Theoretical and Applied Genetics

, Volume 116, Issue 6, pp 815–824 | Cite as

Genomewide selection in oil palm: increasing selection gain per unit time and cost with small populations

  • C. K. Wong
  • R. BernardoEmail author
Original Paper


Oil palm (Elaeis guineensis Jacq.) requires 19 years per cycle of phenotypic selection. The use of molecular markers may reduce the generation interval and the cost of oil-palm breeding. Our objectives were to compare, by simulation, the response to phenotypic selection, marker-assisted recurrent selection (MARS), and genomewide selection with small population sizes in oil palm, and assess the efficiency of each method in terms of years and cost per unit gain. Markers significantly associated with the trait were used to calculate the marker scores in MARS, whereas all markers were used (without significance tests) to calculate the marker scores in genomewide selection. Responses to phenotypic selection and genomewide selection were consistently greater than the response to MARS. With population sizes of N = 50 or 70, responses to genomewide selection were 4–25% larger than the corresponding responses to phenotypic selection, depending on the heritability and number of quantitative trait loci. Cost per unit gain was 26–57% lower with genomewide selection than with phenotypic selection when markers cost US $1.50 per data point, and 35–65% lower when markers cost $0.15 per data point. With population sizes of N = 50 or 70, time per unit gain was 11–23 years with genomewide selection and 14–25 years with phenotypic selection. We conclude that for a realistic yet relatively small population size of N = 50 in oil palm, genomewide selection is superior to MARS and phenotypic selection in terms of gain per unit cost and time. Our results should be generally applicable to other tree species that are characterized by long generation intervals, high costs of maintaining breeding plantations, and small population sizes in selection programs.


Quantitative Trait Locus Small Population Size Phenotypic Selection Typical Scheme Best Linear Unbiased Prediction 
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-Verlag 2008

Authors and Affiliations

  1. 1.Advanced Agriecological Research SDN BDHSelangor Darul EhsanMalaysia
  2. 2.Department of Agronomy and Plant GeneticsUniversity of MinnesotaSt PaulUSA

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