SCZ - Brain Spinal cord cervical c-1
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] 10130#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
974 719 585 395 492 585 485 379 386 397 581 591 215 338 351 444 595 160 773 303
21 22
121 261 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 7997#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7894Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0120866 0.0002603 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.620 8.098 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10130 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01687 0.20105 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07211 1.72482Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10447 ZNF823 19_10 0.9812 29.69 0.0003779 5.485 1
5935 ARFGAP2 11_29 0.9426 25.52 0.0003120 4.839 1
3025 MAP7D1 1_22 0.9221 25.98 0.0003107 5.058 1
2890 SF3B1 2_117 0.9090 45.13 0.0005321 6.784 1
11504 AC012074.2 2_15 0.8675 21.80 0.0002452 4.457 2
8557 MAP3K11 11_36 0.8541 22.88 0.0002535 -4.409 1
104 ELAC2 17_11 0.8446 21.79 0.0002387 4.518 1
3216 HSDL2 9_57 0.7845 22.13 0.0002251 4.378 1
2823 LMAN2L 2_57 0.7344 23.58 0.0002246 -4.454 2
3886 SPECC1 17_16 0.7326 23.29 0.0002213 -4.591 1
2760 PDCD10 3_103 0.7122 20.41 0.0001886 -4.030 1
9840 TMEM222 1_19 0.7116 23.53 0.0002172 3.936 2
3391 TBC1D15 12_44 0.6959 22.66 0.0002045 4.461 2
2445 MDK 11_28 0.6770 38.32 0.0003365 -6.344 1
3741 KLC1 14_54 0.6618 41.08 0.0003526 6.933 1
8880 DIRAS1 19_3 0.6540 21.35 0.0001811 4.119 1
8068 INO80E 16_24 0.6482 38.30 0.0003220 6.230 1
1568 KIAA0391 14_9 0.6447 23.53 0.0001967 -4.788 2
8916 LY6H 8_94 0.6192 23.06 0.0001852 4.236 1
5581 FAM134A 2_129 0.5715 23.55 0.0001746 -4.682 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
12858 HIST1H2BO 6_21 8.042e-11 1705.20 1.779e-12 -7.423 1
9594 HIST1H1B 6_21 1.743e-14 1148.25 2.596e-16 -8.699 1
11457 HIST1H2BN 6_21 1.414e-06 969.11 1.778e-08 10.773 1
6434 MMP16 8_63 0.000e+00 509.49 0.000e+00 3.645 1
11416 HLA-DQB2 6_26 5.551e-16 242.45 1.746e-18 -3.919 1
11576 HLA-DQA2 6_26 5.551e-16 242.45 1.746e-18 -3.919 1
10760 APOM 6_26 1.079e-08 201.55 2.821e-11 8.945 1
10755 ABHD16A 6_26 8.934e-09 201.25 2.332e-11 8.934 1
11740 C4A 6_26 2.019e-10 192.59 5.044e-13 8.445 1
10753 MSH5 6_26 0.000e+00 190.83 0.000e+00 7.722 1
10989 DDAH2 6_26 0.000e+00 187.23 0.000e+00 7.661 1
10266 HLA-DQA1 6_26 0.000e+00 157.40 0.000e+00 -1.344 2
3613 HIST1H2BJ 6_21 0.000e+00 148.31 0.000e+00 1.674 1
10158 HLA-DRB1 6_26 0.000e+00 139.84 0.000e+00 5.148 1
10762 BAG6 6_26 1.110e-16 129.77 1.869e-19 6.613 1
10727 PBX2 6_26 0.000e+00 126.29 0.000e+00 3.355 1
10984 ATF6B 6_26 0.000e+00 117.45 0.000e+00 3.821 1
2073 MPP6 7_21 1.278e-02 109.91 1.822e-05 -3.302 1
10747 C6orf48 6_26 2.220e-16 98.21 2.829e-19 5.387 2
10732 PRRT1 6_26 0.000e+00 95.47 0.000e+00 -7.907 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
2890 SF3B1 2_117 0.9090 45.13 0.0005321 6.784 1
10447 ZNF823 19_10 0.9812 29.69 0.0003779 5.485 1
3741 KLC1 14_54 0.6618 41.08 0.0003526 6.933 1
2445 MDK 11_28 0.6770 38.32 0.0003365 -6.344 1
8068 INO80E 16_24 0.6482 38.30 0.0003220 6.230 1
5935 ARFGAP2 11_29 0.9426 25.52 0.0003120 4.839 1
3025 MAP7D1 1_22 0.9221 25.98 0.0003107 5.058 1
8557 MAP3K11 11_36 0.8541 22.88 0.0002535 -4.409 1
11504 AC012074.2 2_15 0.8675 21.80 0.0002452 4.457 2
104 ELAC2 17_11 0.8446 21.79 0.0002387 4.518 1
3216 HSDL2 9_57 0.7845 22.13 0.0002251 4.378 1
2823 LMAN2L 2_57 0.7344 23.58 0.0002246 -4.454 2
3886 SPECC1 17_16 0.7326 23.29 0.0002213 -4.591 1
7005 MAIP1 2_118 0.3907 43.09 0.0002183 7.321 1
7004 TYW5 2_118 0.3907 43.09 0.0002183 -7.321 1
9840 TMEM222 1_19 0.7116 23.53 0.0002172 3.936 2
3391 TBC1D15 12_44 0.6959 22.66 0.0002045 4.461 2
1568 KIAA0391 14_9 0.6447 23.53 0.0001967 -4.788 2
2760 PDCD10 3_103 0.7122 20.41 0.0001886 -4.030 1
8916 LY6H 8_94 0.6192 23.06 0.0001852 4.236 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11457 HIST1H2BN 6_21 1.414e-06 969.11 1.778e-08 10.773 1
10760 APOM 6_26 1.079e-08 201.55 2.821e-11 8.945 1
10755 ABHD16A 6_26 8.934e-09 201.25 2.332e-11 8.934 1
5732 PPP1R18 6_24 3.681e-04 89.59 4.278e-07 8.730 1
9594 HIST1H1B 6_21 1.743e-14 1148.25 2.596e-16 -8.699 1
11740 C4A 6_26 2.019e-10 192.59 5.044e-13 8.445 1
4810 PGBD1 6_22 6.452e-03 71.16 5.955e-06 -8.295 2
9231 HIST1H2BC 6_20 2.051e-02 50.70 1.349e-05 -7.978 1
10732 PRRT1 6_26 0.000e+00 95.47 0.000e+00 -7.907 1
10753 MSH5 6_26 0.000e+00 190.83 0.000e+00 7.722 1
10989 DDAH2 6_26 0.000e+00 187.23 0.000e+00 7.661 1
10729 RNF5 6_26 0.000e+00 89.08 0.000e+00 7.459 2
12858 HIST1H2BO 6_21 8.042e-11 1705.20 1.779e-12 -7.423 1
7005 MAIP1 2_118 3.907e-01 43.09 2.183e-04 7.321 1
7004 TYW5 2_118 3.907e-01 43.09 2.183e-04 -7.321 1
11468 TRIM26 6_24 2.888e-12 64.86 2.430e-15 -7.007 2
3741 KLC1 14_54 6.618e-01 41.08 3.526e-04 6.933 1
9836 BTN3A2 6_20 1.572e-01 51.01 1.040e-04 6.821 1
2890 SF3B1 2_117 9.090e-01 45.13 5.321e-04 6.784 1
10762 BAG6 6_26 1.110e-16 129.77 1.869e-19 6.613 1Comparing z scores and PIPs
[1] 0.007601GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 21Uploading 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 Overlap
1 positive regulation of MAP kinase activity (GO:0043406) 3/69
Adjusted.P.value Genes
1 0.0112 PDCD10;DIRAS1;MAP3K11
[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
46 Cerebral Cavernous Malformations 3 0.006714 1/8
47 Reticular Dystrophy Of Retinal Pigment Epithelium 0.006714 1/8
50 Familial cerebral cavernous malformation 0.006714 1/8
53 PROSTATE CANCER, HEREDITARY, 2 0.006714 1/8
55 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.006714 1/8
56 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.006714 1/8
57 RETINAL DYSTROPHY WITH OR WITHOUT EXTRAOCULAR ANOMALIES 0.006714 1/8
38 Refractory anemia with ringed sideroblasts 0.011746 1/8
49 Cavernous Hemangioma of Brain 0.015656 1/8
19 Exudative retinopathy 0.016762 1/8
BgRatio
46 1/9703
47 1/9703
50 1/9703
53 1/9703
55 1/9703
56 1/9703
57 1/9703
38 2/9703
49 3/9703
19 4/9703WebGestalt 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] 4.567#number of ctwas genes
length(ctwas_genes)[1] 7#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$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
11504 AC012074.2 2_15 0.8675 21.80 0.0002452 4.457 2
8557 MAP3K11 11_36 0.8541 22.88 0.0002535 -4.409 1
104 ELAC2 17_11 0.8446 21.79 0.0002387 4.518 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.04615 #specificity
print(specificity) ctwas TWAS
0.9996 0.9930 #precision / PPV
print(precision) ctwas TWAS
0.42857 0.07792 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 57#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 667#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.567#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] 15#sensitivity / recall
sensitivity ctwas TWAS
0.05263 0.10526 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9985 0.9865 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
0.75 0.40 
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 50 4
Detected (PIP > 0.8)
3 #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