SCZ - Brain Cerebellum
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] 10083#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
983 727 594 372 493 544 499 367 399 397 614 571 207 339 339 436 631 157 814 300
21 22
29 271 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6721#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6666Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.013760 0.000302 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
11.45 10.62 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10083 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01509 0.19220 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06286 1.04766Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11129 ZNF823 19_10 0.9867 37.18 0.0003483 6.219 2
10100 NPIPA1 16_15 0.9650 24.46 0.0002242 4.689 1
4195 FEZF1 7_74 0.9554 24.24 0.0002199 -4.812 1
12285 AC012074.2 2_15 0.9508 22.31 0.0002014 4.655 1
5873 GALNT2 1_117 0.9489 24.74 0.0002229 4.938 2
1753 PTK6 20_37 0.9122 23.13 0.0002003 -4.486 2
9127 MAP3K11 11_36 0.9062 31.57 0.0002716 -5.401 1
6968 LRP8 1_33 0.9003 26.80 0.0002291 5.050 2
3758 SSPN 12_18 0.8977 23.03 0.0001963 4.516 1
13670 RP11-408A13.3 9_13 0.8920 23.14 0.0001960 4.410 2
5753 SYTL1 1_19 0.8404 21.55 0.0001720 4.216 2
3127 SF3B1 2_117 0.8322 48.68 0.0003847 7.265 1
5541 FANCI 15_41 0.7944 24.01 0.0001811 -4.481 1
7188 TNFRSF13C 22_17 0.7931 40.96 0.0003084 -4.889 2
3233 LAMTOR2 1_76 0.7578 22.82 0.0001642 -4.307 1
6962 TIMP4 3_9 0.7246 21.49 0.0001478 4.200 2
11503 DISP3 1_9 0.6991 21.88 0.0001452 3.703 1
11381 LINC00222 6_73 0.6951 21.47 0.0001417 -4.403 1
412 CTNNA1 5_82 0.6828 26.22 0.0001700 5.512 1
2706 TRPV4 12_66 0.6703 20.76 0.0001321 3.346 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
12543 C4A 6_26 3.821e-07 200.22 7.264e-10 11.326 1
11441 RNF5 6_26 4.651e-08 160.52 7.088e-11 9.714 1
9739 HLA-DQB1 6_26 1.544e-07 149.23 2.188e-10 4.624 1
12183 CYP21A2 6_26 3.772e-12 137.34 4.919e-15 -8.406 2
11924 C4B 6_26 5.686e-11 130.56 7.048e-14 -9.001 1
11439 NOTCH4 6_26 2.414e-08 125.97 2.887e-11 7.767 2
11440 AGER 6_26 1.519e-08 121.48 1.753e-11 -9.071 1
12452 EGFL8 6_26 4.893e-06 110.84 5.150e-09 5.281 1
11449 SKIV2L 6_26 2.022e-09 110.05 2.113e-12 7.169 1
11442 AGPAT1 6_26 1.567e-06 106.76 1.588e-09 -5.190 1
2890 PRSS16 6_21 5.415e-02 106.12 5.456e-05 -11.598 2
10811 HLA-DRB1 6_26 2.172e-10 103.22 2.129e-13 4.363 2
13535 RP1-86C11.7 6_21 2.020e-01 102.16 1.959e-04 10.889 1
10943 ZSCAN16 6_22 1.704e-02 98.62 1.595e-05 -10.284 1
12064 HCG11 6_20 2.560e-02 96.69 2.350e-05 11.015 1
13097 CTA-14H9.5 6_20 2.560e-02 96.69 2.350e-05 11.015 1
11458 C6orf48 6_26 1.887e-11 96.32 1.726e-14 8.171 2
10933 HLA-DQA1 6_26 2.579e-11 95.10 2.329e-14 2.937 1
11437 BTNL2 6_26 8.903e-13 89.85 7.595e-16 4.857 1
5147 FLOT1 6_24 2.356e-01 85.79 1.920e-04 -11.181 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3127 SF3B1 2_117 0.8322 48.68 0.0003847 7.265 1
11129 ZNF823 19_10 0.9867 37.18 0.0003483 6.219 2
7188 TNFRSF13C 22_17 0.7931 40.96 0.0003084 -4.889 2
9127 MAP3K11 11_36 0.9062 31.57 0.0002716 -5.401 1
6968 LRP8 1_33 0.9003 26.80 0.0002291 5.050 2
10100 NPIPA1 16_15 0.9650 24.46 0.0002242 4.689 1
5873 GALNT2 1_117 0.9489 24.74 0.0002229 4.938 2
4195 FEZF1 7_74 0.9554 24.24 0.0002199 -4.812 1
13918 LINC02033 3_28 0.5712 38.68 0.0002098 -6.280 1
2655 MDK 11_28 0.4340 49.04 0.0002021 -7.159 1
12285 AC012074.2 2_15 0.9508 22.31 0.0002014 4.655 1
1753 PTK6 20_37 0.9122 23.13 0.0002003 -4.486 2
3758 SSPN 12_18 0.8977 23.03 0.0001963 4.516 1
13670 RP11-408A13.3 9_13 0.8920 23.14 0.0001960 4.410 2
13535 RP1-86C11.7 6_21 0.2020 102.16 0.0001959 10.889 1
5147 FLOT1 6_24 0.2356 85.79 0.0001920 -11.181 1
11781 AS3MT 10_66 0.4650 43.09 0.0001903 8.051 1
5541 FANCI 15_41 0.7944 24.01 0.0001811 -4.481 1
4178 RNF112 17_17 0.6515 29.13 0.0001802 5.126 2
5753 SYTL1 1_19 0.8404 21.55 0.0001720 4.216 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
2890 PRSS16 6_21 5.415e-02 106.12 5.456e-05 -11.598 2
12543 C4A 6_26 3.821e-07 200.22 7.264e-10 11.326 1
5147 FLOT1 6_24 2.356e-01 85.79 1.920e-04 -11.181 1
12064 HCG11 6_20 2.560e-02 96.69 2.350e-05 11.015 1
13097 CTA-14H9.5 6_20 2.560e-02 96.69 2.350e-05 11.015 1
13535 RP1-86C11.7 6_21 2.020e-01 102.16 1.959e-04 10.889 1
10943 ZSCAN16 6_22 1.704e-02 98.62 1.595e-05 -10.284 1
9834 HIST1H2BC 6_20 3.402e-02 79.02 2.553e-05 -9.909 1
11441 RNF5 6_26 4.651e-08 160.52 7.088e-11 9.714 1
11484 CCHCR1 6_25 1.704e-02 71.45 1.156e-05 -9.521 5
10608 HIST1H1C 6_20 2.236e-02 67.08 1.424e-05 -9.193 2
10473 BTN3A2 6_20 2.312e-02 68.18 1.497e-05 9.184 2
11430 HLA-DMA 6_27 1.201e-01 75.68 8.630e-05 -9.139 2
11440 AGER 6_26 1.519e-08 121.48 1.753e-11 -9.071 1
11924 C4B 6_26 5.686e-11 130.56 7.048e-14 -9.001 1
5150 PGBD1 6_22 1.700e-02 65.77 1.062e-05 -8.437 3
12183 CYP21A2 6_26 3.772e-12 137.34 4.919e-15 -8.406 2
11479 MICB 6_25 8.254e-03 56.75 4.448e-06 -8.172 3
11458 C6orf48 6_26 1.887e-11 96.32 1.726e-14 8.171 2
11724 DDAH2 6_26 3.617e-11 72.62 2.494e-14 8.149 1Comparing z scores and PIPs
[1] 0.01507Gene with high z-score but low PIP, assign to SNP or to gene?
high_z_genes_region <- unique(head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],40)$region_tag)
sum <- 0
for(i in high_z_genes_region){
locus <- ctwas_res[ctwas_res$region_tag==i,]
locus <- head(locus[order(-locus$susie_pip),],20)
snp_pip <- sum(locus[locus$type == 'SNP','susie_pip'])
gene_pip <- sum(locus[locus$type == 'gene','susie_pip'])
print(snp_pip/(snp_pip+gene_pip))
}[1] 0.7954
[1] 0.945
[1] 0.8661
[1] 0.7644
[1] 0.9396
[1] 0.8748
[1] 0.818
[1] 0.8457
[1] 0.1471
[1] 0.3721
[1] 0.8354GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 36Uploading 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"
Term Overlap
1 regulation of protein tyrosine kinase activity (GO:0061097) 3/39
Adjusted.P.value Genes
1 0.01484 DLG4;PTK6;LRP8
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159) 2/17 0.0358
Genes
1 PTPA;PPP2R5B
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase activator activity (GO:0072542) 2/13 0.01626
Genes
1 PTPA;PPP2R5BDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
11 Confusion 0.009834 1/16
60 Speech impairment 0.009834 1/16
61 Derealization 0.009834 1/16
67 Spondylometaphyseal dysplasia, Kozlowski type 0.009834 1/16
68 Metatropic dwarfism 0.009834 1/16
86 Brachyolmia Type 3 0.009834 1/16
91 Sexually disinhibited behavior 0.009834 1/16
98 Hypersomnia, Recurrent 0.009834 1/16
117 Immunodeficiency due to Defect in MAPBP-Interacting Protein 0.009834 1/16
118 FANCONI ANEMIA, COMPLEMENTATION GROUP I 0.009834 1/16
BgRatio
11 1/9703
60 1/9703
61 1/9703
67 1/9703
68 1/9703
86 1/9703
91 1/9703
98 1/9703
117 1/9703
118 1/9703WebGestalt 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] 63#significance threshold for TWAS
print(sig_thresh)[1] 4.567#number of ctwas genes
length(ctwas_genes)[1] 12#number of TWAS genes
length(twas_genes)[1] 152#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5753 SYTL1 1_19 0.8404 21.55 0.0001720 4.216 2
13670 RP11-408A13.3 9_13 0.8920 23.14 0.0001960 4.410 2
3758 SSPN 12_18 0.8977 23.03 0.0001963 4.516 1
1753 PTK6 20_37 0.9122 23.13 0.0002003 -4.486 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.09231 #specificity
print(specificity)ctwas TWAS
0.999 0.986 #precision / PPV
print(precision) ctwas TWAS
0.16667 0.07895 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 63#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 659#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.567#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] 48#sensitivity / recall
sensitivity ctwas TWAS
0.03175 0.19048 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9985 0.9454 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6667 0.2500 
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)
67 51 10
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