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Abstract
BackgroundExisting algorithms and methods for forming diverse core subsets currently address either allele representativeness (breeder's preference) or allele richness (taxonomist's preference). The main objective of this paper is to propose a powerful yet flexible algorithm capable of selecting core subsets that have high average genetic distance between accessions, or rich genetic diversity overall, or a combination of both.ResultsWe present Core Hunter, an advanced stochastic local search algorithm for selecting core subsets. Core Hunter is able to find core subsets having more genetic diversity and better average genetic distance than the current state-of-the-art algorithms for all genetic distance and diversity measures we evaluated. Furthermore, Core Hunter can attempt to optimize any number of genetic measures simultaneously, based on the preference of the user. Notably, Core Hunter is able to select significantly smaller core subsets, which retain all unique alleles from a reference collection, than state-of-the-art algorithms.ConclusionCore Hunter is a highly effective and flexible tool for sampling genetic resources and establishing core subsets. Our implementation, documentation, and source code for Core Hunter is available at
Highlights
IntroductionGenetic resources stored in gene banks are usually sampled with the purpose of evaluating and utilizing them efficiently, as well as studying phenotypic and genotypic diversity, identifying duplicate accessions, and forming core subsets
Existing algorithms and methods for forming diverse core subsets currently address either allele representativeness or allele richness
We have demonstrated that our proposed algorithm for core subset selection, Core Hunter, has improved upon state-of-the-art selection methodologies in several ways
Summary
Genetic resources stored in gene banks are usually sampled with the purpose of evaluating and utilizing them efficiently, as well as studying phenotypic and genotypic diversity, identifying duplicate accessions, and forming core subsets. The aim of the latter activity is to preserve in the sample as much of the diversity present in the original collection as possible. Core subset selection can be based on varying criteria including phenotypic traits or various forms of molecular marker data including, but not limited to, single nucleotide polymorphisms (SNP), amplified fragment length polymorphisms (AFLP), random amplified polymorphic DNA (RAPD), and simple sequence repeats (SSR).
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