Literature DB >> 17219205

BARCSoySNP23: a panel of 23 selected SNPs for soybean cultivar identification.

M S Yoon1, Q J Song, I Y Choi, J E Specht, D L Hyten, P B Cregan.   

Abstract

This report describes a set of 23 informative SNPs (BARCSoySNP23) distributed on 19 of the 20 soybean linkage groups that can be used for soybean cultivar identification. Selection of the SNPs to include in this set was made based upon the information provided by each SNP for distinguishing a diverse set of soybean genotypes as well as the linkage map position of each SNP. The genotypes included the ancestors of North American cultivars, modern North American cultivars and a group of Korean cultivars. The procedure used to identify this subset of highly informative SNP markers resulted in a significant increase in the power of identification versus any other randomly selected set of equal number. This conclusion was supported by a simulation which indicated that the 23-SNP panel can uniquely distinguish 2,200 soybean cultivars, whereas sets of randomly selected 23-SNP panels allowed the unique identification of only about 50 cultivars. The 23-SNP panel can efficiently distinguish each of the genotypes within four maturity group sets of additional cultivars/lines that have identical classical pigmentation and morphological traits. Comparatively, the 13 trinucleotide SSR set published earlier (BARCSoySSR13) has more power on a per locus basis because of the multi-allelic nature of SSRs. However, the assay of bi-allelic SNP loci can be multi-plexed using non-gel based techniques allowing for rapid determination of the SNP alleles present in soybean genotypes, thereby compensating for their relatively low information content. Both BARCSoySNP23 and BARCSoySSR13 were highly congruent relative to identifying genotypes and for estimating population genetic differences.

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Year:  2007        PMID: 17219205     DOI: 10.1007/s00122-006-0487-8

Source DB:  PubMed          Journal:  Theor Appl Genet        ISSN: 0040-5752            Impact factor:   5.699


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