SCZ - Brain Cerebellar Hemisphere
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] 9945#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
941 692 574 371 468 558 511 377 411 395 633 567 188 341 340 419 633 153 782 293
21 22
31 267 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6702#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.6739Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0143819 0.0003031 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
9.894 10.233 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9945 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01344 0.18584 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06308 1.04445Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10988 ZNF823 19_10 0.9851 36.62 0.0003425 6.211 2
5783 GALNT2 1_117 0.9649 25.23 0.0002311 5.083 1
4143 FEZF1 7_74 0.9547 23.85 0.0002162 -4.812 1
12095 AC012074.2 2_15 0.9494 21.97 0.0001981 4.655 1
13452 RP11-408A13.3 9_12 0.9238 22.50 0.0001974 4.536 1
111 ELAC2 17_11 0.8205 21.18 0.0001650 4.752 1
3099 SF3B1 2_117 0.8183 47.43 0.0003685 7.265 1
5667 SYTL1 1_19 0.8065 22.96 0.0001758 4.272 2
10337 TMEM222 1_19 0.8055 22.21 0.0001699 4.303 1
174 ZNF207 17_19 0.7940 23.35 0.0001760 4.599 1
6255 DRD2 11_68 0.7935 26.30 0.0001981 -5.632 1
5591 ZCCHC2 18_34 0.7821 19.60 0.0001456 -3.877 1
1221 EDEM2 20_21 0.7595 20.25 0.0001460 4.057 2
11339 DISP3 1_8 0.7578 20.02 0.0001440 3.696 2
9372 LY6H 8_94 0.7552 21.68 0.0001555 4.186 1
6920 CNNM4 2_57 0.7331 23.15 0.0001612 -4.456 1
7051 ZNF235 19_31 0.7271 20.54 0.0001418 -4.002 2
13014 TBC1D29 17_18 0.7266 22.91 0.0001581 -4.592 1
5459 RLBP1 15_41 0.7242 22.94 0.0001577 -4.280 1
2207 RUNDC3B 7_54 0.6982 23.87 0.0001582 5.102 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11296 C6orf48 6_26 1.273e-04 210.63 2.546e-07 11.5418 1
11553 CLIC1 6_26 1.217e-04 209.14 2.416e-07 11.5063 1
12355 C4A 6_26 9.091e-05 207.34 1.790e-07 11.4403 2
11281 RNF5 6_26 8.237e-06 160.76 1.257e-08 9.7754 2
11986 CYP21A2 6_26 3.463e-07 153.54 5.049e-10 -9.0790 2
11284 PRRT1 6_26 5.771e-05 146.17 8.010e-08 10.0611 1
11280 AGER 6_26 5.527e-06 104.88 5.504e-09 -9.0708 1
5086 PGBD1 6_22 1.907e-02 95.36 1.726e-05 -10.2310 1
11884 HCG11 6_20 2.727e-02 95.33 2.469e-05 11.0152 1
12879 CTA-14H9.5 6_20 2.727e-02 95.33 2.469e-05 11.0152 1
9601 HLA-DQB1 6_26 3.543e-07 92.42 3.109e-10 1.4260 2
11279 NOTCH4 6_26 6.558e-06 89.95 5.601e-09 7.8425 2
12178 HLA-DQA2 6_26 3.764e-07 87.21 3.117e-10 0.9484 1
10334 BTN3A2 6_20 2.289e-02 85.48 1.858e-05 10.5362 1
5083 FLOT1 6_24 4.328e-02 80.14 3.293e-05 -10.9813 1
11282 AGPAT1 6_26 3.493e-07 77.56 2.572e-10 -4.4655 1
6038 ABT1 6_20 5.969e-02 75.30 4.268e-05 9.6693 1
11554 DDAH2 6_26 2.056e-05 73.58 1.436e-08 8.1494 1
11298 HSPA1A 6_26 2.419e-05 71.50 1.643e-08 8.0745 1
2826 TRIM38 6_20 2.508e-02 70.09 1.669e-05 -9.5422 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3099 SF3B1 2_117 0.8183 47.43 0.0003685 7.265 1
10988 ZNF823 19_10 0.9851 36.62 0.0003425 6.211 2
9199 ATG13 11_28 0.6200 44.07 0.0002594 -6.977 1
5783 GALNT2 1_117 0.9649 25.23 0.0002311 5.083 1
4143 FEZF1 7_74 0.9547 23.85 0.0002162 -4.812 1
10942 NMB 15_39 0.6196 34.32 0.0002019 5.881 1
6255 DRD2 11_68 0.7935 26.30 0.0001981 -5.632 1
12095 AC012074.2 2_15 0.9494 21.97 0.0001981 4.655 1
13452 RP11-408A13.3 9_12 0.9238 22.50 0.0001974 4.536 1
174 ZNF207 17_19 0.7940 23.35 0.0001760 4.599 1
5667 SYTL1 1_19 0.8065 22.96 0.0001758 4.272 2
10337 TMEM222 1_19 0.8055 22.21 0.0001699 4.303 1
111 ELAC2 17_11 0.8205 21.18 0.0001650 4.752 1
8510 INO80E 16_24 0.3995 43.47 0.0001649 6.852 1
6920 CNNM4 2_57 0.7331 23.15 0.0001612 -4.456 1
9728 FAM83H 8_94 0.6009 27.99 0.0001597 5.057 2
2207 RUNDC3B 7_54 0.6982 23.87 0.0001582 5.102 1
13014 TBC1D29 17_18 0.7266 22.91 0.0001581 -4.592 1
5459 RLBP1 15_41 0.7242 22.94 0.0001577 -4.280 1
4124 RNF112 17_16 0.5946 27.75 0.0001567 5.122 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11296 C6orf48 6_26 1.273e-04 210.63 2.546e-07 11.542 1
11553 CLIC1 6_26 1.217e-04 209.14 2.416e-07 11.506 1
12355 C4A 6_26 9.091e-05 207.34 1.790e-07 11.440 2
11884 HCG11 6_20 2.727e-02 95.33 2.469e-05 11.015 1
12879 CTA-14H9.5 6_20 2.727e-02 95.33 2.469e-05 11.015 1
5083 FLOT1 6_24 4.328e-02 80.14 3.293e-05 -10.981 1
10334 BTN3A2 6_20 2.289e-02 85.48 1.858e-05 10.536 1
5086 PGBD1 6_22 1.907e-02 95.36 1.726e-05 -10.231 1
11284 PRRT1 6_26 5.771e-05 146.17 8.010e-08 10.061 1
11281 RNF5 6_26 8.237e-06 160.76 1.257e-08 9.775 2
6038 ABT1 6_20 5.969e-02 75.30 4.268e-05 9.669 1
2826 TRIM38 6_20 2.508e-02 70.09 1.669e-05 -9.542 2
11986 CYP21A2 6_26 3.463e-07 153.54 5.049e-10 -9.079 2
11280 AGER 6_26 5.527e-06 104.88 5.504e-09 -9.071 1
12301 HLA-DMB 6_27 1.197e-01 69.61 7.910e-05 -8.812 1
11273 HLA-DMA 6_27 5.302e-02 63.51 3.197e-05 -8.778 2
6221 CNNM2 10_66 9.532e-02 41.33 3.740e-05 -8.161 1
11554 DDAH2 6_26 2.056e-05 73.58 1.436e-08 8.149 1
11298 HSPA1A 6_26 2.419e-05 71.50 1.643e-08 8.075 1
10488 ZSCAN23 6_22 7.885e-02 47.46 3.553e-05 -7.854 1Comparing z scores and PIPs
[1] 0.01227GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 42Uploading 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
161 Newfoundland Rod-Cone Dystrophy 0.03575 1/15
162 Bothnia Retinal Dystrophy 0.03575 1/15
163 Amaurosis hypertrichosis 0.03575 1/15
166 Cone rod dystrophy amelogenesis imperfecta 0.03575 1/15
169 Jalili syndrome 0.03575 1/15
171 PROSTATE CANCER, HEREDITARY, 2 0.03575 1/15
173 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.03575 1/15
175 HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.03575 1/15
118 Acquired Language Disorders 0.05716 1/15
152 Refractory anemia with ringed sideroblasts 0.05716 1/15
BgRatio
161 1/9703
162 1/9703
163 1/9703
166 1/9703
169 1/9703
171 1/9703
173 1/9703
175 1/9703
118 2/9703
152 2/9703WebGestalt 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: 2 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] 55#significance threshold for TWAS
print(sig_thresh)[1] 4.564#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 122#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5667 SYTL1 1_19 0.8065 22.96 0.0001758 4.272 2
10337 TMEM222 1_19 0.8055 22.21 0.0001699 4.303 1
13452 RP11-408A13.3 9_12 0.9238 22.50 0.0001974 4.536 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.12308 #specificity
print(specificity) ctwas TWAS
0.9994 0.9893 #precision / PPV
print(precision) ctwas TWAS
0.3333 0.1311 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 55#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 639#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.564#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] 39#sensitivity / recall
sensitivity ctwas TWAS
0.05455 0.29091 #specificity / (1 - False Positive Rate)
specificityctwas TWAS
1.000 0.964 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4103 
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)
75 39 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