Glycated haemoglobin (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 Glycated haemoglobin (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-30750_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$group)
p_sigma2 <- ggplot(df, aes(x=niter, y=value, group=group)) +
geom_line(aes(color=group)) +
geom_point(aes(color=group)) +
xlab("Iteration") + ylab(bquote(sigma^2)) +
ggtitle("Prior variance") +
theme_cowplot()
plot_grid(p_pi, p_sigma2)
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0169916074 0.0001940152 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
41.55842 21.92967 #report sample size
print(sample_size)[1] 344182#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11095 8697330#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02276317 0.10751416 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04521013 2.40602461Genes with highest PIPs
#distribution of PIPs
hist(ctwas_gene_res$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
4231 LRRC47 1_3 1.000 233.69 6.8e-04 -15.11
2614 LTBR 12_7 1.000 45.07 1.3e-04 4.69
7887 FN3K 17_47 1.000 557.18 1.6e-03 -28.51
5665 CNIH4 1_114 0.998 54.62 1.6e-04 -7.17
8713 GMPPB 3_35 0.998 144.33 4.2e-04 -9.50
9640 CBX6 22_16 0.995 30.01 8.7e-05 4.98
10114 PAQR9 3_87 0.993 46.58 1.3e-04 6.51
8641 OXSR1 3_27 0.990 47.26 1.4e-04 -6.86
9073 HIC1 17_3 0.986 60.36 1.7e-04 8.22
2106 KLF1 19_11 0.984 122.54 3.5e-04 -9.83
7981 PRDX2 19_11 0.984 34.99 1.0e-04 -1.98
5486 SIRT3 11_1 0.980 85.73 2.4e-04 12.59
1837 ABCC1 16_15 0.978 42.78 1.2e-04 6.58
837 ST6GAL1 3_114 0.974 50.22 1.4e-04 7.13
4062 MYO5C 15_21 0.974 77.18 2.2e-04 -9.13
9131 CCDC184 12_30 0.969 114.98 3.2e-04 4.20
7956 GPT 8_94 0.965 28.12 7.9e-05 4.46
11365 LINC01305 2_105 0.957 106.55 3.0e-04 12.37
8981 OR51B6 11_4 0.957 36.45 1.0e-04 5.93
7400 ARFIP1 4_98 0.940 32.80 9.0e-05 5.29
1699 ARFRP1 20_38 0.940 4363.98 1.2e-02 -5.98
3937 HIVEP3 1_27 0.938 50.67 1.4e-04 -7.03
9538 VMO1 17_4 0.931 20.87 5.6e-05 4.08
2306 SPOCK2 10_48 0.930 29.00 7.8e-05 5.10
8978 SMIM19 8_37 0.929 232.60 6.3e-04 15.34
932 EXOSC5 19_30 0.929 25.93 7.0e-05 -5.15
3752 KCNK17 6_30 0.928 25.53 6.9e-05 -4.39
10420 FBXL22 15_29 0.919 28.24 7.5e-05 -4.72
5409 SS18 18_13 0.917 29.98 8.0e-05 -5.05
9736 H1FX 3_80 0.910 25.70 6.8e-05 -5.24
11623 JMJD7 15_15 0.907 37.33 9.8e-05 6.50
11118 AC004540.5 7_23 0.894 22.17 5.8e-05 3.25
2818 SLC12A7 5_2 0.885 67.23 1.7e-04 -7.26
1267 PABPC4 1_24 0.875 64.30 1.6e-04 -8.62
1417 ATP5D 19_2 0.875 25.85 6.6e-05 -4.62
6290 ZFP36L2 2_27 0.869 141.72 3.6e-04 16.55
7091 NEXN 1_48 0.868 41.31 1.0e-04 6.21
4189 ARPC1B 7_61 0.867 72.91 1.8e-04 9.12
5966 VLDLR 9_3 0.856 27.01 6.7e-05 -4.92
2550 MADD 11_29 0.844 40.19 9.9e-05 7.97
5834 TNFAIP8 5_72 0.814 23.62 5.6e-05 4.33
6046 PPRC1 10_65 0.812 24.31 5.7e-05 3.87Genes 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$mu2),report_cols],20) genename region_tag susie_pip mu2 PVE z
12599 HCP5B 6_26 0.000 58476.13 0.0e+00 8.82
10848 TRIM10 6_26 0.000 38638.13 0.0e+00 -8.39
10855 HLA-G 6_26 0.000 35136.37 0.0e+00 -8.99
10853 HCG9 6_26 0.000 24123.62 0.0e+00 2.24
10968 HLA-A 6_26 0.000 20563.14 0.0e+00 1.34
10844 HLA-E 6_26 0.000 16846.01 0.0e+00 6.04
11418 TRIM26 6_26 0.000 16568.52 0.0e+00 2.42
11120 LINC00243 6_26 0.000 14748.28 0.0e+00 8.23
5868 PPP1R18 6_26 0.000 11716.22 0.0e+00 7.91
10841 MRPS18B 6_26 0.000 8836.75 0.0e+00 -1.21
6481 MOV10 1_69 0.000 6251.29 3.4e-07 -4.44
120 ST7L 1_69 0.000 4884.04 2.2e-10 0.01
10381 ZGPAT 20_38 0.001 4720.57 1.0e-05 5.00
1699 ARFRP1 20_38 0.940 4363.98 1.2e-02 -5.98
10847 TRIM15 6_26 0.000 3811.71 0.0e+00 -0.25
3093 CAPZA1 1_69 0.000 3751.76 4.8e-10 -1.10
10436 STMN3 20_38 0.000 3577.38 5.8e-11 4.82
4971 IER3 6_26 0.000 3508.61 0.0e+00 -1.26
10840 C6orf136 6_26 0.000 2226.34 0.0e+00 -4.20
4733 AHI1 6_89 0.000 2213.27 4.6e-10 -1.35Genes with highest PVE
#genes with 20 highest pve
head(ctwas_gene_res[order(-ctwas_gene_res$PVE),report_cols],20) genename region_tag susie_pip mu2 PVE z
1699 ARFRP1 20_38 0.940 4363.98 0.01200 -5.98
7887 FN3K 17_47 1.000 557.18 0.00160 -28.51
4231 LRRC47 1_3 1.000 233.69 0.00068 -15.11
8978 SMIM19 8_37 0.929 232.60 0.00063 15.34
8713 GMPPB 3_35 0.998 144.33 0.00042 -9.50
6290 ZFP36L2 2_27 0.869 141.72 0.00036 16.55
2106 KLF1 19_11 0.984 122.54 0.00035 -9.83
9131 CCDC184 12_30 0.969 114.98 0.00032 4.20
11365 LINC01305 2_105 0.957 106.55 0.00030 12.37
5486 SIRT3 11_1 0.980 85.73 0.00024 12.59
4062 MYO5C 15_21 0.974 77.18 0.00022 -9.13
10567 QRICH1 3_34 0.758 81.87 0.00018 -9.54
4189 ARPC1B 7_61 0.867 72.91 0.00018 9.12
2818 SLC12A7 5_2 0.885 67.23 0.00017 -7.26
9073 HIC1 17_3 0.986 60.36 0.00017 8.22
1267 PABPC4 1_24 0.875 64.30 0.00016 -8.62
5665 CNIH4 1_114 0.998 54.62 0.00016 -7.17
5171 EGF 4_71 0.656 79.61 0.00015 -8.34
3937 HIVEP3 1_27 0.938 50.67 0.00014 -7.03
8641 OXSR1 3_27 0.990 47.26 0.00014 -6.86Genes with largest z scores
#genes with 20 largest z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7887 FN3K 17_47 1.000 557.18 1.6e-03 -28.51
5442 FN3KRP 17_47 0.002 342.46 1.8e-06 -23.76
10438 GYPE 4_94 0.001 254.15 8.2e-07 20.36
12450 RP11-88E10.5 13_61 0.020 318.30 1.9e-05 -18.04
4024 TST 22_14 0.011 309.61 1.0e-05 -17.81
6552 HKDC1 10_46 0.000 644.00 0.0e+00 -17.57
5451 ZNF750 17_47 0.002 141.27 7.5e-07 17.19
8294 GYPA 4_94 0.001 140.38 2.7e-07 -17.17
6290 ZFP36L2 2_27 0.869 141.72 3.6e-04 16.55
11732 GYPB 4_94 0.000 136.07 7.9e-08 16.09
8978 SMIM19 8_37 0.929 232.60 6.3e-04 15.34
4231 LRRC47 1_3 1.000 233.69 6.8e-04 -15.11
11449 SMIM1 1_3 0.016 213.36 9.6e-06 -14.81
3367 ATAD2B 2_14 0.011 83.54 2.8e-06 13.65
4578 TMEM106C 12_30 0.000 135.79 2.7e-10 -13.62
12516 RP1-228P16.8 12_30 0.001 131.24 4.4e-07 13.48
9992 H1FNT 12_30 0.000 115.27 8.3e-08 12.98
11374 CYP21A2 6_26 0.000 788.56 0.0e+00 12.82
12483 HIST1H3A 6_20 0.000 42.46 6.5e-09 12.60
10805 EHMT2 6_26 0.000 760.10 0.0e+00 -12.59Comparing z scores and PIPs
#set nominal signifiance threshold for z scores
alpha <- 0.05
#bonferroni adjusted threshold for z scores
sig_thresh <- qnorm(1-(alpha/nrow(ctwas_gene_res)/2), lower=T)
#Q-Q plot for z scores
obs_z <- ctwas_gene_res$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] 0.03929698#genes with most significant z scores
head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],20) genename region_tag susie_pip mu2 PVE z
7887 FN3K 17_47 1.000 557.18 1.6e-03 -28.51
5442 FN3KRP 17_47 0.002 342.46 1.8e-06 -23.76
10438 GYPE 4_94 0.001 254.15 8.2e-07 20.36
12450 RP11-88E10.5 13_61 0.020 318.30 1.9e-05 -18.04
4024 TST 22_14 0.011 309.61 1.0e-05 -17.81
6552 HKDC1 10_46 0.000 644.00 0.0e+00 -17.57
5451 ZNF750 17_47 0.002 141.27 7.5e-07 17.19
8294 GYPA 4_94 0.001 140.38 2.7e-07 -17.17
6290 ZFP36L2 2_27 0.869 141.72 3.6e-04 16.55
11732 GYPB 4_94 0.000 136.07 7.9e-08 16.09
8978 SMIM19 8_37 0.929 232.60 6.3e-04 15.34
4231 LRRC47 1_3 1.000 233.69 6.8e-04 -15.11
11449 SMIM1 1_3 0.016 213.36 9.6e-06 -14.81
3367 ATAD2B 2_14 0.011 83.54 2.8e-06 13.65
4578 TMEM106C 12_30 0.000 135.79 2.7e-10 -13.62
12516 RP1-228P16.8 12_30 0.001 131.24 4.4e-07 13.48
9992 H1FNT 12_30 0.000 115.27 8.3e-08 12.98
11374 CYP21A2 6_26 0.000 788.56 0.0e+00 12.82
12483 HIST1H3A 6_20 0.000 42.46 6.5e-09 12.60
10805 EHMT2 6_26 0.000 760.10 0.0e+00 -12.59Locus plots for genes and SNPs
ctwas_gene_res_sortz <- ctwas_gene_res[order(-abs(ctwas_gene_res$z)),]
n_plots <- 5
for (region_tag_plot in head(unique(ctwas_gene_res_sortz$region_tag), n_plots)){
ctwas_res_region <- ctwas_res[ctwas_res$region_tag==region_tag_plot,]
start <- min(ctwas_res_region$pos)
end <- max(ctwas_res_region$pos)
ctwas_res_region <- ctwas_res_region[order(ctwas_res_region$pos),]
ctwas_res_region_gene <- ctwas_res_region[ctwas_res_region$type=="gene",]
ctwas_res_region_snp <- ctwas_res_region[ctwas_res_region$type=="SNP",]
#region name
print(paste0("Region: ", region_tag_plot))
#table of genes in region
print(ctwas_res_region_gene[,report_cols])
par(mfrow=c(4,1))
#gene z scores
plot(ctwas_res_region_gene$pos, abs(ctwas_res_region_gene$z), xlab="Position", ylab="abs(gene_z)", xlim=c(start,end),
ylim=c(0,max(sig_thresh, abs(ctwas_res_region_gene$z))),
main=paste0("Region: ", region_tag_plot))
abline(h=sig_thresh,col="red",lty=2)
#significance threshold for SNPs
alpha_snp <- 5*10^(-8)
sig_thresh_snp <- qnorm(1-alpha_snp/2, lower=T)
#snp z scores
plot(ctwas_res_region_snp$pos, abs(ctwas_res_region_snp$z), xlab="Position", ylab="abs(snp_z)",xlim=c(start,end),
ylim=c(0,max(sig_thresh_snp, max(abs(ctwas_res_region_snp$z)))))
abline(h=sig_thresh_snp,col="purple",lty=2)
#gene pips
plot(ctwas_res_region_gene$pos, ctwas_res_region_gene$susie_pip, xlab="Position", ylab="Gene PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
#snp pips
plot(ctwas_res_region_snp$pos, ctwas_res_region_snp$susie_pip, xlab="Position", ylab="SNP PIP", xlim=c(start,end), ylim=c(0,1))
abline(h=0.8,col="blue",lty=2)
}[1] "Region: 17_47"
genename region_tag susie_pip mu2 PVE z
8234 FASN 17_47 0.002 5.04 2.3e-08 -0.04
5434 SLC16A3 17_47 0.010 23.27 7.1e-07 -1.92
5439 CSNK1D 17_47 0.002 5.45 2.5e-08 0.17
12096 LINC01970 17_47 0.016 23.01 1.1e-06 2.66
11992 RP13-516M14.1 17_47 0.012 19.53 6.8e-07 1.98
5448 SECTM1 17_47 0.004 9.36 9.6e-08 1.40
9411 OGFOD3 17_47 0.002 5.52 2.7e-08 -0.78
8226 HEXDC 17_47 0.002 5.66 3.0e-08 0.74
9219 C17orf62 17_47 0.008 14.23 3.1e-07 1.02
5445 FOXK2 17_47 0.003 18.57 1.9e-07 -3.05
5452 WDR45B 17_47 0.007 73.23 1.5e-06 9.81
5437 RAB40B 17_47 0.002 48.04 2.2e-07 -8.46
5442 FN3KRP 17_47 0.002 342.46 1.8e-06 -23.76
7887 FN3K 17_47 1.000 557.18 1.6e-03 -28.51
5441 TBCD 17_47 0.002 10.55 7.5e-08 -1.50
5451 ZNF750 17_47 0.002 141.27 7.5e-07 17.19
8932 B3GNTL1 17_47 0.017 30.03 1.5e-06 0.52
12044 AC144831.1 17_47 0.002 8.85 4.0e-08 -0.89
12430 AC144831.3 17_47 0.003 25.01 2.5e-07 -4.54
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 4_94"
genename region_tag susie_pip mu2 PVE z
11721 RP11-223C24.1 4_94 0.000 6.97 3.0e-09 2.26
8295 USP38 4_94 0.000 12.07 1.2e-08 -0.67
2465 GAB1 4_94 0.000 7.83 4.7e-09 0.27
10438 GYPE 4_94 0.001 254.15 8.2e-07 20.36
11732 GYPB 4_94 0.000 136.07 7.9e-08 16.09
8294 GYPA 4_94 0.001 140.38 2.7e-07 -17.17
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 13_61"
genename region_tag susie_pip mu2 PVE z
3882 TUBGCP3 13_61 0.000 5.47 3.1e-09 0.31
12450 RP11-88E10.5 13_61 0.020 318.30 1.9e-05 -18.04
708 ATP11A 13_61 0.001 26.71 6.5e-08 4.15
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 22_14"
genename region_tag susie_pip mu2 PVE z
1520 HMGXB4 22_14 0.396 28.88 3.3e-05 -5.16
1521 TOM1 22_14 0.202 27.61 1.6e-05 5.07
1528 MCM5 22_14 0.001 5.23 1.4e-08 0.41
1525 HMOX1 22_14 0.006 26.42 4.9e-07 2.53
1532 RASD2 22_14 0.003 15.56 1.4e-07 -1.59
10549 MB 22_14 0.001 9.46 3.6e-08 0.89
11189 APOL6 22_14 0.005 20.62 3.2e-07 1.46
1543 APOL4 22_14 0.001 5.12 1.3e-08 0.33
4020 APOL3 22_14 0.001 7.62 2.9e-08 -0.75
1544 APOL1 22_14 0.002 13.04 8.3e-08 -1.19
4026 APOL2 22_14 0.002 11.78 6.0e-08 1.11
1547 TXN2 22_14 0.001 5.18 1.4e-08 -0.10
1548 FOXRED2 22_14 0.002 9.96 4.6e-08 -0.99
1549 EIF3D 22_14 0.002 11.87 6.6e-08 1.12
1550 IFT27 22_14 0.001 7.44 2.4e-08 0.90
1551 PVALB 22_14 0.025 33.06 2.4e-06 1.96
1553 NCF4 22_14 0.001 5.61 1.6e-08 -0.09
1554 CSF2RB 22_14 0.001 6.37 1.8e-08 -0.79
4024 TST 22_14 0.011 309.61 1.0e-05 -17.81
| Version | Author | Date |
|---|---|---|
| dfd2b5f | wesleycrouse | 2021-09-07 |
[1] "Region: 10_46"
genename region_tag susie_pip mu2 PVE z
5104 DNA2 10_46 0 6.96 0 0.00
5103 TET1 10_46 0 10.55 0 1.43
7627 STOX1 10_46 0 15.00 0 2.26
2297 DDX50 10_46 0 5.83 0 -1.00
7629 DDX21 10_46 0 14.38 0 1.15
3586 SRGN 10_46 0 125.34 0 7.57
3593 VPS26A 10_46 0 8.72 0 0.87
6551 SUPV3L1 10_46 0 70.69 0 -7.67
6552 HKDC1 10_46 0 644.00 0 -17.57
865 TACR2 10_46 0 161.75 0 -5.91
1402 TSPAN15 10_46 0 13.87 0 -3.61
10439 COL13A1 10_46 0 8.47 0 1.01
1403 H2AFY2 10_46 0 5.87 0 -0.14
395 AIFM2 10_46 0 6.53 0 -0.36
6553 TYSND1 10_46 0 25.59 0 -1.65
981 SAR1A 10_46 0 9.91 0 0.12
9363 PPA1 10_46 0 8.32 0 0.87
8609 LRRC20 10_46 0 6.44 0 0.59
6038 EIF4EBP2 10_46 0 5.36 0 0.03
6556 NODAL 10_46 0 5.94 0 -0.41
| 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
33482 rs2779116 1_79 1.000 705.46 2.0e-03 30.86
38083 rs9425587 1_84 1.000 43.17 1.3e-04 -6.79
50690 rs79687284 1_108 1.000 140.40 4.1e-04 13.92
68611 rs1042034 2_13 1.000 38.75 1.1e-04 -5.51
69470 rs565332541 2_14 1.000 100.07 2.9e-04 15.51
70347 rs780093 2_16 1.000 165.24 4.8e-04 11.09
76286 rs2121564 2_28 1.000 75.26 2.2e-04 8.61
111382 rs71397673 2_102 1.000 504.64 1.5e-03 28.67
111390 rs853789 2_102 1.000 1024.25 3.0e-03 38.94
137978 rs56395424 3_9 1.000 157.81 4.6e-04 -13.84
138036 rs10602803 3_9 1.000 70.60 2.1e-04 11.11
169648 rs72964564 3_76 1.000 290.34 8.4e-04 -18.72
188009 rs1027498 3_115 1.000 107.05 3.1e-04 6.97
221606 rs149027545 4_59 1.000 83.15 2.4e-04 8.05
239199 rs11727331 4_94 1.000 161.32 4.7e-04 -17.08
239393 rs34149094 4_94 1.000 68.56 2.0e-04 -7.15
259396 rs529337207 5_12 1.000 74.47 2.2e-04 -8.65
302520 rs6885822 5_93 1.000 63.65 1.8e-04 7.67
312057 rs9378483 6_7 1.000 44.69 1.3e-04 5.38
312167 rs55792466 6_7 1.000 147.34 4.3e-04 -11.10
312203 rs75465676 6_7 1.000 56.86 1.7e-04 -5.10
316759 rs2206734 6_15 1.000 128.69 3.7e-04 15.04
318655 rs75080831 6_19 1.000 177.21 5.1e-04 -20.15
318807 rs34877685 6_20 1.000 165.21 4.8e-04 -9.72
318816 rs72834643 6_20 1.000 496.43 1.4e-03 -21.07
318837 rs115740542 6_20 1.000 844.59 2.5e-03 -28.80
319303 rs6908155 6_21 1.000 377.05 1.1e-03 8.45
319409 rs535096266 6_21 1.000 89.47 2.6e-04 6.25
319679 rs3130253 6_23 1.000 134.98 3.9e-04 13.88
319822 rs6935940 6_27 1.000 89.52 2.6e-04 3.82
323136 rs1005230 6_33 1.000 53.90 1.6e-04 7.06
344544 rs62420266 6_74 1.000 40.34 1.2e-04 -5.70
350939 rs10457576 6_87 1.000 34.89 1.0e-04 5.73
352083 rs199804242 6_89 1.000 9553.62 2.8e-02 2.81
360050 rs60425481 6_104 1.000 8396.80 2.4e-02 -6.69
374034 rs12534523 7_20 1.000 48.04 1.4e-04 5.10
380294 rs138917529 7_32 1.000 108.06 3.1e-04 -12.14
419135 rs758184196 8_11 1.000 1190.75 3.5e-03 -0.53
419164 rs117660512 8_11 1.000 52.69 1.5e-04 -1.46
431152 rs150722768 8_36 1.000 71.90 2.1e-04 -10.55
431316 rs76508735 8_36 1.000 139.58 4.1e-04 -5.99
431329 rs10099921 8_36 1.000 256.12 7.4e-04 -18.49
431336 rs12550646 8_36 1.000 237.55 6.9e-04 -16.78
431344 rs6989331 8_36 1.000 95.74 2.8e-04 -2.86
477818 rs10545172 9_37 1.000 70.29 2.0e-04 9.11
492138 rs57248636 9_62 1.000 36.60 1.1e-04 5.52
495365 rs117561717 9_70 1.000 42.50 1.2e-04 6.47
519093 rs111333451 10_45 1.000 64.10 1.9e-04 8.10
519408 rs4745982 10_46 1.000 1283.98 3.7e-03 -56.67
519409 rs6480402 10_46 1.000 9083.08 2.6e-02 -53.18
519418 rs73267631 10_46 1.000 2137.38 6.2e-03 6.15
524477 rs478839 10_57 1.000 57.90 1.7e-04 -7.51
531524 rs12244851 10_70 1.000 683.97 2.0e-03 24.38
539662 rs234856 11_2 1.000 129.94 3.8e-04 -8.70
541874 rs4910498 11_8 1.000 304.01 8.8e-04 -13.81
557550 rs12294913 11_36 1.000 59.60 1.7e-04 7.56
559925 rs4944832 11_41 1.000 65.62 1.9e-04 -8.05
566319 rs76838754 11_52 1.000 66.37 1.9e-04 -2.44
566322 rs10830962 11_52 1.000 320.81 9.3e-04 19.78
568998 rs73001144 11_57 1.000 34.98 1.0e-04 -5.71
596194 rs150158762 12_33 1.000 88.53 2.6e-04 -9.16
596900 rs7397189 12_36 1.000 42.35 1.2e-04 -6.60
608511 rs55692966 12_56 1.000 41.47 1.2e-04 -6.27
626589 rs576674 13_10 1.000 111.64 3.2e-04 -10.47
641237 rs1327315 13_40 1.000 60.61 1.8e-04 -7.81
653003 rs143614549 13_62 1.000 153.03 4.4e-04 12.51
653023 rs34300741 13_62 1.000 79.20 2.3e-04 -13.40
662717 rs72681869 14_20 1.000 50.20 1.5e-04 -7.31
675021 rs35889227 14_45 1.000 85.86 2.5e-04 -9.34
685266 rs12912777 15_13 1.000 53.81 1.6e-04 6.19
692637 rs66461959 15_31 1.000 89.82 2.6e-04 3.57
692651 rs67453880 15_31 1.000 97.93 2.8e-04 3.50
713444 rs153105 16_23 1.000 55.63 1.6e-04 -4.05
728821 rs2608604 16_54 1.000 410.67 1.2e-03 20.26
728825 rs72813547 16_54 1.000 190.88 5.5e-04 -11.15
730302 rs117100864 17_5 1.000 44.55 1.3e-04 -6.61
731292 rs72829444 17_7 1.000 101.18 2.9e-04 10.35
731454 rs10468482 17_7 1.000 78.85 2.3e-04 -10.14
748457 rs58711252 17_43 1.000 151.36 4.4e-04 14.36
748460 rs3813026 17_43 1.000 177.63 5.2e-04 10.84
748461 rs417780 17_43 1.000 400.90 1.2e-03 19.21
748464 rs61740060 17_43 1.000 151.16 4.4e-04 4.80
748572 rs11658216 17_44 1.000 39.81 1.2e-04 4.87
779037 rs59616136 19_14 1.000 211.42 6.1e-04 -18.27
803387 rs6066141 20_29 1.000 69.52 2.0e-04 -8.59
806880 rs6099616 20_33 1.000 79.78 2.3e-04 8.97
816334 rs2834259 21_14 1.000 60.61 1.8e-04 7.73
820586 rs60426421 21_23 1.000 40.05 1.2e-04 -6.28
827664 rs5756512 22_14 1.000 139.26 4.0e-04 -16.08
827672 rs228924 22_14 1.000 65.29 1.9e-04 1.15
865145 rs200856259 1_69 1.000 12329.29 3.6e-02 3.33
899803 rs56089638 3_20 1.000 13549.91 3.9e-02 2.96
899852 rs143137534 3_20 1.000 13558.31 3.9e-02 3.08
909995 rs142955295 3_35 1.000 352.17 1.0e-03 -2.32
943504 rs760400154 5_2 1.000 14144.28 4.1e-02 2.98
961266 rs1611236 6_26 1.000 129759.80 3.8e-01 8.54
986040 rs10305492 6_30 1.000 44.51 1.3e-04 -6.46
995207 rs201989772 7_61 1.000 413.89 1.2e-03 7.64
1076422 rs4760682 12_30 1.000 564.48 1.6e-03 26.64
1167014 rs5112 19_31 1.000 74.30 2.2e-04 -8.57
1182387 rs202143810 20_38 1.000 6252.46 1.8e-02 -4.13
226824 rs11728350 4_69 0.999 59.98 1.7e-04 7.83
318618 rs10498727 6_19 0.999 56.51 1.6e-04 1.65
318668 rs2281074 6_19 0.999 155.20 4.5e-04 -19.39
380437 rs142235947 7_33 0.999 33.82 9.8e-05 -5.29
419130 rs2428 8_11 0.999 1045.24 3.0e-03 6.08
539660 rs234852 11_2 0.999 68.73 2.0e-04 3.51
586308 rs66720652 12_15 0.999 35.50 1.0e-04 -5.82
616431 rs80019595 12_74 0.999 93.59 2.7e-04 3.88
652667 rs9549304 13_61 0.999 43.84 1.3e-04 7.90
704798 rs11642004 16_4 0.999 34.20 9.9e-05 5.80
728886 rs117425352 16_54 0.999 47.02 1.4e-04 -6.03
734709 rs59503666 17_15 0.999 83.37 2.4e-04 -13.24
1126000 rs371663356 17_28 0.999 43.50 1.3e-04 -6.52
1169858 rs201074739 19_35 0.999 83.99 2.4e-04 -7.84
111383 rs537183 2_102 0.998 990.51 2.9e-03 38.61
320035 rs2856992 6_27 0.998 49.26 1.4e-04 -5.62
519068 rs117731828 10_45 0.998 33.46 9.7e-05 -6.82
541334 rs3750952 11_7 0.998 37.54 1.1e-04 -5.95
748570 rs4371218 17_44 0.998 33.01 9.6e-05 -3.36
860091 rs599134 1_69 0.998 47.61 1.4e-04 6.81
198563 rs34927251 4_17 0.997 31.92 9.2e-05 -5.38
353181 rs540973884 6_92 0.997 58.53 1.7e-04 -8.58
542146 rs79057673 11_8 0.997 37.09 1.1e-04 6.04
596227 rs112538405 12_34 0.997 33.86 9.8e-05 -5.56
744074 rs62062484 17_37 0.997 30.39 8.8e-05 -5.14
111438 rs112308555 2_103 0.996 28.93 8.4e-05 4.91
282698 rs17462893 5_56 0.996 34.88 1.0e-04 6.77
569074 rs11224303 11_58 0.996 255.67 7.4e-04 -15.04
581434 rs3217907 12_4 0.996 35.62 1.0e-04 6.66
600941 rs2137537 12_44 0.996 33.91 9.8e-05 5.73
734651 rs3816511 17_15 0.996 48.40 1.4e-04 -9.10
148253 rs201274656 3_34 0.995 38.12 1.1e-04 1.92
319569 rs3129685 6_23 0.995 72.27 2.1e-04 6.26
544335 rs5215 11_12 0.995 80.66 2.3e-04 -9.02
616423 rs112623431 12_74 0.995 87.16 2.5e-04 -3.50
728769 rs8044367 16_54 0.995 221.80 6.4e-04 -4.43
1135226 rs145500346 17_47 0.995 37.43 1.1e-04 6.29
191077 rs9812813 3_120 0.994 49.25 1.4e-04 7.35
353173 rs590325 6_92 0.994 31.94 9.2e-05 6.70
667254 rs873642 14_30 0.993 43.15 1.2e-04 8.93
820309 rs8129767 21_22 0.993 29.46 8.5e-05 -4.62
1090801 rs45617834 14_52 0.993 34.77 1.0e-04 -5.61
525880 rs1977833 10_59 0.992 129.13 3.7e-04 -11.86
528135 rs6584362 10_64 0.992 29.37 8.5e-05 -4.40
553533 rs2863159 11_28 0.992 40.09 1.2e-04 6.42
615450 rs149837779 12_73 0.992 30.00 8.6e-05 5.96
623396 rs947229 13_5 0.991 27.78 8.0e-05 -4.94
736104 rs9891654 17_18 0.991 46.70 1.3e-04 -6.36
169665 rs6797915 3_76 0.990 44.29 1.3e-04 8.80
311970 rs201036 6_6 0.990 30.28 8.7e-05 -5.27
624899 rs60353775 13_7 0.990 105.35 3.0e-04 11.83
530976 rs11195508 10_70 0.988 34.95 1.0e-04 -5.48
566326 rs271042 11_52 0.988 42.43 1.2e-04 -2.47
724872 rs2927324 16_46 0.988 39.40 1.1e-04 -6.33
884042 rs3811444 1_131 0.988 59.41 1.7e-04 10.10
148252 rs74495218 3_34 0.987 32.05 9.2e-05 4.89
317718 rs191816 6_17 0.987 33.72 9.7e-05 5.41
104666 rs1427297 2_86 0.985 30.73 8.8e-05 -5.27
370803 rs13235534 7_15 0.985 31.49 9.0e-05 5.35
462453 rs10758593 9_4 0.985 46.64 1.3e-04 6.79
775045 rs10410896 19_4 0.985 39.79 1.1e-04 6.42
70134 rs1554481 2_15 0.984 27.20 7.8e-05 4.60
277564 rs12189028 5_45 0.984 33.72 9.6e-05 -2.39
776972 rs11880903 19_7 0.984 28.22 8.1e-05 5.05
807426 rs6026545 20_34 0.984 38.54 1.1e-04 5.83
129730 rs7584554 2_137 0.983 43.11 1.2e-04 6.90
7679 rs557129248 1_18 0.981 27.78 7.9e-05 -4.75
372059 rs7778372 7_17 0.980 36.23 1.0e-04 -5.76
318699 rs115902543 6_20 0.979 30.67 8.7e-05 -3.87
625836 rs9508717 13_9 0.979 39.08 1.1e-04 -5.99
735398 rs2946517 17_16 0.979 50.34 1.4e-04 -8.71
451881 rs138983405 8_78 0.977 72.03 2.0e-04 -9.06
943506 rs563200821 5_2 0.977 14144.77 4.0e-02 3.01
559050 rs3781660 11_39 0.975 27.20 7.7e-05 -4.85
726710 rs11641197 16_49 0.973 33.24 9.4e-05 6.79
775021 rs11878545 19_4 0.972 33.02 9.3e-05 5.69
527038 rs35909109 10_62 0.970 26.51 7.5e-05 4.76
389412 rs374118515 7_48 0.966 31.00 8.7e-05 -5.38
170293 rs7622489 3_78 0.964 46.89 1.3e-04 6.84
667269 rs17245565 14_30 0.964 48.60 1.4e-04 -8.58
713643 rs113675335 16_25 0.963 26.09 7.3e-05 3.80
731303 rs1641549 17_7 0.963 38.30 1.1e-04 8.54
773078 rs531621 18_45 0.962 46.04 1.3e-04 6.73
447928 rs485453 8_69 0.961 27.61 7.7e-05 5.15
496825 rs28624681 9_73 0.961 145.76 4.1e-04 12.54
743260 rs34221578 17_34 0.960 56.86 1.6e-04 7.42
1166961 rs429358 19_31 0.958 59.41 1.7e-04 -7.47
559852 rs11603349 11_41 0.955 107.48 3.0e-04 -11.10
997528 rs41295942 7_62 0.955 30.02 8.3e-05 -5.02
496783 rs1886296 9_73 0.954 25.63 7.1e-05 -4.47
588828 rs7953190 12_19 0.954 80.06 2.2e-04 -8.99
318457 rs34706906 6_19 0.953 54.73 1.5e-04 -11.13
349086 rs1744620 6_83 0.953 25.05 6.9e-05 -4.66
531554 rs66808559 10_70 0.953 31.34 8.7e-05 4.52
609207 rs10777868 12_58 0.953 35.03 9.7e-05 -7.00
745460 rs8070232 17_39 0.953 30.05 8.3e-05 5.35
149961 rs71623875 3_39 0.952 27.43 7.6e-05 4.93
590473 rs7302975 12_21 0.952 25.86 7.2e-05 -4.71
599378 rs2884593 12_40 0.952 30.57 8.5e-05 6.48
170232 rs1260471 3_77 0.950 48.61 1.3e-04 -7.16
410610 rs10227304 7_94 0.950 29.95 8.3e-05 -4.20
740290 rs144216645 17_27 0.949 47.82 1.3e-04 -7.46
783795 rs58526561 19_23 0.949 95.51 2.6e-04 -10.83
313843 rs4357124 6_11 0.948 27.77 7.6e-05 5.26
94612 rs650588 2_66 0.947 50.82 1.4e-04 -6.73
908327 rs13063578 3_33 0.947 84.45 2.3e-04 8.61
667252 rs41307086 14_29 0.946 28.58 7.9e-05 4.70
127282 rs13029395 2_133 0.945 26.68 7.3e-05 3.90
310457 rs318468 6_3 0.942 30.68 8.4e-05 5.40
479188 rs13285167 9_40 0.942 25.19 6.9e-05 4.69
277286 rs13174383 5_44 0.941 54.27 1.5e-04 7.15
808814 rs3901528 20_36 0.941 45.46 1.2e-04 -6.60
553748 rs75065406 11_28 0.938 27.16 7.4e-05 -5.12
500486 rs3824667 10_8 0.937 29.72 8.1e-05 5.17
8460 rs35495299 1_19 0.934 63.91 1.7e-04 -5.95
320185 rs6934244 6_27 0.933 29.19 7.9e-05 5.55
48406 rs17258746 1_105 0.932 39.97 1.1e-04 3.97
349561 rs41285272 6_85 0.932 26.93 7.3e-05 4.76
784102 rs889140 19_23 0.932 28.72 7.8e-05 -5.00
240040 rs10305918 4_95 0.927 26.04 7.0e-05 4.71
153284 rs17775391 3_45 0.925 31.92 8.6e-05 -5.12
50699 rs3754140 1_108 0.924 78.08 2.1e-04 -10.21
662800 rs2883893 14_20 0.921 26.15 7.0e-05 4.66
748381 rs74784618 17_43 0.918 46.83 1.2e-04 5.46
319309 rs7775817 6_21 0.917 290.44 7.7e-04 -2.43
183411 rs10653660 3_104 0.916 162.24 4.3e-04 -16.44
1042638 rs374499153 11_1 0.914 77.33 2.1e-04 9.65
177432 rs28663084 3_94 0.913 63.45 1.7e-04 -7.84
29473 rs72987493 1_67 0.912 37.92 1.0e-04 5.95
81883 rs11886868 2_40 0.910 34.04 9.0e-05 -5.87
495535 rs115478735 9_70 0.909 137.03 3.6e-04 17.60
419151 rs13265731 8_11 0.908 1069.62 2.8e-03 6.18
571343 rs117719056 11_62 0.907 24.19 6.4e-05 -4.22
542164 rs11042847 11_8 0.905 73.21 1.9e-04 9.79
50695 rs340835 1_108 0.902 88.71 2.3e-04 12.37
519009 rs10998007 10_45 0.901 25.10 6.6e-05 3.88
90687 rs4435501 2_57 0.900 30.74 8.0e-05 5.48
163860 rs62258976 3_65 0.900 23.56 6.2e-05 4.36
254616 rs4956970 5_1 0.899 27.66 7.2e-05 -5.09
435333 rs56386732 8_45 0.895 29.75 7.7e-05 5.21
301107 rs74417235 5_91 0.894 30.67 8.0e-05 -5.44
539618 rs231842 11_2 0.891 48.62 1.3e-04 6.45
481437 rs62550974 9_45 0.888 227.22 5.9e-04 -19.55
620877 rs10781644 12_82 0.888 28.63 7.4e-05 -5.43
959808 rs2394122 6_22 0.887 90.22 2.3e-04 -12.62
1076380 rs2408955 12_30 0.887 401.95 1.0e-03 27.11
531518 rs117764423 10_70 0.886 160.77 4.1e-04 -6.70
57 rs201014604 1_1 0.881 25.06 6.4e-05 4.54
639337 rs9530281 13_36 0.881 24.92 6.4e-05 -4.56
380286 rs10259649 7_32 0.873 360.33 9.1e-04 27.49
548242 rs4923464 11_19 0.868 28.97 7.3e-05 -5.03
560704 rs1215071 11_42 0.868 32.92 8.3e-05 5.65
120380 rs231811 2_120 0.867 25.65 6.5e-05 4.53
573303 rs139117557 11_67 0.867 24.01 6.1e-05 -4.35
526424 rs17109928 10_60 0.866 32.01 8.1e-05 5.60
720725 rs72799826 16_38 0.863 25.79 6.5e-05 -5.00
800402 rs61734341 20_19 0.863 27.83 7.0e-05 -5.10
129664 rs6722529 2_137 0.860 34.04 8.5e-05 -5.83
521331 rs58142007 10_51 0.859 24.10 6.0e-05 -4.02
594793 rs55770587 12_31 0.859 50.88 1.3e-04 -7.93
713528 rs2070896 16_25 0.859 68.09 1.7e-04 -7.06
629151 rs374017936 13_16 0.858 30.26 7.5e-05 5.35
4993 rs71014924 1_12 0.857 24.64 6.1e-05 4.51
738719 rs118132312 17_23 0.857 24.99 6.2e-05 4.43
183680 rs2141746 3_105 0.853 78.35 1.9e-04 -8.38
65273 rs10167277 2_7 0.850 26.38 6.5e-05 -4.69
609154 rs10860185 12_58 0.849 24.49 6.0e-05 -3.68
141811 rs2173058 3_17 0.847 35.03 8.6e-05 -5.21
183681 rs11924635 3_105 0.847 29.88 7.3e-05 1.37
558745 rs72932183 11_38 0.846 25.43 6.3e-05 -4.66
713586 rs139044487 16_25 0.845 25.20 6.2e-05 -3.11
473552 rs34280179 9_27 0.843 29.93 7.3e-05 5.01
882512 rs56043070 1_131 0.843 34.44 8.4e-05 -6.21
772977 rs72973445 18_45 0.842 24.17 5.9e-05 4.26
685824 rs77839142 15_14 0.838 25.12 6.1e-05 4.38
1021590 rs12555274 9_16 0.834 101.10 2.5e-04 10.14
252319 rs62336098 4_119 0.831 25.61 6.2e-05 -4.47
599345 rs189339 12_40 0.831 39.88 9.6e-05 -7.92
356623 rs6921399 6_98 0.830 25.44 6.1e-05 4.46
731558 rs116982102 17_8 0.830 24.32 5.9e-05 -4.28
48592 rs2724384 1_105 0.828 33.31 8.0e-05 6.19
749122 rs1285245 17_45 0.828 28.14 6.8e-05 4.90
602886 rs310792 12_47 0.824 25.42 6.1e-05 -4.51
452229 rs28529793 8_78 0.823 102.22 2.4e-04 -7.87
321787 rs6904583 6_31 0.816 25.98 6.2e-05 4.72
738298 rs12938438 17_22 0.811 25.26 6.0e-05 4.20
908853 rs112741837 3_33 0.811 33.76 8.0e-05 4.12
651421 rs754205 13_59 0.809 27.29 6.4e-05 -4.59
81872 rs11884411 2_40 0.807 44.70 1.0e-04 -7.27
109197 rs270920 2_96 0.805 30.25 7.1e-05 -5.47
1029046 rs11257655 10_10 0.805 156.14 3.7e-04 12.81
313595 rs12663475 6_10 0.802 26.85 6.3e-05 -4.74SNPs 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
961266 rs1611236 6_26 1.000 129759.8 3.8e-01 8.54
961254 rs111734624 6_26 0.294 129488.6 1.1e-01 8.55
961251 rs2508055 6_26 0.294 129488.5 1.1e-01 8.55
961282 rs1611252 6_26 0.244 129488.3 9.2e-02 8.55
961291 rs1611260 6_26 0.223 129487.8 8.4e-02 8.55
961280 rs1611248 6_26 0.202 129487.8 7.6e-02 8.55
961296 rs1611265 6_26 0.209 129487.7 7.9e-02 8.55
961209 rs1633033 6_26 0.172 129486.2 6.5e-02 8.56
961299 rs2394171 6_26 0.105 129485.6 3.9e-02 8.55
961298 rs1611267 6_26 0.075 129485.4 2.8e-02 8.55
961249 rs1737020 6_26 0.102 129485.3 3.9e-02 8.55
961250 rs1737019 6_26 0.102 129485.3 3.9e-02 8.55
961301 rs2893981 6_26 0.092 129485.3 3.5e-02 8.55
961257 rs1611228 6_26 0.080 129485.1 3.0e-02 8.55
961217 rs2844838 6_26 0.091 129484.8 3.4e-02 8.55
961221 rs1633032 6_26 0.289 129478.1 1.1e-01 8.57
961244 rs1633020 6_26 0.010 129469.0 3.7e-03 8.54
961247 rs1633018 6_26 0.007 129468.0 2.7e-03 8.54
961264 rs1611234 6_26 0.001 129459.2 4.3e-04 8.53
961185 rs1610726 6_26 0.181 129456.6 6.8e-02 8.58
961215 rs2844840 6_26 0.005 129440.8 2.0e-03 8.55
961402 rs3129185 6_26 0.000 129433.2 2.2e-05 8.53
961410 rs1736999 6_26 0.000 129426.5 8.4e-07 8.51
961278 rs1611246 6_26 0.000 129416.1 3.8e-05 8.53
961416 rs1633001 6_26 0.000 129415.9 5.8e-07 8.51
961501 rs1632980 6_26 0.000 129408.3 7.5e-07 8.51
961232 rs1614309 6_26 0.000 129378.7 7.9e-06 8.55
961231 rs1633030 6_26 0.000 129273.6 3.9e-09 8.54
961309 rs9258382 6_26 0.000 129141.1 2.2e-08 8.63
961306 rs9258379 6_26 0.000 128927.1 0.0e+00 8.60
961273 rs1611241 6_26 0.000 128774.9 0.0e+00 8.65
961235 rs1633028 6_26 0.000 128594.0 0.0e+00 8.55
961275 rs1611244 6_26 0.000 128108.6 0.0e+00 8.66
961245 rs2735042 6_26 0.000 127901.4 0.0e+00 8.36
961297 rs1611266 6_26 0.000 126946.5 0.0e+00 8.83
961281 rs1611249 6_26 0.000 126389.5 0.0e+00 8.81
961260 rs1611230 6_26 0.000 126080.3 0.0e+00 8.82
961287 rs145043018 6_26 0.000 126053.7 0.0e+00 8.82
961295 rs147376303 6_26 0.000 126053.0 0.0e+00 8.82
961304 rs9258376 6_26 0.000 126051.3 0.0e+00 8.82
961310 rs1633016 6_26 0.000 126049.7 0.0e+00 8.82
961207 rs1633035 6_26 0.000 126047.1 0.0e+00 8.81
961227 rs1618670 6_26 0.000 126039.1 0.0e+00 8.82
961337 rs1633014 6_26 0.000 126037.2 0.0e+00 8.81
961246 rs1633019 6_26 0.000 126029.1 0.0e+00 8.80
961394 rs1633010 6_26 0.000 125994.3 0.0e+00 8.79
961447 rs909722 6_26 0.000 125973.3 0.0e+00 8.77
961465 rs1610713 6_26 0.000 125971.7 0.0e+00 8.77
961431 rs1736991 6_26 0.000 125970.5 0.0e+00 8.76
961454 rs1610648 6_26 0.000 125963.0 0.0e+00 8.76SNPs 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
961266 rs1611236 6_26 1.000 129759.80 0.3800 8.54
961221 rs1633032 6_26 0.289 129478.05 0.1100 8.57
961251 rs2508055 6_26 0.294 129488.53 0.1100 8.55
961254 rs111734624 6_26 0.294 129488.55 0.1100 8.55
961282 rs1611252 6_26 0.244 129488.30 0.0920 8.55
961291 rs1611260 6_26 0.223 129487.82 0.0840 8.55
961296 rs1611265 6_26 0.209 129487.66 0.0790 8.55
961280 rs1611248 6_26 0.202 129487.81 0.0760 8.55
961185 rs1610726 6_26 0.181 129456.62 0.0680 8.58
961209 rs1633033 6_26 0.172 129486.20 0.0650 8.56
943504 rs760400154 5_2 1.000 14144.28 0.0410 2.98
943506 rs563200821 5_2 0.977 14144.77 0.0400 3.01
899803 rs56089638 3_20 1.000 13549.91 0.0390 2.96
899852 rs143137534 3_20 1.000 13558.31 0.0390 3.08
961249 rs1737020 6_26 0.102 129485.30 0.0390 8.55
961250 rs1737019 6_26 0.102 129485.30 0.0390 8.55
961299 rs2394171 6_26 0.105 129485.65 0.0390 8.55
865145 rs200856259 1_69 1.000 12329.29 0.0360 3.33
961301 rs2893981 6_26 0.092 129485.29 0.0350 8.55
961217 rs2844838 6_26 0.091 129484.77 0.0340 8.55
961257 rs1611228 6_26 0.080 129485.10 0.0300 8.55
352083 rs199804242 6_89 1.000 9553.62 0.0280 2.81
961298 rs1611267 6_26 0.075 129485.37 0.0280 8.55
865142 rs2932539 1_69 0.745 12330.16 0.0270 -3.42
519409 rs6480402 10_46 1.000 9083.08 0.0260 -53.18
360050 rs60425481 6_104 1.000 8396.80 0.0240 -6.69
352099 rs6923513 6_89 0.633 9592.82 0.0180 2.89
1182387 rs202143810 20_38 1.000 6252.46 0.0180 -4.13
360046 rs3106169 6_104 0.616 8358.50 0.0150 2.33
899792 rs1402975 3_20 0.374 13526.15 0.0150 3.04
360055 rs3106167 6_104 0.458 8358.37 0.0110 2.33
865137 rs1238 1_69 0.299 12326.90 0.0110 -3.41
352082 rs2327654 6_89 0.367 9592.15 0.0100 2.89
1182384 rs6089961 20_38 0.498 6219.46 0.0090 -4.48
1182386 rs2738758 20_38 0.498 6219.46 0.0090 -4.48
360047 rs3127598 6_104 0.365 8358.33 0.0089 2.34
943516 rs118079687 5_2 0.214 14125.15 0.0088 3.03
899829 rs10865811 3_20 0.220 13527.26 0.0087 2.99
519417 rs79086908 10_46 0.547 5426.60 0.0086 11.40
519414 rs35233497 10_46 0.453 5426.14 0.0071 11.40
360039 rs11755965 6_104 0.255 8356.10 0.0062 2.34
519418 rs73267631 10_46 1.000 2137.38 0.0062 6.15
899789 rs67565656 3_20 0.152 13516.57 0.0060 3.06
865143 rs2932538 1_69 0.128 12327.68 0.0046 -3.39
1182367 rs2750483 20_38 0.245 6217.46 0.0044 -4.48
1182365 rs35201382 20_38 0.220 6217.57 0.0040 -4.47
865092 rs10857969 1_69 0.108 12328.25 0.0039 -3.39
899786 rs1402980 3_20 0.100 13514.71 0.0039 3.06
519408 rs4745982 10_46 1.000 1283.98 0.0037 -56.67
961244 rs1633020 6_26 0.010 129469.00 0.0037 8.54SNPs 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
519408 rs4745982 10_46 1.000 1283.98 3.7e-03 -56.67
519409 rs6480402 10_46 1.000 9083.08 2.6e-02 -53.18
519405 rs6480398 10_46 0.000 856.15 0.0e+00 46.60
111390 rs853789 2_102 1.000 1024.25 3.0e-03 38.94
111383 rs537183 2_102 0.998 990.51 2.9e-03 38.61
111384 rs518598 2_102 0.002 972.85 4.3e-06 38.19
111386 rs485094 2_102 0.000 924.72 4.2e-10 37.34
380303 rs2908282 7_32 0.578 914.96 1.5e-03 35.83
380299 rs917793 7_32 0.392 914.00 1.0e-03 35.81
380293 rs4607517 7_32 0.030 908.94 7.9e-05 35.72
380305 rs732360 7_32 0.000 868.17 5.7e-08 35.03
33482 rs2779116 1_79 1.000 705.46 2.0e-03 30.86
111388 rs2544360 2_102 0.000 799.43 5.6e-10 30.12
111389 rs853791 2_102 0.000 792.74 5.1e-10 29.94
519430 rs142196758 10_46 0.000 799.88 0.0e+00 -29.25
318837 rs115740542 6_20 1.000 844.59 2.5e-03 -28.80
33494 rs863327 1_79 0.001 604.42 1.7e-06 28.76
111382 rs71397673 2_102 1.000 504.64 1.5e-03 28.67
111392 rs853785 2_102 0.166 719.89 3.5e-04 28.45
111391 rs860510 2_102 0.404 707.74 8.3e-04 28.07
1135189 rs28485881 17_47 0.000 493.30 6.9e-08 27.93
1135212 rs7208565 17_47 0.000 490.37 6.1e-08 27.91
1135218 rs113373052 17_47 0.000 490.41 6.1e-08 27.91
1135186 rs9909940 17_47 0.000 492.16 6.7e-08 27.90
1135169 rs1046917 17_47 0.000 492.35 6.7e-08 -27.89
1135166 rs1046875 17_47 0.000 491.66 6.6e-08 -27.88
1135168 rs1046896 17_47 0.000 490.71 6.5e-08 27.87
1135174 rs12947062 17_47 0.000 490.41 6.4e-08 -27.87
111385 rs579275 2_102 0.430 694.11 8.7e-04 27.85
33462 rs12042917 1_79 0.001 553.23 9.4e-07 27.53
380286 rs10259649 7_32 0.873 360.33 9.1e-04 27.49
33454 rs12405509 1_79 0.001 549.59 8.9e-07 27.45
1135221 rs2263122 17_47 0.000 483.31 4.9e-08 -27.22
380284 rs2908294 7_32 0.127 351.48 1.3e-04 27.14
1135177 rs2257084 17_47 0.000 484.92 6.1e-08 -27.14
1076380 rs2408955 12_30 0.887 401.95 1.0e-03 27.11
1135188 rs2256833 17_47 0.000 477.43 3.9e-08 -27.01
33420 rs11264980 1_79 0.000 529.45 7.0e-07 26.99
1135200 rs3848403 17_47 0.000 474.77 3.7e-08 26.98
1135178 rs5822544 17_47 0.000 478.56 5.8e-08 -26.97
1135203 rs3859207 17_47 0.000 473.70 3.7e-08 26.97
1135190 rs2459703 17_47 0.000 474.95 3.8e-08 -26.96
1135207 rs8082558 17_47 0.000 471.75 3.6e-08 26.92
1135204 rs8067360 17_47 0.000 470.64 3.6e-08 26.89
1135182 rs3803771 17_47 0.000 469.76 3.6e-08 -26.87
1076094 rs12819124 12_30 0.113 404.13 1.3e-04 -26.83
1135162 rs9895455 17_47 0.000 464.62 1.1e-07 26.71
1076422 rs4760682 12_30 1.000 564.48 1.6e-03 26.64
1135153 rs72634341 17_47 0.000 402.81 3.0e-08 26.52
1135156 rs12449739 17_47 0.000 399.29 2.9e-08 26.47Gene 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] 42if (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)FBXL22 gene(s) from the input list not found in DisGeNET CURATEDJMJD7 gene(s) from the input list not found in DisGeNET CURATEDARFIP1 gene(s) from the input list not found in DisGeNET CURATEDOR51B6 gene(s) from the input list not found in DisGeNET CURATEDCNIH4 gene(s) from the input list not found in DisGeNET CURATEDSMIM19 gene(s) from the input list not found in DisGeNET CURATEDSPOCK2 gene(s) from the input list not found in DisGeNET CURATEDAC004540.5 gene(s) from the input list not found in DisGeNET CURATEDMADD gene(s) from the input list not found in DisGeNET CURATEDKCNK17 gene(s) from the input list not found in DisGeNET CURATEDPPRC1 gene(s) from the input list not found in DisGeNET CURATEDFN3K gene(s) from the input list not found in DisGeNET CURATEDLINC01305 gene(s) from the input list not found in DisGeNET CURATEDCCDC184 gene(s) from the input list not found in DisGeNET CURATEDCBX6 gene(s) from the input list not found in DisGeNET CURATEDATP5D gene(s) from the input list not found in DisGeNET CURATEDH1FX gene(s) from the input list not found in DisGeNET CURATED Description
46 polyps
79 Moderate drinker
105 In(Lu) phenotype (finding)
116 Cardiomyopathy, Dilated, 1CC
119 FETAL HEMOGLOBIN QUANTITATIVE TRAIT LOCUS 6
120 Congenital dyserythropoietic anemia type IV
121 CARDIOMYOPATHY, FAMILIAL HYPERTROPHIC, 20
126 MUSCULAR DYSTROPHY-DYSTROGLYCANOPATHY (LIMB-GIRDLE), TYPE C, 14
127 MUSCULAR DYSTROPHY-DYSTROGLYCANOPATHY (CONGENITAL WITH BRAIN AND EYE ANOMALIES), TYPE A, 14
128 MUSCULAR DYSTROPHY-DYSTROGLYCANOPATHY (CONGENITAL WITH MENTAL RETARDATION), TYPE B, 14
FDR Ratio BgRatio
46 0.03232698 1/25 1/9703
79 0.03232698 1/25 1/9703
105 0.03232698 1/25 1/9703
116 0.03232698 1/25 1/9703
119 0.03232698 1/25 1/9703
120 0.03232698 1/25 1/9703
121 0.03232698 1/25 1/9703
126 0.03232698 1/25 1/9703
127 0.03232698 1/25 1/9703
128 0.03232698 1/25 1/9703******************************************
* *
* Welcome to WebGestaltR ! *
* *
******************************************Loading the functional categories...
Loading the ID list...
Loading the reference list...
Performing the enrichment analysis...Warning in oraEnrichment(interestGeneList, referenceGeneList, geneSet,
minNum = minNum, : No significant gene set is identified based on FDR 0.05!NULL
sessionInfo()R version 3.6.1 (2019-07-05)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0 cowplot_1.0.0
[5] ggplot2_3.3.3
loaded via a namespace (and not attached):
[1] bitops_1.0-6 matrixStats_0.57.0
[3] fs_1.3.1 bit64_4.0.5
[5] doParallel_1.0.16 progress_1.2.2
[7] httr_1.4.1 rprojroot_2.0.2
[9] GenomeInfoDb_1.20.0 doRNG_1.8.2
[11] tools_3.6.1 utf8_1.2.1
[13] R6_2.5.0 DBI_1.1.1
[15] BiocGenerics_0.30.0 colorspace_1.4-1
[17] withr_2.4.1 tidyselect_1.1.0
[19] prettyunits_1.0.2 bit_4.0.4
[21] curl_3.3 compiler_3.6.1
[23] git2r_0.26.1 Biobase_2.44.0
[25] DelayedArray_0.10.0 rtracklayer_1.44.0
[27] labeling_0.3 scales_1.1.0
[29] readr_1.4.0 apcluster_1.4.8
[31] stringr_1.4.0 digest_0.6.20
[33] Rsamtools_2.0.0 svglite_1.2.2
[35] rmarkdown_1.13 XVector_0.24.0
[37] pkgconfig_2.0.3 htmltools_0.3.6
[39] fastmap_1.1.0 BSgenome_1.52.0
[41] rlang_0.4.11 RSQLite_2.2.7
[43] generics_0.0.2 farver_2.1.0
[45] jsonlite_1.6 BiocParallel_1.18.0
[47] dplyr_1.0.7 VariantAnnotation_1.30.1
[49] RCurl_1.98-1.1 magrittr_2.0.1
[51] GenomeInfoDbData_1.2.1 Matrix_1.2-18
[53] Rcpp_1.0.6 munsell_0.5.0
[55] S4Vectors_0.22.1 fansi_0.5.0
[57] gdtools_0.1.9 lifecycle_1.0.0
[59] stringi_1.4.3 whisker_0.3-2
[61] yaml_2.2.0 SummarizedExperiment_1.14.1
[63] zlibbioc_1.30.0 plyr_1.8.4
[65] grid_3.6.1 blob_1.2.1
[67] parallel_3.6.1 promises_1.0.1
[69] crayon_1.4.1 lattice_0.20-38
[71] Biostrings_2.52.0 GenomicFeatures_1.36.3
[73] hms_1.1.0 knitr_1.23
[75] pillar_1.6.1 igraph_1.2.4.1
[77] GenomicRanges_1.36.0 rjson_0.2.20
[79] rngtools_1.5 codetools_0.2-16
[81] reshape2_1.4.3 biomaRt_2.40.1
[83] stats4_3.6.1 XML_3.98-1.20
[85] glue_1.4.2 evaluate_0.14
[87] data.table_1.14.0 foreach_1.5.1
[89] vctrs_0.3.8 httpuv_1.5.1
[91] gtable_0.3.0 purrr_0.3.4
[93] assertthat_0.2.1 cachem_1.0.5
[95] xfun_0.8 later_0.8.0
[97] tibble_3.1.2 iterators_1.0.13
[99] GenomicAlignments_1.20.1 AnnotationDbi_1.46.0
[101] memoise_2.0.0 IRanges_2.18.1
[103] workflowr_1.6.2 ellipsis_0.3.2