SCZ - Brain Spinal cord cervical c-1
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] 10567#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1038 754 605 411 517 537 511 406 401 410 617 617 227 356 367 479
17 18 19 20 21 22
622 168 801 320 127 276 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8626#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8163Check convergence of parameters
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.012559 0.000254 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
10.743 8.561 #report sample size
print(sample_size)[1] 82315#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10567 7573890#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01732 0.20011 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.1174 1.4749Genes with highest PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3283 CRHR1 17_27 0.9975 3588.90 0.0434918 3.362 1
10447 ZNF823 19_10 0.9813 29.51 0.0003518 5.455 1
8557 MAP3K11 11_36 0.8742 23.79 0.0002526 -4.544 1
2890 SF3B1 2_117 0.8616 44.16 0.0004622 6.725 1
104 ELAC2 17_11 0.8479 21.96 0.0002262 4.542 1
3886 SPECC1 17_16 0.8395 25.83 0.0002634 -4.889 1
11504 AC012074.2 2_15 0.8272 21.80 0.0002191 4.447 2
11457 HIST1H2BN 6_21 0.7955 93.49 0.0009035 10.773 1
5935 ARFGAP2 11_29 0.7864 24.28 0.0002320 4.740 1
2497 GPN3 12_67 0.7808 24.83 0.0002355 5.009 1
6055 PLBD2 12_68 0.7752 20.28 0.0001910 3.986 1
5485 RIT1 1_76 0.7722 21.23 0.0001992 -3.496 1
12471 RP11-247A12.7 9_66 0.7587 22.39 0.0002063 4.370 2
3216 HSDL2 9_57 0.7581 22.24 0.0002048 4.322 1
8453 FUT9 6_65 0.7445 29.74 0.0002690 5.427 1
9840 TMEM222 1_19 0.7298 23.01 0.0002040 3.936 2
419 ARID1B 6_102 0.7124 22.04 0.0001908 3.907 1
1568 KIAA0391 14_9 0.7060 26.17 0.0002245 -5.150 2
2760 PDCD10 3_103 0.6947 20.40 0.0001722 -4.038 1
3025 MAP7D1 1_22 0.6914 24.16 0.0002029 4.907 1Genes with largest effect sizes
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
genename region_tag susie_pip mu2 PVE z num_eqtl
3283 CRHR1 17_27 9.975e-01 3588.90 4.349e-02 3.36232 1
6658 ARHGAP27 17_27 0.000e+00 2655.82 0.000e+00 -2.09345 1
4998 PRDM5 4_78 3.307e-13 1816.66 7.299e-15 -2.24071 1
11575 RP11-325F22.2 7_65 0.000e+00 966.29 0.000e+00 4.64948 2
9135 HLA-DQB1 6_26 1.065e-13 867.87 1.123e-15 4.11762 1
11416 HLA-DQB2 6_26 1.030e-13 829.87 1.039e-15 -4.14865 1
11576 HLA-DQA2 6_26 1.030e-13 829.87 1.039e-15 -4.14865 1
10158 HLA-DRB1 6_26 2.827e-13 515.72 1.771e-15 5.15185 1
10266 HLA-DQA1 6_26 4.804e-13 284.17 1.658e-15 -1.06154 2
66 KMT2E 7_65 0.000e+00 227.32 0.000e+00 -2.22870 1
10753 MSH5 6_26 1.508e-12 221.11 4.051e-15 7.25864 1
10740 SKIV2L 6_26 1.756e-12 173.25 3.695e-15 -0.01504 1
4695 NMT1 17_27 0.000e+00 148.07 0.000e+00 2.72086 1
8517 DCAKD 17_27 0.000e+00 116.93 0.000e+00 -2.99967 1
11457 HIST1H2BN 6_21 7.955e-01 93.49 9.035e-04 10.77288 1
10988 CLIC1 6_26 5.148e-13 83.86 5.245e-16 0.46344 1
4529 RINT1 7_65 0.000e+00 74.80 0.000e+00 0.56463 2
9299 ACBD4 17_27 0.000e+00 70.30 0.000e+00 1.73582 1
10270 ZSCAN16 6_22 1.548e-02 67.84 1.276e-05 -8.50932 1
9836 BTN3A2 6_20 2.272e-02 67.49 1.863e-05 9.09444 2Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
3283 CRHR1 17_27 0.9975 3588.90 0.0434918 3.362 1
11457 HIST1H2BN 6_21 0.7955 93.49 0.0009035 10.773 1
2890 SF3B1 2_117 0.8616 44.16 0.0004622 6.725 1
10447 ZNF823 19_10 0.9813 29.51 0.0003518 5.455 1
8068 INO80E 16_24 0.6064 39.54 0.0002913 6.350 1
3741 KLC1 14_54 0.5642 41.29 0.0002830 7.026 1
8453 FUT9 6_65 0.7445 29.74 0.0002690 5.427 1
3886 SPECC1 17_16 0.8395 25.83 0.0002634 -4.889 1
8557 MAP3K11 11_36 0.8742 23.79 0.0002526 -4.544 1
2445 MDK 11_28 0.5321 38.44 0.0002485 -6.357 1
2497 GPN3 12_67 0.7808 24.83 0.0002355 5.009 1
5935 ARFGAP2 11_29 0.7864 24.28 0.0002320 4.740 1
104 ELAC2 17_11 0.8479 21.96 0.0002262 4.542 1
1568 KIAA0391 14_9 0.7060 26.17 0.0002245 -5.150 2
11504 AC012074.2 2_15 0.8272 21.80 0.0002191 4.447 2
12471 RP11-247A12.7 9_66 0.7587 22.39 0.0002063 4.370 2
3216 HSDL2 9_57 0.7581 22.24 0.0002048 4.322 1
9840 TMEM222 1_19 0.7298 23.01 0.0002040 3.936 2
3025 MAP7D1 1_22 0.6914 24.16 0.0002029 4.907 1
5485 RIT1 1_76 0.7722 21.23 0.0001992 -3.496 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11457 HIST1H2BN 6_21 7.955e-01 93.49 9.035e-04 10.773 1
9836 BTN3A2 6_20 2.272e-02 67.49 1.863e-05 9.094 2
10270 ZSCAN16 6_22 1.548e-02 67.84 1.276e-05 -8.509 1
9594 HIST1H1B 6_21 1.726e-02 53.84 1.129e-05 -8.250 1
4810 PGBD1 6_22 1.331e-02 59.13 9.560e-06 -8.142 2
9231 HIST1H2BC 6_20 2.374e-02 53.30 1.537e-05 -8.028 1
10753 MSH5 6_26 1.508e-12 221.11 4.051e-15 7.259 1
7004 TYW5 2_118 3.162e-01 40.60 1.559e-04 -7.226 1
7005 MAIP1 2_118 3.162e-01 40.60 1.559e-04 7.226 1
440 MPHOSPH9 12_75 1.561e-01 46.28 8.779e-05 7.158 1
12858 HIST1H2BO 6_21 1.711e-02 41.74 8.673e-06 -7.075 1
3741 KLC1 14_54 5.642e-01 41.29 2.830e-04 7.026 1
2623 TRIM38 6_20 1.807e-02 40.40 8.869e-06 -7.012 2
2890 SF3B1 2_117 8.616e-01 44.16 4.622e-04 6.725 1
9981 ZSCAN23 6_22 9.773e-02 45.98 5.459e-05 -6.675 2
9354 ARL6IP4 12_75 9.288e-03 39.92 4.504e-06 -6.491 1
3313 SNX19 11_81 1.556e-01 42.66 8.062e-05 6.484 2
6037 ABCB9 12_75 7.382e-03 38.61 3.462e-06 6.404 1
2511 OGFOD2 12_75 6.986e-03 38.28 3.249e-06 6.374 1
2445 MDK 11_28 5.321e-01 38.44 2.485e-04 -6.357 1Comparing z scores and PIPs
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
[1] 0.007665GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 31Uploading 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 |
Term
1 regulation of leukocyte cell-cell adhesion (GO:1903037)
2 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
Overlap Adjusted.P.value Genes
1 2/12 0.02772 FUT9;MDK
2 2/13 0.02772 FUT9;MDK
[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 0.01361 2/11 44/9703
56 Anxiety States, Neurotic 0.01361 2/11 44/9703
84 Anxiety neurosis (finding) 0.01361 2/11 44/9703
92 Cerebral Cavernous Malformations 3 0.01361 1/11 1/9703
96 Familial cerebral cavernous malformation 0.01361 1/11 1/9703
103 PROSTATE CANCER, HEREDITARY, 2 0.01361 1/11 1/9703
104 NOONAN SYNDROME 8 0.01361 1/11 1/9703
105 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.01361 1/11 1/9703
107 Very long chain acyl-CoA dehydrogenase deficiency 0.01361 1/11 1/9703
30 Pain, Postoperative 0.01883 1/11 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: ggrepel: 1 unlabeled data points (too many overlaps). Consider
increasing max.overlaps
| Version | Author | Date |
|---|---|---|
| ff6403a | sq-96 | 2022-02-27 |
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] 7#number of TWAS genes
length(twas_genes)[1] 81#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
11504 AC012074.2 2_15 0.8272 21.80 0.0002191 4.447 2
8557 MAP3K11 11_36 0.8742 23.79 0.0002526 -4.544 1
104 ELAC2 17_11 0.8479 21.96 0.0002262 4.542 1
3283 CRHR1 17_27 0.9975 3588.90 0.0434918 3.362 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03077 0.04615 #specificity
print(specificity) ctwas TWAS
0.9997 0.9929 #precision / PPV
print(precision) ctwas TWAS
0.57143 0.07407 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 728#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 22#sensitivity / recall
sensitivity ctwas TWAS
0.06897 0.10345 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9986 0.9780 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.8000 0.2727 
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 Detected (PIP > 0.8)
72 50 4
Nearby SNP(s)
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