SCZ - Brain Substantia nigra
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] 9743#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
938 694 581 370 465 570 472 380 383 380 570 552 210 311 325 435 585 146 750 300
21 22
107 219 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 7726#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.793Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0075376 0.0002693 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
12.16 8.06 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9743 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01158 0.20700 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04898 1.75131Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
4961 FURIN 15_42 0.9800 46.60 0.0005923 -7.000 1
10000 ZNF823 19_10 0.9719 30.17 0.0003804 5.485 1
5687 ARFGAP2 11_29 0.9119 25.85 0.0003058 4.839 1
11036 AC012074.2 2_15 0.8828 23.06 0.0002641 4.620 2
8494 DIRAS1 19_3 0.8347 23.23 0.0002515 4.571 2
2900 MAP7D1 1_22 0.7766 25.65 0.0002584 5.058 1
8526 LY6H 8_94 0.7106 22.57 0.0002081 4.351 2
6269 PANK4 1_2 0.6903 27.09 0.0002426 4.910 1
8611 ZNF354C 5_108 0.6802 22.61 0.0001995 -4.154 1
98 ELAC2 17_11 0.6773 24.71 0.0002170 4.372 1
2371 MDK 11_28 0.6321 39.24 0.0003217 -6.344 1
2668 PDCD10 3_103 0.5970 22.06 0.0001708 -4.028 2
2720 LMAN2L 2_57 0.5930 23.78 0.0001829 -4.957 2
1618 PPP1R16B 20_23 0.5862 35.88 0.0002728 6.009 1
11951 LINC01415 18_30 0.5846 32.68 0.0002478 -5.655 1
2778 KCNJ13 2_137 0.5784 38.96 0.0002923 -6.658 1
12597 EBLN3P 9_28 0.5337 23.11 0.0001600 -4.442 1
11529 RP11-65M17.3 11_66 0.5146 22.28 0.0001487 4.340 1
3140 HELLS 10_61 0.5070 23.30 0.0001532 -3.886 1
10923 LINC01305 2_105 0.4888 23.44 0.0001486 4.514 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10995 HIST1H2BN 6_21 1.903e-06 984.0 2.429e-08 10.7729 1
6157 MMP16 8_63 0.000e+00 518.4 0.000e+00 3.6478 1
10957 HLA-DQB2 6_26 0.000e+00 297.5 0.000e+00 1.0975 1
8739 HLA-DQB1 6_26 3.331e-16 246.7 1.066e-18 3.9187 1
12122 RP1-153G14.4 6_21 0.000e+00 218.8 0.000e+00 0.4901 2
10307 APOM 6_26 1.696e-08 205.8 4.526e-11 8.9450 1
10295 VWA7 6_26 1.219e-08 205.6 3.250e-11 8.9114 1
10301 ABHD16A 6_26 1.401e-08 205.5 3.734e-11 8.9341 1
11109 HLA-DQA2 6_26 0.000e+00 200.4 0.000e+00 -4.3832 1
11280 C4A 6_26 3.035e-10 196.6 7.737e-13 8.4450 1
10309 BAG6 6_26 0.000e+00 196.0 0.000e+00 8.6525 2
10527 DDAH2 6_26 0.000e+00 191.2 0.000e+00 7.6610 1
10292 HSPA1A 6_26 0.000e+00 169.3 0.000e+00 7.6575 1
10333 PPP1R11 6_24 1.363e-04 158.8 2.807e-07 5.8398 1
3472 HIST1H2BJ 6_21 0.000e+00 150.4 0.000e+00 1.6735 1
2660 PCCB 3_84 1.338e-02 143.8 2.496e-05 -4.3613 1
11004 TRIM26 6_24 3.220e-15 135.5 5.658e-18 -5.5387 1
2012 MPP6 7_21 7.390e-03 111.8 1.072e-05 -3.3024 1
8306 MSL2 3_84 5.309e-03 109.3 7.526e-06 3.7753 1
10521 ATF6B 6_26 0.000e+00 100.7 0.000e+00 3.0904 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
4961 FURIN 15_42 0.9800 46.60 0.0005923 -7.000 1
10000 ZNF823 19_10 0.9719 30.17 0.0003804 5.485 1
2371 MDK 11_28 0.6321 39.24 0.0003217 -6.344 1
5687 ARFGAP2 11_29 0.9119 25.85 0.0003058 4.839 1
2778 KCNJ13 2_137 0.5784 38.96 0.0002923 -6.658 1
1618 PPP1R16B 20_23 0.5862 35.88 0.0002728 6.009 1
11036 AC012074.2 2_15 0.8828 23.06 0.0002641 4.620 2
2900 MAP7D1 1_22 0.7766 25.65 0.0002584 5.058 1
8494 DIRAS1 19_3 0.8347 23.23 0.0002515 4.571 2
11951 LINC01415 18_30 0.5846 32.68 0.0002478 -5.655 1
6269 PANK4 1_2 0.6903 27.09 0.0002426 4.910 1
98 ELAC2 17_11 0.6773 24.71 0.0002170 4.372 1
8526 LY6H 8_94 0.7106 22.57 0.0002081 4.351 2
8611 ZNF354C 5_108 0.6802 22.61 0.0001995 -4.154 1
2720 LMAN2L 2_57 0.5930 23.78 0.0001829 -4.957 2
2668 PDCD10 3_103 0.5970 22.06 0.0001708 -4.028 2
12597 EBLN3P 9_28 0.5337 23.11 0.0001600 -4.442 1
2055 PPP1R17 7_25 0.2926 41.26 0.0001566 3.665 1
3140 HELLS 10_61 0.5070 23.30 0.0001532 -3.886 1
2418 VPS29 12_67 0.4640 24.73 0.0001488 -4.982 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10995 HIST1H2BN 6_21 1.903e-06 984.00 2.429e-08 10.773 1
9418 BTN3A2 6_20 1.405e-02 70.73 1.289e-05 9.206 3
10307 APOM 6_26 1.696e-08 205.77 4.526e-11 8.945 1
10301 ABHD16A 6_26 1.401e-08 205.45 3.734e-11 8.934 1
10295 VWA7 6_26 1.219e-08 205.57 3.250e-11 8.911 1
10309 BAG6 6_26 0.000e+00 196.05 0.000e+00 8.653 2
11280 C4A 6_26 3.035e-10 196.56 7.737e-13 8.445 1
8834 HIST1H2BC 6_20 1.305e-02 52.87 8.946e-06 -7.978 1
10273 RNF5 6_26 0.000e+00 97.92 0.000e+00 7.921 1
10276 PRRT1 6_26 0.000e+00 97.51 0.000e+00 -7.907 1
10527 DDAH2 6_26 0.000e+00 191.22 0.000e+00 7.661 1
10292 HSPA1A 6_26 0.000e+00 169.34 0.000e+00 7.658 1
6323 ZSCAN12 6_22 1.058e-02 47.79 6.557e-06 7.575 2
9552 ZSCAN23 6_22 2.155e-02 57.49 1.607e-05 -7.555 1
10318 CCHCR1 6_25 8.786e-03 43.83 4.995e-06 -7.424 1
4961 FURIN 15_42 9.800e-01 46.60 5.923e-04 -7.000 1
10317 POU5F1 6_25 3.789e-02 45.64 2.243e-05 -6.773 1
5665 CYP17A1 10_66 5.217e-03 31.52 2.133e-06 -6.664 1
2778 KCNJ13 2_137 5.784e-01 38.96 2.923e-04 -6.658 1
9514 ZKSCAN4 6_22 7.582e-03 36.31 3.571e-06 -6.478 1Comparing z scores and PIPs
[1] 0.008108GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 19Uploading 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 regulation of blood vessel endothelial cell proliferation involved in sprouting angiogenesis (GO:1903587)
Overlap Adjusted.P.value Genes
1 2/16 0.02708 PPP1R16B;PDCD10
[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
52 Snowflake vitreoretinal degeneration
53 Cerebral Cavernous Malformations 3
55 Familial cerebral cavernous malformation
57 LEBER CONGENITAL AMAUROSIS 16
58 PROSTATE CANCER, HEREDITARY, 2
60 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17
61 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52
63 IMMUNODEFICIENCY-CENTROMERIC INSTABILITY-FACIAL ANOMALIES SYNDROME 4
36 Immunodeficiency syndrome, variable
54 Cavernous Hemangioma of Brain
FDR Ratio BgRatio
52 0.007305 1/9 1/9703
53 0.007305 1/9 1/9703
55 0.007305 1/9 1/9703
57 0.007305 1/9 1/9703
58 0.007305 1/9 1/9703
60 0.007305 1/9 1/9703
61 0.007305 1/9 1/9703
63 0.007305 1/9 1/9703
36 0.012982 1/9 2/9703
54 0.017518 1/9 3/9703WebGestalt 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] 57#significance threshold for TWAS
print(sig_thresh)[1] 4.559#number of ctwas genes
length(ctwas_genes)[1] 5#number of TWAS genes
length(twas_genes)[1] 79#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols][1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names)#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.04615 #specificity
print(specificity) ctwas TWAS
0.9997 0.9925 #precision / PPV
print(precision) ctwas TWAS
0.40000 0.07595 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 57#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 588#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.559#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] 18#sensitivity / recall
sensitivity ctwas TWAS
0.03509 0.10526 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9796 #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)
73 51 4
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