SCZ - Brain Amygdala
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] 8763#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
857 634 491 344 439 522 412 309 342 339 551 509 196 285 296 349 522 147 694 272
21 22
23 230 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6434#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7342Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0068728 0.0003262 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
17.64 10.18 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 8763 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01009 0.19900 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.03318 1.09729Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10169 ZNF823 19_10 0.9693 38.09 0.0003506 6.143 1
11179 HIST1H2BN 6_21 0.9271 147.71 0.0013003 13.396 1
11222 AC012074.2 2_15 0.9096 23.50 0.0002030 4.671 2
2362 B3GAT1 11_84 0.8902 30.20 0.0002553 -5.211 2
12505 RP11-408A13.3 9_12 0.8618 24.23 0.0001983 4.536 1
12183 RP11-247A12.7 9_66 0.8599 23.99 0.0001959 4.683 1
296 VRK2 2_38 0.6725 39.11 0.0002498 4.977 1
93 ELAC2 17_11 0.6609 25.64 0.0001609 4.752 1
2406 MDK 11_28 0.6495 49.33 0.0003042 -7.159 1
9695 HIST1H1C 6_20 0.6481 26.32 0.0001620 4.586 2
8298 RNASEH2C 11_36 0.6378 26.54 0.0001607 -4.491 1
3251 CYSTM1 5_83 0.6219 26.62 0.0001572 -4.480 1
2889 DLX2 2_104 0.5833 23.13 0.0001281 4.259 1
10971 LINC00390 13_17 0.5453 23.40 0.0001211 -4.536 1
2135 TLE4 9_38 0.5220 22.83 0.0001132 -4.279 1
5997 TMEM56 1_58 0.5177 29.60 0.0001455 -3.907 1
2782 LMAN2L 2_57 0.5148 30.65 0.0001498 -4.276 2
2951 ARHGEF2 1_76 0.5083 26.14 0.0001261 3.816 1
7221 MAMDC2 9_31 0.5037 25.77 0.0001232 4.125 1
377 CTNNA1 5_82 0.4793 27.63 0.0001257 5.491 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10468 ABHD16A 6_27 4.173e-08 234.37 9.286e-11 11.526 1
10465 MSH5 6_27 3.268e-08 233.32 7.241e-11 11.506 1
11458 C4A 6_27 7.516e-09 225.88 1.612e-11 11.326 1
10473 APOM 6_27 6.379e-10 179.20 1.085e-12 9.901 2
11179 HIST1H2BN 6_21 9.271e-01 147.71 1.300e-03 13.396 1
10445 AGPAT1 6_27 1.035e-10 110.58 1.087e-13 -5.190 1
9573 BTN3A2 6_20 9.453e-03 98.01 8.797e-06 10.797 2
8899 HLA-DQB1 6_27 8.804e-09 95.42 7.977e-12 4.624 1
10707 DDAH2 6_27 8.193e-09 91.92 7.151e-12 8.149 1
9980 HLA-DQA1 6_27 1.524e-07 89.62 1.297e-10 4.441 1
10436 HLA-DMA 6_27 2.720e-04 88.71 2.291e-07 -8.845 1
4692 FLOT1 6_24 5.245e-02 85.97 4.282e-05 -10.981 1
9870 HLA-DRB1 6_27 3.229e-09 85.82 2.631e-12 5.077 1
8984 HIST1H2BC 6_20 1.367e-02 84.14 1.092e-05 -9.909 1
11286 HLA-DQA2 6_27 1.754e-08 80.00 1.332e-11 -3.704 2
1135 PPP1R13B 14_54 2.870e-01 63.46 1.729e-04 -7.019 2
11128 CYP21A2 6_27 2.000e-08 57.87 1.099e-11 5.267 2
437 MPHOSPH9 12_75 1.537e-01 56.89 8.301e-05 7.662 1
10231 ZKSCAN8 6_22 5.880e-03 56.79 3.171e-06 7.465 1
10458 SLC44A4 6_27 9.789e-06 55.82 5.188e-09 6.717 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 0.9271 147.71 0.0013003 13.396 1
10169 ZNF823 19_10 0.9693 38.09 0.0003506 6.143 1
2406 MDK 11_28 0.6495 49.33 0.0003042 -7.159 1
2362 B3GAT1 11_84 0.8902 30.20 0.0002553 -5.211 2
296 VRK2 2_38 0.6725 39.11 0.0002498 4.977 1
11222 AC012074.2 2_15 0.9096 23.50 0.0002030 4.671 2
12505 RP11-408A13.3 9_12 0.8618 24.23 0.0001983 4.536 1
12183 RP11-247A12.7 9_66 0.8599 23.99 0.0001959 4.683 1
1135 PPP1R13B 14_54 0.2870 63.46 0.0001729 -7.019 2
9695 HIST1H1C 6_20 0.6481 26.32 0.0001620 4.586 2
93 ELAC2 17_11 0.6609 25.64 0.0001609 4.752 1
8298 RNASEH2C 11_36 0.6378 26.54 0.0001607 -4.491 1
3251 CYSTM1 5_83 0.6219 26.62 0.0001572 -4.480 1
3591 CNOT1 16_31 0.4287 37.81 0.0001539 6.215 1
2782 LMAN2L 2_57 0.5148 30.65 0.0001498 -4.276 2
5997 TMEM56 1_58 0.5177 29.60 0.0001455 -3.907 1
677 PPP2R5B 11_36 0.4723 31.35 0.0001406 -5.093 1
2889 DLX2 2_104 0.5833 23.13 0.0001281 4.259 1
2951 ARHGEF2 1_76 0.5083 26.14 0.0001261 3.816 1
377 CTNNA1 5_82 0.4793 27.63 0.0001257 5.491 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11179 HIST1H2BN 6_21 9.271e-01 147.71 1.300e-03 13.396 1
10468 ABHD16A 6_27 4.173e-08 234.37 9.286e-11 11.526 1
10465 MSH5 6_27 3.268e-08 233.32 7.241e-11 11.506 1
11458 C4A 6_27 7.516e-09 225.88 1.612e-11 11.326 1
4692 FLOT1 6_24 5.245e-02 85.97 4.282e-05 -10.981 1
9573 BTN3A2 6_20 9.453e-03 98.01 8.797e-06 10.797 2
8984 HIST1H2BC 6_20 1.367e-02 84.14 1.092e-05 -9.909 1
10473 APOM 6_27 6.379e-10 179.20 1.085e-12 9.901 2
10436 HLA-DMA 6_27 2.720e-04 88.71 2.291e-07 -8.845 1
5778 CNNM2 10_66 6.889e-02 47.06 3.078e-05 -8.161 1
10707 DDAH2 6_27 8.193e-09 91.92 7.151e-12 8.149 1
437 MPHOSPH9 12_75 1.537e-01 56.89 8.301e-05 7.662 1
10231 ZKSCAN8 6_22 5.880e-03 56.79 3.171e-06 7.465 1
11226 ZSCAN31 6_22 7.515e-03 35.76 2.552e-06 -7.444 2
2406 MDK 11_28 6.495e-01 49.33 3.042e-04 -7.159 1
9987 ZSCAN16 6_22 6.258e-03 49.25 2.927e-06 7.135 1
1135 PPP1R13B 14_54 2.870e-01 63.46 1.729e-04 -7.019 2
8969 HARBI1 11_28 2.046e-01 46.57 9.046e-05 6.977 1
10510 LINC01556 6_22 9.927e-03 37.31 3.517e-06 -6.865 1
2590 TRIM38 6_20 8.548e-03 39.73 3.225e-06 6.798 2Comparing z scores and PIPs
[1] 0.01004GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 19Uploading 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 Adjusted.P.value
1 positive regulation of neuron migration (GO:2001224) 2/13 0.01359
2 regulation of neuron migration (GO:2001222) 2/28 0.03264
Genes
1 MDK;ARHGEF2
2 MDK;ARHGEF2
[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
45 AICARDI-GOUTIERES SYNDROME 3
49 PROSTATE CANCER, HEREDITARY, 2
51 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17
53 MENTAL RETARDATION, AUTOSOMAL RECESSIVE 52
54 NEURODEVELOPMENTAL DISORDER WITH MIDBRAIN AND HINDBRAIN MALFORMATIONS
47 Prostate cancer, familial
18 Schizophrenia
32 AICARDI-GOUTIERES SYNDROME
1 Anxiety Disorders
7 Diabetic Nephropathy
FDR Ratio BgRatio
45 0.01134 1/10 1/9703
49 0.01134 1/10 1/9703
51 0.01134 1/10 1/9703
53 0.01134 1/10 1/9703
54 0.01134 1/10 1/9703
47 0.01982 2/10 69/9703
18 0.06298 4/10 883/9703
32 0.06298 1/10 8/9703
1 0.11644 1/10 44/9703
7 0.11644 1/10 44/9703WebGestalt 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] 46#significance threshold for TWAS
print(sig_thresh)[1] 4.537#number of ctwas genes
length(ctwas_genes)[1] 6#number of TWAS genes
length(twas_genes)[1] 88#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
12505 RP11-408A13.3 9_12 0.8618 24.23 0.0001983 4.536 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.061538 #specificity
print(specificity) ctwas TWAS
0.9994 0.9908 #precision / PPV
print(precision) ctwas TWAS
0.16667 0.09091 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 46#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 443#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.537#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 1#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 18#sensitivity / recall
sensitivity ctwas TWAS
0.02174 0.17391 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9774 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4444 
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)
84 38 7
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_1.1.1 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