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Improving Selection in Forage, Turf, and Biomass Crops Using Molecular Markers

  • E. Charles Brummer
  • Michael D. Casler

Abstract

Selection of improved forage, turf, and bioenergy crops is optimized if measuring the phenotype of interest is rapid, inexpensive, and repeatable. Phenotyping remains the most difficult issue to resolve for many important traits, including biomass yield, abiotic stress tolerance, and long-term persistence. The identification of molecular markers may augment phenotypic selection if markers are identified that are closely linked to or at genes controlling the traits of interest. Simply inherited traits can be easily manipulated with marker assisted selection (MAS), but using markers in more complex situations requires additional thought. In this paper, we put the use of molecular markers into the context of typical perennial forage and turf breeding programs. Identifying markers based on bi-parental mapping populations is likely not the best way to implement a MAS program, although this approach is useful to introgress alleles from wild germplasm. Instead, a more practical approach may be the use of association mapping, measuring both phenotypes and markers directly on the plants in the breeding nursery. Complications of this method include the limited amount of information on linkage disequilibrium that is available for breeding populations, but the increasing availability of gene identification methods and the use of single nucleotide polymorphism (SNP) markers may enable the use of association mapping in many cases. Applying the information to breeding may be done to assist selection, to prescreen plants to determine those on which field-based phenotypic data will later be collected, and to make rapid off-season selections. The practical applications of markers to the breeding programs are discussed.

Keywords

Quantitative Trait Locus Simple Sequence Repeat Marker Association Mapping Genetic Gain Recurrent Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, LLC 2009

Authors and Affiliations

  1. 1.Center for Applied Genetic Technologies, Crop and Soil Science DepartmentUniversity of GeorgiaAthensUSA
  2. 2.USDA-ARSU.S. Dairy Forage Research CenterMadisonUSA

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