SCZ - Brain Substantia nigra
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] 10065#number of imputed weights by chromosome
table(qclist_all$chr)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
962 709 601 389 485 582 471 392 396 396 599 566 214 331 336 458 601 154 778 312
21 22
111 222 #number of imputed weights without missing variants
sum(qclist_all$nmiss==0)[1] 8276#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)[1] 0.8223Check convergence of parameters
#estimated group prior
estimated_group_prior <- group_prior_rec[,ncol(group_prior_rec)]
names(estimated_group_prior) <- c("gene", "snp")
estimated_group_prior["snp"] <- estimated_group_prior["snp"]*thin #adjust parameter to account for thin argument
print(estimated_group_prior) gene snp
0.0125223 0.0002773 #estimated group prior variance
estimated_group_prior_var <- group_prior_var_rec[,ncol(group_prior_var_rec)]
names(estimated_group_prior_var) <- c("gene", "snp")
print(estimated_group_prior_var) gene snp
16.18 12.40 #report sample size
print(sample_size)[1] 161405#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)[1] 10065 7394310#estimated group PVE
estimated_group_pve <- estimated_group_prior_var*estimated_group_prior*group_size/sample_size #check PVE calculation
names(estimated_group_pve) <- c("gene", "snp")
print(estimated_group_pve) gene snp
0.01263 0.15760 #compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))[1] 0.05106 0.81854Genes with highest PIPs
genename region_tag susie_pip mu2 PVE z num_eqtl
655 RASSF1 3_35 0.9946 1055.15 0.0065017 4.532 1
4961 FURIN 15_42 0.9891 93.31 0.0005718 -9.913 1
5588 FAM135B 8_91 0.9828 26.59 0.0001619 -4.167 2
10000 ZNF823 19_10 0.9821 40.70 0.0002476 6.311 1
11036 AC012074.2 2_15 0.9805 30.93 0.0001879 5.469 2
8526 LY6H 8_94 0.9146 29.90 0.0001694 5.333 2
10016 C1orf122 1_23 0.9000 24.51 0.0001367 4.415 1
2111 TLE4 9_38 0.8920 26.97 0.0001491 5.000 1
855 KLHL20 1_85 0.8911 40.22 0.0002220 -5.800 1
6398 ACE 17_37 0.8673 34.74 0.0001867 -5.876 1
4878 C12orf10 12_33 0.8638 24.47 0.0001309 -4.963 1
10995 HIST1H2BN 6_21 0.8557 187.74 0.0009953 13.182 1
4265 DAGLA 11_34 0.8463 22.28 0.0001168 -4.263 1
10007 RPL12 9_66 0.8284 24.28 0.0001246 4.670 2
8783 PUF60 8_94 0.8260 33.93 0.0001736 -5.793 1
3052 DNAH7 2_116 0.8045 26.31 0.0001312 -4.857 2
10823 LINC01268 6_75 0.7995 22.06 0.0001093 -4.406 1
9054 FAM43B 1_14 0.7940 20.66 0.0001017 3.990 2
3209 PTK2B 8_27 0.7930 23.31 0.0001145 3.846 1
2194 NUFIP2 17_18 0.7923 22.64 0.0001111 -4.626 1Genes with largest effect sizes
genename region_tag susie_pip mu2 PVE z num_eqtl
655 RASSF1 3_35 9.946e-01 1055.2 6.502e-03 4.5324 1
8753 LSMEM2 3_35 5.384e-03 1047.1 3.493e-05 4.2709 1
11280 C4A 6_26 4.002e-02 663.0 1.644e-04 10.4180 1
10295 VWA7 6_26 9.998e-02 636.9 3.945e-04 10.5945 1
10301 ABHD16A 6_26 3.507e-01 635.9 1.382e-03 10.7104 1
10307 APOM 6_26 1.703e-01 635.7 6.707e-04 10.6484 1
10276 PRRT1 6_26 4.747e-13 378.2 1.112e-15 -9.2761 1
10273 RNF5 6_26 4.777e-13 378.0 1.119e-15 9.2761 1
10309 BAG6 6_26 4.362e-11 303.2 8.193e-14 9.3662 2
10527 DDAH2 6_26 1.132e-14 255.8 1.795e-17 7.5859 1
119 CACNA2D2 3_35 1.549e-06 239.0 2.293e-09 -0.1044 1
9 SEMA3F 3_35 1.677e-07 229.6 2.385e-10 -1.4379 1
10292 HSPA1A 6_26 1.099e-14 221.5 1.508e-17 7.0119 1
2703 HEMK1 3_35 4.714e-06 203.3 5.936e-09 0.4441 1
33 RBM6 3_35 5.332e-01 194.3 6.420e-04 4.4688 1
10995 HIST1H2BN 6_21 8.557e-01 187.7 9.953e-04 13.1822 1
11109 HLA-DQA2 6_26 1.665e-15 187.6 1.936e-18 -4.4886 1
9447 HYAL3 3_35 7.237e-08 174.5 7.826e-11 -2.5066 1
6937 CAMKV 3_35 1.334e-05 169.2 1.399e-08 -1.7107 1
10957 HLA-DQB2 6_26 1.332e-15 161.1 1.329e-18 1.1165 1Genes with highest PVE
genename region_tag susie_pip mu2 PVE z num_eqtl
655 RASSF1 3_35 0.99456 1055.15 0.0065017 4.532 1
10301 ABHD16A 6_26 0.35071 635.94 0.0013818 10.710 1
10995 HIST1H2BN 6_21 0.85573 187.74 0.0009953 13.182 1
10307 APOM 6_26 0.17029 635.73 0.0006707 10.648 1
33 RBM6 3_35 0.53320 194.34 0.0006420 4.469 1
4961 FURIN 15_42 0.98906 93.31 0.0005718 -9.913 1
10295 VWA7 6_26 0.09998 636.86 0.0003945 10.594 1
10265 HLA-DMA 6_27 0.75856 78.24 0.0003677 -9.498 2
62 KMT2E 7_65 0.78830 54.27 0.0002650 -7.571 2
10000 ZNF823 19_10 0.98211 40.70 0.0002476 6.311 1
855 KLHL20 1_85 0.89108 40.22 0.0002220 -5.800 1
9960 NMB 15_39 0.66224 49.34 0.0002025 7.121 1
8821 HARBI1 11_28 0.50383 60.64 0.0001893 8.046 1
11036 AC012074.2 2_15 0.98046 30.93 0.0001879 5.469 2
6398 ACE 17_37 0.86729 34.74 0.0001867 -5.876 1
9787 ANAPC7 12_67 0.42218 70.21 0.0001836 -7.255 2
8783 PUF60 8_94 0.82596 33.93 0.0001736 -5.793 1
8526 LY6H 8_94 0.91463 29.90 0.0001694 5.333 2
1618 PPP1R16B 20_23 0.54611 49.81 0.0001685 7.550 1
11280 C4A 6_26 0.04002 662.98 0.0001644 10.418 1Genes with largest z scores
genename region_tag susie_pip mu2 PVE z num_eqtl
10995 HIST1H2BN 6_21 8.557e-01 187.74 9.953e-04 13.182 1
10301 ABHD16A 6_26 3.507e-01 635.94 1.382e-03 10.710 1
10307 APOM 6_26 1.703e-01 635.73 6.707e-04 10.648 1
10295 VWA7 6_26 9.998e-02 636.86 3.945e-04 10.594 1
11280 C4A 6_26 4.002e-02 662.98 1.644e-04 10.418 1
4961 FURIN 15_42 9.891e-01 93.31 5.718e-04 -9.913 1
10265 HLA-DMA 6_27 7.586e-01 78.24 3.677e-04 -9.498 2
10309 BAG6 6_26 4.362e-11 303.17 8.193e-14 9.366 2
10276 PRRT1 6_26 4.747e-13 378.21 1.112e-15 -9.276 1
10273 RNF5 6_26 4.777e-13 378.02 1.119e-15 9.276 1
9418 BTN3A2 6_20 1.389e-02 113.44 9.761e-06 9.235 3
6906 PBRM1 3_36 3.983e-02 58.03 1.432e-05 -8.722 1
9143 KMT5A 12_75 3.585e-03 56.08 1.246e-06 -8.158 2
8821 HARBI1 11_28 5.038e-01 60.64 1.893e-04 8.046 1
8834 HIST1H2BC 6_20 1.328e-02 87.23 7.178e-06 -7.993 1
2371 MDK 11_28 1.739e-01 58.15 6.264e-05 -7.898 1
10439 DNAJC19 3_111 3.309e-02 56.57 1.160e-05 7.788 1
3921 C12orf65 12_75 2.607e-04 51.46 8.311e-08 -7.731 1
10527 DDAH2 6_26 1.132e-14 255.83 1.795e-17 7.586 1
62 KMT2E 7_65 7.883e-01 54.27 2.650e-04 -7.571 2Comparing z scores and PIPs
[1] 0.0147GO 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 FDR Ratio BgRatio
31 Dementia, Vascular 0.01978 1/21 1/9703
49 Infant, Premature, Diseases 0.01978 1/21 1/9703
73 Pneumonia, Viral 0.01978 1/21 1/9703
117 Binswanger Disease 0.01978 1/21 1/9703
128 Vascular Dementia, Acute Onset 0.01978 1/21 1/9703
129 Subcortical Vascular Dementia 0.01978 1/21 1/9703
136 Arteriosclerotic Dementia 0.01978 1/21 1/9703
153 Severe Acute Respiratory Syndrome 0.01978 1/21 1/9703
165 THYROID HORMONE RESISTANCE, SELECTIVE PITUITARY 0.01978 1/21 1/9703
166 Deafness, Autosomal Recessive 22 0.01978 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: '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: 2 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] 55#significance threshold for TWAS
print(sig_thresh)[1] 4.566#number of ctwas genes
length(ctwas_genes)[1] 16#number of TWAS genes
length(twas_genes)[1] 148#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$genename %in% novel_genes,report_cols] genename region_tag susie_pip mu2 PVE z num_eqtl
10016 C1orf122 1_23 0.9000 24.51 0.0001367 4.415 1
655 RASSF1 3_35 0.9946 1055.15 0.0065017 4.532 1
5588 FAM135B 8_91 0.9828 26.59 0.0001619 -4.167 2
4265 DAGLA 11_34 0.8463 22.28 0.0001168 -4.263 1#sensitivity / recall
print(sensitivity) ctwas TWAS
0.02308 0.10769 #specificity
print(specificity) ctwas TWAS
0.9987 0.9866 #precision / PPV
print(precision) ctwas TWAS
0.18750 0.09459 
cTWAS is more precise than TWAS in distinguishing silver standard and bystander genes
#number of genes in known annotations (with imputed expression)
print(length(known_annotations))[1] 55#number of bystander genes (with imputed expression)
print(length(unrelated_genes))[1] 620#subset results to genes in known annotations or bystanders
ctwas_gene_res_subset <- ctwas_gene_res[ctwas_gene_res$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] 4.566#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] 45#sensitivity / recall
sensitivity ctwas TWAS
0.05455 0.25455 #specificity / (1 - False Positive Rate)
specificity ctwas TWAS
0.9968 0.9500 #precision / PPV / (1 - False Discovery Rate)
precision ctwas TWAS
0.6000 0.3111 
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)
75 41 10
Detected (PIP > 0.8) Nearby Bystander Gene
3 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