SCZ - Brain Putamen basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-04-19
Checks: 5 2
Knit directory: cTWAS_analysis/
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Weight QC
#number of imputed weights
nrow(qclist_all)[1] 9809#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
968 704 572 395 453 573 498 359 366 394 599 571 207 337 341 396 606 157 750 301
21 22
25 237 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 6827#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.696Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0122063 0.0003085 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
15.31 10.29 #report sample size
print(sample_size)[1] 105318#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 9809 6309950#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01741 0.19029 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05605 1.06023Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
10867 ZNF823 19_10 0.9820 37.56 0.0003502 6.143 1
4092 FEZF1 7_74 0.9532 25.00 0.0002263 -4.812 1
11990 AC012074.2 2_15 0.9478 22.91 0.0002062 4.655 1
3950 IRF3 19_34 0.9150 42.36 0.0003681 -6.590 1
10737 PCBP2 12_33 0.8815 27.36 0.0002290 5.065 1
3043 SF3B1 2_117 0.8725 50.81 0.0004209 7.265 1
11945 HIST1H2BN 6_21 0.8448 105.63 0.0008473 13.396 1
3431 PYROXD2 10_62 0.7840 21.30 0.0001585 3.952 1
5518 ZCCHC2 18_34 0.7666 20.47 0.0001490 -3.877 1
3149 ARHGEF2 1_76 0.7374 22.47 0.0001573 -3.816 1
6842 SPPL3 12_74 0.7325 25.14 0.0001748 -4.648 2
2590 MDK 11_28 0.6904 48.58 0.0003184 -7.159 1
2365 ARHGAP21 10_18 0.6740 23.62 0.0001511 -3.738 2
11117 SOX18 20_38 0.6695 22.76 0.0001447 3.679 2
2638 TRPV4 12_66 0.6616 21.38 0.0001343 3.346 1
6076 FAM135B 8_91 0.6531 21.89 0.0001357 -3.851 1
7176 DBF4B 17_26 0.6514 19.98 0.0001236 3.890 1
10828 NMB 15_39 0.6472 36.10 0.0002219 5.881 1
3364 PTPA 9_66 0.6323 23.70 0.0001423 -4.650 2
9570 TDRD6 6_35 0.6288 20.76 0.0001240 3.788 2Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
11197 APOM 6_26 9.098e-08 234.02 2.022e-10 11.5895 1
12247 C4A 6_26 1.880e-08 222.62 3.975e-11 11.2611 2
11190 MSH5 6_26 2.806e-09 181.57 4.838e-12 9.8879 2
11167 AGER 6_26 1.077e-07 132.12 1.351e-10 -9.0708 1
11183 EHMT2 6_26 1.226e-07 114.50 1.333e-10 7.5336 1
10570 HLA-DRB1 6_26 1.347e-06 111.76 1.429e-09 -5.1060 1
11168 RNF5 6_26 1.708e-10 109.10 1.769e-13 5.1903 1
11169 AGPAT1 6_26 1.708e-10 109.10 1.769e-13 -5.1903 1
11945 HIST1H2BN 6_21 8.448e-01 105.63 8.473e-04 13.3956 1
11186 C6orf48 6_26 8.795e-10 86.13 7.193e-13 7.7844 1
10244 BTN3A2 6_20 1.514e-02 77.69 1.117e-05 9.8139 3
13230 RP1-86C11.7 6_21 4.582e-02 74.03 3.220e-05 10.8893 1
11156 HLA-DMA 6_27 4.778e-02 71.34 3.236e-05 -8.8449 1
11176 STK19 6_26 9.767e-09 67.83 6.290e-12 -3.1148 1
11207 HLA-C 6_26 6.211e-11 63.18 3.726e-14 -7.3708 3
13228 U91328.19 6_20 7.348e-02 55.05 3.841e-05 -7.3880 1
10392 ZSCAN23 6_22 8.978e-02 54.62 4.656e-05 -7.9581 2
12064 HLA-DQA2 6_26 1.189e-07 53.54 6.045e-11 0.8591 1
3043 SF3B1 2_117 8.725e-01 50.81 4.209e-04 7.2652 1
11172 FKBPL 6_26 5.033e-02 50.52 2.414e-05 -5.2136 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 0.8448 105.63 0.0008473 13.396 1
3043 SF3B1 2_117 0.8725 50.81 0.0004209 7.265 1
3950 IRF3 19_34 0.9150 42.36 0.0003681 -6.590 1
10867 ZNF823 19_10 0.9820 37.56 0.0003502 6.143 1
2590 MDK 11_28 0.6904 48.58 0.0003184 -7.159 1
10737 PCBP2 12_33 0.8815 27.36 0.0002290 5.065 1
4092 FEZF1 7_74 0.9532 25.00 0.0002263 -4.812 1
13621 LINC02033 3_27 0.5997 39.12 0.0002228 -6.280 1
10828 NMB 15_39 0.6472 36.10 0.0002219 5.881 1
11990 AC012074.2 2_15 0.9478 22.91 0.0002062 4.655 1
7748 LETM2 8_34 0.5202 38.85 0.0001919 -6.067 1
5872 CCDC39 3_111 0.4213 44.73 0.0001789 -6.797 1
7857 PACSIN3 11_29 0.6204 30.16 0.0001777 5.308 1
6842 SPPL3 12_74 0.7325 25.14 0.0001748 -4.648 2
11497 AS3MT 10_66 0.4127 44.15 0.0001730 8.120 2
5406 FURIN 15_42 0.4964 35.60 0.0001678 -5.772 1
8111 GATAD2A 19_16 0.3646 45.83 0.0001586 -6.577 1
3431 PYROXD2 10_62 0.7840 21.30 0.0001585 3.952 1
3149 ARHGEF2 1_76 0.7374 22.47 0.0001573 -3.816 1
2365 ARHGAP21 10_18 0.6740 23.62 0.0001511 -3.738 2Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 8.448e-01 105.63 8.473e-04 13.396 1
11197 APOM 6_26 9.098e-08 234.02 2.022e-10 11.590 1
12247 C4A 6_26 1.880e-08 222.62 3.975e-11 11.261 2
13230 RP1-86C11.7 6_21 4.582e-02 74.03 3.220e-05 10.889 1
11190 MSH5 6_26 2.806e-09 181.57 4.838e-12 9.888 2
10244 BTN3A2 6_20 1.514e-02 77.69 1.117e-05 9.814 3
11167 AGER 6_26 1.077e-07 132.12 1.351e-10 -9.071 1
11156 HLA-DMA 6_27 4.778e-02 71.34 3.236e-05 -8.845 1
6164 CNNM2 10_66 1.116e-01 42.36 4.488e-05 -8.161 1
11497 AS3MT 10_66 4.127e-01 44.15 1.730e-04 8.120 2
10392 ZSCAN23 6_22 8.978e-02 54.62 4.656e-05 -7.958 2
11186 C6orf48 6_26 8.795e-10 86.13 7.193e-13 7.784 1
10545 ZKSCAN3 6_22 1.584e-02 39.33 5.914e-06 7.765 1
11183 EHMT2 6_26 1.226e-07 114.50 1.333e-10 7.534 1
10732 ZSCAN26 6_22 1.220e-02 46.98 5.440e-06 7.514 3
13228 U91328.19 6_20 7.348e-02 55.05 3.841e-05 -7.388 1
11207 HLA-C 6_26 6.211e-11 63.18 3.726e-14 -7.371 3
3043 SF3B1 2_117 8.725e-01 50.81 4.209e-04 7.265 1
2590 MDK 11_28 6.904e-01 48.58 3.184e-04 -7.159 1
10932 ZKSCAN8 6_22 1.209e-02 50.21 5.764e-06 7.127 2Comparing z scores and PIPs
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.01244GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 37Uploading 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 regulation of leukocyte cell-cell adhesion (GO:1903037)
2 positive regulation of neuron migration (GO:2001224)
3 regulation of leukocyte adhesion to vascular endothelial cell (GO:1904994)
Overlap Adjusted.P.value Genes
1 2/12 0.03453 FUT9;MDK
2 2/13 0.03453 MDK;ARHGEF2
3 2/13 0.03453 FUT9;MDK
[1] "GO_Cellular_Component_2021"
Term Overlap Adjusted.P.value
1 focal adhesion (GO:0005925) 5/387 0.02064
2 cell-substrate junction (GO:0030055) 5/394 0.02064
Genes
1 EFS;RPL12;TRPV4;PCBP2;ARHGEF2
2 EFS;RPL12;TRPV4;PCBP2;ARHGEF2
[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
10 Confusion
47 Speech impairment
48 Derealization
52 Spondylometaphyseal dysplasia, Kozlowski type
53 Metatropic dwarfism
73 Brachyolmia Type 3
78 Sexually disinhibited behavior
84 Hypersomnia, Recurrent
100 SPINAL MUSCULAR ATROPHY, DISTAL, CONGENITAL NONPROGRESSIVE (disorder)
102 HYPOTRICHOSIS-LYMPHEDEMA-TELANGIECTASIA SYNDROME
FDR Ratio BgRatio
10 0.007273 1/14 1/9703
47 0.007273 1/14 1/9703
48 0.007273 1/14 1/9703
52 0.007273 1/14 1/9703
53 0.007273 1/14 1/9703
73 0.007273 1/14 1/9703
78 0.007273 1/14 1/9703
84 0.007273 1/14 1/9703
100 0.007273 1/14 1/9703
102 0.007273 1/14 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: ggrepel: 4 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$genename))[1] 58#significance threshold for TWAS
print(sig_thresh)[1] 4.561#number of ctwas genes
length(ctwas_genes)[1] 7#number of TWAS genes
length(twas_genes)[1] 122#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols][1] genename region_tag susie_pip mu2 PVE z num_eqtl
<0 rows> (or 0-length row.names)#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.10000 #specificity
print(specificity) ctwas TWAS
0.9996 0.9888 #precision / PPV
print(precision) ctwas TWAS
0.4286 0.1066 
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] 666#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$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.561#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 3#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 36#sensitivity / recall
sensitivity ctwas TWAS
0.05172 0.22414 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9655 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3611 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$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 45 10
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