SCZ - Brain Caudate basal ganglia

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Last updated: 2022-03-05

Checks: 5 2

Knit directory: cTWAS_analysis/

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    Modified:   analysis/SCZ_Brain_Cerebellar_Hemisphere.Rmd
    Modified:   analysis/SCZ_Brain_Cerebellum.Rmd
    Modified:   analysis/SCZ_Brain_Cortex.Rmd
    Modified:   analysis/SCZ_Brain_Frontal_Cortex_BA9.Rmd
    Modified:   analysis/SCZ_Brain_Hippocampus.Rmd
    Modified:   analysis/SCZ_Brain_Hypothalamus.Rmd
    Modified:   analysis/SCZ_Brain_Nucleus_accumbens_basal_ganglia.Rmd
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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11590

#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 
1115  851  695  452  579  584  548  433  433  450  693  657  241  393  396  529 
  17   18   19   20   21   22 
 713  185  887  340  130  286 

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

[1] 9058

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

[1] 0.7815

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 
0.0104054 0.0002537 

#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 
10.170  8.703 

#report sample size
print(sample_size)

[1] 82315

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

[1]   11590 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 
0.0149 0.2031 

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

[1] 0.1133 1.4480

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
3517       CRHR1      17_27    0.9961 3564.35 0.0431343  3.362        1
11135     ZNF823      19_10    0.9789   29.83 0.0003547  5.485        2
12311 AC012074.2       2_16    0.8313   23.83 0.0002407  4.623        1
3091       SF3B1      2_117    0.8220   43.62 0.0004356  6.725        1
1678    KIAA0391       14_9    0.7965   25.21 0.0002440 -5.164        1
9254      TMEM81      1_104    0.7956   26.02 0.0002515  4.938        1
110        ELAC2      17_11    0.7948   21.79 0.0002104  4.509        1
7042      CDC25C       5_82    0.7806   25.25 0.0002395 -5.591        1
3750     BHLHE41      12_18    0.7560   22.76 0.0002091 -4.024        1
6310     ARFGAP2      11_29    0.7470   24.29 0.0002204  4.740        1
6443       PLBD2      12_68    0.7438   20.62 0.0001864  3.986        1
13314    TBC1D29      17_18    0.7431   23.05 0.0002081  4.407        1
2684       VPS29      12_67    0.7225   25.07 0.0002201 -4.965        2
8894      ZNF318       6_33    0.6854   24.43 0.0002034 -4.832        1
11792      AS3MT      10_66    0.6837   38.64 0.0003209  6.812        2
12298 AC073283.4       2_30    0.6830   22.95 0.0001904 -3.812        4
10494    TMEM222       1_19    0.6650   24.78 0.0002002  3.902        1
1879       ESRP2      16_36    0.6441   27.28 0.0002134  5.203        2
461       ARID1B      6_102    0.6354   24.30 0.0001876 -3.907        1
11616      ITSN1      21_14    0.6332   24.19 0.0001861  3.954        2

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
3517     CRHR1      17_27 9.961e-01 3564.35 4.313e-02  3.3623        1
12229 HLA-DQB2       6_26 7.161e-14  841.86 7.324e-16 -4.1487        1
10939 HLA-DQA1       6_26 1.436e-13  799.37 1.394e-15  3.4460        1
12390 HLA-DQA2       6_26 1.401e-13  516.30 8.788e-16 -3.5679        1
12115   ARL17B      17_27 0.000e+00  374.03 0.000e+00 -3.0672        1
11731    CLIC1       6_26 9.395e-13  368.28 4.203e-15  8.8118        2
7119  ARHGAP27      17_27 0.000e+00  340.94 0.000e+00  0.3401        1
11469     MSH5       6_26 3.538e-13  318.93 1.371e-15  7.6794        2
11464   HSPA1L       6_26 3.716e-13  230.85 1.042e-15  7.1259        1
9922     ACBD4      17_27 0.000e+00  208.81 0.000e+00  1.3121        1
4990      NMT1      17_27 0.000e+00  184.54 0.000e+00  2.4459        1
12582      C4A       6_26 1.657e-12  156.41 3.148e-15  5.2909        1
10283    FMNL1      17_27 0.000e+00  141.39 0.000e+00 -0.6638        1
10529   HEXIM1      17_27 0.000e+00  130.59 0.000e+00 -3.3358        1
1319      PUS7       7_65 0.000e+00  108.05 0.000e+00  0.6328        2
2439     GOSR2      17_27 0.000e+00   71.67 0.000e+00 -2.5096        1
4823     RINT1       7_65 0.000e+00   68.20 0.000e+00  1.1750        1
5119     PGBD1       6_22 1.352e-02   67.44 1.107e-05 -8.4933        1
10491   BTN3A2       6_20 1.634e-02   64.75 1.285e-05  8.9338        2
9090     DCAKD      17_27 0.000e+00   55.42 0.000e+00 -2.1069        2

Genes with highest PVE

        genename region_tag susie_pip     mu2       PVE      z num_eqtl
3517       CRHR1      17_27    0.9961 3564.35 0.0431343  3.362        1
3091       SF3B1      2_117    0.8220   43.62 0.0004356  6.725        1
11135     ZNF823      19_10    0.9789   29.83 0.0003547  5.485        2
11792      AS3MT      10_66    0.6837   38.64 0.0003209  6.812        2
9254      TMEM81      1_104    0.7956   26.02 0.0002515  4.938        1
1678    KIAA0391       14_9    0.7965   25.21 0.0002440 -5.164        1
12311 AC012074.2       2_16    0.8313   23.83 0.0002407  4.623        1
7042      CDC25C       5_82    0.7806   25.25 0.0002395 -5.591        1
2630         MDK      11_28    0.4877   38.14 0.0002260 -6.357        1
6310     ARFGAP2      11_29    0.7470   24.29 0.0002204  4.740        1
2684       VPS29      12_67    0.7225   25.07 0.0002201 -4.965        2
1879       ESRP2      16_36    0.6441   27.28 0.0002134  5.203        2
110        ELAC2      17_11    0.7948   21.79 0.0002104  4.509        1
3750     BHLHE41      12_18    0.7560   22.76 0.0002091 -4.024        1
13314    TBC1D29      17_18    0.7431   23.05 0.0002081  4.407        1
8894      ZNF318       6_33    0.6854   24.43 0.0002034 -4.832        1
10494    TMEM222       1_19    0.6650   24.78 0.0002002  3.902        1
12298 AC073283.4       2_30    0.6830   22.95 0.0001904 -3.812        4
506      SDCCAG8      1_128    0.5981   25.90 0.0001882 -4.982        1
461       ARID1B      6_102    0.6354   24.30 0.0001876 -3.907        1

Genes with largest z scores

          genename region_tag susie_pip    mu2       PVE      z num_eqtl
10491       BTN3A2       6_20 1.634e-02  64.75 1.285e-05  8.934        2
11731        CLIC1       6_26 9.395e-13 368.28 4.203e-15  8.812        2
5119         PGBD1       6_22 1.352e-02  67.44 1.107e-05 -8.493        1
6289         CNNM2      10_66 1.509e-01  46.14 8.461e-05 -8.118        2
11469         MSH5       6_26 3.538e-13 318.93 1.371e-15  7.679        2
13068 RP11-490G2.2       1_60 1.266e-02  48.12 7.404e-06  7.322        1
7064       ZSCAN12       6_22 1.413e-02  34.93 5.996e-06  7.214        1
9628       C2orf69      2_118 2.481e-01  40.18 1.211e-04  7.151        2
11464       HSPA1L       6_26 3.716e-13 230.85 1.042e-15  7.126        1
11792        AS3MT      10_66 6.837e-01  38.64 3.209e-04  6.812        2
1226      PPP1R13B      14_54 1.408e-01  46.70 7.990e-05  6.798        3
10643      ZSCAN23       6_22 8.404e-02  46.85 4.784e-05 -6.793        1
7500          TYW5      2_118 3.373e-02  36.46 1.494e-05 -6.774        2
3091         SF3B1      2_117 8.220e-01  43.62 4.356e-04  6.725        1
6279       CYP17A1      10_66 5.195e-03  27.80 1.754e-06 -6.720        1
9851     HIST1H2BC       6_20 1.561e-02  39.53 7.498e-06 -6.675        2
10986      ZSCAN26       6_22 1.420e-02  37.06 6.395e-06  6.584        3
4024         XRCC3      14_54 7.584e-02  42.09 3.877e-05  6.524        2
9986       ARL6IP4      12_75 8.382e-03  39.48 4.020e-06  6.491        1
6424         ABCB9      12_75 6.686e-03  38.21 3.104e-06  6.404        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] 0.007075

GO enrichment analysis for genes with PIP>0.5

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

[1] 27

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
27               Neoplasms, Glandular and Epithelial 0.02897  1/13  2/9703
32                               Pain, Postoperative 0.02897  1/13  2/9703
46                               Glandular Neoplasms 0.02897  1/13  2/9703
88        Refractory anemia with ringed sideroblasts 0.02897  1/13  2/9703
93                                       Epithelioma 0.02897  1/13  2/9703
106            CHROMOSOME 6q24-q25 DELETION SYNDROME 0.02897  1/13  2/9703
107                          SENIOR-LOKEN SYNDROME 7 0.02897  1/13  1/9703
111                   PROSTATE CANCER, HEREDITARY, 2 0.02897  1/13  1/9703
113                                NOONAN SYNDROME 8 0.02897  1/13  1/9703
114 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.02897  1/13  1/9703

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] 64

#significance threshold for TWAS
print(sig_thresh)

[1] 4.596

#number of ctwas genes
length(ctwas_genes)

[1] 4

#number of TWAS genes
length(twas_genes)

[1] 82

#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
3517    CRHR1      17_27    0.9961 3564 0.04313 3.362        1

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02308 0.07692 

#specificity
print(specificity)

 ctwas   TWAS 
0.9999 0.9938 

#precision / PPV
print(precision)

ctwas  TWAS 
0.750 0.122 

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] 64

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

[1] 822

#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] 4.596

#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] 30

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.04688 0.15625 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
1.0000 0.9757 

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

 ctwas   TWAS 
1.0000 0.3333 

Past versions of unnamed-chunk-24-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

Past versions of unnamed-chunk-25-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

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")

Past versions of unnamed-chunk-26-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

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) 
                   66                    53                     8 
 Detected (PIP > 0.8) 
                    3 

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

named character(0)

Past versions of unnamed-chunk-30-1.png

Version	Author	Date
4a5db1c	sq-96	2022-03-03

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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] readxl_1.3.1      forcats_0.5.1     stringr_1.4.0     dplyr_1.0.7      
 [5] purrr_0.3.4       readr_2.1.1       tidyr_1.1.4       tidyverse_1.3.1  
 [9] tibble_3.1.6      WebGestaltR_0.4.4 disgenet2r_0.99.2 enrichR_3.0      
[13] cowplot_1.0.0     ggplot2_3.3.5     workflowr_1.7.0  

loaded via a namespace (and not attached):
 [1] fs_1.5.2          lubridate_1.8.0   bit64_4.0.5       doParallel_1.0.17
 [5] httr_1.4.2        rprojroot_2.0.2   tools_3.6.1       backports_1.4.1  
 [9] doRNG_1.8.2       utf8_1.2.2        R6_2.5.1          vipor_0.4.5      
[13] DBI_1.1.2         colorspace_2.0-2  withr_2.4.3       ggrastr_1.0.1    
[17] tidyselect_1.1.1  processx_3.5.2    bit_4.0.4         curl_4.3.2       
[21] compiler_3.6.1    git2r_0.26.1      rvest_1.0.2       cli_3.1.0        
[25] Cairo_1.5-12.2    xml2_1.3.3        labeling_0.4.2    scales_1.1.1     
[29] callr_3.7.0       apcluster_1.4.8   digest_0.6.29     rmarkdown_2.11   
[33] svglite_1.2.2     pkgconfig_2.0.3   htmltools_0.5.2   dbplyr_2.1.1     
[37] fastmap_1.1.0     highr_0.9         rlang_1.0.1       rstudioapi_0.13  
[41] RSQLite_2.2.8     jquerylib_0.1.4   farver_2.1.0      generics_0.1.1   
[45] jsonlite_1.7.2    vroom_1.5.7       magrittr_2.0.2    Matrix_1.2-18    
[49] ggbeeswarm_0.6.0  Rcpp_1.0.8        munsell_0.5.0     fansi_1.0.2      
[53] gdtools_0.1.9     lifecycle_1.0.1   stringi_1.7.6     whisker_0.3-2    
[57] yaml_2.2.1        plyr_1.8.6        grid_3.6.1        blob_1.2.2       
[61] ggrepel_0.9.1     parallel_3.6.1    promises_1.0.1    crayon_1.5.0     
[65] lattice_0.20-38   haven_2.4.3       hms_1.1.1         knitr_1.36       
[69] ps_1.6.0          pillar_1.6.4      igraph_1.2.10     rjson_0.2.20     
[73] rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16  reprex_2.0.1     
[77] glue_1.6.2        evaluate_0.14     getPass_0.2-2     modelr_0.1.8     
[81] data.table_1.14.2 vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[85] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[89] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[93] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2