Genetic diversity of maize inbred lines within and among heterotic groups revealed by RFLPs

The objectives of this study were (1) to investigate genetic diversity for RFLPs in a set of important maize inbreds commonly used in Italian breeding programs, (2) to compare genetic similarities between unrelated lines from the same and different heterotic groups, and (3) to examine the potential...

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Veröffentlicht in:Theoretical and applied genetics 1992-06, Vol.84 (1-2), p.17-25
Hauptverfasser: Livini, C. (Experimental Inst. of Cereal Crops, Bergamo (Italy)), Ajmone-Marsan, P, Melchinger, A.E, Messmer, M.M, Motto, M
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Sprache:eng
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Zusammenfassung:The objectives of this study were (1) to investigate genetic diversity for RFLPs in a set of important maize inbreds commonly used in Italian breeding programs, (2) to compare genetic similarities between unrelated lines from the same and different heterotic groups, and (3) to examine the potential of RFLPs for assigning maize inbreds to heterotic groups. Forty inbreds were analyzed for RFLPs with two restriction enzymes (EcoRI and HindIII) and 82 DNA clones uniformly distributed over the maize genome. Seventy clone-enzyme combinations gave single-banded RFLP patterns, and 79 gave multiple-banded RFLP patterns. The average number of RFLP patterns detected per clone-enzyme combination across all inbreds was 5.8. RFLP data revealed a wide range of genetic diversity within the two heterotic groups assayed, Iowa Stiff Stalk Synthetic (BSSS) and Lancaster Sure Crop (LSC). Genetic similarity (GS) between lines was estimated from binary RFLP data according to the method of Nei and Li (1979). The mean GS for line combinations of type BSSS × LSC (0.498) was substantially smaller than for unrelated line combinations or type BSSS × BSSS (0.584) but almost as great as for un-related line combinations of type LSC × LSC (0.506). Principal coordinate and cluster analyses based on GS values resulted in the separate groupings of lines, which is consistent with known pedigree information. A comparison between both methods for multivariate analyses of RFLP data is presented.
ISSN:0040-5752
1432-2242
DOI:10.1007/BF00223976