SCZ - Brain Caudate basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-03-03
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11590#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1115 851 695 452 579 584 548 433 433 450 693 657 241 393 396 529
17 18 19 20 21 22
713 185 887 340 130 286 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9058#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7815Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0104054 0.0002537 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.170 8.703 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11590 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.0149 0.2031 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1133 1.4480Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3517 CRHR1 17_27 0.9961 3564.35 0.0431343 3.362 1
11135 ZNF823 19_10 0.9789 29.83 0.0003547 5.485 2
12311 AC012074.2 2_16 0.8313 23.83 0.0002407 4.623 1
3091 SF3B1 2_117 0.8220 43.62 0.0004356 6.725 1
1678 KIAA0391 14_9 0.7965 25.21 0.0002440 -5.164 1
9254 TMEM81 1_104 0.7956 26.02 0.0002515 4.938 1
110 ELAC2 17_11 0.7948 21.79 0.0002104 4.509 1
7042 CDC25C 5_82 0.7806 25.25 0.0002395 -5.591 1
3750 BHLHE41 12_18 0.7560 22.76 0.0002091 -4.024 1
6310 ARFGAP2 11_29 0.7470 24.29 0.0002204 4.740 1
6443 PLBD2 12_68 0.7438 20.62 0.0001864 3.986 1
13314 TBC1D29 17_18 0.7431 23.05 0.0002081 4.407 1
2684 VPS29 12_67 0.7225 25.07 0.0002201 -4.965 2
8894 ZNF318 6_33 0.6854 24.43 0.0002034 -4.832 1
11792 AS3MT 10_66 0.6837 38.64 0.0003209 6.812 2
12298 AC073283.4 2_30 0.6830 22.95 0.0001904 -3.812 4
10494 TMEM222 1_19 0.6650 24.78 0.0002002 3.902 1
1879 ESRP2 16_36 0.6441 27.28 0.0002134 5.203 2
461 ARID1B 6_102 0.6354 24.30 0.0001876 -3.907 1
11616 ITSN1 21_14 0.6332 24.19 0.0001861 3.954 2Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3517 CRHR1 17_27 9.961e-01 3564.35 4.313e-02 3.3623 1
12229 HLA-DQB2 6_26 7.161e-14 841.86 7.324e-16 -4.1487 1
10939 HLA-DQA1 6_26 1.436e-13 799.37 1.394e-15 3.4460 1
12390 HLA-DQA2 6_26 1.401e-13 516.30 8.788e-16 -3.5679 1
12115 ARL17B 17_27 0.000e+00 374.03 0.000e+00 -3.0672 1
11731 CLIC1 6_26 9.395e-13 368.28 4.203e-15 8.8118 2
7119 ARHGAP27 17_27 0.000e+00 340.94 0.000e+00 0.3401 1
11469 MSH5 6_26 3.538e-13 318.93 1.371e-15 7.6794 2
11464 HSPA1L 6_26 3.716e-13 230.85 1.042e-15 7.1259 1
9922 ACBD4 17_27 0.000e+00 208.81 0.000e+00 1.3121 1
4990 NMT1 17_27 0.000e+00 184.54 0.000e+00 2.4459 1
12582 C4A 6_26 1.657e-12 156.41 3.148e-15 5.2909 1
10283 FMNL1 17_27 0.000e+00 141.39 0.000e+00 -0.6638 1
10529 HEXIM1 17_27 0.000e+00 130.59 0.000e+00 -3.3358 1
1319 PUS7 7_65 0.000e+00 108.05 0.000e+00 0.6328 2
2439 GOSR2 17_27 0.000e+00 71.67 0.000e+00 -2.5096 1
4823 RINT1 7_65 0.000e+00 68.20 0.000e+00 1.1750 1
5119 PGBD1 6_22 1.352e-02 67.44 1.107e-05 -8.4933 1
10491 BTN3A2 6_20 1.634e-02 64.75 1.285e-05 8.9338 2
9090 DCAKD 17_27 0.000e+00 55.42 0.000e+00 -2.1069 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3517 CRHR1 17_27 0.9961 3564.35 0.0431343 3.362 1
3091 SF3B1 2_117 0.8220 43.62 0.0004356 6.725 1
11135 ZNF823 19_10 0.9789 29.83 0.0003547 5.485 2
11792 AS3MT 10_66 0.6837 38.64 0.0003209 6.812 2
9254 TMEM81 1_104 0.7956 26.02 0.0002515 4.938 1
1678 KIAA0391 14_9 0.7965 25.21 0.0002440 -5.164 1
12311 AC012074.2 2_16 0.8313 23.83 0.0002407 4.623 1
7042 CDC25C 5_82 0.7806 25.25 0.0002395 -5.591 1
2630 MDK 11_28 0.4877 38.14 0.0002260 -6.357 1
6310 ARFGAP2 11_29 0.7470 24.29 0.0002204 4.740 1
2684 VPS29 12_67 0.7225 25.07 0.0002201 -4.965 2
1879 ESRP2 16_36 0.6441 27.28 0.0002134 5.203 2
110 ELAC2 17_11 0.7948 21.79 0.0002104 4.509 1
3750 BHLHE41 12_18 0.7560 22.76 0.0002091 -4.024 1
13314 TBC1D29 17_18 0.7431 23.05 0.0002081 4.407 1
8894 ZNF318 6_33 0.6854 24.43 0.0002034 -4.832 1
10494 TMEM222 1_19 0.6650 24.78 0.0002002 3.902 1
12298 AC073283.4 2_30 0.6830 22.95 0.0001904 -3.812 4
506 SDCCAG8 1_128 0.5981 25.90 0.0001882 -4.982 1
461 ARID1B 6_102 0.6354 24.30 0.0001876 -3.907 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10491 BTN3A2 6_20 1.634e-02 64.75 1.285e-05 8.934 2
11731 CLIC1 6_26 9.395e-13 368.28 4.203e-15 8.812 2
5119 PGBD1 6_22 1.352e-02 67.44 1.107e-05 -8.493 1
6289 CNNM2 10_66 1.509e-01 46.14 8.461e-05 -8.118 2
11469 MSH5 6_26 3.538e-13 318.93 1.371e-15 7.679 2
13068 RP11-490G2.2 1_60 1.266e-02 48.12 7.404e-06 7.322 1
7064 ZSCAN12 6_22 1.413e-02 34.93 5.996e-06 7.214 1
9628 C2orf69 2_118 2.481e-01 40.18 1.211e-04 7.151 2
11464 HSPA1L 6_26 3.716e-13 230.85 1.042e-15 7.126 1
11792 AS3MT 10_66 6.837e-01 38.64 3.209e-04 6.812 2
1226 PPP1R13B 14_54 1.408e-01 46.70 7.990e-05 6.798 3
10643 ZSCAN23 6_22 8.404e-02 46.85 4.784e-05 -6.793 1
7500 TYW5 2_118 3.373e-02 36.46 1.494e-05 -6.774 2
3091 SF3B1 2_117 8.220e-01 43.62 4.356e-04 6.725 1
6279 CYP17A1 10_66 5.195e-03 27.80 1.754e-06 -6.720 1
9851 HIST1H2BC 6_20 1.561e-02 39.53 7.498e-06 -6.675 2
10986 ZSCAN26 6_22 1.420e-02 37.06 6.395e-06 6.584 3
4024 XRCC3 14_54 7.584e-02 42.09 3.877e-05 6.524 2
9986 ARL6IP4 12_75 8.382e-03 39.48 4.020e-06 6.491 1
6424 ABCB9 12_75 6.686e-03 38.21 3.104e-06 6.404 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] 0.007075GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 27Uploading 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"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[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
27 Neoplasms, Glandular and Epithelial 0.02897 1/13 2/9703
32 Pain, Postoperative 0.02897 1/13 2/9703
46 Glandular Neoplasms 0.02897 1/13 2/9703
88 Refractory anemia with ringed sideroblasts 0.02897 1/13 2/9703
93 Epithelioma 0.02897 1/13 2/9703
106 CHROMOSOME 6q24-q25 DELETION SYNDROME 0.02897 1/13 2/9703
107 SENIOR-LOKEN SYNDROME 7 0.02897 1/13 1/9703
111 PROSTATE CANCER, HEREDITARY, 2 0.02897 1/13 1/9703
113 NOONAN SYNDROME 8 0.02897 1/13 1/9703
114 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.02897 1/13 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!NULL
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] 4.596#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 82#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
3517 CRHR1 17_27 0.9961 3564 0.04313 3.362 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.07692 #specificity
print(specificity) ctwas TWAS
0.9999 0.9938 #precision / PPV
print(precision)ctwas TWAS
0.750 0.122 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 64#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 822#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.596#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] 30#sensitivity / recall
sensitivity ctwas TWAS
0.04688 0.15625 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9757 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3333 
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 53 8
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