SCZ - Brain Putamen basal ganglia
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] 10890#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1069 765 627 423 520 642 537 391 403 438 640 628 225 357 364 500
17 18 19 20 21 22
661 173 823 318 118 268 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8221#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7549Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0124812 0.0002562 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
9.991 8.252 #report sample size
print(sample_size)[1] 77096#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10890 7352670#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01761 0.20163 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1259 1.7400Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
905 NT5C2 10_66 1.0000 3129.90 0.0405975 -8.190 1
10867 ZNF823 19_10 0.9810 29.50 0.0003754 5.485 1
4092 FEZF1 7_74 0.9807 27.71 0.0003525 -5.272 1
11990 AC012074.2 2_15 0.9252 22.17 0.0002660 4.623 1
8791 GNG12 1_42 0.9060 22.36 0.0002627 4.530 2
3043 SF3B1 2_117 0.9006 44.05 0.0005145 6.784 1
11497 AS3MT 10_66 0.8748 598.92 0.0067960 8.586 2
10737 PCBP2 12_33 0.8462 21.79 0.0002392 4.496 1
1657 KIAA0391 14_10 0.7730 23.84 0.0002390 -4.760 1
7857 PACSIN3 11_29 0.7560 23.10 0.0002266 4.629 1
7435 SERPINI1 3_103 0.7238 20.15 0.0001891 -4.030 1
6872 CNNM4 2_57 0.7212 22.58 0.0002113 -4.793 1
8900 MAP3K11 11_36 0.7110 22.26 0.0002053 -3.929 2
3935 KLC1 14_54 0.6842 41.27 0.0003663 6.966 1
2590 MDK 11_28 0.6718 38.05 0.0003315 -6.344 1
11110 LIN28B-AS1 6_70 0.6625 23.63 0.0002031 -4.736 2
5277 POC1B 12_54 0.6521 20.40 0.0001725 4.264 1
12516 RP11-65M17.3 11_66 0.6070 20.80 0.0001637 4.301 2
2337 ERLIN1 10_64 0.5760 22.43 0.0001676 4.370 1
700 PPP2R5B 11_36 0.5142 25.23 0.0001683 -4.585 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
905 NT5C2 10_66 1.000e+00 3129.90 4.060e-02 -8.1897 1
6164 CNNM2 10_66 7.960e-06 3031.90 3.130e-07 -7.8764 1
11945 HIST1H2BN 6_21 6.776e-07 984.02 8.648e-09 10.7729 1
11497 AS3MT 10_66 8.748e-01 598.92 6.796e-03 8.5861 2
6711 MMP16 8_63 0.000e+00 520.75 0.000e+00 3.6449 1
13230 RP1-86C11.7 6_21 1.513e-12 426.71 8.377e-15 9.0332 1
5144 CALHM2 10_66 4.301e-11 426.15 2.377e-13 -3.3606 1
6156 INA 10_66 1.623e-10 310.71 6.539e-13 -3.6696 1
13650 HCP5B 6_24 7.559e-12 186.42 1.828e-14 2.4792 1
11190 MSH5 6_26 9.461e-03 175.14 2.149e-05 7.4967 2
11197 APOM 6_26 3.765e-05 154.56 7.548e-08 8.9450 1
2908 PCCB 3_84 1.617e-06 138.22 2.900e-09 -5.9913 1
12247 C4A 6_26 1.041e-07 136.75 1.847e-10 8.4587 2
13080 HCG17 6_24 1.471e-13 121.68 2.322e-16 4.0856 3
2196 MPP6 7_21 3.639e-03 110.55 5.218e-06 -3.4121 1
11165 NOTCH4 6_26 3.331e-16 101.41 4.381e-19 3.2643 2
3798 HIST1H2BJ 6_21 0.000e+00 99.23 0.000e+00 0.2007 2
9879 GRIN2A 16_10 7.640e-07 90.84 9.002e-10 -0.9830 2
11801 SAPCD1 6_26 1.435e-12 86.48 1.610e-15 -2.6196 1
10691 HLA-DQA1 6_26 1.796e-13 81.58 1.901e-16 1.7990 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
905 NT5C2 10_66 1.0000 3129.90 0.0405975 -8.190 1
11497 AS3MT 10_66 0.8748 598.92 0.0067960 8.586 2
3043 SF3B1 2_117 0.9006 44.05 0.0005145 6.784 1
10867 ZNF823 19_10 0.9810 29.50 0.0003754 5.485 1
3935 KLC1 14_54 0.6842 41.27 0.0003663 6.966 1
4092 FEZF1 7_74 0.9807 27.71 0.0003525 -5.272 1
2590 MDK 11_28 0.6718 38.05 0.0003315 -6.344 1
11990 AC012074.2 2_15 0.9252 22.17 0.0002660 4.623 1
8791 GNG12 1_42 0.9060 22.36 0.0002627 4.530 2
10737 PCBP2 12_33 0.8462 21.79 0.0002392 4.496 1
1657 KIAA0391 14_10 0.7730 23.84 0.0002390 -4.760 1
7857 PACSIN3 11_29 0.7560 23.10 0.0002266 4.629 1
6872 CNNM4 2_57 0.7212 22.58 0.0002113 -4.793 1
8900 MAP3K11 11_36 0.7110 22.26 0.0002053 -3.929 2
11110 LIN28B-AS1 6_70 0.6625 23.63 0.0002031 -4.736 2
7435 SERPINI1 3_103 0.7238 20.15 0.0001891 -4.030 1
5406 FURIN 15_42 0.4177 32.97 0.0001786 -5.701 1
5277 POC1B 12_54 0.6521 20.40 0.0001725 4.264 1
700 PPP2R5B 11_36 0.5142 25.23 0.0001683 -4.585 1
2337 ERLIN1 10_64 0.5760 22.43 0.0001676 4.370 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 6.776e-07 984.02 8.648e-09 10.773 1
13230 RP1-86C11.7 6_21 1.513e-12 426.71 8.377e-15 9.033 1
11197 APOM 6_26 3.765e-05 154.56 7.548e-08 8.945 1
11497 AS3MT 10_66 8.748e-01 598.92 6.796e-03 8.586 2
10244 BTN3A2 6_20 1.617e-02 58.46 1.226e-05 8.492 3
12247 C4A 6_26 1.041e-07 136.75 1.847e-10 8.459 2
905 NT5C2 10_66 1.000e+00 3129.90 4.060e-02 -8.190 1
6164 CNNM2 10_66 7.960e-06 3031.90 3.130e-07 -7.876 1
11190 MSH5 6_26 9.461e-03 175.14 2.149e-05 7.497 2
10593 TUBB 6_24 2.334e-08 77.05 2.332e-11 -7.349 1
11957 TRIM26 6_24 5.531e-12 61.93 4.443e-15 -7.107 2
10545 ZKSCAN3 6_22 1.690e-02 40.92 8.968e-06 7.035 1
3935 KLC1 14_54 6.842e-01 41.27 3.663e-04 6.966 1
3043 SF3B1 2_117 9.006e-01 44.05 5.145e-04 6.784 1
10732 ZSCAN26 6_22 9.943e-03 45.38 5.852e-06 6.759 3
13228 U91328.19 6_20 8.134e-02 45.11 4.759e-05 -6.580 1
2590 MDK 11_28 6.718e-01 38.05 3.315e-04 -6.344 1
11209 CCHCR1 6_26 2.433e-10 37.40 1.180e-13 -6.153 3
9596 HARBI1 11_28 1.660e-01 34.49 7.427e-05 6.084 1
12556 APOPT1 14_54 2.347e-02 31.58 9.616e-06 -6.006 2Comparing z scores and PIPs
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.006979GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 20Uploading 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
59 Amaurosis hypertrichosis 0.008233 1/9
60 Familial encephalopathy with neuroserpin inclusion bodies 0.008233 1/9
63 Cone rod dystrophy amelogenesis imperfecta 0.008233 1/9
66 Jalili syndrome 0.008233 1/9
68 SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.008233 1/9
69 CONE-ROD DYSTROPHY 20 0.008233 1/9
70 HYPOGONADOTROPIC HYPOGONADISM 22 WITH OR WITHOUT ANOSMIA 0.008233 1/9
71 SPASTIC PARAPLEGIA 62, AUTOSOMAL RECESSIVE 0.008233 1/9
17 Neoplasms, Glandular and Epithelial 0.010973 1/9
25 Glandular Neoplasms 0.010973 1/9
BgRatio
59 1/9703
60 1/9703
63 1/9703
66 1/9703
68 1/9703
69 1/9703
70 1/9703
71 1/9703
17 2/9703
25 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)
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$genename))[1] 60#significance threshold for TWAS
print(sig_thresh)[1] 4.583#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 76#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
8791 GNG12 1_42 0.9060 22.36 0.0002627 4.530 2
10737 PCBP2 12_33 0.8462 21.79 0.0002392 4.496 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.06154 #specificity
print(specificity) ctwas TWAS
0.9994 0.9937 #precision / PPV
print(precision) ctwas TWAS
0.2500 0.1053 
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] 60#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 776#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$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.583#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 2#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 17#sensitivity / recall
sensitivity ctwas TWAS
0.03333 0.13333 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9884 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.4706 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$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)
70 52 6
Detected (PIP > 0.8)
2 #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