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Metabolite and lipid data from Diversity Outbred mice.

Liver metabolites: 308 phenotypes. Liver lipids: 1354 phenotypes. Plasma lipids: 1767 phenotypes.

QTL Mapping Pipeline

  1. Format and normalize the raw analyte data.
  • Modify file 'attie_liver_metabolites_normalize.R'.
  • If there is no missing data, you don't need to impute data, just batch normalize using ComBat.
  • Name the first column containing the mouse IDs "Mouse.ID" (case-sensitive).
  • Save a *.rds file containing the normalized analytes.
  1. Gather the QTL input data into a single file.
  • Modify file 'gather_qtl_input_data.R'.
  • Read in the analyte file produced in setp 1 and the genoprobs, located on the JAX FTP site.
  • This script will create a single compressed R binary file (*.Rdata) containing objects called:
    • pheno: data.frame containing the normalized phenotypes and covariates.
    • pheno.rz: data.frame containing the Z-score transformed phenotypes and covariates.
    • pheno.descr: a small phenotype dictionary.
    • genoprobs: Haplotype probabilities for all mice in qtl2 format.
    • K: list of kinship matrices.
    • map: data.frame containing marker information.
  1. Map the analytes.
  • Modify file 'qtl2_scan_engine.R'.
  • The script is set up to run 1000's of analytes in chunks on the cluster.
  • You can run all analytes together as long as they all use the same covariates.
  • The output is a *.rds file containing LOD scores for all analytes (num_markers X num_analytes).
  1. Collect the LOD scores for all chunks into one file.
  • Modify file 'gather_qtl_output.R'.
  • This file will gather the chunked QTL files an write them out to a single *.rds file.
  • It will also create a PDF containing all QTL plots.
  1. Harvest the QTL peaks above your desired threshold.
  • Modify 'harvest_thr_qtl.R'.
  • The input file is the *.rds file containing all LOD scores created in step 4.
  • Set a LOD threshold.
  • The output will be a *.csv file containing the highest peak on each chromosome above the LOD threshold.
  1. Create QTL heatmap and histogram.
  • Modify 'qtl_heatmap.R' and 'qtl_histogram.R'.
  • The input for the QTL heatmap is the *.rds file created in step 4 containing all LOD scores at all markers.
  • The output is a large PNG.
  • The input for the QTL histogram is the *.csv file created in step 5 containing the harvested QTL peaks.
  • The output is a PNG that plots the chromosomes that have peaks within 2 Mb.
  1. Calculate founder allele effects and association mapping LODs at selected peaks.
  • Modify 'qtl2_coef_assoc_engine.R'.
  • The input is the *.rds file created in step 4 and the harvested QTL file created in step 5.
  • The output will be one founder allele effects plots and one association mapping plot for each peak in the harvested QTL file.