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Last updated: 2025-05-15

Checks: 2 0

Knit directory: multigroup_ctwas_analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.7.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: up-to-date

<<<<<<< HEAD

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

=======

The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.

>>>>>>> a1e64faf62645b756caec3349c0703f74f4ed211

<<<<<<< HEAD Repository version: c7b1de5

======= Repository version: 8b7dcc7

>>>>>>> a1e64faf62645b756caec3349c0703f74f4ed211

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

<<<<<<< HEAD The results in this page were generated with repository version c7b1de5. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files. ======= The results in this page were generated with repository version 8b7dcc7. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files. >>>>>>> a1e64faf62645b756caec3349c0703f74f4ed211

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    analysis/figure/

Unstaged changes:
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    Modified:   analysis/ctwas_epigenetic_analysis.Rmd
    Modified:   analysis/simulation_one_tissues_three_omics.Rmd
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    Modified:   analysis/index.Rmd
>>>>>>> a1e64faf62645b756caec3349c0703f74f4ed211

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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These are the previous versions of the repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

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Data

GWAS, LD, expr_weights

6 modality weights from Munro et al.

Training stability weights using munro’s RNA data – FUSION pipeline

Training stability weights using munro’s RNA data – FUSION pipeline, use top qtl for non-cs genes

Deciding weights

Comparing results from Munro weights & predictdb weights

• IBD: predictdb eQTL + sQTL VS Munro 6 modalities – thin method V1
• IBD: predictdb eQTL + sQTL + Munro rsQTL + apaQTL VS all 8 weights – thin method V1
• IBD, LDL, SBP, WBC: predictdb eQTL / Munro eQTL / predictdb eQTL + sQTL / predictdb eQTL + sQTL + Munro rsQTL + apaQTL / all 8 weights
• IBD, LDL, SBP, WBC: predictdb eQTL / Munro eQTL / predictdb eQTL + sQTL / predictdb eQTL + sQTL + Munro rsQTL + apaQTL / predictdb eQTL + sQTL + Munro stQTL
• Enrichment analysis using fractional models, for IBD, LDL, SBP, WBC + predictdb eQTL + sQTL / predictdb eQTL + sQTL + Munro stQTL

cTWAS epigenetics results

• cTWAS epigenetic analysis

Simulation results

• Simulation results
• Simulation null weight results
• Simulation Param PIPs
• Simulation no enrichment
• Simulation one tissue no enrichment
• Simulation one tissue enrichment
• Simulation one tissue three omics
• Simulation two tissues no enrichment
• Simulation two tissues two omics enrichment
• Simulation no enrichment four tissues combined
• Simulation pip calibration

Real data application

Real data final round 05/14/2025

Tissue selection

<<<<<<< HEAD
• Min gene 0
• Min gene 1
=======
• eQTL, 45 tissues jointly
• sQTL, 45 tissues jointly
>>>>>>> a1e64faf62645b756caec3349c0703f74f4ed211

Omics

Methylation analysis results

• Methylation_analysis results

Single QTL analysis

Package V1.0

• APA QTL analysis link
• histone QTL analysis link

Package V2.0

• APA QTL analysis (using APA FUSION models by Chen et al.)

Tissues

Deciding matching tissues

Package V1.0

• Deciding the matching tissue – by gene numbers

Package V2.0

• Deciding the matching tissue – by gene numbers
• Deciding the matching tissue – %h2g adaptive method
• Deciding the matching tissue – run ctwas using all tissues, round1
• Deciding the matching tissue – run ctwas using all tissues, G-tests
• Deciding the matching tissue – run ctwas using all tissues, G-tests, scatter plots added

Multi-tissue

Package V1.0

• Multi-tissue analysis, merging the LD blocks
• Validation
• Enrichment analysis – enriched pathways
• LDL ten tissues analysis link

Package V2.0

Multi omics and multi tissues analysis

package v1.0

• LDL - Liver and Adipose - Expression and Splicing link
• LDL - Multi contexts/types link

package v2.0

• 6 Traits, 5 tissues, eQTL + sQTL + apaQTL – load predicdb LD, eqtl, sqtl from predictdb; apa qtl from Chen et al
• 6 Traits, 5 tissues, eQTL + sQTL + apaQTL – compute ukbb LD, eqtl, sqtl from predictdb; apa qtl from Chen et al
• Summary for 6 Traits, 5 tissues, eQTL + sQTL + apaQTL – compute ukbb LD, eqtl, sqtl from predictdb; apa qtl from Chen et al
• 6 Traits, 5 tissues, eQTL + sQTL + apaQTL – compute ukbb LD, all weights are from Munro et al
• Summary for 6 Traits, 5 tissues, eQTL + sQTL + apaQTL – compute ukbb LD, all weights are from Munro et al
• 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing undo
• 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing
• 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing, comparing 2 LD mismatch approaches, problematic genes filtered by PIP & z score
• 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing, comparing 2 LD mismatch approaches, problematic genes filtered by PIP only
• Enrichment analysis for 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al; post-processing undo
• Summary for 6 Traits, 5 tissues, eQTL + sQTL + stQTL – compute ukbb LD, eqtl, sqtl from predictdb; stQTL from Munro et al

Comparing with other methods

• coloc results

Post - processing

LD mismatch diagnosis

• LD mismatch diagnosis using SuSiE_RSS
• Detect LD mismatch for LDL and aFib using DENTIST vs. SuSiE_RSS
• Compare DENTIST and SuSiE_RSS for LD mismatch detection on example locus with manually changed z-scores
• Compare LD mismatch filterng on real data

Region Merge

• Test region merge link

Downstream analysis

• 20 traits

Comparing different settings in multi-group analysis

ctwas settings

• Comparing ctwas running setting: %h2g/gene number/silver standard/go terms – munro st only
• Comparing ctwas running setting: %h2g/gene number/silver standard/go terms – susie st added

QTL settings

• Comparing susie & munro st: parameters only
• Comparing with susie st & apa model
• RNA editing QTLs

postprocessing settings

• Comparing ld-mismatch setting
• CS filtering or not

LDL examples

package v2.0

• Predictdb sQTL from Liver
• Predictdb eQTL&sQTL from Liver
• Comparing single-group & multi-group for LDL – old PIP combining function
• Comparing single-group & multi-group for LDL - new PIP combining function
• Comparing different PIP combining functions
• New tissues comparing LD mismatch
• Downstream analysis, predictdb eQTL, sQTL, Munro stQTL

Summary

Exploring genetic architecture

Old simulation results

• seven uncorrelated tissues simulations link
• seven correlated tissues simulations link
• seven correlated brain tissues simulations link
• Single weight simulations link
• Four expression traits simulations link
• E and S three tissues simulations link