SCZ - Brain Cerebellum
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] 11545#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1117 804 666 419 565 647 572 430 445 459 692 655 227 380 381 540
17 18 19 20 21 22
701 176 906 341 127 295 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8808#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7629Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0140541 0.0002651 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
12.84 12.92 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11545 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.0129 0.1568 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05055 0.78974Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
5873 GALNT2 1_117 0.9848 32.17 1.963e-04 5.728 2
12285 AC012074.2 2_15 0.9813 30.25 1.839e-04 5.469 1
11129 ZNF823 19_10 0.9789 39.12 2.373e-04 6.273 2
4195 FEZF1 7_74 0.9613 23.41 1.394e-04 -4.656 1
9127 MAP3K11 11_36 0.9385 34.92 2.031e-04 -5.790 2
2539 MMD 17_32 0.9186 25.47 1.450e-04 -4.548 1
2706 TRPV4 12_66 0.9062 24.10 1.353e-04 4.416 1
7609 SERPINI1 3_103 0.9022 24.40 1.364e-04 -4.706 2
753 ATP1B3 3_87 0.8849 20.49 1.123e-04 4.085 1
7164 ACE 17_37 0.8757 33.97 1.843e-04 -5.876 1
5541 FANCI 15_41 0.8704 39.54 2.132e-04 -6.308 1
6249 FAM135B 8_91 0.8693 22.03 1.187e-04 -3.461 1
11430 HLA-DMA 6_27 0.8595 78.23 4.166e-04 -9.703 2
4587 ACY3 11_37 0.8400 19.58 1.019e-04 -3.397 2
3420 ABCG2 4_59 0.8328 20.30 1.047e-04 -3.954 1
3521 SLF2 10_64 0.8172 24.41 1.236e-04 -4.618 2
1289 MLF2 12_7 0.7962 21.44 1.058e-04 -3.939 2
7564 ANTXR2 4_54 0.7715 20.82 9.954e-05 3.831 1
3809 PFKFB2 1_105 0.7655 25.40 1.204e-04 4.891 1
10121 ZNRF3 22_9 0.7652 24.55 1.164e-04 -4.646 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
722 RASSF1 3_35 3.464e-14 738.46 1.585e-16 4.3268 1
12132 U73166.2 3_35 3.331e-16 725.82 1.498e-18 -3.8316 1
10677 SLC38A3 3_35 1.352e-12 231.84 1.943e-15 -2.7756 1
122 CACNA2D2 3_35 0.000e+00 211.78 0.000e+00 -0.1392 1
34 RBM6 3_35 3.752e-01 200.14 4.653e-04 4.4688 1
3033 HEMK1 3_35 0.000e+00 183.60 0.000e+00 0.4441 1
7733 CAMKV 3_35 1.323e-04 176.93 1.450e-07 -2.5717 2
10506 HYAL3 3_35 4.805e-13 162.41 4.835e-16 -2.5066 1
11798 IFRD2 3_35 4.805e-13 162.41 4.835e-16 -2.5066 1
207 SEMA3B 3_35 0.000e+00 116.66 0.000e+00 0.6250 1
12064 HCG11 6_20 1.747e-02 114.84 1.243e-05 9.8443 1
13097 CTA-14H9.5 6_20 1.747e-02 114.84 1.243e-05 9.8443 1
13664 LINC02019 3_35 0.000e+00 110.76 0.000e+00 0.3059 2
7729 RNF123 3_35 4.441e-16 98.77 2.718e-19 -2.3252 1
2890 PRSS16 6_21 2.659e-02 95.09 1.567e-05 -11.0498 2
3034 CISH 3_35 0.000e+00 89.05 0.000e+00 -0.8833 1
10473 BTN3A2 6_20 2.928e-02 87.85 1.594e-05 7.8089 2
9834 HIST1H2BC 6_20 1.747e-02 82.66 8.948e-06 -7.9928 1
3007 CYB561D2 3_35 0.000e+00 81.53 0.000e+00 3.5093 1
13535 RP1-86C11.7 6_21 5.223e-01 81.13 2.625e-04 10.5382 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
34 RBM6 3_35 0.3752 200.14 0.0004653 4.469 1
11430 HLA-DMA 6_27 0.8595 78.23 0.0004166 -9.703 2
13535 RP1-86C11.7 6_21 0.5223 81.13 0.0002625 10.538 1
11129 ZNF823 19_10 0.9789 39.12 0.0002373 6.273 2
5541 FANCI 15_41 0.8704 39.54 0.0002132 -6.308 1
7696 GNL3 3_36 0.5554 61.48 0.0002115 9.297 2
9127 MAP3K11 11_36 0.9385 34.92 0.0002031 -5.790 2
5873 GALNT2 1_117 0.9848 32.17 0.0001963 5.728 2
7164 ACE 17_37 0.8757 33.97 0.0001843 -5.876 1
12285 AC012074.2 2_15 0.9813 30.25 0.0001839 5.469 1
12147 ANKRD63 15_14 0.6950 37.72 0.0001624 6.183 1
3127 SF3B1 2_117 0.4697 51.83 0.0001508 7.605 1
5022 ALPK3 15_39 0.4967 48.55 0.0001494 -7.198 1
2539 MMD 17_32 0.9186 25.47 0.0001450 -4.548 1
4195 FEZF1 7_74 0.9613 23.41 0.0001394 -4.656 1
11348 NAGA 22_17 0.6974 32.25 0.0001393 7.211 2
7609 SERPINI1 3_103 0.9022 24.40 0.0001364 -4.706 2
2706 TRPV4 12_66 0.9062 24.10 0.0001353 4.416 1
3521 SLF2 10_64 0.8172 24.41 0.0001236 -4.618 2
7473 SLC9C2 1_85 0.6256 31.86 0.0001235 -6.146 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
2890 PRSS16 6_21 0.0265899 95.09 1.567e-05 -11.050 2
13535 RP1-86C11.7 6_21 0.5223068 81.13 2.625e-04 10.538 1
12543 C4A 6_26 0.1304147 70.64 5.708e-05 10.418 1
12064 HCG11 6_20 0.0174678 114.84 1.243e-05 9.844 1
13097 CTA-14H9.5 6_20 0.0174678 114.84 1.243e-05 9.844 1
11430 HLA-DMA 6_27 0.8594771 78.23 4.166e-04 -9.703 2
12487 HLA-DMB 6_27 0.0844979 74.09 3.879e-05 -9.380 1
7696 GNL3 3_36 0.5553870 61.48 2.115e-04 9.297 2
11441 RNF5 6_26 0.0056087 37.95 1.319e-06 8.765 2
7697 PBRM1 3_36 0.0224563 54.45 7.575e-06 -8.722 1
2725 OGFOD2 12_75 0.0004020 64.42 1.605e-07 8.627 1
9965 ARL6IP4 12_75 0.0003826 64.27 1.524e-07 -8.615 1
8440 GLYCTK 3_36 0.1231524 69.39 5.295e-05 8.577 1
11938 LINC00240 6_21 0.0110127 47.12 3.215e-06 -8.297 1
11484 CCHCR1 6_25 0.0098039 63.87 3.879e-06 -8.245 5
5147 FLOT1 6_24 0.0143588 65.59 5.835e-06 -8.105 3
9818 HARBI1 11_28 0.3192506 58.61 1.159e-04 8.046 1
9327 ATG13 11_28 0.3192506 58.61 1.159e-04 -8.046 1
9834 HIST1H2BC 6_20 0.0174707 82.66 8.948e-06 -7.993 1
10835 TUBB 6_24 0.0138123 60.49 5.177e-06 -7.980 1Comparing z scores and PIPs
[1] 0.01784Gene with high z-score but low PIP, assign to SNP or to gene?
high_z_genes_region <- unique(head(ctwas_gene_res[order(-abs(ctwas_gene_res$z)),report_cols],40)$region_tag)
sum <- 0
for(i in high_z_genes_region){
locus <- ctwas_res[ctwas_res$region_tag==i,]
locus <- head(locus[order(-locus$susie_pip),],20)
snp_pip <- sum(locus[locus$type == 'SNP','susie_pip'])
gene_pip <- sum(locus[locus$type == 'gene','susie_pip'])
print(snp_pip/(snp_pip+gene_pip))
}[1] 0.8582
[1] 0.6929
[1] 0.9413
[1] 0.1099
[1] 0.4675
[1] 0.8757
[1] 0.823
[1] 0.7916
[1] 0.6644
[1] 1
[1] 0.9768
[1] 0.4787
[1] 0.4546
[1] 1
[1] 0.8504
[1] 0.6356
[1] 0.405GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 63Uploading 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"
[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 BgRatio
28 Confusion 0.0293 1/27 1/9703
52 Gingival Hypertrophy 0.0293 1/27 1/9703
70 Infant, Premature, Diseases 0.0293 1/27 1/9703
109 Pneumonia, Viral 0.0293 1/27 1/9703
160 Speech impairment 0.0293 1/27 1/9703
161 Derealization 0.0293 1/27 1/9703
176 Burnett Schwartz Berberian syndrome 0.0293 1/27 1/9703
177 Atrophoderma vermiculatum 0.0293 1/27 1/9703
179 Spondylometaphyseal dysplasia, Kozlowski type 0.0293 1/27 1/9703
180 Metatropic dwarfism 0.0293 1/27 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: 22 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] 68#significance threshold for TWAS
print(sig_thresh)[1] 4.595#number of ctwas genes
length(ctwas_genes)[1] 16#number of TWAS genes
length(twas_genes)[1] 206#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
753 ATP1B3 3_87 0.8849 20.49 0.0001123 4.085 1
3420 ABCG2 4_59 0.8328 20.30 0.0001047 -3.954 1
6249 FAM135B 8_91 0.8693 22.03 0.0001187 -3.461 1
4587 ACY3 11_37 0.8400 19.58 0.0001019 -3.397 2
2706 TRPV4 12_66 0.9062 24.10 0.0001353 4.416 1
2539 MMD 17_32 0.9186 25.47 0.0001450 -4.548 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.16154 #specificity
print(specificity) ctwas TWAS
0.9988 0.9839 #precision / PPV
print(precision) ctwas TWAS
0.1250 0.1019 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 68#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 817#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.595#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] 70#sensitivity / recall
sensitivity ctwas TWAS
0.02941 0.30882 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9976 0.9400 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
0.5 0.3 
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)
62 47 19
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