SCZ - Brain Cerebellum
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] 11176#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1074 779 637 413 547 634 559 420 436 446 669 633 228 361 371 526
17 18 19 20 21 22
674 166 861 329 125 288 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8214#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.735Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0134444 0.0002519 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.169 8.505 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11176 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01592 0.20433 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.08535 1.66076Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11129 ZNF823 19_10 0.9810 28.84 0.0003670 5.510 2
4195 FEZF1 7_74 0.9790 26.97 0.0003425 -5.272 1
12285 AC012074.2 2_15 0.9229 21.68 0.0002595 4.623 1
3267 MAP7D1 1_22 0.9049 25.18 0.0002955 -5.058 1
5873 GALNT2 1_117 0.8975 23.67 0.0002755 4.820 2
3127 SF3B1 2_117 0.8513 42.57 0.0004701 6.784 1
11503 DISP3 1_9 0.8438 20.45 0.0002238 4.095 1
1532 PIK3IP1 22_11 0.8416 21.11 0.0002304 4.375 1
7609 SERPINI1 3_103 0.8004 19.73 0.0002049 -4.078 2
9127 MAP3K11 11_36 0.7962 21.81 0.0002252 -4.232 2
3914 CNOT1 16_31 0.7912 24.72 0.0002537 5.156 2
9461 DIRAS1 19_3 0.7622 20.81 0.0002058 -4.359 1
672 PPP2R5A 1_107 0.7455 21.55 0.0002083 -3.866 2
6829 MOV10 1_69 0.7433 21.61 0.0002083 -4.294 2
4558 DLG4 17_6 0.7309 21.48 0.0002037 3.988 2
10100 NPIPA1 16_15 0.7082 21.56 0.0001980 4.110 1
12147 ANKRD63 15_14 0.6806 27.05 0.0002387 5.222 1
12796 RP11-65M17.3 11_66 0.6705 20.58 0.0001790 4.366 2
3758 SSPN 12_18 0.6585 23.72 0.0002026 3.860 1
6351 ARFGAP2 11_29 0.6551 24.42 0.0002075 4.839 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
6875 MMP16 8_63 0.000e+00 1951.69 0.000e+00 4.391 2
9739 HLA-DQB1 6_26 2.220e-16 252.76 7.280e-19 4.205 1
11502 PPP1R11 6_24 1.346e-04 230.20 4.019e-07 5.399 2
12366 HLA-DQA2 6_26 0.000e+00 186.22 0.000e+00 1.215 1
12543 C4A 6_26 1.497e-11 186.09 3.614e-14 8.445 1
11507 HLA-F 6_24 2.981e-12 179.91 6.957e-15 1.951 1
11724 DDAH2 6_26 0.000e+00 179.44 0.000e+00 7.661 1
13949 HCP5B 6_24 1.733e-13 171.33 3.851e-16 3.125 2
11458 C6orf48 6_26 0.000e+00 167.82 0.000e+00 6.384 2
12183 CYP21A2 6_26 2.331e-15 163.52 4.945e-18 -7.145 2
10933 HLA-DQA1 6_26 2.220e-16 156.56 4.509e-19 1.920 1
2248 DFNA5 7_21 2.330e-03 150.00 4.534e-06 3.260 1
2983 PCCB 3_84 1.714e-02 147.68 3.283e-05 -4.285 1
11449 SKIV2L 6_26 0.000e+00 146.46 0.000e+00 5.396 1
11454 EHMT2 6_26 0.000e+00 111.22 0.000e+00 -5.666 1
2247 MPP6 7_21 3.560e-03 109.60 5.060e-06 -3.302 1
859 PPP2R3A 3_84 6.761e-03 109.55 9.607e-06 4.119 1
10811 HLA-DRB1 6_26 0.000e+00 102.90 0.000e+00 2.449 1
11444 FKBPL 6_26 0.000e+00 97.97 0.000e+00 -4.386 2
11441 RNF5 6_26 0.000e+00 88.81 0.000e+00 7.370 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3127 SF3B1 2_117 0.8513 42.57 0.0004701 6.784 1
11129 ZNF823 19_10 0.9810 28.84 0.0003670 5.510 2
4195 FEZF1 7_74 0.9790 26.97 0.0003425 -5.272 1
11430 HLA-DMA 6_27 0.6421 38.67 0.0003221 -8.071 1
3267 MAP7D1 1_22 0.9049 25.18 0.0002955 -5.058 1
5873 GALNT2 1_117 0.8975 23.67 0.0002755 4.820 2
12285 AC012074.2 2_15 0.9229 21.68 0.0002595 4.623 1
3914 CNOT1 16_31 0.7912 24.72 0.0002537 5.156 2
2655 MDK 11_28 0.5171 37.00 0.0002482 -6.344 1
12147 ANKRD63 15_14 0.6806 27.05 0.0002387 5.222 1
1532 PIK3IP1 22_11 0.8416 21.11 0.0002304 4.375 1
9127 MAP3K11 11_36 0.7962 21.81 0.0002252 -4.232 2
1545 CENPM 22_17 0.4875 35.59 0.0002251 -3.908 1
11503 DISP3 1_9 0.8438 20.45 0.0002238 4.095 1
1753 PTK6 20_37 0.6182 27.07 0.0002170 -5.169 2
672 PPP2R5A 1_107 0.7455 21.55 0.0002083 -3.866 2
6829 MOV10 1_69 0.7433 21.61 0.0002083 -4.294 2
6351 ARFGAP2 11_29 0.6551 24.42 0.0002075 4.839 1
9461 DIRAS1 19_3 0.7622 20.81 0.0002058 -4.359 1
7609 SERPINI1 3_103 0.8004 19.73 0.0002049 -4.078 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
2890 PRSS16 6_21 3.024e-02 42.84 1.680e-05 -9.081 2
13535 RP1-86C11.7 6_21 7.696e-02 51.14 5.105e-05 9.033 1
12064 HCG11 6_20 2.311e-02 61.29 1.837e-05 8.937 1
13097 CTA-14H9.5 6_20 2.311e-02 61.29 1.837e-05 8.937 1
12543 C4A 6_26 1.497e-11 186.09 3.614e-14 8.445 1
12487 HLA-DMB 6_27 3.300e-01 40.27 1.724e-04 -8.273 1
11430 HLA-DMA 6_27 6.421e-01 38.67 3.221e-04 -8.071 1
9834 HIST1H2BC 6_20 2.452e-02 48.55 1.544e-05 -7.978 1
11938 LINC00240 6_21 3.143e-02 33.86 1.380e-05 -7.767 1
11724 DDAH2 6_26 0.000e+00 179.44 0.000e+00 7.661 1
5148 IER3 6_24 4.932e-07 84.74 5.421e-10 7.632 1
11484 CCHCR1 6_25 1.485e-02 43.23 8.330e-06 -7.556 5
11441 RNF5 6_26 0.000e+00 88.81 0.000e+00 7.370 2
10835 TUBB 6_24 1.674e-08 74.88 1.626e-11 -7.349 1
7085 ZSCAN12 6_22 2.899e-02 43.67 1.642e-05 -7.268 1
10608 HIST1H1C 6_20 1.915e-02 40.36 1.002e-05 -7.249 2
5150 PGBD1 6_22 9.208e-03 51.76 6.182e-06 -7.240 3
12183 CYP21A2 6_26 2.331e-15 163.52 4.945e-18 -7.145 2
10787 ZKSCAN3 6_22 1.925e-02 39.23 9.795e-06 7.101 2
3127 SF3B1 2_117 8.513e-01 42.57 4.701e-04 6.784 1Comparing z scores and PIPs
[1] 0.0085Gene with high z-score but low PIP, assign to SNP or to gene?
high_z_genes_region <- unique(head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],40)$region_tag)
sum <- 0
for(i in high_z_genes_region){
locus <- ctwas_res[ctwas_res$region_tag==i,]
locus <- head(locus[order(-locus$susie_pip),],20)
snp_pip <- sum(locus[locus$type == 'SNP','susie_pip'])
gene_pip <- sum(locus[locus$type == 'gene','susie_pip'])
print(snp_pip/(snp_pip+gene_pip))
}[1] 0.8915
[1] 0.6617
[1] 1
[1] 0.5074
[1] 1
[1] 0.9409
[1] 1
[1] 0.1313
[1] 0.9562
[1] 0.5843
[1] 0.9537
[1] 0.7274
[1] 0.8344
[1] 0.6004
[1] 0.9429GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 34Uploading 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 positive regulation of cell projection organization (GO:0031346)
2 negative regulation of lipid kinase activity (GO:0090219)
3 positive regulation of mRNA metabolic process (GO:1903313)
4 regulation of neuron projection arborization (GO:0150011)
5 positive regulation of neuron projection development (GO:0010976)
6 post-transcriptional gene silencing by RNA (GO:0035194)
7 positive regulation of neural precursor cell proliferation (GO:2000179)
8 regulation of neural precursor cell proliferation (GO:2000177)
Overlap Adjusted.P.value Genes
1 4/117 0.01537 MDK;DLG4;PTK6;SERPINI1
2 2/8 0.01537 PIK3IP1;PPP2R5A
3 2/13 0.02840 MOV10;CNOT1
4 2/15 0.02861 MOV10;DLG4
5 3/88 0.03418 MDK;PTK6;SERPINI1
6 2/20 0.03418 MOV10;CNOT1
7 2/22 0.03418 DISP3;MDK
8 2/23 0.03418 DISP3;MDK
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase type 2A complex (GO:0000159) 2/17 0.0259
Genes
1 PPP2R5B;PPP2R5A
[1] "GO_Molecular_Function_2021"
Term Overlap Adjusted.P.value
1 protein phosphatase activator activity (GO:0072542) 2/13 0.007938
2 phosphatase activator activity (GO:0019211) 2/14 0.007938
Genes
1 PPP2R5B;PPP2R5A
2 PPP2R5B;PPP2R5ADisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
73 Disproportionate tall stature 0.02690 1/15
75 Familial encephalopathy with neuroserpin inclusion bodies 0.02690 1/15
79 Hematopoetic Myelodysplasia 0.02690 2/15
85 HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.02690 1/15
86 SPASTIC PARAPLEGIA 62, AUTOSOMAL RECESSIVE 0.02690 1/15
64 Refractory anemia with ringed sideroblasts 0.04051 1/15
74 Macular Dystrophy, Butterfly-Shaped Pigmentary, 2 0.04051 1/15
76 Patterned dystrophy of retinal pigment epithelium 0.04051 1/15
82 MYELODYSPLASTIC SYNDROME 0.04051 2/15
87 Butterfly-shaped pigmentary macular dystrophy 0.04051 1/15
BgRatio
73 1/9703
75 1/9703
79 29/9703
85 1/9703
86 1/9703
64 2/9703
74 3/9703
76 3/9703
82 67/9703
87 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] 62#significance threshold for TWAS
print(sig_thresh)[1] 4.588#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 95#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
11503 DISP3 1_9 0.8438 20.45 0.0002238 4.095 1
7609 SERPINI1 3_103 0.8004 19.73 0.0002049 -4.078 2
1532 PIK3IP1 22_11 0.8416 21.11 0.0002304 4.375 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.04615 #specificity
print(specificity) ctwas TWAS
0.9994 0.9920 #precision / PPV
print(precision) ctwas TWAS
0.22222 0.06316 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 62#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 732#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.588#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] 24#sensitivity / recall
sensitivity ctwas TWAS
0.03226 0.09677 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9754 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.00 0.25 
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 56 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