SCZ - Brain Nucleus accumbens basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-03-03
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11518#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1135 816 688 449 575 581 558 432 426 464 694 651 223 386 374 531
17 18 19 20 21 22
679 185 885 358 134 294 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 9119#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7917Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0137343 0.0002475 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
8.579 8.869 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11518 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01649 0.20198 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.08618 1.45805Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
4131 SPECC1 17_16 0.9939 30.61 0.0003696 5.625 2
11067 ZNF823 19_10 0.9819 28.88 0.0003445 5.483 2
5491 FURIN 15_42 0.9804 44.25 0.0005271 -7.000 1
13453 RP11-230C9.4 6_102 0.9661 23.94 0.0002810 -4.872 2
11808 NPTXR 22_15 0.9254 21.85 0.0002456 4.512 2
13743 CWC25 17_23 0.8673 20.73 0.0002185 -4.015 3
6509 TMEM56 1_58 0.8341 20.15 0.0002041 -3.918 1
3067 SF3B1 2_117 0.8259 42.69 0.0004284 6.725 1
11955 LINC00390 13_17 0.8053 20.17 0.0001973 -4.220 1
6535 TADA1 1_82 0.7967 21.93 0.0002123 -4.168 2
6291 ARFGAP2 11_29 0.7860 23.76 0.0002269 4.740 1
10921 PCBP2 12_33 0.7827 20.28 0.0001928 4.202 1
12231 AC073283.4 2_30 0.7761 20.60 0.0001942 -3.892 2
10150 ACOT1 14_34 0.7648 21.86 0.0002031 4.044 2
2658 VPS29 12_67 0.7276 24.06 0.0002126 -4.937 2
9374 COX8A 11_35 0.7272 24.38 0.0002154 -4.750 1
4719 SOX5 12_17 0.6951 20.31 0.0001715 4.309 1
107 ELAC2 17_11 0.6793 22.42 0.0001850 4.227 1
3206 MAP7D1 1_22 0.6731 23.08 0.0001887 4.907 1
11957 LINC00606 3_8 0.6618 23.04 0.0001853 -3.964 1Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
9682 HLA-DQB1 6_26 1.221e-13 609.57 9.044e-16 4.3395 1
12152 HLA-DQB2 6_26 1.634e-13 513.04 1.019e-15 -3.5679 1
12325 HLA-DQA2 6_26 1.289e-13 385.09 6.030e-16 0.2164 1
11395 MSH5 6_26 4.995e-13 334.97 2.033e-15 8.1001 2
10748 HLA-DRB1 6_26 9.848e-14 166.78 1.995e-16 -1.8363 1
9862 ACBD4 17_27 0.000e+00 163.38 0.000e+00 1.6939 2
10865 HLA-DQA1 6_26 7.232e-13 105.14 9.237e-16 -0.7786 1
11647 CLIC1 6_26 4.017e-13 84.36 4.117e-16 -0.4634 1
10450 HEXIM1 17_27 0.000e+00 67.49 0.000e+00 -2.8451 1
10415 BTN3A2 6_20 2.490e-02 64.62 1.955e-05 9.0374 3
2422 GOSR2 17_27 0.000e+00 56.26 0.000e+00 -3.4300 2
10915 ZSCAN26 6_22 1.946e-02 53.24 1.259e-05 8.6508 3
9788 HIST1H2BC 6_20 2.752e-02 51.86 1.734e-05 -8.0277 1
2790 TRIM38 6_20 2.223e-02 44.29 1.196e-05 -7.4660 2
5491 FURIN 15_42 9.804e-01 44.25 5.271e-04 -7.0004 1
10558 ZSCAN23 6_22 1.061e-01 44.07 5.682e-05 -6.7082 2
3067 SF3B1 2_117 8.259e-01 42.69 4.284e-04 6.7253 1
4135 CDHR3 7_65 0.000e+00 41.22 0.000e+00 2.9707 2
13283 LINC01415 18_30 3.193e-01 39.97 1.550e-04 -5.3243 1
10418 TMEM222 1_19 3.612e-01 39.49 1.733e-04 3.9022 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
5491 FURIN 15_42 0.9804 44.25 0.0005271 -7.000 1
3067 SF3B1 2_117 0.8259 42.69 0.0004284 6.725 1
4131 SPECC1 17_16 0.9939 30.61 0.0003696 5.625 2
11067 ZNF823 19_10 0.9819 28.88 0.0003445 5.483 2
13453 RP11-230C9.4 6_102 0.9661 23.94 0.0002810 -4.872 2
11808 NPTXR 22_15 0.9254 21.85 0.0002456 4.512 2
6291 ARFGAP2 11_29 0.7860 23.76 0.0002269 4.740 1
2602 MDK 11_28 0.5002 37.08 0.0002253 -6.357 1
1571 CACNA1I 22_16 0.5082 35.45 0.0002189 5.841 1
13743 CWC25 17_23 0.8673 20.73 0.0002185 -4.015 3
9374 COX8A 11_35 0.7272 24.38 0.0002154 -4.750 1
2658 VPS29 12_67 0.7276 24.06 0.0002126 -4.937 2
6535 TADA1 1_82 0.7967 21.93 0.0002123 -4.168 2
6304 DRD2 11_67 0.5634 30.91 0.0002115 -5.938 2
6509 TMEM56 1_58 0.8341 20.15 0.0002041 -3.918 1
10150 ACOT1 14_34 0.7648 21.86 0.0002031 4.044 2
11955 LINC00390 13_17 0.8053 20.17 0.0001973 -4.220 1
12231 AC073283.4 2_30 0.7761 20.60 0.0001942 -3.892 2
10921 PCBP2 12_33 0.7827 20.28 0.0001928 4.202 1
3206 MAP7D1 1_22 0.6731 23.08 0.0001887 4.907 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10415 BTN3A2 6_20 2.490e-02 64.62 1.955e-05 9.037 3
10915 ZSCAN26 6_22 1.946e-02 53.24 1.259e-05 8.651 3
11395 MSH5 6_26 4.995e-13 334.97 2.033e-15 8.100 2
9788 HIST1H2BC 6_20 2.752e-02 51.86 1.734e-05 -8.028 1
6275 CNNM2 10_66 6.121e-02 32.97 2.452e-05 -7.547 2
2790 TRIM38 6_20 2.223e-02 44.29 1.196e-05 -7.466 2
5491 FURIN 15_42 9.804e-01 44.25 5.271e-04 -7.000 1
3067 SF3B1 2_117 8.259e-01 42.69 4.284e-04 6.725 1
7442 TYW5 2_118 3.896e-02 35.64 1.686e-05 -6.718 2
10558 ZSCAN23 6_22 1.061e-01 44.07 5.682e-05 -6.708 2
9922 ARL6IP4 12_75 9.368e-03 38.77 4.413e-06 6.491 1
6404 ABCB9 12_75 7.758e-03 37.56 3.540e-06 6.404 1
2602 MDK 11_28 5.002e-01 37.08 2.253e-04 -6.357 1
9771 HARBI1 11_28 1.872e-01 34.44 7.832e-05 6.169 1
11548 DNAJC19 3_111 2.691e-01 36.11 1.180e-04 6.158 1
8554 INO80E 16_24 3.044e-01 36.89 1.364e-04 6.121 2
6304 DRD2 11_67 5.634e-01 30.91 2.115e-04 -5.938 2
7634 GNL3 3_36 1.699e-01 32.47 6.702e-05 5.899 2
1571 CACNA1I 22_16 5.082e-01 35.45 2.189e-04 5.841 1
11136 ZKSCAN8 6_22 1.558e-02 33.88 6.415e-06 5.837 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] 0.00547GO 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"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[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
5 Anxiety Disorders 0.02052 2/14 44/9703
50 Measles 0.02052 1/14 1/9703
51 Memory Disorders 0.02052 2/14 43/9703
92 Memory impairment 0.02052 2/14 44/9703
120 Anxiety States, Neurotic 0.02052 2/14 44/9703
148 Age-Related Memory Disorders 0.02052 2/14 43/9703
149 Memory Disorder, Semantic 0.02052 2/14 43/9703
150 Memory Disorder, Spatial 0.02052 2/14 43/9703
151 Memory Loss 0.02052 2/14 43/9703
169 Anxiety neurosis (finding) 0.02052 2/14 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!NULL
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] 4.594#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 63#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
6509 TMEM56 1_58 0.8341 20.15 0.0002041 -3.918 1
11955 LINC00390 13_17 0.8053 20.17 0.0001973 -4.220 1
13743 CWC25 17_23 0.8673 20.73 0.0002185 -4.015 3
11808 NPTXR 22_15 0.9254 21.85 0.0002456 4.512 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.07692 #specificity
print(specificity) ctwas TWAS
0.9995 0.9954 #precision / PPV
print(precision) ctwas TWAS
0.3333 0.1587 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 59#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 734#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.594#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] 17#sensitivity / recall
sensitivity ctwas TWAS
0.05085 0.16949 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9973 0.9905 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6000 0.5882 
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 49 7
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_1.0.0 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