SCZ - Brain Anterior cingulate cortex BA24
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] 10552#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1030 748 613 403 500 608 511 407 386 416 633 601 216 346 369 482
17 18 19 20 21 22
610 161 797 322 120 273 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8168#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7741Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0151047 0.0002559 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.065 8.056 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10552 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02081 0.19657 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.08811 1.70775Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10719 ZNF823 19_10 0.9904 30.32 0.0003895 5.560 2
13100 KB-226F1.2 22_6 0.9823 27.54 0.0003509 -3.296 2
12952 RP11-230C9.4 6_102 0.9395 21.99 0.0002680 -4.543 2
11817 AC012074.2 2_15 0.9344 22.01 0.0002668 4.623 1
472 TRAPPC3 1_22 0.9286 24.84 0.0002992 5.058 1
2969 SF3B1 2_117 0.9225 44.74 0.0005354 6.784 1
426 FAM120A 9_47 0.8408 23.38 0.0002550 -4.706 2
3999 SPECC1 17_16 0.7912 21.49 0.0002205 4.167 1
9103 DIRAS1 19_3 0.7721 20.80 0.0002083 4.285 1
2838 PDCD10 3_103 0.7656 19.76 0.0001962 -4.030 1
2898 LMAN2L 2_57 0.7652 22.97 0.0002279 -4.528 2
10112 TMEM222 1_19 0.7426 22.15 0.0002133 3.902 1
11362 UBXN2B 8_45 0.7097 20.89 0.0001923 -3.891 2
2963 KCNJ13 2_137 0.7077 37.22 0.0003417 6.658 1
10953 LIN28B-AS1 6_70 0.7052 23.55 0.0002154 -4.732 2
3590 BHLHE41 12_18 0.7035 22.76 0.0002077 3.860 1
6095 ARFGAP2 11_29 0.6638 24.36 0.0002098 4.839 1
13182 RBAKDN 7_6 0.6448 20.65 0.0001727 3.931 2
1043 PLOD1 1_9 0.6179 23.42 0.0001877 -3.849 1
2281 ERLIN1 10_64 0.6034 22.23 0.0001739 4.370 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11773 HIST1H2BN 6_21 1.507e-06 953.07 1.863e-08 10.773 1
6614 MMP16 8_63 0.000e+00 502.03 0.000e+00 3.648 1
8914 MSL2 3_84 2.573e-07 436.85 1.458e-09 5.847 2
13060 RP1-86C11.7 6_21 2.831e-12 415.62 1.526e-14 9.033 1
10534 HLA-DQA1 6_26 1.521e-14 214.18 4.226e-17 3.448 1
11039 APOM 6_26 1.015e-08 199.46 2.626e-11 8.945 1
11035 ABHD16A 6_26 8.414e-09 199.15 2.174e-11 8.934 1
12066 C4A 6_26 2.844e-10 195.76 7.221e-13 8.475 2
11277 DDAH2 6_26 0.000e+00 185.37 0.000e+00 7.661 1
3720 HIST1H2BJ 6_21 0.000e+00 145.75 0.000e+00 1.674 1
2830 PCCB 3_84 0.000e+00 142.15 0.000e+00 -4.361 1
11041 BAG6 6_26 0.000e+00 130.09 0.000e+00 7.267 3
811 PPP2R3A 3_84 0.000e+00 128.74 0.000e+00 4.119 1
10416 HLA-DRB1 6_26 0.000e+00 124.02 0.000e+00 1.172 2
2147 MPP6 7_21 1.985e-04 116.21 2.992e-07 -3.302 1
11014 FKBPL 6_26 0.000e+00 115.58 0.000e+00 -3.789 1
11272 ATF6B 6_26 0.000e+00 115.58 0.000e+00 3.789 1
11710 CYP21A2 6_26 0.000e+00 110.02 0.000e+00 -6.852 2
11008 NOTCH4 6_26 0.000e+00 77.22 0.000e+00 6.098 1
11010 AGER 6_26 0.000e+00 74.57 0.000e+00 -2.627 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
2969 SF3B1 2_117 0.9225 44.74 0.0005354 6.784 1
10719 ZNF823 19_10 0.9904 30.32 0.0003895 5.560 2
13100 KB-226F1.2 22_6 0.9823 27.54 0.0003509 -3.296 2
2963 KCNJ13 2_137 0.7077 37.22 0.0003417 6.658 1
472 TRAPPC3 1_22 0.9286 24.84 0.0002992 5.058 1
2524 MDK 11_28 0.6021 38.29 0.0002991 -6.344 1
12952 RP11-230C9.4 6_102 0.9395 21.99 0.0002680 -4.543 2
11817 AC012074.2 2_15 0.9344 22.01 0.0002668 4.623 1
426 FAM120A 9_47 0.8408 23.38 0.0002550 -4.706 2
455 MPHOSPH9 12_75 0.4389 41.84 0.0002382 6.650 1
2898 LMAN2L 2_57 0.7652 22.97 0.0002279 -4.528 2
3999 SPECC1 17_16 0.7912 21.49 0.0002205 4.167 1
10953 LIN28B-AS1 6_70 0.7052 23.55 0.0002154 -4.732 2
10112 TMEM222 1_19 0.7426 22.15 0.0002133 3.902 1
6095 ARFGAP2 11_29 0.6638 24.36 0.0002098 4.839 1
9103 DIRAS1 19_3 0.7721 20.80 0.0002083 4.285 1
3590 BHLHE41 12_18 0.7035 22.76 0.0002077 3.860 1
5316 FURIN 15_42 0.4872 32.56 0.0002058 -5.701 1
1540 CHADL 22_17 0.4006 39.47 0.0002051 4.950 1
2838 PDCD10 3_103 0.7656 19.76 0.0001962 -4.030 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11773 HIST1H2BN 6_21 1.507e-06 953.07 1.863e-08 10.773 1
13060 RP1-86C11.7 6_21 2.831e-12 415.62 1.526e-14 9.033 1
11039 APOM 6_26 1.015e-08 199.46 2.626e-11 8.945 1
11035 ABHD16A 6_26 8.414e-09 199.15 2.174e-11 8.934 1
12066 C4A 6_26 2.844e-10 195.76 7.221e-13 8.475 2
6075 CNNM2 10_66 2.553e-01 41.00 1.358e-04 -7.876 1
11277 DDAH2 6_26 0.000e+00 185.37 0.000e+00 7.661 1
12530 RP11-490G2.2 1_60 2.956e-02 50.56 1.939e-05 7.551 1
11041 BAG6 6_26 0.000e+00 130.09 0.000e+00 7.267 3
11710 CYP21A2 6_26 0.000e+00 110.02 0.000e+00 -6.852 2
2969 SF3B1 2_117 9.225e-01 44.74 5.354e-04 6.784 1
2963 KCNJ13 2_137 7.077e-01 37.22 3.417e-04 6.658 1
455 MPHOSPH9 12_75 4.389e-01 41.84 2.382e-04 6.650 1
10382 NKAPL 6_22 1.720e-02 35.66 7.954e-06 -6.495 1
11051 POU5F1 6_25 7.404e-02 40.57 3.897e-05 -6.385 2
2524 MDK 11_28 6.021e-01 38.29 2.991e-04 -6.344 1
11008 NOTCH4 6_26 0.000e+00 77.22 0.000e+00 6.098 1
8984 ATG13 11_28 1.505e-01 34.68 6.773e-05 -6.084 1
9474 HARBI1 11_28 1.505e-01 34.68 6.773e-05 6.084 1
8291 INO80E 16_24 4.178e-01 34.94 1.893e-04 5.963 1Comparing z scores and PIPs
[1] 0.006823GO 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"
[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
20 Measles 0.00804 1/10
59 Snowflake vitreoretinal degeneration 0.00804 1/10
60 Cerebral Cavernous Malformations 3 0.00804 1/10
62 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.00804 1/10
64 Familial cerebral cavernous malformation 0.00804 1/10
66 Nevo syndrome (disorder) 0.00804 1/10
67 LEBER CONGENITAL AMAUROSIS 16 0.00804 1/10
74 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.00804 1/10
76 SPASTIC PARAPLEGIA 62, AUTOSOMAL RECESSIVE 0.00804 1/10
77 Ehlers-Danlos syndrome kyphoscoliotic type 0.00804 1/10
BgRatio
20 1/9703
59 1/9703
60 1/9703
62 1/9703
64 1/9703
66 1/9703
67 1/9703
74 1/9703
76 1/9703
77 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] 55#significance threshold for TWAS
print(sig_thresh)[1] 4.576#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 72#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
12952 RP11-230C9.4 6_102 0.9395 21.99 0.0002680 -4.543 2
13100 KB-226F1.2 22_6 0.9823 27.54 0.0003509 -3.296 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.06923 #specificity
print(specificity) ctwas TWAS
0.9995 0.9940 #precision / PPV
print(precision) ctwas TWAS
0.2857 0.1250 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 55#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 680#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.576#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] 22#sensitivity / recall
sensitivity ctwas TWAS
0.03636 0.16364 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9809 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4091 
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)
75 46 7
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