SCZ - Brain Nucleus accumbens basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11501#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1130 797 686 449 570 650 551 432 411 456 694 641 218 385 374 524
17 18 19 20 21 22
671 184 904 356 131 287 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9026#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7848Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0129648 0.0002708 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
13.24 12.79 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11501 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01223 0.15869 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05491 0.79298Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
5491 FURIN 15_42 0.9817 91.36 5.556e-04 -9.913 1
11067 ZNF823 19_10 0.9804 40.18 2.441e-04 6.309 2
6304 DRD2 11_67 0.9731 54.28 3.273e-04 -8.047 2
705 RASSF1 3_35 0.9608 912.47 5.432e-03 4.532 1
6509 TMEM56 1_58 0.9143 31.11 1.762e-04 -4.834 1
13954 MYO19 17_22 0.9113 28.01 1.582e-04 -4.970 2
2651 TRPV4 12_66 0.9008 24.40 1.362e-04 4.416 1
11074 RPL12 9_66 0.8963 23.65 1.313e-04 4.672 2
98 FARP2 2_144 0.8647 21.50 1.152e-04 4.230 3
6180 FAM135B 8_91 0.8575 22.13 1.176e-04 -3.461 1
7086 ACE 17_37 0.8032 32.87 1.636e-04 -5.802 1
4535 ACY3 11_37 0.7800 19.84 9.586e-05 -3.260 1
11957 LINC00606 3_8 0.7601 22.69 1.068e-04 -4.150 1
11400 CSNK2B 6_26 0.7594 31.77 1.495e-04 -6.916 1
13453 RP11-230C9.4 6_102 0.7539 26.85 1.254e-04 -4.906 2
7493 ANTXR2 4_54 0.7518 21.39 9.965e-05 3.831 1
9 ENPP4 6_35 0.7365 21.39 9.760e-05 -3.642 1
8724 FOXN2 2_31 0.7278 25.29 1.140e-04 -5.173 1
6130 GIGYF1 7_62 0.7192 29.31 1.306e-04 5.333 2
4719 SOX5 12_17 0.7112 23.42 1.032e-04 4.089 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
705 RASSF1 3_35 9.608e-01 912.47 5.432e-03 4.5324 1
9699 LSMEM2 3_35 2.549e-01 909.76 1.437e-03 -4.2709 1
10604 SLC38A3 3_35 1.850e-12 230.45 2.642e-15 -2.7756 1
128 CACNA2D2 3_35 0.000e+00 211.01 0.000e+00 -0.1044 1
36 RBM6 3_35 3.893e-01 199.37 4.808e-04 4.4688 1
1217 C3orf18 3_35 0.000e+00 183.45 0.000e+00 -0.4441 1
7671 CAMKV 3_35 1.162e-04 176.27 1.269e-07 -2.5322 2
10442 HYAL3 3_35 5.816e-13 160.77 5.794e-16 -2.5066 1
7673 MST1R 3_35 2.594e-05 151.71 2.438e-08 -4.0250 1
213 SEMA3B 3_35 0.000e+00 115.71 0.000e+00 0.6250 1
10415 BTN3A2 6_20 1.576e-02 113.26 1.106e-05 9.1957 3
13719 LINC02019 3_35 0.000e+00 111.07 0.000e+00 0.3173 1
2972 CISH 3_35 0.000e+00 105.29 0.000e+00 -0.1383 1
7668 RNF123 3_35 4.441e-16 98.43 2.708e-19 -2.3252 1
5491 FURIN 15_42 9.817e-01 91.36 5.556e-04 -9.9133 1
9788 HIST1H2BC 6_20 1.480e-02 86.91 7.967e-06 -7.9928 1
11404 APOM 6_26 4.314e-01 82.97 2.217e-04 10.6484 1
2947 CYB561D2 3_35 0.000e+00 81.60 0.000e+00 3.5093 1
12513 C4A 6_26 7.574e-02 78.98 3.706e-05 10.4180 1
11359 HLA-DMA 6_27 4.761e-01 76.52 2.257e-04 -9.4080 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
705 RASSF1 3_35 0.9608 912.47 0.0054316 4.532 1
9699 LSMEM2 3_35 0.2549 909.76 0.0014366 -4.271 1
5491 FURIN 15_42 0.9817 91.36 0.0005556 -9.913 1
36 RBM6 3_35 0.3893 199.37 0.0004808 4.469 1
6304 DRD2 11_67 0.9731 54.28 0.0003273 -8.047 2
11067 ZNF823 19_10 0.9804 40.18 0.0002441 6.309 2
7634 GNL3 3_36 0.6297 61.42 0.0002396 9.369 2
11359 HLA-DMA 6_27 0.4761 76.52 0.0002257 -9.408 1
11404 APOM 6_26 0.4314 82.97 0.0002217 10.648 1
3067 SF3B1 2_117 0.6674 52.57 0.0002174 7.605 1
8229 GATAD2A 19_15 0.6189 52.47 0.0002012 -7.411 2
6509 TMEM56 1_58 0.9143 31.11 0.0001762 -4.834 1
9771 HARBI1 11_28 0.4557 58.37 0.0001648 8.046 1
7086 ACE 17_37 0.8032 32.87 0.0001636 -5.802 1
13954 MYO19 17_22 0.9113 28.01 0.0001582 -4.970 2
11400 CSNK2B 6_26 0.7594 31.77 0.0001495 -6.916 1
8168 PDIA3 15_16 0.6229 37.53 0.0001448 6.314 1
2651 TRPV4 12_66 0.9008 24.40 0.0001362 4.416 1
11074 RPL12 9_66 0.8963 23.65 0.0001313 4.672 2
6130 GIGYF1 7_62 0.7192 29.31 0.0001306 5.333 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11404 APOM 6_26 0.4313616 82.97 2.217e-04 10.648 1
12513 C4A 6_26 0.0757433 78.98 3.706e-05 10.418 1
5491 FURIN 15_42 0.9816533 91.36 5.556e-04 -9.913 1
11395 MSH5 6_26 0.0161372 75.88 7.586e-06 9.692 2
6275 CNNM2 10_66 0.0933454 38.45 2.224e-05 -9.577 2
11359 HLA-DMA 6_27 0.4760564 76.52 2.257e-04 -9.408 1
7634 GNL3 3_36 0.6297371 61.42 2.396e-04 9.369 2
10415 BTN3A2 6_20 0.0157564 113.26 1.106e-05 9.196 3
12454 HLA-DMB 6_27 0.0774689 73.39 3.522e-05 -9.090 2
6404 ABCB9 12_75 0.0007483 63.16 2.928e-07 8.638 1
9922 ARL6IP4 12_75 0.0006857 62.75 2.666e-07 8.615 1
8377 GLYCTK 3_36 0.1285609 69.60 5.544e-05 8.577 1
8384 SMIM4 3_36 0.0214386 53.83 7.150e-06 -8.494 1
10915 ZSCAN26 6_22 0.0204790 60.98 7.737e-06 8.222 3
6304 DRD2 11_67 0.9731089 54.28 3.273e-04 -8.047 2
9771 HARBI1 11_28 0.4556658 58.37 1.648e-04 8.046 1
9788 HIST1H2BC 6_20 0.0147966 86.91 7.967e-06 -7.993 1
2602 MDK 11_28 0.1623680 55.90 5.624e-05 -7.898 1
2996 NEK4 3_36 0.0113554 44.05 3.099e-06 7.846 1
2995 SPCS1 3_36 0.0111611 43.64 3.018e-06 -7.821 1Comparing z scores and PIPs
[1] 0.01322GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 52Uploading 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
34 Confusion 0.02784 1/21 1/9703
60 Gingival Hypertrophy 0.02784 1/21 1/9703
74 Infant, Premature, Diseases 0.02784 1/21 1/9703
117 Pneumonia, Viral 0.02784 1/21 1/9703
155 Left Ventricular Hypertrophy 0.02784 2/21 25/9703
180 Speech impairment 0.02784 1/21 1/9703
181 Derealization 0.02784 1/21 1/9703
198 Spondylometaphyseal dysplasia, Kozlowski type 0.02784 1/21 1/9703
199 Metatropic dwarfism 0.02784 1/21 1/9703
237 Brachyolmia Type 3 0.02784 1/21 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: 'timedatectl' indicates the non-existent timezone name 'n/a'Warning: Your system is mis-configured: '/etc/localtime' is not a symlinkWarning: It is strongly recommended to set envionment variable TZ to 'America/
Chicago' (or equivalent)Warning: ggrepel: 14 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] 59#significance threshold for TWAS
print(sig_thresh)[1] 4.594#number of ctwas genes
length(ctwas_genes)[1] 11#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$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
98 FARP2 2_144 0.8647 21.50 0.0001152 4.230 3
705 RASSF1 3_35 0.9608 912.47 0.0054316 4.532 1
6180 FAM135B 8_91 0.8575 22.13 0.0001176 -3.461 1
2651 TRPV4 12_66 0.9008 24.40 0.0001362 4.416 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.18462 #specificity
print(specificity) ctwas TWAS
0.9993 0.9888 #precision / PPV
print(precision) ctwas TWAS
0.2727 0.1579 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 59#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 734#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.594#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] 50#sensitivity / recall
sensitivity ctwas TWAS
0.05085 0.40678 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9646 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.00 0.48 
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)
71 35 21
Detected (PIP > 0.8)
3 #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