SCZ - Brain Nucleus accumbens basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-03-14
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11167#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1097 789 659 431 555 642 549 417 406 435 664 627 214 368 359 510
17 18 19 20 21 22
657 174 863 343 131 277 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8459#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7575Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0127798 0.0002541 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.937 8.391 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11167 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01654 0.20332 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.08504 1.66945Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
4131 SPECC1 17_16 0.9996 141.30 0.0018319 5.484 2
5491 FURIN 15_42 0.9813 44.70 0.0005689 -7.000 1
11067 ZNF823 19_10 0.9812 29.18 0.0003714 5.505 2
13453 RP11-230C9.4 6_102 0.9400 21.99 0.0002681 -4.569 2
3206 MAP7D1 1_22 0.9116 24.65 0.0002915 5.058 1
3067 SF3B1 2_117 0.8859 43.72 0.0005024 6.784 1
9374 COX8A 11_35 0.8727 25.22 0.0002854 -4.991 1
10921 PCBP2 12_33 0.8548 21.60 0.0002395 4.496 1
6796 VPS37A 8_18 0.8063 28.29 0.0002958 -5.357 1
107 ELAC2 17_11 0.7884 21.77 0.0002226 4.372 1
4719 SOX5 12_17 0.7868 21.05 0.0002148 4.024 1
2658 VPS29 12_67 0.7855 23.91 0.0002436 -4.896 2
6535 TADA1 1_82 0.7694 22.72 0.0002268 -4.177 2
9395 DIRAS1 19_3 0.7557 21.08 0.0002066 -4.359 1
11808 NPTXR 22_15 0.7465 21.06 0.0002039 4.106 2
10150 ACOT1 14_34 0.7355 22.32 0.0002130 4.167 2
13283 LINC01415 18_30 0.7340 26.96 0.0002567 -5.655 1
6304 DRD2 11_67 0.6852 31.74 0.0002821 -6.045 2
11929 HAR1A 20_37 0.6791 19.59 0.0001726 3.767 1
440 FAM120A 9_47 0.6742 22.84 0.0001998 -4.571 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
6803 MMP16 8_63 0.000e+00 526.79 0.000e+00 3.6478 1
12325 HLA-DQA2 6_26 0.000e+00 347.26 0.000e+00 -0.4490 2
11434 HCG9 6_24 1.997e-10 216.03 5.595e-13 -3.3869 1
11395 MSH5 6_26 2.092e-13 203.29 5.515e-16 8.0439 2
11404 APOM 6_26 1.787e-09 197.10 4.568e-12 8.9450 1
12513 C4A 6_26 3.601e-11 188.79 8.819e-14 8.4450 1
11648 DDAH2 6_26 0.000e+00 182.44 0.000e+00 7.6610 1
11397 LY6G6C 6_26 0.000e+00 156.40 0.000e+00 -7.1790 3
11386 EHMT2 6_26 0.000e+00 152.77 0.000e+00 5.6967 1
10748 HLA-DRB1 6_26 0.000e+00 148.98 0.000e+00 -2.0086 1
2926 PCCB 3_84 0.000e+00 142.96 0.000e+00 -4.3613 1
11375 FKBPL 6_26 3.997e-15 142.53 7.389e-18 -4.6363 2
4131 SPECC1 17_16 9.996e-01 141.30 1.832e-03 5.4844 2
836 PPP2R3A 3_84 0.000e+00 129.68 0.000e+00 4.1188 1
11400 CSNK2B 6_26 1.110e-16 129.30 1.862e-19 -6.6421 1
11642 ATF6B 6_26 0.000e+00 115.50 0.000e+00 3.6260 1
2203 MPP6 7_21 3.245e-03 110.26 4.641e-06 -3.3024 1
11369 NOTCH4 6_26 0.000e+00 94.28 0.000e+00 5.9033 2
11389 C6orf48 6_26 0.000e+00 79.40 0.000e+00 4.1389 3
11370 PBX2 6_26 0.000e+00 78.44 0.000e+00 -0.7005 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
4131 SPECC1 17_16 0.9996 141.30 0.0018319 5.484 2
5491 FURIN 15_42 0.9813 44.70 0.0005689 -7.000 1
3067 SF3B1 2_117 0.8859 43.72 0.0005024 6.784 1
11067 ZNF823 19_10 0.9812 29.18 0.0003714 5.505 2
2602 MDK 11_28 0.6546 37.36 0.0003173 -6.344 1
6796 VPS37A 8_18 0.8063 28.29 0.0002958 -5.357 1
3206 MAP7D1 1_22 0.9116 24.65 0.0002915 5.058 1
9374 COX8A 11_35 0.8727 25.22 0.0002854 -4.991 1
6304 DRD2 11_67 0.6852 31.74 0.0002821 -6.045 2
13453 RP11-230C9.4 6_102 0.9400 21.99 0.0002681 -4.569 2
13283 LINC01415 18_30 0.7340 26.96 0.0002567 -5.655 1
2658 VPS29 12_67 0.7855 23.91 0.0002436 -4.896 2
10921 PCBP2 12_33 0.8548 21.60 0.0002395 4.496 1
1571 CACNA1I 22_16 0.5280 33.45 0.0002291 5.956 1
6535 TADA1 1_82 0.7694 22.72 0.0002268 -4.177 2
107 ELAC2 17_11 0.7884 21.77 0.0002226 4.372 1
4719 SOX5 12_17 0.7868 21.05 0.0002148 4.024 1
6509 TMEM56 1_58 0.4556 36.13 0.0002135 -4.357 1
10150 ACOT1 14_34 0.7355 22.32 0.0002130 4.167 2
2025 FCGRT 19_34 0.5962 27.52 0.0002128 4.874 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10415 BTN3A2 6_20 2.464e-02 67.09 2.144e-05 9.168 3
11404 APOM 6_26 1.787e-09 197.10 4.568e-12 8.945 1
10915 ZSCAN26 6_22 1.060e-02 67.68 9.309e-06 8.718 2
12513 C4A 6_26 3.601e-11 188.79 8.819e-14 8.445 1
11395 MSH5 6_26 2.092e-13 203.29 5.515e-16 8.044 2
9788 HIST1H2BC 6_20 2.377e-02 50.70 1.563e-05 -7.978 1
6275 CNNM2 10_66 1.953e-01 39.06 9.893e-05 -7.876 1
12454 HLA-DMB 6_27 1.410e-01 54.44 9.957e-05 -7.686 2
11648 DDAH2 6_26 0.000e+00 182.44 0.000e+00 7.661 1
11397 LY6G6C 6_26 0.000e+00 156.40 0.000e+00 -7.179 3
5491 FURIN 15_42 9.813e-01 44.70 5.689e-04 -7.000 1
11418 CCHCR1 6_25 1.443e-02 35.34 6.613e-06 -6.927 2
3067 SF3B1 2_117 8.859e-01 43.72 5.024e-04 6.784 1
7442 TYW5 2_118 6.639e-02 38.37 3.304e-05 -6.753 2
11400 CSNK2B 6_26 1.110e-16 129.30 1.862e-19 -6.642 1
2602 MDK 11_28 6.546e-01 37.36 3.173e-04 -6.344 1
11136 ZKSCAN8 6_22 6.617e-03 40.01 3.434e-06 6.128 1
9771 HARBI1 11_28 1.660e-01 33.80 7.277e-05 6.084 1
11446 TRIM27 6_22 1.516e-02 70.98 1.396e-05 6.073 2
6304 DRD2 11_67 6.852e-01 31.74 2.821e-04 -6.045 2Comparing z scores and PIPs
[1] 0.006179GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 33Uploading 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
5 Anxiety Disorders 0.02258 2/15 44/9703
50 Measles 0.02258 1/15 1/9703
51 Memory Disorders 0.02258 2/15 43/9703
93 Memory impairment 0.02258 2/15 44/9703
121 Anxiety States, Neurotic 0.02258 2/15 44/9703
149 Age-Related Memory Disorders 0.02258 2/15 43/9703
150 Memory Disorder, Semantic 0.02258 2/15 43/9703
151 Memory Disorder, Spatial 0.02258 2/15 43/9703
152 Memory Loss 0.02258 2/15 43/9703
170 Anxiety neurosis (finding) 0.02258 2/15 44/9703WebGestalt 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: 1 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] 58#significance threshold for TWAS
print(sig_thresh)[1] 4.588#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 69#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
13453 RP11-230C9.4 6_102 0.9400 21.99 0.0002681 -4.569 2
10921 PCBP2 12_33 0.8548 21.60 0.0002395 4.496 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.07692 #specificity
print(specificity) ctwas TWAS
0.9995 0.9947 #precision / PPV
print(precision) ctwas TWAS
0.3333 0.1449 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 689#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.588#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] 15#sensitivity / recall
sensitivity ctwas TWAS
0.05172 0.17241 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9927 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.6667 
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)
72 48 7
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_1.0.0 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