SCZ - Brain Hypothalamus
sheng Qian
2021-2-6
Last updated: 2022-03-14
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10731#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1062 739 632 417 549 608 523 418 403 413 628 615 202 333 355 476
17 18 19 20 21 22
654 162 826 321 130 265 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8242#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7681Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0150830 0.0002553 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.532 8.151 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10731 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.02211 0.19844 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.09444 1.71361Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
3993 SPECC1 17_16 0.9999 123.23 0.0015982 5.426 2
5324 FURIN 15_42 0.9884 46.00 0.0005898 -7.000 1
10843 ZNF823 19_10 0.9861 29.80 0.0003811 5.502 2
7382 THOC7 3_43 0.9389 37.10 0.0004518 -6.186 1
11997 AC012074.2 2_15 0.9381 22.10 0.0002689 4.620 2
3112 MAP7D1 1_22 0.9365 25.82 0.0003136 5.058 1
2970 SF3B1 2_117 0.9253 44.84 0.0005382 6.784 1
1089 RRN3 16_15 0.8896 24.58 0.0002836 -4.689 2
9203 DIRAS1 19_3 0.8869 22.06 0.0002538 4.504 2
10699 PCBP2 12_33 0.8826 21.89 0.0002506 4.496 1
7356 SERPINI1 3_103 0.7666 19.82 0.0001970 -4.030 1
11817 LINC00242 6_112 0.7600 20.38 0.0002009 3.871 2
1518 ZC3H7B 22_17 0.7435 43.72 0.0004217 4.922 1
2312 TBC1D12 10_60 0.7430 19.56 0.0001885 3.936 1
10218 TMEM222 1_19 0.7352 22.45 0.0002141 3.902 1
3496 TBC1D15 12_44 0.7305 22.01 0.0002086 4.436 2
1685 PPP1R16B 20_23 0.7231 34.83 0.0003267 6.009 1
3367 HELLS 10_61 0.6940 19.90 0.0001791 -3.886 1
394 CTNNA1 5_82 0.6780 23.71 0.0002085 4.946 1
6111 ARFGAP2 11_29 0.6708 25.03 0.0002177 4.839 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11948 HIST1H2BN 6_21 1.479e-06 976.3 1.873e-08 10.7729 1
10837 HIST1H2BK 6_21 0.000e+00 629.4 0.000e+00 -5.7119 1
2829 PCCB 3_84 3.712e-01 513.9 2.474e-03 -6.2860 1
2736 PRSS16 6_21 2.331e-14 371.0 1.122e-16 -8.6315 1
12073 HLA-DQA2 6_26 0.000e+00 283.7 0.000e+00 0.6583 1
13174 RP1-153G14.4 6_21 0.000e+00 237.2 0.000e+00 0.5579 2
11174 APOM 6_26 1.162e-08 201.7 3.039e-11 8.9450 1
11166 MSH5 6_26 8.766e-09 201.4 2.290e-11 8.8864 2
11169 ABHD16A 6_26 9.621e-09 201.3 2.513e-11 8.9341 1
12252 C4A 6_26 2.186e-10 192.8 5.465e-13 8.4450 1
11176 BAG6 6_26 4.441e-16 180.8 1.041e-18 5.6969 2
11172 GPANK1 6_26 4.996e-14 177.9 1.153e-16 7.9727 1
11162 HSPA1A 6_26 0.000e+00 165.7 0.000e+00 7.6575 1
9485 HLA-DQB1 6_26 0.000e+00 162.1 0.000e+00 -1.8861 1
10645 HLA-DQA1 6_26 2.220e-16 161.0 4.638e-19 -1.1464 2
11419 DDAH2 6_26 0.000e+00 157.9 0.000e+00 6.9659 2
13272 RP1-86C11.7 6_21 0.000e+00 149.2 0.000e+00 1.6735 1
9850 GRIN2A 16_10 7.518e-07 142.0 1.385e-09 -0.7161 2
10534 HLA-DRB1 6_26 0.000e+00 140.9 0.000e+00 5.1480 1
806 PPP2R3A 3_84 0.000e+00 129.0 0.000e+00 4.1188 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
2829 PCCB 3_84 0.3712 513.88 0.0024743 -6.286 1
3993 SPECC1 17_16 0.9999 123.23 0.0015982 5.426 2
5324 FURIN 15_42 0.9884 46.00 0.0005898 -7.000 1
2970 SF3B1 2_117 0.9253 44.84 0.0005382 6.784 1
7382 THOC7 3_43 0.9389 37.10 0.0004518 -6.186 1
1518 ZC3H7B 22_17 0.7435 43.72 0.0004217 4.922 1
10843 ZNF823 19_10 0.9861 29.80 0.0003811 5.502 2
1685 PPP1R16B 20_23 0.7231 34.83 0.0003267 6.009 1
3112 MAP7D1 1_22 0.9365 25.82 0.0003136 5.058 1
2505 MDK 11_28 0.5907 38.37 0.0002940 -6.344 1
1089 RRN3 16_15 0.8896 24.58 0.0002836 -4.689 2
11997 AC012074.2 2_15 0.9381 22.10 0.0002689 4.620 2
9203 DIRAS1 19_3 0.8869 22.06 0.0002538 4.504 2
10699 PCBP2 12_33 0.8826 21.89 0.0002506 4.496 1
3041 ALMS1 2_48 0.6519 26.55 0.0002245 -5.177 1
6111 ARFGAP2 11_29 0.6708 25.03 0.0002177 4.839 1
10218 TMEM222 1_19 0.7352 22.45 0.0002141 3.902 1
3496 TBC1D15 12_44 0.7305 22.01 0.0002086 4.436 2
394 CTNNA1 5_82 0.6780 23.71 0.0002085 4.946 1
7454 PBRM1 3_36 0.4876 32.88 0.0002080 -5.790 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11948 HIST1H2BN 6_21 1.479e-06 976.31 1.873e-08 10.773 1
11663 LINC01623 6_22 1.009e-02 111.21 1.456e-05 -10.503 1
11174 APOM 6_26 1.162e-08 201.65 3.039e-11 8.945 1
11169 ABHD16A 6_26 9.621e-09 201.35 2.513e-11 8.934 1
11166 MSH5 6_26 8.766e-09 201.39 2.290e-11 8.886 2
10655 ZSCAN16 6_22 1.234e-02 83.76 1.341e-05 -8.813 1
2736 PRSS16 6_21 2.331e-14 370.96 1.122e-16 -8.631 1
12252 C4A 6_26 2.186e-10 192.77 5.465e-13 8.445 1
9592 HIST1H2BC 6_20 2.446e-02 51.90 1.647e-05 -7.978 1
11172 GPANK1 6_26 4.996e-14 177.89 1.153e-16 7.973 1
11142 RNF5 6_26 0.000e+00 95.74 0.000e+00 7.921 1
11162 HSPA1A 6_26 0.000e+00 165.65 0.000e+00 7.658 1
5324 FURIN 15_42 9.884e-01 46.00 5.898e-04 -7.000 1
11419 DDAH2 6_26 0.000e+00 157.89 0.000e+00 6.966 2
2970 SF3B1 2_117 9.253e-01 44.84 5.382e-04 6.784 1
11139 NOTCH4 6_26 0.000e+00 96.82 0.000e+00 6.623 3
1571 ZFYVE21 14_54 1.666e-01 39.99 8.642e-05 -6.569 1
3855 XRCC3 14_54 1.314e-01 41.74 7.114e-05 6.526 1
10694 ZSCAN26 6_22 1.708e-02 35.98 7.970e-06 6.523 1
11158 SLC44A4 6_26 0.000e+00 92.71 0.000e+00 6.361 1Comparing z scores and PIPs
[1] 0.008014GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 35Uploading 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
66 Alstrom Syndrome
117 SCHIZOPHRENIA 11
118 Familial encephalopathy with neuroserpin inclusion bodies
119 Childhood-onset truncal obesity
122 Hematopoetic Myelodysplasia
125 TREACHER COLLINS SYNDROME 2
128 Very long chain acyl-CoA dehydrogenase deficiency
129 EPILEPSY, FAMILIAL TEMPORAL LOBE, 8
130 IMMUNODEFICIENCY-CENTROMERIC INSTABILITY-FACIAL ANOMALIES SYNDROME 4
67 Metabolic myopathy
FDR Ratio BgRatio
66 0.01814 1/12 1/9703
117 0.01814 1/12 1/9703
118 0.01814 1/12 1/9703
119 0.01814 1/12 1/9703
122 0.01814 2/12 29/9703
125 0.01814 1/12 1/9703
128 0.01814 1/12 1/9703
129 0.01814 1/12 1/9703
130 0.01814 1/12 1/9703
67 0.02720 1/12 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: '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: 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] 59#significance threshold for TWAS
print(sig_thresh)[1] 4.58#number of ctwas genes
length(ctwas_genes)[1] 10#number of TWAS genes
length(twas_genes)[1] 86#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
10699 PCBP2 12_33 0.8826 21.89 0.0002506 4.496 1
9203 DIRAS1 19_3 0.8869 22.06 0.0002538 4.504 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03077 0.06923 #specificity
print(specificity) ctwas TWAS
0.9994 0.9928 #precision / PPV
print(precision) ctwas TWAS
0.4000 0.1047 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 59#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 635#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.58#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] 25#sensitivity / recall
sensitivity ctwas TWAS
0.0678 0.1525 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9748 #precision / PPV / (1 - False Discovery Rate)
precisionctwas TWAS
1.00 0.36 
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)
71 50 5
Detected (PIP > 0.8)
4 #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_1.0.0 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