SCZ - Brain Cortex
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] 11379#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1142 801 644 426 537 655 549 427 432 458 680 647 221 367 377 522
17 18 19 20 21 22
695 179 851 353 123 293 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8519#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7487Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0150804 0.0002497 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.963 8.473 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11379 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01995 0.20174 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1379 1.7253Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
958 NT5C2 10_66 0.9990 2876.64 0.0372745 -8.190 1
1518 DDTL 22_6 0.9987 32.10 0.0004159 -3.206 2
11314 ZNF823 19_10 0.9889 29.71 0.0003811 5.560 2
13518 LINC01415 18_30 0.9807 42.34 0.0005386 -7.512 2
13679 RP11-230C9.4 6_102 0.9414 21.83 0.0002666 -4.558 2
3165 SF3B1 2_117 0.9029 43.80 0.0005130 6.784 1
3085 SPCS1 3_36 0.9013 33.68 0.0003938 -6.382 1
11176 PCBP2 12_33 0.8743 21.44 0.0002432 4.496 1
12719 HLA-DMB 6_27 0.8111 393.39 0.0041389 -8.273 1
421 TRIT1 1_25 0.8089 20.37 0.0002137 -4.060 3
9752 ZNF354C 5_108 0.8015 20.29 0.0002110 -4.154 1
5055 RCBTB1 13_21 0.7780 20.45 0.0002064 -4.141 2
6035 METTL21A 2_122 0.7368 22.25 0.0002126 -4.391 1
451 FAM120A 9_47 0.7113 22.53 0.0002079 -4.571 1
4616 DLG4 17_6 0.6780 22.05 0.0001939 3.863 2
3159 KCNJ13 2_137 0.6742 36.58 0.0003199 6.658 1
6435 ARFGAP2 11_29 0.6699 25.00 0.0002172 4.839 1
9648 LY6H 8_94 0.6544 20.80 0.0001766 4.118 1
6579 FAM177A1 14_9 0.6489 20.82 0.0001753 -4.548 1
947 FMO4 1_84 0.6487 22.82 0.0001920 3.839 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
958 NT5C2 10_66 9.990e-01 2876.6 3.727e-02 -8.1897 1
6413 CNNM2 10_66 6.582e-05 2786.0 2.378e-06 -7.8764 1
6404 INA 10_66 5.789e-06 2049.1 1.539e-07 -7.1401 1
12375 HLA-DPA1 6_27 2.730e-08 1004.3 3.557e-10 5.3356 2
9334 USMG5 10_66 7.687e-11 409.2 4.079e-13 2.4174 1
12719 HLA-DMB 6_27 8.111e-01 393.4 4.139e-03 -8.2728 1
11611 HLA-DMA 6_27 3.220e-15 327.3 1.367e-17 0.4948 1
8214 WBP1L 10_66 5.766e-11 289.3 2.164e-13 2.1272 2
6414 PDCD11 10_66 3.156e-10 261.5 1.070e-12 3.0363 1
9405 MSL2 3_84 1.040e-06 196.8 2.655e-09 5.8137 2
11907 CLIC1 6_26 3.079e-04 176.8 7.062e-07 9.5362 2
5342 CALHM2 10_66 5.007e-11 162.6 1.056e-13 -1.7655 1
11645 LY6G6C 6_26 1.657e-05 151.0 3.247e-08 8.8896 1
11634 ZBTB12 6_26 3.831e-06 146.1 7.259e-09 8.7124 1
11640 HSPA1L 6_26 8.730e-12 140.4 1.590e-14 -7.6575 1
12122 C4B 6_26 3.225e-07 138.7 5.804e-10 -8.4450 1
11638 C6orf48 6_26 3.936e-13 137.8 7.036e-16 7.2997 1
12783 C4A 6_26 2.555e-07 137.2 4.546e-10 8.4728 3
12471 TRIM26 6_24 4.330e-15 135.7 7.619e-18 -5.4551 1
13651 HCG17 6_24 4.552e-15 134.5 7.943e-18 5.5087 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
958 NT5C2 10_66 0.9990 2876.64 0.0372745 -8.190 1
12719 HLA-DMB 6_27 0.8111 393.39 0.0041389 -8.273 1
13518 LINC01415 18_30 0.9807 42.34 0.0005386 -7.512 2
3165 SF3B1 2_117 0.9029 43.80 0.0005130 6.784 1
1518 DDTL 22_6 0.9987 32.10 0.0004159 -3.206 2
3085 SPCS1 3_36 0.9013 33.68 0.0003938 -6.382 1
11314 ZNF823 19_10 0.9889 29.71 0.0003811 5.560 2
3159 KCNJ13 2_137 0.6742 36.58 0.0003199 6.658 1
2682 MDK 11_28 0.5855 38.23 0.0002904 -6.344 1
7729 THOC7 3_43 0.6093 34.55 0.0002730 -5.844 4
13679 RP11-230C9.4 6_102 0.9414 21.83 0.0002666 -4.558 2
11176 PCBP2 12_33 0.8743 21.44 0.0002432 4.496 1
6507 TMEM219 16_24 0.5089 34.44 0.0002273 6.164 1
376 CUL3 2_132 0.6023 28.00 0.0002187 -5.422 1
6435 ARFGAP2 11_29 0.6699 25.00 0.0002172 4.839 1
421 TRIT1 1_25 0.8089 20.37 0.0002137 -4.060 3
6035 METTL21A 2_122 0.7368 22.25 0.0002126 -4.391 1
9752 ZNF354C 5_108 0.8015 20.29 0.0002110 -4.154 1
451 FAM120A 9_47 0.7113 22.53 0.0002079 -4.571 1
5055 RCBTB1 13_21 0.7780 20.45 0.0002064 -4.141 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11907 CLIC1 6_26 3.079e-04 176.84 7.062e-07 9.536 2
11645 LY6G6C 6_26 1.657e-05 151.03 3.247e-08 8.890 1
11129 ZSCAN16 6_22 1.319e-02 79.39 1.358e-05 8.813 1
11634 ZBTB12 6_26 3.831e-06 146.08 7.259e-09 8.712 1
2927 PRSS16 6_21 4.903e-02 42.22 2.685e-05 -8.631 2
12783 C4A 6_26 2.555e-07 137.19 4.546e-10 8.473 3
12122 C4B 6_26 3.225e-07 138.73 5.804e-10 -8.445 1
12719 HLA-DMB 6_27 8.111e-01 393.39 4.139e-03 -8.273 1
958 NT5C2 10_66 9.990e-01 2876.64 3.727e-02 -8.190 1
11649 GPANK1 6_26 3.819e-14 103.62 5.133e-17 7.973 1
12395 CYP21A2 6_26 7.677e-10 129.01 1.285e-12 -7.953 2
6413 CNNM2 10_66 6.582e-05 2786.03 2.378e-06 -7.876 1
11908 DDAH2 6_26 1.042e-13 127.56 1.725e-16 7.661 1
11640 HSPA1L 6_26 8.730e-12 140.43 1.590e-14 -7.658 1
11620 AGER 6_26 2.220e-16 70.05 2.017e-19 -7.547 1
13518 LINC01415 18_30 9.807e-01 42.34 5.386e-04 -7.512 2
11638 C6orf48 6_26 3.936e-13 137.83 7.036e-16 7.300 1
6404 INA 10_66 5.789e-06 2049.08 1.539e-07 -7.140 1
11621 RNF5 6_26 2.220e-16 45.12 1.300e-19 7.104 2
12511 ZSCAN31 6_22 2.921e-02 41.08 1.556e-05 -7.066 3Comparing z scores and PIPs
[1] 0.008173GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 41Uploading 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"
Term Overlap Adjusted.P.value
1 acetylcholine receptor binding (GO:0033130) 2/8 0.007632
2 Notch binding (GO:0005112) 2/24 0.036841
Genes
1 DLG4;LY6H
2 CUL3;HIF1ANDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
20 Measles 0.01082 1/15
64 Disproportionate tall stature 0.01082 1/15
65 Snowflake vitreoretinal degeneration 0.01082 1/15
66 Reticular Dystrophy Of Retinal Pigment Epithelium 0.01082 1/15
69 HEMOLYTIC UREMIC SYNDROME, ATYPICAL, SUSCEPTIBILITY TO, 2 0.01082 1/15
71 LEBER CONGENITAL AMAUROSIS 16 0.01082 1/15
73 PSEUDOHYPOALDOSTERONISM, TYPE IIE 0.01082 1/15
78 SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.01082 1/15
79 CONE-ROD DYSTROPHY 20 0.01082 1/15
82 SPASTIC PARAPLEGIA 62, AUTOSOMAL RECESSIVE 0.01082 1/15
BgRatio
20 1/9703
64 1/9703
65 1/9703
66 1/9703
69 1/9703
71 1/9703
73 1/9703
78 1/9703
79 1/9703
82 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)Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
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] 65#significance threshold for TWAS
print(sig_thresh)[1] 4.592#number of ctwas genes
length(ctwas_genes)[1] 11#number of TWAS genes
length(twas_genes)[1] 93#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
421 TRIT1 1_25 0.8089 20.37 0.0002137 -4.060 3
9752 ZNF354C 5_108 0.8015 20.29 0.0002110 -4.154 1
13679 RP11-230C9.4 6_102 0.9414 21.83 0.0002666 -4.558 2
11176 PCBP2 12_33 0.8743 21.44 0.0002432 4.496 1
1518 DDTL 22_6 0.9987 32.10 0.0004159 -3.206 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.06923 #specificity
print(specificity) ctwas TWAS
0.9992 0.9926 #precision / PPV
print(precision) ctwas TWAS
0.18182 0.09677 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 65#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 820#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.592#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] 26#sensitivity / recall
sensitivity ctwas TWAS
0.03077 0.13846 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9793 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3462 
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)
65 56 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