SCZ - Brain Cortex
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] 10248#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1041 726 590 400 470 587 492 367 394 426 617 601 210 343 347 435
17 18 19 20 21 22
630 168 787 331 25 261 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 7027#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6857Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0131343 0.0003062 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
11.53 10.50 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10248 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01474 0.19261 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06135 1.05163Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11314 ZNF823 19_10 0.9852 37.03 0.0003464 6.181 2
13679 RP11-230C9.4 6_102 0.9579 23.05 0.0002097 -4.712 2
4002 ARMC7 17_42 0.9041 22.49 0.0001931 4.486 2
421 TRIT1 1_25 0.8947 20.82 0.0001768 -4.162 3
3085 SPCS1 3_36 0.8837 37.45 0.0003142 -6.807 1
11176 PCBP2 12_33 0.8775 26.41 0.0002200 5.065 1
3165 SF3B1 2_117 0.8357 48.83 0.0003875 7.265 1
5055 RCBTB1 13_21 0.8072 21.32 0.0001634 -4.251 2
13938 RP11-408A13.3 9_13 0.8005 23.18 0.0001762 4.362 2
2741 VPS29 12_67 0.7991 40.26 0.0003055 -6.461 1
3258 EDEM3 1_92 0.7964 21.59 0.0001633 4.223 2
4239 SPECC1 17_16 0.7887 25.56 0.0001914 4.822 1
376 CUL3 2_132 0.7630 30.14 0.0002184 -5.730 1
6035 METTL21A 2_122 0.7628 21.45 0.0001554 -4.284 1
2796 NT5DC3 12_62 0.7438 22.58 0.0001594 -4.142 2
5968 ITPKB 1_116 0.7154 22.29 0.0001514 -4.033 2
2476 CCDC6 10_39 0.6983 21.24 0.0001408 -3.918 2
3022 PCCB 3_84 0.6976 41.45 0.0002746 -6.724 1
2380 TLE4 9_38 0.6885 21.15 0.0001382 4.279 1
11572 SOX18 20_38 0.6812 21.85 0.0001413 3.659 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
12783 C4A 6_26 5.201e-08 225.05 1.111e-10 11.515 3
11645 LY6G6C 6_26 5.684e-08 222.69 1.202e-10 11.531 1
11634 ZBTB12 6_26 3.825e-08 222.34 8.076e-11 11.521 1
12122 C4B 6_26 1.224e-08 214.67 2.496e-11 -11.326 1
11907 CLIC1 6_26 3.268e-07 211.75 6.571e-10 11.673 2
11649 GPANK1 6_26 3.425e-08 174.72 5.682e-11 10.267 1
11620 AGER 6_26 1.551e-07 148.77 2.191e-10 -9.715 2
11621 RNF5 6_26 7.341e-09 134.80 9.396e-12 9.377 1
11619 NOTCH4 6_26 2.287e-09 131.37 2.853e-12 7.827 3
11622 AGPAT1 6_26 1.380e-10 105.46 1.382e-13 -5.190 1
11908 DDAH2 6_26 1.301e-08 86.85 1.073e-11 8.149 1
11640 HSPA1L 6_26 2.529e-08 84.27 2.023e-11 -8.075 1
5217 FLOT1 6_24 5.756e-02 81.60 4.459e-05 -10.981 1
11657 NFKBIL1 6_26 9.624e-09 74.84 6.839e-12 -5.171 1
10662 BTN3A2 6_20 2.141e-02 68.91 1.401e-05 8.920 2
11611 HLA-DMA 6_27 4.338e-02 63.44 2.613e-05 -8.575 2
9902 HLA-DQB1 6_26 8.953e-10 58.64 4.985e-13 -1.990 1
12594 HLA-DQA2 6_26 2.070e-07 55.73 1.095e-10 -1.505 2
11618 BTNL2 6_26 5.964e-11 54.59 3.092e-14 4.920 2
13386 RP1-265C24.8 6_22 1.483e-02 53.16 7.487e-06 -7.445 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3165 SF3B1 2_117 0.8357 48.83 0.0003875 7.265 1
11314 ZNF823 19_10 0.9852 37.03 0.0003464 6.181 2
3085 SPCS1 3_36 0.8837 37.45 0.0003142 -6.807 1
2741 VPS29 12_67 0.7991 40.26 0.0003055 -6.461 1
3022 PCCB 3_84 0.6976 41.45 0.0002746 -6.724 1
2682 MDK 11_29 0.5619 48.86 0.0002607 -7.159 1
11176 PCBP2 12_33 0.8775 26.41 0.0002200 5.065 1
376 CUL3 2_132 0.7630 30.14 0.0002184 -5.730 1
7729 THOC7 3_43 0.5398 40.99 0.0002101 -6.363 3
13679 RP11-230C9.4 6_102 0.9579 23.05 0.0002097 -4.712 2
4002 ARMC7 17_42 0.9041 22.49 0.0001931 4.486 2
4239 SPECC1 17_16 0.7887 25.56 0.0001914 4.822 1
421 TRIT1 1_25 0.8947 20.82 0.0001768 -4.162 3
13938 RP11-408A13.3 9_13 0.8005 23.18 0.0001762 4.362 2
4088 XRCC3 14_54 0.3806 48.01 0.0001735 7.263 1
5055 RCBTB1 13_21 0.8072 21.32 0.0001634 -4.251 2
3258 EDEM3 1_92 0.7964 21.59 0.0001633 4.223 2
2796 NT5DC3 12_62 0.7438 22.58 0.0001594 -4.142 2
6035 METTL21A 2_122 0.7628 21.45 0.0001554 -4.284 1
5617 FURIN 15_42 0.4686 34.60 0.0001540 -5.772 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11907 CLIC1 6_26 3.268e-07 211.75 6.571e-10 11.673 2
11645 LY6G6C 6_26 5.684e-08 222.69 1.202e-10 11.531 1
11634 ZBTB12 6_26 3.825e-08 222.34 8.076e-11 11.521 1
12783 C4A 6_26 5.201e-08 225.05 1.111e-10 11.515 3
12122 C4B 6_26 1.224e-08 214.67 2.496e-11 -11.326 1
5217 FLOT1 6_24 5.756e-02 81.60 4.459e-05 -10.981 1
11649 GPANK1 6_26 3.425e-08 174.72 5.682e-11 10.267 1
11620 AGER 6_26 1.551e-07 148.77 2.191e-10 -9.715 2
11621 RNF5 6_26 7.341e-09 134.80 9.396e-12 9.377 1
10662 BTN3A2 6_20 2.141e-02 68.91 1.401e-05 8.920 2
11611 HLA-DMA 6_27 4.338e-02 63.44 2.613e-05 -8.575 2
6413 CNNM2 10_66 8.495e-02 46.45 3.747e-05 -8.161 1
11908 DDAH2 6_26 1.301e-08 86.85 1.073e-11 8.149 1
11640 HSPA1L 6_26 2.529e-08 84.27 2.023e-11 -8.075 1
10809 ZSCAN23 6_22 6.241e-02 47.43 2.811e-05 -7.829 1
11619 NOTCH4 6_26 2.287e-09 131.37 2.853e-12 7.827 3
6404 INA 10_66 6.172e-02 50.51 2.960e-05 -7.763 1
11171 ZSCAN26 6_22 1.494e-02 45.44 6.444e-06 7.504 3
13386 RP1-265C24.8 6_22 1.483e-02 53.16 7.487e-06 -7.445 1
11129 ZSCAN16 6_22 1.561e-02 49.13 7.281e-06 7.365 2Comparing z scores and PIPs
[1] 0.01239GO 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"
[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
11 Confusion
54 Speech impairment
55 Derealization
60 Spondylometaphyseal dysplasia, Kozlowski type
61 Metatropic dwarfism
84 Brachyolmia Type 3
90 Sexually disinhibited behavior
96 Hypersomnia, Recurrent
118 SPINAL MUSCULAR ATROPHY, DISTAL, CONGENITAL NONPROGRESSIVE (disorder)
120 HYPOTRICHOSIS-LYMPHEDEMA-TELANGIECTASIA SYNDROME
FDR Ratio BgRatio
11 0.008838 1/14 1/9703
54 0.008838 1/14 1/9703
55 0.008838 1/14 1/9703
60 0.008838 1/14 1/9703
61 0.008838 1/14 1/9703
84 0.008838 1/14 1/9703
90 0.008838 1/14 1/9703
96 0.008838 1/14 1/9703
118 0.008838 1/14 1/9703
120 0.008838 1/14 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] 57#significance threshold for TWAS
print(sig_thresh)[1] 4.57#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 127#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
421 TRIT1 1_25 0.8947 20.82 0.0001768 -4.162 3
13938 RP11-408A13.3 9_13 0.8005 23.18 0.0001762 4.362 2
5055 RCBTB1 13_21 0.8072 21.32 0.0001634 -4.251 2
4002 ARMC7 17_42 0.9041 22.49 0.0001931 4.486 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.12308 #specificity
print(specificity) ctwas TWAS
0.9993 0.9891 #precision / PPV
print(precision) ctwas TWAS
0.2222 0.1260 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 57#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 583#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.57#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] 42#sensitivity / recall
sensitivity ctwas TWAS
0.03509 0.28070 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9554 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.000 0.381 
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)
73 41 14
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