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Identification of Diverse Soybean Germplasm Using RAPD Markers

Jeffrey Thompson, Randall L. Nelson, Lila O. Vodkin

Open publisher page 171 citations

Abstract

The genetic base of soybean [Glycine max (L.) Merr.] breeding in North America is very limited. The focus of this research was to assess the diversity of 18 soybean ancestors and 17 selected plant introductions (PIs) maintained in the USDA Soybean Germplasm Collection. Estimates of genetic relationships among the 35 genotypes were calculated from 281 random amplified polymorphic DNA (RAPD) markers using the simple matching coefficient (SMC) expressed as Euclidean distances. Two forms of hierarchical and nonhierarchical duster analysis as well as multidimensional scaling (MDS) were employed to reveal associations among the genotypes. The average genetic distance among all genotypes was 0.56. All methods of cluster analysis identified distinct groups of ancestors or PIs. Grouping of the ancestors generally agreed with known pedigree, origin, and maturity data. The four methods of clustering produced similar results, and genotypes were assigned to the same cluster 87% of the time. The MDS plots displayed relationships among the genotypes and may be a useful method of selecting genetically distinct individuals. The genotypes within the distinct PI dusters may possess useful genetic diversity that could be exploited by soybean breeders to increase yield.

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What this paper is about

The genetic base of soybean [Glycine max (L.) Merr.] breeding in North America is very limited. The focus of this research was to assess the diversity of 18 soybean ancestors and 17 selected plant introductions (PIs) maintained in the USDA Soybean Germplasm Collection. Estimates of genetic relationships among the 35 genotypes were calculated from 281 random amplified polymorphic DNA (RAPD) markers using the simple matching coefficient (SMC) expressed as Euclidean distances. Two forms of hierarchical and nonhierarchical duster analysis as well as multidimensional scaling (MDS) were employed to reveal associations among the genotypes. The average genetic distance among all genotypes was 0.56. All methods of cluster analysis identified distinct groups of ancestors or PIs. Grouping of the ancestors generally agreed with known pedigree, origin, and maturity data. The four methods of clustering produced similar results, and genotypes were assigned to the same cluster 87% of the time. The MDS plots displayed relationships among the genotypes and may be a useful method of selecting genetically distinct individuals. The genotypes within the distinct PI dusters may possess useful genetic diversity that could be exploited by soybean breeders to increase yield.

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Available abstract

The genetic base of soybean [Glycine max (L.) Merr.] breeding in North America is very limited. The focus of this research was to assess the diversity of 18 soybean ancestors and 17 selected plant introductions (PIs) maintained in the USDA Soybean Germplasm Collection. Estimates of genetic relationships among the 35 genotypes were calculated from 281 random amplified polymorphic DNA (RAPD) markers using the simple matching coefficient (SMC) expressed as Euclidean distances. Two forms of hierarchical and nonhierarchical duster analysis as well as multidimensional scaling (MDS) were employed to reveal associations among the genotypes. The average genetic distance among all genotypes was 0.56. All methods of cluster analysis identified distinct groups of ancestors or PIs. Grouping of the ancestors generally agreed with known pedigree, origin, and maturity data. The four methods of clustering produced similar results, and genotypes were assigned to the same cluster 87% of the time. The MDS plots displayed relationships among the genotypes and may be a useful method of selecting genetically distinct individuals. The genotypes within the distinct PI dusters may possess useful genetic diversity that could be exploited by soybean breeders to increase yield.

Key concepts: Germplasm, Biology, RAPD, Genetic diversity, Genotype, Genetics, Genetic marker, Multidimensional scaling

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