SCZ - Brain Anterior cingulate cortex BA24
sheng Qian
2021-2-6
Last updated: 2022-04-19
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 9453#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
931 670 567 367 441 542 454 359 342 370 583 552 199 327 337 397 555 155 740 289
21 22
29 247 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6793#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7186Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0108717 0.0003169 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
16.97 10.04 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9453 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01656 0.19057 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04975 1.07745Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10719 ZNF823 19_10 0.9845 38.35 0.0003585 6.180 2
12952 RP11-230C9.4 6_102 0.9510 23.70 0.0002140 -4.685 2
11817 AC012074.2 2_15 0.9399 23.13 0.0002064 4.655 1
7971 JSRP1 19_3 0.8892 27.65 0.0002335 4.825 1
3590 BHLHE41 12_18 0.8795 24.00 0.0002004 4.516 1
2969 SF3B1 2_117 0.8722 51.50 0.0004265 7.265 1
11773 HIST1H2BN 6_21 0.8722 108.20 0.0008960 13.396 1
10112 TMEM222 1_19 0.8254 22.21 0.0001740 4.303 1
108 ELAC2 17_11 0.7888 22.90 0.0001715 4.752 1
10725 RPL12 9_66 0.7031 22.54 0.0001505 4.070 2
10939 LINC00862 1_101 0.6920 24.02 0.0001578 4.314 2
2898 LMAN2L 2_57 0.6699 25.27 0.0001607 -4.313 2
11543 LINC00390 13_17 0.6653 23.17 0.0001464 -4.540 1
8291 INO80E 16_24 0.6644 48.98 0.0003090 6.995 1
12860 RP11-247A12.7 9_66 0.6323 23.31 0.0001400 4.468 2
2584 DUSP16 12_11 0.6315 21.72 0.0001302 -3.779 1
7048 DBF4B 17_26 0.6202 20.50 0.0001207 3.890 1
8043 PRDX2 19_10 0.6098 22.76 0.0001318 -4.020 1
12334 RP11-65M17.3 11_66 0.6040 22.85 0.0001311 4.414 1
2524 MDK 11_28 0.5704 49.01 0.0002654 -7.159 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11039 APOM 6_26 4.046e-01 132.87 5.104e-04 11.590 1
12066 C4A 6_26 4.639e-02 131.28 5.783e-05 11.341 2
11035 ABHD16A 6_26 2.649e-01 131.06 3.296e-04 11.526 1
4944 VARS2 6_25 1.322e-01 123.89 1.555e-04 11.413 1
11773 HIST1H2BN 6_21 8.722e-01 108.20 8.960e-04 13.396 1
11008 NOTCH4 6_26 5.774e-04 103.58 5.679e-07 7.712 2
4935 FLOT1 6_24 8.245e-02 85.77 6.715e-05 -10.981 1
11034 LY6G6C 6_26 1.682e-05 82.01 1.310e-08 9.781 2
10534 HLA-DQA1 6_26 4.276e-06 75.34 3.059e-09 3.389 1
13060 RP1-86C11.7 6_21 4.090e-02 74.64 2.899e-05 10.889 1
9388 HLA-DQB1 6_26 7.594e-08 74.18 5.349e-11 4.986 1
11000 HLA-DMA 6_27 3.928e-02 71.09 2.652e-05 -8.720 2
10109 BTN3A2 6_20 1.638e-02 71.09 1.106e-05 9.166 2
10416 HLA-DRB1 6_26 8.466e-09 58.17 4.676e-12 1.359 2
1184 PPP1R13B 14_54 2.996e-01 56.68 1.612e-04 -6.610 2
455 MPHOSPH9 12_75 2.270e-01 56.53 1.219e-04 7.662 1
11277 DDAH2 6_26 1.744e-07 55.33 9.159e-11 8.149 1
10785 ZKSCAN8 6_22 1.025e-02 54.32 5.285e-06 7.317 2
10253 ZSCAN23 6_22 5.184e-02 51.54 2.537e-05 -7.854 1
2969 SF3B1 2_117 8.722e-01 51.50 4.265e-04 7.265 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11773 HIST1H2BN 6_21 0.8722 108.20 0.0008960 13.396 1
11039 APOM 6_26 0.4046 132.87 0.0005104 11.590 1
2969 SF3B1 2_117 0.8722 51.50 0.0004265 7.265 1
10719 ZNF823 19_10 0.9845 38.35 0.0003585 6.180 2
11035 ABHD16A 6_26 0.2649 131.06 0.0003296 11.526 1
8291 INO80E 16_24 0.6644 48.98 0.0003090 6.995 1
2524 MDK 11_28 0.5704 49.01 0.0002654 -7.159 1
7971 JSRP1 19_3 0.8892 27.65 0.0002335 4.825 1
12952 RP11-230C9.4 6_102 0.9510 23.70 0.0002140 -4.685 2
11817 AC012074.2 2_15 0.9399 23.13 0.0002064 4.655 1
3590 BHLHE41 12_18 0.8795 24.00 0.0002004 4.516 1
7325 THOC7 3_43 0.4734 41.15 0.0001850 -6.249 1
5786 CCDC39 3_111 0.4142 45.15 0.0001776 -6.797 1
10112 TMEM222 1_19 0.8254 22.21 0.0001740 4.303 1
108 ELAC2 17_11 0.7888 22.90 0.0001715 4.752 1
5316 FURIN 15_42 0.4801 35.91 0.0001637 -5.772 1
7973 GATAD2A 19_15 0.3678 46.83 0.0001636 -6.577 2
1184 PPP1R13B 14_54 0.2996 56.68 0.0001612 -6.610 2
13014 RP11-350N15.5 8_34 0.4475 37.89 0.0001610 5.963 1
2898 LMAN2L 2_57 0.6699 25.27 0.0001607 -4.313 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11773 HIST1H2BN 6_21 8.722e-01 108.20 8.960e-04 13.396 1
11039 APOM 6_26 4.046e-01 132.87 5.104e-04 11.590 1
11035 ABHD16A 6_26 2.649e-01 131.06 3.296e-04 11.526 1
4944 VARS2 6_25 1.322e-01 123.89 1.555e-04 11.413 1
12066 C4A 6_26 4.639e-02 131.28 5.783e-05 11.341 2
4935 FLOT1 6_24 8.245e-02 85.77 6.715e-05 -10.981 1
13060 RP1-86C11.7 6_21 4.090e-02 74.64 2.899e-05 10.889 1
11034 LY6G6C 6_26 1.682e-05 82.01 1.310e-08 9.781 2
10109 BTN3A2 6_20 1.638e-02 71.09 1.106e-05 9.166 2
11000 HLA-DMA 6_27 3.928e-02 71.09 2.652e-05 -8.720 2
6075 CNNM2 10_66 1.072e-01 48.24 4.911e-05 -8.161 1
11277 DDAH2 6_26 1.744e-07 55.33 9.159e-11 8.149 1
10253 ZSCAN23 6_22 5.184e-02 51.54 2.537e-05 -7.854 1
11008 NOTCH4 6_26 5.774e-04 103.58 5.679e-07 7.712 2
455 MPHOSPH9 12_75 2.270e-01 56.53 1.219e-04 7.662 1
10785 ZKSCAN8 6_22 1.025e-02 54.32 5.285e-06 7.317 2
2969 SF3B1 2_117 8.722e-01 51.50 4.265e-04 7.265 1
11051 POU5F1 6_25 1.734e-02 42.28 6.962e-06 -7.217 2
2524 MDK 11_28 5.704e-01 49.01 2.654e-04 -7.159 1
10542 ZSCAN16 6_22 1.090e-02 48.26 4.994e-06 7.135 1Comparing z scores and PIPs
[1] 0.01195GO 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"
[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
29 Prostatic Neoplasms 0.02411 4/9 616/9703
45 Malignant neoplasm of prostate 0.02411 4/9 616/9703
62 Refractory anemia with ringed sideroblasts 0.02411 1/9 2/9703
74 PROSTATE CANCER, HEREDITARY, 2 0.02411 1/9 1/9703
76 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.02411 1/9 1/9703
78 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.02411 1/9 1/9703
41 Malignant mesothelioma 0.03368 2/9 109/9703
42 Malignant melanoma of iris 0.03368 1/9 5/9703
43 Malignant melanoma of choroid 0.03368 1/9 5/9703
55 Long Sleeper Syndrome 0.03368 1/9 7/9703WebGestalt 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
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] 49#significance threshold for TWAS
print(sig_thresh)[1] 4.553#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 113#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
10112 TMEM222 1_19 0.8254 22.21 0.0001740 4.303 1
3590 BHLHE41 12_18 0.8795 24.00 0.0002004 4.516 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.11538 #specificity
print(specificity) ctwas TWAS
0.9994 0.9896 #precision / PPV
print(precision) ctwas TWAS
0.2500 0.1327 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 49#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 518#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.553#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] 38#sensitivity / recall
sensitivity ctwas TWAS
0.04082 0.30612 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9556 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3947 
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)
81 34 13
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_1.1.1 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