SCZ - Brain Hippocampus
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10982#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1073 766 643 425 536 614 506 404 414 437 662 613 219 376 368 514
17 18 19 20 21 22
661 165 859 332 117 278 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8766#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7982Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0122103 0.0002749 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
20.01 12.33 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10982 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01662 0.15536 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05229 0.79965Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
9365 LSMEM2 3_35 0.9999 1155.66 0.0071596 4.271 1
10687 ZNF823 19_10 0.9838 41.72 0.0002543 6.311 1
2925 ACTR1B 2_57 0.9648 32.65 0.0001952 -5.640 2
11759 HIST1H2BN 6_21 0.9439 194.49 0.0011373 13.182 1
2583 TRPV4 12_66 0.9313 26.68 0.0001539 4.456 2
10694 RPL12 9_66 0.8931 24.30 0.0001345 4.671 2
901 KLHL20 1_85 0.8889 39.92 0.0002199 5.800 1
9488 METTL23 17_43 0.8866 23.19 0.0001274 4.235 1
8759 MAP3K11 11_36 0.8600 34.41 0.0001833 -5.570 1
357 NSUN2 5_6 0.8535 22.20 0.0001174 -4.105 3
5229 C12orf10 12_33 0.8528 24.93 0.0001317 -4.963 1
5977 FAM135B 8_91 0.8516 23.00 0.0001213 -3.461 1
2373 NUP88 17_5 0.8482 28.00 0.0001471 4.765 1
7971 CACNB3 12_32 0.8257 23.40 0.0001197 -3.941 2
6860 ACE 17_37 0.8194 34.16 0.0001734 -5.802 1
3272 ABCG2 4_59 0.8050 21.38 0.0001066 -3.954 1
4606 DAGLA 11_35 0.7909 24.00 0.0001176 -4.263 1
8540 CXXC5 5_82 0.7734 32.98 0.0001580 -5.620 1
11534 LINC00606 3_8 0.7586 23.20 0.0001090 -4.150 1
9119 LY6H 8_94 0.7577 29.41 0.0001380 5.143 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9365 LSMEM2 3_35 9.999e-01 1155.66 7.160e-03 4.2709 1
201 SEMA3B 3_35 1.528e-05 1136.66 1.076e-07 1.0870 1
10261 SLC38A3 3_35 2.154e-07 273.81 3.653e-10 -2.7756 1
123 CACNA2D2 3_35 1.241e-06 258.74 1.989e-09 -0.1392 1
10 SEMA3F 3_35 1.595e-07 249.41 2.465e-10 -1.4379 1
40 RBM6 3_35 6.623e-01 209.39 8.592e-04 4.4688 1
10101 HYAL3 3_35 6.148e-08 198.84 7.574e-11 -1.8436 2
11759 HIST1H2BN 6_21 9.439e-01 194.49 1.137e-03 13.1822 1
12024 NAT6 3_35 2.290e-07 180.42 2.560e-10 0.8292 1
6779 ZSCAN12 6_22 6.233e-01 170.52 6.585e-04 12.8250 1
7425 MST1R 3_35 1.250e-04 155.83 1.207e-07 -4.0250 1
13065 RP1-86C11.7 6_21 1.130e-01 133.35 9.340e-05 -10.5382 1
1182 DOCK3 3_35 2.161e-06 128.31 1.718e-09 -0.3011 1
2889 CISH 3_35 7.474e-07 124.47 5.764e-10 -0.1383 1
12628 CTA-14H9.5 6_20 1.210e-02 120.91 9.061e-06 9.8443 1
4941 FLOT1 6_24 3.037e-02 118.32 2.226e-05 -10.5577 1
10071 BTN3A2 6_20 1.452e-02 110.88 9.972e-06 9.0193 2
7420 RNF123 3_35 5.818e-08 102.19 3.684e-11 -2.3622 1
9448 HIST1H2BC 6_20 1.208e-02 86.81 6.496e-06 -7.9928 1
10999 VWA7 6_26 4.905e-01 86.62 2.632e-04 10.5945 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9365 LSMEM2 3_35 0.9999 1155.66 0.0071596 4.271 1
11759 HIST1H2BN 6_21 0.9439 194.49 0.0011373 13.182 1
40 RBM6 3_35 0.6623 209.39 0.0008592 4.469 1
6779 ZSCAN12 6_22 0.6233 170.52 0.0006585 12.825 1
12006 HLA-DMB 6_27 0.5544 80.44 0.0002763 -9.474 1
10999 VWA7 6_26 0.4905 86.62 0.0002632 10.594 1
7966 GATAD2A 19_15 0.7512 55.30 0.0002574 -7.430 2
10687 ZNF823 19_10 0.9838 41.72 0.0002543 6.311 1
7391 GNL3 3_36 0.6233 62.90 0.0002429 9.102 2
901 KLHL20 1_85 0.8889 39.92 0.0002199 5.800 1
2925 ACTR1B 2_57 0.9648 32.65 0.0001952 -5.640 2
8759 MAP3K11 11_36 0.8600 34.41 0.0001833 -5.570 1
1736 PPP1R16B 20_23 0.5822 50.79 0.0001832 7.550 1
6860 ACE 17_37 0.8194 34.16 0.0001734 -5.802 1
7908 PDIA3 15_16 0.6620 39.22 0.0001608 6.314 1
8540 CXXC5 5_82 0.7734 32.98 0.0001580 -5.620 1
10969 HLA-DMA 6_27 0.3201 79.05 0.0001568 -9.408 1
910 NT5C2 10_66 0.4488 56.09 0.0001560 -9.584 2
4482 TMTC1 12_20 0.6119 40.87 0.0001549 6.192 1
2583 TRPV4 12_66 0.9313 26.68 0.0001539 4.456 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11759 HIST1H2BN 6_21 0.9438741 194.49 1.137e-03 13.182 1
6779 ZSCAN12 6_22 0.6232611 170.52 6.585e-04 12.825 1
10999 VWA7 6_26 0.4905408 86.62 2.632e-04 10.594 1
4941 FLOT1 6_24 0.0303682 118.32 2.226e-05 -10.558 1
13065 RP1-86C11.7 6_21 0.1130468 133.35 9.340e-05 -10.538 1
12063 C4A 6_26 0.1575984 85.02 8.302e-05 10.504 2
12628 CTA-14H9.5 6_20 0.0120960 120.91 9.061e-06 9.844 1
6064 CNNM2 10_66 0.1001205 53.83 3.339e-05 -9.686 1
910 NT5C2 10_66 0.4487794 56.09 1.560e-04 -9.584 2
11006 APOM 6_26 0.0054508 73.07 2.468e-06 9.579 2
12006 HLA-DMB 6_27 0.5544120 80.44 2.763e-04 -9.474 1
10969 HLA-DMA 6_27 0.3200844 79.05 1.568e-04 -9.408 1
10981 PRRT1 6_26 0.0109939 59.83 4.075e-06 9.276 1
10979 RNF5 6_26 0.0108010 59.55 3.985e-06 9.267 1
7391 GNL3 3_36 0.6233135 62.90 2.429e-04 9.102 2
10071 BTN3A2 6_20 0.0145171 110.88 9.972e-06 9.019 2
6186 ABCB9 12_75 0.0008569 66.15 3.512e-07 8.638 1
8097 GLYCTK 3_36 0.1279415 74.32 5.891e-05 8.577 1
9772 KMT5A 12_75 0.0008842 55.06 3.016e-07 -8.551 2
10546 ZSCAN26 6_22 0.0153736 65.33 6.222e-06 8.138 2Comparing z scores and PIPs
[1] 0.01457GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 54Uploading 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
25 Confusion 0.01728 1/18 1/9703
50 Gingival Hypertrophy 0.01728 1/18 1/9703
64 Infant, Premature, Diseases 0.01728 1/18 1/9703
74 Chronic Obstructive Airway Disease 0.01728 2/18 33/9703
94 Pneumonia, Viral 0.01728 1/18 1/9703
123 Left Ventricular Hypertrophy 0.01728 2/18 25/9703
143 Speech impairment 0.01728 1/18 1/9703
144 Derealization 0.01728 1/18 1/9703
155 Spondylometaphyseal dysplasia, Kozlowski type 0.01728 1/18 1/9703
156 Metatropic dwarfism 0.01728 1/18 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: 3 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] 62#significance threshold for TWAS
print(sig_thresh)[1] 4.584#number of ctwas genes
length(ctwas_genes)[1] 16#number of TWAS genes
length(twas_genes)[1] 160#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
9365 LSMEM2 3_35 0.9999 1155.66 0.0071596 4.271 1
3272 ABCG2 4_59 0.8050 21.38 0.0001066 -3.954 1
357 NSUN2 5_6 0.8535 22.20 0.0001174 -4.105 3
5977 FAM135B 8_91 0.8516 23.00 0.0001213 -3.461 1
7971 CACNB3 12_32 0.8257 23.40 0.0001197 -3.941 2
2583 TRPV4 12_66 0.9313 26.68 0.0001539 4.456 2
9488 METTL23 17_43 0.8866 23.19 0.0001274 4.235 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.20000 #specificity
print(specificity) ctwas TWAS
0.9988 0.9877 #precision / PPV
print(precision) ctwas TWAS
0.1875 0.1625 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 62#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 824#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.584#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 5#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 70#sensitivity / recall
sensitivity ctwas TWAS
0.04839 0.41935 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9976 0.9466 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6000 0.3714 
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 36 22
Detected (PIP > 0.8) Nearby Bystander Gene
3 1 #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