SCZ - Brain Nucleus accumbens basal ganglia

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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
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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
    Modified:   analysis/SCZ_Brain_Spinal_cord_cervical_c-1.Rmd
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Weight QC

#number of imputed weights
nrow(qclist_all)

[1] 11518

#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 
1135  816  688  449  575  581  558  432  426  464  694  651  223  386  374  531 
  17   18   19   20   21   22 
 679  185  885  358  134  294 

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

[1] 9119

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

[1] 0.7917

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 
0.0137343 0.0002475 

#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 
8.579 8.869 

#report sample size
print(sample_size)

[1] 82315

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

[1]   11518 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 
0.01649 0.20198 

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

[1] 0.08618 1.45805

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
4131        SPECC1      17_16    0.9939 30.61 0.0003696  5.625        2
11067       ZNF823      19_10    0.9819 28.88 0.0003445  5.483        2
5491         FURIN      15_42    0.9804 44.25 0.0005271 -7.000        1
13453 RP11-230C9.4      6_102    0.9661 23.94 0.0002810 -4.872        2
11808        NPTXR      22_15    0.9254 21.85 0.0002456  4.512        2
13743        CWC25      17_23    0.8673 20.73 0.0002185 -4.015        3
6509        TMEM56       1_58    0.8341 20.15 0.0002041 -3.918        1
3067         SF3B1      2_117    0.8259 42.69 0.0004284  6.725        1
11955    LINC00390      13_17    0.8053 20.17 0.0001973 -4.220        1
6535         TADA1       1_82    0.7967 21.93 0.0002123 -4.168        2
6291       ARFGAP2      11_29    0.7860 23.76 0.0002269  4.740        1
10921        PCBP2      12_33    0.7827 20.28 0.0001928  4.202        1
12231   AC073283.4       2_30    0.7761 20.60 0.0001942 -3.892        2
10150        ACOT1      14_34    0.7648 21.86 0.0002031  4.044        2
2658         VPS29      12_67    0.7276 24.06 0.0002126 -4.937        2
9374         COX8A      11_35    0.7272 24.38 0.0002154 -4.750        1
4719          SOX5      12_17    0.6951 20.31 0.0001715  4.309        1
107          ELAC2      17_11    0.6793 22.42 0.0001850  4.227        1
3206        MAP7D1       1_22    0.6731 23.08 0.0001887  4.907        1
11957    LINC00606        3_8    0.6618 23.04 0.0001853 -3.964        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
9682   HLA-DQB1       6_26 1.221e-13 609.57 9.044e-16  4.3395        1
12152  HLA-DQB2       6_26 1.634e-13 513.04 1.019e-15 -3.5679        1
12325  HLA-DQA2       6_26 1.289e-13 385.09 6.030e-16  0.2164        1
11395      MSH5       6_26 4.995e-13 334.97 2.033e-15  8.1001        2
10748  HLA-DRB1       6_26 9.848e-14 166.78 1.995e-16 -1.8363        1
9862      ACBD4      17_27 0.000e+00 163.38 0.000e+00  1.6939        2
10865  HLA-DQA1       6_26 7.232e-13 105.14 9.237e-16 -0.7786        1
11647     CLIC1       6_26 4.017e-13  84.36 4.117e-16 -0.4634        1
10450    HEXIM1      17_27 0.000e+00  67.49 0.000e+00 -2.8451        1
10415    BTN3A2       6_20 2.490e-02  64.62 1.955e-05  9.0374        3
2422      GOSR2      17_27 0.000e+00  56.26 0.000e+00 -3.4300        2
10915   ZSCAN26       6_22 1.946e-02  53.24 1.259e-05  8.6508        3
9788  HIST1H2BC       6_20 2.752e-02  51.86 1.734e-05 -8.0277        1
2790     TRIM38       6_20 2.223e-02  44.29 1.196e-05 -7.4660        2
5491      FURIN      15_42 9.804e-01  44.25 5.271e-04 -7.0004        1
10558   ZSCAN23       6_22 1.061e-01  44.07 5.682e-05 -6.7082        2
3067      SF3B1      2_117 8.259e-01  42.69 4.284e-04  6.7253        1
4135      CDHR3       7_65 0.000e+00  41.22 0.000e+00  2.9707        2
13283 LINC01415      18_30 3.193e-01  39.97 1.550e-04 -5.3243        1
10418   TMEM222       1_19 3.612e-01  39.49 1.733e-04  3.9022        1

Genes with highest PVE

          genename region_tag susie_pip   mu2       PVE      z num_eqtl
5491         FURIN      15_42    0.9804 44.25 0.0005271 -7.000        1
3067         SF3B1      2_117    0.8259 42.69 0.0004284  6.725        1
4131        SPECC1      17_16    0.9939 30.61 0.0003696  5.625        2
11067       ZNF823      19_10    0.9819 28.88 0.0003445  5.483        2
13453 RP11-230C9.4      6_102    0.9661 23.94 0.0002810 -4.872        2
11808        NPTXR      22_15    0.9254 21.85 0.0002456  4.512        2
6291       ARFGAP2      11_29    0.7860 23.76 0.0002269  4.740        1
2602           MDK      11_28    0.5002 37.08 0.0002253 -6.357        1
1571       CACNA1I      22_16    0.5082 35.45 0.0002189  5.841        1
13743        CWC25      17_23    0.8673 20.73 0.0002185 -4.015        3
9374         COX8A      11_35    0.7272 24.38 0.0002154 -4.750        1
2658         VPS29      12_67    0.7276 24.06 0.0002126 -4.937        2
6535         TADA1       1_82    0.7967 21.93 0.0002123 -4.168        2
6304          DRD2      11_67    0.5634 30.91 0.0002115 -5.938        2
6509        TMEM56       1_58    0.8341 20.15 0.0002041 -3.918        1
10150        ACOT1      14_34    0.7648 21.86 0.0002031  4.044        2
11955    LINC00390      13_17    0.8053 20.17 0.0001973 -4.220        1
12231   AC073283.4       2_30    0.7761 20.60 0.0001942 -3.892        2
10921        PCBP2      12_33    0.7827 20.28 0.0001928  4.202        1
3206        MAP7D1       1_22    0.6731 23.08 0.0001887  4.907        1

Genes with largest z scores

       genename region_tag susie_pip    mu2       PVE      z num_eqtl
10415    BTN3A2       6_20 2.490e-02  64.62 1.955e-05  9.037        3
10915   ZSCAN26       6_22 1.946e-02  53.24 1.259e-05  8.651        3
11395      MSH5       6_26 4.995e-13 334.97 2.033e-15  8.100        2
9788  HIST1H2BC       6_20 2.752e-02  51.86 1.734e-05 -8.028        1
6275      CNNM2      10_66 6.121e-02  32.97 2.452e-05 -7.547        2
2790     TRIM38       6_20 2.223e-02  44.29 1.196e-05 -7.466        2
5491      FURIN      15_42 9.804e-01  44.25 5.271e-04 -7.000        1
3067      SF3B1      2_117 8.259e-01  42.69 4.284e-04  6.725        1
7442       TYW5      2_118 3.896e-02  35.64 1.686e-05 -6.718        2
10558   ZSCAN23       6_22 1.061e-01  44.07 5.682e-05 -6.708        2
9922    ARL6IP4      12_75 9.368e-03  38.77 4.413e-06  6.491        1
6404      ABCB9      12_75 7.758e-03  37.56 3.540e-06  6.404        1
2602        MDK      11_28 5.002e-01  37.08 2.253e-04 -6.357        1
9771     HARBI1      11_28 1.872e-01  34.44 7.832e-05  6.169        1
11548   DNAJC19      3_111 2.691e-01  36.11 1.180e-04  6.158        1
8554     INO80E      16_24 3.044e-01  36.89 1.364e-04  6.121        2
6304       DRD2      11_67 5.634e-01  30.91 2.115e-04 -5.938        2
7634       GNL3       3_36 1.699e-01  32.47 6.702e-05  5.899        2
1571    CACNA1I      22_16 5.082e-01  35.45 2.189e-04  5.841        1
11136   ZKSCAN8       6_22 1.558e-02  33.88 6.415e-06  5.837        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] 0.00547

GO enrichment analysis for genes with PIP>0.5

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

[1] 35

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
5              Anxiety Disorders 0.02052  2/14 44/9703
50                       Measles 0.02052  1/14  1/9703
51              Memory Disorders 0.02052  2/14 43/9703
92             Memory impairment 0.02052  2/14 44/9703
120     Anxiety States, Neurotic 0.02052  2/14 44/9703
148 Age-Related Memory Disorders 0.02052  2/14 43/9703
149    Memory Disorder, Semantic 0.02052  2/14 43/9703
150     Memory Disorder, Spatial 0.02052  2/14 43/9703
151                  Memory Loss 0.02052  2/14 43/9703
169   Anxiety neurosis (finding) 0.02052  2/14 44/9703

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] 59

#significance threshold for TWAS
print(sig_thresh)

[1] 4.594

#number of ctwas genes
length(ctwas_genes)

[1] 9

#number of TWAS genes
length(twas_genes)

[1] 63

#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
6509     TMEM56       1_58    0.8341 20.15 0.0002041 -3.918        1
11955 LINC00390      13_17    0.8053 20.17 0.0001973 -4.220        1
13743     CWC25      17_23    0.8673 20.73 0.0002185 -4.015        3
11808     NPTXR      22_15    0.9254 21.85 0.0002456  4.512        2

#sensitivity / recall
print(sensitivity)

  ctwas    TWAS 
0.02308 0.07692 

#specificity
print(specificity)

 ctwas   TWAS 
0.9995 0.9954 

#precision / PPV
print(precision)

 ctwas   TWAS 
0.3333 0.1587 

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] 59

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

[1] 734

#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] 5

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

[1] 17

#sensitivity / recall
sensitivity

  ctwas    TWAS 
0.05085 0.16949 

#specificity / (1 - False Positive Rate)
specificity

 ctwas   TWAS 
0.9973 0.9905 

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

 ctwas   TWAS 
0.6000 0.5882 

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) 
                   71                    49                     7 
 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