SCZ - Brain Spinal cord cervical c-1
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 10527#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1031 738 609 413 507 592 509 397 395 409 612 610 227 352 364 465
17 18 19 20 21 22
630 170 797 315 124 261 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8543#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8115Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0127710 0.0002755 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.89 12.53 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10527 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01323 0.15811 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04603 0.79565Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10447 ZNF823 19_10 0.9822 40.58 2.470e-04 6.311 1
11504 AC012074.2 2_15 0.9658 29.78 1.782e-04 5.338 2
245 VSIG2 11_77 0.8994 49.79 2.774e-04 -7.361 1
2173 TLE4 9_38 0.8939 26.92 1.491e-04 5.000 1
865 KLHL20 1_85 0.8894 39.68 2.186e-04 -5.800 1
6680 ACE 17_37 0.8677 34.44 1.852e-04 -5.876 1
5097 C12orf10 12_33 0.8633 24.37 1.303e-04 -4.963 1
8557 MAP3K11 11_36 0.8430 33.25 1.737e-04 -5.570 1
7444 GTF2A1 14_39 0.8366 24.69 1.280e-04 -4.850 1
5485 RIT1 1_76 0.8259 24.31 1.244e-04 -4.023 1
5204 CPNE2 16_30 0.8172 21.49 1.088e-04 -4.125 1
3348 PTK2B 8_27 0.8007 23.35 1.159e-04 3.846 1
11457 HIST1H2BN 6_21 0.7935 181.54 8.924e-04 13.182 1
4275 ACY3 11_37 0.7916 19.99 9.804e-05 -3.260 1
2760 PDCD10 3_103 0.7716 23.17 1.107e-04 -4.520 1
12120 CEP95 17_37 0.7631 20.72 9.798e-05 -3.800 1
9567 NIPSNAP1 22_10 0.7619 23.26 1.098e-04 -4.302 2
12740 RP11-47A8.5 10_66 0.7617 37.04 1.748e-04 4.359 1
10453 RPL12 9_66 0.7558 24.49 1.147e-04 4.655 2
3842 ZNF835 19_38 0.7451 27.55 1.272e-04 5.136 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
122 CACNA2D2 3_35 7.123e-01 355.58 1.569e-03 -0.1392 1
2799 HEMK1 3_35 3.002e-04 304.00 5.653e-07 0.4441 1
2800 CISH 3_35 6.345e-05 249.47 9.806e-08 -0.1383 1
11457 HIST1H2BN 6_21 7.935e-01 181.54 8.924e-04 13.1822 1
7229 TEX264 3_35 6.042e-05 136.41 5.106e-08 0.3106 1
38 RBM6 3_35 5.610e-01 120.33 4.182e-04 4.4688 1
5732 PPP1R18 6_24 3.105e-02 116.21 2.236e-05 10.6084 1
7227 MST1R 3_35 4.412e-03 114.51 3.130e-06 -4.0250 1
10032 SLC38A3 3_35 1.933e-02 111.49 1.335e-05 -2.7756 1
9594 HIST1H1B 6_21 2.010e-02 110.54 1.376e-05 -9.5356 1
4928 ARL3 10_66 1.802e-02 85.22 9.514e-06 9.6347 1
9231 HIST1H2BC 6_20 1.399e-02 83.97 7.278e-06 -7.9928 1
10755 ABHD16A 6_26 4.696e-01 83.92 2.442e-04 10.7104 1
10760 APOM 6_26 2.793e-01 82.50 1.428e-04 10.6484 1
4810 PGBD1 6_22 6.233e-02 79.79 3.081e-05 -7.9952 2
12858 HIST1H2BO 6_21 1.080e-02 79.14 5.294e-06 -8.0633 1
11740 C4A 6_26 4.950e-02 78.93 2.421e-05 10.4180 1
10718 HLA-DMA 6_27 5.976e-01 78.21 2.896e-04 -9.4080 1
7223 RNF123 3_35 7.831e-05 77.43 3.757e-08 -2.3622 1
9836 BTN3A2 6_20 1.692e-01 69.76 7.312e-05 6.9759 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
122 CACNA2D2 3_35 0.7123 355.58 0.0015693 -0.1392 1
11457 HIST1H2BN 6_21 0.7935 181.54 0.0008924 13.1822 1
38 RBM6 3_35 0.5610 120.33 0.0004182 4.4688 1
10718 HLA-DMA 6_27 0.5976 78.21 0.0002896 -9.4080 1
7191 PBRM1 3_36 0.6688 67.43 0.0002794 9.4285 1
245 VSIG2 11_77 0.8994 49.79 0.0002774 -7.3608 1
10447 ZNF823 19_10 0.9822 40.58 0.0002470 6.3109 1
10755 ABHD16A 6_26 0.4696 83.92 0.0002442 10.7104 1
2890 SF3B1 2_117 0.7085 53.19 0.0002335 7.6053 1
865 KLHL20 1_85 0.8894 39.68 0.0002186 -5.7996 1
9217 HARBI1 11_28 0.5005 60.02 0.0001861 8.0462 1
6680 ACE 17_37 0.8677 34.44 0.0001852 -5.8759 1
11504 AC012074.2 2_15 0.9658 29.78 0.0001782 5.3381 2
12740 RP11-47A8.5 10_66 0.7617 37.04 0.0001748 4.3592 1
8557 MAP3K11 11_36 0.8430 33.25 0.0001737 -5.5697 1
7697 PDIA3 15_16 0.6457 38.37 0.0001535 6.3137 1
2173 TLE4 9_38 0.8939 26.92 0.0001491 4.9996 1
9176 PUF60 8_94 0.6967 34.26 0.0001479 -5.7929 1
10760 APOM 6_26 0.2793 82.50 0.0001428 10.6484 1
3313 SNX19 11_81 0.6239 36.33 0.0001404 5.7884 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11457 HIST1H2BN 6_21 0.7934544 181.54 8.924e-04 13.182 1
10755 ABHD16A 6_26 0.4695729 83.92 2.442e-04 10.710 1
10760 APOM 6_26 0.2792986 82.50 1.428e-04 10.648 1
5732 PPP1R18 6_24 0.0310507 116.21 2.236e-05 10.608 1
11740 C4A 6_26 0.0495023 78.93 2.421e-05 10.418 1
4928 ARL3 10_66 0.0180206 85.22 9.514e-06 9.635 1
9594 HIST1H1B 6_21 0.0200951 110.54 1.376e-05 -9.536 1
7191 PBRM1 3_36 0.6687926 67.43 2.794e-04 9.429 1
10718 HLA-DMA 6_27 0.5975593 78.21 2.896e-04 -9.408 1
10732 PRRT1 6_26 0.0120671 59.41 4.442e-06 -9.276 1
10729 RNF5 6_26 0.0149009 61.34 5.663e-06 9.132 2
7190 GNL3 3_36 0.1445801 64.38 5.767e-05 9.065 2
6037 ABCB9 12_75 0.0008065 64.68 3.232e-07 8.638 1
9354 ARL6IP4 12_75 0.0007298 64.25 2.905e-07 -8.615 1
2511 OGFOD2 12_75 0.0006966 64.13 2.768e-07 8.602 1
7893 SMIM4 3_36 0.0178696 57.81 6.400e-06 -8.494 1
440 MPHOSPH9 12_75 0.0004506 60.76 1.696e-07 8.479 2
7004 TYW5 2_118 0.3605961 48.60 1.086e-04 -8.344 1
7005 MAIP1 2_118 0.3605961 48.60 1.086e-04 8.344 1
12858 HIST1H2BO 6_21 0.0107971 79.14 5.294e-06 -8.063 1Comparing z scores and PIPs
[1] 0.01596GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 66Uploading 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 BgRatio
164 Follicular thyroid carcinoma 0.02733 2/28 5/9703
162 Thymic Carcinoma 0.02859 2/28 7/9703
5 Alcoholic Intoxication, Chronic 0.03184 5/28 268/9703
63 Infant, Premature, Diseases 0.03184 1/28 1/9703
93 Noonan Syndrome 0.03184 2/28 24/9703
98 Pneumonia, Viral 0.03184 1/28 1/9703
113 Splenic Neoplasms 0.03184 1/28 1/9703
144 Malignant neoplasm of spleen 0.03184 1/28 1/9703
150 LEOPARD Syndrome 0.03184 2/28 22/9703
194 Woolly hair nevus 0.03184 1/28 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: '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: 22 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] 58#significance threshold for TWAS
print(sig_thresh)[1] 4.576#number of ctwas genes
length(ctwas_genes)[1] 12#number of TWAS genes
length(twas_genes)[1] 168#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
5485 RIT1 1_76 0.8259 24.31 0.0001244 -4.023 1
3348 PTK2B 8_27 0.8007 23.35 0.0001159 3.846 1
5204 CPNE2 16_30 0.8172 21.49 0.0001088 -4.125 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.01538 0.16154 #specificity
print(specificity)ctwas TWAS
0.999 0.986 #precision / PPV
print(precision) ctwas TWAS
0.1667 0.1250 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 709#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.576#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] 58#sensitivity / recall
sensitivity ctwas TWAS
0.03448 0.36207 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9958 0.9478 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.4000 0.3621 
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)
72 37 18
Detected (PIP > 0.8) Nearby Bystander Gene
2 1 #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