methylation_analysis
2024-10-22
Last updated: 2024-11-24
Checks: 6 1
Knit directory: multigroup_ctwas_analysis/
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|---|---|---|---|---|
| Rmd | a901ca7 | sq-96 | 2024-11-21 | update |
| Rmd | 770965a | sq-96 | 2024-10-25 | update |
| Rmd | c3e7a9f | sq-96 | 2024-10-25 | update |
| html | c3e7a9f | sq-96 | 2024-10-25 | update |
| Rmd | 2db1bdc | sq-96 | 2024-10-23 | update |
| html | 2db1bdc | sq-96 | 2024-10-23 | update |
| Rmd | f8b659f | sq-96 | 2024-10-23 | update |
| html | f8b659f | sq-96 | 2024-10-23 | update |
| Rmd | b3ff842 | sq-96 | 2024-10-23 | update |
| html | b3ff842 | sq-96 | 2024-10-23 | update |
| Rmd | 919465c | sq-96 | 2024-10-23 | update |
| html | 919465c | sq-96 | 2024-10-23 | update |
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 with methylation lasso models (50kb, h2 pvalue<0.00001)
Percent of heritability with methylation lasso models
Top cTWAS genes with methylation lasso models
2024-11-24 12:02:38 INFO::Annotating susie alpha result ...
2024-11-24 12:02:38 INFO::Map molecular traits to genes
2024-11-24 12:02:39 INFO::Split PIPs for molecular traits mapped to multiple genesTop cTWAS genes from single tissue models
2024-11-24 12:03:21 INFO::Annotating fine-mapping result ...
2024-11-24 12:03:21 INFO::Map molecular traits to genes
2024-11-24 12:03:27 INFO::Add gene positions
2024-11-24 12:03:27 INFO::Add SNP positions2024-11-24 12:04:01 INFO::Annotating fine-mapping result ...
2024-11-24 12:04:01 INFO::Map molecular traits to genes
2024-11-24 12:04:02 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-11-24 12:04:03 INFO::Add gene positions
2024-11-24 12:04:04 INFO::Add SNP positions2024-11-24 12:04:27 INFO::Annotating fine-mapping result ...
2024-11-24 12:04:27 INFO::Map molecular traits to genes
2024-11-24 12:04:29 INFO::Add gene positions
2024-11-24 12:04:30 INFO::Add SNP positions2024-11-24 12:04:55 INFO::Annotating fine-mapping result ...
2024-11-24 12:04:55 INFO::Map molecular traits to genes
2024-11-24 12:04:55 INFO::Split PIPs for molecular traits mapped to multiple genes
2024-11-24 12:04:57 INFO::Add gene positions
2024-11-24 12:04:58 INFO::Add SNP positions
2024-11-24 12:05:16 INFO::Annotating susie alpha result ...
2024-11-24 12:05:16 INFO::Map molecular traits to genes
2024-11-24 12:05:16 INFO::Split PIPs for molecular traits mapped to multiple genes2024-11-24 12:05:23 INFO::Annotating susie alpha result ...
2024-11-24 12:05:23 INFO::Map molecular traits to genes2024-11-24 12:05:30 INFO::Annotating susie alpha result ...
2024-11-24 12:05:30 INFO::Map molecular traits to genes
2024-11-24 12:05:31 INFO::Split PIPs for molecular traits mapped to multiple genesAdding meQTL to eQTL identifies an additional 11 high PIP genes
| Version | Author | Date |
|---|---|---|
| c3e7a9f | sq-96 | 2024-10-25 |
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).
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 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] VennDiagram_1.7.3 futile.logger_1.4.3 RColorBrewer_1.1-3
[4] pheatmap_1.0.12 magrittr_2.0.3 RSQLite_2.3.7
[7] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[10] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[13] tidyr_1.3.1 tibble_3.2.1 tidyverse_2.0.0
[16] ctwas_0.4.15 data.table_1.16.0 gridExtra_2.3
[19] ggVennDiagram_1.5.2 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] GenomeInfoDb_1.34.9 LDlinkR_1.4.0
[7] digest_0.6.37 yulab.utils_0.1.7
[9] ensembldb_2.22.0 htmltools_0.5.8.1
[11] fansi_1.0.6 memoise_2.0.1
[13] tzdb_0.4.0 Biostrings_2.66.0
[15] matrixStats_1.4.1 locuszoomr_0.3.5
[17] timechange_0.3.0 prettyunits_1.2.0
[19] colorspace_2.1-1 blob_1.2.4
[21] rappdirs_0.3.3 ggrepel_0.9.6
[23] xfun_0.47 callr_3.7.2
[25] crayon_1.5.3 RCurl_1.98-1.16
[27] jsonlite_1.8.9 zoo_1.8-12
[29] glue_1.7.0 gtable_0.3.5
[31] zlibbioc_1.44.0 XVector_0.38.0
[33] DelayedArray_0.24.0 BiocGenerics_0.44.0
[35] scales_1.3.0 futile.options_1.0.1
[37] DBI_1.2.3 Rcpp_1.0.13
[39] viridisLite_0.4.2 progress_1.2.3
[41] gridGraphics_0.5-1 bit_4.5.0
[43] DT_0.22 stats4_4.2.0
[45] htmlwidgets_1.6.4 httr_1.4.7
[47] pkgconfig_2.0.3 XML_3.99-0.14
[49] farver_2.1.2 sass_0.4.9
[51] dbplyr_2.5.0 utf8_1.2.4
[53] ggplotify_0.1.2 tidyselect_1.2.1
[55] labeling_0.4.3 rlang_1.1.4
[57] later_1.3.2 AnnotationDbi_1.60.2
[59] munsell_0.5.1 pgenlibr_0.3.7
[61] tools_4.2.0 cachem_1.1.0
[63] cli_3.6.3 generics_0.1.3
[65] evaluate_1.0.0 fastmap_1.2.0
[67] yaml_2.3.10 processx_3.7.0
[69] knitr_1.48 bit64_4.5.2
[71] fs_1.6.4 KEGGREST_1.38.0
[73] AnnotationFilter_1.22.0 whisker_0.4
[75] formatR_1.14 aplot_0.2.3
[77] xml2_1.3.3 biomaRt_2.54.1
[79] compiler_4.2.0 rstudioapi_0.14
[81] plotly_4.10.4 filelock_1.0.3
[83] curl_5.2.3 png_0.1-7
[85] bslib_0.8.0 stringi_1.8.4
[87] highr_0.11 ps_1.7.1
[89] GenomicFeatures_1.50.4 lattice_0.20-45
[91] ProtGenerics_1.30.0 Matrix_1.5-3
[93] vctrs_0.6.5 pillar_1.9.0
[95] lifecycle_1.0.4 jquerylib_0.1.4
[97] cowplot_1.1.3 bitops_1.0-8
[99] irlba_2.3.5.1 httpuv_1.6.5
[101] patchwork_1.3.0 rtracklayer_1.58.0
[103] GenomicRanges_1.50.2 R6_2.5.1
[105] BiocIO_1.8.0 promises_1.3.0
[107] IRanges_2.32.0 codetools_0.2-18
[109] lambda.r_1.2.4 SummarizedExperiment_1.28.0
[111] rprojroot_2.0.3 rjson_0.2.23
[113] withr_3.0.1 GenomicAlignments_1.34.1
[115] Rsamtools_2.14.0 S4Vectors_0.36.2
[117] GenomeInfoDbData_1.2.9 parallel_4.2.0
[119] hms_1.1.3 ggfun_0.1.6
[121] gggrid_0.2-0 rmarkdown_2.28
[123] MatrixGenerics_1.10.0 logging_0.10-108
[125] git2r_0.30.1 mixsqp_0.3-54
[127] getPass_0.2-2 Biobase_2.58.0
[129] restfulr_0.0.15