Haplotype analysis and visualization

This repository features the analyses described in Detection of trait donors and QTL boundaries for early blight resistance using local ancestry inference in a library of genomic sequences for tomato (Anderson TA and Sudermann MA et al. 2023).

It happens in two phases:

1. Generate haplotype cluster information from VCF file
2. Perform comparisons and visualize the results of the cluster analysis

Code is written in R and Python3. The code was run on a Linux-based system.

This analysis method for the identification of homologous haplotypes is both simple and computationally efficient.

Sequences were windowed and stepped according to user specifications. Next, a hierarchical agglomerative clustering model is built using Ward distance. Sequences in a window are then clustered together into homologous haplotypes if their pairwise distance falls under a distance threshold. The program determines the distance threshold, d, by iterating through a user-specified range and identifying the value of d that maximizes the mean silhouette coefficient for all sequences, thus allowing the clustering algorithm to account for unequal information content across genomic windows. The code is built on Scikit-learn’s clustering module.

PART 1

• Generate chromosome-level VCFs that contain no missing data. This can be accomplished by imputing or by filtering
• Navigate to the folder containing the python3 script "cluster_haplotype.py" and run the script using the parameters of your choosing (see example command below)
• Running the script takes a command of the following usage and a total of 8 REQUIRED arguments
• The following command assues that the repository has been cloned and user is working within HaplotypeAnalysis_Visualization directory

python3 ./Haplotype_analysis_scripts/cluster_haplotypes.py [vcf_file] [chromosome_basename] [window_size] [window_step_size] [min_snps_cutoff] [min d] [max d] [step d]

Argument 1: [vcf_file] Specify location of uncompressed chromosome level vcf file
Argument 2: [chromosome_basename] Specify output file basename (typically the chromosome number, i.e. ch09)
Argument 3: [window_size] Set window size to for iterating through the genome - MUST BE AN EVEN NUMBER
Argument 4: [window_step_size] Set step size for window iterations
Argument 5: [min_snps_cutoff] Set the minimum number of SNPs in each window, otherwise outputs NaN
Argument 6: [min d] Set the minimum distance threshold for merging clusters
Argument 7: [max d] Set the maximum distance threshold for merging clusters
Argument 8: [step d] Set the distance (d) step size

See the associated publication and supplementary methods for information on these parameters

The following python dependencies are required:

• scikit-allel we used version 1.2.1
• NumPy)
• pandas)
• scikit-learn
• system commands (os)

EXAMPLE COMMMAND

The following command can be run in the HaplotypeAnalysis_Visualization directory:

python3 ./Haplotype_analysis_scripts/cluster_haplotypes.py ./Example_files/SL4.0ch09_subset.vcf ch09 250000 100000 10 2 80 10

This example code sets the following parameters:

Argument 1: a chromosome 9 file [chr09.vcf]
Argument 2: an output filestem [ch09]
Argument 3: a window size [250]
Argument 4: a window step size [100]
Argument 5: a minimum number of SNPs [10]
Argument 6: a minimum d [2]
Argument 7: a maximum d (even integar) [80]
Argument 8: a step size for d [10]

You can modify the script "parallelize_haplotype_clustering.sh" in order to parallelize by chromosome and by windowing parameters. Each instance of cluster_haplotype.py uses a single thread, so scatter-gather parallelization will save you time in your analysis.

PART 2

• Perform interactive visualizations and analyses of the cluster data in RStudio using the visualize_haplotypes.Rmd script as a starting point.
• Inside the Rmarkdown script are useful contrast and plotting functions that will get you started.
• Download and view the visualize_haplotypes.html document for completed examples to gain insight into the possibilities of this analysis

Happy introgression hunting!