SCZ - Brain Cortex
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
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|---|---|---|---|---|
| Rmd | d57314b | sq-96 | 2022-03-15 | update |
Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11781#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1186 815 684 457 554 665 563 437 444 482 707 665 228 388 388 542
17 18 19 20 21 22
708 183 889 368 129 299 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9161#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7776Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0140025 0.0002678 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
12.74 12.76 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11781 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01302 0.15660 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05811 0.78306Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11314 ZNF823 19_10 0.9923 41.26 2.537e-04 6.363 2
3258 EDEM3 1_92 0.9809 26.07 1.584e-04 4.945 2
735 RASSF1 3_35 0.9562 892.21 5.285e-03 4.532 1
13518 LINC01415 18_30 0.9411 41.13 2.398e-04 -7.082 2
2734 TRPV4 12_66 0.9077 24.28 1.365e-04 4.416 1
2380 TLE4 9_38 0.8988 26.30 1.465e-04 5.000 1
2569 MMD 17_32 0.8833 24.70 1.352e-04 -4.451 1
5055 RCBTB1 13_21 0.8808 30.32 1.655e-04 -5.360 2
8644 SNTB2 16_37 0.8304 26.15 1.345e-04 -4.819 2
5640 CPNE2 16_30 0.8261 20.99 1.074e-04 -4.125 1
13209 CEP95 17_37 0.8207 19.35 9.838e-05 -3.800 1
7241 ACE 17_37 0.8149 32.77 1.655e-04 -5.802 1
7703 SERPINI1 3_103 0.7903 23.11 1.132e-04 -4.181 2
8418 CACNB3 12_31 0.7860 20.76 1.011e-04 -3.513 1
2091 LIN7B 19_34 0.7849 22.58 1.098e-04 4.444 1
9648 LY6H 8_94 0.7779 28.94 1.395e-04 5.143 1
4646 ACY3 11_37 0.7687 19.29 9.188e-05 -3.356 2
3457 ABCG2 4_60 0.7625 21.93 1.036e-04 -3.954 1
3565 SLF2 10_64 0.7605 23.55 1.110e-04 -4.404 1
8593 TNXB 6_26 0.7500 26.01 1.209e-04 2.804 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
735 RASSF1 3_35 9.562e-01 892.2 5.285e-03 4.5324 1
9919 LSMEM2 3_35 2.499e-01 889.5 1.377e-03 4.2709 1
10857 SLC38A3 3_35 3.204e-03 874.9 1.737e-05 -0.8019 1
11 SEMA3F 3_35 6.736e-07 237.5 9.911e-10 0.2075 1
1243 C3orf18 3_35 0.000e+00 203.9 0.000e+00 -0.4916 1
40 RBM6 3_35 3.731e-01 196.5 4.541e-04 4.4688 1
30 RBM5 3_35 1.060e-02 194.3 1.276e-05 3.9872 1
3064 HEMK1 3_35 0.000e+00 179.5 0.000e+00 0.4441 1
7839 CAMKV 3_35 8.993e-08 164.4 9.158e-11 -1.7107 1
10692 HYAL3 3_35 7.226e-13 157.6 7.056e-16 -2.5066 1
12740 NAT6 3_35 0.000e+00 151.3 0.000e+00 0.8525 3
7841 MST1R 3_35 2.688e-05 149.6 2.492e-08 -4.0250 1
11645 LY6G6C 6_26 3.513e-01 114.9 2.501e-04 10.7311 1
12783 C4A 6_26 2.100e-01 114.4 1.489e-04 10.6645 3
11634 ZBTB12 6_26 2.292e-01 113.9 1.617e-04 10.6827 1
11907 CLIC1 6_26 3.574e-02 113.6 2.515e-05 10.8749 2
216 SEMA3B 3_35 0.000e+00 113.2 0.000e+00 0.6250 1
13933 LINC02019 3_35 0.000e+00 108.7 0.000e+00 0.3114 2
12122 C4B 6_26 1.677e-02 108.1 1.123e-05 -10.4180 1
12395 CYP21A2 6_26 8.142e-03 107.6 5.426e-06 -10.4143 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
735 RASSF1 3_35 0.9562 892.21 0.0052854 4.532 1
9919 LSMEM2 3_35 0.2499 889.53 0.0013775 4.271 1
40 RBM6 3_35 0.3731 196.45 0.0004541 4.469 1
7799 GNL3 3_36 0.7014 65.32 0.0002839 9.429 1
11314 ZNF823 19_10 0.9923 41.26 0.0002537 6.363 2
11645 LY6G6C 6_26 0.3513 114.93 0.0002501 10.731 1
13518 LINC01415 18_30 0.9411 41.13 0.0002398 -7.082 2
3165 SF3B1 2_117 0.6745 51.54 0.0002154 7.605 1
12719 HLA-DMB 6_27 0.3690 76.90 0.0001758 -9.380 1
5055 RCBTB1 13_21 0.8808 30.32 0.0001655 -5.360 2
7241 ACE 17_37 0.8149 32.77 0.0001655 -5.802 1
8410 GATAD2A 19_16 0.5065 52.13 0.0001636 -7.419 1
11634 ZBTB12 6_26 0.2292 113.88 0.0001617 10.683 1
3258 EDEM3 1_92 0.9809 26.07 0.0001584 4.945 2
12783 C4A 6_26 0.2100 114.43 0.0001489 10.665 3
2380 TLE4 9_38 0.8988 26.30 0.0001465 5.000 1
3616 SNX19 11_81 0.6390 35.62 0.0001410 5.808 2
9648 LY6H 8_94 0.7779 28.94 0.0001395 5.143 1
376 CUL3 2_132 0.6169 36.03 0.0001377 -6.181 1
2734 TRPV4 12_66 0.9077 24.28 0.0001365 4.416 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11907 CLIC1 6_26 0.0357375 113.57 2.515e-05 10.875 2
11645 LY6G6C 6_26 0.3512849 114.93 2.501e-04 10.731 1
11634 ZBTB12 6_26 0.2291866 113.88 1.617e-04 10.683 1
12783 C4A 6_26 0.2099955 114.43 1.489e-04 10.665 3
12122 C4B 6_26 0.0167691 108.07 1.123e-05 -10.418 1
12395 CYP21A2 6_26 0.0081419 107.57 5.426e-06 -10.414 1
2927 PRSS16 6_21 0.1397855 104.23 9.027e-05 -10.014 2
958 NT5C2 10_66 0.2328363 42.26 6.096e-05 -9.705 1
6413 CNNM2 10_66 0.1764505 41.45 4.532e-05 -9.686 1
7799 GNL3 3_36 0.7013995 65.32 2.839e-04 9.429 1
12719 HLA-DMB 6_27 0.3689835 76.90 1.758e-04 -9.380 1
11611 HLA-DMA 6_27 0.1213633 73.52 5.528e-05 -9.171 2
11621 RNF5 6_26 0.0044675 73.14 2.024e-06 9.003 2
3085 SPCS1 3_36 0.0443594 62.45 1.716e-05 -8.936 1
11649 GPANK1 6_26 0.0017242 74.40 7.947e-07 8.879 1
6550 ABCB9 12_75 0.0007927 62.49 3.069e-07 8.638 1
2756 OGFOD2 12_75 0.0007535 62.22 2.905e-07 8.627 1
8561 SMIM4 3_36 0.0208015 56.08 7.227e-06 -8.494 1
11620 AGER 6_26 0.0014929 56.30 5.208e-07 -8.415 2
9472 ATG13 11_29 0.3160288 60.68 1.188e-04 -8.046 1Comparing z scores and PIPs
[1] 0.01596GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 65Uploading 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 Ratio BgRatio
20 Bone Diseases 0.02505 2/27 10/9703
37 Confusion 0.02505 1/27 1/9703
59 Gingival Hypertrophy 0.02505 1/27 1/9703
77 Infant, Premature, Diseases 0.02505 1/27 1/9703
85 Kienbock Disease 0.02505 1/27 1/9703
104 Maxillary Diseases 0.02505 1/27 1/9703
116 Avascular necrosis of bone 0.02505 1/27 1/9703
120 Nose Neoplasms 0.02505 1/27 1/9703
125 Bone necrosis 0.02505 1/27 1/9703
132 Pneumonia, Viral 0.02505 1/27 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
Warning: ggrepel: 24 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] 66#significance threshold for TWAS
print(sig_thresh)[1] 4.599#number of ctwas genes
length(ctwas_genes)[1] 12#number of TWAS genes
length(twas_genes)[1] 188#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
735 RASSF1 3_35 0.9562 892.21 5.285e-03 4.532 1
2734 TRPV4 12_66 0.9077 24.28 1.365e-04 4.416 1
5640 CPNE2 16_30 0.8261 20.99 1.074e-04 -4.125 1
2569 MMD 17_32 0.8833 24.70 1.352e-04 -4.451 1
13209 CEP95 17_37 0.8207 19.35 9.838e-05 -3.800 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.17692 #specificity
print(specificity) ctwas TWAS
0.9991 0.9859 #precision / PPV
print(precision) ctwas TWAS
0.1667 0.1223 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 66#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 852#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.599#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] 72#sensitivity / recall
sensitivity ctwas TWAS
0.0303 0.3485 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9988 0.9425 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6667 0.3194 
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)
64 43 21
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