SCZ - Brain Caudate 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] 11179#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1069 801 661 428 548 641 529 417 410 427 653 638 232 374 375 505
17 18 19 20 21 22
698 181 864 331 121 276 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8312#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7435Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0096940 0.0002607 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.385 8.436 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11179 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01179 0.20978 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.0693 1.7962Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11135 ZNF823 19_10 0.9727 29.26 0.0003692 5.506 2
12311 AC012074.2 2_16 0.8626 23.50 0.0002629 4.623 1
3091 SF3B1 2_117 0.8337 42.87 0.0004636 6.784 1
110 ELAC2 17_11 0.8068 21.67 0.0002268 4.540 1
3016 LMAN2L 2_57 0.7583 40.87 0.0004020 -4.853 2
2684 VPS29 12_67 0.7448 24.37 0.0002354 -4.923 2
3236 MAP7D1 1_22 0.7347 24.70 0.0002354 5.058 1
1678 KIAA0391 14_9 0.7166 22.30 0.0002073 -4.760 1
1879 ESRP2 16_36 0.6767 25.40 0.0002229 5.047 2
8894 ZNF318 6_33 0.6584 23.98 0.0002048 -4.832 1
179 NISCH 3_36 0.6556 33.67 0.0002863 6.110 1
4159 SPECC1 17_16 0.6420 24.40 0.0002032 4.167 1
13347 LINC01415 18_30 0.6226 30.27 0.0002445 -5.655 1
10494 TMEM222 1_19 0.6204 25.88 0.0002083 3.902 1
2630 MDK 11_28 0.5981 37.22 0.0002888 -6.344 1
13314 TBC1D29 17_18 0.5920 25.26 0.0001939 4.354 1
729 PPP2R5B 11_36 0.5628 24.40 0.0001781 -4.614 1
3750 BHLHE41 12_18 0.5481 27.81 0.0001977 -3.860 1
422 CTNNA1 5_82 0.5379 23.12 0.0001613 4.938 1
506 SDCCAG8 1_128 0.5316 24.71 0.0001704 -4.897 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
6289 CNNM2 10_66 6.684e-04 2517.0 2.182e-05 -8.294 2
6279 CYP17A1 10_66 8.577e-05 346.6 3.856e-07 -6.664 1
2944 PCCB 3_84 0.000e+00 282.0 0.000e+00 -4.695 3
6280 INA 10_66 1.465e-09 281.7 5.352e-12 -3.927 1
12229 HLA-DQB2 6_26 3.331e-16 243.6 1.053e-18 -3.919 1
10939 HLA-DQA1 6_26 6.328e-15 218.1 1.790e-17 3.448 1
11478 APOM 6_26 7.091e-10 194.8 1.792e-12 8.945 1
11467 VWA7 6_26 5.093e-10 194.7 1.286e-12 8.911 1
11731 CLIC1 6_26 4.793e-10 193.9 1.205e-12 8.873 2
11469 MSH5 6_26 1.110e-16 193.7 2.790e-19 7.592 2
12582 C4A 6_26 1.551e-11 191.8 3.859e-14 8.519 2
11732 DDAH2 6_26 0.000e+00 180.2 0.000e+00 7.661 1
11464 HSPA1L 6_26 0.000e+00 159.1 0.000e+00 7.658 1
8111 BORCS7 10_66 1.929e-08 150.4 3.762e-11 3.773 2
11458 EHMT2 6_26 0.000e+00 149.3 0.000e+00 5.405 1
13485 HCG17 6_24 3.109e-15 132.3 5.334e-18 5.533 1
849 PPP2R3A 3_84 0.000e+00 128.5 0.000e+00 4.119 1
11474 CSNK2B 6_26 1.110e-16 127.9 1.841e-19 -6.642 1
673 ZNRD1 6_24 6.230e-07 123.3 9.962e-10 5.354 2
11440 AGER 6_26 0.000e+00 116.6 0.000e+00 -7.547 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3091 SF3B1 2_117 0.8337 42.87 0.0004636 6.784 1
3016 LMAN2L 2_57 0.7583 40.87 0.0004020 -4.853 2
11135 ZNF823 19_10 0.9727 29.26 0.0003692 5.506 2
2630 MDK 11_28 0.5981 37.22 0.0002888 -6.344 1
179 NISCH 3_36 0.6556 33.67 0.0002863 6.110 1
4922 TMEM127 2_57 0.4346 47.44 0.0002674 -3.710 1
12311 AC012074.2 2_16 0.8626 23.50 0.0002629 4.623 1
13347 LINC01415 18_30 0.6226 30.27 0.0002445 -5.655 1
2684 VPS29 12_67 0.7448 24.37 0.0002354 -4.923 2
3236 MAP7D1 1_22 0.7347 24.70 0.0002354 5.058 1
110 ELAC2 17_11 0.8068 21.67 0.0002268 4.540 1
1879 ESRP2 16_36 0.6767 25.40 0.0002229 5.047 2
10494 TMEM222 1_19 0.6204 25.88 0.0002083 3.902 1
1678 KIAA0391 14_9 0.7166 22.30 0.0002073 -4.760 1
8894 ZNF318 6_33 0.6584 23.98 0.0002048 -4.832 1
4159 SPECC1 17_16 0.6420 24.40 0.0002032 4.167 1
3750 BHLHE41 12_18 0.5481 27.81 0.0001977 -3.860 1
13314 TBC1D29 17_18 0.5920 25.26 0.0001939 4.354 1
729 PPP2R5B 11_36 0.5628 24.40 0.0001781 -4.614 1
12520 HLA-DMB 6_27 0.2288 57.84 0.0001716 -7.990 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10491 BTN3A2 6_20 1.746e-02 64.58 1.462e-05 9.089 2
11478 APOM 6_26 7.091e-10 194.82 1.792e-12 8.945 1
11467 VWA7 6_26 5.093e-10 194.67 1.286e-12 8.911 1
11731 CLIC1 6_26 4.793e-10 193.88 1.205e-12 8.873 2
5119 PGBD1 6_22 6.819e-03 76.21 6.741e-06 -8.525 1
12582 C4A 6_26 1.551e-11 191.76 3.859e-14 8.519 2
6289 CNNM2 10_66 6.684e-04 2516.99 2.182e-05 -8.294 2
12520 HLA-DMB 6_27 2.288e-01 57.84 1.716e-04 -7.990 1
11444 PRRT1 6_26 0.000e+00 91.45 0.000e+00 7.907 1
11732 DDAH2 6_26 0.000e+00 180.23 0.000e+00 7.661 1
11464 HSPA1L 6_26 0.000e+00 159.14 0.000e+00 7.658 1
11469 MSH5 6_26 1.110e-16 193.74 2.790e-19 7.592 2
13068 RP11-490G2.2 1_60 1.721e-02 49.56 1.106e-05 7.551 1
11440 AGER 6_26 0.000e+00 116.58 0.000e+00 -7.547 1
7064 ZSCAN12 6_22 6.588e-03 37.55 3.208e-06 7.450 1
9628 C2orf69 2_118 3.015e-01 41.30 1.616e-04 7.234 2
11471 LY6G6C 6_26 0.000e+00 106.34 0.000e+00 -6.903 2
11792 AS3MT 10_66 2.711e-03 80.15 2.819e-06 6.876 2
7500 TYW5 2_118 4.640e-02 37.87 2.279e-05 -6.812 2
3091 SF3B1 2_117 8.337e-01 42.87 4.636e-04 6.784 1Comparing z scores and PIPs
[1] 0.008588GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 22Uploading 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
69 Hematopoetic Myelodysplasia 0.01426 2/10 29/9703
72 SENIOR-LOKEN SYNDROME 7 0.01426 1/10 1/9703
75 PROSTATE CANCER, HEREDITARY, 2 0.01426 1/10 1/9703
77 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.01426 1/10 1/9703
78 BARDET-BIEDL SYNDROME 16 0.01426 1/10 1/9703
80 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.01426 1/10 1/9703
54 Refractory anemia with ringed sideroblasts 0.02138 1/10 2/9703
74 MYELODYSPLASTIC SYNDROME 0.02138 2/10 67/9703
65 Macular Dystrophy, Butterfly-Shaped Pigmentary, 2 0.02331 1/10 3/9703
67 Patterned dystrophy of retinal pigment epithelium 0.02331 1/10 3/9703WebGestalt 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)
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] 64#significance threshold for TWAS
print(sig_thresh)[1] 4.588#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 96#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
110 ELAC2 17_11 0.8068 21.67 0.0002268 4.54 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.08462 #specificity
print(specificity) ctwas TWAS
0.9999 0.9924 #precision / PPV
print(precision) ctwas TWAS
0.7500 0.1146 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 64#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 774#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 26#sensitivity / recall
sensitivity ctwas TWAS
0.04688 0.17188 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9806 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4231 
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)
66 52 9
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