cTWAS_analysis_for_Crohn’s_Disease
Jing Gu
2023-08-22
Last updated: 2023-08-22
Checks: 6 1
Knit directory: m6A_in_disease_genetics/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 688d15c | Jing Gu | 2023-08-22 | immune 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 = FGWAS: 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
Joint analysis of expression, splicing and m6A
[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 7.547e+06 1998.0000 2180.000 911.0000
percent_of_overlaps 8.094e-01 0.9965 0.995 0.9924
SNP eQTL sQTL m6AQTL
estimated_group_prior 0.0001417 0.0022564 2.412e-03 4.056e-03
estimated_group_prior_var 1.6500652 1.7576406 1.483e+00 2.323e+00
estimated_group_pve 0.0052332 0.0000235 2.312e-05 2.546e-05
attributable_group_pve 0.9864132 0.0044293 4.359e-03 4.799e-03$lasso
cTWAS results for individual analysis with m6A
top1 model
Summing up PIPs for m6A peaks located in the same gene
Top m6A PIPs by genes
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
[1] genename susie_pip group region_tag
<0 rows> (or 0-length row.names)
$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 HNF1B 17_22 0.24780 3.492
2 HMGN4 6_20 0.10681 3.370
3 MKKS 20_8 0.10675 2.789
4 ADCY7 16_27 0.10496 3.483
5 ZNF84 12_82 0.09392 -3.050
6 ZNF140 12_82 0.09320 2.998
7 HPS5 11_13 0.09152 2.632
8 SURF6 9_70 0.09148 2.912
9 SEC63 6_72 0.06652 2.303
10 TNRC6C-AS1 17_44 0.06459 -2.623Summing up PIPs for m6A peaks located in the same gene
Top 10 m6A PIPs by genes
# A tibble: 818 × 2
genename total_susie_pip
<chr> <dbl>
1 HNF1B 0.248
2 HMGN4 0.107
3 MKKS 0.107
4 ADCY7 0.105
5 ZNF84 0.0939
6 ZNF140 0.0932
7 HPS5 0.0915
8 SURF6 0.0915
9 PARP14 0.0859
10 GSDMD 0.0709
# ℹ 808 more rowsTop splicing PIPs
peak_id genename pos region_tag susie_pip z
1 chr2:74174015-74185273 DGUOK 74129474 2_48 0.36358 4.557
2 chr1:160680303-160681472 CD48 160588595 1_81 0.10088 -3.035
3 chr12:16052947-16053881 STRAP 15973272 12_13 0.07654 -2.907
4 chr2:231110655-231112631 SP140 231099170 2_135 0.07653 3.570
5 chr2:231109795-231112631 SP140 231097129 2_135 0.07179 -3.535
6 chr2:231109786-231112631 SP140 231110582 2_135 0.06964 -3.516
7 chr5:172410967-172413699 ATP6V0E1 172369769 5_103 0.06929 -2.853
8 chr2:219524466-219525030 BCS1L 219497048 2_129 0.05916 -3.394
9 chr18:33747185-33751010 ELP2 33686857 18_19 0.05700 2.973
10 chr2:176046190-176046384 ATP5MC3 175952853 2_106 0.05145 2.668Summing 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 DGUOK 0.371
2 SP140 0.264
3 CCT7 0.151
4 HMGN4 0.146
5 WARS1 0.136
6 MCOLN2 0.133
7 ERGIC3 0.120
8 CD48 0.114
9 IMMP1L 0.103
10 ELP2 0.0961Top genes by combined PIP
genename combined_pip expression_pip splicing_pip m6A_pip region_tag
714 DGUOK 0.371 0.000000 0.37121 0.00000 2_48
2800 SP140 0.264 0.000000 0.26397 0.00000 2_135
1511 HMGN4 0.253 0.000000 0.14577 0.10681 6_20
1514 HNF1B 0.248 0.000000 0.00000 0.24780 17_22
454 CCT7 0.151 0.000000 0.15076 0.00000 2_48
3240 WARS1 0.145 0.008168 0.13643 0.00000 14_52
1790 MCOLN2 0.144 0.011017 0.13281 0.00000 1_52
1775 MAX 0.137 0.000000 0.07535 0.06163 14_30
2152 PARP14 0.131 0.000000 0.04547 0.08591 3_76
1505 HLA-F 0.124 0.006508 0.07517 0.04247 6_23
1251 ERGIC3 0.120 0.000000 0.12040 0.00000 20_21
3152 TXNL4A 0.116 0.014255 0.07018 0.03170 18_48
473 CD48 0.114 0.000000 0.11360 0.00000 1_81
2383 PTK2B 0.110 0.000000 0.06693 0.04323 8_27
213 ARMC10 0.107 0.107269 0.00000 0.00000 7_63
1854 MKKS 0.107 0.000000 0.00000 0.10675 20_8
2071 NSUN4 0.106 0.009682 0.04999 0.04609 1_29
86 ADCY7 0.105 0.000000 0.00000 0.10496 16_27
1897 MRPL40 0.104 0.013926 0.08995 0.00000 22_4
1572 IMMP1L 0.103 0.000000 0.10329 0.00000 11_21Loading required package: gridWarning: 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