SCZ - Brain Caudate basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-04-19
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10066#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
968 746 594 401 487 566 483 364 374 392 606 585 220 344 352 410 648 165 785 297
21 22
31 248 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6936#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6891Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0118814 0.0003106 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
12.48 10.34 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10066 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01418 0.19242 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05758 1.06338Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11135 ZNF823 19_10 0.9832 37.43 0.0003494 6.187 2
12311 AC012074.2 2_16 0.9179 23.28 0.0002029 4.655 1
13782 RP11-408A13.3 9_12 0.9155 23.13 0.0002011 4.536 1
3750 BHLHE41 12_18 0.8837 23.34 0.0001958 -4.516 1
5717 SYTL1 1_19 0.8828 21.78 0.0001826 4.295 2
8043 PACSIN3 11_29 0.8708 32.42 0.0002681 5.654 2
10494 TMEM222 1_19 0.8413 21.56 0.0001722 4.303 1
3091 SF3B1 2_117 0.8375 49.42 0.0003930 7.265 1
1678 KIAA0391 14_9 0.7915 23.87 0.0001794 -4.843 1
9254 TMEM81 1_104 0.7869 25.90 0.0001935 4.816 1
1226 PPP1R13B 14_54 0.7811 49.97 0.0003706 7.546 3
321 KCNG1 20_31 0.7795 22.53 0.0001668 4.338 1
3502 PYROXD2 10_62 0.7720 21.23 0.0001556 3.952 1
5507 FANCI 15_41 0.7686 24.18 0.0001765 -4.481 1
5632 ZCCHC2 18_34 0.7557 20.39 0.0001463 -3.877 1
328 VRK2 2_38 0.7476 37.52 0.0002663 4.977 1
6401 ADRA2A 10_70 0.7418 23.13 0.0001629 -4.020 1
2944 PCCB 3_84 0.7127 42.04 0.0002845 -6.724 1
422 CTNNA1 5_82 0.7126 25.03 0.0001693 5.512 1
110 ELAC2 17_11 0.7028 23.20 0.0001548 4.654 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11478 APOM 6_26 1.465e-04 221.06 3.076e-07 11.590 1
11462 C6orf48 6_26 1.029e-04 219.19 2.142e-07 11.542 1
11731 CLIC1 6_26 9.808e-05 217.62 2.027e-07 11.506 2
12582 C4A 6_26 9.505e-05 215.92 1.949e-07 11.437 2
11469 MSH5 6_26 5.961e-05 183.45 1.038e-07 10.005 2
10939 HLA-DQA1 6_26 3.109e-07 169.41 5.001e-10 3.389 1
12229 HLA-DQB2 6_26 3.521e-07 168.29 5.626e-10 -4.388 1
11444 PRRT1 6_26 4.594e-05 152.07 6.633e-08 10.061 1
11440 AGER 6_26 6.391e-06 129.99 7.888e-09 -9.693 2
12390 HLA-DQA2 6_26 2.824e-07 122.41 3.282e-10 -3.420 1
11458 EHMT2 6_26 6.246e-04 115.03 6.822e-07 6.899 1
10816 HLA-DRB1 6_26 1.674e-04 108.51 1.725e-07 -5.106 1
11441 RNF5 6_26 2.936e-07 102.06 2.845e-10 5.190 1
11442 AGPAT1 6_26 2.936e-07 102.06 2.845e-10 -5.190 1
5119 PGBD1 6_22 1.444e-02 98.90 1.356e-05 -10.231 1
10491 BTN3A2 6_20 1.784e-02 95.86 1.624e-05 10.719 2
11438 NOTCH4 6_26 3.256e-06 89.28 2.761e-09 7.600 2
11436 HLA-DRA 6_26 3.641e-05 85.16 2.944e-08 4.233 1
5116 FLOT1 6_24 5.944e-02 82.49 4.656e-05 -10.981 1
11732 DDAH2 6_26 1.611e-05 76.38 1.169e-08 8.149 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3091 SF3B1 2_117 0.8375 49.42 0.0003930 7.265 1
1226 PPP1R13B 14_54 0.7811 49.97 0.0003706 7.546 3
11135 ZNF823 19_10 0.9832 37.43 0.0003494 6.187 2
2630 MDK 11_28 0.6687 47.50 0.0003016 -7.159 1
2944 PCCB 3_84 0.7127 42.04 0.0002845 -6.724 1
8639 INO80E 16_24 0.6084 47.47 0.0002742 6.995 1
8043 PACSIN3 11_29 0.8708 32.42 0.0002681 5.654 2
328 VRK2 2_38 0.7476 37.52 0.0002663 4.977 1
12311 AC012074.2 2_16 0.9179 23.28 0.0002029 4.655 1
13782 RP11-408A13.3 9_12 0.9155 23.13 0.0002011 4.536 1
3750 BHLHE41 12_18 0.8837 23.34 0.0001958 -4.516 1
9254 TMEM81 1_104 0.7869 25.90 0.0001935 4.816 1
5717 SYTL1 1_19 0.8828 21.78 0.0001826 4.295 2
7042 CDC25C 5_82 0.6761 28.31 0.0001818 -5.272 1
1678 KIAA0391 14_9 0.7915 23.87 0.0001794 -4.843 1
5507 FANCI 15_41 0.7686 24.18 0.0001765 -4.481 1
10494 TMEM222 1_19 0.8413 21.56 0.0001722 4.303 1
506 SDCCAG8 1_128 0.6759 26.64 0.0001710 -5.076 1
422 CTNNA1 5_82 0.7126 25.03 0.0001693 5.512 1
321 KCNG1 20_31 0.7795 22.53 0.0001668 4.338 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11478 APOM 6_26 1.465e-04 221.06 3.076e-07 11.590 1
11462 C6orf48 6_26 1.029e-04 219.19 2.142e-07 11.542 1
11731 CLIC1 6_26 9.808e-05 217.62 2.027e-07 11.506 2
12582 C4A 6_26 9.505e-05 215.92 1.949e-07 11.437 2
5116 FLOT1 6_24 5.944e-02 82.49 4.656e-05 -10.981 1
10491 BTN3A2 6_20 1.784e-02 95.86 1.624e-05 10.719 2
5119 PGBD1 6_22 1.444e-02 98.90 1.356e-05 -10.231 1
11444 PRRT1 6_26 4.594e-05 152.07 6.633e-08 10.061 1
11469 MSH5 6_26 5.961e-05 183.45 1.038e-07 10.005 2
11440 AGER 6_26 6.391e-06 129.99 7.888e-09 -9.693 2
12520 HLA-DMB 6_27 9.919e-02 72.46 6.824e-05 -8.812 1
11431 HLA-DMA 6_27 4.273e-02 65.95 2.676e-05 -8.769 2
9851 HIST1H2BC 6_20 2.448e-02 67.33 1.565e-05 -8.743 2
7064 ZSCAN12 6_22 1.504e-02 47.49 6.784e-06 8.630 1
6289 CNNM2 10_66 7.127e-02 47.34 3.204e-05 -8.487 2
11732 DDAH2 6_26 1.611e-05 76.38 1.169e-08 8.149 1
11464 HSPA1L 6_26 1.901e-05 74.23 1.340e-08 8.075 1
9628 C2orf69 2_118 1.319e-01 43.56 5.455e-05 7.925 2
10599 ZKSCAN4 6_22 1.522e-02 60.98 8.811e-06 -7.881 1
12213 ZBED9 6_22 3.556e-02 44.85 1.515e-05 7.860 1Comparing z scores and PIPs
[1] 0.01242GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 37Uploading 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)DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR
85 FANCONI ANEMIA, COMPLEMENTATION GROUP I 0.02142
88 HYPOTRICHOSIS-LYMPHEDEMA-TELANGIECTASIA SYNDROME 0.02142
93 Hematopoetic Myelodysplasia 0.02142
97 SENIOR-LOKEN SYNDROME 7 0.02142
100 MYELODYSPLASTIC SYNDROME 0.02142
101 PROSTATE CANCER, HEREDITARY, 2 0.02142
103 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.02142
104 BARDET-BIEDL SYNDROME 16 0.02142
108 Hypotrichosis, lymphedema, telangiectasia, renal defect syndrome 0.02142
24 Leukemia, Myelocytic, Acute 0.02963
Ratio BgRatio
85 1/17 1/9703
88 1/17 1/9703
93 2/17 29/9703
97 1/17 1/9703
100 3/17 67/9703
101 1/17 1/9703
103 1/17 1/9703
104 1/17 1/9703
108 1/17 1/9703
24 3/17 173/9703WebGestalt enrichment analysis for genes with PIP>0.5
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!NULLPIP Manhattan Plot
Sensitivity, specificity and precision for silver standard genes
#number of genes in known annotations
print(length(known_annotations))[1] 130#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))[1] 60#significance threshold for TWAS
print(sig_thresh)[1] 4.566#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 125#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5717 SYTL1 1_19 0.8828 21.78 0.0001826 4.295 2
10494 TMEM222 1_19 0.8413 21.56 0.0001722 4.303 1
13782 RP11-408A13.3 9_12 0.9155 23.13 0.0002011 4.536 1
3750 BHLHE41 12_18 0.8837 23.34 0.0001958 -4.516 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.11538 #specificity
print(specificity) ctwas TWAS
0.9994 0.9890 #precision / PPV
print(precision)ctwas TWAS
0.25 0.12 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 60#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 657#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$genename %in% c(known_annotations, unrelated_genes),]
#assign ctwas and TWAS genes
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.566#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 2#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 43#sensitivity / recall
sensitivity ctwas TWAS
0.03333 0.25000 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9574 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3488 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>=pip]
sensitivity[index] <- sum(ctwas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% ctwas_genes))/length(unrelated_genes)
}
plot(1-specificity, sensitivity, type="l", xlim=c(0,1), ylim=c(0,1), main="", xlab="1 - Specificity", ylab="Sensitivity")
title(expression("ROC Curve for cTWAS (black) and TWAS (" * phantom("red") * ")"))
title(expression(phantom("ROC Curve for cTWAS (black) and TWAS (") * "red" * phantom(")")), col.main="red")
sig_thresh_range <- seq(from=0, to=max(abs(ctwas_gene_res_subset$z)), length.out=length(pip_range))
for (index in 1:length(sig_thresh_range)){
sig_thresh_plot <- sig_thresh_range[index]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>=sig_thresh_plot]
sensitivity[index] <- sum(twas_genes %in% known_annotations)/length(known_annotations)
specificity[index] <- sum(!(unrelated_genes %in% twas_genes))/length(unrelated_genes)
}
lines(1-specificity, sensitivity, xlim=c(0,1), ylim=c(0,1), col="red", lty=1)
abline(a=0,b=1,lty=3)
#add previously computed points from the analysis
ctwas_genes <- ctwas_gene_res_subset$genename[ctwas_gene_res_subset$susie_pip>0.8]
twas_genes <- ctwas_gene_res_subset$genename[abs(ctwas_gene_res_subset$z)>sig_thresh]
points(1-specificity_plot["ctwas"], sensitivity_plot["ctwas"], pch=21, bg="black")
points(1-specificity_plot["TWAS"], sensitivity_plot["TWAS"], pch=21, bg="red")
Undetected silver standard genes have low TWAS z-scores or stronger signal from nearby variants
#table of outcomes for silver standard genes
-sort(-table(silver_standard_case))silver_standard_case
Not Imputed Insignificant z-score Nearby SNP(s)
70 45 13
Detected (PIP > 0.8)
2 #show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]named character(0)
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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 IRanges_2.18.1
[4] S4Vectors_0.22.1 BiocGenerics_0.30.0 biomaRt_2.40.1
[7] readxl_1.3.1 forcats_0.5.1 stringr_1.4.0
[10] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1
[13] tidyr_1.1.4 tidyverse_1.3.1 tibble_3.1.6
[16] WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0
[19] cowplot_1.1.1 ggplot2_3.3.5 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] ggbeeswarm_0.6.0 colorspace_2.0-2 rjson_0.2.20
[4] ellipsis_0.3.2 rprojroot_2.0.2 XVector_0.24.0
[7] fs_1.5.2 rstudioapi_0.13 farver_2.1.0
[10] ggrepel_0.9.1 bit64_4.0.5 AnnotationDbi_1.46.0
[13] fansi_1.0.2 lubridate_1.8.0 xml2_1.3.3
[16] codetools_0.2-16 doParallel_1.0.17 cachem_1.0.6
[19] knitr_1.36 jsonlite_1.7.2 apcluster_1.4.8
[22] Cairo_1.5-12.2 broom_0.7.10 dbplyr_2.1.1
[25] compiler_3.6.1 httr_1.4.2 backports_1.4.1
[28] assertthat_0.2.1 Matrix_1.2-18 fastmap_1.1.0
[31] cli_3.1.0 later_0.8.0 prettyunits_1.1.1
[34] htmltools_0.5.2 tools_3.6.1 igraph_1.2.10
[37] GenomeInfoDbData_1.2.1 gtable_0.3.0 glue_1.6.2
[40] reshape2_1.4.4 doRNG_1.8.2 Rcpp_1.0.8
[43] Biobase_2.44.0 cellranger_1.1.0 jquerylib_0.1.4
[46] vctrs_0.3.8 svglite_1.2.2 iterators_1.0.14
[49] xfun_0.29 ps_1.6.0 rvest_1.0.2
[52] lifecycle_1.0.1 rngtools_1.5.2 XML_3.99-0.3
[55] zlibbioc_1.30.0 getPass_0.2-2 scales_1.1.1
[58] vroom_1.5.7 hms_1.1.1 promises_1.0.1
[61] yaml_2.2.1 curl_4.3.2 memoise_2.0.1
[64] ggrastr_1.0.1 gdtools_0.1.9 stringi_1.7.6
[67] RSQLite_2.2.8 highr_0.9 foreach_1.5.2
[70] rlang_1.0.1 pkgconfig_2.0.3 bitops_1.0-7
[73] evaluate_0.14 lattice_0.20-38 labeling_0.4.2
[76] bit_4.0.4 processx_3.5.2 tidyselect_1.1.1
[79] plyr_1.8.6 magrittr_2.0.2 R6_2.5.1
[82] generics_0.1.1 DBI_1.1.2 pillar_1.6.4
[85] haven_2.4.3 whisker_0.3-2 withr_2.4.3
[88] RCurl_1.98-1.5 modelr_0.1.8 crayon_1.5.0
[91] utf8_1.2.2 tzdb_0.2.0 rmarkdown_2.11
[94] progress_1.2.2 grid_3.6.1 data.table_1.14.2
[97] blob_1.2.2 callr_3.7.0 git2r_0.26.1
[100] reprex_2.0.1 digest_0.6.29 httpuv_1.5.1
[103] munsell_0.5.0 beeswarm_0.2.3 vipor_0.4.5