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<<<<<<< HEAD Last updated: 2025-03-26 ======= Last updated: 2025-03-24 >>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

Checks: 1 1

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: uncommitted changes

The R Markdown file has staged 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.

<<<<<<< HEAD Repository version: 3a5cb13

======= Repository version: d77c711

>>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

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 3a5cb13. 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 d77c711. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files. >>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

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:

<<<<<<< HEAD
Untracked files:
    Untracked:  analysis/simulation_two_tissues_no_enrichment.Rmd

Unstaged changes:
    Modified:   analysis/index.Rmd
    Modified:   analysis/simulation_one_tissue_no_enrichment.Rmd
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Ignored files:
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    Modified:   analysis/index.Rmd
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>>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

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

<<<<<<< HEAD

GWAS, LD, expr_weights

6 modality weights from Munro et al.

Training stability weights using munro’s RNA data

=======

GWAS, LD, expr_weights

6 modality weights from Munro et al.

Training stability weights using munro’s RNA data

>>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

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

Simulation results

• Simulation results
• Simulation null weight results
• Simulation Param PIPs
<<<<<<< HEAD
• Simulation no enrichment
• Simulation one tissue no enrichment
• Simulation two tissues no enrichment
• Simulation no enrichment four tissues combined
• Simulation pip calibration
=======
• Simulation no enrichment
• Simulation one tissue no enrichment
• Simulation no enrichment four tissues combined
• Simulation pip calibration
>>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

Real data application

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

<<<<<<< HEAD
• Comparing ctwas running setting
• Comparing ld-mismatch setting
=======
• Comparing ctwas running setting
• Comparing ld-mismatch setting
>>>>>>> 0fce4b121bf5e09e361da2e8e7d9009108b0e5e7

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

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