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

  • E. Charles Brummer
  • Michael D. Casler


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.


Quantitative Trait Locus Simple Sequence Repeat Marker Association Mapping Genetic Gain Recurrent Selection 
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  1. Andersen JR, Lübberstedt T (2003) Functional markers in plants. Trends Plant Sci 8:554–560CrossRefPubMedGoogle Scholar
  2. Bruckner PL, Raymer PL, Burton GW (1991) Recurrent phenotypic selection for forage yield in rye. Euphytica 54:11–17CrossRefGoogle Scholar
  3. Brummer EC (2005) Thoughts on breeding for increased forage yield. In: O'Mara FP, Wilkins RJ, 't Mannetje L, Lovett DK, Rogers PAM, Boland, TM (eds) XX International Grassland Congress: Offered Papers. Wageningen Academic Publishers, Wageningen, the Netherlands, p. 63Google Scholar
  4. Burton GW (1982) Improved recurrent restricted phenotypic selection increases bahiagrass forage yields. Crop Sci 22:1058–1061Google Scholar
  5. Burton GW, Mullinex BG (1998) Yield distributions of spaced plants within Pensacola bahiagrass populations developed by recurrent restricted phenotypic selection. Crop Sci 38:333–336Google Scholar
  6. Carpenter JA, Casler MD (1990) Divergent phenotypic selection response in smooth bromegrass for forage yield and nutritive value. Crop Sci 30:17–22Google Scholar
  7. Casler MD, Brummer EC (2008) Expected genetic gains for among-and-within-family selection methods in perennial forage crops. Crop Sci 48:890–902CrossRefGoogle Scholar
  8. Casler MD, Fales SL, McElroy AR, Hall MH, Hoffman LD, Undersander DJ, Leath KT (2002) Half-sib family selection for forage yield in orchardgrass. Plant Breed 121:43–48CrossRefGoogle Scholar
  9. Comstock RE (1996) Quantitative genetics with special reference to plant and animal breeding. Iowa State University Press, Ames, IAGoogle Scholar
  10. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4Longman, Harlow, EnglandGoogle Scholar
  11. Fehr W (1987) Principles of cultivar development. Vol. 1 Theory and technique. Macmillian, New YorkGoogle Scholar
  12. Forster JW, Cogan NOI, Dobrowolski MP, Francki MG, Spangenberg GC, Smith KF (2007) Functionally-associated molecular genetic markers for temperate pasture plant improvement. In: Henry RJ (ed) Advances in plant genotyping. CABI Press, Wallingford, Oxford, UKGoogle Scholar
  13. Gallais A (2003) Quantitative genetics and breeding methods in autopolyploid plants. INRA, ParisGoogle Scholar
  14. Hartl DL, Clark AG (2007) Principles of population genetics. 4Sinauer Associates, Sunderland, MAGoogle Scholar
  15. Hayward MD (1983) Selection for yield in Lolium perenne. I. Selection and performance under spaced plant conditions. Euphytica 32:85–95CrossRefGoogle Scholar
  16. Hayward MD, Vivero JL (1984) Selection for yield in Lolium perenne. II. Performance of spaced plant selections under competitive conditions. Euphytica 33:787–800CrossRefGoogle Scholar
  17. Hirschhorn JH, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6:95–108CrossRefPubMedGoogle Scholar
  18. Katepa-Mupondwa FM, Christie BR, Michaels TE (2002) An improved breeding strategy for autotetraploid alfalfa (Medicago sativa. L.) Euphytica 123:139–146CrossRefGoogle Scholar
  19. Lamb JFS, Sheaffer CC, Rhodes LH, Sulc M, Undersander DJ, Brummer EC (2006) Forage yield and quality of alfalfa cultivars released from the 1940s through the 1990s. Crop Sci 46:902–909CrossRefGoogle Scholar
  20. Missaoui AM, Fasoula VA, Bouton JH (2005) The effect of low plant density on response to selection for biomass production in switchgrass. Euphytica 142:1–12CrossRefGoogle Scholar
  21. Moreau LA, Charcosset A, Gallais A (2004) Experimental evaluation of several cycles of marker assisted selection in maize. Euphytica 137:111–118CrossRefGoogle Scholar
  22. Remington DL, Thornsberry JM, Masuoka Y, Wilson LM, Whitt SR, Doebley J, Kresovich S, Goodman MM, Buckler ES (2001) Structure of linkage disequilibrium and phenotypic associations in the maize genome. Nat Genet 98:11479–11484Google Scholar
  23. Robins JG, Bauchan GR, Brummer EC (2007a) Genetic mapping forage yield, plant height, and regrowth at multiple harvests in tetraploid alfalfa (Medicago sativa. L.). Crop Sci 47:11–16CrossRefGoogle Scholar
  24. Robins JG, Luth D, Campbell TA, Bauchan GR, He C, Viands DR, Hansen JL, Brummer EC (2007b) Mapping biomass production in tetraploid alfalfa (Medicago sativa. L.). Crop Sci 47:1–10CrossRefGoogle Scholar
  25. Rose LW, Das MK, Fuentes RG, Taliaferro CM (2007) Effects of high- vs. low-yield environments on selection for increased biomass yield in switchgrass. Euphytica 156:407–415CrossRefGoogle Scholar
  26. Salter R, Melton B, Wilson M, Currier C (1984) Selection in alfalfa for forage yield with three moisture levels in drought boxes. Crop Sci 24:345–349Google Scholar
  27. Shateryan D, Coulman BE, Mather DE (1995) Recurrent restricted phenotypic selection for forage yield in timothy and orchardgrass. Can J Plant Sci 75:871–875Google Scholar
  28. Skøt L, Humphreys MO, Armstead I, Heywood S, Skøt KP, Sanderson R, Thomas ID, Chorlton KH, Sackville Hamilton NR (2005) An association mapping approach to identify flowering time genes in natural populations of Lolium perenne. (L.). Mol Breed 15:233–245CrossRefGoogle Scholar
  29. The Wellcome Trust Case Control Consortium (2007) Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 44:661–678Google Scholar
  30. Thornsberry JM, Goodman MM, Doebley J, Kresovich S, Nielsen D, Buckler ES (2001) Dwarf8 polymorphisms associate with variation in flowering time. Nat Genet 28:286–289CrossRefPubMedGoogle Scholar
  31. Townsend CE (1981) Breeding cicer milkvetch for improved forage yield. Crop Sci 21:363–366Google Scholar
  32. Uddin N, Carver BF, Krenzer EG (1993) Visual selection for forage yield in winter-wheat. Crop Sci 33:41–45Google Scholar
  33. Vogel KP, Pedersen JF (1993) Breeding systems for cross-pollinated perennial grasses. Plant Breed Rev 11:251–274Google Scholar
  34. Wei X, Jackson PA, McIntyre CL, Aitken KS, Croft B (2006) Associations between DNA markers and resistance to diseases in sugarcane and effects of population substructure. Theor Appl Genet 114:155–164CrossRefPubMedGoogle Scholar
  35. Wilkins PW, Humphreys MO (2003) Progress in breeding perennial forage grasses for temperate agriculture. J Agric Sci 140:129–150CrossRefGoogle Scholar

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© 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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