Last updated: 2024-04-17

Checks: 2 0

Knit directory: pop_spec_eqtl_ml/

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    Modified:   paper_figures/sup/pre_mashr_diff_thresh_upset_plots/mak.aa.to.pr,mx.nom_pvalue_thresh_0.1_plots.pdf
    Modified:   pop_spec_eqtl_ml.Rproj
    Modified:   renv/activate.R

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Overview:

This page and the analysis it documents is broken into two sections: paper, and full thesis. The paper section documents analysis completed specifically for the paper comparing definitions of portability. The full thesis section documents a broader set of analyses completed as part of Isobel’s masters thesis project (2021-2022).


Portability paper

Power is a major confounder in the estimation of cross-ancestry human eQTL ‘portability’



Combining summary statistics across studies

The first step in our investigation is to combine eQTL summary statistics from multiple studies in the same tissue. Pages in this section contain information about investigations, decisions and final procedures used to combine single population eQTL summary statistics from relevant tissues together.

  1. Harmonising monocyte eQTL datasets
    1. Combining Mogil et al. 2018 datasets (three populations, African American, European American, Hispanic)
  2. Harmonising whole blood eQTL datasets
    1. Combining Natri et al. datasets 2022
    2. Combining Mak et al. datasets 2022

LD matrix estimation

  1. Filtering of 1000 genomes vcfs for creating LD matrices
  2. Calculating LD matrices from 10000 genomes data
  3. Filtering Indonesian vcfs for creating LD matrices
  4. Calculating Indonesian LD matrices

Determining eQTL portability at the eSNP level

Command line scripts:

Command line scripts for labeling eQTLs as portable across a pair of populations/datasets in the same study group (i.e. Mogil, Mak or Natri) are all found in the code/call_pop_spec_shared folder. These scripts use the datasets found in output/combined_eqtl_summary_stats as input, produced in the above data haramonising analyses.

  1. Statistical significance methods (call_pop_spec_nom_pval.R to use nominimal pvalues, call_pop_spec_lfsr.R to use mashr calculated lfsr values.R)
  2. Effect size based methods (call_pop_spec_raw_effect_size.R to use effect sizes unchanged from publicly available data, call_pop_spec_mashr_effect_size.R to use mashr estimated effect sizes)

Analysis used to produce Figure 2 Upset Plots

Related summary statistics for Figure 2

Analysis used to produce Figure 3 Pairwise sharing plots

Analysis used to produce Figure 3 Power Associated Factor Correlations


Colocalisation Analysiss

  1. Colocalisation analysis step

Command line scripts:

  1. Preparing data from combined summary statistics for colocalisation analysis (code/coloc/coloc_prepare.R)
  2. Running coloc analysis on prepared data (code/coloc/coloc_analysis_pipeline.R)
  3. Use saved coloc results to label eGenes portable (call_pop_spec_coloc.R)

Requires that additional previous analysis steps to make LD matrices from 10000 genomes and Indonesian vcf data have been run. Additionally these command line scripts also call functions from code/coloc/coloc_functions.R and code/utils/paper_plotting_functionsR.


mashr analysis

  1. mashr analysis step

Impact of using mashr to account for power differences across studies

  1. Fig 4 mashr impact on cross study power
    1. Fig 5 mashr upset plots ii.Related summary statistics for Figure 2
    2. Related sup figure - pairwise portability (post mashr analysis)

Supplementary analyses

  1. Sup Note figures on theoretical model of eQTL portability
  2. Different thresholds impact on pre-mashr portability
  3. Pairwise portability plots for mashr portability metrics