Triglycerides (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 | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
| html | da9f015 | wesleycrouse | 2021-08-07 | adding more reports |
Overview
These are the results of a ctwas analysis of the UK Biobank trait Triglycerides (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-30870_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0122979289 0.0002065494 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
56.11433 23.46084 #report sample size
print(sample_size)[1] 343992#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02225793 0.12251943 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.103603 1.259050Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
7089 USP1 1_39 1.000 1055.09 3.1e-03 33.23
1980 FCGRT 19_34 1.000 25707.57 7.5e-02 -3.74
6089 FADS1 11_34 0.999 417.22 1.2e-03 21.00
11023 SIPA1 11_36 0.998 62.32 1.8e-04 8.65
5839 TIMD4 5_92 0.995 278.89 8.1e-04 15.00
3804 OPRL1 20_38 0.985 51.89 1.5e-04 7.76
6590 NTAN1 16_15 0.976 96.82 2.7e-04 12.08
4198 KIAA1683 19_14 0.967 27.60 7.8e-05 4.80
4610 ACP2 11_29 0.956 222.51 6.2e-04 9.99
90 SPATA20 17_29 0.926 29.35 7.9e-05 5.08
5421 NPC1 18_12 0.902 30.06 7.9e-05 0.78
3271 PMFBP1 16_38 0.898 29.06 7.6e-05 4.94
5834 TNFAIP8 5_72 0.888 69.61 1.8e-04 8.38
4564 PSRC1 1_67 0.881 23.59 6.0e-05 -4.40
1386 ITPR3 6_28 0.877 25.45 6.5e-05 4.58
1839 LMF1 16_1 0.859 35.39 8.8e-05 -5.33
9322 F2 11_28 0.852 86.67 2.1e-04 9.17
2119 TMEM147 19_24 0.851 32.68 8.1e-05 5.28
6686 HIST1H2BD 6_20 0.850 30.17 7.5e-05 5.76
7945 DAPK3 19_4 0.832 24.64 6.0e-05 4.57Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 25707.57 7.5e-02 -3.74
12570 RP11-147L13.11 17_39 0.000 8429.34 3.1e-16 -3.63
5520 RCN3 19_34 0.000 8213.14 6.5e-07 -4.91
9505 KPNA2 17_39 0.000 5661.36 4.6e-11 -5.95
12534 RP11-147L13.13 17_39 0.000 5640.39 5.5e-18 -3.29
4687 TMEM60 7_49 0.000 4020.31 0.0e+00 1.47
2551 PTPMT1 11_29 0.000 3213.04 3.8e-14 6.81
2496 ZPR1 11_70 0.044 3041.24 3.9e-04 -47.41
881 ZNF37A 10_28 0.000 2912.27 0.0e+00 0.79
9952 C17orf58 17_39 0.000 2282.52 0.0e+00 -3.27
4609 MYBPC3 11_29 0.000 2281.85 0.0e+00 -3.06
6984 MPP3 17_26 0.000 2214.71 6.7e-09 4.52
8165 CPT1C 19_34 0.001 1869.08 6.4e-06 5.02
8899 LPL 8_21 0.000 1274.24 0.0e+00 -46.68
8552 C1QTNF4 11_29 0.000 1214.79 0.0e+00 -5.12
12146 RP11-147L13.8 17_39 0.000 1150.90 0.0e+00 -2.47
7089 USP1 1_39 1.000 1055.09 3.1e-03 33.23
3102 DOCK7 1_39 0.010 868.15 2.5e-05 30.17
2953 NRBP1 2_16 0.004 761.68 8.2e-06 26.36
2497 FNBP4 11_29 0.000 743.96 0.0e+00 1.29Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1980 FCGRT 19_34 1.000 25707.57 0.07500 -3.74
7089 USP1 1_39 1.000 1055.09 0.00310 33.23
6089 FADS1 11_34 0.999 417.22 0.00120 21.00
5839 TIMD4 5_92 0.995 278.89 0.00081 15.00
7786 CATSPER2 15_16 0.669 362.20 0.00070 19.03
4610 ACP2 11_29 0.956 222.51 0.00062 9.99
2496 ZPR1 11_70 0.044 3041.24 0.00039 -47.41
6590 NTAN1 16_15 0.976 96.82 0.00027 12.08
10765 ZDHHC18 1_18 0.706 122.81 0.00025 10.81
9322 F2 11_28 0.852 86.67 0.00021 9.17
1267 PABPC4 1_24 0.790 77.45 0.00018 -8.95
5834 TNFAIP8 5_72 0.888 69.61 0.00018 8.38
11023 SIPA1 11_36 0.998 62.32 0.00018 8.65
12307 RP11-459I19.1 2_129 0.754 78.68 0.00017 -8.33
5318 USP3 15_29 0.758 77.57 0.00017 9.81
11634 GSTA2 6_39 0.707 73.39 0.00015 8.46
11727 DNAH10OS 12_75 0.549 91.26 0.00015 -11.45
3804 OPRL1 20_38 0.985 51.89 0.00015 7.76
5057 IFT172 2_16 0.527 92.09 0.00014 -12.84
1087 GCKR 2_16 0.439 91.91 0.00012 12.86Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
2496 ZPR1 11_70 0.044 3041.24 3.9e-04 -47.41
8899 LPL 8_21 0.000 1274.24 0.0e+00 -46.68
7089 USP1 1_39 1.000 1055.09 3.1e-03 33.23
3102 DOCK7 1_39 0.010 868.15 2.5e-05 30.17
2953 NRBP1 2_16 0.004 761.68 8.2e-06 26.36
5007 BUD13 11_70 0.000 619.45 0.0e+00 26.11
2956 SNX17 2_16 0.017 688.91 3.5e-05 23.54
6089 FADS1 11_34 0.999 417.22 1.2e-03 21.00
9915 BACE1 11_70 0.000 214.04 0.0e+00 -19.43
7786 CATSPER2 15_16 0.669 362.20 7.0e-04 19.03
4137 MAU2 19_15 0.003 241.95 2.2e-06 17.96
7304 ZNF513 2_16 0.000 335.09 4.6e-07 16.40
5076 ATRAID 2_16 0.001 229.46 3.8e-07 -16.30
4636 FADS2 11_34 0.007 252.22 5.4e-06 15.79
2131 ATP13A1 19_15 0.004 169.52 2.0e-06 -15.71
7782 CASC4 15_17 0.015 247.64 1.1e-05 -15.60
5839 TIMD4 5_92 0.995 278.89 8.1e-04 15.00
1652 PCIF1 20_28 0.007 202.64 4.3e-06 14.40
1087 GCKR 2_16 0.439 91.91 1.2e-04 12.86
5057 IFT172 2_16 0.527 92.09 1.4e-04 -12.84Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.0308247#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
2496 ZPR1 11_70 0.044 3041.24 3.9e-04 -47.41
8899 LPL 8_21 0.000 1274.24 0.0e+00 -46.68
7089 USP1 1_39 1.000 1055.09 3.1e-03 33.23
3102 DOCK7 1_39 0.010 868.15 2.5e-05 30.17
2953 NRBP1 2_16 0.004 761.68 8.2e-06 26.36
5007 BUD13 11_70 0.000 619.45 0.0e+00 26.11
2956 SNX17 2_16 0.017 688.91 3.5e-05 23.54
6089 FADS1 11_34 0.999 417.22 1.2e-03 21.00
9915 BACE1 11_70 0.000 214.04 0.0e+00 -19.43
7786 CATSPER2 15_16 0.669 362.20 7.0e-04 19.03
4137 MAU2 19_15 0.003 241.95 2.2e-06 17.96
7304 ZNF513 2_16 0.000 335.09 4.6e-07 16.40
5076 ATRAID 2_16 0.001 229.46 3.8e-07 -16.30
4636 FADS2 11_34 0.007 252.22 5.4e-06 15.79
2131 ATP13A1 19_15 0.004 169.52 2.0e-06 -15.71
7782 CASC4 15_17 0.015 247.64 1.1e-05 -15.60
5839 TIMD4 5_92 0.995 278.89 8.1e-04 15.00
1652 PCIF1 20_28 0.007 202.64 4.3e-06 14.40
1087 GCKR 2_16 0.439 91.91 1.2e-04 12.86
5057 IFT172 2_16 0.527 92.09 1.4e-04 -12.84Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$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: 11_70"
genename region_tag susie_pip mu2 PVE z
5007 BUD13 11_70 0.000 619.45 0.00000 26.11
2496 ZPR1 11_70 0.044 3041.24 0.00039 -47.41
3237 APOA1 11_70 0.000 164.84 0.00000 3.42
6898 SIK3 11_70 0.000 94.53 0.00000 -5.93
8030 PAFAH1B2 11_70 0.000 443.78 0.00000 -3.65
6104 TAGLN 11_70 0.000 130.16 0.00000 -3.20
6902 PCSK7 11_70 0.000 133.73 0.00000 -4.29
7873 RNF214 11_70 0.000 35.89 0.00000 -1.80
9915 BACE1 11_70 0.000 214.04 0.00000 -19.43
2530 CEP164 11_70 0.000 85.32 0.00000 -5.80
5018 FXYD2 11_70 0.000 5.81 0.00000 0.39
5017 FXYD6 11_70 0.000 6.77 0.00000 0.50
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 8_21"
genename region_tag susie_pip mu2 PVE z
5936 CSGALNACT1 8_21 0 108.25 0 -9.94
1947 INTS10 8_21 0 103.42 0 -8.85
8899 LPL 8_21 0 1274.24 0 -46.68
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 1_39"
genename region_tag susie_pip mu2 PVE z
7088 TM2D1 1_39 0.010 6.66 2.0e-07 1.53
4449 PATJ 1_39 0.041 18.67 2.2e-06 1.86
7089 USP1 1_39 1.000 1055.09 3.1e-03 33.23
3102 DOCK7 1_39 0.010 868.15 2.5e-05 30.17
3822 ATG4C 1_39 0.012 6.82 2.3e-07 -0.32
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 2_16"
genename region_tag susie_pip mu2 PVE z
11045 SLC35F6 2_16 0.000 28.28 3.5e-08 -5.95
3366 TMEM214 2_16 0.003 14.67 1.3e-07 0.21
5074 EMILIN1 2_16 0.001 70.18 1.6e-07 9.33
5061 KHK 2_16 0.004 16.31 2.0e-07 -0.26
5059 CGREF1 2_16 0.003 15.50 1.1e-07 0.15
5070 PREB 2_16 0.000 20.21 2.7e-08 -3.74
5076 ATRAID 2_16 0.001 229.46 3.8e-07 -16.30
1090 CAD 2_16 0.002 50.24 3.2e-07 -7.86
5071 SLC5A6 2_16 0.001 50.74 1.7e-07 5.10
7303 UCN 2_16 0.001 57.96 8.7e-08 -10.37
2952 GTF3C2 2_16 0.001 54.15 8.0e-08 10.16
2956 SNX17 2_16 0.017 688.91 3.5e-05 23.54
7304 ZNF513 2_16 0.000 335.09 4.6e-07 16.40
2953 NRBP1 2_16 0.004 761.68 8.2e-06 26.36
5057 IFT172 2_16 0.527 92.09 1.4e-04 -12.84
1087 GCKR 2_16 0.439 91.91 1.2e-04 12.86
10613 GPN1 2_16 0.001 96.96 2.5e-07 7.15
9018 CCDC121 2_16 0.001 13.49 2.1e-08 -1.90
6660 BRE 2_16 0.002 74.42 4.2e-07 10.36
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 11_34"
genename region_tag susie_pip mu2 PVE z
10165 FAM111B 11_34 0.003 6.98 6.1e-08 -0.95
7794 FAM111A 11_34 0.016 21.29 1.0e-06 -1.71
2506 DTX4 11_34 0.003 5.33 4.1e-08 -0.70
10468 MPEG1 11_34 0.005 11.21 1.5e-07 1.42
2515 MS4A6A 11_34 0.003 14.39 1.2e-07 3.14
7815 PATL1 11_34 0.006 14.52 2.4e-07 -2.00
7817 STX3 11_34 0.003 5.13 3.9e-08 -0.22
7818 MRPL16 11_34 0.003 5.01 3.8e-08 0.08
4634 GIF 11_34 0.007 16.70 3.4e-07 2.21
4638 TCN1 11_34 0.003 5.92 4.8e-08 0.78
6096 MS4A2 11_34 0.008 24.43 5.7e-07 -4.14
11819 AP001257.1 11_34 0.003 5.47 4.5e-08 -0.05
11116 MS4A4E 11_34 0.679 30.85 6.1e-05 5.44
2516 MS4A4A 11_34 0.003 12.02 9.8e-08 2.37
7825 MS4A6E 11_34 0.003 14.85 1.3e-07 -3.47
7826 MS4A7 11_34 0.004 10.64 1.1e-07 2.05
7827 MS4A14 11_34 0.003 7.54 6.8e-08 -1.25
2519 CCDC86 11_34 0.003 5.17 4.0e-08 -0.26
9570 PTGDR2 11_34 0.003 5.73 4.8e-08 -0.29
6093 ZP1 11_34 0.004 9.46 1.0e-07 -1.39
2520 PRPF19 11_34 0.003 5.92 5.1e-08 0.17
2521 TMEM109 11_34 0.004 7.90 8.4e-08 0.65
2546 SLC15A3 11_34 0.003 6.23 5.6e-08 -0.48
2547 CD5 11_34 0.003 6.21 4.7e-08 1.25
8008 VPS37C 11_34 0.003 6.94 6.9e-08 -0.14
11874 PGA5 11_34 0.006 17.95 3.2e-07 3.02
11340 PGA3 11_34 0.006 17.08 2.9e-07 2.93
8009 VWCE 11_34 0.003 11.73 1.2e-07 2.37
6088 TMEM138 11_34 0.016 19.22 9.1e-07 -0.38
7030 CYB561A3 11_34 0.016 19.22 9.1e-07 -0.38
9981 TMEM216 11_34 0.022 27.30 1.7e-06 2.68
11871 RP11-286N22.8 11_34 0.010 18.24 5.3e-07 1.92
4631 DAGLA 11_34 0.005 45.20 6.3e-07 -6.24
3765 MYRF 11_34 0.003 37.67 3.2e-07 5.89
4636 FADS2 11_34 0.007 252.22 5.4e-06 15.79
4637 TMEM258 11_34 0.008 93.35 2.2e-06 9.12
6089 FADS1 11_34 0.999 417.22 1.2e-03 21.00
11190 FADS3 11_34 0.003 11.45 9.9e-08 -2.59
8011 BEST1 11_34 0.003 34.86 3.4e-07 5.66
6092 INCENP 11_34 0.006 17.46 2.8e-07 2.93
7032 ASRGL1 11_34 0.003 5.95 5.0e-08 -0.11
| 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
57719 rs878811 1_116 1.000 37.03 1.1e-04 -5.84
58296 rs11122453 1_117 1.000 294.72 8.6e-04 -19.86
72720 rs1042034 2_13 1.000 730.01 2.1e-03 26.49
72721 rs1801699 2_13 1.000 49.06 1.4e-04 -4.43
74456 rs780093 2_16 1.000 1481.34 4.3e-03 -42.23
185376 rs9817452 3_97 1.000 47.82 1.4e-04 -6.86
199970 rs3748034 4_4 1.000 64.76 1.9e-04 10.20
199971 rs3752442 4_4 1.000 50.77 1.5e-04 -8.94
228245 rs7439032 4_58 1.000 38.02 1.1e-04 -6.59
233405 rs35518360 4_67 1.000 52.42 1.5e-04 7.21
288516 rs115912456 5_49 1.000 39.24 1.1e-04 -6.22
361411 rs540973884 6_92 1.000 145.89 4.2e-04 -12.42
365349 rs746369662 6_99 1.000 237.06 6.9e-04 -4.33
367403 rs56393506 6_104 1.000 195.34 5.7e-04 -8.17
397164 rs13235543 7_47 1.000 731.09 2.1e-03 -37.24
397165 rs12539160 7_47 1.000 92.43 2.7e-04 -5.06
397251 rs199603307 7_48 1.000 87.42 2.5e-04 -9.22
398228 rs761767938 7_49 1.000 8808.29 2.6e-02 -2.70
398236 rs1544459 7_49 1.000 8561.70 2.5e-02 -3.09
432611 rs1495743 8_20 1.000 174.14 5.1e-04 -13.73
433326 rs78963197 8_21 1.000 1377.66 4.0e-03 -50.50
433358 rs75835816 8_21 1.000 482.11 1.4e-03 23.18
433394 rs11986461 8_21 1.000 373.10 1.1e-03 -23.75
444280 rs4738679 8_45 1.000 96.82 2.8e-04 -10.23
463946 rs4604455 8_83 1.000 95.75 2.8e-04 16.84
463984 rs10956254 8_83 1.000 75.67 2.2e-04 12.55
521873 rs71007692 10_28 1.000 6661.57 1.9e-02 2.46
584248 rs1176746 11_67 1.000 3421.59 9.9e-03 -3.60
584250 rs2307599 11_67 1.000 3367.72 9.8e-03 -3.57
585041 rs7927993 11_69 1.000 36.18 1.1e-04 5.98
585163 rs9326246 11_70 1.000 2394.44 7.0e-03 -54.85
585195 rs5130 11_70 1.000 824.69 2.4e-03 -35.89
585388 rs147611518 11_70 1.000 331.40 9.6e-04 19.13
608784 rs7397189 12_36 1.000 67.63 2.0e-04 -10.46
636460 rs1340819 13_7 1.000 35.17 1.0e-04 -5.52
644758 rs7999449 13_25 1.000 22053.06 6.4e-02 3.35
644760 rs775834524 13_25 1.000 22006.33 6.4e-02 3.46
663686 rs143614549 13_62 1.000 65.55 1.9e-04 8.74
663717 rs75680340 13_62 1.000 60.29 1.8e-04 -7.87
702950 rs62000868 15_27 1.000 90.36 2.6e-04 9.84
702956 rs2070895 15_27 1.000 255.53 7.4e-04 16.65
716368 rs895394 15_50 1.000 42.82 1.2e-04 6.42
737510 rs12443634 16_46 1.000 115.98 3.4e-04 -11.82
757578 rs1801689 17_38 1.000 91.24 2.7e-04 -9.37
789079 rs111500536 19_8 1.000 91.63 2.7e-04 -8.89
789082 rs116843064 19_8 1.000 703.88 2.0e-03 -27.25
792090 rs3794991 19_15 1.000 399.41 1.2e-03 -21.01
792121 rs113619686 19_15 1.000 68.76 2.0e-04 1.58
798464 rs440446 19_31 1.000 724.64 2.1e-03 24.46
798469 rs113345881 19_31 1.000 227.06 6.6e-04 22.31
815833 rs6066141 20_29 1.000 46.02 1.3e-04 -7.62
898913 rs9378248 6_26 1.000 157.87 4.6e-04 14.88
941500 rs3072639 11_29 1.000 19414.95 5.6e-02 3.17
1003850 rs202007993 17_26 1.000 4387.00 1.3e-02 -2.66
1003880 rs7209751 17_26 1.000 4371.75 1.3e-02 8.02
1003882 rs72836561 17_26 1.000 492.76 1.4e-03 21.38
1016201 rs764858365 17_39 1.000 30403.23 8.8e-02 -4.05
1054683 rs41523449 19_24 1.000 213.99 6.2e-04 8.26
1054687 rs749726391 19_24 1.000 254.70 7.4e-04 -1.45
1064564 rs374141296 19_34 1.000 24540.91 7.1e-02 3.60
55184 rs56056346 1_111 0.999 91.31 2.7e-04 -4.78
359671 rs212776 6_88 0.999 41.67 1.2e-04 6.61
433324 rs17091881 8_21 0.999 574.52 1.7e-03 22.21
585037 rs28480969 11_69 0.999 37.54 1.1e-04 6.58
594905 rs12824533 12_11 0.999 32.85 9.5e-05 5.49
598104 rs67981690 12_16 0.999 95.75 2.8e-04 9.34
629157 rs35658692 12_75 0.999 92.01 2.7e-04 11.46
788612 rs10401485 19_7 0.999 39.25 1.1e-04 6.88
798462 rs34878901 19_31 0.999 633.71 1.8e-03 -4.29
1003418 rs117380643 17_25 0.999 93.78 2.7e-04 9.72
14831 rs213501 1_34 0.998 36.81 1.1e-04 -5.89
463943 rs13252684 8_83 0.998 323.55 9.4e-04 9.33
819378 rs6099671 20_33 0.998 42.22 1.2e-04 6.56
74457 rs6744393 2_16 0.997 197.47 5.7e-04 -18.28
74481 rs9679004 2_16 0.997 41.38 1.2e-04 2.69
471204 rs1016565 9_1 0.997 30.58 8.9e-05 5.40
528433 rs2393730 10_42 0.997 34.78 1.0e-04 -6.49
643150 rs1326122 13_21 0.997 35.79 1.0e-04 -6.02
813911 rs56206139 20_24 0.997 35.80 1.0e-04 -5.59
597748 rs66720652 12_15 0.996 31.82 9.2e-05 -5.34
373723 rs852386 7_7 0.995 32.78 9.5e-05 5.52
433344 rs11986942 8_21 0.995 718.00 2.1e-03 -42.73
703968 rs10851699 15_28 0.995 38.06 1.1e-04 6.03
331373 rs115482652 6_34 0.994 30.48 8.8e-05 5.86
331374 rs9472126 6_34 0.994 30.62 8.8e-05 -5.22
550693 rs75184896 10_84 0.994 29.56 8.5e-05 5.17
742734 rs11078597 17_2 0.994 48.09 1.4e-04 6.83
280891 rs536916238 5_33 0.993 66.91 1.9e-04 3.25
739722 rs7191098 16_50 0.993 31.43 9.1e-05 -5.37
799327 rs12978750 19_33 0.993 67.24 1.9e-04 9.00
533798 rs2186235 10_51 0.992 28.70 8.3e-05 5.14
556664 rs34623292 11_10 0.992 38.10 1.1e-04 6.41
629460 rs11057830 12_76 0.992 29.96 8.6e-05 5.41
55650 rs61830291 1_112 0.991 62.82 1.8e-04 7.31
428958 rs2251473 8_14 0.991 65.56 1.9e-04 -10.13
497537 rs2254819 9_53 0.990 35.67 1.0e-04 -5.75
328052 rs1233385 6_23 0.989 58.78 1.7e-04 -7.67
605655 rs73108788 12_28 0.989 27.70 8.0e-05 5.07
801441 rs12151142 19_38 0.989 45.99 1.3e-04 8.17
11935 rs1877454 1_27 0.988 32.25 9.3e-05 5.48
82185 rs4566412 2_31 0.986 33.09 9.5e-05 5.35
99093 rs1821968 2_66 0.986 31.22 8.9e-05 -5.43
228417 rs74678260 4_58 0.986 38.25 1.1e-04 -7.72
702874 rs58038553 15_27 0.985 33.63 9.6e-05 -6.85
789019 rs117476590 19_7 0.985 30.67 8.8e-05 -5.67
361420 rs602261 6_93 0.984 27.00 7.7e-05 -3.82
906243 rs28383314 6_26 0.984 250.08 7.2e-04 17.19
48718 rs6427759 1_99 0.983 27.64 7.9e-05 -4.96
55641 rs2642420 1_112 0.983 34.78 9.9e-05 4.32
397763 rs6465120 7_48 0.983 35.74 1.0e-04 -6.43
721573 rs34340800 16_12 0.983 40.45 1.2e-04 6.18
138039 rs4675812 2_144 0.981 31.82 9.1e-05 -5.58
428765 rs7821812 8_14 0.981 80.19 2.3e-04 10.98
567656 rs77377156 11_32 0.979 44.45 1.3e-04 6.26
438777 rs12675945 8_34 0.977 26.02 7.4e-05 -4.71
396901 rs13227753 7_46 0.976 51.34 1.5e-04 -7.26
280873 rs173964 5_33 0.974 278.94 7.9e-04 13.39
413350 rs6961342 7_80 0.974 78.03 2.2e-04 12.34
556936 rs547219635 11_11 0.974 31.30 8.9e-05 -4.99
307012 rs76957426 5_85 0.972 38.73 1.1e-04 5.19
524508 rs71508062 10_33 0.971 26.61 7.5e-05 5.10
527651 rs1171619 10_39 0.966 41.07 1.2e-04 6.18
663666 rs7400029 13_62 0.966 36.11 1.0e-04 6.20
733595 rs71401830 16_37 0.965 25.89 7.3e-05 4.39
228550 rs74939203 4_59 0.959 50.08 1.4e-04 -4.15
937833 rs55971594 11_28 0.959 33.91 9.5e-05 6.64
7503 rs564646712 1_17 0.958 25.51 7.1e-05 4.52
328419 rs181268076 6_27 0.958 69.39 1.9e-04 8.71
585173 rs3135506 11_70 0.956 2976.91 8.3e-03 48.30
40775 rs9425587 1_84 0.955 35.87 1.0e-04 -5.74
130412 rs62191851 2_129 0.953 25.63 7.1e-05 4.15
428081 rs330078 8_12 0.953 31.34 8.7e-05 6.55
382280 rs6959252 7_23 0.952 110.25 3.0e-04 3.68
432350 rs145696392 8_19 0.952 32.84 9.1e-05 5.22
748253 rs139356332 17_16 0.951 24.15 6.7e-05 4.51
449358 rs34582181 8_55 0.945 25.03 6.9e-05 4.66
382358 rs118007210 7_23 0.944 39.47 1.1e-04 6.19
729718 rs12720926 16_31 0.944 96.18 2.6e-04 -13.39
143339 rs10602803 3_9 0.940 44.35 1.2e-04 4.67
429183 rs1736062 8_15 0.940 49.35 1.3e-04 -9.57
113602 rs6722159 2_96 0.938 29.82 8.1e-05 -5.44
583688 rs117978300 11_66 0.938 26.65 7.3e-05 -5.59
16661 rs141797847 1_38 0.934 24.50 6.7e-05 4.51
762421 rs2279396 17_47 0.934 26.01 7.1e-05 -4.72
603835 rs117339363 12_25 0.933 23.88 6.5e-05 -4.64
840078 rs9610329 22_14 0.932 38.13 1.0e-04 6.08
759730 rs1671012 17_42 0.931 29.05 7.9e-05 -5.91
398232 rs11972122 7_49 0.930 8636.61 2.3e-02 -3.36
561607 rs56133711 11_19 0.930 39.54 1.1e-04 6.26
803893 rs6139182 20_5 0.928 27.57 7.4e-05 4.86
41276 rs375413887 1_85 0.926 23.87 6.4e-05 -4.34
492421 rs12236183 9_45 0.922 34.96 9.4e-05 7.02
233471 rs13140033 4_68 0.920 23.97 6.4e-05 4.44
470262 rs7832515 8_94 0.919 27.77 7.4e-05 -5.15
960799 rs71468663 11_36 0.919 122.43 3.3e-04 11.42
297375 rs2165929 5_67 0.918 29.19 7.8e-05 5.68
768615 rs9963938 18_11 0.918 42.58 1.1e-04 -6.38
797517 rs6508974 19_30 0.918 31.77 8.5e-05 -4.80
367367 rs117733303 6_104 0.915 67.82 1.8e-04 -9.86
545155 rs2420477 10_73 0.915 25.42 6.8e-05 -4.86
1016206 rs11079703 17_39 0.915 30395.36 8.1e-02 -4.00
703596 rs72748766 15_27 0.912 27.84 7.4e-05 -4.87
114600 rs13389219 2_100 0.911 169.92 4.5e-04 -15.59
642926 rs6561525 13_21 0.909 27.75 7.3e-05 4.02
290573 rs11741599 5_53 0.896 30.21 7.9e-05 5.36
331367 rs28357093 6_33 0.894 25.07 6.5e-05 -4.60
7468 rs2742962 1_16 0.893 35.54 9.2e-05 5.73
378484 rs38205 7_16 0.892 40.16 1.0e-04 -6.16
279936 rs1694060 5_31 0.886 26.26 6.8e-05 4.47
427696 rs7826654 8_11 0.885 71.95 1.9e-04 -10.03
349610 rs9496567 6_67 0.883 27.99 7.2e-05 -4.86
322206 rs61439239 6_10 0.878 23.79 6.1e-05 4.37
397163 rs13247874 7_47 0.878 748.04 1.9e-03 -37.21
628788 rs529994558 12_75 0.877 43.94 1.1e-04 -7.87
624738 rs73191121 12_66 0.876 29.64 7.6e-05 -4.78
433500 rs74500831 8_22 0.875 35.53 9.0e-05 -5.70
448544 rs10091362 8_54 0.874 25.24 6.4e-05 4.62
815818 rs1412956 20_29 0.872 31.72 8.0e-05 6.56
55171 rs4846295 1_111 0.868 72.48 1.8e-04 1.38
463942 rs2980858 8_83 0.868 852.54 2.2e-03 -34.02
813771 rs932641 20_24 0.867 31.47 7.9e-05 -5.65
101895 rs10864859 2_70 0.859 29.05 7.3e-05 4.85
357784 rs141281941 6_85 0.858 24.53 6.1e-05 4.41
388284 rs149901303 7_32 0.858 23.57 5.9e-05 4.06
199985 rs36205397 4_4 0.857 48.60 1.2e-04 10.94
365340 rs818442 6_99 0.853 218.30 5.4e-04 1.26
435133 rs145706224 8_24 0.850 24.68 6.1e-05 4.37
585014 rs10047413 11_69 0.849 31.25 7.7e-05 6.72
840864 rs4821764 22_16 0.849 88.70 2.2e-04 9.53
311681 rs56034935 5_96 0.847 24.19 6.0e-05 4.30
698336 rs148470008 15_15 0.844 76.45 1.9e-04 8.88
538087 rs7074206 10_60 0.842 25.21 6.2e-05 4.44
702934 rs28594460 15_27 0.842 31.22 7.6e-05 5.05
228437 rs28529445 4_58 0.840 40.38 9.9e-05 8.05
673966 rs72681869 14_20 0.832 24.55 5.9e-05 -4.32
190474 rs234043 3_106 0.831 24.68 6.0e-05 4.46
937692 rs149903077 11_28 0.831 45.65 1.1e-04 -6.38
284003 rs40087 5_41 0.830 24.55 5.9e-05 -4.33
682226 rs1848401 14_36 0.828 27.33 6.6e-05 4.99
439034 rs72638505 8_34 0.827 24.54 5.9e-05 4.29
143316 rs4684848 3_9 0.822 114.40 2.7e-04 -8.10
328290 rs9276189 6_27 0.820 47.20 1.1e-04 6.86
330366 rs3734554 6_32 0.820 25.03 6.0e-05 4.42
521300 rs7912430 10_27 0.820 24.57 5.9e-05 4.30
254408 rs13110927 4_109 0.817 24.79 5.9e-05 4.38
729715 rs12446515 16_31 0.816 122.60 2.9e-04 -14.28
592232 rs79988477 12_4 0.814 26.03 6.2e-05 -4.47
716268 rs7175132 15_50 0.814 26.97 6.4e-05 -4.82
367363 rs3127599 6_104 0.812 183.73 4.3e-04 7.74
208661 rs56147366 4_22 0.805 52.20 1.2e-04 8.62
367410 rs374071816 6_104 0.804 95.21 2.2e-04 -7.93
199888 rs112820797 4_4 0.801 52.44 1.2e-04 -7.53
428560 rs13249929 8_13 0.801 46.22 1.1e-04 -9.07
744568 rs4968186 17_7 0.801 28.28 6.6e-05 -5.54SNPs 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
1016201 rs764858365 17_39 1.000 30403.23 8.8e-02 -4.05
1016206 rs11079703 17_39 0.915 30395.36 8.1e-02 -4.00
1016211 rs8079835 17_39 0.523 30387.09 4.6e-02 -4.04
1016191 rs11870061 17_39 0.314 30386.57 2.8e-02 -4.03
1016209 rs8075040 17_39 0.026 30385.39 2.3e-03 -4.03
1016189 rs12938098 17_39 0.101 30383.46 8.9e-03 -4.02
1016193 rs2365412 17_39 0.032 30381.52 2.8e-03 -4.04
1016203 rs9895905 17_39 0.009 30380.15 8.1e-04 -4.02
1016198 rs8072248 17_39 0.047 30378.37 4.1e-03 -4.05
1016227 rs71139197 17_39 0.000 30362.77 4.3e-05 -3.95
1016214 rs11651507 17_39 0.000 30360.42 5.8e-06 -4.00
1016215 rs12949073 17_39 0.000 30360.41 6.0e-06 -4.00
1016217 rs8080933 17_39 0.000 30360.22 6.1e-06 -4.00
1016230 rs62084204 17_39 0.000 30359.24 2.4e-05 -3.95
1016222 rs12941792 17_39 0.000 30348.90 1.8e-07 -3.98
1016224 rs12602159 17_39 0.000 30348.34 1.6e-07 -3.98
1016229 rs8065171 17_39 0.000 30347.34 1.5e-07 -3.98
1016225 rs8077847 17_39 0.000 30344.79 5.4e-08 -3.97
1016231 rs9904849 17_39 0.000 30338.93 1.5e-06 -4.00
1016232 rs9905298 17_39 0.000 30332.64 2.7e-08 -3.98
1016234 rs9911497 17_39 0.000 30331.90 2.6e-08 -3.99
1016235 rs9912436 17_39 0.000 30330.80 9.0e-09 -3.98
1016241 rs12451934 17_39 0.000 30328.43 3.1e-07 -4.00
1016185 rs10744997 17_39 0.000 30306.14 1.1e-09 -3.99
1016244 rs4791080 17_39 0.000 30278.32 3.5e-10 -4.00
1016186 rs10775402 17_39 0.000 30250.49 1.4e-11 -3.96
1016220 rs35098173 17_39 0.000 30219.33 5.0e-12 -3.83
1016248 rs34297832 17_39 0.000 30183.61 1.2e-11 -3.99
1016178 rs66467955 17_39 0.000 28886.15 1.2e-12 3.61
1016261 rs11079704 17_39 0.000 28428.83 2.4e-10 -4.47
1016265 rs9902223 17_39 0.000 28417.27 3.0e-10 -4.50
1016260 rs9894804 17_39 0.000 28415.31 1.7e-10 -4.43
1016130 rs7224576 17_39 0.000 28320.22 7.9e-12 -3.95
1016129 rs7225648 17_39 0.000 28172.99 1.5e-11 -4.05
1016125 rs4791129 17_39 0.000 28080.21 8.8e-12 -4.07
1016146 rs9989439 17_39 0.000 27493.90 8.3e-12 3.85
1016145 rs9303520 17_39 0.000 27492.35 1.7e-12 3.85
1016122 rs146749646 17_39 0.000 26726.78 2.0e-13 -3.46
1016124 rs7209992 17_39 0.000 26509.59 1.8e-12 -3.96
1016126 rs4791128 17_39 0.000 26461.64 1.7e-12 -3.95
1016131 rs7224839 17_39 0.000 26329.98 4.6e-12 -4.03
1016127 rs4791127 17_39 0.000 26184.79 8.3e-12 -4.12
1016128 rs7224665 17_39 0.000 26183.63 8.7e-12 -4.13
1016172 rs4791109 17_39 0.000 25408.22 2.1e-14 -3.41
1016100 rs9912547 17_39 0.000 25205.78 8.9e-14 3.48
1064564 rs374141296 19_34 1.000 24540.91 7.1e-02 3.60
1064568 rs2946865 19_34 0.000 24499.10 1.2e-10 3.74
1064561 rs113176985 19_34 0.000 24482.07 2.7e-09 3.75
1064554 rs35295508 19_34 0.000 24430.91 1.4e-09 3.72
1064559 rs73056069 19_34 0.000 24357.93 8.4e-10 3.68SNPs 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
1016201 rs764858365 17_39 1.000 30403.23 0.0880 -4.05
1016206 rs11079703 17_39 0.915 30395.36 0.0810 -4.00
1064564 rs374141296 19_34 1.000 24540.91 0.0710 3.60
644758 rs7999449 13_25 1.000 22053.06 0.0640 3.35
644760 rs775834524 13_25 1.000 22006.33 0.0640 3.46
941500 rs3072639 11_29 1.000 19414.95 0.0560 3.17
1016211 rs8079835 17_39 0.523 30387.09 0.0460 -4.04
1016191 rs11870061 17_39 0.314 30386.57 0.0280 -4.03
398228 rs761767938 7_49 1.000 8808.29 0.0260 -2.70
398236 rs1544459 7_49 1.000 8561.70 0.0250 -3.09
398232 rs11972122 7_49 0.930 8636.61 0.0230 -3.36
521873 rs71007692 10_28 1.000 6661.57 0.0190 2.46
521872 rs2474565 10_28 0.657 6694.49 0.0130 2.57
941506 rs11039670 11_29 0.236 19414.37 0.0130 3.26
1003850 rs202007993 17_26 1.000 4387.00 0.0130 -2.66
1003880 rs7209751 17_26 1.000 4371.75 0.0130 8.02
941515 rs11039671 11_29 0.221 19414.44 0.0120 3.26
941541 rs9651621 11_29 0.201 19414.36 0.0110 3.26
941521 rs4436573 11_29 0.185 19414.33 0.0100 3.26
941527 rs10838872 11_29 0.183 19411.86 0.0100 3.27
941529 rs11039675 11_29 0.185 19414.33 0.0100 3.26
941538 rs7124318 11_29 0.179 19414.12 0.0100 3.26
584248 rs1176746 11_67 1.000 3421.59 0.0099 -3.60
584250 rs2307599 11_67 1.000 3367.72 0.0098 -3.57
521879 rs2472183 10_28 0.471 6694.33 0.0092 2.56
1016189 rs12938098 17_39 0.101 30383.46 0.0089 -4.02
585173 rs3135506 11_70 0.956 2976.91 0.0083 48.30
521882 rs11011452 10_28 0.407 6694.34 0.0079 2.55
585163 rs9326246 11_70 1.000 2394.44 0.0070 -54.85
941502 rs7949513 11_29 0.086 19412.49 0.0049 3.24
74456 rs780093 2_16 1.000 1481.34 0.0043 -42.23
1016198 rs8072248 17_39 0.047 30378.37 0.0041 -4.05
433326 rs78963197 8_21 1.000 1377.66 0.0040 -50.50
521870 rs9299760 10_28 0.159 6690.23 0.0031 2.56
1003893 rs4793041 17_26 0.256 4205.34 0.0031 8.38
941546 rs12295434 11_29 0.051 19405.46 0.0029 3.21
941555 rs11039677 11_29 0.050 19405.38 0.0028 3.21
1016193 rs2365412 17_39 0.032 30381.52 0.0028 -4.04
941556 rs7119161 11_29 0.049 19408.15 0.0027 3.23
941564 rs7946068 11_29 0.047 19407.94 0.0026 3.23
585195 rs5130 11_70 1.000 824.69 0.0024 -35.89
1016209 rs8075040 17_39 0.026 30385.39 0.0023 -4.03
463942 rs2980858 8_83 0.868 852.54 0.0022 -34.02
72720 rs1042034 2_13 1.000 730.01 0.0021 26.49
397164 rs13235543 7_47 1.000 731.09 0.0021 -37.24
433344 rs11986942 8_21 0.995 718.00 0.0021 -42.73
798464 rs440446 19_31 1.000 724.64 0.0021 24.46
1003892 rs34462326 17_26 0.168 4207.10 0.0021 8.37
789082 rs116843064 19_8 1.000 703.88 0.0020 -27.25
397163 rs13247874 7_47 0.878 748.04 0.0019 -37.21SNPs 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
585163 rs9326246 11_70 1.000 2394.44 7.0e-03 -54.85
585161 rs1558861 11_70 0.000 2376.95 2.5e-06 -54.63
433326 rs78963197 8_21 1.000 1377.66 4.0e-03 -50.50
433340 rs6999813 8_21 0.000 1391.01 1.4e-07 -50.45
433319 rs10645926 8_21 0.000 1340.62 0.0e+00 -50.15
433331 rs17489226 8_21 0.000 1311.95 2.2e-15 -49.74
433357 rs7816447 8_21 0.000 1365.32 8.6e-16 -49.70
433359 rs11984698 8_21 0.000 1352.77 5.8e-17 -49.64
433352 rs28675909 8_21 0.000 1345.26 1.0e-17 -49.60
433355 rs11989309 8_21 0.000 1345.82 1.0e-17 -49.60
433354 rs7004149 8_21 0.000 1341.94 4.3e-18 -49.58
433353 rs79198716 8_21 0.000 1341.35 3.5e-18 -49.57
585173 rs3135506 11_70 0.956 2976.91 8.3e-03 48.30
433321 rs1569209 8_21 0.000 1226.93 0.0e+00 -47.91
433323 rs80073370 8_21 0.000 1156.75 0.0e+00 -47.40
585172 rs140050044 11_70 0.000 2883.15 0.0e+00 47.24
433335 rs2410620 8_21 0.000 789.27 1.7e-10 -46.93
433342 rs1441762 8_21 0.000 786.79 1.4e-10 -46.91
433345 rs4126104 8_21 0.000 771.07 8.7e-12 -46.86
585171 rs12274192 11_70 0.000 2823.59 0.0e+00 46.83
585170 rs17120029 11_70 0.000 2819.17 0.0e+00 46.78
433341 rs35878331 8_21 0.000 769.68 7.1e-12 -46.67
585167 rs11825181 11_70 0.000 2743.46 0.0e+00 46.49
433384 rs80026582 8_21 0.000 1194.54 0.0e+00 -46.30
433346 rs35369244 8_21 0.000 737.95 2.1e-14 -46.24
585165 rs12280724 11_70 0.000 2644.21 0.0e+00 45.54
433315 rs149553676 8_21 0.000 691.70 0.0e+00 -45.10
433314 rs287 8_21 0.000 703.87 0.0e+00 -44.83
433344 rs11986942 8_21 0.995 718.00 2.1e-03 -42.73
74456 rs780093 2_16 1.000 1481.34 4.3e-03 -42.23
585176 rs4938313 11_70 0.000 1297.91 0.0e+00 -40.88
585156 rs4938302 11_70 0.000 731.21 0.0e+00 -40.78
463931 rs2980875 8_83 0.076 553.20 1.2e-04 -38.83
585120 rs509728 11_70 0.000 1153.95 0.0e+00 38.77
463927 rs2001844 8_83 0.465 543.30 7.3e-04 -38.69
463929 rs2980886 8_83 0.457 543.26 7.2e-04 -38.69
433337 rs4083261 8_21 0.005 725.81 1.1e-05 -38.68
585162 rs57232565 11_70 0.000 1862.64 0.0e+00 38.33
585155 rs74849419 11_70 0.000 1534.61 0.0e+00 38.22
463938 rs10808546 8_83 0.002 568.04 3.2e-06 -38.16
433336 rs12541912 8_21 0.000 720.30 1.4e-13 -37.86
463937 rs2954031 8_83 0.000 530.39 1.1e-08 -37.70
585144 rs57984552 11_70 0.000 623.08 0.0e+00 37.37
397164 rs13235543 7_47 1.000 731.09 2.1e-03 -37.24
397163 rs13247874 7_47 0.878 748.04 1.9e-03 -37.21
397156 rs35659126 7_47 0.066 738.23 1.4e-04 -36.99
397158 rs9638180 7_47 0.057 737.87 1.2e-04 -36.99
397160 rs35173225 7_47 0.000 689.47 2.4e-07 -36.32
397154 rs368981495 7_47 0.000 703.15 2.4e-07 -36.15
585195 rs5130 11_70 1.000 824.69 2.4e-03 -35.89Gene 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] 20if (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)KIAA1683 gene(s) from the input list not found in DisGeNET CURATEDHIST1H2BD gene(s) from the input list not found in DisGeNET CURATEDSPATA20 gene(s) from the input list not found in DisGeNET CURATEDPSRC1 gene(s) from the input list not found in DisGeNET CURATEDTMEM147 gene(s) from the input list not found in DisGeNET CURATEDUSP1 gene(s) from the input list not found in DisGeNET CURATEDNTAN1 gene(s) from the input list not found in DisGeNET CURATEDFCGRT gene(s) from the input list not found in DisGeNET CURATED Description FDR Ratio BgRatio
9 Brucellosis 0.009677344 1/12 1/9703
18 Dysarthria 0.009677344 1/12 2/9703
25 Inherited Factor II deficiency 0.009677344 1/12 1/9703
37 Ophthalmoplegia 0.009677344 1/12 2/9703
41 Sinus Thrombosis, Intracranial 0.009677344 1/12 2/9703
57 External Ophthalmoplegia 0.009677344 1/12 2/9703
58 Skin Diseases, Vascular 0.009677344 1/12 1/9703
60 Niemann-Pick Disease, Type C 0.009677344 1/12 2/9703
64 Dysarthria, Scanning 0.009677344 1/12 2/9703
68 Mesenteric Venous Thrombosis 0.009677344 1/12 2/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...
description size overlap FDR database
1 Dyslipidaemia 81 5 0.001448182 disease_GLAD4U
2 Therapeutic abortion 12 3 0.002147689 disease_GLAD4U
userId
1 PSRC1;TIMD4;F2;FADS1;LMF1
2 F2;ACP2;SIPA1
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