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 
7549540     843 
                               SNP      gene
estimated_group_prior     0.000248 0.0094812
estimated_group_prior_var 9.409679 7.2407510
estimated_group_pve       0.293787 0.0009652
attributable_group_pve    0.996725 0.0032748
$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 2191.0000 918.0000
group_size          7.550e+06 1834.0000 2039.0000 843.0000
percent_of_overlaps 8.097e-01    0.9147    0.9306   0.9183
                                SNP    eQTL      sQTL    m6AQTL
estimated_group_prior     0.0002292 0.04629  0.001540 0.0061797
estimated_group_prior_var 9.2477617 7.45709 26.327399 7.4376303
estimated_group_pve       0.2669012 0.01056  0.001379 0.0006462
attributable_group_pve    0.9549740 0.03778  0.004933 0.0023122
[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 2005.000 2191.0000 918.0000
group_size          7.550e+06 1993.000 2168.0000 906.0000
percent_of_overlaps 8.097e-01    0.994    0.9895   0.9869
                                SNP     eQTL      sQTL   m6AQTL
estimated_group_prior     2.023e-04 0.031993 3.557e-05 0.019938
estimated_group_prior_var 1.037e+01 6.651734 3.954e+00 7.688136
estimated_group_pve       2.640e-01 0.007074 5.085e-06 0.002316
attributable_group_pve    9.656e-01 0.025873 1.860e-05 0.008472
$top1


$lasso

cTWAS results for individual analysis with m6A

top1 model

    genename region_tag susie_pip      z
1      IP6K2       3_34    0.8041 -4.767
2     ICOSLG      21_22    0.5716  9.960
3      CYTH1      17_44    0.3671 -4.097
4  MMP24-AS1      20_21    0.2748 -3.579
5      DHX38      16_38    0.2530 -3.560
6      RGS10      10_74    0.2472 -3.797
7      AKAP8      19_12    0.2463 -3.754
8      RNGTT       6_60    0.2267  2.851
9      TRIM8      10_66    0.2174 -3.259
10     CRTC3      15_42    0.2150 -3.841
11   ZFP36L2       2_27    0.2088  5.896
12    KIF21B      1_103    0.2059 -8.739
13     NEAT1      11_36    0.1911 -3.440
14      COG4      16_37    0.1902  2.841
15     TAF6L      11_35    0.1889 -3.091
16     STK24      13_50    0.1336 -2.750
17   C1orf43       1_77    0.1311 -2.570
18     CD320       19_8    0.1299 -2.217
19     DHX38      16_38    0.1269 -3.102
20     GSDMD       8_94    0.1252  2.543

Summing up PIPs for m6A peaks located in the same gene

Top m6A PIPs by genes

# A tibble: 760 × 2
   genename  total_susie_pip
   <chr>               <dbl>
 1 IP6K2               0.804
 2 ICOSLG              0.615
 3 DHX38               0.380
 4 CYTH1               0.367
 5 MMP24-AS1           0.275
 6 GSDMD               0.250
 7 RGS10               0.247
 8 AKAP8               0.246
 9 RNGTT               0.227
10 TRIM8               0.217
# ℹ 750 more rows

cTWAS results for joint analysis using a lasso model

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
1960            GLRX    0.9484  eQTL       5_56
1974           PTGIR    0.9191  eQTL      19_34
1952          ENOPH1    0.9082  eQTL       4_56
1938            CD28    0.8974  eQTL      2_120
1936            ABL2    0.8072  eQTL       1_91
1964          KDELR2    0.7994  eQTL        7_9
1867 ENSG00000211659    0.7943  eQTL       22_5
1444            CBFB    0.7281  eQTL      16_37
29             TMCO4    0.6598  eQTL       1_13
9           TNFRSF14    0.6438  eQTL        1_2
1880           MTMR3    0.5969  eQTL      22_10
106              DR1    0.5717  eQTL       1_57
285           NDUFS1    0.5555  eQTL      2_122
795           SMIM19    0.5523  eQTL       8_37
476              DAP    0.5301  eQTL        5_9
419          TMEM128    0.5269  eQTL        4_5
1462            WWP2    0.5240  eQTL      16_37
1067 ENSG00000245532    0.5114  eQTL      11_36

$m6AQTL
     genename susie_pip  group region_tag
5047    IP6K2    0.9080 m6AQTL       3_35
5009 C21orf33    0.7936 m6AQTL      21_23
4946    AKAP8    0.7020 m6AQTL      19_12
4214   KIF21B    0.6662 m6AQTL      1_103
4890    CYTH1    0.5689 m6AQTL      17_44
4821    DHX38    0.5541 m6AQTL      16_38

$sQTL
[1] genename   susie_pip  group      region_tag
<0 rows> (or 0-length row.names)

Top m6A modification pip

   genename region_tag susie_pip      z
1     IP6K2       3_35    0.9080 -4.767
2  C21orf33      21_23    0.7936 -5.739
3     AKAP8      19_12    0.7020 -4.074
4    KIF21B      1_103    0.6662 -8.732
5     CYTH1      17_44    0.5689 -4.120
6     DHX38      16_38    0.5541 -3.918
7     CRTC3      15_42    0.4149 -4.466
8     DHX38      16_38    0.3841 -3.682
9  C19orf54      19_28    0.3077 -3.376
10   LHFPL2       5_46    0.3025 -2.940

Summing up PIPs for m6A peaks located in the same gene

Top 10 m6A PIPs by genes

# A tibble: 814 × 2
   genename total_susie_pip
   <chr>              <dbl>
 1 DHX38              0.938
 2 IP6K2              0.908
 3 C21orf33           0.794
 4 AKAP8              0.702
 5 KIF21B             0.666
 6 CYTH1              0.569
 7 CRTC3              0.415
 8 LHFPL2             0.382
 9 CENPF              0.363
10 KIAA1147           0.329
# ℹ 804 more rows

Top splicing PIPs

Splicing PIPs are quite low.

                     peak_id  genename       pos region_tag susie_pip      z
1   chr1:155226210-155226433    SCAMP3 155135335       1_79  0.022818  6.753
2   chr1:155226210-155226465    SCAMP3 155135335       1_79  0.013819 -6.456
3   chr2:231054571-231065601     SP110 231050715      2_135  0.012014  4.938
4   chr2:231110655-231112631     SP140 231099170      2_135  0.007161 -4.812
5     chr7:26233314-26235467 HNRNPA2B1  26151190       7_23  0.004200  4.279
6      chr11:1908544-1908704      LSP1   1841555       11_2  0.003660  4.304
7   chr2:231079833-231084589     SP140 230992212      2_135  0.003155  4.548
8        chr11:810039-810234     RPLP2    716765       11_1  0.002695  3.548
9        chr11:810357-811597     RPLP2    752059       11_1  0.002639 -3.523
10 chr10:114186117-114186976    ZDHHC6 114169664      10_70  0.002388  3.592

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 SCAMP3            0.0369 
 2 SP140             0.0149 
 3 SP110             0.0120 
 4 RPLP2             0.00544
 5 LSP1              0.00451
 6 HNRNPA2B1         0.00420
 7 EPSTI1            0.00348
 8 ORAI2             0.00329
 9 RPSA              0.00277
10 IMMP1L            0.00255

Top genes by combined PIP

            genename combined_pip expression_pip splicing_pip  m6A_pip
1389            GLRX        1.044         0.9484    0.0000000 0.095273
725            DHX38        0.938         0.0000    0.0000000 0.938280
2370           PTGIR        0.919         0.9191    0.0000000 0.000000
847           ENOPH1        0.908         0.9082    0.0000000 0.000000
1586           IP6K2        0.908         0.0000    0.0000173 0.907997
462             CD28        0.897         0.8974    0.0000000 0.000000
21              ABL2        0.807         0.8072    0.0000000 0.000000
1623          KDELR2        0.799         0.7994    0.0000000 0.000000
368         C21orf33        0.794         0.0000    0.0000000 0.793555
905  ENSG00000211659        0.794         0.7943    0.0000000 0.000000
416             CBFB        0.728         0.7281    0.0000000 0.000000
111            AKAP8        0.702         0.0000    0.0000000 0.701968
1639          KIF21B        0.666         0.0000    0.0000000 0.666226
2977           TMCO4        0.660         0.6598    0.0000000 0.000000
3025        TNFRSF14        0.644         0.6438    0.0000000 0.000000
665              DAP        0.615         0.5301    0.0002289 0.084182
1934           MTMR3        0.597         0.5969    0.0000000 0.000000
2986         TMEM128        0.575         0.5269    0.0000000 0.048111
761              DR1        0.574         0.5717    0.0020552 0.000000
661            CYTH1        0.569         0.0000    0.0000000 0.568940
2005          NDUFS1        0.555         0.5555    0.0000000 0.000000
2748          SMIM19        0.552         0.5523    0.0000000 0.000000
3254            WWP2        0.532         0.5240    0.0000000 0.007832
1039 ENSG00000245532        0.511         0.5114    0.0000000 0.000000
1190 ENSG00000269958        0.496         0.4958    0.0000000 0.000000
2893          TAMM41        0.488         0.4883    0.0000000 0.000000
339           BTN3A3        0.482         0.4411    0.0000000 0.041386
2550            RPL9        0.481         0.4806    0.0000000 0.000000
2190            PEX7        0.474         0.4740    0.0000000 0.000000
985  ENSG00000233429        0.455         0.4545    0.0000000 0.000000
1659           KYAT3        0.450         0.4495    0.0000000 0.000000
2716         SLC37A2        0.449         0.1561    0.0000000 0.293129
2300            PPIH        0.445         0.4449    0.0000000 0.000000
3121           TTC32        0.443         0.4425    0.0000000 0.000000
2879          SYNGR1        0.432         0.4317    0.0000000 0.000000
1346        FRA10AC1        0.431         0.4309    0.0000000 0.000000
618            CRTC3        0.415         0.0000    0.0000000 0.414926
3299           ZFP90        0.415         0.4146    0.0000000 0.000000
220            ARPC5        0.413         0.2451    0.0002714 0.167322
360         C19orf54        0.410         0.1015    0.0009512 0.307729
     region_tag
1389       5_56
725       16_38
2370      19_34
847        4_56
1586       3_35
462       2_120
21         1_91
1623        7_9
368       21_23
905        22_5
416       16_37
111       19_12
1639      1_103
2977       1_13
3025        1_2
665         5_9
1934      22_10
2986        4_5
761        1_57
661       17_44
2005      2_122
2748       8_37
3254      16_37
1039      11_36
1190      14_54
2893        3_9
339        6_20
2550       4_32
2190       6_90
985        7_23
1659       1_54
2716      11_77
2300       1_27
3121       2_12
2879      22_16
1346      10_60
618       15_42
3299      16_37
220        1_93
360       19_28

Compared with the putative genes identified by ABC model

Only two genes annotated with ABC model are imputable with our current QTL datasets.

  genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1     NCF4        0.038        0.03762    2.803e-05       0      22_15
2     CD40        0.001        0.00000    8.489e-04       0      20_28
Joining with `by = join_by(genename)`
[1] "The causal genes identified by both methods:"
  genename                       m6A.PEAK.ID TWAS.P.Bonferroni m6A_pip
1    IP6K2    chr3:48731165-48731510_IP6K2_-         1.602e-03  0.9080
2    AKAP8   chr19:15483795-15484043_AKAP8_-         4.592e-02  0.7020
3   KIF21B chr1:200939513-200939662_KIF21B_-         2.367e-09  0.6662
[1] "The causal genes specifically identified by ctwas:"
  genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1    DHX38        0.938              0     0.00e+00  0.9383      16_38
2    IP6K2        0.908              0     1.73e-05  0.9080       3_35
3 C21orf33        0.794              0     0.00e+00  0.7936      21_23
4    AKAP8        0.702              0     0.00e+00  0.7020      19_12
5   KIF21B        0.666              0     0.00e+00  0.6662      1_103
6    CYTH1        0.569              0     0.00e+00  0.5689      17_44

Locus plots for specific examples

Loading required package: grid
Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'
     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1389     GLRX        1.044         0.9484            0 0.09527       5_56

    genename combined_pip expression_pip splicing_pip m6A_pip region_tag
725    DHX38        0.938              0            0  0.9383      16_38

     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1586    IP6K2        0.908              0     1.73e-05   0.908       3_35

Locus plot for the prioritized TWAS genes with FUSION

     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
3065 TRAF3IP3        0.057              0    0.0002895 0.05718      1_108

   genename combined_pip expression_pip splicing_pip m6A_pip region_tag
86    ADCY7        0.098              0            0 0.09821      16_27

     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
1541   ICOSLG        0.052              0            0 0.05211      21_23


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