SCZ - Brain Hippocampus
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] 10654#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1035 753 624 411 532 604 502 391 402 420 627 592 214 352 363 495
17 18 19 20 21 22
649 161 821 318 114 274 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8204#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.77Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.012095 0.000259 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.570 8.219 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10654 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01767 0.20297 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.07701 1.77546Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10687 ZNF823 19_10 0.9809 29.59 0.0003764 5.485 1
6084 ARFGAP2 11_29 0.9434 25.73 0.0003148 4.839 1
8759 MAP3K11 11_36 0.8669 22.56 0.0002537 -4.409 1
7929 ENDOG 9_66 0.8524 23.46 0.0002593 4.814 2
104 ELAC2 17_11 0.7884 22.09 0.0002259 4.372 1
4009 SPECC1 17_16 0.7772 22.05 0.0002223 4.167 1
12742 TBC1D29 17_18 0.7667 22.68 0.0002255 4.504 2
10621 SLC28A3 9_42 0.7388 20.69 0.0001982 3.743 1
10075 TMEM222 1_19 0.7030 23.44 0.0002137 3.902 1
2469 TBC1D19 4_22 0.7018 21.22 0.0001932 4.178 1
1516 TTLL1 22_18 0.6929 23.38 0.0002101 -4.510 2
3506 TBC1D15 12_44 0.6802 44.94 0.0003965 4.584 2
424 FAM120A 9_47 0.6753 23.25 0.0002037 4.571 1
1736 PPP1R16B 20_23 0.6717 34.97 0.0003047 6.009 1
2981 KCNJ13 2_137 0.6658 37.90 0.0003273 6.658 1
6538 MOV10 1_69 0.6539 22.16 0.0001879 -4.165 2
8275 INO80E 16_24 0.6509 38.24 0.0003229 6.230 1
3607 BHLHE41 12_18 0.6378 24.96 0.0002065 -3.860 1
2916 LMAN2L 2_57 0.6234 26.42 0.0002136 -4.473 2
9119 LY6H 8_94 0.5960 21.15 0.0001635 4.118 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11759 HIST1H2BN 6_21 9.808e-07 988.31 1.257e-08 10.7729 1
12967 RP1-153G14.4 6_21 0.000e+00 545.34 0.000e+00 1.8537 1
13065 RP1-86C11.7 6_21 2.000e-12 428.96 1.113e-14 -9.0332 1
10999 VWA7 6_27 6.840e-05 157.52 1.398e-07 8.9114 1
3217 STAG1 3_84 0.000e+00 154.32 0.000e+00 3.7399 2
10508 HIST1H2AG 6_21 0.000e+00 151.61 0.000e+00 1.6735 1
12063 C4A 6_27 9.696e-09 130.50 1.641e-11 8.3522 2
11006 APOM 6_27 1.374e-08 125.29 2.232e-11 7.8900 2
6779 ZSCAN12 6_22 4.256e-02 125.06 6.903e-05 10.9401 1
2147 MPP6 7_21 2.839e-03 111.44 4.103e-06 -3.3024 1
10995 C6orf48 6_27 5.034e-10 92.92 6.067e-13 3.4168 3
10782 HLA-DRB5 6_27 5.340e-14 81.74 5.662e-17 2.8311 1
10979 RNF5 6_27 6.906e-14 80.93 7.249e-17 7.9213 1
10981 PRRT1 6_27 6.839e-14 80.56 7.146e-17 7.9069 1
4942 IER3 6_24 5.995e-15 76.18 5.924e-18 2.1673 1
10546 ZSCAN26 6_22 1.016e-02 70.00 9.229e-06 8.5444 2
12006 HLA-DMB 6_27 4.488e-01 67.97 3.957e-04 -8.0771 1
11769 TRIM26 6_24 2.298e-14 67.96 2.026e-17 -4.5226 2
10071 BTN3A2 6_20 1.990e-02 66.16 1.708e-05 9.1074 2
9707 GRIN2A 16_10 3.721e-07 65.62 3.167e-10 0.3187 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3506 TBC1D15 12_44 0.6802 44.94 0.0003965 4.584 2
12006 HLA-DMB 6_27 0.4488 67.97 0.0003957 -8.077 1
10687 ZNF823 19_10 0.9809 29.59 0.0003764 5.485 1
2981 KCNJ13 2_137 0.6658 37.90 0.0003273 6.658 1
8275 INO80E 16_24 0.6509 38.24 0.0003229 6.230 1
6084 ARFGAP2 11_29 0.9434 25.73 0.0003148 4.839 1
1736 PPP1R16B 20_23 0.6717 34.97 0.0003047 6.009 1
2535 MDK 11_28 0.5855 38.49 0.0002923 -6.344 1
7929 ENDOG 9_66 0.8524 23.46 0.0002593 4.814 2
8759 MAP3K11 11_36 0.8669 22.56 0.0002537 -4.409 1
104 ELAC2 17_11 0.7884 22.09 0.0002259 4.372 1
12742 TBC1D29 17_18 0.7667 22.68 0.0002255 4.504 2
4009 SPECC1 17_16 0.7772 22.05 0.0002223 4.167 1
10075 TMEM222 1_19 0.7030 23.44 0.0002137 3.902 1
2916 LMAN2L 2_57 0.6234 26.42 0.0002136 -4.473 2
1516 TTLL1 22_18 0.6929 23.38 0.0002101 -4.510 2
3607 BHLHE41 12_18 0.6378 24.96 0.0002065 -3.860 1
424 FAM120A 9_47 0.6753 23.25 0.0002037 4.571 1
8075 BATF2 11_36 0.5647 27.27 0.0001998 -4.859 2
2590 VPS29 12_67 0.5781 26.61 0.0001996 -4.982 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
6779 ZSCAN12 6_22 4.256e-02 125.06 6.903e-05 10.940 1
11759 HIST1H2BN 6_21 9.808e-07 988.31 1.257e-08 10.773 1
10071 BTN3A2 6_20 1.990e-02 66.16 1.708e-05 9.107 2
13065 RP1-86C11.7 6_21 2.000e-12 428.96 1.113e-14 -9.033 1
12628 CTA-14H9.5 6_20 1.914e-02 64.08 1.591e-05 8.937 1
10999 VWA7 6_27 6.840e-05 157.52 1.398e-07 8.911 1
10546 ZSCAN26 6_22 1.016e-02 70.00 9.229e-06 8.544 2
12063 C4A 6_27 9.696e-09 130.50 1.641e-11 8.352 2
12006 HLA-DMB 6_27 4.488e-01 67.97 3.957e-04 -8.077 1
9448 HIST1H2BC 6_20 2.014e-02 50.53 1.320e-05 -7.978 1
10979 RNF5 6_27 6.906e-14 80.93 7.249e-17 7.921 1
10981 PRRT1 6_27 6.839e-14 80.56 7.146e-17 7.907 1
11006 APOM 6_27 1.374e-08 125.29 2.232e-11 7.890 2
6064 CNNM2 10_66 1.510e-01 40.14 7.862e-05 -7.876 1
910 NT5C2 10_66 3.130e-01 40.94 1.662e-04 -7.507 2
10219 ZSCAN23 6_22 2.302e-02 47.21 1.409e-05 -7.124 1
10362 ZKSCAN3 6_22 1.140e-02 39.61 5.855e-06 6.866 1
2981 KCNJ13 2_137 6.658e-01 37.90 3.273e-04 6.658 1
10290 DPYD 1_60 1.525e-02 38.86 7.688e-06 -6.455 1
2535 MDK 11_28 5.855e-01 38.49 2.923e-04 -6.344 1Comparing z scores and PIPs
[1] 0.00657GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 26Uploading 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 plasma membrane bounded cell projection assembly (GO:0120032)
Overlap Adjusted.P.value Genes
1 3/70 0.02821 PPP1R16B;TBC1D19;TBC1D15
[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
45 Snowflake vitreoretinal degeneration 0.007421 1/8
47 LEBER CONGENITAL AMAUROSIS 16 0.007421 1/8
48 PROSTATE CANCER, HEREDITARY, 2 0.007421 1/8
50 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.007421 1/8
52 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.007421 1/8
53 Bile acid CoA ligase deficiency and defective amidation 0.007421 1/8
33 Long Sleeper Syndrome 0.025918 1/8
34 Short Sleeper Syndrome 0.025918 1/8
35 Sleep-Related Neurogenic Tachypnea 0.025918 1/8
36 Subwakefullness Syndrome 0.025918 1/8
BgRatio
45 1/9703
47 1/9703
48 1/9703
50 1/9703
52 1/9703
53 1/9703
33 7/9703
34 7/9703
35 7/9703
36 7/9703WebGestalt 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] 67#significance threshold for TWAS
print(sig_thresh)[1] 4.578#number of ctwas genes
length(ctwas_genes)[1] 4#number of TWAS genes
length(twas_genes)[1] 70#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
8759 MAP3K11 11_36 0.8669 22.56 0.0002537 -4.409 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.076923 #specificity
print(specificity) ctwas TWAS
0.9997 0.9943 #precision / PPV
print(precision) ctwas TWAS
0.2500 0.1429 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 67#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 813#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.578#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 3#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 19#sensitivity / recall
sensitivity ctwas TWAS
0.01493 0.14925 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9975 0.9889 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.3333 0.5263 
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)
63 57 9
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_1.0.0 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