methylation_analysis
2024-10-22
Last updated: 2024-11-25
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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Fusion Lasso model of DNA methylation
I built lasso model of DNA methylation with FUSION for Whole Blood and Colon Transverse. Similar to meQTL mapping, for each CpG site, I extracted surrounding 50kb genptypes and train lasso models with cross validation. With heritability cutoff p<0.0001, I have about 16,000 and 48,000 CpG sites in whole blood and colon transverse. Among which, 5,000 and 40,000 CpG sites are also in QTL mapping. Colon have more overlaps than whole blood. The average cross-validation R2 for lasso in whole blood and colon transverse are 0.393 and 0.248 In the single QTL approach (qval < 0.001), we have 7,720 and 91,466 CpG sites.
IBD results
cTWAS parameters (50kb, h2 pvalue<0.00001)
meQTLs explained 10% heritability
cTWAS with eQTLs and meQTLs identifies 28 genes with PIP > 0.8
2024-11-25 16:41:15 INFO::Annotating susie alpha result ...
2024-11-25 16:41:15 INFO::Map molecular traits to genes
2024-11-25 16:41:16 INFO::Split PIPs for molecular traits mapped to multiple genesTop cTWAS genes from single group models have very little overlap
Adding eQTL to meQTL identifies an additional 16 high PIP genes
- 12/17 meQTL genes still have combined PIP > 0.8 after adding eQTL
- 3/17 meQTL genes have decreased combined PIP < 0.8 after adding eQTL
- 2/17 meQTL genes are lost, due to region selection after adding eQTL
- Among the 12 overalpped genes:
- One gene (BRD7) is mediation (meQTL pip decreases from 0.96 to 0.09, eQTL pip=0.90).
- Two genes (TNFSF15 and ATG16L1) are competition (eQTL pip = 0.2 and meQTL pip is decreased by 0.2).
- Nine genes are meQTL alone (no eQTL pip).
BRD7
TNFSF15
Adding meQTL to eQTL identifies an additional 11 high PIP genes
Genes that identified by meQTL not eQTL
[1] "CYP2C19" "HLA-DRA" "AMZ1" "ADCY3" "RGS14" "PAX8" "TMEM52"
[8] "ZFP36L2" "ETS1" "ITLN1" "SEC16A" ETS1
Supporting a possible role for Ets1 in inflammatory syndromes of the gut is the identification of SNPs in the human ETS1 gene locus as a susceptibility alleles for celiac disease. https://pmc.ncbi.nlm.nih.gov/articles/PMC10842644/ 
RGS14
Examples conserved between the mouse and human DNMT3A-deficient state comprise RGS14 (Regulator of G-protein signaling 14) and IFITM3 (Interferon-induced transmembrane protein 3), which showed a canonically increased expression with reduced methylation in the promoter region (https://www.nature.com/articles/s41467-022-33844-2#MOESM1)
Colocalization analysis revealed eight candidate genetic variants and risk genes (including LINC00824, CDKAL1, IL10, IL23R, DNAJC27, LPP, RUNX3, and RGS14) associated with a shared genetic basis. Among these, IL23R, DNAJC27, LPP, and RGS14 were further validated by MVMR analysis. (https://www.tandfonline.com/doi/full/10.1080/07853890.2023.2281658#abstract) 
SEC16A
Both patients harboured other potentially damaging mutations in the GSDMB, ERAP2 and SEC16A genes.(https://pubmed.ncbi.nlm.nih.gov/22543157/) 
HLA_DRA
ADCY3
sessionInfo()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
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] VennDiagram_1.7.3 futile.logger_1.4.3
[3] RColorBrewer_1.1-3 EnsDb.Hsapiens.v86_2.99.0
[5] ensembldb_2.22.0 AnnotationFilter_1.22.0
[7] GenomicFeatures_1.50.4 AnnotationDbi_1.60.2
[9] Biobase_2.58.0 GenomicRanges_1.50.2
[11] GenomeInfoDb_1.34.9 IRanges_2.32.0
[13] S4Vectors_0.36.2 BiocGenerics_0.44.0
[15] pheatmap_1.0.12 magrittr_2.0.3
[17] RSQLite_2.3.7 lubridate_1.9.3
[19] forcats_1.0.0 stringr_1.5.1
[21] dplyr_1.1.4 purrr_1.0.2
[23] readr_2.1.5 tidyr_1.3.1
[25] tibble_3.2.1 tidyverse_2.0.0
[27] ctwas_0.4.15 data.table_1.16.0
[29] gridExtra_2.3 ggVennDiagram_1.5.2
[31] ggplot2_3.5.1 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] BiocFileCache_2.6.1 lazyeval_0.2.2
[3] crosstalk_1.2.1 BiocParallel_1.32.6
[5] LDlinkR_1.4.0 digest_0.6.37
[7] yulab.utils_0.1.7 htmltools_0.5.8.1
[9] fansi_1.0.6 memoise_2.0.1
[11] tzdb_0.4.0 Biostrings_2.66.0
[13] matrixStats_1.4.1 locuszoomr_0.3.5
[15] timechange_0.3.0 prettyunits_1.2.0
[17] colorspace_2.1-1 blob_1.2.4
[19] rappdirs_0.3.3 ggrepel_0.9.6
[21] xfun_0.47 callr_3.7.2
[23] crayon_1.5.3 RCurl_1.98-1.16
[25] jsonlite_1.8.9 zoo_1.8-12
[27] glue_1.7.0 gtable_0.3.5
[29] zlibbioc_1.44.0 XVector_0.38.0
[31] DelayedArray_0.24.0 scales_1.3.0
[33] futile.options_1.0.1 DBI_1.2.3
[35] Rcpp_1.0.13 viridisLite_0.4.2
[37] progress_1.2.3 gridGraphics_0.5-1
[39] bit_4.5.0 DT_0.22
[41] htmlwidgets_1.6.4 httr_1.4.7
[43] pkgconfig_2.0.3 XML_3.99-0.14
[45] farver_2.1.2 sass_0.4.9
[47] dbplyr_2.5.0 utf8_1.2.4
[49] ggplotify_0.1.2 tidyselect_1.2.1
[51] labeling_0.4.3 rlang_1.1.4
[53] later_1.3.2 munsell_0.5.1
[55] pgenlibr_0.3.7 tools_4.2.0
[57] cachem_1.1.0 cli_3.6.3
[59] generics_0.1.3 evaluate_1.0.0
[61] fastmap_1.2.0 yaml_2.3.10
[63] processx_3.7.0 knitr_1.48
[65] bit64_4.5.2 fs_1.6.4
[67] KEGGREST_1.38.0 whisker_0.4
[69] formatR_1.14 aplot_0.2.3
[71] xml2_1.3.3 biomaRt_2.54.1
[73] compiler_4.2.0 rstudioapi_0.14
[75] plotly_4.10.4 filelock_1.0.3
[77] curl_5.2.3 png_0.1-7
[79] bslib_0.8.0 stringi_1.8.4
[81] highr_0.11 ps_1.7.1
[83] lattice_0.20-45 ProtGenerics_1.30.0
[85] Matrix_1.5-3 vctrs_0.6.5
[87] pillar_1.9.0 lifecycle_1.0.4
[89] jquerylib_0.1.4 cowplot_1.1.3
[91] bitops_1.0-8 irlba_2.3.5.1
[93] patchwork_1.3.0 httpuv_1.6.5
[95] rtracklayer_1.58.0 R6_2.5.1
[97] BiocIO_1.8.0 promises_1.3.0
[99] codetools_0.2-18 lambda.r_1.2.4
[101] SummarizedExperiment_1.28.0 rprojroot_2.0.3
[103] rjson_0.2.23 withr_3.0.1
[105] GenomicAlignments_1.34.1 Rsamtools_2.14.0
[107] GenomeInfoDbData_1.2.9 parallel_4.2.0
[109] hms_1.1.3 ggfun_0.1.6
[111] gggrid_0.2-0 rmarkdown_2.28
[113] MatrixGenerics_1.10.0 logging_0.10-108
[115] git2r_0.30.1 mixsqp_0.3-54
[117] getPass_0.2-2 restfulr_0.0.15