SCZ - Brain Cortex

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Last updated: 2022-03-05

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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    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] 11805

#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 
1182  833  689  456  564  597  567  438  449  491  708  673  233  392  390  558 
  17   18   19   20   21   22 
 697  184  896  372  130  306 

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

[1] 9268

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

[1] 0.7851

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 
0.0117819 0.0002498 

#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 
10.115  8.745 

#report sample size
print(sample_size)

[1] 82315

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

[1]   11805 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 
0.01709 0.20104 

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

[1] 0.07764 1.58587

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
11314       ZNF823      19_10    0.9894 30.41 0.0003655  5.576        2
13679 RP11-230C9.4      6_102    0.9655 24.38 0.0002859 -4.864        2
3165         SF3B1      2_117    0.8406 43.74 0.0004467  6.725        1
3085         SPCS1       3_36    0.8248 35.15 0.0003522 -6.504        1
11176        PCBP2      12_33    0.7873 20.60 0.0001970  4.202        1
5055        RCBTB1      13_21    0.7808 21.04 0.0001996 -4.143        2
421          TRIT1       1_25    0.7795 21.04 0.0001992 -4.073        3
11969        AS3MT      10_66    0.7577 38.26 0.0003522  6.688        3
6435       ARFGAP2      11_29    0.7533 24.88 0.0002277  4.740        1
6035      METTL21A      2_122    0.7032 22.51 0.0001923 -4.406        1
13958        CWC25      17_23    0.6607 22.97 0.0001843 -3.926        2
376           CUL3      2_132    0.6584 29.41 0.0002352 -5.491        1
3183        CNPPD1      2_129    0.6333 23.74 0.0001827 -4.678        2
4002         ARMC7      17_42    0.5918 23.73 0.0001706  4.133        2
9752       ZNF354C      5_108    0.5861 21.75 0.0001549 -3.965        1
12379    LINC01305      2_105    0.5824 22.86 0.0001617  4.523        1
4909       CCDC146       7_49    0.5702 20.79 0.0001441  3.799        3
5958        CEP170      1_128    0.5671 24.30 0.0001674  4.678        1
752        PPP2R5B      11_36    0.5634 24.18 0.0001655 -4.577        1
10297        PCBP3      21_23    0.5553 21.27 0.0001435  4.308        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
7218  ARHGAP27      17_27   0.00000 2129.27 0.000e+00 -1.847        2
3591     CRHR1      17_27   0.00000 2068.72 0.000e+00 -3.270        1
11645   LY6G6C       6_26   0.00000  960.79 0.000e+00  8.872        1
11907    CLIC1       6_26   0.00000  949.93 0.000e+00  9.312        2
10504   SPPL2C      17_27   0.00000  752.27 0.000e+00 -1.978        1
10996 HLA-DRB1       6_26   0.00000  525.39 0.000e+00  4.535        1
11640   HSPA1L       6_26   0.00000  401.95 0.000e+00 -7.126        1
11118 HLA-DQA1       6_26   0.00000  166.39 0.000e+00  1.889        1
4915     SRPK2       7_65   0.00000  128.12 0.000e+00 -1.338        1
10699   HEXIM1      17_27   0.00000  122.54 0.000e+00 -3.372        1
10447    FMNL1      17_27   0.00000  109.85 0.000e+00  1.802        2
12300   SAPCD1       6_26   0.00000  107.97 0.000e+00 -2.781        1
9224     DCAKD      17_27   0.00000   91.74 0.000e+00 -2.216        3
12783      C4A       6_26   0.00000   77.97 0.000e+00  3.137        2
9902  HLA-DQB1       6_26   0.00000   76.18 0.000e+00  1.677        2
10083    ACBD4      17_27   0.00000   68.16 0.000e+00  1.719        2
2927    PRSS16       6_21   0.11206   62.33 8.485e-05 -8.564        2
2503     GOSR2      17_27   0.00000   57.01 0.000e+00 -3.444        2
958      NT5C2      10_66   0.50836   51.68 3.192e-04 -8.066        1
6404       INA      10_66   0.02367   48.25 1.387e-05 -7.264        1

Genes with highest PVE

          genename region_tag susie_pip   mu2       PVE      z num_eqtl
3165         SF3B1      2_117    0.8406 43.74 0.0004467  6.725        1
11314       ZNF823      19_10    0.9894 30.41 0.0003655  5.576        2
3085         SPCS1       3_36    0.8248 35.15 0.0003522 -6.504        1
11969        AS3MT      10_66    0.7577 38.26 0.0003522  6.688        3
958          NT5C2      10_66    0.5084 51.68 0.0003192 -8.066        1
13679 RP11-230C9.4      6_102    0.9655 24.38 0.0002859 -4.864        2
376           CUL3      2_132    0.6584 29.41 0.0002352 -5.491        1
6435       ARFGAP2      11_29    0.7533 24.88 0.0002277  4.740        1
2682           MDK      11_29    0.4345 39.31 0.0002075 -6.357        1
5055        RCBTB1      13_21    0.7808 21.04 0.0001996 -4.143        2
421          TRIT1       1_25    0.7795 21.04 0.0001992 -4.073        3
11176        PCBP2      12_33    0.7873 20.60 0.0001970  4.202        1
6035      METTL21A      2_122    0.7032 22.51 0.0001923 -4.406        1
13958        CWC25      17_23    0.6607 22.97 0.0001843 -3.926        2
13401        CORO7       16_4    0.5211 28.97 0.0001834 -5.016        2
3183        CNPPD1      2_129    0.6333 23.74 0.0001827 -4.678        2
6507       TMEM219      16_24    0.4011 37.11 0.0001808  6.243        1
4002         ARMC7      17_42    0.5918 23.73 0.0001706  4.133        2
5958        CEP170      1_128    0.5671 24.30 0.0001674  4.678        1
752        PPP2R5B      11_36    0.5634 24.18 0.0001655 -4.577        1

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
11907     CLIC1       6_26  0.000000 949.93 0.000e+00  9.312        2
11645    LY6G6C       6_26  0.000000 960.79 0.000e+00  8.872        1
2927     PRSS16       6_21  0.112056  62.33 8.485e-05 -8.564        2
958       NT5C2      10_66  0.508364  51.68 3.192e-04 -8.066        1
6413      CNNM2      10_66  0.047944  45.59 2.655e-05 -7.691        1
10662    BTN3A2       6_20  0.016326  46.66 9.254e-06  7.313        3
6404        INA      10_66  0.023665  48.25 1.387e-05 -7.264        1
13518 LINC01415      18_30  0.027640  32.82 1.102e-05 -7.188        2
11640    HSPA1L       6_26  0.000000 401.95 0.000e+00 -7.126        1
12511   ZSCAN31       6_22  0.029845  37.53 1.361e-05 -6.820        3
3165      SF3B1      2_117  0.840602  43.74 4.467e-04  6.725        1
11969     AS3MT      10_66  0.757723  38.26 3.522e-04  6.688        3
11171   ZSCAN26       6_22  0.016156  37.52 7.365e-06  6.645        3
2756     OGFOD2      12_75  0.010145  39.52 4.870e-06  6.518        1
3085      SPCS1       3_36  0.824822  35.15 3.522e-04 -6.504        1
3616      SNX19      11_81  0.134870  41.83 6.853e-05  6.459        2
10809   ZSCAN23       6_22  0.050592  38.63 2.374e-05 -6.415        1
6550      ABCB9      12_75  0.007503  37.92 3.457e-06  6.404        1
2682        MDK      11_29  0.434514  39.31 2.075e-04 -6.357        1
3159     KCNJ13      2_137  0.209359  34.40 8.748e-05  6.333        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] 0.006607

GO enrichment analysis for genes with PIP>0.5

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

[1] 26

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

                                                                         Term
1 regulation of nucleobase-containing compound metabolic process (GO:0019219)
  Overlap Adjusted.P.value       Genes
1    2/12          0.02022 PCBP3;PCBP2
[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
55                           Disproportionate tall stature 0.009895   1/9
56       Reticular Dystrophy Of Retinal Pigment Epithelium 0.009895   1/9
60                       PSEUDOHYPOALDOSTERONISM, TYPE IIE 0.009895   1/9
62              SPASTIC PARAPLEGIA 45, AUTOSOMAL RECESSIVE 0.009895   1/9
63 RETINAL DYSTROPHY WITH OR WITHOUT EXTRAOCULAR ANOMALIES 0.009895   1/9
64        COMBINED OXIDATIVE PHOSPHORYLATION DEFICIENCY 35 0.009895   1/9
17                     Neoplasms, Glandular and Epithelial 0.011869   1/9
29                                     Glandular Neoplasms 0.011869   1/9
48              Refractory anemia with ringed sideroblasts 0.011869   1/9
51                                             Epithelioma 0.011869   1/9
   BgRatio
55  1/9703
56  1/9703
60  1/9703
62  1/9703
63  1/9703
64  1/9703
17  2/9703
29  2/9703
48  2/9703
51  2/9703

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] 66

#significance threshold for TWAS
print(sig_thresh)

[1] 4.599

#number of ctwas genes
length(ctwas_genes)

[1] 4

#number of TWAS genes
length(twas_genes)

[1] 78

#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 
0.01538 0.06154 

#specificity
print(specificity)

 ctwas   TWAS 
0.9998 0.9940 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.5000 0.1026 

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] 66

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

[1] 856

#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] 4.599

#number of ctwas genes (in known annotations or bystanders)
length(ctwas_genes)

[1] 2

#number of TWAS genes (in known annotations or bystanders)
length(twas_genes)

[1] 28

#sensitivity / recall
sensitivity

 ctwas   TWAS 
0.0303 0.1212 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
1.0000 0.9766 

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

 ctwas   TWAS 
1.0000 0.2857 

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

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

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) 
                   64                    58                     6 
 Detected (PIP > 0.8) 
                    2 

#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