Last updated: 2023-08-16

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File Version Author Date Message
Rmd 08eaf44 Jing Gu 2023-08-16 run ctwas for multiple traits

Load ctwas results

# top 1 method
res <- impute_expr_z(z_snp, weight = weight, ld_R_dir = ld_R_dir,
                         method = NULL, outputdir = outputdir, outname = outname.e,
                         harmonize_z = T, harmonize_wgt = T, scale_by_ld_variance=F,
                         strand_ambig_action_z = "recover", 
                         recover_strand_ambig_wgt = T
# lasso/elastic-net method
res <- impute_expr_z(z_snp, weight = weight, ld_R_dir = ld_R_dir,
                         method = NULL, outputdir = outputdir, outname = outname.e,
                         harmonize_z = T, harmonize_wgt = T, scale_by_ld_variance=F,
                         strand_ambig_action_z = "none", 
                         recover_strand_ambig_wgt = F

GWAS: UK Biobank GWAS summary statistics - European individuals

Weights: FUSION weights using top1, lasso, or elastic-net models were converted into PredictDB format and were not needed to do scaling when running ctwas.

Check convergence of parameters

cTWAS analysis on m6A alone

[1] "Check convergence for the top1 model:"
[1] "Table of group size:"
    SNP    gene 
8713250     888 
                                SNP     gene
estimated_group_prior     7.333e-05 0.050687
estimated_group_prior_var 5.562e+01 7.859621
estimated_group_pve       1.034e-01 0.001030
attributable_group_pve    9.901e-01 0.009857
$top1

Joint analysis of expression, splicing and m6A

[1] "Check convergence for the top1 model when jointly analyzing expression, splicing and m6A:"
[1] "Table of group size before/after matching with UKBB SNPs:"
                          SNP      eQTL     sQTL   m6AQTL
prior_group_size    9.324e+06 2005.0000 2191.000 918.0000
group_size          8.713e+06 1928.0000 2123.000 888.0000
percent_of_overlaps 9.345e-01    0.9616    0.969   0.9673
                                SNP     eQTL    sQTL    m6AQTL
estimated_group_prior     5.872e-05 0.021347 0.03260 0.0290148
estimated_group_prior_var 6.634e+01 9.304196 6.55138 9.1182195
estimated_group_pve       9.878e-02 0.001114 0.00132 0.0006837
attributable_group_pve    9.694e-01 0.010937 0.01295 0.0067097
[1] "Check convergence for the lasso model when jointly analyzing expression, splicing and m6A:"
[1] "Table of group size before/after matching with UKBB SNPs:"
                          SNP      eQTL     sQTL   m6AQTL
prior_group_size    9.324e+06 2005.0000 2191.000 918.0000
group_size          8.713e+06 1998.0000 2180.000 912.0000
percent_of_overlaps 9.345e-01    0.9965    0.995   0.9935
                                SNP      eQTL      sQTL    m6AQTL
estimated_group_prior     9.786e-05 0.0116409 0.0239019 1.470e-02
estimated_group_prior_var 2.182e+01 7.4765802 5.1365664 1.663e+01
estimated_group_pve       5.414e-02 0.0005061 0.0007789 6.489e-04
attributable_group_pve    9.655e-01 0.0090243 0.0138897 1.157e-02
$top1


$lasso

cTWAS results for individual analysis with m6A

top1 model

   genename region_tag susie_pip       z
1     PMPCA       9_73    0.9816   6.364
2     PGAM5      12_82    0.9385   4.457
3     TAF6L      11_35    0.9366   4.016
4   C7orf50        7_2    0.9165  -4.260
5    RABEP1       17_5    0.8514  -3.553
6   TMEM199      17_17    0.8416  -6.098
7    MYL12B       18_3    0.8288   3.524
8     HERC1      15_29    0.7916  -2.865
9     TCTN3      10_61    0.7764   3.980
10      MVK      12_66    0.7549  -3.789
11   ICOSLG      21_22    0.7456  -3.467
12   VKORC1      16_24    0.7376  -3.587
13    POLD4      11_37    0.7330  -3.379
14   DIABLO      12_75    0.6965   3.143
15    HMGCR       5_44    0.6849 -19.989

Summing up PIPs for m6A peaks located in the same gene

Top m6A PIPs by genes

# A tibble: 22 × 2
   genename total_susie_pip
   <chr>              <dbl>
 1 ICOSLG             1.06 
 2 PMPCA              0.982
 3 RABEP1             0.969
 4 PGAM5              0.938
 5 TAF6L              0.937
 6 C7orf50            0.916
 7 TMEM199            0.842
 8 MYL12B             0.829
 9 HERC1              0.792
10 TCTN3              0.776
# ℹ 12 more rows

cTWAS results for joint analysis using a lasso model

Top m6A modification pip

Top expression/splicing/m6A units

For m6A or splicing QTLs, they are assigned to the nearest genes (m6A needs to be confirmed with Kevin).

Top SNPs or genes with PIP > 0.6

$eQTL
     genename susie_pip group region_tag
1987    ABHD8    0.9777  eQTL      19_14
1966    TRIM5    0.9565  eQTL       11_4
1960   SPRED2    0.9248  eQTL       2_42
1832   PCMTD2    0.6698  eQTL      20_38
911      CDK9    0.6393  eQTL       9_66

$m6AQTL
     genename susie_pip  group region_tag
5082  TMEM199    0.9698 m6AQTL      17_18
5078    PMPCA    0.9584 m6AQTL       9_73
5073     TAP2    0.7896 m6AQTL       6_27
4702    PGAM5    0.7499 m6AQTL      12_82
4905   MYL12B    0.7133 m6AQTL       18_3

$sQTL
     genename susie_pip group region_tag
4176   ABHD12    0.9996  sQTL      20_19
4135   SCAMP3    0.8896  sQTL       1_79
2453    GSK3B    0.7947  sQTL       3_74
4152     RAF1    0.7858  sQTL        3_9
3109   ENTPD1    0.6742  sQTL      10_61
2932      BLK    0.6438  sQTL       8_15
3082  ANAPC16    0.6368  sQTL      10_49

Top m6A modification pip

   genename region_tag susie_pip      z
1   TMEM199      17_18    0.9698 -6.649
2     PMPCA       9_73    0.9584  6.350
3      TAP2       6_27    0.7896 -8.346
4     PGAM5      12_82    0.7499  4.457
5    MYL12B       18_3    0.7133  3.653
6     POLD4      11_37    0.5925 -4.179
7   C7orf50        7_2    0.5123 -4.212
8     HERC1      15_29    0.5029 -3.818
9     TAF6L      11_35    0.4885  4.021
10     FGD3       9_47    0.2726 -3.213

Summing up PIPs for m6A peaks located in the same gene

Top 10 m6A PIPs by genes

# A tibble: 819 × 2
   genename total_susie_pip
   <chr>              <dbl>
 1 TMEM199            0.970
 2 PMPCA              0.958
 3 TAP2               0.790
 4 PGAM5              0.750
 5 MYL12B             0.713
 6 POLD4              0.593
 7 C7orf50            0.512
 8 HERC1              0.503
 9 TAF6L              0.488
10 DIDO1              0.283
# ℹ 809 more rows

Top splicing PIPs

                    peak_id genename       pos region_tag susie_pip      z
1   chr20:25275666-25282855   ABHD12  25260931      20_19    0.9996  6.644
2  chr1:155230450-155231448   SCAMP3 155149718       1_79    0.8896  4.305
3  chr3:119582452-119624602    GSK3B 119503971       3_74    0.7947 -5.789
4    chr3:12650834-12660014     RAF1  12574512        3_9    0.7858 -5.628
5   chr10:97602251-97602973   ENTPD1  97507473      10_61    0.6742 -4.590
6    chr8:11397080-11400733      BLK  11368731       8_15    0.6438  4.373
7   chr10:73980137-73983646  ANAPC16  73949708      10_49    0.6368  4.415
8    chr2:85823772-85824227   RNF181  85818886       2_54    0.5906  3.595
9    chr6:29691304-29691460    HLA-F  29644502       6_23    0.5565  5.293
10  chr12:53856351-53859716    PCBP2  53770941      12_33    0.5476  4.063

Summing up PIPs for spliced introns located in the same gene

Top 10 splicing PIPs by genes

# A tibble: 10 × 2
   genename total_susie_pip
   <chr>              <dbl>
 1 RMDN1               1.31
 2 LBP                 1.16
 3 WARS1               1.13
 4 SCAMP3              1.10
 5 ANAPC16             1.07
 6 ERGIC3              1.07
 7 HLA-F               1.05
 8 IFI44L              1.02
 9 CCT7                1.01
10 ABHD12              1.00

Top genes by combined PIP

     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
2503    RMDN1        1.314        0.00000      1.31366 0.00000       8_62
1505    HLA-F        1.201        0.05645      1.05500 0.08978       6_23
1680      LBP        1.162        0.00000      1.16237 0.00000      20_23
3241    WARS1        1.160        0.03006      1.13042 0.00000      14_52
2623   SCAMP3        1.129        0.00000      1.10088 0.02771       1_79
149   ANAPC16        1.072        0.00000      1.07160 0.00000      10_49
1251   ERGIC3        1.066        0.00000      1.06575 0.00000      20_21
1552   IFI44L        1.053        0.00000      1.01540 0.03795       1_48
3095    TRIM5        1.043        0.95648      0.08641 0.00000       11_4
454      CCT7        1.010        0.00000      1.00965 0.00000       2_48
14     ABHD12        1.000        0.00000      0.99960 0.00000      20_19
18      ABHD8        0.978        0.97771      0.00000 0.00000      19_14
3010  TMEM199        0.970        0.00000      0.00000 0.96979      17_18
2259    PMPCA        0.958        0.00000      0.00000 0.95843       9_73
1572   IMMP1L        0.940        0.00000      0.94015 0.00000      11_21
1790   MCOLN2        0.933        0.03269      0.90027 0.00000       1_52
2815   SPRED2        0.925        0.92476      0.00000 0.00000       2_42
1928   MTERF4        0.909        0.03773      0.67259 0.19843      2_144
2809     SPG7        0.889        0.00000      0.81819 0.07106      16_54
1233   ENTPD1        0.886        0.02109      0.86474 0.00000      10_61
638      CTSH        0.858        0.18503      0.60610 0.06653      15_37
1862     MMAB        0.857        0.01508      0.84150 0.00000      12_67
1968  NADSYN1        0.855        0.13755      0.71720 0.00000      11_40
2942     TDP1        0.846        0.06640      0.77954 0.00000      14_45
310       BLK        0.825        0.00000      0.82520 0.00000       8_15
508     CENPU        0.795        0.09120      0.70348 0.00000      4_119
1439    GSK3B        0.795        0.00000      0.79472 0.00000       3_74
2910     TAP2        0.790        0.00000      0.00000 0.78964       6_27
2428     RAF1        0.786        0.00000      0.78581 0.00000        3_9
2166    PCBP2        0.776        0.00000      0.77585 0.00000      12_33
2801    SP140        0.774        0.00000      0.77396 0.00000      2_135
1689   LGALS8        0.763        0.00000      0.68077 0.08214      1_124
2560     RPL8        0.763        0.04376      0.71959 0.00000       8_94
3260    WDR91        0.756        0.02902      0.55332 0.17384       7_82
2201    PGAM5        0.750        0.00000      0.00000 0.74988      12_82
3073    TRAF1        0.733        0.00000      0.73315 0.00000       9_63
2889  SYNCRIP        0.716        0.00000      0.71582 0.00000       6_58
1953   MYL12B        0.713        0.00000      0.00000 0.71328       18_3
207     ARIH2        0.700        0.00000      0.63860 0.06122       3_35
471      CD46        0.700        0.00000      0.70025 0.00000      1_107
Loading required package: grid
Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'

Compared with the results from Zhao et al.

[1] "Table of combined PIPs for LCL silver standard genes:"
   genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1     HMGCR        0.216        0.00000       0.0000 0.21569       5_44
2     VDAC1        0.174        0.00000       0.1738 0.00000       5_80
3      CETP        0.145        0.14520       0.0000 0.00000      16_31
4     DHCR7        0.129        0.00000       0.0000 0.12937      11_40
5      VAPA        0.087        0.00000       0.0868 0.00000       18_7
6      PLTP        0.082        0.08167       0.0000 0.00000      20_28
7      VAPB        0.061        0.06068       0.0000 0.00000      20_34
8    STARD3        0.054        0.05385       0.0000 0.00000      17_23
9      TNKS        0.035        0.02106       0.0000 0.01417       8_12
10     LIPA        0.030        0.02955       0.0000 0.00000      10_57
11    EPHX2        0.029        0.02887       0.0000 0.00000       8_27
12    ITIH4        0.027        0.02655       0.0000 0.00000       3_36
13     LDLR        0.000        0.00000       0.0000 0.00000      19_10
   annotation
1       known
2       known
3       known
4       known
5       known
6       known
7       known
8       known
9       known
10      known
11      known
12      known
13      known
[1] "Table of combined PIPs for LCL bystander genes:"
   genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1   NADSYN1        0.855        0.13755       0.7172 0.00000      11_40
2      RAF1        0.786        0.00000       0.7858 0.00000        3_9
3     ACOT8        0.493        0.01311       0.4801 0.00000      20_28
4   ITGB3BP        0.469        0.03102       0.4381 0.00000       1_40
5      GNL3        0.435        0.00000       0.3376 0.09772       3_36
6     SMUG1        0.420        0.00000       0.2614 0.15811      12_33
7    NT5DC2        0.407        0.02316       0.3837 0.00000       3_36
8     YWHAB        0.327        0.07149       0.2553 0.00000      20_28
9     DNPEP        0.313        0.00000       0.3126 0.00000      2_129
10    NUMA1        0.306        0.00000       0.3062 0.00000      11_40
   annotation
1   bystander
2   bystander
3   bystander
4   bystander
5   bystander
6   bystander
7   bystander
8   bystander
9   bystander
10  bystander
[1] "Overlaps with previously identified high PIP genes that are either silver standard or bystander genes:"
  genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1     USP1        0.094        0.02297       0.0712 0.00000       1_39
2     PLTP        0.082        0.08167       0.0000 0.00000      20_28
3     TNKS        0.035        0.02106       0.0000 0.01417       8_12
  annotation
1  bystander
2      known
3      known

Locus plots for specific examples

    genename combined_pip expression_pip splicing_pip m6A_pip region_tag
412    HMGCR        0.216              0            0  0.2157       5_44
    annotation
412      known


R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

locale:
 [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
 [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
 [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
[10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 

attached base packages:
[1] grid      stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] biomaRt_2.52.0       Gviz_1.40.1          cowplot_1.1.1       
 [4] ggplot2_3.4.3        GenomicRanges_1.48.0 GenomeInfoDb_1.32.2 
 [7] IRanges_2.30.1       S4Vectors_0.34.0     BiocGenerics_0.42.0 
[10] ctwas_0.1.38         dplyr_1.1.2          workflowr_1.7.0     

loaded via a namespace (and not attached):
  [1] colorspace_2.1-0            deldir_1.0-6               
  [3] rjson_0.2.21                rprojroot_2.0.3            
  [5] biovizBase_1.44.0           htmlTable_2.4.0            
  [7] XVector_0.36.0              base64enc_0.1-3            
  [9] fs_1.6.3                    dichromat_2.0-0.1          
 [11] rstudioapi_0.15.0           farver_2.1.1               
 [13] bit64_4.0.5                 AnnotationDbi_1.58.0       
 [15] fansi_1.0.4                 xml2_1.3.3                 
 [17] codetools_0.2-18            logging_0.10-108           
 [19] cachem_1.0.8                knitr_1.39                 
 [21] Formula_1.2-4               jsonlite_1.8.7             
 [23] Rsamtools_2.12.0            cluster_2.1.3              
 [25] dbplyr_2.3.3                png_0.1-7                  
 [27] compiler_4.2.0              httr_1.4.6                 
 [29] backports_1.4.1             lazyeval_0.2.2             
 [31] Matrix_1.6-1                fastmap_1.1.1              
 [33] cli_3.6.1                   later_1.3.0                
 [35] htmltools_0.5.2             prettyunits_1.1.1          
 [37] tools_4.2.0                 gtable_0.3.3               
 [39] glue_1.6.2                  GenomeInfoDbData_1.2.8     
 [41] rappdirs_0.3.3              Rcpp_1.0.11                
 [43] Biobase_2.56.0              jquerylib_0.1.4            
 [45] vctrs_0.6.3                 Biostrings_2.64.0          
 [47] rtracklayer_1.56.0          iterators_1.0.14           
 [49] xfun_0.30                   stringr_1.5.0              
 [51] ps_1.7.0                    lifecycle_1.0.3            
 [53] ensembldb_2.20.2            restfulr_0.0.14            
 [55] XML_3.99-0.14               getPass_0.2-2              
 [57] zlibbioc_1.42.0             scales_1.2.1               
 [59] BSgenome_1.64.0             VariantAnnotation_1.42.1   
 [61] ProtGenerics_1.28.0         hms_1.1.3                  
 [63] promises_1.2.0.1            MatrixGenerics_1.8.0       
 [65] parallel_4.2.0              SummarizedExperiment_1.26.1
 [67] AnnotationFilter_1.20.0     RColorBrewer_1.1-3         
 [69] yaml_2.3.5                  curl_5.0.2                 
 [71] memoise_2.0.1               gridExtra_2.3              
 [73] sass_0.4.1                  rpart_4.1.16               
 [75] latticeExtra_0.6-30         stringi_1.7.12             
 [77] RSQLite_2.3.1               highr_0.9                  
 [79] BiocIO_1.6.0                foreach_1.5.2              
 [81] checkmate_2.1.0             GenomicFeatures_1.48.4     
 [83] filelock_1.0.2              BiocParallel_1.30.3        
 [85] rlang_1.1.1                 pkgconfig_2.0.3            
 [87] matrixStats_0.62.0          bitops_1.0-7               
 [89] evaluate_0.15               lattice_0.20-45            
 [91] htmlwidgets_1.5.4           GenomicAlignments_1.32.0   
 [93] labeling_0.4.2              bit_4.0.5                  
 [95] processx_3.8.0              tidyselect_1.2.0           
 [97] magrittr_2.0.3              R6_2.5.1                   
 [99] generics_0.1.3              Hmisc_5.1-0                
[101] DelayedArray_0.22.0         DBI_1.1.3                  
[103] pgenlibr_0.3.6              pillar_1.9.0               
[105] whisker_0.4                 foreign_0.8-82             
[107] withr_2.5.0                 KEGGREST_1.36.2            
[109] RCurl_1.98-1.7              nnet_7.3-17                
[111] tibble_3.2.1                crayon_1.5.2               
[113] interp_1.1-4                utf8_1.2.3                 
[115] BiocFileCache_2.4.0         rmarkdown_2.14             
[117] jpeg_0.1-10                 progress_1.2.2             
[119] data.table_1.14.8           blob_1.2.4                 
[121] callr_3.7.3                 git2r_0.30.1               
[123] digest_0.6.33               httpuv_1.6.5               
[125] munsell_0.5.0               bslib_0.3.1