SCZ - Brain Nucleus accumbens basal ganglia
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] 10019#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
984 716 600 396 489 570 494 366 377 401 621 577 200 337 331 411 611 164 776 316
21 22
32 250 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6976#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6963Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0135548 0.0003062 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.98 10.50 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10019 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01416 0.19266 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06609 1.10055Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
11067 ZNF823 19_10 0.9830 36.50 0.0003406 6.184 2
4131 SPECC1 17_16 0.9649 28.01 0.0002566 5.295 2
13453 RP11-230C9.4 6_102 0.9623 23.13 0.0002113 -4.738 2
5491 FURIN 15_42 0.9589 46.05 0.0004193 -6.990 1
13724 RP11-408A13.3 9_12 0.9236 22.78 0.0001998 4.536 1
10921 PCBP2 12_33 0.8789 26.37 0.0002201 5.065 1
3067 SF3B1 2_117 0.8314 48.79 0.0003852 7.265 1
10418 TMEM222 1_19 0.8165 22.08 0.0001712 4.303 1
6509 TMEM56 1_58 0.8064 20.82 0.0001594 -3.907 1
268 VSIG2 11_77 0.7953 30.05 0.0002269 -4.616 2
3468 PYROXD2 10_62 0.7908 20.64 0.0001550 3.952 1
320 VRK2 2_38 0.7514 36.84 0.0002628 4.977 1
5689 SYTL1 1_19 0.7452 24.35 0.0001723 4.306 2
13743 CWC25 17_23 0.7406 21.86 0.0001537 -4.095 3
11955 LINC00390 13_17 0.6989 22.10 0.0001467 -4.536 1
1843 SETD6 16_31 0.6968 37.01 0.0002448 -6.343 1
4135 CDHR3 7_65 0.6967 22.67 0.0001499 4.315 1
7855 MAMDC2 9_31 0.6876 21.71 0.0001418 4.125 1
11298 LINC00862 1_101 0.6826 22.93 0.0001486 4.339 2
7266 DBF4B 17_26 0.6789 19.28 0.0001243 3.890 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
12325 HLA-DQA2 6_26 0.000e+00 1505.7 0.000e+00 -0.6489 2
12152 HLA-DQB2 6_26 0.000e+00 797.3 0.000e+00 -3.4195 1
12513 C4A 6_26 4.570e-02 674.8 2.928e-04 11.3259 1
11404 APOM 6_26 4.547e-01 647.0 2.793e-03 11.5895 1
11389 C6orf48 6_26 9.823e-05 601.4 5.609e-07 10.9169 2
11395 MSH5 6_26 2.863e-06 580.6 1.578e-08 10.5589 2
11386 EHMT2 6_26 0.000e+00 436.6 0.000e+00 7.5336 1
10865 HLA-DQA1 6_26 0.000e+00 411.1 0.000e+00 3.6960 2
11372 AGPAT1 6_26 0.000e+00 405.0 0.000e+00 -5.1903 1
11371 AGER 6_26 1.776e-15 402.3 6.786e-18 -9.0708 1
11369 NOTCH4 6_26 0.000e+00 315.7 0.000e+00 7.5989 2
11367 BTNL2 6_26 0.000e+00 302.3 0.000e+00 3.4859 1
11391 HSPA1L 6_26 0.000e+00 286.0 0.000e+00 1.4874 1
11648 DDAH2 6_26 0.000e+00 259.2 0.000e+00 8.1494 1
11397 LY6G6C 6_26 0.000e+00 243.4 0.000e+00 -7.8392 3
11375 FKBPL 6_26 0.000e+00 187.8 0.000e+00 -5.2840 2
11647 CLIC1 6_26 0.000e+00 160.9 0.000e+00 0.0146 1
11366 HLA-DRA 6_26 0.000e+00 159.0 0.000e+00 3.7977 1
11642 ATF6B 6_26 0.000e+00 157.2 0.000e+00 4.3893 1
11370 PBX2 6_26 0.000e+00 153.9 0.000e+00 -1.0290 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11404 APOM 6_26 0.4547 647.02 0.0027931 11.590 1
5491 FURIN 15_42 0.9589 46.05 0.0004193 -6.990 1
3067 SF3B1 2_117 0.8314 48.79 0.0003852 7.265 1
11067 ZNF823 19_10 0.9830 36.50 0.0003406 6.184 2
2602 MDK 11_28 0.6640 46.66 0.0002941 -7.159 1
12513 C4A 6_26 0.0457 674.80 0.0002928 11.326 1
320 VRK2 2_38 0.7514 36.84 0.0002628 4.977 1
4131 SPECC1 17_16 0.9649 28.01 0.0002566 5.295 2
1843 SETD6 16_31 0.6968 37.01 0.0002448 -6.343 1
268 VSIG2 11_77 0.7953 30.05 0.0002269 -4.616 2
10921 PCBP2 12_33 0.8789 26.37 0.0002201 5.065 1
13453 RP11-230C9.4 6_102 0.9623 23.13 0.0002113 -4.738 2
13724 RP11-408A13.3 9_12 0.9236 22.78 0.0001998 4.536 1
7851 LETM2 8_34 0.5510 37.51 0.0001962 -6.067 1
5689 SYTL1 1_19 0.7452 24.35 0.0001723 4.306 2
10418 TMEM222 1_19 0.8165 22.08 0.0001712 4.303 1
410 CTNNA1 5_82 0.6507 26.43 0.0001633 5.512 1
6509 TMEM56 1_58 0.8064 20.82 0.0001594 -3.907 1
3468 PYROXD2 10_62 0.7908 20.64 0.0001550 3.952 1
13743 CWC25 17_23 0.7406 21.86 0.0001537 -4.095 3Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11404 APOM 6_26 4.547e-01 647.02 2.793e-03 11.590 1
12513 C4A 6_26 4.570e-02 674.80 2.928e-04 11.326 1
5093 FLOT1 6_24 5.725e-02 79.39 4.315e-05 -10.981 1
11389 C6orf48 6_26 9.823e-05 601.40 5.609e-07 10.917 2
10415 BTN3A2 6_20 2.445e-02 94.77 2.200e-05 10.822 3
11395 MSH5 6_26 2.863e-06 580.55 1.578e-08 10.559 2
10915 ZSCAN26 6_22 1.614e-02 73.96 1.134e-05 10.158 3
2826 BTN2A1 6_20 5.570e-02 83.47 4.415e-05 -10.131 1
9788 HIST1H2BC 6_20 3.494e-02 81.03 2.688e-05 -9.909 1
11431 RNF39 6_24 1.261e-01 59.40 7.110e-05 9.536 1
11371 AGER 6_26 1.776e-15 402.34 6.786e-18 -9.071 1
2790 TRIM38 6_20 2.329e-02 65.72 1.453e-05 -9.032 2
11359 HLA-DMA 6_27 5.353e-02 66.65 3.388e-05 -8.845 1
12454 HLA-DMB 6_27 6.309e-02 67.59 4.049e-05 -8.701 2
11648 DDAH2 6_26 0.000e+00 259.20 0.000e+00 8.149 1
6275 CNNM2 10_66 3.990e-02 40.21 1.523e-05 -8.125 2
11397 LY6G6C 6_26 0.000e+00 243.41 0.000e+00 -7.839 3
10558 ZSCAN23 6_22 7.201e-02 47.91 3.275e-05 -7.769 2
11369 NOTCH4 6_26 0.000e+00 315.72 0.000e+00 7.599 2
11386 EHMT2 6_26 0.000e+00 436.64 0.000e+00 7.534 1Comparing z scores and PIPs
[1] 0.01008GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 39Uploading 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 Overlap
1 positive regulation of neuron migration (GO:2001224) 2/13
2 cell-cell junction assembly (GO:0007043) 3/66
3 cell-cell junction organization (GO:0045216) 3/82
4 positive regulation of cartilage development (GO:0061036) 2/18
5 regulation of cartilage development (GO:0061035) 2/18
6 cellular response to osmotic stress (GO:0071470) 2/22
7 cellular response to chemical stress (GO:0062197) 3/101
8 regulation of microtubule depolymerization (GO:0031114) 2/25
9 regulation of regulatory T cell differentiation (GO:0045589) 2/26
10 regulation of chondrocyte differentiation (GO:0032330) 2/26
Adjusted.P.value Genes
1 0.04580 MDK;ARHGEF2
2 0.04580 TRPV4;CTNNA1;CDHR3
3 0.04580 TRPV4;CTNNA1;CDHR3
4 0.04580 MDK;SOX5
5 0.04580 MDK;SOX5
6 0.04817 TRPV4;ARHGEF2
7 0.04817 PRDX2;TRPV4;VRK2
8 0.04817 TRPV4;ARHGEF2
9 0.04817 MDK;CD46
10 0.04817 MDK;SOX5
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 focal adhesion (GO:0005925) 6/387 0.003283
2 cell-substrate junction (GO:0030055) 6/394 0.003283
3 catenin complex (GO:0016342) 2/31 0.033461
4 adherens junction (GO:0005912) 3/132 0.033461
Genes
1 RPL12;TRPV4;PCBP2;CTNNA1;ARHGEF2;CD46
2 RPL12;TRPV4;PCBP2;CTNNA1;ARHGEF2;CD46
3 CTNNA1;CDHR3
4 TRPV4;CTNNA1;CDHR3
[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
15 Confusion
40 Measles
66 Speech impairment
67 Derealization
73 Spondylometaphyseal dysplasia, Kozlowski type
74 Metatropic dwarfism
91 Brachyolmia Type 3
99 Sexually disinhibited behavior
106 Hypersomnia, Recurrent
129 SPINAL MUSCULAR ATROPHY, DISTAL, CONGENITAL NONPROGRESSIVE (disorder)
FDR Ratio BgRatio
15 0.01027 1/15 1/9703
40 0.01027 1/15 1/9703
66 0.01027 1/15 1/9703
67 0.01027 1/15 1/9703
73 0.01027 1/15 1/9703
74 0.01027 1/15 1/9703
91 0.01027 1/15 1/9703
99 0.01027 1/15 1/9703
106 0.01027 1/15 1/9703
129 0.01027 1/15 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
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] 52#significance threshold for TWAS
print(sig_thresh)[1] 4.565#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 101#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
10418 TMEM222 1_19 0.8165 22.08 0.0001712 4.303 1
6509 TMEM56 1_58 0.8064 20.82 0.0001594 -3.907 1
13724 RP11-408A13.3 9_12 0.9236 22.78 0.0001998 4.536 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.12308 #specificity
print(specificity) ctwas TWAS
0.9994 0.9915 #precision / PPV
print(precision) ctwas TWAS
0.3333 0.1584 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 52#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 539#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.565#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] 27#sensitivity / recall
sensitivity ctwas TWAS
0.05769 0.30769 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9796 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.5926 
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)
78 36 13
Detected (PIP > 0.8)
3 #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