SCZ - Brain Amygdala
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] 10286#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1006 733 594 399 509 598 475 371 400 392 626 587 221 325 335 447
17 18 19 20 21 22
599 161 817 312 117 262 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8327#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8095Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0154121 0.0002731 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.33 12.45 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10286 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01505 0.15578 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04957 0.79248Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
2362 B3GAT1 11_84 0.9924 37.59 0.0002311 -6.495 2
10169 ZNF823 19_10 0.9851 40.56 0.0002475 6.311 1
11222 AC012074.2 2_15 0.9837 30.89 0.0001883 5.474 2
5997 TMEM56 1_58 0.9321 31.82 0.0001838 -4.834 1
2451 TRPV4 12_66 0.9155 24.52 0.0001391 4.416 1
2135 TLE4 9_38 0.9110 26.81 0.0001513 -5.000 1
3264 SNX19 11_81 0.8837 35.54 0.0001946 6.046 3
10793 UBXN2B 8_45 0.8776 25.14 0.0001367 -4.429 2
4374 DAGLA 11_34 0.8749 21.89 0.0001186 -4.263 1
7247 PIGO 9_27 0.8659 24.34 0.0001306 4.667 1
1564 CD40 20_28 0.8547 22.95 0.0001215 -4.470 2
11179 HIST1H2BN 6_21 0.8455 184.26 0.0009652 13.182 1
10176 RPL12 9_66 0.8422 23.98 0.0001251 4.663 2
6535 ACE 17_37 0.8414 33.78 0.0001761 -5.802 1
5060 CPNE2 16_30 0.8396 21.17 0.0001101 -4.125 1
8947 TDRD6 6_35 0.7941 20.44 0.0001006 3.520 2
8675 LY6H 8_94 0.7935 28.77 0.0001415 5.143 1
5344 RIT1 1_76 0.7935 23.31 0.0001146 -4.023 1
6891 ANTXR2 4_54 0.7883 20.64 0.0001008 3.831 1
3212 BCL11A 2_40 0.7823 21.17 0.0001026 -4.103 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
111 CACNA2D2 3_35 7.165e-01 347.96 1.545e-03 -0.1392 1
2756 HEMK1 3_35 3.617e-04 297.72 6.672e-07 0.4441 1
12501 LINC02019 3_35 1.176e-04 247.07 1.800e-07 0.3204 2
2757 CISH 3_35 7.702e-05 244.27 1.166e-07 0.1383 1
11179 HIST1H2BN 6_21 8.455e-01 184.26 9.652e-04 13.1822 1
7065 TEX264 3_35 9.295e-05 121.51 6.998e-08 1.8775 1
7061 CAMKV 3_35 3.879e-04 118.19 2.840e-07 1.7107 1
9573 BTN3A2 6_20 1.722e-02 112.82 1.203e-05 9.2080 3
7063 MST1R 3_35 1.137e-02 112.82 7.946e-06 -4.0250 1
36 ZMYND10 3_35 3.134e-03 111.88 2.172e-06 -1.0310 1
9746 SLC38A3 3_35 2.053e-02 109.15 1.388e-05 -2.7756 1
8984 HIST1H2BC 6_20 1.627e-02 86.78 8.746e-06 -7.9928 1
10465 MSH5 6_27 4.464e-01 85.35 2.361e-04 10.7311 1
10436 HLA-DMA 6_27 5.637e-01 85.16 2.974e-04 -9.4080 1
10468 ABHD16A 6_27 3.851e-01 85.03 2.029e-04 10.7104 1
11458 C4A 6_27 3.384e-02 79.73 1.672e-05 10.4180 1
192 SEMA3B 3_35 8.086e-03 78.31 3.923e-06 1.4494 2
437 MPHOSPH9 12_75 1.091e-01 75.83 5.126e-05 9.4596 1
7058 RNF123 3_35 9.276e-05 75.75 4.353e-08 -2.3252 1
5903 ABCB9 12_75 6.975e-04 65.88 2.847e-07 8.6382 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
111 CACNA2D2 3_35 0.7165 347.96 0.0015447 -0.1392 1
11179 HIST1H2BN 6_21 0.8455 184.26 0.0009652 13.1822 1
10436 HLA-DMA 6_27 0.5637 85.16 0.0002974 -9.4080 1
10169 ZNF823 19_10 0.9851 40.56 0.0002475 6.3109 1
10465 MSH5 6_27 0.4464 85.35 0.0002361 10.7311 1
2362 B3GAT1 11_84 0.9924 37.59 0.0002311 -6.4953 2
10468 ABHD16A 6_27 0.3851 85.03 0.0002029 10.7104 1
7026 GNL3 3_36 0.5387 60.39 0.0002015 9.0984 2
3264 SNX19 11_81 0.8837 35.54 0.0001946 6.0459 3
8969 HARBI1 11_28 0.5259 59.61 0.0001942 8.0462 1
11222 AC012074.2 2_15 0.9837 30.89 0.0001883 5.4735 2
242 VSIG2 11_77 0.7263 41.40 0.0001863 -5.2191 1
5997 TMEM56 1_58 0.9321 31.82 0.0001838 -4.8337 1
6535 ACE 17_37 0.8414 33.78 0.0001761 -5.8021 1
5866 TAOK2 16_24 0.5441 51.21 0.0001726 7.4740 1
7515 PDIA3 15_16 0.6646 38.12 0.0001569 6.3137 1
2135 TLE4 9_38 0.9110 26.81 0.0001513 -4.9996 1
10458 SLC44A4 6_27 0.7186 33.90 0.0001509 6.8910 1
3591 CNOT1 16_31 0.7412 31.46 0.0001445 5.4349 1
8675 LY6H 8_94 0.7935 28.77 0.0001415 5.1432 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 0.8454839 184.26 9.652e-04 13.182 1
10465 MSH5 6_27 0.4463900 85.35 2.361e-04 10.731 1
10468 ABHD16A 6_27 0.3851216 85.03 2.029e-04 10.710 1
11458 C4A 6_27 0.0338413 79.73 1.672e-05 10.418 1
5778 CNNM2 10_66 0.2323050 53.26 7.666e-05 -9.686 1
437 MPHOSPH9 12_75 0.1091160 75.83 5.126e-05 9.460 1
10436 HLA-DMA 6_27 0.5636670 85.16 2.974e-04 -9.408 1
10444 RNF5 6_27 0.0045866 62.10 1.765e-06 9.276 1
9573 BTN3A2 6_20 0.0172168 112.82 1.203e-05 9.208 3
10469 LY6G5C 6_27 0.0018383 56.61 6.447e-07 9.105 1
7026 GNL3 3_36 0.5386542 60.39 2.015e-04 9.098 2
10473 APOM 6_27 0.0012429 58.04 4.470e-07 8.655 2
5903 ABCB9 12_75 0.0006975 65.88 2.847e-07 8.638 1
7705 SMIM4 3_36 0.0275029 54.84 9.344e-06 -8.494 1
8969 HARBI1 11_28 0.5259293 59.61 1.942e-04 8.046 1
8984 HIST1H2BC 6_20 0.0162680 86.78 8.746e-06 -7.993 1
2406 MDK 11_28 0.1835682 57.12 6.497e-05 -7.898 1
2778 NEK4 3_36 0.0156524 46.67 4.526e-06 7.898 1
10617 DNAJC19 3_111 0.0396696 56.33 1.385e-05 7.788 1
10520 TCTN1 12_67 0.3150257 57.66 1.125e-04 7.586 1Comparing z scores and PIPs
[1] 0.01361GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 73Uploading 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
1 calcium-dependent protein serine/threonine phosphatase activity (GO:0004723)
2 mitogen-activated protein kinase kinase binding (GO:0031434)
Overlap Adjusted.P.value Genes
1 2/7 0.02013 PPP3R1;PPP3CC
2 2/8 0.02013 ACE;TAOK2DisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio BgRatio
30 Confusion 0.02608 1/31 1/9703
38 Dementia, Vascular 0.02608 1/31 1/9703
53 Gingival Hypertrophy 0.02608 1/31 1/9703
68 Infant, Premature, Diseases 0.02608 1/31 1/9703
104 Pneumonia, Viral 0.02608 1/31 1/9703
133 Left Ventricular Hypertrophy 0.02608 2/31 25/9703
155 Speech impairment 0.02608 1/31 1/9703
156 Derealization 0.02608 1/31 1/9703
169 Spondylometaphyseal dysplasia, Kozlowski type 0.02608 1/31 1/9703
170 Metatropic dwarfism 0.02608 1/31 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: 28 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] 51#significance threshold for TWAS
print(sig_thresh)[1] 4.571#number of ctwas genes
length(ctwas_genes)[1] 15#number of TWAS genes
length(twas_genes)[1] 140#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
10793 UBXN2B 8_45 0.8776 25.14 0.0001367 -4.429 2
4374 DAGLA 11_34 0.8749 21.89 0.0001186 -4.263 1
2451 TRPV4 12_66 0.9155 24.52 0.0001391 4.416 1
5060 CPNE2 16_30 0.8396 21.17 0.0001101 -4.125 1
1564 CD40 20_28 0.8547 22.95 0.0001215 -4.470 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.12308 #specificity
print(specificity) ctwas TWAS
0.9987 0.9879 #precision / PPV
print(precision) ctwas TWAS
0.1333 0.1143 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 51#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 610#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.571#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] 40#sensitivity / recall
sensitivity ctwas TWAS
0.03922 0.31373 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9607 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.0 0.4 
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)
79 35 14
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_1.1.1 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