SCZ - Brain Cerebellar Hemisphere
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] 10988#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1044 751 636 406 524 614 548 415 425 434 669 618 208 358 360 526
17 18 19 20 21 22
701 163 854 326 130 278 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8105#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7376Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0174262 0.0002447 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.252 8.234 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10988 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.0205 0.1921 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1006 1.6108Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
4143 FEZF1 7_74 0.9848 26.98 0.0003447 -5.272 1
10988 ZNF823 19_10 0.9837 28.72 0.0003664 5.472 2
6241 ARFGAP2 11_29 0.9557 24.78 0.0003072 4.839 1
2207 RUNDC3B 7_54 0.9489 24.43 0.0003006 5.373 1
12095 AC012074.2 2_15 0.9395 21.46 0.0002615 4.623 1
499 TRAPPC3 1_22 0.9258 25.13 0.0003018 5.058 1
5783 GALNT2 1_117 0.9192 23.44 0.0002795 4.843 1
11339 DISP3 1_8 0.8992 19.26 0.0002246 4.062 2
3099 SF3B1 2_117 0.8960 42.31 0.0004917 6.784 1
6920 CNNM4 2_57 0.8493 39.51 0.0004353 -4.793 1
4331 TRIM28 19_39 0.8229 20.97 0.0002238 4.253 2
13214 RP11-230C9.4 6_102 0.8150 19.57 0.0002069 -3.928 3
9329 DIRAS1 19_3 0.8104 20.61 0.0002166 -4.359 1
1148 RRN3 16_15 0.7966 20.46 0.0002114 -4.310 1
7481 SERPINI1 3_103 0.7816 19.25 0.0001951 -4.030 1
13246 RP1-224A6.9 1_15 0.7795 19.35 0.0001957 -4.000 1
9372 LY6H 8_94 0.7777 21.16 0.0002135 4.236 1
3842 ABCC10 6_33 0.7726 21.94 0.0002199 -4.790 2
174 ZNF207 17_19 0.7610 20.37 0.0002011 4.200 1
4509 REEP2 5_82 0.7590 22.70 0.0002235 4.541 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
12178 HLA-DQA2 6_26 0.000e+00 276.24 0.000e+00 0.7376 1
11986 CYP21A2 6_26 2.938e-07 215.77 8.222e-10 -7.7309 2
11296 C6orf48 6_26 2.065e-09 193.18 5.173e-12 8.9003 1
11553 CLIC1 6_26 1.193e-09 191.96 2.969e-12 8.8731 1
12355 C4A 6_26 3.523e-11 186.85 8.539e-14 8.5099 2
11554 DDAH2 6_26 0.000e+00 178.91 0.000e+00 7.6610 1
11298 HSPA1A 6_26 0.000e+00 158.24 0.000e+00 7.6575 1
9601 HLA-DQB1 6_26 0.000e+00 155.76 0.000e+00 -1.8861 1
6764 MMP16 8_63 0.000e+00 150.60 0.000e+00 -0.8409 1
2957 PCCB 3_84 0.000e+00 146.43 0.000e+00 -4.2854 1
855 PPP2R3A 3_84 0.000e+00 125.82 0.000e+00 4.1188 1
12887 CTA-254O6.1 7_54 3.569e-04 123.95 5.738e-07 -3.7199 2
11754 C4B 6_26 0.000e+00 114.25 0.000e+00 -6.9370 4
11281 RNF5 6_26 0.000e+00 108.76 0.000e+00 7.6002 2
2227 MPP6 7_21 4.725e-03 106.50 6.528e-06 -3.3024 1
11285 FKBPL 6_26 1.110e-16 104.82 1.510e-19 -4.2267 1
11078 HLA-DRB5 6_26 0.000e+00 103.53 0.000e+00 2.8311 1
10673 HLA-DRB1 6_26 0.000e+00 100.37 0.000e+00 2.4486 1
11279 NOTCH4 6_26 0.000e+00 93.85 0.000e+00 6.0856 2
11284 PRRT1 6_26 0.000e+00 91.07 0.000e+00 7.9069 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3099 SF3B1 2_117 0.8960 42.31 0.0004917 6.784 1
6920 CNNM4 2_57 0.8493 39.51 0.0004353 -4.793 1
5826 CIAO1 2_57 0.6905 43.68 0.0003912 -3.710 1
10988 ZNF823 19_10 0.9837 28.72 0.0003664 5.472 2
4143 FEZF1 7_74 0.9848 26.98 0.0003447 -5.272 1
12301 HLA-DMB 6_27 0.5417 47.15 0.0003313 -7.990 1
8510 INO80E 16_24 0.6705 36.20 0.0003148 6.230 1
1612 ZC3H7B 22_19 0.5687 42.56 0.0003140 4.922 1
6241 ARFGAP2 11_29 0.9557 24.78 0.0003072 4.839 1
499 TRAPPC3 1_22 0.9258 25.13 0.0003018 5.058 1
2207 RUNDC3B 7_54 0.9489 24.43 0.0003006 5.373 1
5783 GALNT2 1_117 0.9192 23.44 0.0002795 4.843 1
12095 AC012074.2 2_15 0.9395 21.46 0.0002615 4.623 1
9199 ATG13 11_28 0.5211 34.13 0.0002307 -6.084 1
5875 FAM134A 2_129 0.7209 24.45 0.0002286 -4.940 1
11339 DISP3 1_8 0.8992 19.26 0.0002246 4.062 2
4331 TRIM28 19_39 0.8229 20.97 0.0002238 4.253 2
4509 REEP2 5_82 0.7590 22.70 0.0002235 4.541 2
3842 ABCC10 6_33 0.7726 21.94 0.0002199 -4.790 2
9329 DIRAS1 19_3 0.8104 20.61 0.0002166 -4.359 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11884 HCG11 6_20 2.774e-02 59.08 2.126e-05 8.937 1
12879 CTA-14H9.5 6_20 2.774e-02 59.08 2.126e-05 8.937 1
11296 C6orf48 6_26 2.065e-09 193.18 5.173e-12 8.900 1
11553 CLIC1 6_26 1.193e-09 191.96 2.969e-12 8.873 1
2870 PRSS16 6_21 5.521e-02 41.38 2.963e-05 -8.631 1
5086 PGBD1 6_22 1.341e-02 74.86 1.302e-05 -8.525 1
12355 C4A 6_26 3.523e-11 186.85 8.539e-14 8.510 2
12301 HLA-DMB 6_27 5.417e-01 47.15 3.313e-04 -7.990 1
6038 ABT1 6_20 4.778e-02 47.51 2.945e-05 7.913 1
11284 PRRT1 6_26 0.000e+00 91.07 0.000e+00 7.907 1
6221 CNNM2 10_66 2.427e-01 38.62 1.216e-04 -7.876 1
11986 CYP21A2 6_26 2.938e-07 215.77 8.222e-10 -7.731 2
11554 DDAH2 6_26 0.000e+00 178.91 0.000e+00 7.661 1
11298 HSPA1A 6_26 0.000e+00 158.24 0.000e+00 7.658 1
11281 RNF5 6_26 0.000e+00 108.76 0.000e+00 7.600 2
10845 ZSCAN26 6_22 1.980e-02 38.58 9.907e-06 7.054 2
11754 C4B 6_26 0.000e+00 114.25 0.000e+00 -6.937 4
10334 BTN3A2 6_20 2.185e-01 48.42 1.372e-04 6.875 1
7375 TYW5 2_118 1.251e-01 38.28 6.214e-05 -6.844 2
3099 SF3B1 2_117 8.960e-01 42.31 4.917e-04 6.784 1Comparing z scores and PIPs
[1] 0.007372GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 48Uploading 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
17 Measles 0.01058 1/16
51 Amaurosis hypertrichosis 0.01058 1/16
52 Familial encephalopathy with neuroserpin inclusion bodies 0.01058 1/16
54 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.01058 1/16
55 ALPHA-KETOGLUTARATE DEHYDROGENASE DEFICIENCY 0.01058 1/16
56 Cone rod dystrophy amelogenesis imperfecta 0.01058 1/16
58 JOUBERT SYNDROME 13 0.01058 1/16
61 Jalili syndrome 0.01058 1/16
67 SPASTIC PARAPLEGIA 72, AUTOSOMAL RECESSIVE 0.01058 1/16
68 SPASTIC PARAPLEGIA 72, AUTOSOMAL DOMINANT 0.01058 1/16
BgRatio
17 1/9703
51 1/9703
52 1/9703
54 1/9703
55 1/9703
56 1/9703
58 1/9703
61 1/9703
67 1/9703
68 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)
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] 60#significance threshold for TWAS
print(sig_thresh)[1] 4.585#number of ctwas genes
length(ctwas_genes)[1] 13#number of TWAS genes
length(twas_genes)[1] 81#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
11339 DISP3 1_8 0.8992 19.26 0.0002246 4.062 2
13214 RP11-230C9.4 6_102 0.8150 19.57 0.0002069 -3.928 3
9329 DIRAS1 19_3 0.8104 20.61 0.0002166 -4.359 1
4331 TRIM28 19_39 0.8229 20.97 0.0002238 4.253 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.06154 #specificity
print(specificity) ctwas TWAS
0.9990 0.9933 #precision / PPV
print(precision) ctwas TWAS
0.15385 0.09877 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 60#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 710#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.585#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 4#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 24#sensitivity / recall
sensitivity ctwas TWAS
0.03333 0.13333 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9972 0.9775 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.5000 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)
70 52 6
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_1.0.0 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