SCZ - Brain Amygdala

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Last updated: 2022-03-03

Checks: 5 2

Knit directory: cTWAS_analysis/

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/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/data/	data
/project2/xinhe/shengqian/cTWAS/cTWAS_analysis/code/ctwas_config.R	code/ctwas_config.R

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File	Version	Author	Date	Message
html	75a1466	sq-96	2022-02-27	Build site.
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Rmd	3dd5b4c	sq-96	2022-02-27	update

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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 10292

#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 
1005  740  588  392  518  542  481  374  407  397  632  597  222  335  340  460 
  17   18   19   20   21   22 
 598  163  797  309  120  275 

#number of imputed weights without missing variants
sum(qclist_all$nmiss==0)

[1] 8408

#proportion of imputed weights without missing variants
mean(qclist_all$nmiss==0)

[1] 0.8169

Check convergence of parameters

Past versions of unnamed-chunk-5-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

#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 
0.0072071 0.0002653 

#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 
14.431  8.448 

#report sample size
print(sample_size)

[1] 82315

#report group size
group_size <- c(nrow(ctwas_gene_res), n_snps)
print(group_size)

[1]   10292 7573890

#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 
0.0130 0.2062 

#compare sum(PIP*mu2/sample_size) with above PVE calculation
c(sum(ctwas_gene_res$PVE),sum(ctwas_snp_res$PVE))

[1] 0.04663 1.55649

Genes with highest PIPs

Past versions of unnamed-chunk-7-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

          genename region_tag susie_pip    mu2       PVE      z num_eqtl
10169       ZNF823      19_10    0.9717  30.47 0.0003597  5.455        1
11179    HIST1H2BN       6_21    0.8621 100.93 0.0010571 10.773        1
11222   AC012074.2       2_15    0.8529  23.33 0.0002418  4.648        2
2362        B3GAT1      11_84    0.7950  23.70 0.0002289 -4.459        2
242          VSIG2      11_77    0.7863  26.81 0.0002560 -3.818        1
5997        TMEM56       1_58    0.7396  21.89 0.0001967 -3.918        1
3251        CYSTM1       5_83    0.7299  23.06 0.0002045 -4.025        1
10971    LINC00390      13_17    0.6968  22.04 0.0001866 -4.220        1
5798       ARFGAP2      11_29    0.6909  25.35 0.0002128  4.740        1
5920         PLBD2      12_68    0.6614  22.50 0.0001808  3.986        1
3172         HSDL2       9_57    0.6477  25.20 0.0001983 -4.322        1
10503        ZFP57       6_23    0.6316  73.92 0.0005672  7.267        1
10520        TCTN1      12_67    0.6306  24.15 0.0001850  4.840        1
5344          RIT1       1_76    0.5471  23.38 0.0001554 -3.496        1
377         CTNNA1       5_82    0.5417  25.60 0.0001685  5.064        1
9014        FAM83H       8_94    0.5412  24.57 0.0001616  4.317        2
677        PPP2R5B      11_36    0.5310  25.56 0.0001649 -4.623        1
11888 RP11-220I1.5       9_28    0.5132  23.80 0.0001484 -4.450        1
9316         ACOT1      14_34    0.4857  28.81 0.0001700  3.967        2
2406           MDK      11_28    0.4793  39.90 0.0002323 -6.357        1

Genes with largest effect sizes

Past versions of unnamed-chunk-8-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

           genename region_tag susie_pip    mu2       PVE       z num_eqtl
8899       HLA-DQB1       6_26 5.651e-14 882.98 6.062e-16  4.2352        1
9980       HLA-DQA1       6_26 7.838e-14 755.62 7.195e-16  4.0876        1
9870       HLA-DRB1       6_26 8.471e-14 504.68 5.194e-16  4.3158        1
10465          MSH5       6_26 8.843e-13 381.27 4.096e-15  8.8122        1
9057          ACBD4      17_27 0.000e+00 196.81 0.000e+00  1.1059        1
10458       SLC44A4       6_26 5.934e-12 170.69 1.230e-14  6.2502        1
9377          FMNL1      17_27 0.000e+00 123.32 0.000e+00 -0.6638        1
11179     HIST1H2BN       6_21 8.621e-01 100.93 1.057e-03 10.7729        1
10706         CLIC1       6_26 2.934e-13  86.02 3.066e-16  0.4634        1
1218           PUS7       7_65 0.000e+00  77.22 0.000e+00 -3.2022        1
10503         ZFP57       6_23 6.316e-01  73.92 5.672e-04  7.2673        1
9573         BTN3A2       6_20 1.147e-02  68.67 9.569e-06  9.0494        3
2240          GOSR2      17_27 0.000e+00  68.45 0.000e+00 -2.5096        1
4578           NMT1      17_27 0.000e+00  66.09 0.000e+00  2.2782        1
8918          RPRML      17_27 0.000e+00  63.52 0.000e+00  1.5727        1
8984      HIST1H2BC       6_20 1.263e-02  55.02 8.443e-06 -8.0277        1
437        MPHOSPH9      12_75 1.291e-01  48.60 7.624e-05  7.1580        1
4868          PRDM5       4_78 0.000e+00  45.06 0.000e+00  0.3272        2
6704          LEMD2       6_28 1.496e-01  42.65 7.752e-05  4.2792        2
12183 RP11-247A12.7       9_66 3.131e-01  41.87 1.593e-04  4.3022        1

Genes with highest PVE

        genename region_tag susie_pip    mu2       PVE      z num_eqtl
11179  HIST1H2BN       6_21    0.8621 100.93 0.0010571 10.773        1
10503      ZFP57       6_23    0.6316  73.92 0.0005672  7.267        1
10169     ZNF823      19_10    0.9717  30.47 0.0003597  5.455        1
242        VSIG2      11_77    0.7863  26.81 0.0002560 -3.818        1
11222 AC012074.2       2_15    0.8529  23.33 0.0002418  4.648        2
2406         MDK      11_28    0.4793  39.90 0.0002323 -6.357        1
2362      B3GAT1      11_84    0.7950  23.70 0.0002289 -4.459        2
5798     ARFGAP2      11_29    0.6909  25.35 0.0002128  4.740        1
3251      CYSTM1       5_83    0.7299  23.06 0.0002045 -4.025        1
3172       HSDL2       9_57    0.6477  25.20 0.0001983 -4.322        1
5997      TMEM56       1_58    0.7396  21.89 0.0001967 -3.918        1
10971  LINC00390      13_17    0.6968  22.04 0.0001866 -4.220        1
10520      TCTN1      12_67    0.6306  24.15 0.0001850  4.840        1
5920       PLBD2      12_68    0.6614  22.50 0.0001808  3.986        1
413       ARID1B      6_102    0.3600  39.03 0.0001707  3.907        1
9316       ACOT1      14_34    0.4857  28.81 0.0001700  3.967        2
5866       TAOK2      16_24    0.3554  39.04 0.0001685  6.189        1
377       CTNNA1       5_82    0.5417  25.60 0.0001685  5.064        1
677      PPP2R5B      11_36    0.5310  25.56 0.0001649 -4.623        1
9014      FAM83H       8_94    0.5412  24.57 0.0001616  4.317        2

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11179 HIST1H2BN       6_21 8.621e-01 100.93 1.057e-03 10.773        1
9573     BTN3A2       6_20 1.147e-02  68.67 9.569e-06  9.049        3
10465      MSH5       6_26 8.843e-13 381.27 4.096e-15  8.812        1
8984  HIST1H2BC       6_20 1.263e-02  55.02 8.443e-06 -8.028        1
5778      CNNM2      10_66 1.144e-01  38.59 5.363e-05 -7.691        1
10503     ZFP57       6_23 6.316e-01  73.92 5.672e-04  7.267        1
437    MPHOSPH9      12_75 1.291e-01  48.60 7.624e-05  7.158        1
5903      ABCB9      12_75 4.331e-03  40.53 2.133e-06  6.404        1
2406        MDK      11_28 4.793e-01  39.90 2.323e-04 -6.357        1
11226   ZSCAN31       6_22 9.442e-03  28.74 3.296e-06 -6.270        2
10458   SLC44A4       6_26 5.934e-12 170.69 1.230e-14  6.250        1
9777       DPYD       1_60 6.071e-03  37.20 2.744e-06 -6.222        1
5866      TAOK2      16_24 3.554e-01  39.04 1.685e-04  6.189        1
8969     HARBI1      11_28 1.658e-01  37.21 7.496e-05  6.169        1
10617   DNAJC19      3_111 2.294e-01  39.04 1.088e-04  6.158        1
2590     TRIM38       6_20 9.834e-03  30.60 3.656e-06  5.841        2
10231   ZKSCAN8       6_22 6.487e-03  37.40 2.948e-06  5.829        1
3264      SNX19      11_81 4.623e-02  37.69 2.117e-05  5.761        3
7375        CKB      14_54 9.933e-03  29.53 3.563e-06 -5.704        1
9987    ZSCAN16       6_22 7.220e-03  35.90 3.149e-06  5.677        1

Comparing z scores and PIPs

Past versions of unnamed-chunk-11-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

Past versions of unnamed-chunk-11-2.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[1] 0.005733

GO enrichment analysis for genes with PIP>0.5

#number of genes for gene set enrichment
length(genes)

[1] 18

Uploading 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"

Past versions of unnamed-chunk-14-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Cellular_Component_2021"

Past versions of unnamed-chunk-14-2.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[1] Term             Overlap          Adjusted.P.value Genes           
<0 rows> (or 0-length row.names)
[1] "GO_Molecular_Function_2021"

Past versions of unnamed-chunk-14-3.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

[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
45                               JOUBERT SYNDROME 13 0.02597   1/9  1/9703
50                                 NOONAN SYNDROME 8 0.02597   1/9  1/9703
36 Macular Dystrophy, Butterfly-Shaped Pigmentary, 2 0.03114   1/9  3/9703
38 Patterned dystrophy of retinal pigment epithelium 0.03114   1/9  3/9703
54     Butterfly-shaped pigmentary macular dystrophy 0.03114   1/9  3/9703
22                 Amelogenesis Imperfecta, Type III 0.03459   1/9  4/9703
35          Diabetes Mellitus, Transient Neonatal, 1 0.03704   1/9  5/9703
1                            Amelogenesis Imperfecta 0.06312   1/9 12/9703
2       Hereditary Nonpolyposis Colorectal Neoplasms 0.06312   1/9 26/9703
3                                  Diabetes Mellitus 0.06312   1/9 32/9703

WebGestalt 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!

NULL

PIP Manhattan Plot

Past versions of unnamed-chunk-17-1.png

Version	Author	Date
ff6403a	sq-96	2022-02-27

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] 52

#significance threshold for TWAS
print(sig_thresh)

[1] 4.571

#number of ctwas genes
length(ctwas_genes)

[1] 3

#number of TWAS genes
length(twas_genes)

[1] 59

#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 
0.007692 0.023077 

#specificity
print(specificity)

 ctwas   TWAS 
0.9998 0.9945 

#precision / PPV
print(precision)

  ctwas    TWAS 
0.33333 0.05085 

Past versions of unnamed-chunk-20-1.png

Version	Author	Date
75a1466	sq-96	2022-02-27
ff6403a	sq-96	2022-02-27

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] 52

#number of bystander genes (with imputed expression)
print(length(unrelated_genes))

[1] 628

#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] 4.571

#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] 12

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.01923 0.05769 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
1.0000 0.9857 

#precision / PPV / (1 - False Discovery Rate)
precision

ctwas  TWAS 
 1.00  0.25 

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) 
                   78                    49                     2 
 Detected (PIP > 0.8) 
                    1 

#show inconclusive genes
silver_standard_case[silver_standard_case=="Inconclusive"]

named character(0)

Session information

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_1.0.0        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