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Population structure and genetic diversity in a commercial maize breeding program assessed with SSR and SNP markers
Authors:Delphine Van Inghelandt  Albrecht E Melchinger  Claude Lebreton  Benjamin Stich
Institution:1. Institute of Plant Breeding, Seed Science, and Population Genetics, University of Hohenheim, 70593, Stuttgart, Germany
2. Limagrain Verneuil Holding, Ferme de l’étang, BP3, 77390, Verneuil l’étang, France
3. Max Planck Institute for Plant Breeding Research, Carl-von-Linné-Weg 10, 50829, Cologne, Germany
Abstract:Information about the genetic diversity and population structure in elite breeding material is of fundamental importance for the improvement of crops. The objectives of our study were to (a) examine the population structure and the genetic diversity in elite maize germplasm based on simple sequence repeat (SSR) markers, (b) compare these results with those obtained from single nucleotide polymorphism (SNP) markers, and (c) compare the coancestry coefficient calculated from pedigree records with genetic distance estimates calculated from SSR and SNP markers. Our study was based on 1,537 elite maize inbred lines genotyped with 359 SSR and 8,244 SNP markers. The average number of alleles per locus, of group specific alleles, and the gene diversity (D) were higher for SSRs than for SNPs. Modified Roger’s distance (MRD) estimates and membership probabilities of the STRUCTURE matrices were higher for SSR than for SNP markers but the germplasm organization in four heterotic pools was consistent with STRUCTURE results based on SSRs and SNPs. MRD estimates calculated for the two marker systems were highly correlated (0.87). Our results suggested that the same conclusions regarding the structure and the diversity of heterotic pools could be drawn from both markers types. Furthermore, although our results suggested that the ratio of the number of SSRs and SNPs required to obtain MRD or D estimates with similar precision is not constant across the various precision levels, we propose that between 7 and 11 times more SNPs than SSRs should be used for analyzing population structure and genetic diversity.
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