SCZ - Brain Hypothalamus
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] 9567#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
943 663 573 390 482 553 471 366 357 395 569 552 184 321 332 385 578 154 739 282
21 22
30 248 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6811#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7119Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0159161 0.0003071 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.29 10.13 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9567 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02211 0.18629 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.06816 1.05969Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10843 ZNF823 19_10 0.9887 37.85 0.0003553 6.177 2
3993 SPECC1 17_16 0.9816 30.03 0.0002799 5.366 2
5324 FURIN 15_42 0.9769 47.90 0.0004443 -6.990 1
11997 AC012074.2 2_15 0.9585 22.81 0.0002076 4.653 2
13402 RP11-408A13.3 9_12 0.9399 23.43 0.0002091 4.536 1
13055 RP11-247A12.7 9_66 0.9372 23.41 0.0002083 4.683 1
5526 SYTL1 1_19 0.9100 21.63 0.0001869 4.307 2
10699 PCBP2 12_33 0.9062 26.83 0.0002308 5.065 1
2970 SF3B1 2_117 0.9027 50.93 0.0004365 7.265 1
1089 RRN3 16_15 0.8937 21.71 0.0001843 -4.264 2
6683 VPS8 3_113 0.8883 21.34 0.0001800 -4.258 1
11948 HIST1H2BN 6_21 0.8851 106.45 0.0008946 13.396 1
105 ELAC2 17_11 0.8803 22.00 0.0001839 4.811 2
10218 TMEM222 1_19 0.8801 21.61 0.0001806 4.303 1
3872 IRF3 19_35 0.8691 41.43 0.0003419 -6.461 1
11817 LINC00242 6_112 0.8632 21.69 0.0001778 4.288 2
5315 FANCI 15_41 0.8333 24.57 0.0001944 -4.481 1
706 GAL 11_38 0.8285 25.84 0.0002033 -4.946 2
307 VRK2 2_38 0.8260 38.46 0.0003016 4.977 1
1685 PPP1R16B 20_23 0.7927 60.76 0.0004573 7.738 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11174 APOM 6_26 2.279e-04 226.12 4.893e-07 11.5895 1
11169 ABHD16A 6_26 1.873e-04 223.25 3.970e-07 11.5262 1
11166 MSH5 6_26 1.656e-04 223.12 3.508e-07 11.5179 2
12252 C4A 6_26 4.277e-05 216.43 8.790e-08 11.3259 1
10534 HLA-DRB1 6_26 3.042e-05 178.62 5.159e-08 6.2222 1
11172 GPANK1 6_26 6.166e-05 172.29 1.009e-07 10.2672 1
11663 LINC01623 6_22 2.055e-02 156.89 3.062e-05 -12.9094 1
11142 RNF5 6_26 6.939e-05 154.88 1.020e-07 10.0454 1
11139 NOTCH4 6_26 1.102e-03 153.59 1.607e-06 8.4528 3
11948 HIST1H2BN 6_21 8.851e-01 106.45 8.946e-04 13.3956 1
11143 AGPAT1 6_26 3.964e-07 103.89 3.910e-10 -5.1903 1
10645 HLA-DQA1 6_26 4.324e-07 103.71 4.258e-10 -1.5380 1
11423 GTF2H4 6_25 3.997e-01 102.09 3.874e-04 11.1544 1
12073 HLA-DQA2 6_26 3.608e-07 92.84 3.180e-10 0.8591 1
9485 HLA-DQB1 6_26 3.438e-07 88.99 2.905e-10 -1.9898 1
11176 BAG6 6_26 3.345e-05 84.95 2.698e-08 -3.5825 1
9592 HIST1H2BC 6_20 3.365e-02 84.08 2.687e-05 -9.9088 1
4935 FLOT1 6_24 8.121e-02 83.84 6.465e-05 -10.9213 1
2696 TRIM38 6_20 2.313e-02 76.36 1.677e-05 -9.5948 2
11162 HSPA1A 6_26 2.791e-05 75.84 2.010e-08 8.0745 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11948 HIST1H2BN 6_21 0.8851 106.45 0.0008946 13.396 1
1685 PPP1R16B 20_23 0.7927 60.76 0.0004573 7.738 1
5324 FURIN 15_42 0.9769 47.90 0.0004443 -6.990 1
2970 SF3B1 2_117 0.9027 50.93 0.0004365 7.265 1
11423 GTF2H4 6_25 0.3997 102.09 0.0003874 11.154 1
10843 ZNF823 19_10 0.9887 37.85 0.0003553 6.177 2
3872 IRF3 19_35 0.8691 41.43 0.0003419 -6.461 1
10406 SLC38A3 3_35 0.7871 45.54 0.0003403 -1.402 1
2829 PCCB 3_84 0.7304 45.14 0.0003130 -6.724 1
307 VRK2 2_38 0.8260 38.46 0.0003016 4.977 1
3993 SPECC1 17_16 0.9816 30.03 0.0002799 5.366 2
2505 MDK 11_28 0.5826 48.64 0.0002690 -7.159 1
38 RBM6 3_35 0.5159 54.01 0.0002645 3.221 1
7669 LETM2 8_34 0.6763 38.57 0.0002476 -6.067 1
10797 NMB 15_39 0.7173 35.79 0.0002438 5.881 1
10699 PCBP2 12_33 0.9062 26.83 0.0002308 5.065 1
3041 ALMS1 2_48 0.7001 34.07 0.0002265 -5.898 1
7382 THOC7 3_43 0.5650 40.56 0.0002176 -6.249 1
13402 RP11-408A13.3 9_12 0.9399 23.43 0.0002091 4.536 1
13055 RP11-247A12.7 9_66 0.9372 23.41 0.0002083 4.683 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11948 HIST1H2BN 6_21 8.851e-01 106.45 8.946e-04 13.396 1
11663 LINC01623 6_22 2.055e-02 156.89 3.062e-05 -12.909 1
11174 APOM 6_26 2.279e-04 226.12 4.893e-07 11.590 1
11169 ABHD16A 6_26 1.873e-04 223.25 3.970e-07 11.526 1
11166 MSH5 6_26 1.656e-04 223.12 3.508e-07 11.518 2
12252 C4A 6_26 4.277e-05 216.43 8.790e-08 11.326 1
11423 GTF2H4 6_25 3.997e-01 102.09 3.874e-04 11.154 1
4935 FLOT1 6_24 8.121e-02 83.84 6.465e-05 -10.921 1
11172 GPANK1 6_26 6.166e-05 172.29 1.009e-07 10.267 1
11142 RNF5 6_26 6.939e-05 154.88 1.020e-07 10.045 1
9592 HIST1H2BC 6_20 3.365e-02 84.08 2.687e-05 -9.909 1
10512 ZKSCAN3 6_22 2.276e-02 67.06 1.449e-05 9.707 2
2696 TRIM38 6_20 2.313e-02 76.36 1.677e-05 -9.595 2
10214 BTN3A2 6_20 2.116e-02 72.00 1.446e-05 9.294 2
11131 HLA-DMA 6_27 5.646e-02 68.13 3.652e-05 -8.590 2
11139 NOTCH4 6_26 1.102e-03 153.59 1.607e-06 8.453 3
11162 HSPA1A 6_26 2.791e-05 75.84 2.010e-08 8.075 1
11479 AS3MT 10_66 4.244e-01 45.06 1.816e-04 8.051 1
10360 ZSCAN23 6_22 7.931e-02 49.55 3.732e-05 -7.778 2
1685 PPP1R16B 20_23 7.927e-01 60.76 4.573e-04 7.738 1Comparing z scores and PIPs
[1] 0.0138GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 56Uploading 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
58 Alstrom Syndrome
105 FANCONI ANEMIA, COMPLEMENTATION GROUP I
107 HYPOTRICHOSIS-LYMPHEDEMA-TELANGIECTASIA SYNDROME
109 Childhood-onset truncal obesity
115 MITOCHONDRIAL COMPLEX V (ATP SYNTHASE) DEFICIENCY, NUCLEAR TYPE 1
117 PROSTATE CANCER, HEREDITARY, 2
119 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17
120 OVARIAN DYSGENESIS 4
121 ENCEPHALOPATHY, ACUTE, INFECTION-INDUCED (HERPES-SPECIFIC), SUSCEPTIBILITY TO, 7
122 EPILEPSY, FAMILIAL TEMPORAL LOBE, 8
FDR Ratio BgRatio
58 0.02273 1/21 1/9703
105 0.02273 1/21 1/9703
107 0.02273 1/21 1/9703
109 0.02273 1/21 1/9703
115 0.02273 1/21 1/9703
117 0.02273 1/21 1/9703
119 0.02273 1/21 1/9703
120 0.02273 1/21 1/9703
121 0.02273 1/21 1/9703
122 0.02273 1/21 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: ggrepel: 4 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] 57#significance threshold for TWAS
print(sig_thresh)[1] 4.555#number of ctwas genes
length(ctwas_genes)[1] 19#number of TWAS genes
length(twas_genes)[1] 132#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5526 SYTL1 1_19 0.9100 21.63 0.0001869 4.307 2
10218 TMEM222 1_19 0.8801 21.61 0.0001806 4.303 1
6683 VPS8 3_113 0.8883 21.34 0.0001800 -4.258 1
11817 LINC00242 6_112 0.8632 21.69 0.0001778 4.288 2
13402 RP11-408A13.3 9_12 0.9399 23.43 0.0002091 4.536 1
5315 FANCI 15_41 0.8333 24.57 0.0001944 -4.481 1
1089 RRN3 16_15 0.8937 21.71 0.0001843 -4.264 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03846 0.10769 #specificity
print(specificity) ctwas TWAS
0.9985 0.9876 #precision / PPV
print(precision) ctwas TWAS
0.2632 0.1061 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 57#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 596#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.555#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 5#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 39#sensitivity / recall
sensitivity ctwas TWAS
0.08772 0.24561 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9581 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.000 0.359 
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)
73 43 9
Detected (PIP > 0.8)
5 #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