Diversity parameter calculation from SSR data with varying and higher ploidy levels: an example on pear (Pyrus ssp.)...

Area of Science:

• Plant genetics
• Bioinformatics
• Population genetics

Background:

• Simple Sequence Repeat (SSR) markers are vital for genetic analyses due to high polymorphism and ubiquity.
• Standard genetic analysis tools often require diploid datasets, posing challenges for polyploid plant species.
• SSR markers can exhibit multi-locus behavior and genotyping errors, complicating data analysis.

Purpose of the Study:

• To evaluate the impact of data adjustments on genetic diversity parameters in plant datasets.
• To assess the consistency of genetic diversity statistics after handling multi-allelic markers and ploidy variations.
• To provide a robust method for adapting complex plant genetic datasets for downstream analyses.

Main Methods:

• Comparison of genetic diversity statistics between an original dataset and modified subsets.
• Modification of a pear cultivar dataset by excluding additional alleles and selected markers.
• Evaluation of consistency in genetic diversity parameters across different data treatment scenarios.

Main Results:

• Genetic diversity statistics were largely consistent across the original and modified datasets.
• Data adjustments, including the exclusion of multi-allelic markers, did not significantly alter key diversity metrics.
• The study demonstrates the robustness of genetic diversity estimation despite data complexities.

Conclusions:

• Adapting plant genetic datasets, particularly those with polyploidy or multi-allelic SSR markers, is feasible while preserving biological validity.
• The developed script offers a reliable approach for pre-analysis data adjustments in plant genetics.
• Accurate estimation of genetic diversity is achievable even with complex, adjusted datasets.