Last updated: 2023-08-22

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Knit directory: m6A_in_disease_genetics/

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File Version Author Date Message
Rmd 0560ec9 Jing Gu 2023-08-22 analyzed neutrph

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     2.387e-04 1.829e-02
estimated_group_prior_var 1.683e+01 1.718e+01
estimated_group_pve       1.000e-01 7.976e-04
attributable_group_pve    9.921e-01 7.909e-03
$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      0.000234  0.015405  0.008797 7.876e-04
estimated_group_prior_var 15.762762 16.480563 25.106901 1.010e+01
estimated_group_pve        0.091849  0.001399  0.001340 2.020e-05
attributable_group_pve     0.970832  0.014788  0.014167 2.135e-04
[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     2.067e-04  0.01459 1.479e-03 3.526e-04
estimated_group_prior_var 1.838e+01 13.68762 3.091e+01 1.039e+01
estimated_group_pve       9.463e-02  0.00114 2.848e-04 9.552e-06
attributable_group_pve    9.851e-01  0.01187 2.965e-03 9.943e-05
$top1


$lasso

cTWAS results for individual analysis with m6A

top1 model

  genename region_tag susie_pip      z
1 SLC9A3R1      17_42    0.9664 -7.384
2   LETMD1      12_31    0.8989 -5.107
3    HMGN4       6_20    0.7958  3.861
4    ASCC1      10_48    0.7928  3.989
5   HNRNPK       9_41    0.7313  7.884
6    BANF1      11_36    0.7053  4.664
7   SQSTM1      5_108    0.6327 -5.743
8  THEMIS2       1_19    0.6237  3.913

Summing up PIPs for m6A peaks located in the same gene

Top m6A PIPs by genes

# A tibble: 9 × 2
  genename total_susie_pip
  <chr>              <dbl>
1 SLC9A3R1           0.966
2 LETMD1             0.899
3 HMGN4              0.796
4 ASCC1              0.793
5 HNRNPK             0.731
6 BANF1              0.705
7 SH2D3C             0.634
8 SQSTM1             0.633
9 THEMIS2            0.624

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
1956         CSNK1G1    0.9995  eQTL      15_29
1933           ZMIZ1    0.9909  eQTL      10_51
1993          TTLL12    0.9888  eQTL      22_18
1916          NDUFS2    0.9267  eQTL       1_81
1971           CCDC9    0.8776  eQTL      19_34
1923            MXD3    0.8298  eQTL      5_106
256             KYNU    0.7896  eQTL       2_85
1959        RAPGEFL1    0.7579  eQTL      17_23
970           BORCS7    0.7408  eQTL      10_66
782  ENSG00000255310    0.7163  eQTL       8_14
68   ENSG00000229431    0.6882  eQTL       1_27
1905          ZNF593    0.6366  eQTL       1_18

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

$sQTL
     genename susie_pip group region_tag
4108    MYO1G    0.9619  sQTL       7_33
3558     ETFA    0.7622  sQTL      15_36
2456    GSK3B    0.7306  sQTL       3_74

Top m6A modification pip

   genename region_tag susie_pip      z
1     TAPBP       6_28   0.48279 -8.315
2    TGOLN2       2_54   0.33394 -7.771
3  SLC9A3R1      17_42   0.08616 -7.384
4    LETMD1      12_31   0.06916 -4.766
5     BANF1      11_36   0.04132  4.670
6   THEMIS2       1_19   0.02911  3.876
7    C2CD2L      11_71   0.02885  3.482
8   PPP2R5C      14_54   0.02779 -3.812
9     TRIT1       1_25   0.02766  3.964
10    ASCC1      10_49   0.02600  4.016

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 TAPBP             0.507 
 2 TGOLN2            0.334 
 3 SLC9A3R1          0.0862
 4 LETMD1            0.0692
 5 BANF1             0.0413
 6 THEMIS2           0.0291
 7 C2CD2L            0.0289
 8 PPP2R5C           0.0278
 9 TRIT1             0.0277
10 ASCC1             0.0260
# ℹ 809 more rows

Top splicing PIPs

                    peak_id genename       pos region_tag susie_pip      z
1    chr7:45009474-45009639    MYO1G  44925489       7_33    0.9619 -8.315
2   chr15:76588078-76602273     ETFA  76496232      15_36    0.7622 -5.564
3  chr3:119582452-119624602    GSK3B 119503971       3_74    0.7306  6.695
4    chr2:85823772-85824227   RNF181  85818886       2_54    0.3726  3.817
5   chr10:97007123-97023621   PDLIM1  97001124      10_61    0.2946 -7.031
6    chr9:86593367-86595418   HNRNPK  86592026       9_41    0.2547  7.912
7    chr7:56120178-56123317    CCT6A  56033141       7_40    0.2220 -4.773
8     chr19:1036561-1037624     CNN2   1038445       19_2    0.1991  3.367
9     chr19:1036999-1037624     CNN2   1038445       19_2    0.1991 -3.367
10 chr1:207940540-207943666     CD46 207923081      1_107    0.1979 -9.808

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 MYO1G                0.962
 2 ETFA                 0.765
 3 GSK3B                0.731
 4 CD46                 0.463
 5 CNN2                 0.399
 6 RNF181               0.397
 7 HNRNPK               0.356
 8 PDLIM1               0.295
 9 AC253536.7           0.236
10 CCT6A                0.224

Top genes by combined PIP

            genename combined_pip expression_pip splicing_pip  m6A_pip
624          CSNK1G1        1.001        0.99947     0.000000 0.001205
3320           ZMIZ1        0.991        0.99091     0.000000 0.000000
3141          TTLL12        0.989        0.98885     0.000000 0.000000
1957           MYO1G        0.962        0.00000     0.961931 0.000000
2012          NDUFS2        0.927        0.92667     0.000000 0.000000
437            CCDC9        0.878        0.87764     0.000000 0.000000
1947            MXD3        0.830        0.82980     0.000000 0.000000
1663            KYNU        0.791        0.78963     0.001790 0.000000
1265            ETFA        0.776        0.01132     0.764910 0.000000
2438        RAPGEFL1        0.758        0.75791     0.000000 0.000000
317           BORCS7        0.741        0.74077     0.000000 0.000000
1439           GSK3B        0.731        0.00000     0.730646 0.000000
1085 ENSG00000255310        0.716        0.71625     0.000000 0.000000
967  ENSG00000229431        0.688        0.68816     0.000000 0.000000
3371          ZNF593        0.637        0.63657     0.000000 0.000000
3230           VPS16        0.576        0.57565     0.000000 0.000000
360         C19orf54        0.538        0.53245     0.004023 0.001566
1264           ESYT2        0.537        0.53457     0.002218 0.000000
1626          KDELR2        0.520        0.52024     0.000000 0.000000
1195 ENSG00000270081        0.510        0.51042     0.000000 0.000000
     region_tag
624       15_29
3320      10_51
3141      22_18
1957       7_33
2012       1_81
437       19_34
1947      5_106
1663       2_85
1265      15_36
2438      17_23
317       10_66
1439       3_74
1085       8_14
967        1_27
3371       1_18
3230       20_3
360       19_28
1264       7_99
1626        7_9
1195       2_75
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_1.4.7                 
 [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