SCZ - Brain Caudate basal ganglia
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] 11549#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1113 836 688 449 569 642 534 428 425 443 690 658 238 386 385 524
17 18 19 20 21 22
714 185 888 345 129 280 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8945#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7745Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0111227 0.0002745 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
13.61 12.66 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11549 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01083 0.15925 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04685 0.80048Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
8043 PACSIN3 11_29 0.9920 48.73 0.0002995 7.013 2
6997 SPPL3 12_74 0.9891 34.38 0.0002107 -5.663 2
10518 MPPED1 22_19 0.9889 29.58 0.0001812 -5.318 2
11135 ZNF823 19_10 0.9774 40.41 0.0002447 6.308 2
12311 AC012074.2 2_16 0.9758 30.43 0.0001840 5.469 1
101 FARP2 2_144 0.9020 22.41 0.0001253 4.355 3
2677 TRPV4 12_66 0.8820 24.20 0.0001322 4.416 1
110 ELAC2 17_11 0.8757 24.49 0.0001329 5.507 1
12520 HLA-DMB 6_27 0.8715 79.57 0.0004296 -9.679 1
14012 MYO19 17_22 0.8505 26.46 0.0001394 -4.886 1
7145 ACE 17_37 0.8389 33.79 0.0001756 -5.876 1
6200 FAM135B 8_91 0.8356 22.25 0.0001152 -3.461 1
11142 RPL12 9_66 0.8267 23.97 0.0001228 4.663 2
7959 GTF2A1 14_39 0.8119 24.39 0.0001227 -4.850 1
5922 METTL21A 2_122 0.8042 25.32 0.0001261 -4.404 1
12134 AC008269.2 2_122 0.7729 22.36 0.0001071 4.336 1
5830 RIT1 1_76 0.7673 23.85 0.0001134 -4.023 1
11616 ITSN1 21_14 0.7568 22.63 0.0001061 4.315 2
8810 FOXN2 2_31 0.7510 26.11 0.0001215 -5.260 2
6653 SLC25A27 6_35 0.7493 25.86 0.0001200 -3.899 3Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10 SEMA3F 3_35 6.666e-02 769.81 3.179e-04 0.2075 1
38 RBM6 3_35 3.483e-01 456.56 9.852e-04 4.4688 1
9765 LSMEM2 3_35 5.565e-01 399.87 1.379e-03 4.2709 1
7736 MST1R 3_35 1.603e-05 382.04 3.794e-08 -3.4420 2
12539 NAT6 3_35 1.332e-05 360.61 2.975e-08 1.6917 3
10521 HYAL3 3_35 3.000e-05 337.77 6.278e-08 -2.5066 1
712 RASSF1 3_35 1.092e-05 322.64 2.184e-08 4.3268 1
7732 RNF123 3_35 9.944e-06 272.41 1.678e-08 -2.3622 1
130 CACNA2D2 3_35 4.435e-05 113.02 3.106e-08 -0.1392 1
10491 BTN3A2 6_20 1.445e-02 111.96 1.002e-05 9.0087 2
702 RHOA 3_35 1.187e-05 101.93 7.499e-09 -1.9997 1
1222 C3orf18 3_35 1.169e-04 99.42 7.203e-08 -0.4441 1
2990 HEMK1 3_35 1.169e-04 99.42 7.203e-08 0.4441 1
11731 CLIC1 6_26 4.557e-01 82.93 2.341e-04 10.7310 2
5119 PGBD1 6_22 2.865e-02 82.31 1.461e-05 -8.4603 1
11478 APOM 6_26 2.228e-01 81.12 1.120e-04 10.6484 1
2958 USP4 3_35 1.441e-04 80.77 7.213e-08 2.9885 2
12520 HLA-DMB 6_27 8.715e-01 79.57 4.296e-04 -9.6790 1
5990 AMT 3_35 1.828e-05 78.60 8.901e-09 -1.5571 1
12582 C4A 6_26 6.111e-02 78.41 2.968e-05 10.4799 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9765 LSMEM2 3_35 0.55648 399.87 0.0013786 4.2709 1
38 RBM6 3_35 0.34830 456.56 0.0009852 4.4688 1
12520 HLA-DMB 6_27 0.87145 79.57 0.0004296 -9.6790 1
10 SEMA3F 3_35 0.06666 769.81 0.0003179 0.2075 1
8043 PACSIN3 11_29 0.99198 48.73 0.0002995 7.0133 2
11135 ZNF823 19_10 0.97742 40.41 0.0002447 6.3077 2
7700 PBRM1 3_36 0.57790 66.13 0.0002368 -9.4285 1
11731 CLIC1 6_26 0.45566 82.93 0.0002341 10.7310 2
6997 SPPL3 12_74 0.98914 34.38 0.0002107 -5.6634 2
3091 SF3B1 2_117 0.63631 51.76 0.0002041 7.6053 1
8307 GATAD2A 19_15 0.60643 52.85 0.0001986 -7.4194 1
12090 HCG11 6_20 0.74660 40.25 0.0001862 -0.5804 1
12311 AC012074.2 2_16 0.97584 30.43 0.0001840 5.4694 1
10518 MPPED1 22_19 0.98894 29.58 0.0001812 -5.3176 2
1226 PPP1R13B 14_54 0.59527 48.93 0.0001805 7.4786 2
8639 INO80E 16_24 0.54928 51.79 0.0001762 7.5514 1
7145 ACE 17_37 0.83886 33.79 0.0001756 -5.8759 1
9836 HARBI1 11_28 0.43587 58.70 0.0001585 8.0462 1
11474 CSNK2B 6_26 0.73898 32.14 0.0001472 -6.9161 1
8241 PDIA3 15_16 0.62403 37.68 0.0001457 6.3137 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11731 CLIC1 6_26 0.4556596 82.93 2.341e-04 10.731 2
11478 APOM 6_26 0.2228140 81.12 1.120e-04 10.648 1
12582 C4A 6_26 0.0611069 78.41 2.968e-05 10.480 2
6289 CNNM2 10_66 0.1236064 41.57 3.184e-05 -9.914 2
12520 HLA-DMB 6_27 0.8714547 79.57 4.296e-04 -9.679 1
7700 PBRM1 3_36 0.5779011 66.13 2.368e-04 -9.429 1
11431 HLA-DMA 6_27 0.0804636 75.86 3.782e-05 -9.408 1
11444 PRRT1 6_26 0.0105908 56.70 3.721e-06 9.276 1
11469 MSH5 6_26 0.0057968 67.44 2.422e-06 9.136 2
7699 GNL3 3_36 0.1773205 63.53 6.980e-05 9.127 3
10491 BTN3A2 6_20 0.0144493 111.96 1.002e-05 9.009 2
6424 ABCB9 12_75 0.0006627 63.47 2.606e-07 8.638 1
9986 ARL6IP4 12_75 0.0006047 63.06 2.362e-07 8.615 1
2697 OGFOD2 12_75 0.0005826 62.95 2.272e-07 8.602 1
8450 SMIM4 3_36 0.0174944 56.68 6.144e-06 -8.494 1
5119 PGBD1 6_22 0.0286547 82.31 1.461e-05 -8.460 1
11440 AGER 6_26 0.0043172 40.24 1.076e-06 -8.380 2
9836 HARBI1 11_28 0.4358733 58.70 1.585e-04 8.046 1
2630 MDK 11_28 0.1542184 56.23 5.372e-05 -7.898 1
3012 NEK4 3_36 0.0107185 48.35 3.211e-06 7.898 1Comparing z scores and PIPs
[1] 0.01455GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 59Uploading 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
26 Confusion 0.02379 1/27 1/9703
51 Gingival Hypertrophy 0.02379 1/27 1/9703
62 Infant, Premature, Diseases 0.02379 1/27 1/9703
94 Pneumonia, Viral 0.02379 1/27 1/9703
96 Prostatic Neoplasms 0.02379 7/27 616/9703
103 Schizophrenia 0.02379 8/27 883/9703
125 Left Ventricular Hypertrophy 0.02379 2/27 25/9703
142 Speech impairment 0.02379 1/27 1/9703
143 Derealization 0.02379 1/27 1/9703
154 Spondylometaphyseal dysplasia, Kozlowski type 0.02379 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: 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] 65#significance threshold for TWAS
print(sig_thresh)[1] 4.595#number of ctwas genes
length(ctwas_genes)[1] 15#number of TWAS genes
length(twas_genes)[1] 168#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5922 METTL21A 2_122 0.8042 25.32 0.0001261 -4.404 1
101 FARP2 2_144 0.9020 22.41 0.0001253 4.355 3
6200 FAM135B 8_91 0.8356 22.25 0.0001152 -3.461 1
2677 TRPV4 12_66 0.8820 24.20 0.0001322 4.416 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.16154 #specificity
print(specificity) ctwas TWAS
0.9990 0.9872 #precision / PPV
print(precision)ctwas TWAS
0.200 0.125 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 65#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 775#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 3#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 53#sensitivity / recall
sensitivity ctwas TWAS
0.04615 0.32308 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9587 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3962 
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)
65 44 18
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