Agrigenomic Diversity Unleashed: Current Single Nucleotide Polymorphism Genotyping Methods for the Agricultural Sciences - Roger D. Lawrie; Steven E. Massey (Applied Biosciences, 2023)
Single Nucleotide Polymorphisms (SNPs) are variations that occur at single nucleotides in the genome and are present at an appreciable level in a population. SNPs can be linked to phenotypes of interest, for example diseases, recent adaptations, or species hybridization. They can also be used to study phylogeny and evolutionary history. Technologies that rapidly identify and catalog the presence of SNPs in a DNA sample are known as SNP genotyping panels, and they continue to undergo rapid development. Such methods have great utility across the agricultural sciences in diverse areas such as plant and animal breeding, pathogen and pesticide resistance identification, outbreak tracing, and hybridization detection. Here, we provide an overview of 14 different SNP genotyping technologies and weigh some of the pros and cons associated with each platform. This review is not comprehensive or technical, nor does it aim to be. Rather, the objective is to provide an introduction to the landscape of genotyping technologies for researchers who do not have experience with these methods. Three classes of SNP genotyping methods are Polymerase Chain Reaction (PCR)-based (nine different methods), microarray-based (one method), and Next-Generation Sequencing (NGS)-based (four different methods). We discuss how each genotyping class is suited for different niches; PCR-based has a low SNP count and high sample number, microarray-based has a very high SNP count and a moderate sample number, and Next-Generation Sequencing-based has a moderate SNP count and moderate number of samples. Included are basics about how the methods function and example use cases of each method. Additionally, we introduce and discuss the potential for the MinION sequencer in SNP genotyping. For each technology, we provide insights into cost, equipment needs, labor costs, experimental complexity, data output complexity, and accessibility. These considerations address the feasibility of deploying the technologies in an agricultural science environment.