Skip to main content
ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Plant, Soil and Nutrition Research » Research » Publications at this Location » Publication #332004

Research Project: Enhancing Breeding of Small Grains through Improved Bioinformatics

Location: Plant, Soil and Nutrition Research

Title: A simple language to script and simulate breeding schemes: the breeding scheme language

Author
item Yabe, Shiori - University Of Tokyo
item Iwata, Hiroyoshi - University Of Tokyo
item Jannink, Jean-luc

Submitted to: Crop Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/16/2016
Publication Date: 6/16/2017
Citation: Yabe, S., Iwata, H., Jannink, J. 2017. A simple language to script and simulate breeding schemes: the breeding scheme language. Crop Science. 57(3):1347-1354.

Interpretive Summary: It is difficult for plant breeders to determine an optimal breeding strategy given that the problem involves many factors, from how many genes affect the trait to the level of resources available to the breeding program. There are many possible breeding schemes for any given level of resources. Although simulation may be useful to help choose a better (or the best) breeding scheme, it is difficult for breeders to take the first step in conducting breeding simulation because of the complexity of building a simulation platform or even using existing simulation tools. We present here a simple and flexible simulation platform, the Breeding Scheme Language (BSL). This simulation platform works in the statistical computing environment, R. Users define their target species, genetic characteristics of the trait, and breeding schemes by writing simple, self-explanatory scripts. We believe the BSL will be useful for breeders to evaluate breeding schemes and to choose an optimal breeding strategy among a number of possible ones, as well as for training plant breeders.

Technical Abstract: It is difficult for plant breeders to determine an optimal breeding strategy given that the problem involves many factors, such as target trait genetic architecture and breeding resource availability. There are many possible breeding schemes for each breeding program. Although simulation study may be useful to help choose a better (or the best) breeding scheme, it is difficult for breeders to take the first step in conducting breeding simulation because of the complexity of building a simulation platform or even using existing simulation tools. We present here a simple and flexible simulation platform, the Breeding Scheme Language (BSL). This simulation platform works in the statistical computing environment, R. Users define their target species, trait genetic architectures and breeding schemes by writing simple, self-explanatory scripts. We believe the BSL will be useful for breeders to evaluate breeding schemes and to choose an optimal breeding strategy among a number of possible ones, as well as for training plant breeders.