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snpAIMeR: R package for evaluating ancestry informative marker contributions in non-model population diagnostics

Producción científica: Articlerevisión exhaustiva

Resumen

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.

Idioma originalEnglish
Número de artículobtae377
PublicaciónBioinformatics
Volumen40
N.º6
DOI
EstadoPublished - jun 1 2024

Nota bibliográfica

Publisher Copyright:
© 2024 The Author(s). Published by Oxford University Press.

Financiación

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].

FinanciadoresNúmero del financiador
United States Department of Agriculture-Animal and Plant Health Inspection Service-Plant Protection ActT00C154, T00C063, T00C070
U.S. Department of AgricultureKY008091
U.S. Department of Agriculture
United States Department of Agriculture National Institute of Food and Agriculture, Agriculture and Food Research Initiative CARE2020–67013-30978

    ASJC Scopus subject areas

    • Statistics and Probability
    • Biochemistry
    • Molecular Biology
    • Computer Science Applications
    • Computational Theory and Mathematics
    • Computational Mathematics

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