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Genotypes Module

geno.Rmd

Developer’s Guide to the qtl2shiny Package

Overview

The Genotypes panel is a high-level visual container designed to investigate genotype probabilities, strain distribution patterns (SDPs), and phenotypic effects at specific chromosomal locations. It acts as an integration layer, coordinating three sub-modules:

  1. genoDataApp: Loads and filters pairwise founder genotype probabilities.
  2. genoPlotApp: Plots individual genotype probabilities along chromosomes.
  3. genoEffectApp: Evaluates phenotype associations (averages/BLUPs) by genotype class or pattern.

Module Hierarchy & Entrypoints

  • Top-Level Container:
    • Standalone Application: genoApp()
    • Server Module: genoServer(id, hotspot_list, pattern_list, snp_list, pairprobs_obj, project_df)
    • UI Input: genoInput(id)
    • UI Output: genoOutput(id)
  • Sub-Modules:
    • Genotype Data (genoDataApp):
      • Server: genoDataServer(id, hotspot_list, snp_list, pairprobs_obj, project_df)
      • UI Input: genoDataInput(id)
      • UI Output: genoDataOutput(id)
    • Genotype Plot (genoPlotApp):
      • Server: genoPlotServer(id, hotspot_list, geno_list)
      • UI Output: genoPlotOutput(id)
    • Genotype Effect (genoEffectApp):
      • Server: genoEffectServer(id, hotspot_list, pattern_list, snp_list, geno_list, pairprobs_obj, project_df)
      • UI Input: genoEffectUI(id) (Displays Summary DataTable)
      • UI Output: genoEffectOutput(id) (Displays Effect Plot)

1. Top-Level Container (genoApp)

Server Logic & Reactive Flow (genoServer)

The main server orchestrates parameters from global sidebars (hotspot_list, pattern_list, snp_list) and routes coordinates from the data submodule to the plot and effect panels:

  1. Instantiation:
    • Calls genoDataServer to create the positional coordinate reactive (pos_Mbp) and the main genotype probabilities table.
    • Passes the resulting geno_list to genoPlotServer and genoEffectServer.
  2. Download Handling:
    • Renders download handlers (download_Plot, download_Table, download_Filename, download_Type) depending on which tab panel is active:
      • Genotypes / GenoTable: Downloads plots/tables from genoPlotServer / genoDataServer.
      • Effects / EffectTable: Downloads plots/tables from genoEffectServer.
graph TD
    HotspotList[hotspot_list] --> genoServer
    PatternList[pattern_list] --> genoServer
    SnpList[snp_list] --> genoServer
    PairProbs[pairprobs_obj] --> genoServer
    
    genoServer --> genoData[genoDataServer]
    genoData -->|geno_list| genoPlot[genoPlotServer]
    genoData -->|geno_list| genoEffect[genoEffectServer]
    
    genoPlot -->|Plot| download[downloadServer]
    genoEffect -->|Plot/Table| download
    genoData -->|Table| download

2. Genotype Data Module (genoDataApp)

genoDataApp() is a module designed to retrieve, filter, and format pairwise genotype probability tables (geno_table) for a selected chromosomal region, strain distribution patterns (SDPs), and physical map positions (Mbp).

Data Used by the Module

  • Genotype Probabilities (genoprob/ directory / FST format): 36-state (or collapsed 8-state) genotype probabilities.
  • Physical Map (pmap.rds): Map of markers and physical coordinates.
  • Strain Distribution Patterns: Alleles mapping founder lines to distinct strain distribution groups (from snp_list$patterns).

Logic and Code Workflow 

graph TD
    ProjectDF[project_df] --> WinPar[hotspot_list$win_par]
    WinPar --> ChrID[chr_id]
    WinPar --> PeakMbp[peak_Mbp]
    
    PairProbs[pairprobs_obj] --> MapRange[Map/Rng Calculation]
    ChrID --> MapRange
    PeakMbp --> MapRange
    
    MapRange --> OutputSlider[output$pos_Mbp_input]
    OutputSlider --> PosMbp[input$pos_Mbp]
    
    PosMbp --> GenotypesCalc[geno_table reactive]
    PairProbs --> GenotypesCalc
    Patterns[snp_list$patterns] --> GenotypesCalc
    
    GenotypesCalc --> OutputDT[output$geno_table_output]
  1. Parameters & Coordinate Selection:

    • Extracts chr_id and peak_Mbp from hotspot_list$win_par.

    • Renders a slider selector pos_Mbp dynamically inside output$pos_Mbp_input mapped to the chromosome physical map boundaries.

    • Observes resets to update slider values when chromosome selection changes:

      observeEvent(shiny::req(win_par()), {
  if (shiny::isTruthy(input$pos_Mbp)) {
    map <- shiny::req(pairprobs_obj()$map)
    chr <- shiny::req(chr_id())
    rng <- round(2 * range(map[[chr]])) / 2
    value <- shiny::req(peak_Mbp())
    if(value < rng[1] | value > rng[2]) value <- mean(rng)
    shiny::updateSliderInput(session, "pos_Mbp", NULL, value, rng[1], rng[2], step=.1)
  }
})
  2. Genotype Table Calculation:
    • Computes geno_table reactively using qtl2pattern::pair_geno_table() at the current coordinate.
  3. Data Table Output:
  4. Return Structure:
    • pos_Mbp: Current slider coordinate reactive.
    • Table: Computed genotype table reactive.

3. Genotype Plot Module (genoPlotApp)

genoPlotApp visualizes individual genotype probabilities along chromosomes to help developers and researchers examine founder ancestry.

Data Used by the Module

  • Phenotype Matrix (pheno_mx): Passed reactively from hotspot_list$pheno_mx.
  • Genotype Table (geno_table): Passed reactively from geno_list$Table.

Logic and Code Workflow

  • geno_plot: Computes the ggplot object reactively by calling geno_ggplot from the qtl2pattern package, passing the active genotypes table and phenotype matrix:

    geno_plot <- shiny::reactive({
  geno_ggplot(shiny::req(geno_table()), shiny::req(pheno_mx()))
})

  • Rendering: Outputs the ggplot object in output$geno_plot using shiny::renderPlot.

4. Genotype Effect Module (genoEffectApp)

genoEffectApp models and visualizes phenotypic effects by grouping experimental individuals according to their genotype probabilities and patterns at the selected locus.

Data Used by the Module

  • Genotype Probabilities (pairprobs_obj)
  • Phenotypes (pheno_mx): Selected phenotype column.
  • Covariates & Kinship: covar_df and LOCO kinship_list for statistical regression.
  • Strain Distribution Patterns & Scans: patterns() and scan_pattern() from the pattern module (retaining BLUP attributes).

Logic and Code Workflow

  1. Effect Scanning (effect_obj):

    • Executes the statistical scan by running effect_scan() when genotypes, phenotypes, and patterns become valid:

      effect_obj <- shiny::reactive({
  shiny::req(snp_action(), project_df(), pairprobs_obj(),
             hotspot_list$kinship_list(), hotspot_list$covar_df(),
             hotspot_list$peak_df(), pattern_list$scan_pattern())
  pheno_name <- shiny::req(pattern_list$pat_par$pheno_name)
  pheno_mx <- shiny::req(hotspot_list$pheno_mx())[, pheno_name, drop = FALSE]
  blups <- attr(pattern_list$scan_pattern(), "blups")

  appProgress('Effect scans', pheno_name, {
    shiny::setProgress(1)
    effect_scan(pheno_mx, hotspot_list$covar_df(),
                pairprobs_obj(), hotspot_list$kinship_list(),
                hotspot_list$peak_df(), patterns(),
                pattern_list$scan_pattern(), blups)
  })
})
  2. Visualizing Effects (geno_effect_plot):

    • Generates the ggplot object by calling ggplot2::autoplot() on the effect scan object at the chosen physical position pos_Mbp():

      p <- ggplot2::autoplot(eff, pos = pos_Mbp())
p + ggplot2::ggtitle(colnames(hotspot_list$pheno_mx()))
  3. Summary Table (geno_effect_table):
    • Summarizes effect estimates at pos_Mbp() by calling effect_summary(eff, pos = pos_Mbp()).
    • Renders the summary in output$geno_effect_table_output using DT::renderDataTable.