Pipeline for performing GWAS mappings with C. elegans phenotype data
R Python
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Install package:


Install bcftools using homebrew

brew install bcftools

A set of functions to process phenotype data, perform GWAS, and perform post-mapping data processing for C. elegans.

The pipeline is split into three steps
  1. Process phenotypes
  2. Perform GWAS mappings
  3. Process mapping data frame
Process Phenotypes

Input data frame for this step contains properly formatted phenotype data. The first column should be named trait all additional columns should be strains. One row corresponding to one trait for all strains.

Example Usage

processed_phenotypes <- process_pheno(data)

This function outputs a list object. Outputs a list. The first element of the list is an ordered vector of traits. The second element of the list is a dataframe containing one column for each strain, with values corresponding to traits in element 1 for rows.

GWAS Mappings

Input data for this step is the output from the process_pheno function. GWAS mappings are performed using the GWAS function from the rrBLUP package with a 5% minor allele frequency cutoff for SNPs. Additional input data for this function are built into the package (SNP set & kinship matrix)

Example Usage

mapping_df <- gwas_mappings(processed_phenotypes, cores = 4, only_sig = TRUE)

The output for function is a data frame that contains SNP information, trait information, and log transformed p-values.

Process Mappings

The input data sets for this step are:

  1. The output from the mapping step
  2. The processed phenotype data set
  3. SNP set

Example Usage

processed_mapping_df <- process_mappings(mapping_df, snp_df = snps, processed_phenotypes, CI_size = 50, snp_grouping = 200)

The resulting dataframe contains all information output from the gwas_mappings function as well as

  1. Variance explained calculations for SNPs above the bonferroni corrected p-value
  2. Confidence intervals information for all identified peaks

NOTE the process_mappings function is also broken up into three separate functions, which have their own documentation:

  1. calculate_VE
  2. find_peaks
  3. identify_CI

Generate custom kinship and mapping datasets

Although this package comes with pre-built kinship and mapping datasets, it is possible to generate your own for use. This functionality requires bcftools. Use generate_mapping and generate_kinship to generate mapping and kinship dataframes, respectively. These data can be used in conjunction with gwas_mappings.

Plotting functions

  1. manplot- a manhattan plot to visualize GWAS mapping results
  2. pxg_plot - a boxplot of phenotypes split by genotype at QTL peak position
  3. gene_variants - a strain by variant plot for a particular gene(s) of interest

Example Script

pheno <- spike(snps, c(80, 1020))
processed_phenotypes <- process_pheno(pheno)
mapping_df <- gwas_mappings(processed_phenotypes)
processed_mapping_df <- process_mappings(mapping_df, phenotype_df = processed_phenotypes, CI_size = 50, snp_grouping = 200)