quickQTL

Dead simple QTL mapping for haploids based on per-site test of mean differences by genotype (defaults to ANOVA). Returns a plot and a CSV file of p-values.

Example usage

Generate a file giving F-statistics and p-values on a per site basis:

./quickQTL.py stats example-geno-table.csv example-pheno-table.csv example.out

Generate an Matplotlib interactive plot from the output of the command above.

./quickQTL.py plot example.out example-chrom.csv

Generate an PNG plot without interactive plot (e.g. appropriate from a non-GUI session):

./quickQTL.py plot example.out example-chrom.csv --output example.png

Generate an interactive genome-wide QTL plot via Bokeh:

./bokehQTL.py genomewide example.out example-chrom.csv

Generate a plot for a specific QTL in Bokeh, including markers indicating annotated genes:

./bokehQTL.py chromosome -n 8 example.out example-chrom.csv example.gff

Dependencies

• Numpy
• Scipy
• Matplotlib
• Pandas
• Click
• pandarallel
• Bokeh

All of these dependencies can be installed via Conda.

Input file specifications

See the include example-*.csv files for exemplar versions of these file formats.

Phenotype file

• CSV file with at least 2 columns
• Column 1: Sample Names (header Sample_Name)
• Columns 2... : Phenotypes (headers Phenotype_XXXX, Phenotype_YYYY, etc).

Genotype file

• CSV file with at least 3 columns
• Column 1: Chromosome Name (header Chromosome)
• Column 2: Coordinate (header Coordinate)
• Column 3:... : Genotypes for Sample_N where each Sample_N header matches a Sample_N in the phenotype files.

NOTE: the order of sample headers in the genotype file does not need to match the order of sample names in the phenotype file. There can also be samples in the phenotype file that are not in the genotype file and vice versa; those cases are ignored in the analysis.

Chromosome file

• CSV file with exactly 2 columns
• Column 2: Chromosome Name (header Chromosome)
• Column 3: Chromosome Length (header Length)