SCZ - Brain Frontal Cortex BA9

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Last updated: 2022-03-05

Checks: 5 2

Knit directory: cTWAS_analysis/

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    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
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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11507

#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 
1142  839  648  452  570  593  533  445  426  459  695  674  240  377  369  524 
  17   18   19   20   21   22 
 705  179  882  347  120  288 

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

[1] 9036

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

[1] 0.7853

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 
0.0101748 0.0002529 

#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 
8.988 8.832 

#report sample size
print(sample_size)

[1] 82315

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

[1]   11507 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 
0.01278 0.20549 

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

[1] 0.07922 1.52765

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
13483 RP11-230C9.4      6_102    0.9873 24.01 0.0002879 -4.866        2
7629         THOC7       3_43    0.9813 34.05 0.0004059 -6.066        2
11134       ZNF823      19_10    0.9749 29.06 0.0003441  5.468        2
12304   AC012074.2       2_15    0.8746 21.96 0.0002333  4.623        1
10221        ACOT1      14_34    0.8412 22.58 0.0002308  4.284        3
9133       MAP3K11      11_36    0.8338 23.52 0.0002382 -4.544        1
108          ELAC2      17_11    0.8056 21.71 0.0002124  4.542        1
6584         TADA1       1_82    0.7488 23.41 0.0002130 -4.174        2
3758       BHLHE41      12_18    0.7421 22.88 0.0002063  4.024        1
6336       ARFGAP2      11_29    0.7316 23.92 0.0002126  4.740        1
6470         PLBD2      12_68    0.7284 20.64 0.0001827  3.986        1
9457        LPCAT4      15_10    0.7113 20.24 0.0001749 -4.205        2
14019        ERICD       8_92    0.7082 21.16 0.0001821 -4.157        1
6317         CNNM2      10_66    0.6998 48.44 0.0004117 -8.902        2
9024          FUT9       6_65    0.6687 29.04 0.0002360  5.427        1
12293   AC073283.4       2_30    0.6190 20.75 0.0001561 -3.969        2
491        TRAPPC3       1_22    0.6176 23.44 0.0001759  4.907        1
733        PPP2R5B      11_36    0.6117 24.40 0.0001813 -4.623        1
4755          SOX5      12_17    0.6076 25.53 0.0001884  3.966        1
7965        GTF2A1      14_39    0.5965 20.91 0.0001515 -4.352        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
3530         CRHR1      17_27 2.970e-01 3095.21 1.117e-02 -3.36232        1
7121      ARHGAP27      17_27 0.000e+00 2291.37 0.000e+00 -2.08012        1
11430      HLA-DOA       6_26 6.350e-14  565.12 4.360e-16  6.84691        1
10942     HLA-DQA1       6_26 1.136e-13  486.81 6.717e-16  1.95455        1
10825     HLA-DRB1       6_26 1.346e-13  364.90 5.965e-16 -1.49219        1
11728        CLIC1       6_26 7.577e-13  363.20 3.343e-15  8.81238        2
11464         MSH5       6_26 6.363e-13  260.67 2.015e-15  7.40963        2
12571          C4A       6_26 1.336e-12  154.49 2.508e-15  5.29092        1
5338         PRDM5       4_78 0.000e+00  134.51 0.000e+00  0.06252        1
10287        FMNL1      17_27 0.000e+00  123.57 0.000e+00  0.66376        1
9925         ACBD4      17_27 0.000e+00  108.05 0.000e+00  0.26990        2
5014          NMT1      17_27 0.000e+00  100.35 0.000e+00  2.52018        2
10493       BTN3A2       6_20 1.836e-02   62.84 1.401e-05  8.94434        2
9090         DCAKD      17_27 0.000e+00   58.82 0.000e+00 -0.72756        1
2463         GOSR2      17_27 0.000e+00   56.03 0.000e+00 -3.44243        2
8482          TNXB       6_26 1.119e-13   55.82 7.589e-17  3.42145        1
6317         CNNM2      10_66 6.998e-01   48.44 4.117e-04 -8.90156        2
2871        PRSS16       6_21 5.667e-02   47.78 3.290e-05 -7.60149        1
13323    LINC01415      18_30 1.919e-01   46.63 1.087e-04 -5.32426        1
13051 RP11-490G2.2       1_60 1.276e-02   46.39 7.190e-06  7.32158        1

Genes with highest PVE

          genename region_tag susie_pip     mu2       PVE      z num_eqtl
3530         CRHR1      17_27    0.2970 3095.21 0.0111688 -3.362        1
6317         CNNM2      10_66    0.6998   48.44 0.0004117 -8.902        2
7629         THOC7       3_43    0.9813   34.05 0.0004059 -6.066        2
11134       ZNF823      19_10    0.9749   29.06 0.0003441  5.468        2
13483 RP11-230C9.4      6_102    0.9873   24.01 0.0002879 -4.866        2
9133       MAP3K11      11_36    0.8338   23.52 0.0002382 -4.544        1
9024          FUT9       6_65    0.6687   29.04 0.0002360  5.427        1
12304   AC012074.2       2_15    0.8746   21.96 0.0002333  4.623        1
10221        ACOT1      14_34    0.8412   22.58 0.0002308  4.284        3
1619        ZC3H7B      22_17    0.3965   45.66 0.0002200  5.015        3
6584         TADA1       1_82    0.7488   23.41 0.0002130 -4.174        2
6336       ARFGAP2      11_29    0.7316   23.92 0.0002126  4.740        1
108          ELAC2      17_11    0.8056   21.71 0.0002124  4.542        1
3758       BHLHE41      12_18    0.7421   22.88 0.0002063  4.024        1
4755          SOX5      12_17    0.6076   25.53 0.0001884  3.966        1
6470         PLBD2      12_68    0.7284   20.64 0.0001827  3.986        1
14019        ERICD       8_92    0.7082   21.16 0.0001821 -4.157        1
733        PPP2R5B      11_36    0.6117   24.40 0.0001813 -4.623        1
491        TRAPPC3       1_22    0.6176   23.44 0.0001759  4.907        1
748         ATP1B3       3_87    0.5394   26.72 0.0001751  3.663        1

Genes with largest z scores

          genename region_tag susie_pip    mu2       PVE      z num_eqtl
10493       BTN3A2       6_20 1.836e-02  62.84 1.401e-05  8.944        2
6317         CNNM2      10_66 6.998e-01  48.44 4.117e-04 -8.902        2
11728        CLIC1       6_26 7.577e-13 363.20 3.343e-15  8.812        2
7067       ZSCAN12       6_22 1.489e-02  41.30 7.471e-06 -8.008        1
939          NT5C2      10_66 2.700e-01  37.11 1.217e-04  7.804        1
2871        PRSS16       6_21 5.667e-02  47.78 3.290e-05 -7.601        1
11464         MSH5       6_26 6.363e-13 260.67 2.015e-15  7.410        2
13051 RP11-490G2.2       1_60 1.276e-02  46.39 7.190e-06  7.322        1
11430      HLA-DOA       6_26 6.350e-14 565.12 4.360e-16  6.847        1
10634      ZSCAN23       6_22 8.161e-02  45.53 4.514e-05 -6.793        1
9986       ARL6IP4      12_75 7.424e-03  38.54 3.476e-06  6.491        1
12308      ZSCAN31       6_22 2.258e-02  29.34 8.050e-06 -6.446        2
6452         ABCB9      12_75 6.069e-03  37.31 2.751e-06  6.404        1
10988      ZSCAN26       6_22 1.391e-02  33.86 5.721e-06  6.349        3
6407         TAOK2      16_24 3.620e-01  37.85 1.665e-04  6.300        1
9343         ATG13      11_28 2.963e-01  35.09 1.263e-04 -6.169        1
11633      DNAJC19      3_111 2.203e-01  36.38 9.736e-05  6.158        1
11089          NMB      15_39 1.795e-01  40.21 8.768e-05  6.132        1
7629         THOC7       3_43 9.813e-01  34.05 4.059e-04 -6.066        2
8634        INO80E      16_24 1.278e-01  36.26 5.630e-05  6.051        2

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] 0.006431

GO enrichment analysis for genes with PIP>0.5

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

[1] 28

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
21                                     Spasmophilia 0.0055   1/9  1/9703
24                                           Tetany 0.0055   1/9  1/9703
31                                 Tetany, Neonatal 0.0055   1/9  1/9703
56                                        Tetanilla 0.0055   1/9  1/9703
63                          SENIOR-LOKEN SYNDROME 7 0.0055   1/9  1/9703
64                          HYPOMAGNESEMIA 6, RENAL 0.0055   1/9  1/9703
67                   PROSTATE CANCER, HEREDITARY, 2 0.0055   1/9  1/9703
68       SPASTIC PARAPLEGIA 53, AUTOSOMAL RECESSIVE 0.0055   1/9  1/9703
70 COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 17 0.0055   1/9  1/9703
71                         BARDET-BIEDL SYNDROME 16 0.0055   1/9  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] 4.594

#number of ctwas genes
length(ctwas_genes)

[1] 7

#number of TWAS genes
length(twas_genes)

[1] 74

#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
9133   MAP3K11      11_36    0.8338 23.52 0.0002382 -4.544        1
10221    ACOT1      14_34    0.8412 22.58 0.0002308  4.284        3
108      ELAC2      17_11    0.8056 21.71 0.0002124  4.542        1

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02308 0.06154 

#specificity
print(specificity)

 ctwas   TWAS 
0.9997 0.9942 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.4286 0.1081 

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] 879

#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] 4.594

#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] 25

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.04688 0.12500 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
0.9989 0.9807 

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

ctwas  TWAS 
 0.75  0.32 

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

Version	Author	Date
4a5db1c	sq-96	2022-03-03
75a1466	sq-96	2022-02-27
ff6403a	sq-96	2022-02-27

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Version	Author	Date
4a5db1c	sq-96	2022-03-03
75a1466	sq-96	2022-02-27
ff6403a	sq-96	2022-02-27

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                    55                     6 
 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