Abstract
Motivation: Single nucleotide polymorphism (SNP) markers are increasingly popular for population genomics and inferring ancestry for individuals of unknown origin. Because large SNP datasets are impractical for rapid and routine analysis, diagnostics rely on panels of highly informative markers. Strategies exist for selecting these markers, however, resources for efficiently evaluating their performance are limited for non-model systems.
Results: snpAIMeR is a user-friendly R package that evaluates the efficacy of genomic markers for the cluster assignment of unknown individuals. It is intended to help minimize panel size and genotyping effort by determining the informativeness of candidate diagnostic markers. Provided genotype data from individuals of known origin, it uses leave-one-out cross-validation to determine population assignment rates for individual markers and marker combinations.
Document Type
Article
Publication Date
6-2024
Digital Object Identifier (DOI)
https://doi.org/10.1093/bioinformatics/btae377
Funding Information
This work has been supported by the United States Department of Agriculture-National Institute of Food and Agriculture- Agriculture and Food Research Initiative [2020–67013-30978]; United States Department of Agriculture-Animal and Plant Health Inspection Service-Plant Protection Act [AP20PPQS&T00C154, AP21PPQS&T00C063, AP22PPQS&T00C070]; and United States Department of Agriculture Hatch Grant [KY008091].
Repository Citation
Vertacnik, Kim L.; Vernygora, Oksana V.; and Dupuis, Julian R., "snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics" (2024). Entomology Faculty Publications. 257.
https://uknowledge.uky.edu/entomology_facpub/257
Included in
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Notes/Citation Information
© The Author(s) 2024. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.