A needle in a seedstack: an improved method for detection of rare alleles in bulk seed testing through KASP

Anthony Brusa, Eric L. Patterson, Todd A. Gaines, Kevin Dorn, Philip Westra, Crystal D. Sparks, Don Wyse

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


BACKGROUND: Amaranthus palmeri is an aggressive and prolific weed species with major impact on agricultural yield and is a prohibited noxious weed across the Midwest. Morphological identification of A. palmeri from other Amaranthus species is extremely difficult in seeds, which has led to genetic testing for seed identification in commercial seed lots.RESULTS: We created an inexpensive and reliable genetic test based on novel, species-specific, single nucleotide polymorphisms (SNPs) from GBS (Genotyping by Sequencing) data. We report three SNP-based genetic tests for identifying A. palmeri alone or in a mixed pool of Amaranthus spp. Sensitivity ranged from 99.8 to 100%, specificity from 99.59 to 100%. Accuracy for all three tests is > 99.7%. All three are capable of reliably detecting one A. palmeri seed in a pool of 200 Amaranthus spp. seeds. The test was validated across 20 populations of A. palmeri, along with eight other Amaranthus species, the largest and most genetically diverse panel of Amaranthus samples to date.CONCLUSION: Our work represents a marked improvement over existing commercial assays resulting in an identification assay that is (i) accurate, (ii) robust, (iii) easy to interpret and (iv) applicable to both leaf tissue and pools of up to 200 seeds. Included is a data transformation method for calling of closely grouped competitive fluorescence assays. We also present a comprehensive GBS dataset from the largest geographic panel of Amaranthus populations sequenced. Our approach serves as a model for developing markers for other difficult to identify species. (c) 2021 Society of Chemical Industry
Original languageEnglish
Pages (from-to)2477-2484
Number of pages8
JournalPest Management Science
Issue number5
Early online dateFeb 2021
Publication statusPublished - May 2021
Externally publishedYes


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