SCZ - Brain Cerebellar Hemisphere
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] 11328#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1087 766 651 425 543 628 553 423 439 442 695 635 209 381 371 537
17 18 19 20 21 22
707 170 905 332 134 295 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8719#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7697Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0151400 0.0002659 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
11.59 12.56 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11328 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01232 0.15296 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.0538 0.7832Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
2523 YWHAE 17_2 0.9821 51.13 0.0003111 0.1008 2
12095 AC012074.2 2_15 0.9818 29.78 0.0001812 5.4694 1
5783 GALNT2 1_117 0.9806 31.50 0.0001914 5.6989 1
10988 ZNF823 19_10 0.9672 38.21 0.0002290 6.1841 2
4143 FEZF1 7_74 0.9631 23.24 0.0001387 -4.6555 1
1283 MLF2 12_7 0.9499 26.45 0.0001557 -4.9033 1
380 NSUN2 5_6 0.9400 22.60 0.0001316 -4.2792 3
2207 RUNDC3B 7_54 0.9365 27.25 0.0001581 5.4540 1
3040 ACTR1B 2_57 0.9188 30.79 0.0001753 -5.5513 1
929 KLHL20 1_85 0.8931 36.68 0.0002029 -5.7996 1
8411 SNTB2 16_37 0.8895 26.82 0.0001478 -4.8247 2
6611 SEMA3D 7_53 0.8861 21.87 0.0001201 -4.0278 3
9042 C11orf80 11_37 0.8847 19.92 0.0001092 -4.0883 2
7758 SLC4A2 7_94 0.8764 20.09 0.0001091 -4.0287 2
12301 HLA-DMB 6_27 0.8737 75.80 0.0004103 -9.6790 1
417 RETSAT 2_54 0.8699 20.37 0.0001098 3.9626 1
8715 KCNMB3 3_110 0.8539 19.36 0.0001024 -3.8661 1
3479 SLF2 10_64 0.8402 24.28 0.0001264 -4.5939 2
7031 ACE 17_37 0.8299 32.91 0.0001692 -5.8021 1
3381 ABCG2 4_59 0.8289 20.15 0.0001035 -3.9541 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
10533 SLC38A3 3_35 1.465e-05 683.9 6.206e-08 -2.77559 1
33 RBM5 3_35 2.313e-02 449.6 6.443e-05 3.98715 1
41 RBM6 3_35 3.409e-01 440.5 9.304e-04 4.46875 1
9614 LSMEM2 3_35 1.958e-01 385.8 4.681e-04 -4.27088 1
10363 HYAL3 3_35 4.474e-05 326.0 9.037e-08 -2.50662 1
11633 IFRD2 3_35 4.474e-05 326.0 9.037e-08 -2.50662 1
724 RASSF1 3_35 1.644e-05 312.1 3.178e-08 4.32685 1
11946 U73166.2 3_35 5.847e-05 280.9 1.018e-07 -4.59660 1
7604 RNF123 3_35 1.491e-05 262.4 2.424e-08 -2.32524 1
2981 CYB561D2 3_35 1.486e-05 256.7 2.362e-08 -2.19612 2
9825 UBA7 3_35 1.710e-05 202.3 2.143e-08 -1.08001 1
12318 NAT6 3_35 1.488e-05 188.4 1.737e-08 0.79523 2
11884 HCG11 6_20 1.858e-02 115.4 1.328e-05 9.84429 1
12879 CTA-14H9.5 6_20 1.858e-02 115.4 1.328e-05 9.84429 1
12038 GPX1 3_35 1.524e-05 111.7 1.055e-08 -0.09215 2
130 CACNA2D2 3_35 6.978e-05 109.8 4.748e-08 -0.10441 1
10334 BTN3A2 6_20 2.035e-02 107.3 1.353e-05 9.14834 2
11553 CLIC1 6_26 4.178e-01 103.9 2.691e-04 10.73111 1
2870 PRSS16 6_21 1.288e-01 103.6 8.272e-05 -10.00016 1
11296 C6orf48 6_26 2.724e-01 102.9 1.736e-04 10.68269 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
41 RBM6 3_35 0.3409 440.55 0.0009304 4.4688 1
9614 LSMEM2 3_35 0.1958 385.82 0.0004681 -4.2709 1
12301 HLA-DMB 6_27 0.8737 75.80 0.0004103 -9.6790 1
2523 YWHAE 17_2 0.9821 51.13 0.0003111 0.1008 2
7572 GNL3 3_36 0.6855 64.38 0.0002735 9.4161 2
11553 CLIC1 6_26 0.4178 103.94 0.0002691 10.7311 1
10988 ZNF823 19_10 0.9672 38.21 0.0002290 6.1841 2
929 KLHL20 1_85 0.8931 36.68 0.0002029 -5.7996 1
9199 ATG13 11_28 0.5347 58.39 0.0001934 -8.0462 1
5783 GALNT2 1_117 0.9806 31.50 0.0001914 5.6989 1
10942 NMB 15_39 0.6314 47.51 0.0001859 7.1213 1
3099 SF3B1 2_117 0.5996 49.54 0.0001841 7.6053 1
5201 ARL3 10_66 0.6666 44.35 0.0001832 -9.6347 1
12095 AC012074.2 2_15 0.9818 29.78 0.0001812 5.4694 1
3040 ACTR1B 2_57 0.9188 30.79 0.0001753 -5.5513 1
11296 C6orf48 6_26 0.2724 102.85 0.0001736 10.6827 1
7031 ACE 17_37 0.8299 32.91 0.0001692 -5.8021 1
11777 PLEKHM1 17_27 0.6631 41.12 0.0001689 6.4041 2
2207 RUNDC3B 7_54 0.9365 27.25 0.0001581 5.4540 1
1283 MLF2 12_7 0.9499 26.45 0.0001557 -4.9033 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11553 CLIC1 6_26 0.4177893 103.94 2.691e-04 10.731 1
11296 C6orf48 6_26 0.2723513 102.85 1.736e-04 10.683 1
12355 C4A 6_26 0.1019645 101.06 6.384e-05 10.542 2
2870 PRSS16 6_21 0.1288225 103.64 8.272e-05 -10.000 1
11884 HCG11 6_20 0.0185817 115.38 1.328e-05 9.844 1
12879 CTA-14H9.5 6_20 0.0185817 115.38 1.328e-05 9.844 1
6221 CNNM2 10_66 0.2381535 40.61 5.992e-05 -9.686 1
12301 HLA-DMB 6_27 0.8737245 75.80 4.103e-04 -9.679 1
5201 ARL3 10_66 0.6666110 44.35 1.832e-04 -9.635 1
7572 GNL3 3_36 0.6855456 64.38 2.735e-04 9.416 2
11273 HLA-DMA 6_27 0.0880475 72.06 3.931e-05 -9.408 1
11281 RNF5 6_26 0.0142254 63.98 5.639e-06 9.278 2
11284 PRRT1 6_26 0.0135224 56.97 4.773e-06 9.276 1
11986 CYP21A2 6_26 0.0146039 78.38 7.091e-06 -9.197 1
10334 BTN3A2 6_20 0.0203464 107.34 1.353e-05 9.148 2
7573 PBRM1 3_36 0.0221295 56.08 7.688e-06 -8.722 1
11754 C4B 6_26 0.0520478 85.41 2.754e-05 -8.656 2
6038 ABT1 6_20 0.0288050 83.66 1.493e-05 8.650 1
1323 PITPNM2 12_75 0.0008402 61.35 3.194e-07 -8.615 1
2710 OGFOD2 12_75 0.0007551 61.45 2.875e-07 8.615 1Comparing z scores and PIPs
[1] 0.01677GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 69Uploading 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"
Term
1 positive regulation of establishment of protein localization to mitochondrion (GO:1903749)
Overlap Adjusted.P.value Genes
1 4/56 0.02547 YWHAE;USP36;YWHAB;ATG13
[1] "GO_Cellular_Component_2021"
[1] Term Overlap Adjusted.P.value Genes
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"
Term Overlap
1 phosphoserine residue binding (GO:0050815) 2/9
2 ABC-type xenobiotic transporter activity (GO:0008559) 2/11
3 anion transmembrane transporter activity (GO:0008509) 2/17
4 MHC class II protein complex binding (GO:0023026) 2/17
Adjusted.P.value Genes
1 0.03351 YWHAE;YWHAB
2 0.03351 ABCC10;ABCG2
3 0.04088 SLC4A2;ABCG2
4 0.04088 YWHAE;HLA-DMBDisGeNET enrichment analysis for genes with PIP>0.5
Description FDR Ratio
56 Gingival Hypertrophy 0.04643 1/32
71 Infant, Premature, Diseases 0.04643 1/32
110 Pneumonia, Viral 0.04643 1/32
118 Schizophrenia 0.04643 9/32
206 Gorlin Chaudhry Moss syndrome 0.04643 1/32
216 Symmetrical dyschromatosis of extremities 0.04643 1/32
284 Severe Acute Respiratory Syndrome 0.04643 1/32
303 URIC ACID CONCENTRATION, SERUM, QUANTITATIVE TRAIT LOCUS 1 0.04643 1/32
304 Ehlers-Danlos syndrome caused by tenascin-X deficiency 0.04643 1/32
305 Familial encephalopathy with neuroserpin inclusion bodies 0.04643 1/32
BgRatio
56 1/9703
71 1/9703
110 1/9703
118 883/9703
206 1/9703
216 1/9703
284 1/9703
303 1/9703
304 1/9703
305 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: 12 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] 64#significance threshold for TWAS
print(sig_thresh)[1] 4.591#number of ctwas genes
length(ctwas_genes)[1] 20#number of TWAS genes
length(twas_genes)[1] 190#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
417 RETSAT 2_54 0.8699 20.37 0.0001098 3.9626 1
8715 KCNMB3 3_110 0.8539 19.36 0.0001024 -3.8661 1
3381 ABCG2 4_59 0.8289 20.15 0.0001035 -3.9541 1
380 NSUN2 5_6 0.9400 22.60 0.0001316 -4.2792 3
6611 SEMA3D 7_53 0.8861 21.87 0.0001201 -4.0278 3
7758 SLC4A2 7_94 0.8764 20.09 0.0001091 -4.0287 2
9042 C11orf80 11_37 0.8847 19.92 0.0001092 -4.0883 2
2523 YWHAE 17_2 0.9821 51.13 0.0003111 0.1008 2#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.18462 #specificity
print(specificity) ctwas TWAS
0.9985 0.9853 #precision / PPV
print(precision) ctwas TWAS
0.1500 0.1263 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 64#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 785#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.591#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 3#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 62#sensitivity / recall
sensitivity ctwas TWAS
0.04688 0.37500 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9516 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3871 
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)
66 40 21
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_1.1.1 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