Diversity parameter calculation from SSR data with varying and higher ploidy levels: an example on pear (Pyrus ssp.) data
TL;DR
This study examines an example dataset of pear cultivars to evaluate how genetic diversity parameters are affected by data adjustments and provides a robust basis for making dataset adjustments before further analyses.
Abstract
SSR markers are used for a myriad of genetic analyses, such as genotyping, parentage analysis or genetic structure, due to their high polymorphism, co-dominance and genomic ubiquity. Most downstream analysis tools usually require a diploid dataset but some plant species may have higher ploidy levels or there may be genotypes within the species that differ in ploidy level. Additionally, SSR markers can show multi-locus behaviour and are prone to genotyping errors. Consequently, existing analysis tools often do not meet the requirements of plant datasets. Nevertheless, accurately estimating genetic diversity parameters remains essential, even for polyploid datasets. This emphasizes the importance of adapting such datasets appropriately to enable subsequent analysis while preserving their biological validity. This study examines an example dataset of pear cultivars to evaluate how genetic diversity parameters are affected by data adjustments. The original dataset, which includes numerous markers with more than two alleles, was compared with three modified subsets in which additional alleles (more than two) and selected markers were excluded. The results show that genetic diversity statistics remain largely consistent across datasets. The script used in this work thus provides a robust basis for making dataset adjustments before further analyses.