Last updated: 2023-08-16

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Knit directory: m6A_in_disease_genetics/

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Rmd 08eaf44 Jing Gu 2023-08-16 run ctwas for multiple traits
html 3a4bab9 Jing Gu 2023-08-11 Build site.
Rmd d1d6b2a Jing Gu 2023-08-11 wflow_publish(c("analysis/wbc_m6A_output_hg19.Rmd", "analysis/index.Rmd",
Rmd c94ee10 Jing Gu 2023-08-11 wflow_publish(c("analysis/wbc_m6A_output_hg19.Rmd", "analysis/index.Rmd",

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     2.481e-04 1.227e-02
estimated_group_prior_var 1.920e+01 2.631e+01
estimated_group_pve       1.184e-01 8.178e-04
attributable_group_pve    9.931e-01 6.858e-03
[1] "Check convergence for the lasso model:"
[1] "Table of group size:"
    SNP    gene 
8713250     912 
                                SNP      gene
estimated_group_prior     2.414e-04 1.016e-02
estimated_group_prior_var 1.898e+01 3.699e+01
estimated_group_pve       1.139e-01 9.778e-04
attributable_group_pve    9.915e-01 8.513e-03
$top1

Version Author Date
3a4bab9 Jing Gu 2023-08-11 

$lasso

Version Author Date
3a4bab9 Jing Gu 2023-08-11

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     2.406e-04 8.895e-03  0.012934 1.236e-02
estimated_group_prior_var 1.858e+01 1.683e+01 36.589120 2.554e+01
estimated_group_pve       1.112e-01 8.236e-04  0.002867 7.999e-04
attributable_group_pve    9.612e-01 7.120e-03  0.024783 6.916e-03
[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      0.000217 9.483e-04  0.005262  0.012026
estimated_group_prior_var 19.281964 2.155e+01 38.383079 33.143952
estimated_group_pve        0.104003 1.165e-04  0.001256  0.001037
attributable_group_pve     0.977353 1.095e-03  0.011805  0.009747
$top1

Version Author Date
3a4bab9 Jing Gu 2023-08-11 

$lasso

cTWAS results for individual analysis with m6A

Lasso model

  genename region_tag susie_pip       z
1 SLC9A3R1      17_42    0.9473  -7.630
2  ZKSCAN5       7_61    0.7976   7.112
3    ADCY7      16_27    0.7817   4.382
4    TRIT1       1_25    0.7516   5.554
5  THEMIS2       1_19    0.7034   6.243
6   BTN3A3       6_20    0.6855 -13.445
7  WAC-AS1      10_20    0.6102  11.178

Summing up PIPs for m6A peaks located in the same gene

Top m6A PIPs by genes

# A tibble: 7 × 2
  genename total_susie_pip
  <chr>              <dbl>
1 SLC9A3R1           0.947
2 ZKSCAN5            0.798
3 ADCY7              0.782
4 TRIT1              0.752
5 THEMIS2            0.703
6 BTN3A3             0.686
7 WAC-AS1            0.615

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
1981    CSNK1G1    0.9988  eQTL      15_29
43   AL391650.1    0.7867  eQTL       1_18
143      NDUFS2    0.7360  eQTL       1_81
1915     TTLL12    0.6001  eQTL      22_18

$m6AQTL
     genename susie_pip  group region_tag
5089 SLC9A3R1    0.9586 m6AQTL      17_42
5072  ZKSCAN5    0.8403 m6AQTL       7_61
4202    TRIT1    0.7973 m6AQTL       1_25
4196  THEMIS2    0.7560 m6AQTL       1_19
4425   BTN3A3    0.7494 m6AQTL       6_20
5084    ADCY7    0.7069 m6AQTL      16_27
4529   DENND3    0.6867 m6AQTL       8_92
4586  WAC-AS1    0.6349 m6AQTL      10_20

$sQTL
     genename susie_pip group region_tag
4119   RNF181    1.0000  sQTL       2_54
4126    HLA-F    1.0000  sQTL       6_23
4136    MYO1G    0.9983  sQTL       7_33
4164    FNBP4    0.9889  sQTL      11_29
2471    GSK3B    0.6478  sQTL       3_74
3084   PDLIM1    0.6441  sQTL      10_61

Top m6A modification pip

ZKSCAN5: RNA Polymerase II Cis-Regulatory Region Sequence-Specific DNA Binding (GO:0000978). THEMIS2 is involved in the biological process T Cell Receptor Signaling Pathway (GO:0050852). BANF: DNA binding factor|Regulation Of Innate Immune Response (GO:0045088). TRIT1 has the molecular function of Catalytic Activity, Acting On A tRNA (GO:0140101). TRIT1 is involved in the biological process RNA Modification (GO:0009451). S1PR2 is involved in the biological process Regulation Of Cell Population Proliferation (GO:0042127). WAC has the molecular function of RNA Polymerase II Complex Binding (GO:0000993). CD320 is involved in the biological process Regulation Of B Cell Proliferation (GO:0030888).

   genename region_tag susie_pip       z
1  SLC9A3R1      17_42    0.9586  -7.630
2   ZKSCAN5       7_61    0.8403   7.112
3     TRIT1       1_25    0.7973   5.554
4   THEMIS2       1_19    0.7560   6.243
5    BTN3A3       6_20    0.7494 -13.445
6     ADCY7      16_27    0.7069   4.382
7    DENND3       8_92    0.6867   5.979
8   WAC-AS1      10_20    0.6349  11.178
9      SMG9      19_30    0.5570   4.092
10   SQSTM1      5_108    0.4403  -4.393

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 SLC9A3R1           0.959
 2 ZKSCAN5            0.840
 3 TRIT1              0.797
 4 THEMIS2            0.756
 5 BTN3A3             0.749
 6 ADCY7              0.707
 7 DENND3             0.687
 8 WAC-AS1            0.642
 9 SMG9               0.557
10 SQSTM1             0.440
# ℹ 809 more rows

Top splicing PIPs

Some loci contain variants in the same credible set but having opposite z scores. For instance, the predicted splicing levels of two introns of CNN2 based on the same variant (position=1038445) have opposite associations with traits. Is this variant more likely to affect traits by altering the splicing levels of both transcripts, rather than one of them since they have equal PIP?

                    peak_id   genename       pos region_tag susie_pip       z
1    chr2:85823772-85824227     RNF181  85818886       2_54    1.0000   5.175
2    chr6:29693340-29694660      HLA-F  29646165       6_23    1.0000 -17.265
3    chr7:45009474-45009639      MYO1G  44925489       7_33    0.9983 -11.848
4   chr11:47761655-47765505      FNBP4  47684908      11_29    0.9889  10.996
5  chr3:119582452-119624602      GSK3B 119503971       3_74    0.6478   5.631
6   chr10:97007123-97023621     PDLIM1  97001124      10_61    0.6441  -7.375
7    chr9:86593367-86595418     HNRNPK  86592026       9_41    0.5989   9.019
8   chr22:24268707-24316496 AC253536.7  24182500       22_7    0.5457   6.311
9    chr1:43852637-43853174       MED8  43843649       1_27    0.4548   4.806
10 chr5:122111457-122130961       SNX2 122050927       5_74    0.4518  -6.744

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 HLA-F              1.00 
 2 RNF181             1    
 3 MYO1G              0.998
 4 FNBP4              0.989
 5 HNRNPK             0.910
 6 MED8               0.886
 7 CNN2               0.852
 8 CD46               0.725
 9 GSK3B              0.648
10 PDLIM1             0.644

Top genes by combined PIP

       genename combined_pip expression_pip splicing_pip   m6A_pip region_tag
624     CSNK1G1        1.018      9.988e-01     0.000000 1.903e-02      15_29
1505      HLA-F        1.000      1.021e-08     1.000037 3.634e-07       6_23
2517     RNF181        1.000      0.000e+00     1.000000 0.000e+00       2_54
1957      MYO1G        0.998      0.000e+00     0.998301 0.000e+00       7_33
1340      FNBP4        0.989      0.000e+00     0.988933 0.000e+00      11_29
2741   SLC9A3R1        0.959      0.000e+00     0.000000 9.586e-01      17_42
1518     HNRNPK        0.937      0.000e+00     0.910055 2.663e-02       9_41
1807       MED8        0.886      0.000e+00     0.885964 0.000e+00       1_27
567        CNN2        0.852      0.000e+00     0.852357 0.000e+00       19_2
3317    ZKSCAN5        0.840      0.000e+00     0.000000 8.403e-01       7_61
3103      TRIT1        0.799      4.314e-05     0.001363 7.973e-01       1_25
129  AL391650.1        0.787      7.867e-01     0.000000 0.000e+00       1_18
2962    THEMIS2        0.756      0.000e+00     0.000000 7.560e-01       1_19
339      BTN3A3        0.751      1.570e-03     0.000000 7.494e-01       6_20
2012     NDUFS2        0.736      7.360e-01     0.000000 0.000e+00       1_81
471        CD46        0.725      0.000e+00     0.724809 0.000e+00      1_107
700      DENND3        0.724      0.000e+00     0.037520 6.867e-01       8_92
86        ADCY7        0.707      0.000e+00     0.000000 7.069e-01      16_27
1439      GSK3B        0.648      0.000e+00     0.647811 0.000e+00       3_74
2184     PDLIM1        0.644      0.000e+00     0.644131 0.000e+00      10_61
Loading required package: grid
Warning: replacing previous import 'utils::download.file' by
'restfulr::download.file' when loading 'rtracklayer'

Locus plots for specific examples

     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
2012   NDUFS2        0.736          0.736            0       0       1_81

Version Author Date
3a4bab9 Jing Gu 2023-08-11 
    genename combined_pip expression_pip splicing_pip m6A_pip region_tag
624  CSNK1G1        1.018         0.9988            0 0.01903      15_29
Warning in asMethod(object): sparse->dense coercion: allocating vector of size
1.1 GiB

Version Author Date
3a4bab9 Jing Gu 2023-08-11 
     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
2962  THEMIS2        0.756              0            0   0.756       1_19

Version Author Date
3a4bab9 Jing Gu 2023-08-11 
     genename combined_pip expression_pip splicing_pip m6A_pip region_tag
3317  ZKSCAN5         0.84              0            0  0.8403       7_61

Version Author Date
3a4bab9 Jing Gu 2023-08-11 

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