SCZ - Brain Frontal Cortex BA9
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] 11132#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1109 821 615 436 539 650 516 425 404 442 665 646 232 357 357 491
17 18 19 20 21 22
689 169 851 331 113 274 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8333#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7486Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0102061 0.0002575 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
7.050 8.453 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11132 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01039 0.20758 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07017 1.67791Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
7629 THOC7 3_43 0.9890 39.37 0.0005051 -6.686 2
11134 ZNF823 19_10 0.9688 28.25 0.0003550 5.496 2
13483 RP11-230C9.4 6_102 0.9562 21.34 0.0002647 -4.560 2
12304 AC012074.2 2_15 0.8923 21.73 0.0002515 4.623 1
9133 MAP3K11 11_36 0.7999 22.00 0.0002283 -4.409 1
108 ELAC2 17_11 0.7856 21.19 0.0002159 4.518 1
491 TRAPPC3 1_22 0.7794 23.87 0.0002413 5.058 1
4755 SOX5 12_17 0.7373 22.17 0.0002120 4.068 1
12511 HLA-DMB 6_27 0.7127 51.09 0.0004723 -8.273 1
6584 TADA1 1_82 0.7017 24.29 0.0002211 -4.185 2
14019 ERICD 8_92 0.6929 21.71 0.0001951 -4.064 1
3036 LMAN2L 2_57 0.6766 24.53 0.0002153 -4.586 2
6336 ARFGAP2 11_29 0.6231 23.80 0.0001924 4.839 1
10221 ACOT1 14_34 0.6223 23.24 0.0001876 4.128 2
13323 LINC01415 18_30 0.6193 29.78 0.0002392 -5.655 1
733 PPP2R5B 11_36 0.5811 23.56 0.0001776 -4.610 1
440 FAM120A 9_47 0.5782 23.09 0.0001732 -4.571 1
6317 CNNM2 10_66 0.5620 48.88 0.0003563 -8.991 2
14039 EBLN3P 9_29 0.5564 21.65 0.0001562 -4.442 1
7965 GTF2A1 14_39 0.5533 20.67 0.0001484 -4.376 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
6869 MMP16 8_63 0.000e+00 504.15 0.000e+00 3.645 1
2963 PCCB 3_84 0.000e+00 200.91 0.000e+00 -2.836 2
11472 APOM 6_26 1.476e-10 187.39 3.588e-13 8.945 1
11728 CLIC1 6_26 1.011e-10 186.52 2.445e-13 8.873 2
12571 C4A 6_26 2.097e-13 174.89 4.757e-16 8.295 3
10825 HLA-DRB1 6_26 0.000e+00 173.98 0.000e+00 0.697 2
11729 DDAH2 6_26 0.000e+00 173.33 0.000e+00 7.661 1
11464 MSH5 6_26 1.110e-16 150.94 2.174e-19 7.294 2
13456 HCG17 6_24 3.664e-15 128.65 6.114e-18 5.593 1
11465 MPIG6B 6_26 1.110e-16 121.05 1.743e-19 5.897 2
11430 HLA-DOA 6_26 0.000e+00 120.45 0.000e+00 7.189 1
11474 BAG6 6_26 0.000e+00 114.49 0.000e+00 7.046 3
12191 CYP21A2 6_26 0.000e+00 106.58 0.000e+00 -6.996 2
11446 FKBPL 6_26 0.000e+00 102.70 0.000e+00 -4.227 1
10942 HLA-DQA1 6_26 1.110e-16 102.32 1.473e-19 1.013 2
11443 RNF5 6_26 0.000e+00 88.32 0.000e+00 7.921 1
11440 NOTCH4 6_26 0.000e+00 88.11 0.000e+00 5.998 3
12101 SAPCD1 6_26 0.000e+00 83.62 0.000e+00 5.609 1
11723 ATF6B 6_26 0.000e+00 77.42 0.000e+00 2.835 1
5139 IER3 6_24 4.108e-15 71.95 3.834e-18 2.126 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
7629 THOC7 3_43 0.9890 39.37 0.0005051 -6.686 2
12511 HLA-DMB 6_27 0.7127 51.09 0.0004723 -8.273 1
6317 CNNM2 10_66 0.5620 48.88 0.0003563 -8.991 2
11134 ZNF823 19_10 0.9688 28.25 0.0003550 5.496 2
1619 ZC3H7B 22_17 0.5514 41.17 0.0002945 4.954 3
13483 RP11-230C9.4 6_102 0.9562 21.34 0.0002647 -4.560 2
12304 AC012074.2 2_15 0.8923 21.73 0.0002515 4.623 1
491 TRAPPC3 1_22 0.7794 23.87 0.0002413 5.058 1
13323 LINC01415 18_30 0.6193 29.78 0.0002392 -5.655 1
9133 MAP3K11 11_36 0.7999 22.00 0.0002283 -4.409 1
6584 TADA1 1_82 0.7017 24.29 0.0002211 -4.185 2
108 ELAC2 17_11 0.7856 21.19 0.0002159 4.518 1
3036 LMAN2L 2_57 0.6766 24.53 0.0002153 -4.586 2
4755 SOX5 12_17 0.7373 22.17 0.0002120 4.068 1
14019 ERICD 8_92 0.6929 21.71 0.0001951 -4.064 1
6336 ARFGAP2 11_29 0.6231 23.80 0.0001924 4.839 1
3758 BHLHE41 12_18 0.5275 28.06 0.0001920 3.860 1
10221 ACOT1 14_34 0.6223 23.24 0.0001876 4.128 2
733 PPP2R5B 11_36 0.5811 23.56 0.0001776 -4.610 1
440 FAM120A 9_47 0.5782 23.09 0.0001732 -4.571 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10493 BTN3A2 6_20 2.076e-02 61.32 1.651e-05 9.057 2
6317 CNNM2 10_66 5.620e-01 48.88 3.563e-04 -8.991 2
11472 APOM 6_26 1.476e-10 187.39 3.588e-13 8.945 1
11728 CLIC1 6_26 1.011e-10 186.52 2.445e-13 8.873 2
12571 C4A 6_26 2.097e-13 174.89 4.757e-16 8.295 3
12511 HLA-DMB 6_27 7.127e-01 51.09 4.723e-04 -8.273 1
7067 ZSCAN12 6_22 8.691e-03 45.73 5.155e-06 -8.039 1
11443 RNF5 6_26 0.000e+00 88.32 0.000e+00 7.921 1
11729 DDAH2 6_26 0.000e+00 173.33 0.000e+00 7.661 1
13051 RP11-490G2.2 1_60 1.833e-02 46.92 1.116e-05 7.551 1
2871 PRSS16 6_21 2.662e-02 31.60 1.091e-05 -7.550 1
11464 MSH5 6_26 1.110e-16 150.94 2.174e-19 7.294 2
11430 HLA-DOA 6_26 0.000e+00 120.45 0.000e+00 7.189 1
11474 BAG6 6_26 0.000e+00 114.49 0.000e+00 7.046 3
12191 CYP21A2 6_26 0.000e+00 106.58 0.000e+00 -6.996 2
12308 ZSCAN31 6_22 1.708e-02 33.77 7.483e-06 -6.742 2
7629 THOC7 3_43 9.890e-01 39.37 5.051e-04 -6.686 2
4032 XRCC3 14_54 8.495e-02 39.78 4.383e-05 6.526 1
10988 ZSCAN26 6_22 8.723e-03 38.70 4.379e-06 6.435 3
11486 CCHCR1 6_25 8.454e-03 28.78 3.156e-06 -6.298 2Comparing z scores and PIPs
[1] 0.006917GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 23Uploading 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"
Term
1 endoplasmic reticulum to Golgi vesicle-mediated transport (GO:0006888)
Overlap Adjusted.P.value Genes
1 4/185 0.007218 TRAPPC3;LMAN2L;ARFGAP2;TMED4
[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
20 Spasmophilia 0.005635 1/8 1/9703
23 Tetany 0.005635 1/8 1/9703
30 Tetany, Neonatal 0.005635 1/8 1/9703
55 Tetanilla 0.005635 1/8 1/9703
65 HYPOMAGNESEMIA 6, RENAL 0.005635 1/8 1/9703
68 PROSTATE CANCER, HEREDITARY, 2 0.005635 1/8 1/9703
70 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.005635 1/8 1/9703
73 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.005635 1/8 1/9703
74 LAMB-SHAFFER SYNDROME 0.005635 1/8 1/9703
75 HYPOMAGNESEMIA, SEIZURES, AND MENTAL RETARDATION 0.005635 1/8 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] 62#significance threshold for TWAS
print(sig_thresh)[1] 4.587#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 77#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
13483 RP11-230C9.4 6_102 0.9562 21.34 0.0002647 -4.56 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.05385 #specificity
print(specificity) ctwas TWAS
0.9998 0.9937 #precision / PPV
print(precision) ctwas TWAS
0.50000 0.09091 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 62#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 790#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.587#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 2#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 17#sensitivity / recall
sensitivity ctwas TWAS
0.03226 0.11290 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9873 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4118 
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)
68 55 5
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