672558 15_47 1.000 79.34 2.3e-04 5.56
703557 rs3743250 15_48 1.000 55.58 1.6e-04 -6.91
705155 rs117544769 16_1 1.000 86.22 2.5e-04 -10.97
705166 rs11248852 16_1 1.000 142.55 4.2e-04 -16.99
705174 rs2076421 16_1 1.000 119.91 3.5e-04 15.41
725459 rs9931108 16_45 1.000 96.52 2.8e-04 5.65
742289 rs1801689 17_38 1.000 128.63 3.8e-04 11.78
765252 rs77728352 18_32 1.000 41.53 1.2e-04 -6.23
771790 rs77169818 18_46 1.000 77.68 2.3e-04 -8.89
781059 rs73924758 19_22 1.000 46.15 1.3e-04 -5.31
785051 rs814573 19_32 1.000 36.67 1.1e-04 5.88
791220 rs200167482 20_8 1.000 35.33 1.0e-04 -5.77
794631 rs6112780 20_14 1.000 77.94 2.3e-04 -10.08
794707 rs10470054 20_14 1.000 55.95 1.6e-04 8.31
804643 rs79723704 20_34 1.000 42.12 1.2e-04 -6.38
806424 rs6122476 20_37 1.000 35.27 1.0e-04 -5.50
855823 rs35130213 1_19 1.000 2596.49 7.6e-03 -3.96
855825 rs2236854 1_19 1.000 2595.88 7.6e-03 -3.85
892941 rs145990041 1_112 1.000 70.82 2.1e-04 8.95
909185 rs1260326 2_16 1.000 477.84 1.4e-03 25.55
1044750 rs145982925 11_21 1.000 7176.60 2.1e-02 3.22
1044751 rs35827570 11_21 1.000 7167.73 2.1e-02 1.83
1105207 rs112720618 16_2 1.000 19426.13 5.7e-02 -7.54
1105208 rs56404919 16_2 1.000 19267.26 5.6e-02 -7.54
1163159 rs5388 17_37 1.000 434.31 1.3e-03 22.63
1191823 rs142998071 19_33 1.000 44.05 1.3e-04 6.85
1227156 rs1005694 21_17 1.000 84.23 2.5e-04 -1.93
269105 rs113088001 5_31 0.999 47.96 1.4e-04 7.38
293972 rs11242237 5_80 0.999 88.78 2.6e-04 -8.00
295986 rs853161 5_84 0.999 45.34 1.3e-04 -6.61
305843 rs2340010 5_104 0.999 33.62 9.8e-05 5.60
365270 rs13226659 7_2 0.999 67.40 2.0e-04 8.62
365638 rs4719415 7_4 0.999 60.38 1.8e-04 7.92
457634 rs12674961 8_88 0.999 50.88 1.5e-04 -8.89
522289 rs10823504 10_46 0.999 34.09 9.9e-05 5.62
591334 rs7967974 12_22 0.999 46.07 1.3e-04 -8.27
611948 rs882409 12_61 0.999 120.92 3.5e-04 16.43
614709 rs75622376 12_67 0.999 62.04 1.8e-04 7.66
740074 rs11079157 17_32 0.999 40.58 1.2e-04 6.51
991162 rs662138 6_103 0.999 94.04 2.7e-04 9.66
16277 rs11209239 1_43 0.998 33.84 9.9e-05 5.63
57793 rs150491879 1_129 0.998 34.09 9.9e-05 5.60
91901 rs3789066 2_66 0.998 35.14 1.0e-04 5.93
172105 rs12489068 3_85 0.998 92.10 2.7e-04 -10.65
371361 rs34124255 7_15 0.998 38.19 1.1e-04 -4.46
381621 rs9658238 7_33 0.998 66.35 1.9e-04 9.39
382081 rs7791050 7_35 0.998 36.93 1.1e-04 -6.88
555905 rs12797220 11_30 0.998 41.75 1.2e-04 4.67
612005 rs1580715 12_62 0.998 113.50 3.3e-04 -9.65
827382 rs5765672 22_20 0.998 31.66 9.2e-05 -5.23
48955 rs1223802 1_108 0.997 55.99 1.6e-04 -6.76
359066 rs9479504 6_100 0.997 78.39 2.3e-04 9.02
504253 rs60100723 10_12 0.997 38.06 1.1e-04 6.26
1174269 rs34536443 19_9 0.997 86.58 2.5e-04 -8.42
182780 rs28507699 3_105 0.996 150.70 4.4e-04 -10.47
316802 rs9467715 6_20 0.996 45.85 1.3e-04 -2.60
413251 rs7810268 7_98 0.996 36.12 1.1e-04 5.54
69405 rs72787520 2_20 0.995 38.19 1.1e-04 -5.31
371364 rs6954572 7_15 0.995 76.55 2.2e-04 -7.97
550533 rs56133711 11_19 0.995 38.71 1.1e-04 -6.15
296052 rs6894302 5_84 0.994 40.87 1.2e-04 5.82
304022 rs2974438 5_100 0.994 260.35 7.6e-04 -17.69
38199 rs10913276 1_86 0.993 118.95 3.5e-04 16.90
62177 rs13018091 2_4 0.993 42.94 1.2e-04 -6.64
343477 rs7763983 6_67 0.993 33.28 9.7e-05 6.37
367729 rs186587982 7_9 0.993 150.24 4.4e-04 -13.53
521904 rs2305196 10_46 0.993 38.68 1.1e-04 -5.79
529105 rs3740365 10_60 0.993 56.66 1.6e-04 -5.74
134865 rs139232179 3_9 0.992 36.58 1.1e-04 5.90
747444 rs36000545 17_46 0.992 33.72 9.8e-05 -5.70
304030 rs6885027 5_100 0.991 45.48 1.3e-04 8.79
463831 rs7032169 9_6 0.991 36.30 1.1e-04 3.67
646402 rs57684439 13_45 0.991 30.20 8.7e-05 4.33
715490 rs17616063 16_27 0.991 29.68 8.6e-05 -5.05
146264 rs1605068 3_36 0.989 29.43 8.5e-05 5.00
79850 rs1621048 2_40 0.988 32.88 9.5e-05 -4.94
123608 rs4674919 2_132 0.988 38.03 1.1e-04 6.33
596705 rs117564283 12_32 0.988 32.54 9.4e-05 5.83
694073 rs143717852 15_31 0.988 84.19 2.4e-04 -8.48
725533 rs112290554 16_45 0.988 84.92 2.4e-04 -9.40
1189830 rs75621460 19_28 0.988 32.71 9.4e-05 -5.82
528896 rs12355020 10_59 0.987 30.68 8.8e-05 -6.10
591238 rs113987763 12_21 0.987 158.06 4.6e-04 10.27
794593 rs6136911 20_14 0.987 56.52 1.6e-04 -9.34
67760 rs62127724 2_15 0.984 286.23 8.2e-04 17.32
319476 rs2524082 6_25 0.984 43.24 1.2e-04 -6.71
725676 rs72823102 16_46 0.982 28.38 8.1e-05 5.44
522001 rs11597602 10_46 0.981 31.15 8.9e-05 -4.85
550482 rs3741407 11_19 0.981 27.92 8.0e-05 -3.87
612155 rs4764939 12_62 0.979 40.68 1.2e-04 6.25
427501 rs11780047 8_27 0.977 36.12 1.0e-04 -5.84
293944 rs35914524 5_80 0.976 32.78 9.3e-05 4.56
781058 rs7249790 19_22 0.976 30.58 8.7e-05 -2.65
824959 rs138703 22_15 0.976 129.00 3.7e-04 -11.01
275952 rs77561962 5_45 0.975 33.95 9.7e-05 5.78
407697 rs12155147 7_84 0.975 30.71 8.7e-05 5.40
244621 rs17540470 4_109 0.974 33.25 9.5e-05 5.79
351756 rs142620810 6_85 0.974 28.69 8.2e-05 5.13
794710 rs3827963 20_14 0.973 34.52 9.8e-05 -6.07
48912 rs3754140 1_108 0.972 66.15 1.9e-04 6.87
534220 rs12244851 10_70 0.972 33.52 9.5e-05 5.60
160365 rs4928057 3_64 0.970 32.06 9.1e-05 -7.36
423109 rs75886735 8_17 0.970 27.77 7.9e-05 4.94
558974 rs1203614 11_37 0.970 26.92 7.6e-05 4.20
612019 rs1874872 12_62 0.969 46.20 1.3e-04 -1.20
677131 rs17090693 14_48 0.969 33.44 9.5e-05 4.21
727386 rs558760274 17_1 0.969 25.49 7.2e-05 4.74
703560 rs58060839 15_48 0.968 37.30 1.1e-04 -5.24
725485 rs12934751 16_45 0.967 130.75 3.7e-04 11.08
771916 rs62104512 18_46 0.967 48.51 1.4e-04 -6.88
567000 rs12795994 11_53 0.965 26.55 7.5e-05 -5.31
460340 rs13253652 8_92 0.964 27.99 7.9e-05 2.53
1174287 rs12720356 19_9 0.964 123.22 3.5e-04 -14.75
1119519 rs4782568 16_48 0.963 122.73 3.5e-04 -11.28
546646 rs61885960 11_11 0.960 29.87 8.4e-05 5.10
316980 rs140967207 6_21 0.959 30.54 8.6e-05 5.10
785403 rs7249509 19_32 0.958 28.96 8.1e-05 -4.98
362991 rs76523601 6_108 0.957 49.01 1.4e-04 -3.70
371285 rs7802610 7_15 0.955 26.60 7.4e-05 5.19
799590 rs6103338 20_27 0.955 31.71 8.8e-05 5.45
192341 rs3748034 4_4 0.954 30.84 8.6e-05 -6.03
764877 rs9953884 18_31 0.951 55.23 1.5e-04 6.80
401256 rs1868757 7_70 0.950 27.17 7.5e-05 5.35
627127 rs7999704 13_9 0.950 29.44 8.2e-05 -5.10
125911 rs12478406 2_136 0.949 85.53 2.4e-04 -2.12
740747 rs12947269 17_34 0.948 27.54 7.6e-05 -5.71
382201 rs13230267 7_35 0.945 31.04 8.6e-05 5.19
389972 rs11762191 7_47 0.944 58.63 1.6e-04 8.71
554918 rs11039134 11_29 0.944 55.04 1.5e-04 -10.08
664147 rs10136844 14_21 0.944 27.38 7.6e-05 -4.95
686996 rs3803361 15_13 0.943 25.73 7.1e-05 -4.74
692287 rs2414752 15_28 0.943 30.77 8.5e-05 -4.32
709645 rs34967165 16_12 0.943 33.04 9.1e-05 5.36
48908 rs340835 1_108 0.942 42.95 1.2e-04 -6.14
591366 rs11051788 12_22 0.941 32.70 9.0e-05 -6.27
578390 rs765386 11_80 0.939 26.62 7.3e-05 -4.73
649246 rs892252 13_51 0.938 25.28 6.9e-05 4.66
1000975 rs35887778 7_61 0.938 39.18 1.1e-04 6.85
420585 rs77304020 8_14 0.937 40.01 1.1e-04 -5.57
135486 rs2227998 3_10 0.936 43.32 1.2e-04 6.10
528925 rs78382982 10_59 0.936 26.42 7.2e-05 5.10
172804 rs58020426 3_87 0.935 24.67 6.7e-05 -4.30
807037 rs2823025 21_2 0.934 25.03 6.8e-05 -4.70
279359 rs557184468 5_52 0.933 38.51 1.0e-04 -7.51
317897 rs3130253 6_23 0.931 41.22 1.1e-04 3.99
688603 rs12050772 15_20 0.931 56.29 1.5e-04 -7.07
760530 rs991014 18_24 0.931 34.74 9.4e-05 5.69
306562 rs62389092 5_105 0.929 24.49 6.6e-05 -4.55
507384 rs750689165 10_16 0.929 38.91 1.1e-04 -7.35
322162 rs72880536 6_28 0.928 26.81 7.3e-05 -4.75
172752 rs4683606 3_86 0.927 194.72 5.3e-04 -13.36
512525 rs2505692 10_27 0.926 24.86 6.7e-05 3.78
91977 rs2166862 2_66 0.924 30.08 8.1e-05 5.18
740740 rs8074463 17_34 0.924 29.15 7.9e-05 -5.88
736881 rs17614452 17_26 0.923 28.11 7.6e-05 5.04
798182 rs2246443 20_23 0.921 25.08 6.7e-05 4.15
460331 rs56114972 8_92 0.919 24.24 6.5e-05 -3.81
522718 rs780662 10_47 0.918 25.23 6.8e-05 4.65
274320 rs10062008 5_43 0.914 25.60 6.8e-05 4.34
567044 rs509723 11_54 0.914 31.78 8.5e-05 -5.29
669289 rs34489253 14_32 0.911 46.32 1.2e-04 -7.04
489217 rs817854 9_54 0.909 27.50 7.3e-05 3.66
222436 rs1813867 4_66 0.908 32.23 8.5e-05 -6.79
800319 rs577036133 20_28 0.908 25.93 6.9e-05 4.55
38896 rs4442334 1_89 0.907 43.67 1.2e-04 -6.82
669340 rs3784139 14_32 0.907 29.63 7.9e-05 -6.39
186803 rs6782470 3_114 0.906 25.68 6.8e-05 4.51
182328 rs10653660 3_104 0.905 58.02 1.5e-04 7.76
584303 rs12824533 12_11 0.905 26.32 7.0e-05 3.80
616732 rs149837779 12_73 0.905 24.82 6.6e-05 -4.56
345813 rs2354558 6_71 0.903 24.01 6.3e-05 4.36
669880 rs4902841 14_33 0.903 25.61 6.8e-05 4.62
781006 rs73019624 19_21 0.899 38.40 1.0e-04 -6.29
455740 rs2648832 8_84 0.897 24.53 6.4e-05 -4.50
182182 rs4955590 3_104 0.894 26.95 7.0e-05 -5.25
57727 rs61833239 1_128 0.892 26.14 6.8e-05 -2.13
54893 rs4006577 1_122 0.890 24.83 6.5e-05 4.51
798811 rs62209440 20_24 0.889 25.27 6.6e-05 -4.64
345130 rs4515420 6_70 0.888 32.12 8.3e-05 5.30
164407 rs148695018 3_70 0.886 25.50 6.6e-05 4.53
754197 rs8093352 18_11 0.886 24.70 6.4e-05 4.28
623189 rs4294650 13_2 0.885 53.93 1.4e-04 -7.29
471037 rs10965488 9_17 0.883 28.58 7.4e-05 4.98
362054 rs777679051 6_106 0.881 30.19 7.8e-05 -5.19
110939 rs141607132 2_107 0.880 24.55 6.3e-05 4.41
702650 rs1464445 15_46 0.880 49.47 1.3e-04 -6.81
588393 rs74842514 12_17 0.879 32.42 8.3e-05 -5.42
697490 rs72734182 15_38 0.879 25.23 6.5e-05 4.39
92466 rs4849177 2_67 0.878 57.18 1.5e-04 7.63
414173 rs12698259 7_99 0.878 26.26 6.7e-05 3.95
12690 rs55869368 1_35 0.875 25.09 6.4e-05 -4.48
74102 rs2121564 2_28 0.875 26.57 6.8e-05 -4.80
23244 rs146501986 1_55 0.874 259.25 6.6e-04 16.90
483367 rs1360200 9_45 0.872 27.70 7.1e-05 -5.47
711617 rs6497339 16_18 0.871 34.43 8.8e-05 -5.53
1086977 rs11620783 14_3 0.870 67.08 1.7e-04 -7.45
397467 rs75082775 7_62 0.869 24.25 6.1e-05 4.27
435223 rs71519448 8_44 0.866 46.84 1.2e-04 2.30
573219 rs75794878 11_67 0.863 33.52 8.5e-05 -5.55
310709 rs2765359 6_7 0.861 36.13 9.1e-05 -4.63
1231463 rs62223645 21_17 0.859 37.77 9.5e-05 5.69
703612 rs35477848 15_48 0.857 25.68 6.4e-05 -4.01
693749 rs36120854 15_30 0.855 24.77 6.2e-05 4.28
526694 rs2607863 10_55 0.854 24.36 6.1e-05 -4.38
710631 rs35512524 16_15 0.854 27.03 6.7e-05 5.24
362924 rs118014721 6_108 0.853 85.75 2.1e-04 4.80
270931 rs12656462 5_35 0.851 38.27 9.5e-05 -5.81
746018 rs8065893 17_43 0.851 25.38 6.3e-05 4.44
789414 rs74273659 20_5 0.849 24.55 6.1e-05 -4.38
310758 rs545632 6_7 0.848 27.61 6.8e-05 -5.66
721277 rs71403855 16_38 0.847 26.22 6.5e-05 4.98
64327 rs5829382 2_8 0.844 25.69 6.3e-05 4.62
104620 rs834837 2_93 0.844 25.55 6.3e-05 4.57
575593 rs10892819 11_74 0.842 28.77 7.1e-05 -5.26
774171 rs10408455 19_5 0.842 46.20 1.1e-04 -6.30
577053 rs10893498 11_77 0.841 34.18 8.4e-05 -5.75
826844 rs136908 22_20 0.841 28.55 7.0e-05 5.05
134658 rs11128570 3_9 0.840 27.59 6.8e-05 5.33
185153 rs6793063 3_111 0.838 28.18 6.9e-05 4.88
630176 rs61630147 13_15 0.838 159.29 3.9e-04 12.88
147415 rs79987842 3_38 0.837 31.24 7.6e-05 -4.74
353332 rs765215967 6_89 0.837 25.08 6.1e-05 -4.40
666755 rs6573307 14_27 0.836 92.87 2.3e-04 10.00
138522 rs17400314 3_17 0.835 26.15 6.4e-05 5.14
310782 rs9379083 6_7 0.835 49.95 1.2e-04 5.84
825934 rs2267452 22_18 0.835 26.65 6.5e-05 4.72
172091 rs940191 3_85 0.832 38.48 9.4e-05 -6.92
430428 rs10087804 8_33 0.831 28.57 6.9e-05 4.98
803620 rs6127693 20_33 0.831 30.15 7.3e-05 5.95
381886 rs11773764 7_34 0.828 86.99 2.1e-04 12.06
574860 rs56246162 11_72 0.828 24.60 6.0e-05 -4.42
776977 rs146213062 19_12 0.828 24.74 6.0e-05 -4.44
308080 rs77507057 5_110 0.825 44.55 1.1e-04 6.48
744428 rs7216472 17_41 0.825 35.52 8.6e-05 -5.77
381581 rs10246245 7_33 0.822 87.38 2.1e-04 7.96
775198 rs10401485 19_7 0.822 36.41 8.7e-05 6.15
1227176 rs113455659 21_17 0.822 168.47 4.0e-04 15.87
821669 rs9608723 22_9 0.820 37.00 8.9e-05 -6.39
405653 rs4507692 7_79 0.819 35.54 8.5e-05 -5.67
494035 rs569990989 9_63 0.818 24.48 5.8e-05 4.45
824065 rs5755943 22_14 0.818 56.89 1.4e-04 7.67
322369 rs1187117 6_28 0.816 55.86 1.3e-04 7.74
706019 rs76814483 16_6 0.816 87.98 2.1e-04 -9.51
146233 rs34789050 3_35 0.811 37.38 8.8e-05 5.74
555293 rs5791853 11_29 0.811 140.11 3.3e-04 14.63
598027 rs2657880 12_35 0.810 35.25 8.3e-05 -5.97
458089 rs2315839 8_88 0.809 54.47 1.3e-04 7.50
1226914 rs149331216 21_17 0.808 60.12 1.4e-04 8.75
135578 rs1038300 3_10 0.807 25.94 6.1e-05 -4.30
677141 rs10151359 14_48 0.806 25.18 5.9e-05 -2.51
351551 rs2184968 6_84 0.801 746.96 1.7e-03 27.80
652663 rs1079971 13_59 0.801 25.64 6.0e-05 4.33SNPs 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
185023 rs146797780 3_110 1.000 92556.81 0.2700 -5.92
185024 rs7636471 3_110 1.000 92458.89 0.2700 -5.66
185026 rs6769162 3_110 0.000 89796.79 0.0000 -5.56
185007 rs6807293 3_110 0.000 82366.01 0.0000 -5.50
184995 rs6794252 3_110 0.000 82271.39 0.0000 -5.52
185027 rs9838117 3_110 0.000 71294.88 0.0000 -4.83
185001 rs9844482 3_110 0.000 57802.40 0.0000 -5.15
185005 rs34435186 3_110 0.000 45724.25 0.0000 -3.87
634704 rs775834524 13_25 1.000 37910.53 0.1100 -4.23
634702 rs7999449 13_25 1.000 37832.08 0.1100 -4.29
634694 rs7337153 13_25 0.032 37797.15 0.0036 -4.27
634699 rs9537143 13_25 0.142 37685.46 0.0160 4.38
634698 rs9597193 13_25 0.033 37681.88 0.0036 4.38
634697 rs9527401 13_25 0.033 37681.66 0.0037 4.38
634695 rs9527399 13_25 0.042 37681.49 0.0046 4.38
634693 rs9537125 13_25 0.012 37655.70 0.0013 4.37
634692 rs9527398 13_25 0.012 37655.54 0.0013 4.37
634690 rs9537123 13_25 0.024 37653.21 0.0027 4.39
634684 rs2937326 13_25 0.000 36976.23 0.0000 -4.31
634683 rs3013347 13_25 0.000 36976.08 0.0000 -4.30
634685 rs9597179 13_25 0.000 36855.00 0.0000 4.38
634709 rs9537159 13_25 0.000 36210.34 0.0000 -4.46
634715 rs539380 13_25 0.000 36166.27 0.0000 -4.45
634686 rs9537116 13_25 0.000 36155.29 0.0000 4.30
634708 rs35800055 13_25 0.000 36079.30 0.0000 4.56
634705 rs4536353 13_25 0.000 36076.43 0.0000 4.57
634707 rs67100646 13_25 0.000 36076.22 0.0000 4.58
634706 rs4296148 13_25 0.000 36075.00 0.0000 4.57
634712 rs7994036 13_25 0.000 36066.22 0.0000 4.56
634710 rs9597201 13_25 0.000 36064.82 0.0000 4.57
634714 rs9537174 13_25 0.000 36063.45 0.0000 4.55
634681 rs3105089 13_25 0.000 34300.16 0.0000 -4.31
634680 rs3124374 13_25 0.000 34092.70 0.0000 -4.40
634679 rs2315886 13_25 0.000 34081.88 0.0000 -4.42
634678 rs2315887 13_25 0.000 34081.80 0.0000 -4.42
634670 rs2315898 13_25 0.000 34043.08 0.0000 -4.40
634672 rs3105045 13_25 0.000 34035.60 0.0000 -4.44
634673 rs2315895 13_25 0.000 34035.50 0.0000 -4.43
634674 rs3124405 13_25 0.000 34035.07 0.0000 -4.44
634668 rs7317475 13_25 0.000 33998.21 0.0000 -4.34
634676 rs3124402 13_25 0.000 33983.83 0.0000 -4.37
634660 rs4635225 13_25 0.000 33919.33 0.0000 -4.32
634662 rs616312 13_25 0.000 33919.10 0.0000 -4.31
634665 rs520268 13_25 0.000 33919.10 0.0000 -4.31
634657 rs1960704 13_25 0.000 33917.24 0.0000 -4.32
634721 rs9569325 13_25 0.000 33607.09 0.0000 -3.92
634726 rs2095219 13_25 0.000 33481.64 0.0000 -3.81
634718 rs480215 13_25 0.000 33457.57 0.0000 -3.84
634725 rs4885924 13_25 0.000 33409.39 0.0000 -3.82
634724 rs4885918 13_25 0.000 33320.21 0.0000 -3.81SNPs 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
185023 rs146797780 3_110 1.000 92556.81 0.27000 -5.92
185024 rs7636471 3_110 1.000 92458.89 0.27000 -5.66
634702 rs7999449 13_25 1.000 37832.08 0.11000 -4.29
634704 rs775834524 13_25 1.000 37910.53 0.11000 -4.23
1105207 rs112720618 16_2 1.000 19426.13 0.05700 -7.54
1105208 rs56404919 16_2 1.000 19267.26 0.05600 -7.54
512782 rs71007692 10_28 1.000 9582.85 0.02800 2.95
1044750 rs145982925 11_21 1.000 7176.60 0.02100 3.22
1044751 rs35827570 11_21 1.000 7167.73 0.02100 1.83
53599 rs766167074 1_118 1.000 5864.16 0.01700 2.75
634699 rs9537143 13_25 0.142 37685.46 0.01600 4.38
512779 rs9299760 10_28 0.506 9562.78 0.01400 2.94
512788 rs2472183 10_28 0.499 9567.10 0.01400 2.91
512791 rs11011452 10_28 0.394 9567.43 0.01100 2.89
512781 rs2474565 10_28 0.343 9566.86 0.00960 2.90
855823 rs35130213 1_19 1.000 2596.49 0.00760 -3.96
855825 rs2236854 1_19 1.000 2595.88 0.00760 -3.85
381950 rs700752 7_34 1.000 2229.11 0.00650 47.41
53597 rs2486737 1_118 0.296 5896.79 0.00510 2.27
53598 rs971534 1_118 0.281 5896.77 0.00480 2.27
634695 rs9527399 13_25 0.042 37681.49 0.00460 4.38
634697 rs9527401 13_25 0.033 37681.66 0.00370 4.38
53605 rs2248646 1_118 0.208 5894.78 0.00360 2.28
634694 rs7337153 13_25 0.032 37797.15 0.00360 -4.27
634698 rs9597193 13_25 0.033 37681.88 0.00360 4.38
53593 rs2790891 1_118 0.166 5896.19 0.00290 2.26
53594 rs2491405 1_118 0.166 5896.19 0.00290 2.26
53606 rs2211176 1_118 0.169 5894.90 0.00290 2.27
53607 rs2790882 1_118 0.169 5894.90 0.00290 2.27
157632 rs768688512 3_58 1.000 976.27 0.00290 -3.54
634690 rs9537123 13_25 0.024 37653.21 0.00270 4.39
53596 rs10489611 1_118 0.141 5896.45 0.00240 2.26
157616 rs56320121 3_58 1.000 787.64 0.00230 -3.10
53590 rs2256908 1_118 0.123 5896.07 0.00210 2.26
351551 rs2184968 6_84 0.801 746.96 0.00170 27.80
512986 rs199841839 10_28 0.129 3904.31 0.00150 5.64
1104191 rs80253441 16_2 0.719 691.87 0.00150 -12.35
52633 rs287613 1_116 1.000 468.58 0.00140 3.46
52639 rs71180790 1_116 1.000 464.95 0.00140 2.98
405874 rs125124 7_80 1.000 474.47 0.00140 22.58
909185 rs1260326 2_16 1.000 477.84 0.00140 25.55
157631 rs6765538 3_58 0.467 970.48 0.00130 -3.39
634692 rs9527398 13_25 0.012 37655.54 0.00130 4.37
634693 rs9537125 13_25 0.012 37655.70 0.00130 4.37
1163159 rs5388 17_37 1.000 434.31 0.00130 22.63
512990 rs141987073 10_28 0.105 3905.54 0.00120 5.62
541789 rs11042594 11_2 1.000 399.03 0.00120 17.70
541799 rs17885785 11_2 1.000 360.79 0.00110 24.68
53611 rs2790874 1_118 0.060 5887.45 0.00100 2.28
541801 rs3842762 11_2 1.000 329.30 0.00096 -19.28SNPs 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
381950 rs700752 7_34 1.000 2229.11 6.5e-03 47.41
381949 rs1917609 7_34 0.000 1616.43 1.7e-17 -39.95
381939 rs7801650 7_34 0.000 1540.26 1.3e-17 -39.07
381942 rs7782135 7_34 0.000 1539.07 1.3e-17 -39.06
381940 rs7788438 7_34 0.000 1537.10 1.2e-17 -39.03
381933 rs35692095 7_34 0.000 1517.39 1.0e-17 -38.79
381935 rs4724488 7_34 0.000 1518.13 1.1e-17 -38.78
611932 rs5742678 12_61 0.531 535.69 8.3e-04 -29.02
611924 rs1520222 12_61 0.469 535.13 7.3e-04 -28.95
351551 rs2184968 6_84 0.801 746.96 1.7e-03 27.80
351549 rs4897179 6_84 0.192 744.80 4.2e-04 27.76
351552 rs1361109 6_84 0.009 736.72 1.9e-05 27.64
351554 rs4895808 6_84 0.007 735.64 1.5e-05 27.62
351555 rs1844594 6_84 0.006 734.55 1.2e-05 27.60
351559 rs9398810 6_84 0.005 733.05 1.0e-05 27.57
351560 rs9401885 6_84 0.005 733.72 1.1e-05 27.57
351557 rs9372839 6_84 0.004 729.06 8.1e-06 27.50
351543 rs2326387 6_84 0.003 712.19 6.7e-06 27.14
351546 rs1361262 6_84 0.003 712.38 6.8e-06 27.14
351542 rs9375435 6_84 0.003 703.38 6.6e-06 26.97
351565 rs6921183 6_84 0.011 633.18 2.1e-05 25.91
351566 rs9401890 6_84 0.011 631.98 2.0e-05 25.89
351567 rs9375448 6_84 0.011 630.02 2.0e-05 25.85
351573 rs9491653 6_84 0.008 613.08 1.4e-05 25.55
909185 rs1260326 2_16 1.000 477.84 1.4e-03 25.55
351572 rs4629707 6_84 0.007 611.29 1.3e-05 25.53
351571 rs7738836 6_84 0.007 611.06 1.3e-05 25.52
351575 rs9375449 6_84 0.007 611.46 1.3e-05 25.52
351577 rs4895813 6_84 0.007 611.00 1.3e-05 25.51
351580 rs11154367 6_84 0.008 611.24 1.4e-05 25.51
351581 rs853987 6_84 0.007 606.94 1.2e-05 -25.44
541800 rs2239681 11_2 1.000 233.35 6.8e-04 -25.38
611926 rs6539035 12_61 0.000 471.85 4.1e-15 -25.18
611933 rs6539036 12_61 0.000 470.85 3.4e-15 -25.16
611930 rs4764696 12_61 0.000 470.35 3.1e-15 -25.14
351563 rs6925689 6_84 0.007 589.09 1.2e-05 25.05
381951 rs856541 7_34 0.000 716.93 1.8e-12 24.79
909205 rs780094 2_16 0.000 437.98 1.1e-07 24.70
909207 rs780093 2_16 0.000 438.02 1.1e-07 24.69
541799 rs17885785 11_2 1.000 360.79 1.1e-03 24.68
908972 rs4665972 2_16 0.000 450.56 1.3e-07 24.67
351582 rs1101563 6_84 0.006 568.61 9.3e-06 -24.62
351585 rs979197 6_84 0.005 566.88 8.9e-06 -24.58
351586 rs1015446 6_84 0.005 563.38 8.7e-06 -24.51
381944 rs856586 7_34 0.000 652.03 3.5e-15 24.07
909236 rs11127048 2_16 0.000 421.88 1.4e-07 23.98
909192 rs6547692 2_16 0.000 367.07 6.6e-08 22.87
909203 rs780096 2_16 0.000 364.85 9.4e-08 22.75
909204 rs780095 2_16 0.000 364.62 9.3e-08 22.75
909221 rs1260334 2_16 0.000 361.24 9.0e-08 22.66Gene 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] 53if (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"
Term Overlap Adjusted.P.value
1 protein kinase activator activity (GO:0030295) 3/63 0.04245936
2 insulin-like growth factor binding (GO:0005520) 2/15 0.04245936
Genes
1 MAP2K2;RICTOR;GAS6
2 IGFALS;IGF2RZFPM1 gene(s) from the input list not found in DisGeNET CURATEDZNF276 gene(s) from the input list not found in DisGeNET CURATEDKMT5C gene(s) from the input list not found in DisGeNET CURATEDTMEM176B gene(s) from the input list not found in DisGeNET CURATEDTLCD2 gene(s) from the input list not found in DisGeNET CURATEDGTF2H1 gene(s) from the input list not found in DisGeNET CURATEDUBE2K gene(s) from the input list not found in DisGeNET CURATEDRP11-442O1.3 gene(s) from the input list not found in DisGeNET CURATEDCCDC97 gene(s) from the input list not found in DisGeNET CURATEDPALM3 gene(s) from the input list not found in DisGeNET CURATEDB3GNT3 gene(s) from the input list not found in DisGeNET CURATEDACTR1B gene(s) from the input list not found in DisGeNET CURATEDZNF845 gene(s) from the input list not found in DisGeNET CURATEDLRGUK gene(s) from the input list not found in DisGeNET CURATEDAC004540.5 gene(s) from the input list not found in DisGeNET CURATEDC11orf96 gene(s) from the input list not found in DisGeNET CURATEDAES gene(s) from the input list not found in DisGeNET CURATEDZNF582 gene(s) from the input list not found in DisGeNET CURATEDSYTL1 gene(s) from the input list not found in DisGeNET CURATEDMPPED2 gene(s) from the input list not found in DisGeNET CURATEDFAAP20 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
54 Leukemia, T-Cell, Chronic 0.02721088 1/31 1/9703
109 Paramyotonia Congenita (disorder) 0.02721088 1/31 1/9703
136 Acute Undifferentiated Leukemia 0.02721088 1/31 1/9703
147 Trichorhinophalangeal dysplasia type I 0.02721088 1/31 1/9703
148 Enteropathy-Associated T-Cell Lymphoma 0.02721088 1/31 1/9703
157 Myotonic Disorders 0.02721088 1/31 1/9703
178 Myotonia Fluctuans (disorder) 0.02721088 1/31 1/9703
179 Undifferentiated type acute leukemia 0.02721088 1/31 1/9703
192 Acute myeloid leukemia, 11q23 abnormalities 0.02721088 1/31 1/9703
198 Leukemia, Large Granular Lymphocytic 0.02721088 1/31 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