SCZ - Brain Hypothalamus
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] 11075#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1102 753 663 439 556 620 531 428 412 428 658 631 212 356 369 488
17 18 19 20 21 22
679 167 846 327 134 276 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8842#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.7984Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0092792 0.0002797 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
16.99 12.46 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 11075 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01082 0.15970 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.04506 0.80755Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
679 RASSF1 3_35 0.9998 1010.97 6.262e-03 4.532 1
5324 FURIN 15_42 0.9865 93.98 5.744e-04 -9.913 1
10843 ZNF823 19_10 0.9768 40.83 2.471e-04 6.310 2
11997 AC012074.2 2_15 0.9739 31.12 1.878e-04 5.469 2
3872 IRF3 19_35 0.9153 56.18 3.186e-04 -7.641 1
2551 TRPV4 12_66 0.8672 24.86 1.336e-04 4.416 1
6907 ACE 17_38 0.8341 35.37 1.828e-04 -5.876 1
4594 DAGLA 11_34 0.8069 22.96 1.148e-04 -4.263 1
11948 HIST1H2BN 6_21 0.7920 180.20 8.842e-04 13.182 1
10850 RPL12 9_66 0.7825 24.72 1.198e-04 4.671 2
10823 HIST4H4 12_12 0.7792 23.09 1.115e-04 -4.038 2
13339 CTA-246H3.12 22_7 0.7627 23.19 1.096e-04 4.019 2
8828 KAT5 11_36 0.7462 32.09 1.483e-04 5.224 1
5639 RIT1 1_76 0.7327 24.28 1.102e-04 -4.023 1
2933 APC2 19_2 0.7277 24.44 1.102e-04 4.108 1
8406 CALML6 1_1 0.7157 23.43 1.039e-04 4.505 2
7356 SERPINI1 3_103 0.7084 23.39 1.027e-04 -4.520 1
3844 MAX 14_30 0.7044 22.80 9.951e-05 4.329 1
783 ACADVL 17_6 0.7037 22.96 1.001e-04 -4.325 1
3951 ZNF835 19_38 0.6839 28.50 1.208e-04 5.136 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
679 RASSF1 3_35 9.998e-01 1010.97 6.262e-03 4.5324 1
208 SEMA3B 3_35 0.000e+00 818.63 0.000e+00 1.4290 2
10406 SLC38A3 3_35 5.910e-13 250.62 9.176e-16 -2.7756 1
125 CACNA2D2 3_35 0.000e+00 234.19 0.000e+00 -0.1392 1
38 RBM6 3_35 5.359e-01 213.63 7.093e-04 4.4688 1
11948 HIST1H2BN 6_21 7.920e-01 180.20 8.842e-04 13.1822 1
7487 CAMKV 3_35 1.002e-07 178.89 1.110e-10 1.7107 1
10243 HYAL3 3_35 3.250e-13 176.06 3.545e-16 -2.5066 1
2875 HEMK1 3_35 0.000e+00 169.08 0.000e+00 -0.8999 1
7489 MST1R 3_35 2.336e-05 161.53 2.337e-08 -4.0250 1
12211 NAT6 3_35 3.077e-12 138.78 2.646e-15 1.5617 2
13397 LINC02019 3_35 0.000e+00 123.39 0.000e+00 0.3148 2
2736 PRSS16 6_21 7.452e-02 110.59 5.106e-05 -10.0002 1
7484 RNF123 3_35 2.220e-16 105.34 1.449e-19 -2.3622 1
2876 CISH 3_35 0.000e+00 98.55 0.000e+00 -0.8833 1
5324 FURIN 15_42 9.865e-01 93.98 5.744e-04 -9.9133 1
9592 HIST1H2BC 6_20 9.537e-03 89.18 5.270e-06 -7.9928 1
11166 MSH5 6_26 3.639e-01 88.14 1.987e-04 10.7348 2
11169 ABHD16A 6_26 2.925e-01 87.60 1.588e-04 10.7104 1
11174 APOM 6_26 1.715e-01 86.17 9.154e-05 10.6484 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
679 RASSF1 3_35 0.9998 1010.97 0.0062622 4.532 1
11948 HIST1H2BN 6_21 0.7920 180.20 0.0008842 13.182 1
38 RBM6 3_35 0.5359 213.63 0.0007093 4.469 1
5324 FURIN 15_42 0.9865 93.98 0.0005744 -9.913 1
3872 IRF3 19_35 0.9153 56.18 0.0003186 -7.641 1
11131 HLA-DMA 6_27 0.5425 79.13 0.0002660 -9.408 1
10843 ZNF823 19_10 0.9768 40.83 0.0002471 6.310 2
7453 GNL3 3_36 0.5480 64.08 0.0002176 9.162 2
2970 SF3B1 2_117 0.6501 53.51 0.0002155 7.605 1
2829 PCCB 3_84 0.4515 74.67 0.0002089 -7.445 1
11166 MSH5 6_26 0.3639 88.14 0.0001987 10.735 2
11997 AC012074.2 2_15 0.9739 31.12 0.0001878 5.469 2
10797 NMB 15_39 0.5971 49.81 0.0001843 7.121 1
6907 ACE 17_38 0.8341 35.37 0.0001828 -5.876 1
11169 ABHD16A 6_26 0.2925 87.60 0.0001588 10.710 1
8828 KAT5 11_36 0.7462 32.09 0.0001483 5.224 1
1685 PPP1R16B 20_23 0.4755 50.16 0.0001478 7.550 1
6178 TAOK2 16_24 0.4305 50.75 0.0001353 7.474 1
2551 TRPV4 12_66 0.8672 24.86 0.0001336 4.416 1
8764 FUT9 6_65 0.6000 33.25 0.0001236 5.446 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
11948 HIST1H2BN 6_21 0.7919507 180.20 8.842e-04 13.182 1
11166 MSH5 6_26 0.3639443 88.14 1.987e-04 10.735 2
11169 ABHD16A 6_26 0.2925358 87.60 1.588e-04 10.710 1
11174 APOM 6_26 0.1714513 86.17 9.154e-05 10.648 1
12252 C4A 6_26 0.0284680 82.47 1.455e-05 10.418 1
2736 PRSS16 6_21 0.0745211 110.59 5.106e-05 -10.000 1
5324 FURIN 15_42 0.9864665 93.98 5.744e-04 -9.913 1
11131 HLA-DMA 6_27 0.5424704 79.13 2.660e-04 -9.408 1
11142 RNF5 6_26 0.0069454 60.98 2.624e-06 9.267 1
7453 GNL3 3_36 0.5480209 64.08 2.176e-04 9.162 2
11172 GPANK1 6_26 0.0026378 52.42 8.567e-07 8.879 1
7454 PBRM1 3_36 0.0190804 59.72 7.060e-06 -8.722 1
9715 ARL6IP4 12_75 0.0005083 65.04 2.048e-07 8.615 1
8184 SMIM4 3_36 0.0156714 56.91 5.525e-06 -8.494 1
6103 DGKZ 11_28 0.2250490 61.32 8.550e-05 8.064 1
9577 HARBI1 11_28 0.2470633 60.56 9.269e-05 8.046 1
9085 ATG13 11_28 0.2470633 60.56 9.269e-05 -8.046 1
11139 NOTCH4 6_26 0.0364096 66.48 1.500e-05 8.033 3
9592 HIST1H2BC 6_20 0.0095369 89.18 5.270e-06 -7.993 1
2505 MDK 11_28 0.0864733 57.98 3.107e-05 -7.898 1Comparing z scores and PIPs
[1] 0.01652GO enrichment analysis for genes with PIP>0.5
#number of genes for gene set enrichment
length(genes)[1] 48Uploading 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
32 Confusion 0.01977 1/21 1/9703
50 Gingival Hypertrophy 0.01977 1/21 1/9703
63 Infant, Premature, Diseases 0.01977 1/21 1/9703
75 Chronic Obstructive Airway Disease 0.01977 2/21 33/9703
99 Pneumonia, Viral 0.01977 1/21 1/9703
145 Speech impairment 0.01977 1/21 1/9703
146 Derealization 0.01977 1/21 1/9703
154 Spondylometaphyseal dysplasia, Kozlowski type 0.01977 1/21 1/9703
155 Metatropic dwarfism 0.01977 1/21 1/9703
182 Brachyolmia Type 3 0.01977 1/21 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: 3 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] 4.586#number of ctwas genes
length(ctwas_genes)[1] 8#number of TWAS genes
length(twas_genes)[1] 183#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
679 RASSF1 3_35 0.9998 1010.97 0.0062622 4.532 1
4594 DAGLA 11_34 0.8069 22.96 0.0001148 -4.263 1
2551 TRPV4 12_66 0.8672 24.86 0.0001336 4.416 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.03077 0.15385 #specificity
print(specificity) ctwas TWAS
0.9996 0.9852 #precision / PPV
print(precision) ctwas TWAS
0.5000 0.1093 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 58#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 704#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.586#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)[1] 4#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)[1] 62#sensitivity / recall
sensitivity ctwas TWAS
0.06897 0.34483 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
1.0000 0.9403 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
1.0000 0.3226 
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 38 16
Detected (PIP > 0.8)
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_1.1.1 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