SCZ - Brain Frontal Cortex BA9
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] 9968#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
988 727 567 399 488 584 459 370 362 400 616 581 206 335 326 411 640 164 776 295
21 22
26 248 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6860#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6882Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0104967 0.0003109 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
9.587 10.407 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9968 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.009524 0.193868 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05471 1.10715Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11134 ZNF823 19_10 0.9705 35.92 0.0003310 6.156 2
13483 RP11-230C9.4 6_102 0.9654 22.67 0.0002078 -4.717 2
12304 AC012074.2 2_15 0.9319 22.04 0.0001950 4.655 1
7629 THOC7 3_43 0.9310 40.69 0.0003597 -6.638 2
11400 FOXO6 1_25 0.8661 25.49 0.0002097 -4.548 1
3758 BHLHE41 12_18 0.8579 22.87 0.0001863 4.516 1
9133 MAP3K11 11_36 0.8486 31.26 0.0002518 -5.401 1
2748 NT5DC3 12_62 0.8353 23.21 0.0001841 -4.585 2
4755 SOX5 12_17 0.7652 22.32 0.0001621 4.477 1
6859 VPS37A 8_18 0.7615 24.06 0.0001739 -4.738 1
9027 AFF1 4_59 0.7451 21.63 0.0001530 -4.096 1
3216 ARHGEF2 1_76 0.7071 22.39 0.0001503 -3.816 1
324 VRK2 2_38 0.6683 36.06 0.0002288 4.977 1
3443 PTPA 9_66 0.6571 22.84 0.0001425 -4.670 2
3036 LMAN2L 2_57 0.6373 25.18 0.0001524 -4.360 2
13738 RP11-408A13.3 9_12 0.6261 22.94 0.0001364 4.225 4
2339 TLE4 9_38 0.6207 21.02 0.0001239 4.279 1
2963 PCCB 3_84 0.6161 40.50 0.0002369 -6.724 1
4053 SCAF1 19_34 0.6148 37.64 0.0002197 -6.374 1
9166 NUDT4 12_55 0.5940 25.79 0.0001455 3.796 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10825 HLA-DRB1 6_26 0.000e+00 1178.11 0.000e+00 0.9851 2
11430 HLA-DOA 6_26 0.000e+00 740.27 0.000e+00 7.6357 1
10942 HLA-DQA1 6_26 0.000e+00 709.98 0.000e+00 2.8586 4
11458 C6orf48 6_26 1.387e-01 632.85 8.333e-04 11.5418 1
11728 CLIC1 6_26 8.801e-02 629.20 5.258e-04 11.5065 2
12571 C4A 6_26 2.033e-03 627.55 1.212e-05 11.1096 3
11472 APOM 6_26 1.873e-01 625.74 1.113e-03 11.5895 1
11464 MSH5 6_26 1.357e-08 545.14 7.024e-11 10.1409 2
11443 RNF5 6_26 4.240e-12 465.75 1.875e-14 10.0454 1
11444 AGPAT1 6_26 0.000e+00 389.99 0.000e+00 -5.1903 1
11440 NOTCH4 6_26 0.000e+00 309.20 0.000e+00 7.7180 2
11729 DDAH2 6_26 0.000e+00 251.21 0.000e+00 8.1494 1
12101 SAPCD1 6_26 0.000e+00 158.42 0.000e+00 7.1109 1
11723 ATF6B 6_26 0.000e+00 154.84 0.000e+00 3.7369 1
11446 FKBPL 6_26 0.000e+00 149.71 0.000e+00 -5.2136 1
11469 CSNK2B 6_26 0.000e+00 110.95 0.000e+00 0.9231 2
11452 SKIV2L 6_26 0.000e+00 108.69 0.000e+00 1.5067 3
5147 VARS2 6_25 2.886e-01 103.75 2.843e-04 11.4130 1
11474 BAG6 6_26 0.000e+00 101.40 0.000e+00 5.0995 2
11462 VWA7 6_26 0.000e+00 97.37 0.000e+00 -2.5080 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11472 APOM 6_26 0.18730 625.74 0.0011128 11.590 1
11458 C6orf48 6_26 0.13868 632.85 0.0008333 11.542 1
11728 CLIC1 6_26 0.08801 629.20 0.0005258 11.506 2
7629 THOC7 3_43 0.93101 40.69 0.0003597 -6.638 2
11134 ZNF823 19_10 0.97046 35.92 0.0003310 6.156 2
5147 VARS2 6_25 0.28864 103.75 0.0002843 11.413 1
9133 MAP3K11 11_36 0.84856 31.26 0.0002518 -5.401 1
2963 PCCB 3_84 0.61610 40.50 0.0002369 -6.724 1
324 VRK2 2_38 0.66830 36.06 0.0002288 4.977 1
4053 SCAF1 19_34 0.61481 37.64 0.0002197 -6.374 1
9343 ATG13 11_28 0.51818 43.39 0.0002135 -6.977 1
11400 FOXO6 1_25 0.86614 25.49 0.0002097 -4.548 1
13483 RP11-230C9.4 6_102 0.96536 22.67 0.0002078 -4.717 2
12304 AC012074.2 2_15 0.93187 22.04 0.0001950 4.655 1
3758 BHLHE41 12_18 0.85788 22.87 0.0001863 4.516 1
2748 NT5DC3 12_62 0.83533 23.21 0.0001841 -4.585 2
6407 TAOK2 16_24 0.41685 45.47 0.0001800 6.997 1
11089 NMB 15_39 0.52494 34.99 0.0001744 5.881 1
6859 VPS37A 8_18 0.76150 24.06 0.0001739 -4.738 1
733 PPP2R5B 11_36 0.56616 30.24 0.0001626 -5.093 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11472 APOM 6_26 1.873e-01 625.74 1.113e-03 11.590 1
11458 C6orf48 6_26 1.387e-01 632.85 8.333e-04 11.542 1
11728 CLIC1 6_26 8.801e-02 629.20 5.258e-04 11.506 2
5147 VARS2 6_25 2.886e-01 103.75 2.843e-04 11.413 1
12571 C4A 6_26 2.033e-03 627.55 1.212e-05 11.110 3
5138 FLOT1 6_24 3.665e-02 78.74 2.740e-05 -10.944 1
10493 BTN3A2 6_20 1.910e-02 88.94 1.613e-05 10.632 1
11464 MSH5 6_26 1.357e-08 545.14 7.024e-11 10.141 2
11443 RNF5 6_26 4.240e-12 465.75 1.875e-14 10.045 1
2871 PRSS16 6_21 8.024e-02 72.45 5.520e-05 -9.217 1
6317 CNNM2 10_66 1.841e-01 49.66 8.681e-05 -9.113 2
12511 HLA-DMB 6_27 5.549e-02 68.55 3.612e-05 -8.962 1
11432 HLA-DMA 6_27 4.432e-02 64.71 2.723e-05 -8.771 2
11729 DDAH2 6_26 0.000e+00 251.21 0.000e+00 8.149 1
11790 AS3MT 10_66 2.365e-01 43.64 9.800e-05 8.051 1
10634 ZSCAN23 6_22 5.722e-02 47.45 2.578e-05 -7.854 1
10802 ZKSCAN3 6_22 1.617e-02 37.25 5.719e-06 7.765 1
11440 NOTCH4 6_26 0.000e+00 309.20 0.000e+00 7.718 2
11430 HLA-DOA 6_26 0.000e+00 740.27 0.000e+00 7.636 1
939 NT5C2 10_66 2.016e-01 37.08 7.096e-05 7.614 1Comparing z scores and PIPs
[1] 0.01234GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 29Uploading 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"
Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159) 2/17 0.01008
Genes
1 PTPA;PPP2R5B
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase activator activity (GO:0072542) 2/13 0.009564
Genes
1 PTPA;PPP2R5BDisGeNET enrichment analysis for genes with PIP>0.5
Description
46 SPASTIC PARAPLEGIA 53, AUTOSOMAL RECESSIVE
49 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52
50 LAMB-SHAFFER SYNDROME
51 NEURODEVELOPMENTAL DISORDER WITH MIDBRAIN AND HINDBRAIN MALFORMATIONS
52 Developmental and speech delay due to SOX5 deficiency
53 12p12.1 microdeletion syndrome
44 Propionicaciduria
25 Propionic acidemia
28 Long Sleeper Syndrome
29 Short Sleeper Syndrome
FDR Ratio BgRatio
46 0.009105 1/10 1/9703
49 0.009105 1/10 1/9703
50 0.009105 1/10 1/9703
51 0.009105 1/10 1/9703
52 0.009105 1/10 1/9703
53 0.009105 1/10 1/9703
44 0.015601 1/10 2/9703
25 0.020466 1/10 3/9703
28 0.027238 1/10 7/9703
29 0.027238 1/10 7/9703WebGestalt 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
Warning: ggrepel: 2 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
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] 52#significance threshold for TWAS
print(sig_thresh)[1] 4.564#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 123#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
11400 FOXO6 1_25 0.8661 25.49 0.0002097 -4.548 1
3758 BHLHE41 12_18 0.8579 22.87 0.0001863 4.516 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.08462 #specificity
print(specificity) ctwas TWAS
0.9994 0.9887 #precision / PPV
print(precision) ctwas TWAS
0.25000 0.08943 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 52#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 558#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.564#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 3#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 36#sensitivity / recall
sensitivity ctwas TWAS
0.03846 0.21154 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9982 0.9552 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6667 0.3056 
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)
78 41 9
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