SCZ - Brain Putamen basal ganglia
sheng Qian
2021-2-6
Last updated: 2022-03-16
Checks: 5 2
Knit directory: cTWAS_analysis/
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|---|---|---|---|---|
| Rmd | d57314b | sq-96 | 2022-03-15 | update |
Weight QC
#number of imputed weights
nrow(qclist_all)[1] 11271#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1120 786 659 444 526 660 552 400 410 451 667 642 225 382 381 514
17 18 19 20 21 22
684 177 856 347 121 267 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8824#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7829Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0103659 0.0002745 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var)gene snp
16.2 12.6 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11271 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01173 0.15837 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04683 0.79781Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
6842 SPPL3 12_74 0.9849 34.38 2.098e-04 -5.6124 2
9521 LSMEM2 3_35 0.9794 1012.56 6.144e-03 4.2709 1
10867 ZNF823 19_10 0.9784 40.71 2.467e-04 6.3109 1
11990 AC012074.2 2_15 0.9765 31.00 1.876e-04 5.4694 1
4092 FEZF1 7_74 0.9513 24.14 1.423e-04 -4.6555 1
3781 BTN2A2 6_20 0.9347 26.05 1.508e-04 -0.9406 2
2638 TRPV4 12_66 0.8883 25.17 1.385e-04 4.4157 1
4466 ACY3 11_37 0.8301 20.36 1.047e-04 -3.3965 1
7789 GTF2A1 14_39 0.8059 24.82 1.239e-04 -4.8497 1
75 OSBPL7 17_28 0.7966 23.11 1.141e-04 4.3242 2
3080 QPCT 2_23 0.7948 38.26 1.884e-04 6.2812 2
10872 RPL12 9_66 0.7918 24.58 1.206e-04 4.6699 2
9612 GSX2 4_39 0.7915 24.78 1.215e-04 4.7860 1
2678 MRPL51 12_7 0.7804 22.34 1.080e-04 3.9435 1
6521 SLC25A27 6_35 0.7746 23.03 1.105e-04 -3.8945 3
5424 CPNE2 16_30 0.7725 22.06 1.056e-04 -4.1250 1
11945 HIST1H2BN 6_21 0.7651 177.22 8.400e-04 13.1822 1
5311 C12orf10 12_33 0.7513 26.18 1.219e-04 -4.9630 1
13295 RP11-47A8.5 10_66 0.7454 36.08 1.666e-04 4.2823 1
12644 CEP95 17_37 0.7366 21.37 9.752e-05 -3.8003 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
9521 LSMEM2 3_35 9.794e-01 1012.56 6.144e-03 4.2709 1
206 SEMA3B 3_35 9.526e-07 989.37 5.839e-09 1.0870 1
10436 SLC38A3 3_35 1.970e-07 247.64 3.022e-10 -2.7756 1
123 CACNA2D2 3_35 9.002e-07 225.92 1.260e-09 -0.1392 1
36 RBM6 3_35 4.844e-01 193.33 5.802e-04 4.4688 1
12210 NAT6 3_35 6.637e-08 179.02 7.361e-11 1.8009 2
11945 HIST1H2BN 6_21 7.651e-01 177.22 8.400e-04 13.1822 1
10270 HYAL3 3_35 6.470e-08 170.13 6.820e-11 -2.5066 1
7563 CAMKV 3_35 9.880e-06 168.16 1.029e-08 -1.7107 1
7565 MST1R 3_35 1.296e-04 145.52 1.168e-07 -4.0250 1
13230 RP1-86C11.7 6_21 1.374e-01 123.77 1.054e-04 10.5382 1
10244 BTN3A2 6_20 1.059e-02 116.76 7.657e-06 8.0974 3
1208 DOCK3 3_35 1.604e-06 112.81 1.121e-09 0.3011 1
7560 RNF123 3_35 6.096e-08 94.80 3.580e-11 -2.3622 1
11197 APOM 6_26 3.717e-01 87.55 2.016e-04 10.6484 1
12247 C4A 6_26 3.016e-01 87.12 1.628e-04 10.6070 1
11156 HLA-DMA 6_27 5.528e-01 78.48 2.688e-04 -9.4095 2
13228 U91328.19 6_20 6.651e-02 72.58 2.991e-05 -6.2195 1
11190 MSH5 6_26 8.706e-04 69.73 3.761e-07 9.0192 2
6302 ABCB9 12_75 5.159e-04 66.63 2.130e-07 8.6382 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
9521 LSMEM2 3_35 0.9794 1012.56 0.0061444 4.2709 1
11945 HIST1H2BN 6_21 0.7651 177.22 0.0008400 13.1822 1
36 RBM6 3_35 0.4844 193.33 0.0005802 4.4688 1
11156 HLA-DMA 6_27 0.5528 78.48 0.0002688 -9.4095 2
3950 IRF3 19_34 0.7190 55.82 0.0002487 -7.5059 1
10867 ZNF823 19_10 0.9784 40.71 0.0002467 6.3109 1
3043 SF3B1 2_117 0.6646 52.94 0.0002180 7.6053 1
6842 SPPL3 12_74 0.9849 34.38 0.0002098 -5.6124 2
8111 GATAD2A 19_16 0.6292 52.66 0.0002053 -7.4194 1
11197 APOM 6_26 0.3717 87.55 0.0002016 10.6484 1
7527 GNL3 3_36 0.4880 63.17 0.0001910 9.0882 2
10828 NMB 15_39 0.6170 49.70 0.0001900 7.1213 1
3080 QPCT 2_23 0.7948 38.26 0.0001884 6.2812 2
11990 AC012074.2 2_15 0.9765 31.00 0.0001876 5.4694 1
9596 HARBI1 11_28 0.4666 60.14 0.0001739 8.0462 1
13295 RP11-47A8.5 10_66 0.7454 36.08 0.0001666 4.2823 1
12247 C4A 6_26 0.3016 87.12 0.0001628 10.6070 1
3781 BTN2A2 6_20 0.9347 26.05 0.0001508 -0.9406 2
4092 FEZF1 7_74 0.9513 24.14 0.0001423 -4.6555 1
2638 TRPV4 12_66 0.8883 25.17 0.0001385 4.4157 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11945 HIST1H2BN 6_21 0.7650567 177.22 8.400e-04 13.182 1
11197 APOM 6_26 0.3717378 87.55 2.016e-04 10.648 1
12247 C4A 6_26 0.3016414 87.12 1.628e-04 10.607 1
13230 RP1-86C11.7 6_21 0.1374458 123.77 1.054e-04 10.538 1
905 NT5C2 10_66 0.1439382 53.47 4.769e-05 -9.705 1
6164 CNNM2 10_66 0.1187419 52.87 3.889e-05 -9.686 1
11156 HLA-DMA 6_27 0.5528062 78.48 2.688e-04 -9.409 2
7527 GNL3 3_36 0.4880252 63.17 1.910e-04 9.088 2
11190 MSH5 6_26 0.0008706 69.73 3.761e-07 9.019 2
7528 PBRM1 3_36 0.0213831 59.35 7.863e-06 -8.722 1
6302 ABCB9 12_75 0.0005159 66.63 2.130e-07 8.638 1
8250 SMIM4 3_36 0.0180570 56.86 6.361e-06 -8.494 1
11497 AS3MT 10_66 0.0020186 57.12 7.144e-07 8.363 1
10244 BTN3A2 6_20 0.0105851 116.76 7.657e-06 8.097 3
9596 HARBI1 11_28 0.4665954 60.14 1.739e-04 8.046 1
10593 TUBB 6_24 0.0083018 59.21 3.046e-06 -7.980 1
2590 MDK 11_28 0.1614738 57.66 5.768e-05 -7.898 1
2971 NEK4 3_36 0.0103125 48.29 3.085e-06 7.898 1
11346 DNAJC19 3_111 0.0216825 56.90 7.644e-06 7.788 1
245 GLT8D1 3_36 0.0092730 46.07 2.647e-06 7.782 1Comparing z scores and PIPs
#proportion of significant z scores
mean(abs(ctwas_gene_res$z) > sig_thresh)[1] 0.01473GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 57Uploading 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
15 Confusion
30 Chronic Lymphocytic Leukemia
72 Speech impairment
73 Derealization
82 Spondylometaphyseal dysplasia, Kozlowski type
83 Metatropic dwarfism
107 Brachyolmia Type 3
114 Sexually disinhibited behavior
123 Hypersomnia, Recurrent
145 SPINAL MUSCULAR ATROPHY, DISTAL, CONGENITAL NONPROGRESSIVE (disorder)
FDR Ratio BgRatio
15 0.01737 1/25 1/9703
30 0.01737 3/25 55/9703
72 0.01737 1/25 1/9703
73 0.01737 1/25 1/9703
82 0.01737 1/25 1/9703
83 0.01737 1/25 1/9703
107 0.01737 1/25 1/9703
114 0.01737 1/25 1/9703
123 0.01737 1/25 1/9703
145 0.01737 1/25 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: 13 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$genename))[1] 61#significance threshold for TWAS
print(sig_thresh)[1] 4.59#number of ctwas genes
length(ctwas_genes)[1] 9#number of TWAS genes
length(twas_genes)[1] 166#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
9521 LSMEM2 3_35 0.9794 1012.56 0.0061444 4.2709 1
3781 BTN2A2 6_20 0.9347 26.05 0.0001508 -0.9406 2
4466 ACY3 11_37 0.8301 20.36 0.0001047 -3.3965 1
2638 TRPV4 12_66 0.8883 25.17 0.0001385 4.4157 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.007692 0.138462 #specificity
print(specificity) ctwas TWAS
0.9993 0.9868 #precision / PPV
print(precision) ctwas TWAS
0.1111 0.1084 
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] 61#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 820#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$z)>sig_thresh]
#significance threshold for TWAS
print(sig_thresh)[1] 4.59#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 1#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 57#sensitivity / recall
sensitivity ctwas TWAS
0.01639 0.29508 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9524 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3158 
pip_range <- (0:1000)/1000
sensitivity <- rep(NA, length(pip_range))
specificity <- rep(NA, length(pip_range))
for (index in 1:length(pip_range)){
pip <- pip_range[index]
ctwas_genes <- ctwas_gene_res_subset$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)
69 43 17
Detected (PIP > 0.8)
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