Aspartate aminotransferase (quantile) - Whole_Blood
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 | 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 Aspartate aminotransferase (quantile) using Whole_Blood 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-30650_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 Whole_Blood 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] 11095#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
1129 747 624 400 479 621 560 383 404 430 682 652 192 362 331
16 17 18 19 20 21 22
551 725 159 911 313 130 310 #proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.762776Load 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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0168494018 0.0001829734 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
13.28972 13.76141 #report sample size
print(sample_size)[1] 342990#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.007243461 0.063849180 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.03063955 0.88253267Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
11229 RP11-441O15.3 10_64 1.000 201.94 5.9e-04 -20.11
5690 WDPCP 2_41 0.995 29.86 8.7e-05 5.22
11318 MIR34AHG 1_6 0.989 67.35 1.9e-04 -7.83
9863 LAMP1 13_62 0.989 54.41 1.6e-04 7.28
4881 BIN1 2_74 0.985 24.42 7.0e-05 -5.18
6290 ZFP36L2 2_27 0.976 47.02 1.3e-04 -6.94
6206 ZNF827 4_95 0.973 23.81 6.8e-05 5.48
9181 BEND3 6_71 0.961 21.56 6.0e-05 -4.42
10946 KLRC3 12_10 0.960 55.58 1.6e-04 8.32
4894 TOR1B 9_67 0.958 25.87 7.2e-05 4.52
3758 ATXN1 6_13 0.945 40.30 1.1e-04 6.40
11297 LINC01624 6_112 0.941 24.93 6.8e-05 -4.79
9363 PPA1 10_46 0.941 35.86 9.8e-05 6.03
6670 RHPN1 8_94 0.918 21.75 5.8e-05 -4.18
4223 CHMP2A 19_39 0.918 22.24 6.0e-05 4.51
9523 NDN 15_2 0.912 27.72 7.4e-05 5.11
3194 TSPAN1 1_28 0.898 25.15 6.6e-05 6.14
6460 KLF10 8_69 0.887 20.52 5.3e-05 -4.04
3804 OPRL1 20_38 0.882 23.97 6.2e-05 5.10
8229 LRRC45 17_46 0.868 19.85 5.0e-05 4.03
4643 COL4A2 13_59 0.859 27.82 7.0e-05 4.90
11143 TMEM167B 1_67 0.848 20.88 5.2e-05 4.19
6439 SLFN13 17_21 0.847 18.71 4.6e-05 -2.84
6674 CNNM4 2_57 0.827 20.91 5.0e-05 4.34
3347 IRF2BPL 14_36 0.825 30.44 7.3e-05 5.40
6704 ZC3H18 16_54 0.824 40.47 9.7e-05 7.19
208 PPP5C 19_32 0.812 30.21 7.1e-05 5.17Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 0.001 14960.68 5.9e-05 4.83
8003 KCTD5 16_2 0.000 10884.40 0.0e+00 4.70
5520 RCN3 19_34 0.000 4813.08 0.0e+00 4.81
6481 MOV10 1_69 0.000 2925.93 1.1e-10 -6.12
120 ST7L 1_69 0.000 2236.96 4.5e-15 -1.26
3093 CAPZA1 1_69 0.000 1715.56 2.5e-15 -1.16
2528 PANX1 11_53 0.000 1055.79 0.0e+00 9.40
8165 CPT1C 19_34 0.000 1042.35 0.0e+00 -1.47
2551 PTPMT1 11_29 0.659 716.01 1.4e-03 8.23
881 ZNF37A 10_28 0.004 540.86 6.2e-06 -1.43
4609 MYBPC3 11_29 0.000 499.21 2.0e-16 3.44
3657 GPR83 11_53 0.000 460.03 0.0e+00 -1.59
6024 TMEM236 10_14 0.000 371.04 7.3e-08 -19.58
8552 C1QTNF4 11_29 0.128 328.91 1.2e-04 9.11
571 SLC6A16 19_34 0.000 287.31 0.0e+00 -2.17
11652 C4A 6_26 0.000 258.00 2.0e-11 21.14
10492 CTC-301O7.4 19_34 0.000 255.66 0.0e+00 -0.76
11047 CLIC1 6_26 0.000 255.53 2.5e-12 20.97
10808 NEU1 6_26 0.000 255.06 3.2e-12 20.97
7712 C2 6_26 0.000 253.54 3.4e-12 -21.06Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
2551 PTPMT1 11_29 0.659 716.01 1.4e-03 8.23
11229 RP11-441O15.3 10_64 1.000 201.94 5.9e-04 -20.11
11318 MIR34AHG 1_6 0.989 67.35 1.9e-04 -7.83
10946 KLRC3 12_10 0.960 55.58 1.6e-04 8.32
9863 LAMP1 13_62 0.989 54.41 1.6e-04 7.28
6290 ZFP36L2 2_27 0.976 47.02 1.3e-04 -6.94
8552 C1QTNF4 11_29 0.128 328.91 1.2e-04 9.11
5235 SUOX 12_35 0.724 52.14 1.1e-04 6.77
3758 ATXN1 6_13 0.945 40.30 1.1e-04 6.40
6423 FAM69A 1_56 0.776 44.93 1.0e-04 6.18
9363 PPA1 10_46 0.941 35.86 9.8e-05 6.03
6704 ZC3H18 16_54 0.824 40.47 9.7e-05 7.19
8809 TMEM81 1_104 0.529 60.73 9.4e-05 5.54
5690 WDPCP 2_41 0.995 29.86 8.7e-05 5.22
5004 SDCBP 8_45 0.611 48.34 8.6e-05 7.07
10392 SND1 7_79 0.659 42.80 8.2e-05 6.63
10746 LIME1 20_38 0.716 39.20 8.2e-05 -6.35
7500 GPR146 7_3 0.773 35.06 7.9e-05 5.99
3101 MEF2D 1_77 0.798 33.74 7.9e-05 5.76
9523 NDN 15_2 0.912 27.72 7.4e-05 5.11Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11218 C4B 6_26 0.000 244.94 1.1e-12 -21.16
11652 C4A 6_26 0.000 258.00 2.0e-11 21.14
7712 C2 6_26 0.000 253.54 3.4e-12 -21.06
10808 NEU1 6_26 0.000 255.06 3.2e-12 20.97
11047 CLIC1 6_26 0.000 255.53 2.5e-12 20.97
10825 APOM 6_26 0.000 251.94 6.3e-13 20.85
11229 RP11-441O15.3 10_64 1.000 201.94 5.9e-04 -20.11
6024 TMEM236 10_14 0.000 371.04 7.3e-08 -19.58
1366 CWF19L1 10_64 0.000 249.47 4.3e-09 -18.50
3390 GOT1 10_64 0.000 162.98 1.5e-09 -17.13
10204 BLOC1S2 10_64 0.000 184.96 1.1e-09 -16.23
10789 PBX2 6_26 0.000 101.43 0.0e+00 15.89
10848 TRIM10 6_26 0.000 232.41 6.7e-12 15.50
10807 SLC44A4 6_26 0.000 181.10 0.0e+00 -14.12
10790 AGER 6_26 0.000 165.01 0.0e+00 -13.97
6593 FCHO2 5_43 0.022 160.61 1.0e-05 -13.96
8055 PDHB 3_40 0.051 167.16 2.5e-05 13.27
10781 HLA-DMA 6_27 0.000 97.61 1.5e-11 -12.92
6712 ZSCAN12 6_22 0.031 124.91 1.1e-05 12.58
10021 ZKSCAN4 6_22 0.033 124.49 1.2e-05 -12.53Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.02712934#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
11218 C4B 6_26 0.000 244.94 1.1e-12 -21.16
11652 C4A 6_26 0.000 258.00 2.0e-11 21.14
7712 C2 6_26 0.000 253.54 3.4e-12 -21.06
10808 NEU1 6_26 0.000 255.06 3.2e-12 20.97
11047 CLIC1 6_26 0.000 255.53 2.5e-12 20.97
10825 APOM 6_26 0.000 251.94 6.3e-13 20.85
11229 RP11-441O15.3 10_64 1.000 201.94 5.9e-04 -20.11
6024 TMEM236 10_14 0.000 371.04 7.3e-08 -19.58
1366 CWF19L1 10_64 0.000 249.47 4.3e-09 -18.50
3390 GOT1 10_64 0.000 162.98 1.5e-09 -17.13
10204 BLOC1S2 10_64 0.000 184.96 1.1e-09 -16.23
10789 PBX2 6_26 0.000 101.43 0.0e+00 15.89
10848 TRIM10 6_26 0.000 232.41 6.7e-12 15.50
10807 SLC44A4 6_26 0.000 181.10 0.0e+00 -14.12
10790 AGER 6_26 0.000 165.01 0.0e+00 -13.97
6593 FCHO2 5_43 0.022 160.61 1.0e-05 -13.96
8055 PDHB 3_40 0.051 167.16 2.5e-05 13.27
10781 HLA-DMA 6_27 0.000 97.61 1.5e-11 -12.92
6712 ZSCAN12 6_22 0.031 124.91 1.1e-05 12.58
10021 ZKSCAN4 6_22 0.033 124.49 1.2e-05 -12.53Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 6_26"
genename region_tag susie_pip mu2 PVE z
10855 HLA-G 6_26 0 160.40 0.0e+00 11.83
12599 HCP5B 6_26 0 159.99 0.0e+00 -12.45
10968 HLA-A 6_26 0 25.76 0.0e+00 4.85
10853 HCG9 6_26 0 26.02 0.0e+00 4.51
10851 PPP1R11 6_26 0 23.95 0.0e+00 3.66
661 ZNRD1 6_26 0 42.75 0.0e+00 3.30
10850 RNF39 6_26 0 5.83 0.0e+00 0.93
10848 TRIM10 6_26 0 232.41 6.7e-12 15.50
10847 TRIM15 6_26 0 45.06 0.0e+00 4.96
11418 TRIM26 6_26 0 54.35 0.0e+00 -5.74
10845 TRIM39 6_26 0 9.22 0.0e+00 0.04
11563 RPP21 6_26 0 75.33 1.5e-19 -2.98
10844 HLA-E 6_26 0 37.11 0.0e+00 -5.91
10841 MRPS18B 6_26 0 12.63 0.0e+00 -2.38
10840 C6orf136 6_26 0 49.93 0.0e+00 4.25
10839 DHX16 6_26 0 29.28 0.0e+00 2.44
5868 PPP1R18 6_26 0 30.42 0.0e+00 -5.11
4976 NRM 6_26 0 39.72 0.0e+00 3.38
4970 FLOT1 6_26 0 15.99 0.0e+00 -3.10
10230 TUBB 6_26 0 13.49 0.0e+00 -1.74
4971 IER3 6_26 0 13.41 0.0e+00 -2.95
11120 LINC00243 6_26 0 116.40 0.0e+00 -12.44
10843 DDR1 6_26 0 14.55 0.0e+00 2.44
11052 GTF2H4 6_26 0 5.23 0.0e+00 0.44
4978 VARS2 6_26 0 15.92 0.0e+00 0.60
10838 CCHCR1 6_26 0 93.00 1.8e-18 -3.01
4969 TCF19 6_26 0 192.31 3.2e-14 6.77
10966 HCG27 6_26 0 51.05 0.0e+00 5.35
10837 POU5F1 6_26 0 22.93 0.0e+00 0.97
10836 HLA-C 6_26 0 123.35 0.0e+00 -9.71
10788 NOTCH4 6_26 0 249.31 1.3e-14 10.30
11439 HLA-B 6_26 0 75.27 2.9e-19 -1.49
12270 XXbac-BPG181B23.7 6_26 0 41.85 0.0e+00 -5.34
10834 MICA 6_26 0 8.51 0.0e+00 -0.47
10833 MICB 6_26 0 15.70 0.0e+00 -0.87
10830 LST1 6_26 0 4.82 0.0e+00 -0.08
10619 DDX39B 6_26 0 12.05 0.0e+00 0.56
11050 ATP6V1G2 6_26 0 41.51 0.0e+00 -1.67
10831 NFKBIL1 6_26 0 116.12 2.5e-18 -5.91
11282 LTA 6_26 0 29.20 0.0e+00 5.27
11296 LTB 6_26 0 28.40 0.0e+00 5.13
11395 TNF 6_26 0 45.97 0.0e+00 2.29
10829 NCR3 6_26 0 37.63 0.0e+00 -4.48
10828 AIF1 6_26 0 6.14 0.0e+00 0.36
10827 PRRC2A 6_26 0 34.59 0.0e+00 6.01
10826 BAG6 6_26 0 59.29 2.7e-19 -8.54
10825 APOM 6_26 0 251.94 6.3e-13 20.85
10824 C6orf47 6_26 0 9.30 0.0e+00 -1.09
10822 CSNK2B 6_26 0 35.18 0.0e+00 -8.45
10823 GPANK1 6_26 0 80.95 0.0e+00 11.68
11539 LY6G5B 6_26 0 96.29 0.0e+00 -7.92
10821 LY6G5C 6_26 0 84.04 0.0e+00 -6.58
11639 LY6G6D 6_26 0 85.28 0.0e+00 -7.06
10818 MPIG6B 6_26 0 13.14 0.0e+00 -0.36
10819 LY6G6C 6_26 0 45.83 0.0e+00 -5.52
11048 DDAH2 6_26 0 67.94 0.0e+00 -3.96
10817 MSH5 6_26 0 10.68 0.0e+00 -2.24
11047 CLIC1 6_26 0 255.53 2.5e-12 20.97
11327 SAPCD1 6_26 0 9.09 0.0e+00 -2.64
10814 VWA7 6_26 0 23.54 0.0e+00 3.06
10809 C6orf48 6_26 0 9.65 0.0e+00 0.08
10813 VARS 6_26 0 28.05 0.0e+00 1.44
10812 LSM2 6_26 0 27.64 0.0e+00 -1.25
10811 HSPA1L 6_26 0 34.20 0.0e+00 4.90
10808 NEU1 6_26 0 255.06 3.2e-12 20.97
10807 SLC44A4 6_26 0 181.10 0.0e+00 -14.12
7712 C2 6_26 0 253.54 3.4e-12 -21.06
10805 EHMT2 6_26 0 47.73 0.0e+00 2.12
10802 NELFE 6_26 0 14.63 0.0e+00 -1.78
10801 SKIV2L 6_26 0 49.77 0.0e+00 4.57
10797 STK19 6_26 0 28.38 0.0e+00 2.96
10800 DXO 6_26 0 19.40 0.0e+00 0.34
11652 C4A 6_26 0 258.00 2.0e-11 21.14
11218 C4B 6_26 0 244.94 1.1e-12 -21.16
11374 CYP21A2 6_26 0 73.63 0.0e+00 -11.94
11193 PPT2 6_26 0 21.03 0.0e+00 -3.66
11043 ATF6B 6_26 0 14.66 0.0e+00 1.14
10795 FKBPL 6_26 0 22.21 0.0e+00 -3.53
10794 PRRT1 6_26 0 30.69 1.9e-18 1.52
10791 RNF5 6_26 0 5.47 0.0e+00 1.90
11565 EGFL8 6_26 0 17.96 0.0e+00 -2.83
10792 AGPAT1 6_26 0 18.64 0.0e+00 7.47
10790 AGER 6_26 0 165.01 0.0e+00 -13.97
10789 PBX2 6_26 0 101.43 0.0e+00 15.89
10608 HLA-DRB5 6_26 0 53.54 0.0e+00 -0.05
10325 HLA-DQA1 6_26 0 46.31 0.0e+00 -1.33
11490 HLA-DQA2 6_26 0 45.11 2.6e-18 2.66
11389 HLA-DQB2 6_26 0 107.63 3.8e-15 -10.75
9260 HLA-DQB1 6_26 0 90.93 8.8e-17 9.21
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 10_64"
genename region_tag susie_pip mu2 PVE z
3390 GOT1 10_64 0 162.98 1.5e-09 -17.13
11229 RP11-441O15.3 10_64 1 201.94 5.9e-04 -20.11
6475 SLC25A28 10_64 0 8.39 2.8e-11 -4.25
11988 RP11-85A1.3 10_64 0 8.46 2.8e-11 4.24
10532 ENTPD7 10_64 0 7.25 3.1e-11 0.36
3379 CUTC 10_64 0 20.85 2.4e-10 3.97
244 COX15 10_64 0 9.92 3.5e-11 2.60
290 ABCC2 10_64 0 18.87 5.6e-09 3.81
2289 DNMBP 10_64 0 30.80 2.5e-10 -7.22
2292 ERLIN1 10_64 0 91.43 2.6e-08 -7.73
1366 CWF19L1 10_64 0 249.47 4.3e-09 -18.50
10204 BLOC1S2 10_64 0 184.96 1.1e-09 -16.23
2294 PKD2L1 10_64 0 46.64 1.1e-09 5.04
11463 OLMALINC 10_64 0 11.43 6.1e-11 -0.84
891 SEC31B 10_64 0 25.27 7.9e-10 -2.59
7681 HIF1AN 10_64 0 16.73 2.5e-10 -1.81
7682 NDUFB8 10_64 0 17.39 2.1e-10 -2.05
3375 SLF2 10_64 0 18.94 2.4e-10 2.23
1367 SEMA4G 10_64 0 16.90 1.9e-10 1.73
2313 TWNK 10_64 0 8.03 3.0e-11 1.42
504 MRPL43 10_64 0 8.64 3.7e-11 -0.94
2314 LZTS2 10_64 0 16.67 1.8e-10 1.68
9967 PDZD7 10_64 0 4.70 1.2e-11 0.21
2315 SFXN3 10_64 0 6.28 1.8e-11 0.62
2316 KAZALD1 10_64 0 6.10 1.8e-11 0.32
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 10_14"
genename region_tag susie_pip mu2 PVE z
6025 RSU1 10_14 0 6.54 0.0e+00 -0.29
2295 CUBN 10_14 0 90.54 0.0e+00 -3.66
2296 TRDMT1 10_14 0 253.49 8.2e-19 8.13
6026 ST8SIA6 10_14 0 7.06 0.0e+00 1.29
7664 HACD1 10_14 0 248.42 1.5e-18 -9.46
6024 TMEM236 10_14 0 371.04 7.3e-08 -19.58
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 5_43"
genename region_tag susie_pip mu2 PVE z
4316 MAP1B 5_43 0.012 4.99 1.8e-07 0.92
2789 MRPS27 5_43 0.018 7.77 4.1e-07 -0.45
1055 TNPO1 5_43 0.080 64.06 1.5e-05 8.34
6593 FCHO2 5_43 0.022 160.61 1.0e-05 -13.96
6595 TMEM171 5_43 0.054 19.57 3.1e-06 1.82
5832 BTF3 5_43 0.013 5.07 1.9e-07 -0.61
7436 UTP15 5_43 0.013 4.76 1.7e-07 0.08
7434 ANKRA2 5_43 0.021 9.77 5.9e-07 1.39
11122 ARHGEF28 5_43 0.072 19.72 4.1e-06 -2.28
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 3_40"
genename region_tag susie_pip mu2 PVE z
7284 DNASE1L3 3_40 0.013 7.34 2.8e-07 -2.10
7283 ABHD6 3_40 0.013 67.09 2.6e-06 -10.86
7282 RPP14 3_40 0.013 96.01 3.6e-06 10.77
8056 PXK 3_40 0.067 15.28 3.0e-06 -2.15
8055 PDHB 3_40 0.051 167.16 2.5e-05 13.27
8058 KCTD6 3_40 0.012 15.22 5.2e-07 -2.39
8059 ACOX2 3_40 0.024 55.47 3.9e-06 8.48
8060 FAM107A 3_40 0.018 7.76 4.2e-07 0.05
10630 FAM3D 3_40 0.032 11.18 1.0e-06 0.05
| 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
4517 rs4336844 1_11 1.000 155.78 4.5e-04 12.99
34427 rs1771599 1_79 1.000 80.93 2.4e-04 9.94
34436 rs61804205 1_79 1.000 131.61 3.8e-04 15.58
47814 rs4951163 1_104 1.000 80.77 2.4e-04 7.21
61970 rs12239046 1_131 1.000 34.17 1.0e-04 -5.82
129950 rs4973550 2_136 1.000 71.08 2.1e-04 8.55
149320 rs7648467 3_32 1.000 100.42 2.9e-04 10.70
152147 rs35327008 3_39 1.000 55.52 1.6e-04 -7.18
186417 rs149368105 3_105 1.000 56.56 1.6e-04 -9.86
186438 rs234043 3_106 1.000 42.09 1.2e-04 -6.53
229369 rs35518360 4_67 1.000 153.22 4.5e-04 13.08
229435 rs13140033 4_68 1.000 74.74 2.2e-04 8.87
271911 rs2859493 5_26 1.000 142.14 4.1e-04 10.33
322475 rs115740542 6_20 1.000 51.47 1.5e-04 7.10
326785 rs9272364 6_26 1.000 291.23 8.5e-04 20.71
327242 rs9276192 6_27 1.000 306.88 8.9e-04 -19.53
327435 rs2244458 6_27 1.000 93.24 2.7e-04 1.51
425884 rs758184196 8_11 1.000 547.84 1.6e-03 -4.11
432513 rs2293400 8_23 1.000 66.34 1.9e-04 7.54
466638 rs307738 8_92 1.000 98.41 2.9e-04 1.32
466639 rs56114972 8_92 1.000 144.00 4.2e-04 6.32
490369 rs113609637 9_47 1.000 55.48 1.6e-04 -7.84
497561 rs7040440 9_59 1.000 54.75 1.6e-04 -4.69
502565 rs115478735 9_70 1.000 235.47 6.9e-04 -16.44
511282 rs72782512 10_14 1.000 231.91 6.8e-04 20.05
511313 rs17657502 10_14 1.000 546.34 1.6e-03 33.32
511323 rs553304 10_14 1.000 557.46 1.6e-03 -35.11
511474 rs16917138 10_15 1.000 50.52 1.5e-04 7.23
511478 rs79666207 10_15 1.000 47.56 1.4e-04 7.13
518183 rs71007692 10_28 1.000 1326.96 3.9e-03 -1.99
529336 rs5786398 10_51 1.000 42.72 1.2e-04 -5.40
535742 rs112255710 10_63 1.000 39.69 1.2e-04 -7.34
554962 rs7481951 11_15 1.000 130.26 3.8e-04 12.24
581855 rs2307599 11_67 1.000 57.97 1.7e-04 -1.37
585851 rs4937122 11_77 1.000 48.00 1.4e-04 -6.92
605835 rs6581124 12_35 1.000 37.16 1.1e-04 5.73
605854 rs7397189 12_36 1.000 113.20 3.3e-04 11.38
609895 rs2137537 12_44 1.000 103.48 3.0e-04 -10.77
631186 rs504366 13_3 1.000 43.78 1.3e-04 -6.70
670481 rs72681869 14_20 1.000 76.29 2.2e-04 -11.71
670529 rs142004400 14_20 1.000 67.36 2.0e-04 -11.39
683627 rs1243165 14_49 1.000 40.70 1.2e-04 3.48
698544 rs2070895 15_27 1.000 49.66 1.4e-04 -7.15
724678 rs17257349 16_29 1.000 71.46 2.1e-04 9.26
732885 rs11645522 16_46 1.000 44.28 1.3e-04 6.11
752836 rs1801689 17_38 1.000 81.62 2.4e-04 9.38
789975 rs3794991 19_15 1.000 154.73 4.5e-04 13.27
796743 rs73045223 19_30 1.000 54.36 1.6e-04 7.25
867065 rs333947 1_69 1.000 214.20 6.2e-04 -14.64
875851 rs200856259 1_69 1.000 5967.95 1.7e-02 4.22
990466 rs3072639 11_29 1.000 4269.84 1.2e-02 3.11
997311 rs148050219 11_53 1.000 33519.65 9.8e-02 -12.67
997321 rs111443113 11_53 1.000 33489.73 9.8e-02 -0.39
1043893 rs116985006 16_2 1.000 15036.21 4.4e-02 5.81
1043897 rs774104952 16_2 1.000 15148.46 4.4e-02 5.75
1086615 rs113176985 19_34 1.000 15120.35 4.4e-02 -4.88
1086618 rs374141296 19_34 1.000 15237.14 4.4e-02 -4.72
1100971 rs12975366 19_37 1.000 130.84 3.8e-04 -12.07
147541 rs2649750 3_28 0.999 32.97 9.6e-05 -5.78
181340 rs9817452 3_97 0.999 32.48 9.5e-05 5.50
271929 rs76142317 5_26 0.999 35.63 1.0e-04 4.22
400784 rs740047 7_56 0.999 33.46 9.7e-05 5.03
492515 rs1226592 9_50 0.999 67.01 2.0e-04 8.37
549113 rs10838525 11_4 0.999 36.19 1.1e-04 -5.16
567350 rs75592015 11_37 0.999 32.54 9.5e-05 -5.66
594818 rs66720652 12_15 0.999 33.84 9.9e-05 -5.72
753161 rs56213591 17_39 0.999 35.58 1.0e-04 5.81
838299 rs11090617 22_19 0.999 759.96 2.2e-03 28.80
271922 rs34209642 5_26 0.998 38.59 1.1e-04 2.40
271957 rs2962478 5_26 0.998 36.89 1.1e-04 5.86
203867 rs2970862 4_20 0.997 31.74 9.2e-05 6.07
295808 rs112801206 5_74 0.997 29.20 8.5e-05 5.22
298758 rs6894249 5_79 0.997 47.61 1.4e-04 -5.98
427137 rs11250151 8_15 0.997 75.09 2.2e-04 -9.51
626435 rs12425627 12_76 0.997 31.47 9.1e-05 -5.67
323613 rs1233385 6_23 0.996 119.20 3.5e-04 -14.33
511518 rs7070430 10_15 0.995 39.02 1.1e-04 -3.99
798064 rs12978750 19_33 0.995 55.05 1.6e-04 7.95
563132 rs77897592 11_30 0.994 27.30 7.9e-05 4.42
756911 rs4969183 17_44 0.994 72.71 2.1e-04 9.26
784295 rs576338566 19_4 0.993 30.46 8.8e-05 -5.44
866342 rs140584594 1_67 0.993 33.17 9.6e-05 5.41
485845 rs34084620 9_38 0.992 27.92 8.1e-05 5.09
326017 rs204887 6_26 0.991 101.91 2.9e-04 -11.22
511479 rs7089228 10_15 0.990 48.46 1.4e-04 -7.75
224594 rs77094191 4_59 0.989 56.02 1.6e-04 -5.02
683623 rs941594 14_49 0.989 49.03 1.4e-04 4.34
425648 rs7833103 8_11 0.988 250.23 7.2e-04 10.85
724675 rs190752012 16_29 0.988 30.12 8.7e-05 6.36
982132 rs76744182 10_64 0.988 45.58 1.3e-04 -6.86
186325 rs17461279 3_105 0.987 29.60 8.5e-05 -5.36
480685 rs1137642 9_25 0.986 138.62 4.0e-04 -11.65
911357 rs4835265 4_95 0.986 141.72 4.1e-04 12.80
1086606 rs61371437 19_34 0.986 15080.94 4.3e-02 -4.73
590133 rs7976853 12_3 0.985 35.54 1.0e-04 5.78
73372 rs71409634 2_21 0.980 27.73 7.9e-05 5.09
300951 rs769204262 5_84 0.980 27.34 7.8e-05 5.11
358087 rs212776 6_88 0.978 28.54 8.1e-05 5.31
585555 rs11220136 11_77 0.978 61.12 1.7e-04 8.41
738951 rs12601581 17_7 0.977 44.63 1.3e-04 -6.19
783196 rs351988 19_2 0.977 31.49 9.0e-05 5.50
179843 rs7610095 3_94 0.975 35.00 1.0e-04 -6.40
497570 rs10739409 9_59 0.975 52.74 1.5e-04 -8.90
78416 rs4952901 2_30 0.974 30.94 8.8e-05 5.28
785768 rs10401485 19_7 0.973 31.02 8.8e-05 5.36
325582 rs2853999 6_26 0.972 358.86 1.0e-03 -20.00
426623 rs11777976 8_13 0.970 73.13 2.1e-04 -9.65
77020 rs72800939 2_28 0.968 25.46 7.2e-05 4.81
756876 rs12449451 17_44 0.967 26.93 7.6e-05 5.57
838310 rs9626057 22_19 0.967 303.13 8.5e-04 15.73
1053097 rs75303800 16_54 0.961 38.62 1.1e-04 7.06
15808 rs7556224 1_37 0.960 25.37 7.1e-05 4.55
318797 rs2841572 6_12 0.960 97.56 2.7e-04 10.45
209572 rs12639940 4_32 0.959 24.03 6.7e-05 -4.14
511318 rs145553078 10_14 0.958 153.26 4.3e-04 -16.45
481093 rs6476453 9_27 0.957 26.89 7.5e-05 -4.89
575479 rs74717621 11_54 0.957 24.93 7.0e-05 4.72
497581 rs10759697 9_59 0.956 89.95 2.5e-04 -10.40
431439 rs11986461 8_21 0.955 31.26 8.7e-05 -5.69
671984 rs6572976 14_24 0.955 63.70 1.8e-04 -8.09
152101 rs559993437 3_39 0.951 25.71 7.1e-05 -4.50
427162 rs1809356 8_15 0.949 28.14 7.8e-05 5.74
774947 rs12373325 18_31 0.949 117.50 3.3e-04 -12.23
575295 rs144988974 11_52 0.943 24.77 6.8e-05 4.62
726171 rs9922575 16_31 0.943 55.92 1.5e-04 -3.28
114214 rs12464787 2_108 0.942 79.78 2.2e-04 9.23
185715 rs61436251 3_104 0.942 25.95 7.1e-05 -3.27
116899 rs17576323 2_112 0.941 33.87 9.3e-05 -6.02
172139 rs9870956 3_77 0.940 26.12 7.2e-05 4.87
833580 rs11704551 22_10 0.938 69.77 1.9e-04 -9.17
732884 rs13334801 16_46 0.937 28.07 7.7e-05 4.30
604785 rs10876377 12_33 0.936 36.96 1.0e-04 5.98
327392 rs1871664 6_27 0.933 68.10 1.9e-04 -8.00
812095 rs1412956 20_29 0.931 27.14 7.4e-05 5.13
835964 rs132642 22_14 0.931 74.42 2.0e-04 8.89
78435 rs56030357 2_31 0.930 55.48 1.5e-04 7.52
535744 rs117780022 10_63 0.927 25.33 6.8e-05 4.28
353131 rs78485454 6_77 0.921 26.42 7.1e-05 -3.13
271953 rs13183079 5_26 0.920 123.29 3.3e-04 9.38
726209 rs71400028 16_31 0.919 246.86 6.6e-04 -15.88
322211 rs62392365 6_19 0.915 38.04 1.0e-04 -6.58
622515 rs141105880 12_67 0.914 35.69 9.5e-05 -6.95
738997 rs307627 17_7 0.913 28.67 7.6e-05 -5.11
832916 rs133902 22_7 0.911 24.74 6.6e-05 4.71
179009 rs6774253 3_92 0.903 28.44 7.5e-05 -5.22
138458 rs56395424 3_9 0.902 33.15 8.7e-05 -5.76
280045 rs150892208 5_42 0.901 43.66 1.1e-04 -7.16
625357 rs571529125 12_74 0.900 47.92 1.3e-04 8.15
774574 rs2849421 18_30 0.900 148.24 3.9e-04 -12.71
693360 rs17659152 15_15 0.899 23.51 6.2e-05 4.31
328059 rs4713999 6_29 0.893 26.06 6.8e-05 4.64
373562 rs10279376 7_9 0.893 49.03 1.3e-04 -7.15
322319 rs72838866 6_19 0.892 29.54 7.7e-05 5.77
776630 rs71162605 18_35 0.890 27.13 7.0e-05 4.53
34437 rs10917685 1_79 0.889 102.27 2.7e-04 -12.02
776628 rs73963711 18_35 0.889 30.82 8.0e-05 -5.25
603131 rs12313103 12_29 0.888 26.32 6.8e-05 4.75
736777 rs558760274 17_1 0.885 23.55 6.1e-05 -4.37
41258 rs2500119 1_91 0.884 141.75 3.7e-04 12.46
785544 rs339399 19_7 0.883 31.46 8.1e-05 5.35
532895 rs7094510 10_57 0.881 29.19 7.5e-05 -5.20
586534 rs71480000 11_80 0.880 24.17 6.2e-05 -4.43
704144 rs12592898 15_37 0.880 29.08 7.5e-05 -6.10
94524 rs4849369 2_66 0.877 29.77 7.6e-05 -5.28
547803 rs2583438 11_2 0.877 55.53 1.4e-04 -7.56
303365 rs12110157 5_88 0.871 27.83 7.1e-05 5.24
195949 rs36205397 4_4 0.869 27.87 7.1e-05 5.63
142619 rs734866 3_18 0.863 25.96 6.5e-05 -4.80
290081 rs163895 5_63 0.861 24.36 6.1e-05 -4.18
353161 rs7758190 6_77 0.860 25.19 6.3e-05 -3.91
327137 rs1794274 6_26 0.849 270.32 6.7e-04 -22.47
255033 rs3814419 4_118 0.848 31.95 7.9e-05 6.05
69960 rs1042034 2_13 0.845 25.14 6.2e-05 4.63
333188 rs941968 6_39 0.840 26.36 6.5e-05 4.73
101270 rs10928493 2_79 0.839 24.95 6.1e-05 4.90
410924 rs77506340 7_79 0.839 28.53 7.0e-05 5.34
330338 rs2025704 6_34 0.832 29.91 7.3e-05 -5.55
973562 rs143378550 9_67 0.832 65.79 1.6e-04 -7.17
633829 rs1756957 13_7 0.829 37.44 9.0e-05 -6.17
458322 rs146373428 8_78 0.826 25.22 6.1e-05 -4.40
798750 rs28875253 19_38 0.824 27.31 6.6e-05 4.92
789723 rs12162221 19_15 0.822 48.55 1.2e-04 4.23
841356 rs12484572 22_24 0.822 24.84 6.0e-05 4.65
693156 rs11070250 15_13 0.819 58.62 1.4e-04 -9.17
592228 rs6488516 12_11 0.817 26.49 6.3e-05 4.76
125495 rs149146451 2_129 0.816 25.44 6.0e-05 4.31
660640 rs1760940 14_1 0.815 54.90 1.3e-04 7.72
584501 rs10892865 11_74 0.809 30.55 7.2e-05 -6.03
794305 rs33824 19_23 0.809 46.94 1.1e-04 -8.54
581706 rs55697087 11_67 0.808 27.24 6.4e-05 -4.50
707510 rs72754570 15_41 0.804 45.01 1.1e-04 -6.65
693029 rs530892566 15_13 0.803 27.86 6.5e-05 5.05
797445 rs56010181 19_33 0.802 53.13 1.2e-04 7.29
481114 rs3808868 9_27 0.801 26.63 6.2e-05 4.81
501312 rs13302576 9_66 0.801 26.39 6.2e-05 -4.68SNPs 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
997311 rs148050219 11_53 1.000 33519.65 9.8e-02 -12.67
997321 rs111443113 11_53 1.000 33489.73 9.8e-02 -0.39
997320 rs60550219 11_53 0.125 33474.24 1.2e-02 -12.67
997307 rs7105405 11_53 0.206 33466.52 2.0e-02 -12.70
997345 rs67167563 11_53 0.049 33461.29 4.8e-03 -12.66
997352 rs113426210 11_53 0.025 33446.28 2.4e-03 -12.67
997303 rs9888156 11_53 0.000 33416.66 2.8e-05 -12.66
997358 rs950878 11_53 0.010 33407.00 9.6e-04 -12.69
997301 rs67232024 11_53 0.000 33358.81 1.1e-08 -12.62
997280 rs7927828 11_53 0.000 33357.70 6.1e-09 -12.61
997298 rs9888266 11_53 0.000 33313.72 5.0e-09 -12.65
997286 rs67812366 11_53 0.000 33313.60 6.3e-09 -12.65
997289 rs7109132 11_53 0.000 33313.30 4.0e-09 -12.65
997281 rs57856352 11_53 0.000 33302.62 3.1e-10 -12.62
997302 rs16919533 11_53 0.000 33296.05 1.1e-10 -12.64
997300 rs67549397 11_53 0.000 33247.72 6.2e-15 -12.54
997299 rs9888143 11_53 0.000 33196.15 4.2e-16 -12.56
997291 rs60546087 11_53 0.000 33192.69 4.4e-16 -12.56
997290 rs60351354 11_53 0.000 33192.66 4.3e-16 -12.56
997295 rs1573567 11_53 0.000 33192.43 2.9e-16 -12.56
997292 rs7109819 11_53 0.000 33192.40 2.9e-16 -12.56
997258 rs7932290 11_53 0.000 33044.33 2.2e-13 -12.79
997226 rs7934467 11_53 0.000 32775.06 0.0e+00 -12.59
997621 rs72966603 11_53 0.000 27604.22 0.0e+00 -13.54
997751 rs12419615 11_53 0.000 26126.77 0.0e+00 -13.58
997802 rs58964858 11_53 0.000 22329.00 0.0e+00 -13.18
997804 rs72968738 11_53 0.000 22285.01 0.0e+00 -13.11
997828 rs138626734 11_53 0.000 22005.73 0.0e+00 -13.07
997814 rs72968745 11_53 0.000 22000.68 0.0e+00 -13.14
997813 rs4491178 11_53 0.000 21999.40 0.0e+00 -13.14
997846 rs4408267 11_53 0.000 21995.42 0.0e+00 -13.07
997874 rs11604580 11_53 0.000 21973.97 0.0e+00 -13.13
997879 rs4342991 11_53 0.000 21971.52 0.0e+00 -13.13
997445 rs72962880 11_53 0.000 21957.46 0.0e+00 -10.63
997732 rs7945841 11_53 0.000 21936.14 0.0e+00 -12.54
997818 rs4753124 11_53 0.000 21914.40 0.0e+00 -13.08
997851 rs16919942 11_53 0.000 21899.10 0.0e+00 -13.10
997434 rs55659547 11_53 0.000 21891.75 0.0e+00 -10.57
997433 rs7950356 11_53 0.000 21888.30 0.0e+00 -10.57
997444 rs56359140 11_53 0.000 21865.40 0.0e+00 -10.56
997437 rs72962872 11_53 0.000 21862.78 0.0e+00 -10.56
997439 rs140989262 11_53 0.000 21576.66 0.0e+00 -10.53
997770 rs7119800 11_53 0.000 21490.65 0.0e+00 -12.41
997459 rs72962891 11_53 0.000 21397.07 0.0e+00 -10.41
997478 rs72964604 11_53 0.000 21357.17 0.0e+00 -10.51
997774 rs2176565 11_53 0.000 21213.84 0.0e+00 -12.54
997775 rs7949551 11_53 0.000 20577.62 0.0e+00 -12.79
997241 rs1506657 11_53 0.000 20289.29 0.0e+00 10.56
997778 rs72968710 11_53 0.000 20090.45 0.0e+00 -12.72
997781 rs16919917 11_53 0.000 19939.03 0.0e+00 -12.85SNPs 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
997311 rs148050219 11_53 1.000 33519.65 0.09800 -12.67
997321 rs111443113 11_53 1.000 33489.73 0.09800 -0.39
1043893 rs116985006 16_2 1.000 15036.21 0.04400 5.81
1043897 rs774104952 16_2 1.000 15148.46 0.04400 5.75
1086615 rs113176985 19_34 1.000 15120.35 0.04400 -4.88
1086618 rs374141296 19_34 1.000 15237.14 0.04400 -4.72
1086606 rs61371437 19_34 0.986 15080.94 0.04300 -4.73
997307 rs7105405 11_53 0.206 33466.52 0.02000 -12.70
875851 rs200856259 1_69 1.000 5967.95 0.01700 4.22
990466 rs3072639 11_29 1.000 4269.84 0.01200 3.11
997320 rs60550219 11_53 0.125 33474.24 0.01200 -12.67
997345 rs67167563 11_53 0.049 33461.29 0.00480 -12.66
518183 rs71007692 10_28 1.000 1326.96 0.00390 -1.99
875748 rs6537746 1_69 0.210 5877.17 0.00360 -3.96
875848 rs2932539 1_69 0.197 5881.57 0.00340 -3.93
875798 rs10857969 1_69 0.171 5881.40 0.00290 -3.93
875751 rs4838961 1_69 0.161 5880.09 0.00280 -3.94
875840 rs12048528 1_69 0.147 5879.78 0.00250 -3.93
875795 rs10745332 1_69 0.142 5879.83 0.00240 -3.93
997352 rs113426210 11_53 0.025 33446.28 0.00240 -12.67
875801 rs3013441 1_69 0.137 5880.28 0.00230 -3.93
838299 rs11090617 22_19 0.999 759.96 0.00220 28.80
875773 rs4240534 1_69 0.131 5880.25 0.00220 -3.93
875849 rs2932538 1_69 0.130 5880.53 0.00220 -3.92
875776 rs6691025 1_69 0.121 5880.15 0.00210 -3.93
518180 rs9299760 10_28 0.527 1299.94 0.00200 -2.01
518189 rs2472183 10_28 0.448 1300.46 0.00170 -1.99
425884 rs758184196 8_11 1.000 547.84 0.00160 -4.11
511313 rs17657502 10_14 1.000 546.34 0.00160 33.32
511323 rs553304 10_14 1.000 557.46 0.00160 -35.11
875843 rs1238 1_69 0.093 5879.57 0.00160 -3.91
518182 rs2474565 10_28 0.405 1300.36 0.00150 -1.98
518192 rs11011452 10_28 0.380 1300.44 0.00140 -1.97
990472 rs11039670 11_29 0.099 4308.93 0.00120 3.16
990504 rs7124318 11_29 0.099 4308.89 0.00120 3.16
875833 rs6682678 1_69 0.065 5877.92 0.00110 -3.92
990468 rs7949513 11_29 0.088 4308.57 0.00110 3.16
325582 rs2853999 6_26 0.972 358.86 0.00100 -20.00
990495 rs11039675 11_29 0.078 4308.79 0.00098 3.16
990481 rs11039671 11_29 0.077 4308.79 0.00096 3.16
990507 rs9651621 11_29 0.077 4308.79 0.00096 3.16
997358 rs950878 11_53 0.010 33407.00 0.00096 -12.69
990487 rs4436573 11_29 0.075 4308.77 0.00094 3.16
425900 rs13265731 8_11 0.529 583.26 0.00090 8.51
327242 rs9276192 6_27 1.000 306.88 0.00089 -19.53
326785 rs9272364 6_26 1.000 291.23 0.00085 20.71
838310 rs9626057 22_19 0.967 303.13 0.00085 15.73
425896 rs6993494 8_11 0.471 582.79 0.00080 8.49
990493 rs10838872 11_29 0.060 4308.18 0.00075 3.16
425648 rs7833103 8_11 0.988 250.23 0.00072 10.85SNPs 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
511323 rs553304 10_14 1.000 557.46 1.6e-03 -35.11
511313 rs17657502 10_14 1.000 546.34 1.6e-03 33.32
838299 rs11090617 22_19 0.999 759.96 2.2e-03 28.80
838302 rs1977081 22_19 0.020 750.85 4.4e-05 28.52
838305 rs2072905 22_19 0.016 729.82 3.5e-05 28.11
838306 rs2401512 22_19 0.016 729.90 3.5e-05 28.11
838307 rs4823176 22_19 0.016 729.80 3.5e-05 28.11
838308 rs4823178 22_19 0.016 729.83 3.5e-05 28.11
838309 rs13056555 22_19 0.017 730.09 3.5e-05 28.11
838304 rs1883348 22_19 0.013 717.71 2.7e-05 27.88
511330 rs2478571 10_14 0.000 583.51 0.0e+00 -25.93
511325 rs113334738 10_14 0.000 198.26 0.0e+00 23.64
511262 rs113414299 10_14 0.078 492.81 1.1e-04 -23.42
327137 rs1794274 6_26 0.849 270.32 6.7e-04 -22.47
327168 rs9275576 6_26 0.151 265.19 1.2e-04 -22.32
326704 rs9271690 6_26 0.000 222.21 9.4e-18 -21.86
326708 rs9271727 6_26 0.000 224.13 0.0e+00 -21.67
326307 rs7748925 6_26 0.000 235.10 4.5e-12 -21.47
326319 rs3135383 6_26 0.000 234.02 3.2e-12 -21.44
326628 rs9271342 6_26 0.000 225.20 0.0e+00 -21.34
326131 rs9268152 6_26 0.000 236.92 1.5e-12 -21.33
326184 rs2395149 6_26 0.000 236.40 1.4e-12 -21.33
326623 rs642093 6_26 0.000 224.54 0.0e+00 -21.33
326194 rs3129927 6_26 0.000 235.54 1.0e-12 -21.30
326714 rs539509361 6_26 0.000 211.55 0.0e+00 -21.30
326540 rs592362 6_26 0.000 222.38 0.0e+00 -21.27
326541 rs3998183 6_26 0.000 223.82 0.0e+00 -21.26
325986 rs1270942 6_26 0.000 253.62 6.5e-13 -21.14
325954 rs3130478 6_26 0.000 263.95 2.7e-12 -21.13
325984 rs1265905 6_26 0.000 253.36 3.9e-13 -21.07
325938 rs3130491 6_26 0.000 263.59 1.7e-12 -21.05
152289 rs11719192 3_40 0.232 291.93 2.0e-04 21.04
325955 rs3130679 6_26 0.000 259.02 7.0e-13 -21.02
326195 rs2143462 6_26 0.000 245.94 1.7e-17 -21.02
326080 rs3130303 6_26 0.000 255.32 5.7e-16 -21.01
152283 rs11925862 3_40 0.118 290.00 9.9e-05 21.00
152286 rs62259778 3_40 0.097 289.47 8.2e-05 20.99
152287 rs11919206 3_40 0.100 289.55 8.5e-05 20.99
152288 rs62259780 3_40 0.094 289.39 7.9e-05 20.99
152269 rs11705721 3_40 0.057 288.04 4.8e-05 20.96
152270 rs55727087 3_40 0.056 288.02 4.7e-05 20.96
152271 rs11130637 3_40 0.057 288.07 4.8e-05 20.96
326779 rs9272309 6_26 0.000 223.19 7.2e-20 -20.95
152256 rs7647184 3_40 0.038 287.14 3.2e-05 20.94
326620 rs9271304 6_26 0.000 212.34 0.0e+00 -20.94
152263 rs62258103 3_40 0.033 286.59 2.8e-05 20.93
152264 rs6445978 3_40 0.034 286.61 2.8e-05 20.93
152274 rs11915190 3_40 0.038 286.86 3.2e-05 20.93
511251 rs72638788 10_14 0.000 355.06 1.1e-14 -20.90
152275 rs34579268 3_40 0.020 285.21 1.7e-05 20.89Gene 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] 27if (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)ZNF827 gene(s) from the input list not found in DisGeNET CURATEDLAMP1 gene(s) from the input list not found in DisGeNET CURATEDLRRC45 gene(s) from the input list not found in DisGeNET CURATEDZC3H18 gene(s) from the input list not found in DisGeNET CURATEDMIR34AHG gene(s) from the input list not found in DisGeNET CURATEDBEND3 gene(s) from the input list not found in DisGeNET CURATEDLINC01624 gene(s) from the input list not found in DisGeNET CURATEDCHMP2A gene(s) from the input list not found in DisGeNET CURATEDTMEM167B gene(s) from the input list not found in DisGeNET CURATEDPPP5C gene(s) from the input list not found in DisGeNET CURATEDRP11-441O15.3 gene(s) from the input list not found in DisGeNET CURATEDTSPAN1 gene(s) from the input list not found in DisGeNET CURATEDKLRC3 gene(s) from the input list not found in DisGeNET CURATEDSLFN13 gene(s) from the input list not found in DisGeNET CURATED Description
66 Orstavik Lindemann Solberg syndrome
67 Amaurosis hypertrichosis
72 Heart defect, tongue hamartoma and polysyndactyly
73 Cone rod dystrophy amelogenesis imperfecta
76 BARDET-BIEDL SYNDROME 15
77 PORENCEPHALY 2
80 Jalili syndrome
91 NEURODEVELOPMENTAL DISORDER WITH REGRESSION, ABNORMAL MOVEMENTS, LOSS OF SPEECH, AND SEIZURES
40 Congenital porencephaly
68 PORENCEPHALY, FAMILIAL
FDR Ratio BgRatio
66 0.01524170 1/13 1/9703
67 0.01524170 1/13 1/9703
72 0.01524170 1/13 1/9703
73 0.01524170 1/13 1/9703
76 0.01524170 1/13 1/9703
77 0.01524170 1/13 1/9703
80 0.01524170 1/13 1/9703
91 0.01524170 1/13 1/9703
40 0.01624776 1/13 2/9703
68 0.01624776 1/13 2/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!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