SCZ - Brain Hippocampus
sheng Qian
2021-2-6
Last updated: 2022-04-19
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 9529#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
933 692 571 374 467 524 440 350 368 387 579 538 199 328 331 401 589 153 755 277
21 22
25 248 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6774#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7109Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0127004 0.0003112 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.52 10.20 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9529 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01783 0.19011 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05526 1.06662Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10687 ZNF823 19_10 0.9828 37.78 0.0003526 6.143 1
13199 RP11-408A13.3 9_12 0.9245 23.54 0.0002066 4.536 1
8759 MAP3K11 11_36 0.9130 32.10 0.0002782 -5.401 1
3607 BHLHE41 12_18 0.8948 23.72 0.0002016 -4.516 1
5515 SYTL1 1_19 0.8899 21.93 0.0001853 4.307 2
10255 C19orf35 19_3 0.8864 26.73 0.0002250 -4.525 1
11759 HIST1H2BN 6_21 0.8609 106.83 0.0008732 13.396 1
10075 TMEM222 1_19 0.8550 21.79 0.0001769 4.303 1
9632 GRID1 10_55 0.8518 21.94 0.0001774 4.415 2
12846 RP11-247A12.7 9_66 0.7983 23.61 0.0001790 4.683 1
12742 TBC1D29 17_18 0.7572 23.88 0.0001717 4.742 2
3085 ARHGEF2 1_76 0.7557 22.43 0.0001610 -3.816 1
1736 PPP1R16B 20_23 0.7508 60.85 0.0004338 7.738 1
10982 FKBPL 6_27 0.7437 56.86 0.0004015 -5.277 2
10261 SLC38A3 3_35 0.7374 45.50 0.0003186 -1.402 1
11532 LINC00390 13_17 0.7005 22.93 0.0001526 -4.540 1
12176 YJEFN3 19_15 0.6948 50.28 0.0003317 -6.827 1
2183 EIF3B 7_4 0.6790 23.43 0.0001511 4.478 2
10932 SOX18 20_38 0.6767 22.52 0.0001447 3.659 1
2597 DUSP16 12_11 0.6681 21.01 0.0001333 -3.779 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10999 VWA7 6_27 1.135e-08 245.13 2.642e-11 11.555 1
12063 C4A 6_27 2.120e-09 234.83 4.727e-12 11.326 1
11006 APOM 6_27 1.032e-09 216.99 2.126e-12 10.730 2
10981 PRRT1 6_27 1.709e-06 156.38 2.538e-09 10.061 1
10979 RNF5 6_27 1.564e-06 155.80 2.314e-09 10.045 1
10995 C6orf48 6_27 2.515e-11 140.32 3.351e-14 7.823 2
10976 NOTCH4 6_27 5.420e-07 120.86 6.220e-10 6.390 1
11759 HIST1H2BN 6_21 8.609e-01 106.83 8.732e-04 13.396 1
12628 CTA-14H9.5 6_20 2.080e-02 101.46 2.004e-05 11.015 1
10383 HLA-DRB1 6_27 9.548e-08 96.00 8.704e-11 5.077 1
10071 BTN3A2 6_20 1.819e-02 94.79 1.637e-05 10.733 2
12006 HLA-DMB 6_27 4.825e-04 87.87 4.025e-07 -8.860 1
10969 HLA-DMA 6_27 2.371e-04 85.86 1.933e-07 -8.774 2
4941 FLOT1 6_24 7.310e-02 84.59 5.872e-05 -10.944 1
9448 HIST1H2BC 6_20 2.786e-02 82.72 2.189e-05 -9.909 1
10546 ZSCAN26 6_22 1.373e-02 76.96 1.004e-05 9.757 2
2719 TRIM38 6_20 1.952e-02 74.97 1.389e-05 -9.572 2
13065 RP1-86C11.7 6_21 4.838e-02 73.98 3.398e-05 -10.889 1
10974 BTNL2 6_27 5.623e-10 62.87 3.357e-13 3.884 1
1736 PPP1R16B 20_23 7.508e-01 60.85 4.338e-04 7.738 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11759 HIST1H2BN 6_21 0.8609 106.83 0.0008732 13.396 1
1736 PPP1R16B 20_23 0.7508 60.85 0.0004338 7.738 1
10982 FKBPL 6_27 0.7437 56.86 0.0004015 -5.277 2
10687 ZNF823 19_10 0.9828 37.78 0.0003526 6.143 1
12176 YJEFN3 19_15 0.6948 50.28 0.0003317 -6.827 1
10261 SLC38A3 3_35 0.7374 45.50 0.0003186 -1.402 1
8759 MAP3K11 11_36 0.9130 32.10 0.0002782 -5.401 1
2535 MDK 11_28 0.5725 48.69 0.0002647 -7.159 1
626 SNAP91 6_57 0.6312 43.26 0.0002593 6.969 1
40 RBM6 3_35 0.4575 54.18 0.0002354 3.221 1
10255 C19orf35 19_3 0.8864 26.73 0.0002250 -4.525 1
8275 INO80E 16_24 0.4958 46.89 0.0002207 6.852 1
13199 RP11-408A13.3 9_12 0.9245 23.54 0.0002066 4.536 1
3607 BHLHE41 12_18 0.8948 23.72 0.0002016 -4.516 1
5515 SYTL1 1_19 0.8899 21.93 0.0001853 4.307 2
12846 RP11-247A12.7 9_66 0.7983 23.61 0.0001790 4.683 1
9632 GRID1 10_55 0.8518 21.94 0.0001774 4.415 2
10075 TMEM222 1_19 0.8550 21.79 0.0001769 4.303 1
8075 BATF2 11_36 0.5632 32.97 0.0001763 -5.318 2
12742 TBC1D29 17_18 0.7572 23.88 0.0001717 4.742 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11759 HIST1H2BN 6_21 8.609e-01 106.83 8.732e-04 13.396 1
10999 VWA7 6_27 1.135e-08 245.13 2.642e-11 11.555 1
12063 C4A 6_27 2.120e-09 234.83 4.727e-12 11.326 1
12628 CTA-14H9.5 6_20 2.080e-02 101.46 2.004e-05 11.015 1
4941 FLOT1 6_24 7.310e-02 84.59 5.872e-05 -10.944 1
13065 RP1-86C11.7 6_21 4.838e-02 73.98 3.398e-05 -10.889 1
10071 BTN3A2 6_20 1.819e-02 94.79 1.637e-05 10.733 2
11006 APOM 6_27 1.032e-09 216.99 2.126e-12 10.730 2
10981 PRRT1 6_27 1.709e-06 156.38 2.538e-09 10.061 1
10979 RNF5 6_27 1.564e-06 155.80 2.314e-09 10.045 1
9448 HIST1H2BC 6_20 2.786e-02 82.72 2.189e-05 -9.909 1
10546 ZSCAN26 6_22 1.373e-02 76.96 1.004e-05 9.757 2
2719 TRIM38 6_20 1.952e-02 74.97 1.389e-05 -9.572 2
12006 HLA-DMB 6_27 4.825e-04 87.87 4.025e-07 -8.860 1
10969 HLA-DMA 6_27 2.371e-04 85.86 1.933e-07 -8.774 2
10219 ZSCAN23 6_22 3.074e-02 50.74 1.481e-05 -8.180 1
6064 CNNM2 10_66 1.179e-01 44.36 4.968e-05 -8.161 1
10995 C6orf48 6_27 2.515e-11 140.32 3.351e-14 7.823 2
1736 PPP1R16B 20_23 7.508e-01 60.85 4.338e-04 7.738 1
10362 ZKSCAN3 6_22 1.549e-02 39.74 5.844e-06 7.690 1Comparing z scores and PIPs
[1] 0.01175GO 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"
Term
1 regulation of leukocyte cell-cell adhesion (GO:1903037)
2 positive regulation of neuron migration (GO:2001224)
3 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
Overlap Adjusted.P.value Genes
1 2/12 0.04655 FUT9;MDK
2 2/13 0.04655 MDK;ARHGEF2
3 2/13 0.04655 FUT9;MDK
[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
17 Confusion
61 Speech impairment
62 Derealization
67 Spondylometaphyseal dysplasia, Kozlowski type
68 Metatropic dwarfism
86 Brachyolmia Type 3
91 Sexually disinhibited behavior
98 Hypersomnia, Recurrent
124 SPINAL MUSCULAR ATROPHY, DISTAL, CONGENITAL NONPROGRESSIVE (disorder)
125 HYPOTRICHOSIS-LYMPHEDEMA-TELANGIECTASIA SYNDROME
FDR Ratio BgRatio
17 0.01335 1/19 1/9703
61 0.01335 1/19 1/9703
62 0.01335 1/19 1/9703
67 0.01335 1/19 1/9703
68 0.01335 1/19 1/9703
86 0.01335 1/19 1/9703
91 0.01335 1/19 1/9703
98 0.01335 1/19 1/9703
124 0.01335 1/19 1/9703
125 0.01335 1/19 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: ggrepel: 8 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] 58#significance threshold for TWAS
print(sig_thresh)[1] 4.555#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 112#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5515 SYTL1 1_19 0.8899 21.93 0.0001853 4.307 2
10075 TMEM222 1_19 0.8550 21.79 0.0001769 4.303 1
13199 RP11-408A13.3 9_12 0.9245 23.54 0.0002066 4.536 1
9632 GRID1 10_55 0.8518 21.94 0.0001774 4.415 2
3607 BHLHE41 12_18 0.8948 23.72 0.0002016 -4.516 1
10255 C19orf35 19_3 0.8864 26.73 0.0002250 -4.525 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.115385 #specificity
print(specificity) ctwas TWAS
0.9992 0.9898 #precision / PPV
print(precision) ctwas TWAS
0.1111 0.1339 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 670#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.555#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] 44#sensitivity / recall
sensitivity ctwas TWAS
0.01724 0.25862 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9985 0.9567 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.5000 0.3409 
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)
72 43 14
Detected (PIP > 0.8)
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