Ascochyta rabiei is a necrotrophic fungal disease of chickpea (Cicer arietnum) with the potential to cause total yield loss under conducive conditions. The Australian A. rabiei population presents a challenge to produce reproducible SNPs due to its clonal nature and repetitive genome. To be able to study A. rabiei in the field using an experimental population a workflow needs to be developed that will produce both high-resolution genome wide-SNP haplotypes that are applicable for downstream genetic analysis and re-identify clonal lineages.
An experimental population of six A. rabiei genotypes were defined as genetically distinct by principal component analysis (PCA). Low-medium coverage whole genome sequencing was used on field population of 35 A. rabiei genotypes that had been re-sampled two years after the experimental population was released in the field and allowed to proliferate. A variant calling pipeline using Freebayes, followed by variant filtering for polymorphic informative loci with sequencing depth > 30 and genotype call rate > 99% resulted in a VCF calling pipeline with high confidence SNP variants. High-quality haplotypes and clonal lineages of the experimental population were successfully defined using this variant calling method. This strategy utilised discriminatory analysis of principal components (DAPC) where 8 clusters were defined and separated the experimental population into separate groups, which was confirmed by phylogenetic tree constructed using maximum-likelihood analysis.
The workflow demonstrated by this study presents the opportunity to trace an experimental population while still capturing sufficient resolution for downstream genetic analysis and taking into consideration the handling of clonal data.
This study aims to develop a workflow utilising low to medium coverage whole genome sequencing to determine a reliable genome-wide SNP-based haplotype for use in experimental evolution of fungal clonal pathosystem for which there are two main outcomes. The first is the obtainment of high-resolution haplotype clones within a cluster for the analysis of selection and divergence. The second, is the reidentification of an experimental population in the field for fitness purposed and assignment of haplotype-based clusters. This repository contains the bioinformatics pipeline used to perform the following research aims:
- Compare variant detection pipelines that allow accurate identification of Multi-Locus-Haplotypes (MLH)
- Identify MLHs descending from an original population and measure their frequencies
- Asses the impact of host genotype on MLHs frequencies
- Identify genomic loci and regions that drive isolate adaptation/evolution under different selection pressures
Detailed information on the experimental and analysis approaches and methods, including detailed bioinformatics pipelines and code can be found at https://github.com/IdoBar/HPC_SNP_calling_documentation/index.html.
This repository was created by Ido Bar (i.bar@griffith.edu.au) and
Hayley Wilson (hayley.wilson7@griffithuni.edu.au).
Please contact us for any additional information or collaboration
opportunities.