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Last updated: 2025-05-28

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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

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.

Repository version: 6fc9c34

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

The results in this page were generated with repository version 6fc9c34. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:
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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 five uncorrelated tissues three omics results
• Simulation five correlated brain tissues three omics results
• Simulation one tissue three omics
• Simulation two tissues two omics enrichment

Real data application

Real data final round 05/14/2025

Exploring genetic architecture

LDL, IBD: different qtl combination

Tissue selection

Tissue filtered by sample size, irrelevant tissues filtered out, brian tissue removed from non-psychiatric traits

• eQTL - some tissues removed
• sQTL - some tissues removed
• Summary

Multi group analysis results

Data analysis

• Summary for parameters, number of genes in single/multigroup analysis, heatmap for new genes

Enrichment analysis

• Comparing enriched terms found by fractional model/enrichR for multigroup&singlegroup

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

Finalized package

45 tissues jointly

• eQTL
• sQTL

Tissue filtered by sample size, irrelevant tissues filtered out

• eQTL - some tissues removed
• sQTL - some tissues removed

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