cTWAS paper figures
2025-8-18
Last updated: 2025-09-11
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Figure 1: Workflow of M-cTWAS
Figure 2: cTWAS simulations (Correlated Brain Tissues)
Figure 2a: Inflated PHE by single-group analysis
Figure 2b: PHE
Figure S: Enrichment (original scale)
Figure 2c: PIP calibration
Figure 2d: Power and False discovery rate
Figure 3: cTWAS estimates genetic architecture of complex traits from GTEx
Figure 3a: Tissues are often correlated: but one tissue is not enough.
Figure 3b: Modalities are mostly independent
Figure 3b: Shared pattern
Figure 3c: Partition of h2g (Multiple tissues contribute and Contribution of each modality)
Figure 4: Multi-cTWAS improves the discovery of candidate genes
Figure 4a: Incorporating multiple modality and tissues improves discovery power
Figure 4b: M-cTWAS identified genes with higher POPS scores
Averaged POPS scores across all traits stratified by M-cTWAS PIPs
Figure 4c: Validation with Silver Standand Genes
Figure 4d: Comparison of number of signals per region between M-cTWAS, Coloc and TWAS
cTWAS tends to report single genes per locus, while coloc or TWAS report many. The number of signals of Coloc and TWAS are calcuated in regions with TWAS signals (after bonferroni correction). M-cTWAS signals are culated in regions selected by screen region step.
Figure 4e: Compare with TGFM: compare with genes found in the paper. Show that unique genes by cTWAS are valid
For unique genes identified by M-cTWAS and TGFM, I plotted the
distribution of POPS scores and showed that M-cTWAS unique genes have
higher POPS score than TGFM unique genes.
| Version | Author | Date |
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| d5606c1 | sq-96 | 2025-09-03 |
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| d5606c1 | sq-96 | 2025-09-03 |
Figure 5: cTWAS discovers candidate genes for complex traits and provides insights on their molecular mechanisms
Figure 5a: cTWAS helps identify causal modality and context
Figure 5b: Gene set enrichment analysis
Figure 5c: Network analysis of candidate genes suggests novel pathways
Figure 5d: A substantial fraction of cTWAS genes are novel
Figure 6: Brain epiQTLs explain a large fraction of missing heritability by eQTLs
Figure 6a: EpiQTLs explain a larger fraction of h2g than eQTLs
Figure 6b: Using epiQTL discovered novel CREs not explained by eQTLs
| Version | Author | Date |
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| d5606c1 | sq-96 | 2025-09-03 |
Figure 6c: Using epiQTL discovered novel CREs missed by eQTLs
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| d5606c1 | sq-96 | 2025-09-03 |
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Proportion of CpGs present in exactly k cell-type peaks: peaks n proportion
<int> <int> <num>
1: 1 29 0.16292135
2: 2 20 0.11235955
3: 3 22 0.12359551
4: 4 22 0.12359551
5: 5 10 0.05617978
6: 6 12 0.06741573
7: 7 63 0.35393258
| Version | Author | Date |
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| d5606c1 | sq-96 | 2025-09-03 |
[1] 203
intergenic
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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 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] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] grex_1.9 patchwork_1.3.0
[3] scales_1.3.0 circlize_0.4.15
[5] plotrix_3.8-2 cowplot_1.1.3
[7] ggpubr_0.6.0 RColorBrewer_1.1-3
[9] RSQLite_2.3.7 mapgen_0.5.12
[11] ComplexHeatmap_2.12.0 EnsDb.Hsapiens.v86_2.99.0
[13] ensembldb_2.22.0 AnnotationFilter_1.22.0
[15] GenomicFeatures_1.50.4 AnnotationDbi_1.60.2
[17] Biobase_2.58.0 GenomicRanges_1.50.2
[19] GenomeInfoDb_1.34.9 IRanges_2.32.0
[21] S4Vectors_0.36.2 BiocGenerics_0.44.0
[23] pheatmap_1.0.12 dplyr_1.1.4
[25] egg_0.4.5 gridExtra_2.3
[27] ggrepel_0.9.6 ggplot2_3.5.1
[29] data.table_1.16.0 ctwas_0.5.32
[31] workflowr_1.7.0
loaded via a namespace (and not attached):
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[17] Biostrings_2.66.0 readr_2.1.5
[19] AMR_2.1.1 matrixStats_1.4.1
[21] locuszoomr_0.3.5 prettyunits_1.2.0
[23] colorspace_2.1-1 skimr_2.1.4
[25] blob_1.2.4 rappdirs_0.3.3
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[29] crayon_1.5.3 RCurl_1.98-1.16
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[35] gtable_0.3.5 zlibbioc_1.44.0
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[43] DBI_1.2.3 rstatix_0.7.2
[45] Rcpp_1.0.13 viridisLite_0.4.2
[47] progress_1.2.3 clue_0.3-61
[49] bit_4.5.0 htmlwidgets_1.6.4
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[81] xml2_1.3.3 biomaRt_2.54.1
[83] compiler_4.2.0 rstudioapi_0.14
[85] susieR_0.12.35 plotly_4.10.4
[87] filelock_1.0.3 curl_5.2.3
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[97] ProtGenerics_1.30.0 Matrix_1.5-3
[99] vctrs_0.6.5 pillar_1.9.0
[101] lifecycle_1.0.4 jquerylib_0.1.4
[103] GlobalOptions_0.1.2 bitops_1.0-8
[105] irlba_2.3.5.1 httpuv_1.6.5
[107] rtracklayer_1.58.0 R6_2.6.1
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[129] git2r_0.30.1 mixsqp_0.3-54
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[133] restfulr_0.0.15