Last updated: 2023-08-16

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Knit directory: m6A_in_disease_genetics/

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File Version Author Date Message
Rmd a1e2443 Jing Gu 2023-08-16 added rbc

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     2.452e-04 2.534e-08
estimated_group_prior_var 2.551e+01 1.357e+01
estimated_group_pve       1.555e-01 8.711e-10
attributable_group_pve    1.000e+00 5.601e-09
[1] "Check convergence for the lasso model:"
[1] "Table of group size:"
    SNP    gene 
8713250     912 
                                SNP      gene
estimated_group_prior     2.389e-04 1.809e-06
estimated_group_prior_var 2.384e+01 2.900e+01
estimated_group_pve       1.416e-01 1.365e-07
attributable_group_pve    1.000e+00 9.643e-07
$top1


$lasso

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     2.364e-04  0.019161 8.127e-03 3.872e-09
estimated_group_prior_var 2.553e+01 28.495736 1.868e+01 8.508e+00
estimated_group_pve       1.500e-01  0.003004 9.196e-04 8.348e-11
attributable_group_pve    9.745e-01  0.019509 5.973e-03 5.422e-10
[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     2.143e-04  0.013424 2.429e-04 9.197e-11
estimated_group_prior_var 2.336e+01 62.619390 1.820e+01 2.083e+01
estimated_group_pve       1.245e-01  0.004792 2.749e-05 4.986e-12
attributable_group_pve    9.627e-01  0.037062 2.126e-04 3.856e-11
$top1


$lasso

cTWAS results for individual analysis with m6A

Lasso model

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

Summing up PIPs for m6A peaks located in the same gene

Top m6A PIPs by genes

# A tibble: 0 × 2
# ℹ 2 variables: genename <chr>, total_susie_pip <dbl>

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
1914          ADAM15    0.9934  eQTL       1_79
1921           STK36    0.9925  eQTL      2_129
1995 ENSG00000205559    0.9801  eQTL      22_24
1924          SMIM19    0.9462  eQTL       8_37
1929 ENSG00000180139    0.8333  eQTL      10_57
1933           RBMS2    0.7782  eQTL      12_36
1527           SKAP1    0.7562  eQTL      17_28
1979             ELL    0.7499  eQTL      19_16
749          TSC22D4    0.6867  eQTL       7_61
1922           NSUN2    0.6741  eQTL        5_6
1202            GPN3    0.6468  eQTL      12_67
1368         CSNK1G1    0.6460  eQTL      15_29
912             PTPA    0.6099  eQTL       9_66

$m6AQTL
[1] genename   susie_pip  group      region_tag
<0 rows> (or 0-length row.names)

$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      TAP2       6_27 3.170e-08  -6.439
2    C2CD2L      11_71 1.818e-08   4.267
3     PSKH1      16_37 1.489e-08 -11.068
4    FERMT3      11_36 1.443e-08  -3.254
5     DHX38      16_38 1.261e-08   3.456
6    ZNF282       7_92 1.259e-08   3.636
7     ISG20      15_41 1.254e-08  -3.563
8    CD2BP2      16_24 9.926e-09  11.194
9     PARVB      22_19 7.998e-09   2.964
10     SMG9      19_30 6.096e-09   6.864

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 TAP2       0.0000000317 
 2 C2CD2L     0.0000000182 
 3 PSKH1      0.0000000149 
 4 FERMT3     0.0000000144 
 5 ISG20      0.0000000138 
 6 DHX38      0.0000000126 
 7 ZNF282     0.0000000126 
 8 CD2BP2     0.00000000993
 9 PARVB      0.00000000800
10 SMG9       0.00000000610
# ℹ 809 more rows

Top splicing PIPs

                    peak_id genename       pos region_tag susie_pip      z
1   chr20:25204793-25205798   ENTPD6  25206654      20_19   0.31803  5.780
2  chr1:155679615-155686797     DAP3 155666961       1_79   0.03517 -1.429
3   chr11:66053043-66053172    YIF1A  65998757      11_37   0.03010 -2.807
4   chr15:75197572-75198619  FAM219B  75125645      15_35   0.02974  8.047
5   chr16:31503661-31504299    RUSF1  31470540      16_25   0.02597 -3.560
6     chr19:5717627-5719715    LONP1   5655792       19_5   0.02525  3.858
7     chr19:5714282-5719715    LONP1   5655792       19_5   0.02253 -3.822
8    chr8:38852924-38853732    TM2D2  38762056       8_35   0.02052  4.076
9  chr1:155658965-155664582     DAP3 155625602       1_79   0.01965 -1.188
10   chr1:93815558-93819465      DR1  93816400       1_57   0.01954 -6.505

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 ENTPD6            0.318 
 2 DAP3              0.0553
 3 LONP1             0.0478
 4 DR1               0.0384
 5 CIAO2A            0.0361
 6 ENOSF1            0.0311
 7 ATXN2L            0.0306
 8 YIF1A             0.0304
 9 FAM219B           0.0297
10 TRAF3IP3          0.0272

Top genes by combined PIP

            genename combined_pip expression_pip splicing_pip   m6A_pip
81            ADAM15        0.993         0.9934    0.0000000 0.000e+00
2862           STK36        0.993         0.9925    0.0000000 0.000e+00
900  ENSG00000205559        0.980         0.9801    0.0000000 0.000e+00
2760          SMIM19        0.946         0.9462    0.0000000 0.000e+00
874  ENSG00000180139        0.833         0.8333    0.0000000 0.000e+00
2459           RBMS2        0.778         0.7782    0.0000000 0.000e+00
2687           SKAP1        0.760         0.7562    0.0037724 0.000e+00
826              ELL        0.750         0.7499    0.0000000 0.000e+00
3120         TSC22D4        0.687         0.6867    0.0000000 0.000e+00
2070           NSUN2        0.676         0.6741    0.0024136 0.000e+00
1420            GPN3        0.647         0.6468    0.0000000 0.000e+00
624          CSNK1G1        0.646         0.6460    0.0000000 4.822e-09
2386            PTPA        0.610         0.6099    0.0000000 0.000e+00
829             ELOA        0.579         0.5790    0.0000000 0.000e+00
2758            SMG9        0.572         0.5724    0.0000000 6.096e-09
460             CD22        0.569         0.5691    0.0001497 5.656e-11
306              BIK        0.552         0.5519    0.0000000 0.000e+00
1874             MPI        0.544         0.5259    0.0176415 0.000e+00
945  ENSG00000226526        0.519         0.5187    0.0000000 0.000e+00
665              DAP        0.500         0.4977    0.0018242 5.655e-10
     region_tag
81         1_79
2862      2_129
900       22_24
2760       8_37
874       10_57
2459      12_36
2687      17_28
826       19_16
3120       7_61
2070        5_6
1420      12_67
624       15_29
2386       9_66
829        1_17
2758      19_30
460       19_25
306       22_18
1874      15_35
945        1_12
665         5_9
Loading required package: grid
Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'

Locus plots for specific examples


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