Single large-scale marker-assisted selection (SLS-MAS)


This paper presents a new approach for plant improvement that interactively combines the use of DNA markers and conventional breeding. This approach involves selecting plants at early generation with a fixed, favorable genetic background at specific loci, conducting a single large-scale marker-assisted selection (SLS-MAS) while maintaining as much as possible the allelic segregation in the rest of the genome. First, the identification of elite lines presenting high allelic complementarity and being outstanding for traits of interest is required to capture favorable alleles from different parental lines. Second, after identification of the most favorable genomic regions for each selected parental line, those lines are intercrossed to develop segregating populations from which plants homozygous for favorable alleles at target loci are selected. One objective of the scheme is to conduct the marker-assisted selection only once, and it requires the selection of a minimum number of plants to maintain sufficient allelic variability at the unselected loci. Therefore, the selection pressure exerted on the segregating population is quite high and the screening of large populations is required to achieve the objectives of the scheme. No selection is applied outside the target genomic regions, to maintain as much as possible the Mendelian allelic segregation among the selected genotypes. After selection with DNA markers, the genetic diversity at un-selected loci may allow breeders to generate new varieties and hybrids through conventional breeding in response to various local needs. Although the single large-scale MAS scheme described here is oriented toward maize and large-scale breeding programs with substantial resources, the flexibility of this scheme would allow breeding programs to develop options compatible with local resources.

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Ribaut, JM., Betrán, J. Single large-scale marker-assisted selection (SLS-MAS). Molecular Breeding 5, 531–541 (1999).

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  • crop improvement
  • marker-assisted selection
  • quantitative trait loci
  • Zea mays L.