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a Dep. of Plant Sciences, Rutgers Univ., New Brunswick, NJ 08901
b Dep. of Ecology and Evolutionary Biology, Univ. of California, 321 Steinhaus Hall, Irvine, CA 92697
* Corresponding author (bgaut{at}uci.edu)
An essential prerequisite to cultivar identification is to determine whether cultivars are differentiated genetically. We investigated genetic diversity among and within seven perennial ryegrass (Lolium perenne L.) cultivars (Loretta, Linn, Manhattan II, Affinity, Jet, Pennfine, and Palmer III) using simple sequence repeat (SSR) markers, with the goal of determining whether cultivars could be differentiated on the basis of genetic data. In each cultivar we genotyped 30 individuals with 22 SSR markers, 18 of which had not been reported previously. Our results indicated that each of the seven cultivars contained high but similar levels of genetic diversity. Within cultivar heterozygosity ranged from 0.589 to 0.643. The cultivars could be distinguished by a number of statistical criteria, including: (i) a small but significant proportion (14.6%) of among-cultivar genetic variation, based on analysis of molecular variance (AMOVA); (ii) significant between cultivar FST values that ranged from 0.065 to 0.197; (iii) separation of individuals in principal component analysis (PCA); and (iv) correct identification of individuals by the genotype assignment test, which is related to discriminant analysis. The genotype assignment test worked particularly well; it correctly assigned all 210 individuals to their cultivar of origin. Sampling analysis indicated that the genotype assignment requires data from at least 15 SSRs to be >99% accurate, suggesting future studies of genetic diversity in perennial ryegrass cultivars should use at least 15 SSR markers. Overall, the SSRs reported in this paper were highly effective for differentiating among cultivars.
Abbreviations: RFLP, restriction fragment length polymorphism RAPD, random amplified polymorphic DNA AFLP, amplified fragment length polymorphism SSR, simple sequence repeat AMOVA, analysis of molecular variance HWE, Hardy-Weinberg equilibrium PCA, principle components analysis
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