SCZ - Brain Spinal cord cervical c-1
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] 9040#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
894 649 536 348 429 514 446 349 342 345 531 533 200 304 324 368 557 145 690 274
21 22
27 235 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6617#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.732Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0124907 0.0003133 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.39 10.05 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9040 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.0165 0.1886 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.0542 1.0708Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10447 ZNF823 19_10 0.9824 37.28 0.0003477 6.143 1
12796 RP11-408A13.3 9_12 0.9224 23.54 0.0002061 4.536 1
11504 AC012074.2 2_15 0.9223 22.27 0.0001950 4.557 2
12471 RP11-247A12.7 9_66 0.9213 23.50 0.0002056 4.694 2
8557 MAP3K11 11_36 0.9091 32.66 0.0002820 -5.401 1
2890 SF3B1 2_117 0.8780 50.87 0.0004241 7.265 1
9840 TMEM222 1_19 0.8758 22.09 0.0001837 4.367 2
11457 HIST1H2BN 6_21 0.8598 106.35 0.0008682 13.396 1
5374 SYTL1 1_19 0.8536 22.00 0.0001783 4.274 2
11163 ZNF844 19_10 0.8510 23.96 0.0001936 4.487 1
299 VRK2 2_38 0.8164 40.02 0.0003103 4.693 2
3886 SPECC1 17_16 0.7927 26.23 0.0001974 -4.822 1
10032 SLC38A3 3_35 0.7482 45.64 0.0003243 -1.402 1
4312 TRPC4 13_14 0.7357 22.41 0.0001566 -4.518 1
1568 KIAA0391 14_9 0.7197 26.15 0.0001787 -4.897 2
2445 MDK 11_28 0.6937 48.75 0.0003211 -7.159 1
2823 LMAN2L 2_57 0.6853 24.21 0.0001575 -4.252 2
2173 TLE4 9_38 0.6819 21.78 0.0001410 4.279 1
4659 RCBTB1 13_21 0.6800 21.07 0.0001361 -3.996 1
459 SDCCAG8 1_128 0.6391 26.41 0.0001603 -4.660 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10760 APOM 6_26 1.688e-04 225.84 3.621e-07 11.590 1
10755 ABHD16A 6_26 1.387e-04 222.97 2.937e-07 11.526 1
11740 C4A 6_26 3.161e-05 216.08 6.486e-08 11.326 1
10158 HLA-DRB1 6_26 2.554e-05 177.63 4.307e-08 6.222 1
9135 HLA-DQB1 6_26 2.701e-07 173.12 4.440e-10 4.182 1
10137 ZKSCAN3 6_22 5.433e-02 170.18 8.780e-05 13.312 1
11576 HLA-DQA2 6_26 3.378e-07 169.00 5.421e-10 -4.388 1
11416 HLA-DQB2 6_26 3.378e-07 169.00 5.421e-10 -4.388 1
10732 PRRT1 6_26 5.758e-05 155.10 8.480e-08 -10.061 1
10729 RNF5 6_26 2.902e-05 146.18 4.028e-08 9.582 2
10726 NOTCH4 6_26 3.686e-03 135.32 4.735e-06 6.793 1
10728 AGER 6_26 4.522e-06 111.23 4.775e-09 -9.071 1
11457 HIST1H2BN 6_21 8.598e-01 106.35 8.682e-04 13.396 1
10730 AGPAT1 6_26 2.914e-07 103.42 2.862e-10 -5.190 1
10270 ZSCAN16 6_22 1.317e-02 102.08 1.276e-05 -10.284 1
9836 BTN3A2 6_20 1.890e-02 96.21 1.726e-05 10.739 2
9231 HIST1H2BC 6_20 2.817e-02 84.16 2.251e-05 -9.909 1
10753 MSH5 6_26 2.157e-05 80.01 1.639e-08 8.219 1
10989 DDAH2 6_26 1.771e-05 78.13 1.313e-08 8.149 1
10718 HLA-DMA 6_27 5.650e-02 73.21 3.927e-05 -8.951 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11457 HIST1H2BN 6_21 0.8598 106.35 0.0008682 13.396 1
2890 SF3B1 2_117 0.8780 50.87 0.0004241 7.265 1
10447 ZNF823 19_10 0.9824 37.28 0.0003477 6.143 1
10032 SLC38A3 3_35 0.7482 45.64 0.0003243 -1.402 1
2445 MDK 11_28 0.6937 48.75 0.0003211 -7.159 1
299 VRK2 2_38 0.8164 40.02 0.0003103 4.693 2
8557 MAP3K11 11_36 0.9091 32.66 0.0002820 -5.401 1
38 RBM6 3_35 0.4540 54.10 0.0002332 3.221 1
8068 INO80E 16_24 0.4941 46.81 0.0002196 6.852 1
7404 LETM2 8_34 0.5919 38.65 0.0002172 6.067 1
12796 RP11-408A13.3 9_12 0.9224 23.54 0.0002061 4.536 1
12471 RP11-247A12.7 9_66 0.9213 23.50 0.0002056 4.694 2
11046 AS3MT 10_66 0.4854 44.38 0.0002045 8.051 1
3886 SPECC1 17_16 0.7927 26.23 0.0001974 -4.822 1
11504 AC012074.2 2_15 0.9223 22.27 0.0001950 4.557 2
11163 ZNF844 19_10 0.8510 23.96 0.0001936 4.487 1
9840 TMEM222 1_19 0.8758 22.09 0.0001837 4.367 2
1568 KIAA0391 14_9 0.7197 26.15 0.0001787 -4.897 2
5374 SYTL1 1_19 0.8536 22.00 0.0001783 4.274 2
5186 FURIN 15_42 0.5063 35.59 0.0001711 -5.772 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11457 HIST1H2BN 6_21 8.598e-01 106.35 8.682e-04 13.396 1
10137 ZKSCAN3 6_22 5.433e-02 170.18 8.780e-05 13.312 1
10760 APOM 6_26 1.688e-04 225.84 3.621e-07 11.590 1
10755 ABHD16A 6_26 1.387e-04 222.97 2.937e-07 11.526 1
11740 C4A 6_26 3.161e-05 216.08 6.486e-08 11.326 1
9594 HIST1H1B 6_21 1.727e-02 62.76 1.029e-05 -10.766 1
9836 BTN3A2 6_20 1.890e-02 96.21 1.726e-05 10.739 2
10270 ZSCAN16 6_22 1.317e-02 102.08 1.276e-05 -10.284 1
10732 PRRT1 6_26 5.758e-05 155.10 8.480e-08 -10.061 1
9231 HIST1H2BC 6_20 2.817e-02 84.16 2.251e-05 -9.909 1
10729 RNF5 6_26 2.902e-05 146.18 4.028e-08 9.582 2
12858 HIST1H2BO 6_21 1.571e-02 49.84 7.433e-06 -9.187 1
10728 AGER 6_26 4.522e-06 111.23 4.775e-09 -9.071 1
10718 HLA-DMA 6_27 5.650e-02 73.21 3.927e-05 -8.951 2
2623 TRIM38 6_20 2.040e-02 67.24 1.302e-05 -8.903 2
10753 MSH5 6_26 2.157e-05 80.01 1.639e-08 8.219 1
10989 DDAH2 6_26 1.771e-05 78.13 1.313e-08 8.149 1
11046 AS3MT 10_66 4.854e-01 44.38 2.045e-04 8.051 1
7005 MAIP1 2_118 3.199e-01 46.24 1.404e-04 7.980 1
7004 TYW5 2_118 3.199e-01 46.24 1.404e-04 -7.980 1Comparing z scores and PIPs
[1] 0.01316GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 37Uploading 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 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
Overlap Adjusted.P.value Genes
1 2/12 0.04834 FUT9;MDK
2 2/13 0.04834 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 FDR Ratio
78 Reticular Dystrophy Of Retinal Pigment Epithelium 0.02511 1/14
80 SENIOR-LOKEN SYNDROME 7 0.02511 1/14
84 BARDET-BIEDL SYNDROME 16 0.02511 1/14
85 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52 0.02511 1/14
87 RETINAL DYSTROPHY WITH OR WITHOUT EXTRAOCULAR ANOMALIES 0.02511 1/14
69 Refractory anemia with ringed sideroblasts 0.04182 1/14
6 Bronchopulmonary Dysplasia 0.04472 1/14
32 Schizophrenia 0.04472 5/14
37 Exudative retinopathy 0.04472 1/14
50 Familial Exudative Vitreoretinopathy 0.04472 1/14
BgRatio
78 1/9703
80 1/9703
84 1/9703
85 1/9703
87 1/9703
69 2/9703
6 3/9703
32 883/9703
37 4/9703
50 5/9703WebGestalt 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: 1 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] 49#significance threshold for TWAS
print(sig_thresh)[1] 4.544#number of ctwas genes
length(ctwas_genes)[1] 11#number of TWAS genes
length(twas_genes)[1] 119#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5374 SYTL1 1_19 0.8536 22.00 0.0001783 4.274 2
9840 TMEM222 1_19 0.8758 22.09 0.0001837 4.367 2
12796 RP11-408A13.3 9_12 0.9224 23.54 0.0002061 4.536 1
11163 ZNF844 19_10 0.8510 23.96 0.0001936 4.487 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.09231 #specificity
print(specificity) ctwas TWAS
0.9990 0.9881 #precision / PPV
print(precision) ctwas TWAS
0.1818 0.1008 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 49#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 522#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.544#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 4#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 36#sensitivity / recall
sensitivity ctwas TWAS
0.04082 0.24490 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9962 0.9540 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.5000 0.3333 
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)
81 37 10
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