cTWAS paper figures
2025-8-18
Last updated: 2025-09-28
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Knit directory: multigroup_ctwas_analysis/
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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
Proportion of heritability explained by gene expression of each tissue estimated by MESC and cTWAS. cTWAS used pre-trained GTEx v8 gene expression prediction models from PredictDB. MESC used pre-computed GTEx v8 gene expression scores. Dashlines show the true PVE value. 
Figure S1: Inflated PHE by single-group analysis
Figure 2b: PHE (correlated tissues)
Proportion of heritability explained by gene expression, splicing and RNA stability of causal and non-causal tissues estimated by M-cTWAS. Causal groups contain two tissues and non-causal groups contain three tissues. Each dot represents one of the ten replicates in simulations. Dashlines show the true PVE value. 
Figure S2: PHE (uncorrelated tissues)
Figure S3: Enrichment (correlated and uncorrelated tissues)
Proportion of heritability explained by gene expression, splicing and RNA stability of each tissue estimated by M-cTWAS. Each dot represents one of the ten replicates in simulations. Dashlines show the true PVE value.
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Figure 2c: PIP calibration (correlated)
Gene PIP calibration. Gene PIPs from all simulations are grouped into bins. The plot shows the proportion of true causal genes (y axis) against the average PIPs (x axis) under each bin. A well-calibrated method should produce points along the diagonal lines (red). The ±s.e. is shown for each point in the vertical bars calculated over ten independent simulations. 
Figure S4: PIP calibration (uncorrelated)
Figure 2d: Power and False discovery rate (correlated)
Number of genes identified by various methods. We used the following significance thresholds for each method: PIP>0.8 for cTWAS. We use different colors for causal genes identified by each method, and the top gray bars indicate noncausal genes. The height of the bar is the mean over ten independent simulations; the standard error is shown for each bar in vertical lines from the same ten simulations. 
Figure S5: Power and False discovery rate (uncorrelated)
Figure 3: cTWAS estimates genetic architecture of complex traits from GTEx
Figure 3a: Modalities are mostly independent
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Figure S6: Tissues are often correlated: but one tissue is not enough.
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Figure 3b: Shared pattern (eQTL)
Figure S7: Shared pattern (sQTL)
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Figure 3c: Partition of h2g (Multiple tissues contribute and Contribution of each modality)
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Figure S8: Partition of h2g for other traits
https://sq-96.github.io/multigroup_ctwas_analysis/partition_of_h2g_supplementary.html
Figure 3d: Existing methods may overestimate the contribution of molQTL.
Figure S9: Existing methods may overestimate the contribution of molQTL (other traits).
https://sq-96.github.io/multigroup_ctwas_analysis/overestimate_eQTL_supplementary.html
Figure 4: Multi-cTWAS improves the discovery of candidate genes
Figure 4a: Incorporating multiple modality and tissues improves discovery power
Figure S10: Incorporating multiple modality and tissues improves discovery power (other traits)
https://sq-96.github.io/multigroup_ctwas_analysis/increase_power_supplementary.html
Figure 4b: M-cTWAS identified genes with higher POPS scores
Averaged POPS scores across all traits stratified by M-cTWAS PIPs 
Figure S11: POPS scores of individual trait
https://sq-96.github.io/multigroup_ctwas_analysis/pops_supplementary_figures.html
Figure 4c: Validation with LDL Silver Standand Genes
Figure S12: Validation with IBD deepseek curated 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 S12: Comparison of number of signals per region between M-cTWAS, Coloc and TWAS (LDL, histogram)
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Figure S13: Comparison of number of signals per region between M-cTWAS, Coloc and TWAS (other traits)
https://sq-96.github.io/multigroup_ctwas_analysis/number_of_signals_per_locus_supplementary.html
Figure 4e: cTWAS locus plot at FADS1 locus
Figure S14: Coloc examples at FASD1 locus
Figure 4e: Compare with TGFM: (LDL)
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. 
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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
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Figure S16: cTWAS helps identify causal modality and context (other traits)
https://sq-96.github.io/multigroup_ctwas_analysis/causal_modality_context_supplementary.html
Figure 5b: A substantial fraction of cTWAS genes are novel
Figure S17: Novel gene category for other traits
https://sq-96.github.io/multigroup_ctwas_analysis/novel_gene_category_supplementary_figures.html
Figure 5c: Network analysis of candidate genes suggests novel pathways (LDL)
Figure S18: Network analysis of candidate genes suggests novel pathways (IBD)
Figure S19: Locus plot of highlighted gene
LDLR
PCSK9
SNX17
Figure 6: Brain epiQTLs explain a large fraction of missing heritability by eQTLs
Figure 6a: EpiQTLs together explain a larger fraction of h2g than eQTLs
Figure 6b: Using epiQTL discovered novel CREs not explained by eQTLs
Figure 6c: Do eQTLs explain epiQTLs? Independent PHE
Figure 6d: Do eQTLs explain epiQTLs? few PIPs decreased after adding eQTLs
Figure S20: Do eQTLs explain epiQTLs? Why eQTLs do not explain epiQTLs. Cell type specific mQTLs
Figure S21: Do eQTLs explain epiQTLs? GTEx is limited in power. Most mQTLs and caQTLs are eQTLs in MetaBrain eQTL
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| 087fabe | sq-96 | 2025-09-24 |
| 09f6128 | sq-96 | 2025-09-24 |
| 382a925 | sq-96 | 2025-09-24 |
| c05ac83 | sq-96 | 2025-09-20 |
| 564fb14 | sq-96 | 2025-09-19 |
| 97b028e | sq-96 | 2025-09-19 |
| 5837ab1 | sq-96 | 2025-09-17 |
| 4c0a409 | sq-96 | 2025-09-16 |
| 12b30a2 | sq-96 | 2025-09-15 |
| d3a926c | sq-96 | 2025-09-15 |
| 6c90f51 | sq-96 | 2025-09-15 |
Figure 6e: Do epiQTLs explain eqtls? Independent PHE
Figure 6f: Do epiQTLs explain eqtlS? few PIPs decreased aftering adding epiQTLs
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] stats4 grid stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] patchwork_1.3.0 circlize_0.4.15
[3] purrr_1.0.2 plotrix_3.8-2
[5] cowplot_1.1.3 ggpubr_0.6.0
[7] grex_1.9 ggraph_2.2.2
[9] igraph_2.1.4 scales_1.3.0
[11] RColorBrewer_1.1-3 RSQLite_2.3.7
[13] mapgen_0.5.12 ComplexHeatmap_2.12.0
[15] EnsDb.Hsapiens.v86_2.99.0 ensembldb_2.22.0
[17] AnnotationFilter_1.22.0 GenomicFeatures_1.50.4
[19] AnnotationDbi_1.60.2 Biobase_2.58.0
[21] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
[23] IRanges_2.32.0 S4Vectors_0.36.2
[25] BiocGenerics_0.44.0 pheatmap_1.0.12
[27] dplyr_1.1.4 egg_0.4.5
[29] gridExtra_2.3 ggrepel_0.9.6
[31] ggplot2_3.5.1 data.table_1.16.0
[33] ctwas_0.5.32 workflowr_1.7.0
loaded via a namespace (and not attached):
[1] utf8_1.2.4 R.utils_2.11.0
[3] tidyselect_1.2.1 htmlwidgets_1.6.4
[5] BiocParallel_1.32.6 munsell_0.5.1
[7] codetools_0.2-18 withr_3.0.1
[9] colorspace_2.1-1 filelock_1.0.3
[11] highr_0.11 knitr_1.48
[13] rstudioapi_0.14 ggsignif_0.6.3
[15] MatrixGenerics_1.10.0 labeling_0.4.3
[17] git2r_0.30.1 pgenlibr_0.3.7
[19] AMR_2.1.1 repr_1.1.4
[21] GenomeInfoDbData_1.2.9 mixsqp_0.3-54
[23] polyclip_1.10-0 bit64_4.5.2
[25] farver_2.1.2 rprojroot_2.0.3
[27] vctrs_0.6.5 generics_0.1.3
[29] xfun_0.47 BiocFileCache_2.6.1
[31] R6_2.6.1 doParallel_1.0.17
[33] clue_0.3-61 graphlayouts_1.2.2
[35] bitops_1.0-8 cachem_1.1.0
[37] reshape_0.8.9 DelayedArray_0.24.0
[39] promises_1.3.0 BiocIO_1.8.0
[41] gtable_0.3.5 Cairo_1.6-0
[43] processx_3.7.0 tidygraph_1.3.1
[45] rlang_1.1.4 GlobalOptions_0.1.2
[47] rtracklayer_1.58.0 rstatix_0.7.2
[49] lazyeval_0.2.2 broom_1.0.5
[51] gggrid_0.2-0 yaml_2.3.10
[53] abind_1.4-5 backports_1.4.1
[55] httpuv_1.6.5 tools_4.2.0
[57] logging_0.10-108 jquerylib_0.1.4
[59] Rcpp_1.0.13 plyr_1.8.7
[61] base64enc_0.1-3 progress_1.2.3
[63] zlibbioc_1.44.0 RCurl_1.98-1.16
[65] ps_1.7.1 prettyunits_1.2.0
[67] GetoptLong_1.0.5 viridis_0.6.2
[69] zoo_1.8-12 SummarizedExperiment_1.28.0
[71] cluster_2.1.3 fs_1.6.4
[73] magrittr_2.0.3 LDlinkR_1.4.0
[75] whisker_0.4 ProtGenerics_1.30.0
[77] matrixStats_1.4.1 hms_1.1.3
[79] evaluate_1.0.0 XML_3.99-0.14
[81] shape_1.4.6 compiler_4.2.0
[83] biomaRt_2.54.1 tibble_3.2.1
[85] crayon_1.5.3 R.oo_1.24.0
[87] htmltools_0.5.8.1 later_1.3.2
[89] tzdb_0.4.0 tidyr_1.3.1
[91] DBI_1.2.3 tweenr_1.0.2
[93] dbplyr_2.5.0 MASS_7.3-56
[95] rappdirs_0.3.3 Matrix_1.5-3
[97] car_3.1-1 readr_2.1.5
[99] cli_3.6.3 R.methodsS3_1.8.1
[101] parallel_4.2.0 locuszoomr_0.3.5
[103] pkgconfig_2.0.3 getPass_0.2-2
[105] GenomicAlignments_1.34.1 skimr_2.1.4
[107] plotly_4.10.4 xml2_1.3.3
[109] foreach_1.5.2 bslib_0.8.0
[111] XVector_0.38.0 stringr_1.5.1
[113] callr_3.7.2 digest_0.6.37
[115] Biostrings_2.66.0 rmarkdown_2.28
[117] restfulr_0.0.15 curl_5.2.3
[119] Rsamtools_2.14.0 rjson_0.2.23
[121] lifecycle_1.0.4 jsonlite_1.8.9
[123] carData_3.0-5 viridisLite_0.4.2
[125] fansi_1.0.6 pillar_1.9.0
[127] susieR_0.12.35 lattice_0.20-45
[129] KEGGREST_1.38.0 fastmap_1.2.0
[131] httr_1.4.7 glue_1.7.0
[133] png_0.1-7 iterators_1.0.14
[135] bit_4.5.0 ggforce_0.3.4
[137] stringi_1.8.4 sass_0.4.9
[139] blob_1.2.4 memoise_2.0.1
[141] tidyverse_2.0.0 irlba_2.3.5.1