Last updated: 2022-02-27

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Rmd 3dd5b4c sq-96 2022-02-27 update

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

#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

          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

           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

[1] 0.005733
       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

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"

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

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

[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

Sensitivity, specificity and precision for silver standard genes

#number of genes in known annotations
print(length(known_annotations))
[1] 41
#number of genes in known annotations with imputed expression
print(sum(known_annotations %in% ctwas_gene_res$genename))
[1] 20
#significance threshold for TWAS
print(sig_thresh)
[1] 4.571
#number of ctwas genes
length(ctwas_genes)
[1] 3
#number of TWAS genes
length(twas_genes)
[1] 59
#show novel genes (ctwas genes with not in TWAS genes)
ctwas_gene_res[ctwas_gene_res$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     0 
#specificity
print(specificity)
 ctwas   TWAS 
0.9997 0.9943 
#precision / PPV
print(precision)
ctwas  TWAS 
    0     0 


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_1.6.2  

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  bit_4.0.4         curl_4.3.2        compiler_3.6.1   
[21] git2r_0.26.1      rvest_1.0.2       cli_3.1.0         Cairo_1.5-12.2   
[25] xml2_1.3.3        labeling_0.4.2    scales_1.1.1      apcluster_1.4.8  
[29] digest_0.6.29     rmarkdown_2.11    svglite_1.2.2     pkgconfig_2.0.3  
[33] htmltools_0.5.2   dbplyr_2.1.1      fastmap_1.1.0     highr_0.9        
[37] rlang_1.0.1       rstudioapi_0.13   RSQLite_2.2.8     jquerylib_0.1.4  
[41] farver_2.1.0      generics_0.1.1    jsonlite_1.7.2    vroom_1.5.7      
[45] magrittr_2.0.2    Matrix_1.2-18     ggbeeswarm_0.6.0  Rcpp_1.0.8       
[49] munsell_0.5.0     fansi_1.0.2       gdtools_0.1.9     lifecycle_1.0.1  
[53] stringi_1.7.6     whisker_0.3-2     yaml_2.2.1        plyr_1.8.6       
[57] grid_3.6.1        blob_1.2.2        ggrepel_0.9.1     parallel_3.6.1   
[61] promises_1.0.1    crayon_1.5.0      lattice_0.20-38   haven_2.4.3      
[65] hms_1.1.1         knitr_1.36        pillar_1.6.4      igraph_1.2.10    
[69] rjson_0.2.20      rngtools_1.5.2    reshape2_1.4.4    codetools_0.2-16 
[73] reprex_2.0.1      glue_1.6.2        evaluate_0.14     data.table_1.14.2
[77] modelr_0.1.8      vctrs_0.3.8       tzdb_0.2.0        httpuv_1.5.1     
[81] foreach_1.5.2     cellranger_1.1.0  gtable_0.3.0      assertthat_0.2.1 
[85] cachem_1.0.6      xfun_0.29         broom_0.7.10      later_0.8.0      
[89] iterators_1.0.14  beeswarm_0.2.3    memoise_2.0.1     ellipsis_0.3.2